THE EFFECT OF RATE STRUCTURE UPON THE
AVAILABILITY OF CREDIT AT CONSUMER
FINANCE COMPANIES

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MICHIGAN STATE UNIVERSITY
Maurice B. Goucizwaard

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This is to certify that the

thesis entitled

The Effect of Rate Structure Upon the
Availability of Credit at
Consumer Finance Companies

presented by

Maurice Goudzwaard ’

has been accepted towards fulfillment
of the requirements for

Ph.D. degree in Business Administration

flights/ii

Major professor

Date September 31 1965

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ABSTRACT

THE EFFECT OF RATE STRUCTURE UPON THE AVAILABILITY
OF CREDIT AT CONSUMER FINANCE COMPANIES

by Maurice Goudzwaard

The principal objective of this dissertation is to mea-
sure the extent that higher rates persuade consumer finance
companies to lend to riskier borrowers, or conversely, the
extent that lower rates cause them to deny credit to marginal
applicants. Since each state regulates the maximum allowable
rate of charge, consumer finance companies impose varying
finance rates depending on the legal regulations in the differ-
ent states which they operate. This study concerns itself
with the resulting effects of this variability on credit
standards.

Three methods were used to evaluate this relationship:

1) interviews to determine management policies and
attitudes toward credit standard flexibility.

2) a regression analysis of state chargeoff rates,
turndown rates, and finance rates of three major
consumer finance companies.

3) a detailed comparison of the characteristics of
persons receiving credit and those denied credit
in Albany, New York, where lenders are permitted
to charge only a relatively low rate, with loan
applicants in Charleston, west Virginia where
lenders charge a higher rate.

u
-

 

 

 

 

 

 

Maurice Goudzwaard

Each of these tests confirms the hypothesis of a positive
refilationship between the finance rate charged by lenders and
‘the risk.accepted into their loan portfolios.

The interviews indicated that major finance companies do
not consciously adjust risk criteria because of rate differ—
entials. Company executives indicate the profits earned in
higher rate states compensate for the smaller earnings in
lower rate states, resulting in credit standards being deter-
mined on the basis of average cost and revenue analysis.
However, most loan supervisors or "middle managers" who
evaluate branch manager credit decisions tend to adjust loan
quality criteria to earnings potentials implicit in rate
levels and to adOpt more lenient credit standards in higher
rate states. The mechanism of adjustment, however, is inexact.

The first quantitative test uses data from three major
finance companies and correlates state chargeoff rates, turn-
down rates, gross yields, and average loan sizes to measure
the relationship between risk acceptance and finance charge
levels. A multiple correlation analysis using chargeoff
rates as the dependent variable indicates a moderate rela—
tionship between risk acceptance and gross yield, average
loan size, and state per capita income. Simple and partial

correlatitni analyses of chargeoff rates and gross yields

Maurice Goudzwaard
(lisplay a moderate correlation at each of the three firms.

Turndown rates, used as a measure of risk acceptance,
Iaave virtually no relationship with finance charges, both
xdhen tested by simple and partial correlation.

The third test analyzes data from branch offices of
three major finance companies in Albany, New York and in
Charleston, West Virginia. Characteristics of 200 borrowers
and 200 rejected applicants from each office were compared
to determine whether Charleston lenders accepted more risk
because of their greater rate-earnings protection. As a
means of measuring risk, two credit scoring systems were
used--one based on consumer finance company data and the
other on commercial bank experiences. The application of
both scoring systems suggests there is virtually no difference
between applicant personal characteristics in the two cities.
An extended analysis of financial characteristics reveals
applicants in Charleston have lower incomes and greater
instalment debt and monthly payments, implying lenders in
Charleston assume greater risks. This lower income and higher
debt of applicants exists in spite of the higher income of
the general pOpulation in Charleston as compared with Albany.
It appears that borrowers in Charleston are eligible for

approximately $400 more credit. The conclusiveness of the

i

 

‘ I

Maurice Goudzwaard
evidence is somewhat mitigated by the larger prOportion of
applicants living in owner occupied dwellings and the greater
residence stability of applicants in Albany. On balance,
lenders in Charleston accept greater risk and offer more
credit to their borrowers.

The management attitude survey and both quantitative
tests confirm the hypothesis of a positive relationship be-
tween finance charges and credit availability at consumer
finance companies. This finding must be used in a guarded
‘way, however, since the reliability and validity of the con—
clusions depend on the assumptions that l) chargeoff rates
measure risk acceptance standards, and 2) the "independent"
and smaller finance companies adOpt credit standards similar
to those of the three major finance companies. Chargeoff
rates presumably reflect credit quality, but in some instances
collection and investigation expenses substitute for chargeoff
expense, thereby rendering chargeoff rate a less valid mea-
surement. Previous studies show the credit standards of
smaller finance companies are similar to those of the larger
firms, but further research is needed to confirm or refute
this.

The empirical evidence suggests higher rate ceilings

permit borrowers to obtain more credit, although further

Maurice Goudzwaard
research is needed to accurately measure the extent of this

variability. Whether or not more credit should be available
tt) a larger segment of society is not a matter of economic

science, but a social value judgment.

THE EFFECT OF RATE STRUCTURE
UPON THE AVAILABILITY OF

CREDIT AT CONSUMER FINANCE COMPANIES

BY
A e)

. rr
Maurice B? Goudzwaard

A THESIS

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

DOCTOR OF PHILOSOPHY

Department of Accounting and Financial Administration

1965

ACKNOWLEDGEMENTS

I thank the many peOple who encouraged and assisted me
during my academic endeavors at Michigan State University. A
jpartial listing includes only a few who have made my college
education more rewarding and complete.

The members of my dissertation committee were very con-
structive and helpful with their refreshing and stimulating
ideas. I am grateful to Professor James Stapleton for his
.assistance, Professor James D. Edwards for his academic stimu-
lation and financial assistance, and to Professor Roland I.
Inabinson, my dissertation committee chairman, whose vitality
arui sincere deducation I will always appreciate.

I also thank Professor Robert W. Johnson for introducing
nus to the world of Finance in general and the field of Consumer
(:redit in particular. His academic and financial concern for
xny education is most appreciated.

The representatives of several consumer finance companies
twere most helpful in allowing me to use and analyze their data.
INithout them this dissertation would not be possible. I espe-
cially thank Dr. Ernst A. Bauer and the management of Household
Finance Corporation for providing the financial assistance
needed to complete the dissertation research.

My parents were very understanding and considerate of my
educational pursuits and provided the family environment

ii

necessary to sustain my motivation and enthusiasm.
My sincere appreciation to Judy whose thoughtful under-
standing and patience made my educational efforts less arduous

arui eminently worthwhile.

iii

TABLE OF CONTENTS

ACKNOWLEDGEMENTS...................

ILIST OF CHARTS. . . . . . . . . . . . . . . . . . . .

LIST OF TABLES 0 O O O O O O O O 0 O O O O O O O O O .

ILIST OF APPENDICES. . . . . . . . . . . . . . . . . .

Chapter

I

II

INTRODUCTION

General Background . . . . . . . . . . . .
Nature of the Study. . . . . . . . . . . .
Purpose of the Project . . . . . . . . . .
Significance of the Study. . . . . . . . .
Importance and Role of Consumer Credit in
our Economy. . . . . . . . . . . . . . .
Consumer Finance Company Role in Credit
Expansion. . . . . . . . . . . . . . . .
Approach to the Study. . . . . . . . . . .
Organization of the Study. . . . . . . . .

MANAGEMENT VIEW OF THE RELATIONSHIP BETWEEN
RISK AND RATE

Introduction . . . . . . . . . . . . . . .
Top Management View. . . . . . . . . . . .
Middle and Lower Management View . . . .

Effect of Management Evaluation Criteria on

Loan Quality . . . . . . . . . . . . .
Management Policies and Attitudes Toward
Rate Levels. . . . . . . . . . . .
Preferential Rates . . . . . . . . .

C 0 0

Effect of Loan Size on Borrower Loan Eligi—

bility . . . . . . . . . . . . . . . . .
Management Attitude Toward Rate Regulation
Influence of Other Variables . . . .
Summary and Conclusions. . . . . .

iv

Page

ii

viii

xiv

\IO\~I>I-‘

ll

13
18
23

25
26
3O

32

35
4o

43
44
45
46

Chapter Page
III EMPIRICAL RELATIONSHIP OF CREDIT STANDARDS
AND RATE LEVELS TEST I
Introduction . . . . . . . . . . . . . . . . 48

Data Used in Test I. . . . . . . . . . . . . 49
Measures of Risk . . . . . . . . . . . . . . 51
Methods of Measuring Association . . . . . . 53
Expected Relationship Between Gross Yield and
Chargeoff Rates. . . . . . . . . . . . . . 54
Expected Relationship Between Gross Yield and

Turndown Rates . . . . . . . . . . . . . . 55
Expected Relationship Between Per Capita In—

come and Chargeoff Rates . . . . . . . . . 55
Expected Relationship Between Per Capita In-

come and Turndown Rates. . . . . . . . . . 56
Expected Relationship Between Average Loan

Size and Risk Acceptance . . . . . . . . . 56

Expected Relationship of Variables Indirectly
Associated with Hypothesis . . . . . . . . 57

Empirical Evidence . . . . . . . . . . . . . 60
Multiple Correlations. . . . . . . . . . 60
Relationship Between Chargeoff Rates and

Gross Yields . . . . . . . . . . . . . 62
Relationship Between Turndown Rates and
Gross Yield. . . . . . . . . . . . . . 67
Relationship Between Chargeoff Rates and
Average Loan Sizes . . . . . . . . . . 71
Relationship Between Turndown Rates and
Average Loan Sizes . . . . . . . . . . 72
Relationship Between Chargeoff Rates and
Per Capita Income. . . . . . . . . . . 73
Relationship Between Turndown Rates and
Per Capita Income. . . . . . . . . . . 74
Summary of Relationship Between Risk and
Independent Variables. . . . . . . . . . . 76
Regression Coefficients Indirectly Related
to Hypothesis. . . . . . . . . . . . . . . 77
Relationship of Per Capita Income and
Gross Yield. . . . . . . . . . . . . . 78
Relationship of Average Loan Size and
Gross Yield. . . . . . . . . . . . . . 79
Summary and Conclusions. . . . . . . . . . . 8O

Chapter Page

IV EMPIRICAL RELATIONSHIP BETWEEN RISK ACCEPTANCE
AND RATE LEVEL TEST II

Introduction . . . . . . . . . . . . . . . . 82
Source and Location of Data. . . . . . . . . 83
Demographic Characteristics of Albany, New
York and Charleston, West Virginia POpula-
tions. . . . . . . . . . . . . . . . . . . 84
Nature of Data and Structure of Sample . . . 87
Elements of Loan Quality . . . . . . . . . . 88
Application of Credit Scoring System to
Test II. . . . . . . . . . . . . . . . . . 95
Expected Score Relationships . . . . . . . . 100
Actual Score Results of Test II . . . . . . 102
Analysis of Credit Factors Included in Score
System . . . . . . . . . . . . . . . . . . 109
Monthly Income . . . . . . . . . . . . . 109
Personal Characteristics of Applicants . 115
Age . . . . . . . . . . . . . . . . . 115
Sex . . . . . . . . . . . . . . . . . 119
Marital Status. . . . . . . . . . . . 121
Residential Characteristics of Applicants 123
Years in Residence and Home Ownership '123
Telephone Possession. . . . . . . . . 128
Occupational Characteristics . . . . . . 130
Stability of Employment . . . . . . . 130
Nature of Borrower Occupation . . . . 133
Purpose of Loan . . . . . . . . . . . 138
Analysis of Credit Factors Not Included in
Score Systems. . . . . . . . . . . . . . . 141
Size of Loan . . . . . . . . . . . . . . 142
Amount of Debt . . . . . . . . . . . . . 145
Monthly Payments . . . . . . . . . . . . 148
Security for Loan. . . . . . . . . . . . 150~
Present, Former, or New Borrower . . . . 153
Race . . . . . . . . . . . . . . . . . . 155
Possession of Checking Account . . . . . 157
Summary and Conclusions. . . . . . . . . . . 157

V EVALUATION OF THE EMPIRICAL EVIDENCE
Is the Hypothesis Confirmed? . . . . . . . . 161
Tests of the Hypothesis. . . . . . . . . . . 162

Managerial Policy. . . . . . . . . . . . . 162
Test I . . . . . . . . . . . . . . . . . . 163

vi

Chapter Page

V Validity of Test I as a Measure of Risk
Acceptance and Rate Relationship . . . . 168
Significance of Hypothesis to Intra-firm
Risk Discrimination. . . . . . . . . . . . 172
Significance of Findings to Public Policy. . 174
General Summary. . . . . . . . . . . . . . . 178

APPENDICES. . . . . . . . . . . . . . . . . . . . . . . 180

BIBLIOGRAPI-IYO 0 O O 0 O O O 0 C O O O O 0 O O O 0 O O O 198

vii

Chart

1.1

LIST OF CHARTS
Page

Amount of Instalment Credit by Holder,
Trends Since 1956. . . . . . . . . . . . . . 15

Top Management View of Risk Standards. . . . . 27

Middle Management View of Risk Standards . . . 33
TOp Management View of Risk-Cost Relationship. 38
TOp Management View of Output Determination. . 41

Relationship Between Gross Yield and Chargeoff
Rate for Firm X, Based on Experiences in 27
States . . . . . . . . . . . . . . . . . . . 64

Relationship Between Gross Yield and Chargeoff
Rate for Firm Y, Based on Experiences in 41
States 0 0 O O O O O O O O O O O O O O O O 0 65

Relationship Between Gross Yield and Chargeoff
Rate for Firm Z, Based on Experiences in 45
States . . . . . . . . . . . . . . . . . . . 66

Relationship Between Turndown Rate and Gross
Yield for Firm X, Based on Experiences in 27
States . . . . . . . . . . . . . . . . . . . 69

Relationship Between Turndown Rate and Gross
Yield for Firm Y, Based on Experiences in 41
States 0 O O O 0 O 0 O O O O O 0 O O O O O O 70

Expected Relationship Between Score and Finance
Rate Level Under Two Score Systems . . . . . 101

Percentage Distribution of Loans Made in Charles—
ton and Albany, by Smith Score System. . . . 106

Percentage Distribution of Rejected Applicants

in Charleston and Albany, by Smith Score
System . . . . . . . . . . . . . . . . . . . 107

viii

Chart Page
4.4 Percentage Distribution of Accepted and
Rejected Loan Applicants, for Albany, By
Smith Score System . . . . . . . . . . . . . 108

4.5 Actual Relationship Between Score and Finance
Rate Levels Under Two Score Systems. . . . . 110

L IST OF EXHIBITS
Exhibit

.1.1. Outline of Research Design . . . . . . . . . . 24

ix

Table

1.1

1.2

LIST OF TABLES

Amount and Percentage of Consumer Credit for
Selected Years . . . . . . . . . . . . . . .

Amount and Percentage of Total Debt in U.S. for
Selected Years . . . . . . . . . . . . . . .

Amount and Percentage of Instalment Loans Held
by Institutions for Selected Years . . . . .

Amount and Percentage of Instalment Credit by
Purpose. 0 O O O O O O O O O O O O O O O O 0

Percentage of Personal Loan Holdings Held by
Financial Institutions . . . . . . . . . . .

Summary of Regression Equations. . . . . . . .

Multiple Correlation Coefficients of Chargeoff
Rate and Gross Yield, Average Loan Size, and
Per Capita Income by State, for Three Finance
Companies. . . . . . . . . . . . . . . . . .

Multiple Correlation Coefficients of Turndown
Rate and Gross Yield, Average Loan Size, and
Per Capita Income, by State for Two Finance
Companies. . . . . . . . . . . . . . . . . .

Simple and Partial Correlation Coefficients of
Chargeoff Rates and Gross Yield, by State and
for Three Finance Companies. . . . . . . . .

Simple and Partial Correlation Coefficients of
Turndown Rate with Gross Yield, By State and
for Finance Companies. . . . . . . . . . . .

Simple and Partial Correlation Coefficients of
Chargeoff Rates and Average Loan Sizes, by
State for Three Finance Companies. . . . . .

Simple and Partial Correlation Coefficients of
Turndown Rates with Average Loan Sizes, by
State for Two Finance Companies. . . . . . .

Page

12

12

14

16

17

59

61

61

63

67

71

72

Table

3.8

3.9

Simple and Partial Correlation Coefficients of
Chargeoff Rates and Per Capita Income, by
State for Three Finance Companies. . . . . .

Simple and Partial Correlation Coefficients of
Turndown Rate and Per Capita Income, by
State for Two Finance Companies. . . . . . .

Multiple Correlation Coefficients of Gross Yield
and Average Loan Size and Per Capita Income,
by State for Three Finance Companies . . . .

Simple and Partial Correlation Coefficients of
Gross Yield and Per Capita Income, by State
and Three Finance Companies. . . . . . . . .

Simple and Partial Correlation Coefficients of
Gross Yield and Average Loan Size by State
for Two Finance Companies. . . . . . . . . .

Selected Social and Economic Characteristics
of POpulation in Sample Cities . . . . . . .

Relative Importance of Borrower Characteristics
as Predictors of Loan Quality. . . . . . . .

Risk Elements in Score Systems . . . . . . . .

Percentage Distribution of Loan Applicants in
Albany and Charleston, by Smith Score System

Percentage Distribution of Loan Applicants in
Albany and Charleston, by Consumer Finance
Company Score System . . . . . . . . . . . .

Comparison of Percentage Distribution of Loan
Applicants by Size of Monthly Income, for
Borrowers and Rejected Accounts. . . . . . .

Comparison of Percentage Distribution of Loan
Applicants by Size of Annual Income, for
Borrowers and Rejected Applicants. . . . . .

Comparison of Percentage Distribution of Loan

Applicants by Age, for Borrowers and Rejected
Accounts . . . . . . . . . . . . . . . . . .

xi

Page

73

75

77

78

79

86

93

100

103

104

112

113

117

Table Page

4.9 Comparison of Percentage Distribution of Loan
Applicants by Sex, for Borrowers and
Rejected Applicants. . . . . . . . . . . . . 120

4.10 Comparison of Percentage Distribution of Loan
Applicants by Marital Status, for Borrowers
and Rejected Accounts. . . . . . . . . . . . 121

4.11 Comparison of Percentage Distribution of Loan
Applicants, by Years in Present Residence,
for Borrowers and Rejected Applicants. . . . 125

44.12 Comparison of Percentage Distributions of Loan
Applicants, by Occupancy of Owner Occupied
Homes, for Borrowers and Rejected Applicants 127

‘1-Ll3 Comparison of Percentage Distribution of Loan

Applicants, by Telephone in Residence, for
Borrowers and Rejected Accounts. . . . . . . 129

4-.Ju4 Comparison of Percentage Distribution of Loan

Applicants, by Years in Present Occupation,
for Borrowers and Rejected Loan Applicants . 132

4° 1—55 Comparison of Percentage Distribution of Loan

Applicants, by Occupational Groups, for
Borrowers and Rejected Accounts. . . . . . . 136

4‘ J~€S Comparison of Percentage Distribution of Loan

Applicants, by intended Purpose of Loans, for
Borrowers and Rejected Accounts. . . . . . . 140

4 .
“1—77 Comparison of Percent and Number of Loans by

Size of Loans, for Borrowers . . . . . . . . 143

4
“J~EB Comparison of Percent of Loans by Size of

Additional Credit, for Borrowers . . . . . . 144

4
‘:1~S9 Comparison of Percentage Distribution of Loan

Applicants by Size of Instalment Debt, for
Borrowers and Rejected Accounts. . . . . . . 146

‘ :3(D Comparison of Percentage Distribution of Loan

Applications by Size of Instalment Debt
Including Present Loan, for Borrowers. . . . 147

xii

Table Page

4.21 Comparison of Percentage Distribution of Loan
Applicants by Size of Monthly Payments, for
Borrowers and Rejected Accounts. . . . . . . 149

4.22 Comparison of Percentage Distribution of Loan
Applicants by Size of Monthly Payment, in-
cluding Present Loan, for Borrowers. . . . . 149

4.23 Comparison of Percentage Distribution of Loans
by Type of Security, for Borrowers . . . . . 151

4.24 Comparison of Percentage Distribution of Loan
Applicants by Source--Present, Former, or
New Applicant, for Borrowers and Rejected
Applicants . . . . . . . . . . . . . . . . . 154

'4.H25 Comparison of Percentage Distribution of Loan
Applicants by Race, for Borrowers and
Rejected Accounts. . . . . . . . . . . . . . 156

4- 26 Comparison of Risk Elements and Credit Scores
for Borrowers in Albany, N.Y. and Charleston,
W. Va. . . 0 . . . . . . . . . . . . . . . O 160

53- 1. Simple and Partial Correlation Coefficient (r)
of Chargeoff Rate and Gross Yield, for Three
Finance Companies. . . . . . . . . . . . . . 164

ES-'22 Simple and Partial Correlation Coefficients (r)
of Turndown Rate and Gross Yield, for Three
Finance Companies. . . . . . . . . . . . . . 165

55 ~ :3 Summary of Characteristics of Borrowers and

Rejected Loan Applicants in Albany and
Charleston, 1964—1965, by Mean Average 167

xiii

j,

Appendix

A

(3:3

LIST OF APPENDICES
Page

Dollar Charges on Different Size Loans Repaid
According to Contract at Lawful Maximum
Rates of Charge Under 44 Small Loan Laws
Arranged by Loan Size Ceiling. . . . . . . . 181

Consumer Finance Companies Supplying Information
Used in Study. . . . . . . . . . . . . . . . 182

Simple and Partial Correlation Coefficient
Formulas O O O O O O O O O O O O O O O O O Q 183

Risk Element Weights, Smith Credit Score
System . . . . . . . . . . . . . . . . . . . 184

Mean and Standard Deviation Values of States
Experiences for Variables Used in Correlation
Analysis, for Three Firms. . . . . . . . . . 186

Comparison of Percentage Distribution of
Borrowers, by Bank Account . . . . . . . . . 187

Supplementary Tables to Chapter IV . . . . . . 188

Comparison of Borrower Instalment Debt in
Various Income Classifications, for Albany
and Charleston . . . . . . . . . . . . . . . 189

Comparison of Borrower Monthly Payments in
Various Income Classifications, for Albany
and Charleston . . . . . . . . . . . . . . . 190

Comparison of Borrower Residence Stability for
Various Income Classifications, for Albany

and Charleston . . . . . . . . . . . . . . . 191

Borrower Monthly Income for Renters and Non-
Renters, for Albany and Charleston . . . . . 192

Borrower Resident Stability for Renters and
Non-Renters, for Albany and Charleston . . . 193

Monthly Income for Those with Telephone in
Residence, for Albany and Charleston . . . . 194

xiv

 

Appendix

G7

G8

G9

Residence Stability of Borrowers with Tele-
phone in Residence, for Albany and

Charleston . .

Monthly Income for Present, Former,

and New

Borrowers, for Albany and Charleston .

Borrower Income for Whites and Non—whites,

for Albany and Charleston.

XV

Page

195

196

197

CHAPTER I

INTRODUCTION

General Background

Since the early 1900's when Small Loan Laws were enacted
through the efforts of the Russell Sage Foundation, consumer

credit has played an increasingly important role in our econ-

Omy. In 1964, $59.4 billion of instalment consumer credit

a rate of

. 1
increase greater than that of gross national product. Ap-

Was outstanding compared to $11.6 billion in 1949,

Proximately 54%of the population owes some form of consumer
dEBIDt: to one of many types of financial institutions ranging
fITC>Dn a small financial c00perative to a multimillion dollar
fjjrliince company. The variety of terms and financial arrange-
me’r11:s available at financial institutions is as wide as the
per<:es charged for their assortment of services. Some insti—
ttltitions, as commercial banks, tend to charge lower prices and
grant credit to preferred risk customers, whereas consumer
fit"tance companies generally charge higher prices and special-

12 . . . . .
Q in serViCing those With a greater degree of risk.
1Federal Reserve Bulletin, March, 1965, p. 472.

l

‘- 1'. .I,
\c «II I.
nth.

 

2
As is true in most commodities, the greater the cost of
producing financial services the higher the equilibrium price.
Since commercial banks tend to accept better risks relative
to finance companies, their rate of finance charge is somewhat
less. However, before drawing definite conclusions between
cost and price relationships the historical, moral, and legal
restrictions applied to consumer credit must be investigated.
Throughout history, society has regarded consumer debt

with apprehension and has legally regulated its use.3 Part

of this regulation stems from our Judeo-Christian heritage
where the virtues of hard work and thrift were the pillars

of the Protestant Ethic. While past saving was considered
meritorious, borrowing on future income was held in disrespect
and disfavor. The attitude toward past saving diverged from
the View toward future saving. Loans used primarily for pro—
ductive purposes were deemed economically imperative while
loans used for immediate consumption reasons were looked upon
With askance. As the Western World made greater economic
13rogress and as social mores changed, consumer credit was

Elradually regarded as economically necessary and socially
._“‘~

2
This statement assumes a normal equilibrium price situ-

E3”ttion where the demand and supply of funds are somewhat elas-
ic.
3
Nugent, Rolf, and Robinson, Louis, Regulation of the
Ԥ-ll4all Loan Business, Russell Sage Foundation, New YOrk, 1935.

3
desirable. Today, though "productive" loans have gone un-
regulated, states still have jurisdiction over the terms of
consumer credit. Most states have usury laws permitting a
maximum rate of 6% or 7% for all types of credit. Because of
the higher costs associated with consumer credit, the usury
rate makes it virtually impossible to economically attract
sufficient capital to establish consumer credit enterprises.
Each state, except for Arkansas, now allows higher rates and
encourages firms to enter the small loan market by permitting
them to operate under a Small Loan Law, modeled after the
Model Small Loan Laws first promolgated by the Russell Sage
4

Foundations in 1916.

The impetus for the enactment of enabling legislation
to permit consumer credit at higher rates was the "loan shark
evil" existing in the late 1800's and early 1900's. It was
not uncommon for illegal lenders to charge rates as high as
200% and 300% per annum and often use somewhat brutal collec-
tion tactics to attain their goals. The small loan statutes
1"lave contributed to the practical elimination of the "loan

5
‘3}Iark evil." The social objective of these regulations is

t2<> have a freely accessible source of funds at reasonable
\ I

4 I .

Hubachek, F.B., "The Development of Regulatory Small
ian Laws,f Law and Contemporary Problems, Vol. VIII, No. 1,
later, 1941, pp. 108-138.

JC.<:>
wi

5
Ibido' pp. 108-138.

 

 

 

Im‘

4

rates; however, to achieve this social goal, the price must
be sufficient to induce suppliers of capital to enter the
market and earn a reasonable profit.

The result of these state regulations is a conglomera-
tion of 49 different Small Loan Laws all uniquely different
and all allowing diverse maximum rates of charge. The National
Conference of Commissioners on Uniform State Laws is now work—

ing to unify these laws.

Nature of the Study

The focus of this study is on the relationship between
rate levels and risk acceptance standards used by consumer
finance companies. While it may be true that consumer finance
companies as a whole specialize in servicing higher risk mar—
kets, there are Operational differences within this segment
of the consumer credit industry.

This study is an intra-consumer finance company survey
dealing with one segment of the entire consumer credit in-
dustry--the consumer finance industry. The consumer credit

iUdustry consists of all legal financial institutions grant-

‘iJfig direct and indirect consumer loans and includes commercial
13Einks, sales finance companies, credit unions, retail stores,
‘Eilfixi consumer finance companies. The consumer finance industry

‘i~s a collection of firms granting direct cash loans and oper-

1tLing under the jurisdiction of the Small Loan Law or Industrial

, .

O“

 

 

 

..o-

-.-h

a .

t

5

Loan Law within a particular state. The amount of cash loans
held by consumer finance companies at the end of 1964 was $4.4
billion, with the 10 major national firms accounting for

over 15% of this amount.

The constitutional basis for the Small Loan Laws rests

on the right of a state to correct a social evil. By permit-
ting a certain rate, each state strives to make the terms both
economically feasible and socially and morally desirable. The
crucial issue is hgw socially desirable and hgw economically
feasible. The issue this study hOpes to face is the degree

of desirability achieved through a particular rate. Each
person or group of persons has its own concept of fairness and
desirability and what is fair and conscionable to one is quite
the contrary to another. The rate in each state reflects the
attitude of its legislative assembly to correct a social evil
and insures the provisions of facilities to those who need
Credit for consumption purposes. With a particular rate, an
entrepreneur must determine the level of expenses he can toler-
ate in order to retain a normal profit. The higher the rate
lie charges his customer, the more expense he can afford in

nnaintaining a reasonable rate of return.‘
Empirical evidence indicates that costs tend to be greater

jLIa'higher rate states. In a recent study by Paul E. Smith,
\

6
Federal Reserve Bulletin, March, 1965, p. 472.

.ar‘:

v-.

a...

 

 

 

6
three finance companies charging higher rates also sustained
higher levels of Operating expenses in all categories--rent,
salaries, etc., compared with two firms charging lower rates.7
The higher rate firms also incurred more bad debt losses,
possibly indicating a higher amount of repayment uncertainty
within their portfolios. While occupancy costs, investigation
costs, salaries, advertising costs, etc., are a large part of
the firm's expense, bad debt losses are also a significant
portion. As the allowable rate rises, a finance company may
be able to tolerate a greater debt loss either by reducing
collection efforts or by accepting more repayment uncertainty

from the borrower. Whether or not it does and to what extent

is the interest of this study.

Purpose of the Project

The primary purpose of this dissertation is to examine
the influence of legal finance charge ceilings on the quality
of credit accepted by consumer finance companies.

Assuming a positively sloped supply schedule of funds,
the most general appriori hypothesis is that significant rate
differences alter acceptable risk levels, therefore showing

ii- positive relationship between risk and rate. Risk is de-

i§lined.as the probability of loan repayment in accordance with
\

I Smith Paul, Cost of Providing Consumer Credit, National
‘klreau of Economic Research, New York, 1962, p. 2.

 

 

 

 

the terms of the lender-borrower contract.

The test of this assertion relies on a study of manage-
ment policies, a regression analysis, and an empirical study
of loan applicant quality.

The conclusions derived might prove or give evidence to
positive, negative, or uncertain relationships. From the
empirical study it is hoped to derive a supply schedule of
funds available to potential borrowers at various rates. What-
ever the conclusions, the goal is to provide a rational, logical
method (or approach to a method) for establishing legal finance
charge ceilings. The approach of this study is market oriented,
suggesting borrower eligibility rather than cost differentials

as a key measure of rate structure adequacy.

Significance of the Study
The apparent need for information concerning those ex-

cluded loans due to finance charge ceilings is expressed in a
recent bulletin by the National Conference of Commissioners
On Uniform State Laws. "The Commissioners on Uniform State
Laws have undertaken their consumer credit project at the
irequest of the Council of State Governments. . .The Commis-
fisioner's project contemplates studies with a view toward
(Zomprehensive and simplified legislation on substantially all
Eispects of consumer credit trade practices, including disclo-

sSure of cost of credit, rate controls or ceilings, the time

 

 

.n.

 

 

 

 

 

8
price doctrine and usury, contract provisions, etc." (under-
lining mine).

There are two approaches to use when determining an 0p-
timum finance charge ceiling.9 One approach is that of setting
rates on the basis of the lender's costs. Since the cost per
dollar of loan is larger on small loans, higher rates may be
justified; as loan sizes becomes larger, however, the fixed
expense is spread over more loan dollars and average costs

are reduced resulting in lower rates. In the report, The Con-

sumer Finance Industgy submitted by the National Consumer Fi-

 

nance Association to the National Commission on Money and
Credit, it states that "small loan rates of charge are designed
to permit the necessary higher costs of their services, of the
more complete investigation, and the special services attached
to instalment lending of relatively small sums of money."
Professor Paul Smith, in a recent monograph issued by
the National Bureau of Economic Research, shows the cost of
Providing consumer credit of four major types of financial in-

stitutions. His goal was to "ascertain the costscnfdifferent

\

T

SNational Conference of Commissioners on Uniform State
1¥Ehws, Project on Retail Installment Sales, Consumer Credit,
séiflgall Loans and Usury, August 22, 1964, p. 1.
It should be noted that a "finance charge" is not the
‘Ssiame as an "interest charge", since interest expense is but
tie small portion of consumer credit costs. Lenders need
<3 charge a finance charge greater than the interest rate
‘:;<b recoup service and risk assumption costs.

 

.c-
s .

»>"

 

u.

9
types of consumer credit obligations in order to understand
the level and structure of the finance rates paid by con-
10
sumers." In an earlier monograph issued by the National
Bureau of Economic Research, Ernst A. Dauer also shows evi-
ll
dence of the relationship between costs and rates.

The approach of correlating each finance company's costs
with the level of finance charges emphasizes the lender rather
than the borrower. In a cost study, the goal is to determine
the minimum rate that finance companies must charge in order
to earn a fair return. This rate does not indicate that it
should be the maximum rate. While the setting of a minimum
rate at this level does cover normal operating costs, it
does not necessarily cover the risk of loss for those bor-
rowers whose financial risk warrants a rate greater than the
"cost rate."

The second approach to rate setting is to base a rate
on the portion of the public that can be served at a given
rate. While it may be true that the "cost rate"--let us

aSsume 3%F-does provide credit for those who are in the 3%

Iiisk class, it does not provide credit for those whose risk

-\

W7

10 . . . .
Smith, Paul E., Cost of Prov1ding Consumer Credit,

IQWEitional Bureau of Economic Research, New YOrk, 1962, . 2.
P
11
Dauer, Ernst A., Comparative Operating Experiences
‘5313: Consumer Instalment Financing Agencies and Commercial
‘J§ES§nk§Ll929-4l, National Bureau of Economic Research,
:3 Qw York, 1944 .

 

 

 

 

4..

10
is greater than 3%. If a maximum rate is set, a private firm
cannot serve those who are willing to pay a higher rate be—
cause it would not be prOperly matching returns to costs and
a fair return. The excessive losses would offset any return
on capital.
The result of setting a minimum price on capital has the
same effect as a minimum wage for labor. If the employee
adds value to the firm of only $1.00 per hour and the minimum
wage is $1.25 per hour, he will not be hired. Unless he raises
his marginal productivity, the worker will remain unemployed.
Similarly, with a maximum rate the finance company will not
serve the high risk borrower because the risk is not worth
the allowed finance charge. By fixing a maximum rate for
credit we say that some consumers are not worthy of credit.
The prOper or fair rate which provides the greatest bene-
fit to the consumer can be determined if we know how many
people are "cut out" of the market at any given rate. If this
information is available we can reliably say, for example,
that a 3% rate will make 20% of the pOpulation ineligible to
receive a loan from a consumer finance company. Whether or
rust this is desirable is a matter of value judgment. This
:jIJdgment is usually made by the state legislatures as they
Getermine the finance charge ceilings. Therefore, it is very

dLImportant they possess evidence showing the effects of certain

A-a' "

J"-

,1.-

 

11
rates upon the pOpulation they represent. Only through a
full knowledge of probable effects can an enlightened decision
be made.
This dissertation uses the second approach to study this
problem. By showing the results that rate ceilings have had
in the past, we can be more certain of the significance they

might have in the future.

Importance and Role of Consumer Credit in our Economy
The importance of consumer credit in our economy cannot
be overemphasized. Since 1939 the amount of consumer credit
rose from $7.2 billion to over ten times this amount in 1964.
Gross National Product, meanwhile, has increased a little over
six times. Instalment credit has increased more than propor-
tionately during the past 25 years as it now comprises 77.3%
of total consumer credit compared to 62.5% in 1939. Matter
of fact, since 1959, the gross national product has increased
3l%--certainly an impressive increase—~but consumer instalment
cmedit has increased even more and shows a 49% growth rate.
(Table 1.1) While the level of federal, state, and local
Sgovernment debt has increased at a very large rate, consumer
cZredit has kept pace and now comprises 22% of total govern-
fruent, private and consumer credit. (Table 1.2)
There are many reasons for this large rate of consumer

cI‘edit increase, but probably the most important reasons are

 

12

TABLE 1.1

AMOUNT AND PERCENTAGE OF CONSUMER CREDIT

FOR SELECTED YEARS

(Amounts in billions of dollars)

 

1939 1959

 

1964

 

 

Amt. Percent Amt. Percent Amt. Percent

 

Instalment
Credit

Non-Instal.
Credit

Total Con-
sumer Credit $7.2 100.0% $51.5 100.0%

$4.5 62.5% $39.2 76.0%

2.7 37.5 12.3 24.0

 

 

Gross National

$59.4 77.3%

17.4 22.7

$76.8 100.0%

 

 

Product $482.8 $634.6

Source: Federal Reserve Bulletin, March, 1965.
TABLE 1.2

AMOUNT AND PERCENTAGE OF TOTAL DEBT IN U.S.

_

FOR SELECTED YEARS

(Amounts in billions of dollars)

1959

 

Amount Percent

IPublic Debt $298 35.2%
-I?rivate and Business 372 44.0
COnsumer Debt 175 20 . 8

Total

\

Source:

 

$845 100.0%

‘—"

Survey of Current Business.

1964

 

Amount Percent

$341 31.2%
511 46.7
242 22.1

 

 

$1094 100.0%

13
the changing social attitudes and the incidence of greater
economic stability encouraging and conditioning consumers to
assume more debt. Since most consumers prefer to purchase
products and durable goods rather than rent or lease them,
there has been a greater need to finance these larger pur-

l2
chases through more debt.

Consumer Finance Company Role in Credit Expansion
While the level of consumer credit has increased at a
growing rate, the proportion served by the consumer finance
companies has remained steady. In 1959 and 1964 their rela-
tive share of the instalment credit market amounted to 8.5%.
Meanwhile the market share of commercial banks and credit
unions has increased substantially while retail stores have
suffered a strong decline in credit. During the past ten
years banks have aggressively competed for consumer credit
in contrast to their conservative policies of 25 years ago.
(Table 1.3, Chart 1.1)
Part of the explanation for the shifts within the
-industry is the increased emphasis of commercial banks on
Jsetail automobile credit. Personal cash loans have increased

in importance since 1959 when they amounted to 24.6% of total

 

 

12
Neifeld, M. R., The Personal Finance Business, Harper
& Bros., New York, 1933, pp. 189—194.

 

._‘

.t.

 

14
TABLE 1.3
AMOUNT AND PERCENTAGE OF INSTALMENT LOANS HELD BY INSTITUTIONS

FOR SELECTED YEARS
(Amounts in billions of dollars)

 

 

1939 1959 1964

 

 

 

Amt. Percent Amt. Percent Amt. Percent

 

Commercial
Banks $1.1 24.5% $15.2 38.8% $23.9 40.3%

Sales Finance
Companies 1.2 26.7 10.3 26.3 14.8 24.9

Consumer Fin.
Companies* —— -- 3.3 8.5 5.1 8.5

Credit Unions .1 2.2 3.3 8.4 6.5 10.9

Other Financial

 

 

 

Ins. .7 15.4 1.4 3.5 1.7 2.9
Retail Outlets 1.4 31.2 5.7 14.5 7.4, 12.5
Total $4.5 100.0% $39.2 100.0% $59.4 100.0%

*Consumer Finance Company data included in "Other Fi—
Ilancial Institutions" until Sept. 1950.

Source: Federal Reserve Bulletin, March, 1965.

Jinstalment credit, but rose to 27% in 1964. (Table 1.4)

UDhe personal cash loan market has largely been dominated by
1:11e consumer finance industry but it has recently faced
Esevere competition from sales finance companies who have
‘51C3ubled their personal cash loan holdings and have increased

‘tllleeir share of the total instalment credit market from 10.1%

15

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16
TABLE 1.4
AMOUNT AND PERCENTAGE OF INSTALMENT CREDIT BY PURPOSE

(Amounts in billions of dollars)

1939 1959 1964

 

 

 

Amt. Percent Amt. Percent Amt. Percent

 

Automobiles $1.5 33.3% $16.4 41.8% $24.5 41.3%

Other Consumer
Goods 1.6 35.5 10.6 27.1 15.3 25.8

Repair and
Modernization .3 6.6 2.8 7.2 3.5 5.9

Personal Cash
Loans 1.1 24.6 9.4 23.9 16.1 27.0

 

Total $4.5 100.0% $39.2 100.0% $59.4 100.0%

 

Source: Federal Reserve Bulletin, March, 1965.

to 12.6%. (Table 1.5) Consumer finance companies, meanwhile,
have decreased their share of the personal cash loan market
from 30.4% to 27.6%.

Since many sales finance companies have merged and con-
solidated operations with small personal loan companies and
Consumer finance companies have expanded their sales finance
Ifunctions, a separate distinction between these institutions

:Lssnc longer clear cut; therefore, a combined total is useful.
TIflhe sales finance companies and consumer finance companies

1:<>ta1 approximately 40%.of the total personal loans outstanding

17

TABLE 1.5

PERCENTAGE OF PERSONAL LOAN HOLDINGS HELD BY
FINANCIAL INSTITUTIONS

 

 

1959 1964
Commercial Banks 34.1% 33.5%
Credit Unions 17.6% 20.2%
Other Financial Institutions 7.8% 6.1%
Consumer Finance Companies 30.4% 27.6%
Sales Finance Companies __;g;y% _1246%
Total 100.0% 100.0%

 

Source: Federal Reserve Bulletin, March, 1965.

 

while the commercial banks have 33% with credit unions holding
20%.of the total.

There is no doubt that consumer credit is becoming a
nwre important influence in the stability of the economy,

since an expansion or contraction of credit can have serious
13
.repercussions. In large measure, the maintenance of

<economic growth is becoming more dependent on a continuous
13
There are many discussions of consumer credit expan—
Eszion and contraction effects upon the economic system. One
Of the most succinct is by: Smithies, Arthur, "Uses of
Selective Credit Controls," United States Monetary Policy,
{IFITe American Assembly, Columbia University, 1958, pp. 73—89.

18
and steady growth of instalment credit. As pointed out by a
leading industry economist, "if the role of consumer instal-
ment credit grows at the same rate as total output, the total
amount outstanding, now $60 billions, would become $100 billion
by 1975. If consumer instalment continues to outpace the
growth in total output to the same degree as it has in the
past ten years, it will be 2% times what it is today (i.e.,
$150 billion). And if we just cut that rate of acceleration
in half, consumer instalment credit will still more than double
14

in the next ten years (i.e., $120 billion)."

Because of the importance of consumer credit to the econ-
omy, now and in the future, it is imperative that a logical

rate setting mechanism be established and maintained to control

the proper use and amount of consumer credit.

_Approach to the Study

Test I attempts to explain a relationship between gross
yield and risk standards by three finance companies, each
Operating in more than 40 states. By correlating their ex-
.periences of gross yield with chargeoff and turndown rates on
a.state basis, it will show whether or not risk is positively
Eissociated with rate structure. The hypothesis states there

-i.s a direct and positive relationship between these variables.
x
14
. Dauer, Ernst A., Report presented to the Household
lgfllynance Corporation Executives Conference, April 30, 1965,p. 4.

 

.oO'

 

 

 

19
In addition, other variables are correlated with chargeoff
rates to determine their influence on risk levels.

The hypothesis will also be tested through the following
procedure. (Test 11) Relevant data will be collected from a
sample of loan applications taken by three companies all located
in a large metrOpolitan area in each of two selected states.
Each state differs according to the legally imposed finance
charge ceiling, but most other legal regulations are similar.
The only variable allowed to change will be the rate ceiling;
all other variables--for example, a legally imposed loan size
maximum is the same in each state. The objective is to trace
any risk differences to variances in finance charge ceilings.

New York and West Virginia have the same loan ceiling
of $800 but vary in the legal rates of charge as the average
gross yield on loans outstanding for New York in 1964 was

15 16
20.33% compared to the 29.04% for West Virginia. Gross
yield is the amount of interest collected as a percentage of
average loan outstanding during a particular year. While
these two states vary geographically, demographically, and

—industrially, the cities of Albany, New York and Charleston,

tVest Virginia are quite similar in these respects and provide

 

 

15
Annual Report of the Superintendent of Banks, State
of New York, 1964, p. 97.
16
Consolidated Report of the Small Loan Licensees of
We st Virginia, 1964, p. 5.

 

 

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.o.

 

on."

"an

 

'~

'!

 

20
excellent areas for comparing the effects of rate on risk
acceptance. The Standard Metropolitan Statistical Areas of
each city are approximately the same in size and industrial
characteristics.

A random sample of 200 written applications refused by
each of the three finance companies in each city will be
analyzed. In addition, 200 written loan applications accepted
by each firm will be studied. In total, the data consist of
borrower characteristics from 2400 loan applications from
two cities with 600 accepted loan applications and 600 turn-
down loan applications from each city.

All of the relevant material included in the loan appli-
cation will be collected, and comparisons will be made among
companies in the two states. The most pertinent and relevant
information concerns the nature of those risk elements which
help the lender determine whether or not he should grant the
loan. The final assessment of risks is compared to the rate
charged for the loan. Obviously, if the risk or the lender's
perception of risk is greater than the rate warrants, he will
not grant the loan. There are many considerations which the
lender takes into account when he assesses the probability of
loan repayment. These are classified as the risk elements of
a loan. The intensity and number of risk elements indicated

in the written application will be compared by state.

21

The specific risk elements of borrowers are: (not in

order or importance).

The

The

The

The

The

The

The

The

The

The

The

The

The

sex of the borrower

race of the borrower

age of the borrower

marital status of the borrower
borrower's residence stability
borrower's occupational stability
nature of the borrower's occupation
monthly income of the borrower
borrower's instalment payment schedule
borrower's instalment debt outstanding
purpose of the loan

security for the loan

finance company's credit evaluation score

It is expected, for instance, that the percentage of lower

income applicants who were refused loans will be greater in a

state where the legally imposed rate ceiling is low compared

to a "high rate" state. This agrees with the principle that

the lenders prefer less risk to more and will conclude, for

example, that a prospective borrower with $3,000 annual income

carries a higher degree of risk than a prospective borrower

with $5,000 annual income, other things being equal.

This type of evidence may indicate that rate variations

22
account for the degree of discrimination shown by consumer
finance companies when they assess the risk of each prospec-
tive borrower.

These data should assist to determining the proportion
of the population eligible to receive loans from finance com—
panies. The comparison of differences in the prOportions of
those eligible to receive loans under various rates will aid
in determining approximately what segment of society can be
served at any given rate. The result will be an approximate
"supply curve" showing who is "cut out“ of the market at any
rate.

In addition to Tests I and II, a survey will be made to
determine consumer finance company management policies and
attitudes toward risk discrimination and rates. Representa-
tives from seven of the 10 largest finance companies will be
interviewed about their company's role in the risk adjustment
process.

Through a management attitude and policy study and through
empirical observation, the hypothesis will be analyzed and
tested from different but not necessarily compatible perspec-
tives. One of the primary reasons for this study is to deter-
mine the divergence and compatibility of these perspectives
and use this acquired information to analyze a potential bor-
rower's loan eligibility.

An outline of the research design if shown in Chart 1.1.

23

Organization of the Study

Chapter II reports and analyzes risk discrimination
policies and attitudes toward the role of rates and risk
acceptance standards by top, middle, and lower management
personnel. In Chapter III, the results of regression analy—
ses between relevant variables are shown and their implica-
tions analyzed. Chapter IV presents the findings from tabu-
lating and analyzing loan application data from loan offices
in the two sample cities. The conclusions and implications

of the findings are presented in Chapter V.

24
EXHIBIT 1.1
OUTLINE OF RESEARCH DESIGN

Test I

Data Source: Three major national consumer finance
companies

Data Analyzed: 1) Chargeoff rate in each of approximately

40 states

2) Turndown rate in each of approximately
40 states

3) Gross yield earned in each of approximately
40 states

4) Average loan size in each of approximately
40 states

5) Per capita income in each of approximately
40 states

Data Correlated: Chargeoff rate and gross yield

Turndown rate and gross yield
Chargeoff rate and average loan size
Turndown rate and average loan size
Chargeoff rate and per capita income
Turndown rate and per capita income

Test II

NEW YORK (av.rate: 20.33%) % WEST VIRGINIA (av.rate: 29.04%)

Albany Charleston
a. Three finance companies Three finance companies
1) accepted loan accepted loan
applications applications

BORROWER CHARACTERISTICS BORROWER CHARACTERISTICS
2) rejected loan rejected loan
applications applications

II
M

BORROWER CHARACTERISTICS ? BORROWER CHARACTERISTICS

Management Attitude Survey

LOW RATE STATES # HIGH RATE STATES
Seven finance companies Seven finance companies
POLICIES = 9 POLICIES

ATTITUDES ATTITUDES

CHAPTER II

MANAGEMENT VIEW OF THE RELATIONSHIP BETWEEN RISK AND RATE

Introduction

While there is evidence that cost levels influence rate
levels, is there evidencecfiéacausal relationship between rate
levels and risk acceptance costs? To help answer this ques-
tion, representatives from seven of the ten largest finance
companies were interviewed to determine the influence of
finance charges on their loan quality decisions. Operating
personnel as Vice Presidents, Supervisors, Branch Managers,
Research Directors, Legislative Agents, Economists, and
Financial Analysts expressed their Opinions and attitudes
concerning rate and its relationship to formal and informal
policies. The purpose of the interviews was: 1) to deter-
mine whether or not and to what degree managers consider
rate in formulating Operating policy, and 2) to determine
the extent of policy implementation at tOp, middle, and lower
management levels. This portion of the study attempts to
measure management intentions whereas chapters three and four
empirically measure borrower characteristics to indicate risk

and rate relationships.

25

26

Top Management View

 

Vice Presidents and other top management personnel from
six of the seven companies interviewed state that rate differ-
entials play little or no role in their decisions to accept
or reject loan applications. This response was elicited
from the question: If a marginal risk borrower was able to
obtain credit in a ”high rate" state, would your acceptance
decision differ if the same applicant applied for a loan in
a "low rate" state where only the rate varies? The most com—
mon response was: No, our decision would not be different.
Their general concensus of Opinion shows little flexibility
in loan acceptance criteria once an acceptable rate is permitted.
In management directives and policies the standards for quali-
fied and unqualified loan applications take definite form;
the standards for acceptability are fairly rigid and most
loans are considered either "good" or "bad". This two—fold
classification leads to an arbitrary distinction and a firm
line of demarcation between the acceptable and the unaccept-
able. At national finance companies conducting business in
many states and under various regulations, this loan quality
demarcation line appears the same in all states regardless of
the particular rate charged. Chart. 2.1 graphically displays

this relationship.

27

CHART 2.1 TOP MANAGEMENT VIEW
OF RISK STANDARDS

Finance

Charge %

Minimum
Rate

 

 

Maximum Risk Accepted

1. There is a minimum rate where the Consumer Finance Companies
will not Operate.

2. At any given acceptable or adequate rate the maximum risk is
accepted.

 

 

\-

28

One major finance company uses a quantitative scoring
system and sets a minimum score which each loan applicant
must attain before he is eligible for credit. The minimum
score "cut off point“ is almost exclusively dependent upon
the delinquency rate in that area. A company official states:
"In determining the cut off point we did take into consider—
ation the gross income of the average loan made. However,
our cut off point was not significantly affected by this
factor. By far the most significant factor affecting the
cut off point was the delinquency rate and the ability of
the system to discriminate in a particular area."

It must be pointed out, however, that a Vice President
of one of seven finance companies takes a diametrically
opposed position on this subject. At his firm, rate is con—
sidered when adopting loan criteria policies; as rate ceil-
ings change, his company's credit standards also change.

In the home office training given to branch managers,
very little emphasis is placed on adjusting risk to accom—
modate a particular rate. Most of the training emphasizes
the recognition of "good" and ”bad” loan characteristics.
The discernment of a good loan from a bad loan entails a
concept of absolute rather than relative risk and suggests
a discontinuous risk scale. For example, a manager states:

”We never make a loan with the idea that it will be uncollect-

 

...

‘K

- a

'n.

29
able. Every loan is good when we make it: it turns out to
be bad after we make it."

The formulation of a fixed cut—off level of risk accept—
ance indicates the use of average revenue and average cost
relationship analysis in determining output. In setting
optimum output levels the most profitable level of output is
at the point where marginal cost equals marginal revenue.l
Using marginal relationships, managers would adjust output
when marginal revenue changes and would grant loans only to
those customers whose expected value of repayment matches
their expected costs. Company manager's decisions do not
reflect this pricing and output policy since output levels
are not consciously altered from state to state. Of course,
other costs besides the risk acceptance costs can and are
adjusted as revenue levels change, but managers state they
do not intend to adjust charge off rates by altering the
number of acceptable marginal accounts as finance charges
vary. Rather than using each state as a separate pricing
and output entity, the total of company operations is the
unit used when setting optimum credit volume levels. This

mechanism, in effect, uses average costs and revenues of

all states as the basis for output decisions.

_‘

Leftwich, Richard H., The Price System and Resource
‘ALLocation, HOlt, Rinehart and Winston, New York, 1961,
ChGpter 9 .

 

 

All

 

 

~ .

~._.

'-.,

30

Middle and Lower Management View

There is another side of the story, however, as there is
evidence that middle management does, in fact, determine out?
put levels on a marginal cost basis. Supervisors of branch
managers agree that company policies are not implemented as
rigidly as they are formulated. Somewhere in the chain of
command from the board of directors to the branch manager,
risk adjustments are made. Probably the most revealing mech-
anism for imperfect policy implementation occurs at one particu-
lar finance company. TOp management at this company allows a
branch office to have a bad debt reserve which varies with
the gross yield rate. In a state where the gross yield is
relatively high the bad reserve is also high, and the branch
manager can tolerate a higher level of loan "chargeoffs" by
either accepting higher risk customers or curtailing collec-
tion efforts. As long as the branch manager's bad debts are
within the parameters of the allowable reserve, his performance
is judged satisfactory. The same level of “chargeoffs“ would
be unacceptable in a state having a lower gross yield. Here,
in effect, is a direct relationship between gross yield and
risk acceptance, however subtle it may be. While most managers
on all levels recognize the relationship of yield and chargeoff
rate, the association of rate and risk tolerance is not recog-

nized by managers in the upper echelon of the organization structure.

31

Middle management---the supervisors of branch managers---
is generally quite aware of the role of finance charges upon
their loan quality standards. Supervisory personnel operating
in more than one state recognize that while a marginal risk
loan applicant might be acceptable in a high rate state as
Florida, the identical loan applicant would be rejected in a
low rate state as New York. The rationale is economic —————
if the rate is adequate, they can "afford" to take the risk.

In the process of communicating and implementing decisions
from policy makers to branch managers, most of the adjustment
is pivoted at the general supervisory level where the actual
operating and short range decisions are made. The vital adjust—
ment is made by the supervisor who evaluates the branch mana—
ger's performance by reviewing the accepted and turndown loan
applications in a branch office. By deciding whether or not
the branch manager is too lenient or too strict, the supervisor
tends to sway the judgment of the branch manager, thereby estab-
lishing a future risk acceptance pattern. An experienced su—
pervisor states that branch managers are not aware of these
differences in acceptance standards because they are less

mobile and have little Opportunity to compare Operations in

 

2Supervisory personnel recognize that an applicant's re-
payment habits depends on his cultural and social environment
and that two diverse states as Florida and New York may not
provide an adequate basis for measurement. Assuming applicant
characteristics do not differ in each state, however, their
10an acceptance decision would depend on rate differences.

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states where rate, among other things, is quite different.
The supervisors are quite aware of these differences, and
most of them do vary their evaluation standards accordingly.
Chart 2.2 graphically displays middle management's view of
the risk and rate relationship.

All branch managers who were interviewed say they are
not cognizant of rate differentials when making loan decisions.
Since their training at the home office does not specify any
rate consideration and their informal training is usually in
the state where they presently operate, there is little Oppor-

tunity to consider and recognize rate differentials.

Effect of Management Evaluation Criteria on Loanfiguality

Most companies base their evaluation of branch managers
on the amount of net yield earned by their office. Of course,
the concept of net yield is ambiguous because net yield on
average loan balances is the net result of considering both
fixed and variable costs. In determining net yield there is
a problem of consistently allocating a fair amount of fixed
costs to each office. Most corporate firms charge a branch
office only for its controllable costs and, therefore, evalu-
ate branch managers on the adequacy of earnings after deducting
Controllable expenses. Generally, the higher the net yield
Iflle more satisfactory the manager's performance. Net yield

is: not the sole consideration, however, as the delinquency

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33

CHART 2.2 MIDDLE MANAGEMENT VIEW
OF RISK STANDARDS

 

Finance

Charge %

Minimum
Rate

 

 

 

Maximum Risk Accepted

1. Given an operational rate, operating supervisors vary their
risk acceptance standards to accommodate rate differences.

2. The higher the rate permitted, the greater the percentage
of loan market accepted.

 

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34

rate, charge—off rate, and collection expense level are vital
portions of controllable management expense and all have an
impact on net earnings. These amounts are measured against
company norms. All of the firms are interested in the growth
rate of outstanding loan balances. One firm's representative
states that a high growth rate is the primary requirement of
its branch managers. The primary requirement of most branch
managers, however, is an adequate net yield.

While a specific minimum net yield is not stated, some
measure of adequate yield apparently exists. In low rate
states, earnings are sufficient to encourage firms to continue
and expand Operations, indicating the adequacy of present and
expected yields. In states where the net yield is relatively
high, the gross yield is more than proportionately higher
than the gross yield in a low rate state. Because each branch
manager is guided by the same net yield norms, those Operating
in states allowing higher rates can afford higher costs.

While general operating costs of labor and rent may be higher,
branch managers can also afford greater bad debt losses. This
a priori supposition is validated by Test I which shows a
marked relationship between gross yield and chargeoff rate,
indicating a probable adjustment of credit standards. Other
empirical evidence also shows that higher rate companies tend

to have higher operating costs, but it is not altogether clear

35
which is the cause and which is the effect.3 Do higher rates
cause higher costs or vice versa? Some representatives allege
the former to be true due to their company's ability to "afford"

higher costs.

.Management Policies and Attitudes Toward Rate Levels

The most common reason cited by legislative agents and
company officials for their Opposition to proposed rate re-
ductions or their encouragement for rate increases is the
adequacy or inadequacy of the rate structure to provide a
sufficient net yield. In many states frequent prOposals for
rate reductions are made by legislators who are either genu-
inely interested in furthering the consumer interest or
advancing their own political careers by using the high rates
charged by Small Loan Licensees as political "whipping boys".
Company representatives suspect the latter presents a more
accurate description. Regardless of the cause for legisla—
tive prOposals, legislative agents frequently argue for a
certain gross yield in order to achieve a fair rate of return,
for the ability of the rate structure to produce a sufficient
net yield is the benchmark of an adequate rate structure.

Just what this particular rate structure is, of course,

is the object of this dissertation, but the actual setting of

 

3
Smith, Paul F., Consumer Credit Costs, 1949-1959,
National Bureau of Economic Research, New York. 1964. p. 25.

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36
rates is usually the result of political compromises among
legislators and legislative agents representing various and
diverse groups interested in Small Loan legislation. Con-
sumer finance companies argue that an inadequate rate struc-
ture will force firms in their industry to cease loan opera-
tions within the area.

In statements concerning Optimum rate levels, there appears
to be a fixed definition of a ”good“ and "bad” loan. In states
with relatively higher rate levels, firms will Operate and
expand Operations if the rate sufficiently covers normal
Operating expenses and normal bad debt losses, predicated on
a normal acceptance of risk. If the rate structure is inade-
quate to cover normal bad debt losses, management contends an
unwillingness to reduce the bad debt amount by restricting
loans to only the better risk customers. This is somewhat
understandable as restricting loans to the better risks would
force consumer finance companies to more actively compete
with commercial banks and sales finance companies. Management
reaction to rate reduction, however, indicates a reluctance
to adjust risk with rate differences.

In order to ascertain whether or not managers would accept

more risk if a greater than normal rate was legally permitted,

 

4History has borne this out. In 1930, for example, the
rate in New Jersey was reduced sharply and loans outstanding
dropped more than 75%. Similar experiences happened in
Georgia, New Hampshire, and west Virginia.

Niefield, M.R., Personal Finance Comes of Age, Harper &
Bros., New York, 1939, pp. 25-27.

 

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37
managers were asked to indicate the amount of risk they would
tolerate if rates increased substantially. Most managers
indicated that they would not accept significantly greater
risk than accepted at the normal rate. When it is pointed
out that they can earn more profit by accepting previously
submarginal accounts, their counter-argument is two-fold:
l) to accept marginal risk accounts would add to marginal
costs by a disprOportionate amount and, therefore, the marginal
revenue would not warrant the acceptance of additional risk.
The marginal cost curve is practically vertical at the "bad
loan" point., 2) it is morally and socially inadvisable to
grant credit to submarginal risk accounts.

Managers contend that granting credit to customers be—
yond the normal risk level would increase costs by a dispro—
portionate amount, and beyond the bad loan point the incre-
mental costs would outweigh the benefits from the added rate.
The costs of more thoroughly investigating and collecting
delinquent accounts in this higher "risk class” would not
warrant granting them credit. Their judgment is apparently
based on past experience with higher risk clientele. Chart
2.3 shows the nature of the verticle marginal cost curve and
its relationship to output determination.

The argument of moral responsibility might appear altru-

istic but it does have an economic explanation. By accepting

38

CHART 2.3 TOP MANAGEMENT VIEW
OF RISK-COST RELATIONSHIP

.Marginal Average

 

Cost Cost
Finance
%
Charge
P' -——' Price', Average Revenue',
'Marginal Revenue'
P

 

Price, Average Revenue,
Marginal Revenue

 

 

 

 

O 0'

Maximum Risk (percentage of loan market)

At a certain level of output, the costs associated with accepting
a prOportionately greater amount of customers rise drastically.

Consequently, the output or share of market accepted does not
noticeably change as rates rise.

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39
the added risk customer, the firm is lending to a "type" of
borrower with social and economic characteristics considered
undesirable by a large segment of society. This attitude is
reflected by the general public's desire to disassociate with
this "type" borrower, therefore tending to restrict their
borrowing from a particular firm. In other words, by dealing
with this "class" of customers the consumer finance company
contends it is tarnishing and altering its reputation, having
possible negative long-run implications.

In order to alleviate the "image" problem, some firms
have Opened allied branches under different company names to
provide funds to customers with better credit. This, in effect,
differentiates credit and establishes a separate and unique
company image. In this respect, we have evidence that man-
agement does recognize the benefits of allowing reduced rates
to better risks. Of course many of the better risks need
larger amounts of funds, and therefore, can be allowed a
more favorable rate on this basis.

An example of moral responsibility usually cited by
managers is the rate charged under the Texas Small Loan Act,
where actual rates for small loans are substantially lower
than that permitted by law. In this instance, moral respon-
Sibility is cited as the primary reason for charging lower

rates, although there certainly is an economic explanation.

 

 

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Preferential Rates

As another indication of risk flexibility, managers were
asked about their policies and attitudes toward preferential
or discriminating rates granted to borrowers of varying risks.
Company representatives state they do not anticipate granting
preferential rates to customers of varying risks. While the
marginal cost principle of economic theory dictates that each
applicant should be granted credit if the expected gain ex-
ceeds expected loss, management assumes that economic rent and
producer's surplus derived from the better risks in all risk
classes offsets the losses on marginal risk and submarginal
risk accounts. Chart. 2.4 illustrates the nature of consumer
finance company price determination where output is set on
the basis of average costs and average revenue. If we assume
the demand for credit does respond to price differentials--
price elastic--it might be argued that one can compete more
effectively by varying the rate to meet the risk of the bor-
rower.5 This situation is not unusual in finance, of course,
because commercial banks vary the rate to match the risk of
commercial enterprises who borrow money. The application of
the marginal principle is accepted quite widely in commercial

banking circles.

5A study of consumer credit demand shows elasticity
among "unrationed" borrowers. Juster, F. Thomas, and Shay,
RObert P., Consumer Sensitivity to Finance Rates: An Empirical
.§Q§_Analytical Investigation,National Bureau of Economic Re-
search, New YOrk, N. Y., 1964.

41

CHART 2.4 TOP MANAGEMENT VIEW
OF OUTPUT DETERMINATION

Marginal Average
Cost Cost

      

Finance w

Price, Average Revenue,
Charge

Marginal Revenue

   

I
I
I
. I
' I
i I I
I l

 

 

Optimum actual
output output

Percentage of Market (all risk classes)

The maximum risk level is determined by the intersection of
average cost and average revenue curves.

42

The majority of firms express the following reasons for
not using the variable rate scale: 1) the added costs would
far outweigh the added benefits, 2) other financial institu-
tions provide credit for the better risks and consumer finance
companies do not want to invade their market. The rise in
cost is largely reflected in the additional training necessary
for branch managers. These costs of training and general
introduction to the system are more fixed than variable;
however, variable costs of higher salaries, more personnel
expenses, and more equipment costs would exist.

One prominent executive suggests his company would grant
preferential rates to better risks if it could develop an
accurate and reliable credit scoring system whereby prob—
ability of loss is expressed as a numerical score, but it will
be sometime in the future before this idea is translated into
actual policy. One large finance company gave preferential
rates to school teachers a few years ago but has now dis—
continued this practice.

If firms would voluntarily adopt flexible rates for their
own borrowers it is more likely that various interstate legal

rates would cause flexibility in interstate credit standards.

 

In practice, however, firms tend to charge the maximum legal

rate to all borrowers regardless of risk.

 

 

, -

 

 

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,

 

 

43

Effect of Loan Size on Borrower Loan Eligibility

As a further measure of risk flexibility managers were
asked whether or not they rejected small loan applicants when
the loan revenue is substantially less than the cost incurred.
All of the managers agreed that if a person could qualify for
a large loan, the same applicant would be able to secure a
small loan even if the company would sustain a loss on this
particular transaction. The economic rationale for accepting
a present loss is the expectation of gains from future busi—
ness with this customer, therefore providing a short-run loss
and a long—run gain. However, in cases where there is little
expectation of gain from future business, loan grantors still
accept qualified small loan applicants. Branch managers are
instructed not to adjust their risk standards solely because
the rate will not compensate for the costs and risks incurred
to grant and collect a small loan.

Rate structures are not designed to make smaller loans
profitable since legislatures expect lenders to subsidize
the smaller loans from more profitable larger loans. For
example, a directive from the New York state banking superin—
tendent states:

"If licensed lenders are to perform the function
which the statute contemplates, they should be

6This assumes an accurate measurement of variable and
fixed costs. It could be argued that even a $25 loan is
Profitable since the only marginal cost is the interest ex-
Pense.

44

willing to meet the requirements of the borrower

who is in need of a $25 or $50 loan. To fix a

rate which would make very small loans profitable

would not be feasible because such a rate would

necessarily be prohibitive.

The Department believes that the licensed lenders

must recognize that the State will not continue

indefinitely to permit them to earn excessive

profits on larger loans unless they utilize a

portion of such income to support a reasonable

volume of very small loans.”7
In general, the loan size effect on revenue is not a deciding
factor in determining loan eligibility. Loan size is an im-
portant consideration in determining the borrower's ability
to repay a loan, however, since a marginal borrower's prob-
ability of loan repayment varies with loan size. A borrower
with a small income would have less probability of repaying
a large loan of $1,000 than a small loan of $50, other things
being equal. Risk is considered when making loans of all
sizes, but credit is granted even in situations where the

variable and direct costs exceed the revenue derived from

the loan.

Management Attitude Toward Rate Regulatiop
Most executives believe Small Loan Laws should not be
eliminated. The most frequently cited reason for this is the

fear of abuse by the finance companies themselves. While

k

Second Special Report of Superintendent of Banks
Bplative to Licensed Lenders, New York State Banking Dept.,
1941 pp. 19-20.

45
managers believe competitive pressure would temper exorbinate
rates, they, in effect, believe the market is too imperfect
on both the demand and supply sides to warrant the absence
of governmental regulation. Some express the belief that
very few small loans would be made due to the very high rate
required to warrant the incurred costs. Most consider this
morally and socially undesirable. It is interesting to note
the apparent lack of confidence in industry competition to
determine a fair price in which no excess monOpoly profits
are earned. All agree that consumer demand is relatively
price inelastic, but while the market may be imperfect on the
demand side competition on the supply side would force prices

to a level permitting only normal profits to be earned.

Influence on other Variables

In addition to rate regulations, other provisions of the
Small Loan Law influence levels of loan quality. The legal
regulations regarding permissible collection practices vary
and significantly affect loan Operations. In New York, for
instance, the law permits a lender to assign a borrower's
Wages without rate rescheduling, whereas in other states it
is not possible to assign wages unless the lender agrees to
reState the charge at the usury rate level. This, of course,
can have an important bearing upon the risk level assumed

beCause the expected gain is greater when wages can be more

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easily assigned. Representatives from two companies feel

the legal prOVISion dealing with collection practices has

a greater influence on credit standards flexibility than rate
differentials, while others take an Opposite View. There is
no general concensus of opinion on this issue from the sample
of interviews solicited. One cannot deny the fact that legal
restrictions on collection tactics are relevant and play a

significant role in consumer finance company risk evaluation.

Summary and Conclusions

TOp management representatives from seven of the ten
largest U.S. consumer finance companies state that finance
charge level variations play little or no part in influencing
their company's maximum risk acceptance standard. While rate
level variations play only a small role in the determination
of general policies, rate does influence the implementation
of delinquency and chargeoff rate policies at middle and
lower management levels.

These discussions indicate three significant conclusions
Concerning management policies: 1) within the broad range of
Present rates, tOp management contends it accepts a constant
leVel of maximum risk in all states regardless of rate, 2) a
minimum rate must be provided before a finance company will
undertake long-run Operations within a state. A small in_

Crease beyond this minimum amount will not cause them to

47
accept less creditworthy borrowers, 3) “Middle managers" or
loan supervisors alter risk standards to accommodate rate
differentials.

Because loan supervisors are closer to actual Operations,
it must be concluded that finance companies alter credit
standards to compensate for rate differences.

These conclusions are also based on management attitudes
toward governmental regulation, loan sizes, preferential rates,
and rate reductions to prospective borrowers.

The legal restrictions concerning collection practices
also play an important role in company policies and attitudes

toward risk acceptance.

CHAPTER III

EMPIRICAL RELATIONSHIP OF CREDIT STANDARDS AND RATE LEVELS

TEST I

Introduction

 

In the previous chapter company representatives assert
they do not alter their risk acceptance standards in states
permitting lower or higher rates, but maintain stable criteria
of acceptability. The purpose of the next two chapters is to
test empirically the hypothesis of a positive relationship
between credit availability and the legally determined rate
structure. The empirical test is two fold. The first test
(Test I) measures the association between finance company
earnings in each state with their corresponding chargeoff
rate and turndown experiences. The second test (Test II)
analyses characteristics of loan applicants from consumer
finance companies with branch offices in a high rate state
and a low rate state. The tests differ in scope and depth
Since Test I uses chargeoff rates and turndown rates from
many states as a measure of credit availability, while Test II
measures risk by analyzing individual characteristics of bor-
r0Wers from two states. While borrower characteristic analysis

48

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49
provides a more accurate measure of risk acceptance patterns,
the use of chargeoff and turndown rate analysis has the advant-
age of being broader in sc0pe since it includes the experiences
of three finance companies, each conducting business in more

than 40 states.

Data Used in Test I

Each of the three finance companies reported their various
1964 state experiences with gross yield, percentage of new
direct loan applications rejected, the amount of charged off
loans as a percentage of average loans outstanding, and aver-
age loan balances. Gross yield is calculated as the amount of
interest collected as a percentage of average daily loans out-
standing. It provides an estimate of average rates since rate

structures are usually multibracket, with different rates for

1
various loan sizes.

All three companies reported their chargeoff rates while

only two indicated their turndown rates. Therefore, the rela-

‘

For instance, the New York law states: "Every licensee
hereunder may loan any sum of money not exceeding eight hun-
dred dollars in amount, and may charge, contract for, and
receive thereon interest, consideration, or charges not ex-
ceeding two and one-half per centum per month on any part of
the unpaid principal balances of the loan not in excess of one
hundred dollars and two per centum per month on any part of
the unpaid principal balance of the loan in excess of one
hundred dollars but not in excess of three hundred dollars
and three quarters of one per centum per month on any remainder
of the unpaid principal balance of the loan." Section 352a of
Article IX of Banking Law of the State of New York with Re-
-§E§gt to Licensed Lenders.

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tionship between the independent variables and turndown rates
is more restrictive. One firm's data include only their
experiences in states with more than 10 offices, since the
inclusion of experiences in states with more than 10 offices
may bias the conclusion; a firm may alter its short—run credit
standards to attain maximum longvrun gains.

All three firms conduct lending Operations in approxi-
mately the same states and face similar regulatory restrictions.
Their experiences with yields, chargeoffs, etc. are similar,
but there are a few dissimiliarities. The average state
gross yield for Firm X is approximately 3 percentage points
less than the average state yield for Firm Y. The gross yield
variance is also lower for Firm X. The average chargeoff rate
for Firm Y is greater than for the other two firms, tending
to substantiate the hypothesis of a positive risk-rate rela-
tionship when using aggregate interstate data.2 The average
loan size in Firm X is approximately $50 greater than that for
Firm Y and $75 greater than Firm Z, partially explaining the
lower gross yield experiences of Firm X.(Appendix E)

The average turndown rates are very similar for both

Firm X and Firm Y.

‘.

 

 

This View is corroborated in Smith's study of consumer
credit costs concluding higher rate companies accept greater
Chargeoff rates. Smith, Paul F., Consumer Credit Costs, 1949-
ifijfiJ National Bureau of Economic Research, New York, 1964,

p. 25.

 

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The intrafirm data is supplemented with state per capita
income figures to provide an overall measure of state risk,
although the association of average income and risk is some-

what nebulous.

Measures of Risk

 

The analysis assumes turndown rates and chargeoff rates
are measures of consumer finance company risk acceptance be-
havior. These assumptions must be qualified since higher
chargeoff rates also reflect different collection methods in
addition to loan repayment delinquency. If the same "type"
borrower receives credit in two states, for example, but the
intensity of collection effort varies, there will be different
chargeoff rates because loan repayment then also depends on
the lender's repayment "encouragement." However, since branch
managers in all states have the same general training from
their home office personnel and general loan supervisors, each
firm tends to maintain fairly uniform collection policies in
all states. Methods of charging off accounts also are con-
tingent on the accounting procedures employed in each finance
COmpany as some firms "write off" accounts when a borrower is
delinquent for 120 days, while others use shorter or longer
'Periods of delinquency. Each branch office in a major finance
COmpany, however, uses the same criteria for “charging off"

an account.

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In spite of some inconsistency and variation, it is valid
to assert that higher chargeoff rates represent more lenient
credit policies, although the relationship probably is not
proportional.

The use of turndown rates as a measure of risk acceptance
or rejection is, at best, an inexact measurement of credit
policy since many other factors affecting this ratio. Risk
differences in the general pOpulation affect turndown rates
because more peOple would be rejected for credit in areas
where the risk level is lower. On the other hand, if wealthier
people live in a particular finance company market area, re-
jection rates would be lower. Other important and influencing
factors as population levels, employment stability, debt and
loan repayment attitudes, and the general economic position
of the community also influence rejection patterns. The com-
petitive nature of the state and local markets is also impor-
tant in situations where banks, credit unions, and sales finance
companies aggressively compete with consumer finance companies.
Legal regulations on all forms of financial institutions also
affect finance companies practices. Since all of these factors
influence the nature of consumer finance company markets, turn-
down rates are less reliable indicators of credit standard

Variability.

53
Methods of Measuring Association

The correlation analysis attempts to statistically hold
certain variables constant in order to isolate the influence
of finance charges on turndown and chargeoff rate levels. If
other factors are held relatively constant, the measurement
of these associations is useful in suggesting a risk and rate
relationship.

The regression equations show chargeoff rates and turn-
down rates as the independent or explained variables and
gross yield, average loan size, and per capita state income
as the independent or explaining variables. The analysis is
extended further with a correlation analysis of the relation-
ship between gross yield and non-risk measuring variables to
lend insight into rate structure patterns.

While multiple regression coefficients do not show causal
relationships, they do provide a measure of association among
variables, either causal or resulting. Since gross yield
and per capita income figures are uncontrollable by the
lender,3 the relationship of these variables with the depend—
ent variables suggests a causal relationship since chargeoff
and turndown rates are more flexible and controllable.

In all cases the simple, partial, and multiple coeffi-

cients are calculated to determine the coefficients of

x

3
Assuming loan sizes do not change and the maximum rate

is charged.

54
correlation (r) and the coefficients of association (A).
The simple correlations relate one independent variable
with a dependent variable, while the partial coefficients
measure association by considering one independent variable
and holding one or more other independent variables constant.
The multiple correlation coefficients indicate the influence
of all the independent variables associated with the depend-
ent variables. They are very difficult to interpret because
the addition of two or more variables makes it difficult to
distinguish which variable is more closely related. For
this reason a partial correlation coefficient is more useful.
The coefficients of association (A) measure the percentage
of dependent variable standard deviation "explained" by the

4
independent variable(s). (Appendix C)

Expected Relationship between Gross Yield and Chargeoff Rate
There most likely is a positive relationship between

gross yields and chargeoff rates since loan supervisors indi-
Cate they are somewhat flexible in their credit standards.
“Haapter II) As discussed earlier, it seems economically

SOLnnd for firms to accept higher credit losses when charging

hi4gfuar rates because a higher marginal revenue per loan permits

 

 

4
Ekeblad, Frederick A., The Statistical Method in Busi—
£§E§§§,. John Wiley & Sons, Inc., New York, 1962, p. 574.

fZekial’ Mordecai, and Fox, Karl A., Methods of Correlation
‘EEZ__JE§?g£ession Analysis, John Wiley & Sons, New York, 1959,

p0 532.

55
them to accept higher marginal costs at Optimum output levels.
Of course, additional expected risk assumption loss is not
the only expense consideration since salaries, rents, and
other expenses vary geographically. The influence of factor

costs is not included in the regression equation.

Expected Relationship between Gross Yield and Turndown Rates

There probably is a negative relationship between turn-
down rates and gross yields since firms charging higher rates
may accept a greater percentage of applicants as borrowers.
If lenders accept more risk in higher rate states their turn—
down rates may not vary because of a greater or smaller pro-
portion of "riskier" applicants in the loan market area.
Turndown rates are also affected by the degree of financial
institution competition in each loan market area.
Expected Relationship between State Per Capita Income and
Chargeoff Rates

It is doubtful whether there is a strong relationship
between state per capita income and chargeoff rates since
each finance company asserts that it accepts the maximum
amount of risk at a minimum acceptable rate, regardless of
the state income level. (Chapter II) However, it is possible
that companies accept a fixed level of credit in all states
and extend little effort to secure additional borrowers once

this level is attained. In higher income states lenders may

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56

sustain increased loan balances, and economies of scale may
influence them to sustain higher chargeoff rates. Each of
these possibilities could cause chargeoff rates to be corre-
lated with state income. These possibilities are doubtful,
however, and suggest only possible rather than probable
policies.
Expected Relationship between State Per Capita Income
and Turndown Rates

It seems likely that states with higher per capita in-
comes have relatively lower turndown rates, although rate
structure may simultaneously influence rejection levels. If
borrower income is a measure of risk, then finance companies
are rational in accepting a greater percentage of higher
income applicants, other factors being equal.

Expected Relationship between Average Loan Size
and Risk Acceptance

 

Most consumer credit cost studies infer an inverse relae
tionship between average loan size and the amount of borrower
risk. A recent study showing data accumulated from one state
indicates a relatively high inverse correlation coefficient

5

of -055 between loan size and chargeoff rate. Part of the

reason for the more favorable experience with larger loans

 

5

Comiskey, Eugene E., A Study of Loan Cost Behavior In
Consumer Finance Companies, unpublished Ph.D. dissertation,
Michigan State University, 1965.

57

15 the tendency of present borrowers to be more readily
accepted for them since their creditworthiness has been
confirmed. In addition, credit investigation of applicants
seeking larger loans is more thorough and complete.
Expected Relationship of Variables Indirectly Associated
with Hypothesis

The purpose of Small Loan Legislation is to fulfill con-
sumer capital needs because usury rates are inadequate to
attract sufficient funds. Rate is not the only important
determinate of credit availability, however, as Small Loan
Legislation limits the maximum amount a borrower can obtain
from one finance company. Some legal loan size limits, while
designed to prevent a borrower from going into too much debt
have not been adequate to fulfill the growing demand of bor-
rowers with higher incomes who want more credit. In a large
sense, loan size ceilings are interdependent with rate levels
since states allowing larger loans also tend to allow lower
average rates. A full explanation and description of this
relationship is not the object of this study, but one should

be aware of the existence and influence of loan size ceilings.

 

For a more complete discussion of the need for higher
loan size ceilings see: Phelps, Clyde W., "Consumer Finance
Charges II," The Journal of Marketing, July, 1952, pp. 22-36.
Kawaja, Michael, "On the Inflexibility of Small Loan Rates
and Loan Size Ceilings," Personal Finance Lawfguarterly Report,
Spring, 1965, pp. 57—59.

58

A statistical correlation of rate levels and average
loan sizes balances shows a partial indication of this inter—
action. Since both rate ceilings and loan size ceilings are
determined by law, they show the extent of legislative aware-
ness of this relationship. These two factors in a sense, are
mutually determined by legislatures who try to establish a
rate ceiling and loan size ceiling capable of serving the
borrowing community without allowing lenders to earn exorbitant
profits.. Lenders allowed to grant larger loans can afford to
charge lower rates since a large portion of unit costs is
relatively fixed and therefore lenders are concerned with
the amount of revenue rather than the gate of revenue for them
to breakeven and earn a fair return. Obviously, the breakeven
revenue needed for a $500 loan is not five times greater than
the revenue needed for a $100 loan. The breakeven relation-
ship between rate and loan size is not linear. This is accom-
modated with the multibracket rate structure where incremental
revenues decrease as loan sizes increase, but it is doubtful
whether the decreasing rates of revenue are equal to decreasing
rates of cost. Because of these relationships, there probably
is an inverse correlation between average loan sizes and aver-
age gross yields.

In addition, a correlation of state per capita income

and finance charge levels is measured to partially explain

59
the logic of state finance charge ceilings. The most likely
relationship is inverse, since each higher income state would
tend to allow a higher loan size ceiling which, in turn, is
associated with a lower average yield.
A summary of the regression equations used in this chapter

is shown in Table 3.1.

 

 

 

 

 

TABLE 3.1
E
The variables and their symbols are:
A = Chargeoff rate
B = Turndown rate
C = Gross Yield
D = Average Loan Size
E = Per Capita Income
The multiple correlation equations are:
Dependent Variables Independent Variables Table
A = C,D,E 3.2
B = C,D,E 3.3
C = D,E 3.10
The partial correlation equations are:
Dependent Variables Independent Variables Table
A = C1D,E 3.4
B = C1D,E 3.5
A = DlC,E 3.6
B = DlC,E 3.7
A = ElC,D 3.8
B = ElC,D 3.9
C = ElD 3.11
C = DlE 3.12

 

Indicates variables held constant.

6O

Empirical Evidence

Multiple Correlations

The multiple correlation coefficients shown on Tables 3.2
and 3.3 indicate regression coefficients (r) when chargeoff
rate or turndown rate is correlated with all of the independ-
ent variables-—gross yield, average loan size, and state per
capita income. Only two companies reported their turndown
ratios but each supplied all of the other data. Correlation
coefficients are computed for each company separately and for
the combined experiences of all the firms.

The multiple correlation of the combined experiences for
the three companies indicates a coefficient of .47 when corre—
lating chargeoff rate with the independent variables. (Table
3.2) The coefficient is not unusually high but shows that all
or some of the independent variables are associated with charge-
off rates. The relationship among companies differs as shown
by the multiple correlation coefficient for Firm X which is
twice that of Firm Y and 30 percent greater than Firm Z's
coefficient. The coefficients of association (A) show an
even greater coefficient spread as the A of .23 for Firm X
is four times that of Firm Y and about twice the A.of Firm Z.

The relationship between turndown rate and the three
variables for the combined experience of the two companies

is not as strong as the chargeoff relationship and there is

61
greater inconsistency in individual company experience. Firm
X shows a multiple correlation coefficient of .75 compared to
the r of .25 for Firm Y. Firm Z did not report turndown data.

(Table 3.3)

TABLE 3.2

MULTIPLE CORRELATION COEFFICIENTS OF CHARGEOFF RATE AND
GROSS YIELD, AVERAGE LOAN SIZE, AND PER CAPITA INCOME
BY STATE, FOR THREE FINANCE COMPANIES

 

 

 

 

A.= C,D,E

Coefficient of Coefficient of

Correlation (r) Association (A)
Firm X .64 .23
Firm Y .32 .06
Firm Z .47 .13
Firms X,Y,Z .47 .13

TABLE 3.3

MULTIPLE CORRELATION COEFFICIENTS OF TURNDOWN RATE AND
GROSS YIELD, AVERAGE LOAN SIZE, AND PER CAPITA
INCOME, BY STATE FOR TWO FINANCE COMPANIES

 

 

 

B = C,D,E
Coefficient of Coefficient of
Correlation (r) Association (A)
Firm X .75 .36
Firm Y .25 .04

Firms X,Y .21 .03

 

62
Relationship between Chargeoff Rate and Gross Yield

A simple correlation analysis of chargeoff rate and gross
yield shows an r of .47 for the combined experiences of the
three companies, indicating a fairly strong relationship
among variables. (Table 3.4) These data indicate that lenders
in states with higher gross yields experience higher chargeoff
levels, although the simple correlation figure does not tell
whether yield alone is associated with chargeoff rate because
other variables are allowed to vary. The simple correlation
suggests that yield levels are an important influence on
chargeoff rates when other variables may actually be more
influential. The partial correlation coefficient of .44
indicates the direct influence or association of yield on
chargeoff rates when holding other variables included in the
analysis relatively constant. In effect, the .44 partial
coefficient indicates that gross yield "explains" 10 percent
of the chargeoff rate standard deviation.

The simple and partial correlation coefficients are mod~
erately high for each of the three firms with Firm Z showing
the highest simple correlation and Firm Y showing the largest
partial coefficient. The data used to correlate Firm Z's
experiences exclude certain observations thought to be non-
representative of company policy. Chart 3.3 shows a simple
relationship between yield and chargeoff rate with three

observations clearly out of line. When all of the experiences

63

are considered, the partial correlation coefficient is -.15,
an obvious refutation of the hypothesis. While it is always
convenient to exclude certain data to obtain higher coeffici—
ents, the observations in this instance are too extreme to
justify their inclusion. The data from Firm X include only
those obtained from states where 10 or more branch offices
are located since their inclusion would less likely be repre—
sentative of overall company policy. When all of Firm X's
experiences are included, however, the partial correlation
coefficient between chargeoff rate and gross yield is only
.04, indicating no relationship. It does not appear this
relationship is representative of overall company behavior
because in smaller Operations, firms make short-run adjust-
ments to attain longer-run goals. Therefore, the data would
be biased if they include these shorter—run experiences.

The data in Table 3.4 and Charts 3.1, 3.2 and 3.3 reveal
the strong simple and partial relationships.

TABLE 3.4

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF CHARGEOFF RATES
AND GROSS YIELD, BY STATE AND FOR THREE FINANCE COMPANIES

 

 

A = ClDE
Simple Partial Partial
Correlation Correlation Coefficient of

Coefficient (r) Coefficient (r) Association (A)

Firm X .38 .26 -04
Firm Y .28 .31 .05
Firm Z .41 .28 .05

Firms X,Y,Z .47 .44 .10

 

 

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67

Relationship between Turndown Rates and Gross Yields

There appears to be little relationship between yields
and turndown rates since the simple r of -.07 and the partial
r of —.16 for the combined experiences for Firms X and Y are
barely significant at the .10 level. The individual companies
also show little relationship between turndown rates and gross
yield levels as their coefficients are equally small. (Table

3.5)
TABLE 3.5

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF TURNDOWN RATE
WITH GROSS YIELD, BY STATE AND FOR FINANCE COMPANIES

 

m
B = C1D,E
Simple Partial Partial
Correlation Correlation Coefficient Of

Coefficient (r) Coefficient (r) Association (A)

 

Firm X -.03 -.12 -.01

Firm Y —.05 -.10 ,, —.01
Firms X,Y -.07 -.16 —.02

 

While the r's indicate an inverse relationship, the magni»
tude is much smaller than expected. It may be that higher risk
applicants in the higher rate states more readily apply for
credit resulting in little change in turndown rates. If finance
companies accept more risk in higher rate states, and the evim
dence shown in the preceding section indicates they do, it

might be that poorer risk applicants can more easily obtain

68

credit. If this knowledge is disseminated tO the community,
more people will apply for credit. As information of lower
credit standards and consumer finance company credit expecta-
tions filter through the community via direct and indirect
means, the market becomes enlarged resulting in finance com-
panies making the same turndown Eggs, even though the appli-
cant risk level is somewhat poorer. In effect, it may be that
the number and risk level Of applicants depends on yield levels,
resulting in a relatively stable turndown rate. As rates rise,
more people from lower risk classes apply for credit. Previ-
ously, these additional applicants excluded themselves from the
market since they perceived little Opportunity of securing funds.
The analysis in Test II examines the validity of this assertion.

An even more plausible explanation for a stable turndown
rate, however is the tendency for loan managers to disregard
those applications from poorer risk applicants as "official"
turndowns, simply because they see little hope of the applicant
securing credit even if subsequent investigation is favorable.
{Branch managers may also be reluctant to let their supervisors
know their turndown rates). Therefore, the data really don't
reflect rejection and acceptance policies at finance companies.

From the statistical analysis alone, however, it is evident
turndown rate is not associated with gross yield. Charts 3.4

and 3.5 graphically show the small relationship.

 

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71

Relationship between Chargeoff Rates and Average Loan Sizes

While there is a moderate correlation between chargeoff
rates and gross yield levels, are there other variables associ-
ated with risk levels to explain their behavior? In an attempt
to determine these other relationships, a simple and partial
regression analysis is presented showing chargeoff rates,
average loan sizes, and state per capita income relationships.

The data on Table 3.6 show an inconsistent relationship
between chargeoff levels and average loan sizes at the three
finance companies. Firm Z experienced a -.45 partial coefficient
between variables while Firm X shows a partial correlation coeffi-
cient of only -.08. The latter coefficient is not statistically
significant at the .05 level and, therefore, is not reliable.
The experiences of Firm Y, on the other hand, show a .14 partial

coefficient which is also not significant at the .05 level.

TABLE 3.6

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF CHARGEOFF
RATES AND AVERAGE LOAN SIZES, BY STATE FOR THREE
FINANCE COMPANIES

 

 

: ==============
A = DlC,E
Simple Partial Partial
Correlation Correlation Coefficient Of

Coefficient (r) Coefficient (r) Association (A)

 

 

Firm x -.25 —.08 -.01
Firm Y .05 .14 .01
Firm 2 --.45 —.45 -.1o
Firms X,Y,Z —.25 —.13 -.01

x

72
Other published evidences shows an inconsistent pattern
among financial institutions as commercial banks experience
more losses with smaller loans than finance companies who
. . 7
have been quite successful at collecting smaller accounts.
The inter finance company analysis presented here also shows

an inconclusive relationship between loan size and risk

acceptance among firms within the same industry.

Relationship between Turndown Rates and Average Loan Size

The relationship between turndown rates and loan size
is slightly more consistent but hardly more conclusive. The
coefficients in Table 3.7 show a negative relationship for
experiences of Firms X and Y and neither is significant at
the .05 level. There is a slight tendency for branch Offices
granting larger loans to reject fewer applicants, although the
reasons for this are not clear.

TABLE 3.7

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF TURNDOWN
RATES WITH AVERAGE LOAN SIZES, BY STATE FOR TWO FINANCE

 

COMPANIES
i m
B = DlC,E
Simple Partial Partial
Correlation Correlation Coefficient Of

Coefficient (r) Coefficient (r) Association (A)

 

Firm X .09 -.01 -.Ol
Firm Y —.19 -.24 -.02
Firms X,Y -.07 —.12 -.01

 

7 . . .
Robbins, W. DaVid, Consumer Instalment Loans, Ohio
State University, 1955, p. 103.

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73

Relationship between Chargeoff Rates and Per Capita Income

 

The coefficients shown in Table 3.8 reveal an inconsistent
correlation between company chargeoff rates and the population
income levels in the various states of Operation. A partial
correlation coefficient of -.56 for Firm X is relatively high
but there is virtually no correlation for the experiences in
Firms Y and Z. The simple correlation coefficients indicate
a stronger negative relationship for experiences in Firms Y
and Z principally because states with lower per capita incomes
also tend to allow higher rates. When holding the effect of
gross yield and average loan size constant there is less

relationship between chargeoff rates and state income.

TABLE 3.8

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF CHARGEOFF
RATES AND PER CAPITA INCOME, BY STATE FOR THREE
FINANCE COMPANIES

 

A = ElC,D
Simple Partial Partial
Correlation Correlation Coefficient of

Coefficient (r) Coefficient (r) Association (A)

 

Firm X -.55 -.56 -.17
Firm Y —.01 —.06 .01
Firm Z ~.l6 —.09 -.01
Firms X,Y,Z -.l4 ~.07 -.01

 

 

The inconsistent relationship is interesting when con—

Sidering that Firm Y analyzes borrower loan eligibility by

74

quantitatively scoring each applicant while Firm X relies on
branch manager credit judgment. The chargeoff rates of Firm
Y reflects little influence of state income levels, apparently
because they use the scoring system. Of course, factors not
included in the partial correlation equation might simultane-
ously relate with chargeoff ratios and therefore affect the
relationship. Factor costs and prices vary by state because
of local demand and supply conditions and may influence the

apparent correlation.

Relationship between Turndown Rates and Per Capita Income

The coefficients listed in Table 3.9 reveal the correla—
tion between turndown rates and per capita income. While the
partial correlation coefficient for Firm X is a very high -.75
there is virtually no correlation between Firm Y's experiences.
If state income levels are lower, it is expected a greater
percentage of applicants would be rejected since they have
lower incomes and tend to be "riskier", other things being
equal. This does not appear to be the case with Firm Y since
it does not have higher rejection rates in lower income
states, but this is true in Firm X.

Since Operations Of Firm X are somewhat larger and
broader in sc0pe than that of Firm Y, more applicants in
Firm X are garnered from the smaller urban areas where average

incomes may be smaller. Firm Y, on the other hand, conducts

75
TABLE 3.9

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF TURNDOWN RATE
AND PER CAPITA INCOME, BY STATE FOR TWO FINANCE

 

 

 

COMPANIES
B = ElC,D
Simple Partial Partial
Correlation Correlation Coefficient of

Coefficient (r) Coefficient (r) Association (A)

 

Firm X -.74 -.75 -.33
Firm Y .07 .ll °01
Firms X,Y ~.l4 —.16 -.Ol

 

a larger portion of its Operations in large metrOpolitan areas
where average incomes tend to be higher. These differences in
firm Operations could explain the inconsistent findings, since
firms Operating in lower income areas would tend to have a
greater percentage of lower risk applicants.

It is also possible that Firm Y maintains a stable turn-
down rate as a matter of policy regardless of applicant risk,
but this is a most tenuous assertion. If the firm rejects the
same portion of applicants in each state because Of a pre~
determined target turndown ratio, then there would be little
Correlation between state income and the number Of turndowns.
15 more plausible explanation for this small correlation is

tile higher income Of persons in Firm Y's market.

76
Summary of Relationship between Risk and Independent Variables

The simple, partial, and multiple regression analysis
indicates a moderate relationship between chargeoff rate and
gross yield, but virtually no relationship between turndown
rate and gross yield. While the partial coefficients of .31,
.26, and .41 are not extremely high, they do reflect a definite
association. It must be remembered the hypothesis does not
state that a more lenient credit policy is the only result
of a higher rate since other costs may be influenced by rate,
and vice versa. While there may be a tendency for higher
chargeoff rates, it is not the only cost element varying in
each state. (Note the regression equation doesn't account
for other cost differences). In essence, however, the charge-
off rate and gross yield correlation tends to confirm the
hypothesis that firms accept more risk in states permitting
higher rates.

The use of turndown rates as a measure of risk provides
little basis for asserting an inverse relationship between
gross yields and rejection rates, although the coefficients
are slightly negative. While the coefficients are inverse,
tluey clearly are not significant. The use of turndown ex-
Pelriences as a basis for risk acceptance and rejection policies
d0e3s not confirm the hypothesis because they are inaccurate

anti unreliable measures of risk.

77

Regression Coefficients Indirectly Related to Hypothesis

The foregoing analysis attempts to explain relationships
between risk measurements and other variables in order to
determine the influence of other legal regulations on the
quality of credit. If there is a relationship between risk
and rate as defined in the study, then it is useful to extend
the analysis one step further by associating other variables
with rate level to determine their relationship, separately
and collectively, with rate levels. Table 3.10 shows the
multiple correlation coefficients for the association of gross
yield with average loan size and per capita income, indicating
whether the independent variables collectively can explain
rate levels. The r of .67 for Firm X and .43 for both Firms

Y and Z show a consistent and strong relationship.

TABLE 3.10

MULTIPLE CORRELATION COEFFICIENTS OF GROSS YIELD AND
AVERAGE LOAN SIZE AND PER CAPITA INCOME, BY
STATE FOR THREE FINANCE COMPANIES

 

 

 

 

C = D,E
Multiple Multiple
Correlation Coefficient of
Coefficient (r) Association (A)
Firm x .67 .27
Firm Y .43 .10
Firm 2 .43 .10
FiITm X,Y,Z .42 .10

78

Relationshipgof Per Capita Income and Gross Yield

The coefficients listed in Table 3.11 show that states
with lower pOpulation income levels generally allow finance
companies to charge higher rates, although the evidence is
not strong. In the states where Firm Y operates the rela—
tionship is higher than in those where Firms X and Z conduct
operations. Though the -.27 partial coefficient of Firm Y is
significant at the .05 level, the magnitude of the correlation
does not indicate particularly conclusive relationship since
only 3 percent of the gross yield standard deviation can be

explained by state per capita income.

TABLE 3.11

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF GROSS YIELD
AND PER CAPITA INCOME, BY STATE FOR THREE FINANCE

 

 

COMPANIES
C = D1E
Simple Partial Partial
Correlation Correlation Coefficient Of

Coefficient (r) Coefficient (r) Association (A)

Firm X -.O9 -.16 -.02
Firm Y ”.35 “.27 -.03
Firm Z -.05 —.12 -.01
Firm X,Y,Z -.20 -.15 -.02

 

 

A negative relationship is logical to expect, Of course,

51n63e legislatures in lower income states probably are aware

79
that higher rates are needed to enable a fair portion of the
pOpulation to be served. The statistical evidence indicates

this is somewhat valid.

Relationship of Average Loan Size and Gross Yield

Closely associated with rate determination is the setting
of maximum loan sizes. Because Of the decreasing cost nature
of consumer credit, firms can afford to charge lower rates
when loan balances are larger. This is reflected in the multi—
bracket rate structure used in most states. The statistical
association of average loan size and gross yield clearly is
inverse in direction. The strong relationship for Firm X is
indicated by the -.67 partial coefficient and the partial r of
~.26 and -.43 for Firms Y and Z. (Table 3.12) These data
largely explain the higher multiple correlation coefficients
and confirm the Observation that states allowing higher loan

sizes also restrict maximum rates of charge.

TABLE 3.12

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS OF GROSS YIELD
AND AVERAGE LOAN SIZE BY STATE FOR TWO
FINANCE COMPANIES

 

 

C = DlE
Simple Partial Partial
Correlation Correlation Coefficient of

Coefficient (r) Coefficient (r) Association (A)

 

Firm X -.66 ~.67 -.27
Firm Y ~.34 -.26 —.04
Firm Z -.42 -.43 —.10

Firms X,Y,Z -.39 -.37 —.07

0‘...

80
Summary and Conclusions

The statistical analysis presented in this chapter shows
that the three firms included in this study sustain higher
chargeoff rates in states allowing higher finance charges.

In each case significant and moderately strong simple and
partial correlation coefficients validate the basis assertion

of the hypothesis. While the correlation coefficients are
moderately strong, the coefficients of association showing

the percentage of chargeoff rate standards deviation “explained"
by gross yield are less convincing.

When using loan applicant rejection rates as a dependent
variable and as a proxy for risk, there is little correlation
between risk and finance charge, indicating firms do not
reject significantly fewer applicants in higher rate states.

A statistical correlation of average loan size and
chargeoff rate shows one firm sustaining higher chargeoffs in
states where average loan sizes are smaller. The two other
firms show little correlation between these two variables.

One major firm tends to charge Off more loans and reject
more applicants in lower income states while another major
finance company shows little correlation between pOpulation
income and their chargeoff rates. Part of this inconsistency
is attributable to differences in lending practices and methods

Of operations by the two firms. The larger size firm is

81
probably more suseptible to higher rejection rates.

There is a tendency for lower average income states to
permit higher finance charges, although the statistical evi~
dence is not strong. All three firms report an inverse rela-
tionship but the largest correlation coefficient is too low
to be convincing, even though it is significant at the .05
level.

As expected, there is a large inverse relationship be-
tween average loan size and gross yield since states permitting
higher loan size ceilings recognize the decreasing average cost
nature Of consumer credit and restrict maximum rates of charge.

In general, a regression analysis supports the risk-rate
hypothesis. Test II provides a micro-analystic View Of bor-

rower characteristics and risk acceptance differences.

CHAPTER IV

EMPIRICAL RELATIONSHIP OF RISK ACCEPTANCE AND RATE LEVEL

TEST II

Introduction

 

The results of Test I suggest there is a positive and
direct correlation between the risk assumed by consumer
finance companies and the rates charged for loans. The
hypothesis was tested in the preceding chapter by examining
the relationship between chargeoff rate and gross yield.
The hypothesis is further tested in this chapter by a compara-
tive analysis of the characteristics of those borrowers located
in a "high rate" state with those in a "low rate" state. If
borrower characteristic analysis suggests there is poorer loan
quality in a high rate state compared to a low rate state, it
might be concluded that companies offer more credit when allowed
to charge higher rates. Conversely, a small difference in loan
quality between borrowers in these states would tend to re-
pudiate the hypothesis.

The first task is to describe the source of the data and

then to discuss and define loan quality.

82

83

Source and location of data

 

In order to test the hypothesis by comparative analysis,

loan applicants from two states with contrasting rate ceilings
5nm\m(

but with smaller loan size ceilings were studied. These two
states are New York and West Virginia. The maximum allowable
rate in New York is 2%% on the first $100, 2% on the next $200,
and 3/4 of 1% for the balance over $300. The West Virginia
law permits a licensed lender to charge 3% per month on the
first $200, 2% on the next $400, and l%% on the remaining
balance.1 For 1964 the average gross yield was 29.33%.per
annum for all finance companies operating in West Virginia
and 20.33% for those firms Operating under the Small Loan Law
in New York, for an annual spread of nine percentage points.2
Since the maximum loan size is $800 in both states, the effect
of different maximum loan sizes on gross yield is mitigated,
although companies in states making a larger percentage of
smaller loans would tend to show a higher gross yield, even
with the same legal loan size ceiling. Other legal restrictions
are similar in both states except New York permits lenders to
more easily assign wages than does West Virginia. Most managers

from major finance companies are uncertain of the influence of

collection practice regulations on loan quality.

 

lSmall Loan Law, New York and West Virginia.

 

2State Supervisors Reports, 1964, New York and west

Virginia.

 

84

Demographic Characteristics of Albany, New York
and Charlestony West Virginia Populations

 

At first it might seem that borrowers in West Virginia
and New York are not comparable since the social and economic
characteristics of these states are very different. New York
is primarily an industrial state while West Virginia's econ-
omy is more heavily oriented to the mining and agriculture
industries. Since the general nature Of borrowers would tend
to differ too much, it might be argued, it is difficult to
compare risk in each state. Given the difficulty in risk
measurement, these reservations are valid-—in each state
borrowers probably have different characteristics because of
general economic variations. The contents Of the test sample,
however, are derived from a single metropolitan area in each
state. The pOpulation of Charleston, West Virginia has economic
characteristics similar to those of Albany, New York since each
is of similar size, median income, type of employment, per~
manency of residence, etc. In some instances the economic
position of the Charleston community is better than that Of
Albany, although the differences are slight. For example, the
annual median family income in Charleston is $368 greater than
in Albany. In addition there appears to be a higher percentage
of "white collar" workers in Charleston. The data in Table
4.1 also show the ratio of divorced and separated persons as

approximately the same in each city, while the percent Of

85
whites in the total pOpulation and the incidence of telephone
possession are fairly similar. Charleston tends to have a
greater percentage of workers engaged in manufacturing. Manu—
facturing is concentrated primarily in the chemical industry
in Charleston, as 46% Of the manufacturing employees are en-
gaged in this industry, while no single industry in Albany
employs more than this percentage of the work force. There is
only a 5% difference between the proportion of workers employed
in retail trade and public administration in each city. The
characteristic most different between the two cities is the
owner occupancy status as 71% of the population in Albany live
in renter occupied homes compared to 54% in Charleston. Since
home ownership is a significant indicator of risk, it is im-
portant to note the tendency for greater home ownership in
Charleston.

It should be noted that the demographic data of Charleston
and Albany are based on the city population comprising an
"urban place" as defined by the U.S. Bureau of Census. The
classification appears more representative of the loan market
for a "mature, downtown office" than "urbanized area" and
”standard metrOpolitan area" classifications.

Variations in the competitiveness of consumer credit.
suppliers could influence the nature of the consumer finance

company market since prospective loan applicants would more

86

likely be attracted to banks and credit unions if these insti—
tutions competed more vigorously with finance companies. While
the more liberal convenience and advantage laws of West Virginia
permit a larger number of finance companies it is uncertain
whether or not there is more inter-institutional competition.

Differences in social attitudes toward consumer credit are
also influential in determining relevant loan markets but it is
difficult to judge its effect on consumer credit demand and the

acceptance and rejection criteria of consumer credit lenders.

TABLE 4.1

SELECTED SOCIAL-AND ECONOMIC CHARACTERISTICS
OF POPULATION IN SAMPLE CITIES

 

m

 

Albany, N.Y. Charleston, W.Va.
Median annual income $5778 $6146
Median age 35.3 yrs. 31.9 yrs.
Percent of white persons
in total population 91.5% 90.3%
Percent having telephones
in household 81.5% 83.1%
Percent living in owner
occupied housing 29.1% 46.0%
Population 129,726 85,796
Percent of pOpulation
divorced or separated 3.0% ' 3.4%
Percent of "white collar"
workers in work force 43.8% 48.7%

 

Source: U.S. Bureau of Census, based on 1959 data for "Urban

D1 aha" are:

87

Nature of Data and Structure of Sample

 

Loan applications of borrowers were Obtained from three
major finance company branch Offices located in the downtown
section of each community. Each of the Offices is classified
as a "mature, downtown office" with a loan portfolio Of approxi-
mately 1,000 accounts. A sample of 200 borrower loan applica-
tions from each office was Obtained through random selection
to make a total sample of 600 borrower applications from each
city. The method of sample selection consisted of choosing
loan applications on the basis of every fifth account in the
file drawer if there were 1,000 accounts and every 6th appli-
cant if there were 1,200 accounts, etc. While the use Of
random numbers to select each application gives a more unbiased
sample because each account would then have the same probability
of being selected, the chosen sample fairly represents the
total loan portfolio. Present, former, and new borrowers
were all selected together so that the frequency of each class
in the sample is representative of the whole population. In
the case of Charleston, West Virginia, only loans made after
June 1, 1963 were included in the sample since the present
Small Loan Law went into effect at that time. The Albany
sample includes loans made from April, 1963 to April, 1965.

No indirect loan applications were included in the sample.

In addition to appraising loan characteristics of actual

88
borrowers, an analysis is made of characteristics of rejected
loan applicants. While the general loan market in each city
appears to be similar there might be some differences in the
characteristics of persons applying for credit. With a higher
rate ceiling it is conceivable that a different "type" of
applicant is encouraged to apply for small cash loans. The
analysis of turndowns also allows a look at the risk problem
from two perspectives--that of borrowers and that of rejected
loan applicants. If the risk level is substantially lower
for both turndowns and borrowers in Albany there would then
be some evidence of hypothesis validation. In effect, the
turndown or rejected account data either confirm or conflict
with the conclusions from borrower characteristic analysis.
Again, 200 rejected loan applications were selected from each
office for a total of 600 from each city. These turndown
applications represent most of the rejections during the period

of March, 1964 to April, 1965.

Elements of loan quality

Since the empirical test attempts to measure differences
in overall borrower loan quality under two different rate
structures, the question of determining the proper elements
of loan quality must be considered before making general
statements and judgments concerning risk levels. The term

loan quality has many meanings. It has been.pointed out that

89
deterioration of credit quality can mean at least four differ-
ent things: “1) a decline in actual quality, as indicated by
loss rates, foreclosure rates, default or delinquency rates
determined after the event, 2) a decline in estimated quality
as indicated by loss reserves, interest differential, economic
appraisals, and the like, or 3) a shift toward types Of loan
contracts or types Of borrowers that may be expected to in-
volve higher delinquency or loss rates, or 4) a change for
the worse in the economic prospects of debtors."3 Test I uses
chargeoff rates as a measure of loan quality, while Test II
compares actual applicant characteristics to indicate credit
quality differences.

A change in the economic prospects of a community also
affects loan quality, but this factor does not apply here since
loan applicants in both cities applied for credit when economic
conditions and forecasts were relatively stable. Employment
was fairly stable in each city and the prospect of future
business activity was bright. Any Observed differences in
loan quality should be interpreted as typical of long run
situations since short run economic conditions were stable in
each city. In April, 1965 when the sample was drawn, there
was some indication of possible increased unemployment in

Charleston, but employment prospects were generally favorable

 

3Moore, Geoffrey H., "The Quality of Credit in Booms and
Depressions," Journal of Finance, Vol. XI, NO. 2, May, 1956,
p. 293.

 

90
during the preceding two-year period.

Several studies concerning credit quality determination
at the time a loan is granted have shown some SOphisticated
and imaginative analysis. One of the pioneering studies on
the risk assessment of consumer credit was made by David
Durand in 1941.4 Durand studied borrowers at commercial banks,
at credit unions, and at two personal finance companies. He
used two measurements of risk for each borrower characteristic
and risk element--the bad loan relative and the efficiency
index. The bad loan relative is derived by analyzing and
comparing the incidence of borrower losses in a particular
classification with the number of "good" accounts in the same
classification. Out of a sample of 200 "good" accounts and
200 "bad" accounts--a bad account being defined by the lender--
if those borrowers with an income Of less than $100 per month,
for example, comprise 13.8% of the good accounts and 17.7% Of
bad accounts a bad loan relative of 1.3 is derived, indicating
that borrowers in this income range have a 30%.greater chance
of going "bad". The higher the bad loan relative for any
particular characteristic the greater the chance of loans in

this category being uncollectable. As another measure of risk,

an efficiency index shows the relative importance Of each

 

4 . . . ' '

Durand, DaVid, Risk Elements in Consumer Instalment
Financing, National Bureau of Economic Research, New York,
1941.

 

91
characteristic as a risk indicator. The efficiency index
shows, for example, that if 78% of bad loans had job tenure
of less than 10 years, while 54.5% of good borrowers had
tenure of less than 10 years the net difference of 23.5
would be the efficiency index. When the index is zero, the
distribution Of good and bad loans is identical and the charac-
teristic would be a poor measure of risk prediction. Likewise,
a high efficiency index indicates a particular characteristic
is a good measure of risk.

As Durand points out, his analysis suffers from the lack
of certain information. The credit history and general charac-
ter of the borrowers as well as their mental and physical health
are not included in the analysis. Variation in collection
methods were not considered, either. This is an important
consideration since a more lenient credit policy might be Off-
set by a more vigorous collection policy and may, therefore,
bias the conclusions.

A more serious drawback, however, is the built-in bias
of the data inputs used in the statistical analysis. The
indices Of risk are derived from loss records on borrOwers
who already have been selected by experienced credit analysts.
Therefore, the bad loan probabilities apply only to those who
have passed an initial acceptance test and may or may not

apply to other classes of loan applicants analyzed by credit

92
grantors using different criteria of credit judgment.

Other measurements Of risk have been developed using other
bases for determining a "bad" loan. Plumber and Young used
repossession rates and loss measures, while Wonderlic6 used the
actual loss rates. Robbins used the percentage of loans charged
off as a measure of loan quality. These studies, while making
a contribution toward rational, quantitative measurement of
risk are not as detailed as the work of Durand, Smith, and
others.

Table 4.2 shows the relative importance attributed to each
borrower characteristic by various studies measuring consumer
risk. In column 1, Smith's results show that possession Of a
telephone is the best signal predictor of loan default, while
monthly payments as a percentage of monthly income is one of
the poorest measurements.9 This is in contrast to the criteria
most often used by credit interviewers to reject applicants, as

shown in column 2. Durand's commercial bank analysis shown in

 

Plummer, Wilbur C., and Young, Ralph A., Sales Finance
Companies and Their Credit Practices, National Bureau of
Economic Research, Financial Research Program, 1940, p. 186.

 

 

6W'onderlic, E.V., "Control of Losses In Installment
Credit," Time Sales Financing, Vol. 20, No. 2, February,
1956' ppo 6-89

7

Robbins, W. David, Consumer Installment Loans, Ohio State
University, Columbus, Ohio, 1955, p. 106.

Smith, Paul F., "Measuring Risk on Consumer Installment
Credit," Management Science, Vol. 11, NO. 2, November, 1964,
pp. 327—340.

91bid., p. 337.

93

TABLE 4.2

RELATIVE IMPORTANCE OF BORROWER CHARACTERISTICS
AS PREDICTORS OF LOAN QUALITY

 

 

1 2 3 4 5
Smitha
Smitha rejected Durandb BankersC Retailersd
(borrowers) accounts (banks) attitudes attitudes

Telephone in residence 1 l6 --
Ownership or rental of

residence 2 l 5 -— —-
Bank account 3 10 l -— —-
Purpose of loan 4 ll —- -- --
Time in last residence 5 l4 4 -- --
Time in last job 6 7 2 l 1
Sex and marital status 7 9 6 l3 -—
Neighborhood 8 4 -— -- --
Time in last two jobs 9 6 -- -- --
Maturity of loan 10 3 9 10 5
Time in last two

residences ll 13 -- -- —-
Occupation 12 12 3 7 1
Monthly income 13 17 8 6 --
Age 14 8 —- 12 ——
Number in family 15 15 -- 9 --
Monthly payment as %

of income 16 5 -- -— 3
Interviewer's appraisal 17 2 -- -- --
Life insurance -- —- 7 -- ~—
Amount of loan -- -- 9 -- --
Past payment record -- -- -— 2 ——
Character and reputation -— -- -- 3 --
Debt -- -- -- 4 4
Credit rating -— -- —- 5 2
Assets -- —- -— 8 --
Security -- -- -- ll --

 

Source: aSmith, Paul F., ”Measuring Risk on Consumer Instalment Credit,“
November, 1964, p.

Management Science,

 

Vol. 11, No.

337.

bDurand, David, Risk Elements in Consumer Instalment Financing,

Studies in Consumer Instalment Financing, NO. 8, National

Bureau of Economic Research, New York, 1941, p.

c . .
Chapman, John M., CommerCial Banks and Instalment Credit,
National Bureau of Economic Research, Financial Research

Program, New York,

Plummer, Wilbur C.,

panies and Their Credit Practices,

1940, p. 138.

and Young,

Ralph A.,

80.

Sales Finance Com-

 

National Bureau of Economic

Research, Financial Research Program, 1940, p.

137.

94
column 3 suggests a similar ranking of characteristics, although
10
there are some differences. For example, occupation and occu-
pational stability are more important indicators of risk under
Durand's analysis.

It is interesting to note the dissimilarity of rankings
between the statistical indices and the credit guides of credit
managers. In column 4, the relative ranking assigned to each
characteristic by 126 commercial banks show that moral charac-
teristics as past payment record and credit history are the
most important criteria of loan quality.11 Financial charac-
teristics as income and assets and loan characteristics are
judged less important than personal and vocational character-
istics. A study of the relative importance attached to various
credit factors by 688 retail establishments indicates occupa—
tion and occupational stability are most important, closely
followed by the quality of past payment record.12 Again, length
of loan contract is least important in their ranking systems.

These attitudes substantiate the view that past payment

record and minimum income are two primary considerations. The

statistical studies do not consider credit history directly

 

10Durand, Op.cit., p. 80.

ll .

' Chapman, John M., CommerCial Banks and Installment
Credit, National Bureau of Economic Research, Financial Re-
search Program, 1940, p. 138.

12Plummer, Wilber C., and Young, Ralph A., Sales Finance

Companies and Their Credit Practices, National Bureau of Eco-
nomic Research, Financial Research Program, 1940, p. 137.

 

95

but consider other factors which indirectly indicate borrower
ability and willingness to repay a loan. Monthly income is
ranked 12th by Smith and 8th in importance by Durand, showing
less emphasis on this factor than placed by credit managers.
It is also interesting to note that Smith's study Of rejected
accounts indicates that the interviewer's appraisal is the
second most important credit determing factor while the bor-

rower data shows it to be least important.

Application Of Credit Score Systems to Test II

A method for evaluating the total risk Of a loan applicant
is to use a score system which assigns values to various charac—
teristics to arrive at a composite score. The purpose of a
scoring system is to measure the overall risk of a consumer
credit applicant. Credit managers have long contended, and
still contend, that the basis for a credit decision rests
upon the artful judgment of the manager rather than the sum—
mation Of a few numbers. Tests indicate that when comparisons
are made between the experiences of applicants granted credit
on the basis of a score and those granted loans on the basis
of credit "judgment", the Odds definitely favor the use of the

13
credit score system.

 

13 . . .
Zaegel, R. J., "A POint Rating System for Evaluating
Customers," The Credit WOrld, Vol. 52, #1, October, 1963,
pp. 9-13.

 

96

The reason for using the scoring system in this study is
to assess the overall risk of each borrower and each rejected
applicant included in the sample of 2,400 loan applications.
Rather than comparing individual characteristics of borrowers
in Albany and Charleston, all the factors are combined to Ob-
tain an overall measurement of relative risk based on bad
account probabilities. While the income level of borrowers
in one city may be higher than in the other, this factor might
be Offset, for example, by the higher stability of employment
in the other city. The use of scoring facilitates analysis
by combining 8 or 10 characteristics and consistently and
accurately weighing each.

Borrower characteristics of those granted loans and those
refused credit in both cities were scored to more easily com-
pare the average scores of the 600 borrowers in Albany with
the 600 borrowers in Charleston. Likewise, the scores of the
600 rejected applicants in each city are compared. If the
average scores are the same in each city, it tends to refute
the hypothesis while a substantial difference in scores indi-
cates a confirmation of the hypothesis.

One of the most recent developments of a credit scoring sys—

tem has been made by Paul Smith Of the University of Pennsylvania.14

 

l4 . . .
Smith, Paul F., "Measuring Risk on Consumer Installment

Credit," Management Science, Vol. 11, No. 2, November, 1964,
pp. 327—340.

97
Using data from commercial banks on loans extended during a
5% year period, Smith analyzed loan characteristics in an
attempt to determine posterior bad account probabilities.
Calculation of bad account probabilities was made by dividing
the number of bad accounts with a given trait by the total
number Of accounts in the sample with that trait. Using 13
separate risk characteristics, Smith was able to determine the
probability of an account becoming "bad" or delinquent by
knowing only one variable. In order to obtain an overall
estimate of future loss, probabilities of all the various
combinations of traits would have to be completed, and since
this is an arduous and time consuming process to be practically
feasible for actual Operation, Smith uses an intermediate step
for ranking of accounts to give an order of priority. By
adding the separate probabilities of each trait classification,
the resulting sum serves as a basis for ranking. Applicants
with the most points are those with a greater probability of
becoming "bad". His score formula is: Rank = (pai+pbi+pci+ — — —
pni) 1000 where p is the posterior delinquency probability for
each trait and the small letters indicate different traits with
i classifications. The results from using this score system
as a basis for granting loans appears to be favorable. Actual
use of the system indicates that nearly 20%.Of the bad accounts

fell within a range of the index that would have permitted the

98
use of index values to reject applicants with a loss Of less
than three good accounts for each bad account. Most lending
institutions consider this a profitable ratio.

The American Investment Co. of Illinois is a pioneer in
the development of a quantitative credit scoring system and
they have been using their system effectively during the past
few years. Their system uses discriminate analysis to deter—
mine the relative weights to be given each characteristic.
The General Finance Company of Evanston, Illinois and the
Associates Investment Co. of South Bend, Indiana also use
credit scoring systems to guide their decision making. Other
large firms are presently conducting statistical tests of loan
probabilities to use in a workable scoring system.

Smith's system has deficiencies similar to Durand's be-
cause credit history is not included into the analysis. How-
ever, some firms have scoring systems which include credit
rating information and company representatives allege credit
history is somewhat quantifiable.

In order to check the scores, Smith's criteria using com-
mercial bank data was supplemented by the use of a credit
scoring system developed by a major consumer finance company.
Therefore, there is a double score for the applicants--one
based on consumer finance company data and the other based on

recently published commercial bank data. Smith's system

99
considers 10 borrower characteristics while the consumer fi-
nance company score system weighs only 8 elements of loan
quality, as indicated in Table 4.3. Smith's system differs
in that it considers the level Of income, loan purpose, and
borrower sex, while the finance company system uses borrower
occupation as an element of loan quality, but neither system
includes consideration Of a borrower's financial position.
Smith's probability analysis indicates that borrower financial
ability to cover monthly payments was the least effective dis—
criminator of good and bad accounts. It is not readily ap-
parent why this is true but the preselection bias is the most
likely reason.

The amount of funds borrowed by the applicant is not con-
sidered in the score. An applicant seeking $800 certainly
would demand closer scrutiny than one borrowing $50, but both
systems do not alter the repayment probability because of loan
size. In use, however, a credit manager could alter the cutoff
point depending on the size of loan.

The calculation of scores for the sample considers only
those accounts showing 2;; 8 or 10 borrower characteristics and
the analysis is not affected by a large portion of accounts
showing only a few traits. Since the information of borrower
applicants tends to be more complete than for rejected appli-
cants, the sample of borrowers is larger and somewhat more

representative.

100

TABLE 4.3

RISK ELEMENTS IN SCORE SYSTEMS

 

 

m m
Finance Company System Smith Scoring System
Age Age

Marital Status Marital Status

Time at Present Address Time at Present Address
Telephone in Residence Telephone in Residence
Occupation Number in Family

Time at Present Job Rent or Own Home

Number in Family Sex

Rent or own Home Time at Present Job

Monthly Income
Purpose of Loan

 

Expected Score Relationships

The hypothesis is that consumer finance companies tend
to accept more risk and reject fewer applicants in states
allowing lenders to charge a higher rate. Since the annual
gross yield is approximately nine percentage points higher
in Charleston, a higher risk score for these borrowers is
expected when using Smith's system and a lower score when
using the consumer finance company system if lenders in West
Virginia do, in fact, accept more borrower risk.15 Chart 4.1

indicates the expected relationships between average scores

and finance charges under both systems. A table showing the

 

5 . . . . .

Notice the pOSitive relationship between score and
risk under Smith's system and the negative relationship under
the consumer finance company system.

101

CHART 4.1 EXPECTED RELATIONSHIP BETWEEN SCORE AND
FINANCE RATE LEVEL UNDER TWO SCORE SYSTEMS

Consumer Finance Company
Smith System System

mean mean
score score

 

 

 

 

Finance Charge % Finance Charge %

Note: There is a positive relationship between score and
risk level under Smith's system and a negative rela—
tionship under the consumer finance company system.

102

the components of the Smith Score System is given in Appendix D.

Actual Score Results of Test II

The actual scores indicate an almost identical mean score
of borrowers in Albany and Charleston and a very similar rela-
tionship for rejected accounts. Table 4.4 shows that the
Smith mean score of 1211 points for borrowers in Albany is
very close to the mean score of 1200 points for borrowers
located in Charleston, a less than 1% variation. The standard
deviation of scores is also quite similar in both cities. These
tendencies are confirmed in using the Consumer Finance Company
Score system as borrowers in Albany averaged 773 points com-
pared to 779 in Charleston. The average score for rejected
accounts in Albany differs only slightly from those in Charleston,
when using Smith's system, but the disparity is larger under
the consumer finance company system. (Table 4.5)

These conclusions of borrower characteristics are based
on 531 Observations in Albany and 570 in Charleston which are
less than the sample of 600 in each city due to the absence
of information on some accounts. Of those applicants report-
ing all Of their characteristics, there is a strikingly close
mean score between groups of borrowers paying widely different
rates Of charge.

The turndown data of Albany show a mean score of 400

points greater than the mean borrower score in Albany for a

103
TABLE 4.4
PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS IN

ALBANY AND CHARLESTON, BY
SMITH SCORE SYSTEM

 

 

 

 

 

Borrowers Rejected accounts
Albany Charleston Albany Charleston
Points
1-500 1.17% 2.50% 1.20% 1.17%
501-700 1.17 1.33 1.00 .83
701-900 12.50 16.66 1.00 .83
901-1100 21.33 23.33 3.50 5.67
1101-1300 23.67 19.67 11.17 11.50
1301-1500 12.17 12.67 16.00 11.83
1501-1700 4.17 7.17 10.67 8.67
1701-1900 4.83 6.00 7.83 7.67
1901-2100 4.33 3.50 10.33 9.00
2101-Over 2.17 2.17 10.00 12.33
No Report 12.49 5.00 27.30 30.50
Total 100.00% 100.00% 100.00%. 100.00%
#Mean Score 1211 1200 1591 1599
#Std. Dev. 375 377 427 458

 

Note: Notice the positive relationship between number of

points and risk level.

system, see Appendix D.

For explanation of point

# Includes only those applicants supplying all of the re-
quired information.

104
TABLE 4.5
PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS IN

ALBANY AND CHARLESTON, BY
CONSUMER FINANCE COMPANY SCORE SYSTEM

 

 

 

 

 

Borrowers Rejected accounts
Albany Charleston Albany Charleston
Points
1-200 .54% .34% 4.00% .33%
201-400 2.83 4.00 7.50 6.00
401-600 12.67 15.34 21.50 32.00
601—800 35.50 32.33 19.17 27.67
801-1000 27.83 28.83 4.50 9.67
1001-1200 13.83 16.17 .67 2.00
1201-1400 0 .33 0 0
1401-Over 0 0 0 0
NO Report 6.80 2.66 42.66 22.33
Total 100.00% 100.00% 100.00% 100.00%
#Mean Score 773 777 526 613
#Std. Dev. 197 205 231 177

 

Note: Notice the negative relationship between number of
points and risk level.

# Includes only those applicants supplying all of the re-
quired information.

105
30% difference, but a very similar mean score to the scores
of rejected applicants in Charleston who averaged 1599 points.
The consumer finance company system score shows a greater vari-
ation of scores for the rejected loan applicants indicating
that sex, occupation, income, or loan purpose differences
exist since the systems differ in the weighting Of these
factors. The differences in mean scores are too small to draw
any other conclusion about the correlation of risk and rate
except that the data show little difference in risk acceptance
patterns in loan companies Operating in cities with different
finance rate ceilings.

The percentage distribution of borrower scores shows a
tendency for scores to be grouped in the same categories.
Table 4.2 shows that approximately 45% Of the borrowers have
scores of 900-1300 points, while only 30% of the rejected
applicants attain a score of this size. The similarity of
borrower scores is shown in Chart 4.2 and the similarity is
rejected account scores is shown in Chart 4.3. Chart 4.4
contrasts the borrower distributions with the rejected appli-
cant distributions for the Albany experiences. Since the
borrower and rejected applicant experiences are similar in
both cities, a comparison of the Charleston borrower and
'rejected loan applicant experiences is about the same as

Albany's. While it is always dangerous to make generalizations,

106

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108

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109
the data are so similar that it is compelling to categori-
cally state there is practically no difference in borrower
quality between cities. This limited evidence definitely
suggests a refutation of the hypothesis. Even though lenders
are charging a higher finance rate in West Virginia, this
analysis suggests they are not accepting more risk into their
portfolios. These stable risk tendencies are graphically
shown in Chart 4.5. Of course, this assertion is subject to
the aforementioned qualifications and assumes the score sys-

tems include all Of the relevant variables.

Analysis of Credit Factors Included in Score Systems
While many borrower characteristics are included in the
scoring systems used in the analysis, it is also useful to
analyze each credit factor separately. The scoring systems
account for variations Of certain characteristics but a de-
tailed Observation aids in providing more insight into lender's

preferences.

Monthiy income
One of the most surprising findings from both Durand's
and Smith's studies is the relatively small influence of

monthly income information on loan repayment probability.

 

6
Smith, Op.cit., p. 337.

16
Smith's risk index shows income to be only 13th on a list Of 17,

llO

CHART 4.5 ACTUAL RELATIONSHIP BETWEEN SCORE AND
FINANCE RATE LEVELS UNDER TWO SCORE SYSTEMS

Consumer Finance Company
Smith System System

Mean
Mean Score
Score

 

 

 

 

 

 

Finance Charge % Finance Charge %

Based on 600 Samples Of loan applications for both borrowers
and rejected accounts from each of two cities.

111
while Durand shows it only the 8th most reliable indicator of
loan repayment.17 While a minimum income certainly is a neces-
sary condition for debt assumption, the incremental amount
above the minimum level apparently is not a solid indicator
Of risk. As income rises, people tend to assume larger finan-
cial Obligations and increase their scale of living. There-
fore the amount of discretionary income above the amount needed
for current expense and present Obligations may not change
proportionately with total income.

The data in Tables 4.6 and 4.7 show a tendency for bor-
rowers in Albany to have a slightly higher level of income
compared to those in Charleston as the mean monthly income is
$468 for borrowers in Albany compared to $447 for borrowers
in Charleston. Approximately 18% of Albany's borrowers have
a monthly income of $300 or less, while 24% have this income
in Charleston. Approximately 7% Of the borrowers in each
city have incomes greater than $750 per month. By far, the
greatest number of borrowers have incomes between $300 to
$500 in both cities. In Albany, 75% of the borrowers have
monthly incomes between $300 and $750, while there are 6%
fewer borrowers in this classification in Charleston. The
slight tendency for Albany borrowers to have higher incomes

is verified in the turndown data which show that 56% of the

 

l7
Durand, op.cit., p. 80.

112

TABLE 4.6

COMPARISON OF PERCENTAGE_DISTRIBUTION OF LOAN APPLICANTS
BY SIZE OF MONTHLY INCOME, FOR.
BORROWERS AND REJECTED ACCOUNTS

 

 

 

 

g—
Monthly Borrowers Rejected accounts
income Albany Charleston NCFA data* Albany Charleston
$ 0 0% 0% 0% 1.50% 1.33%
1-100 0 .67 3.3 1.67 3.17
101-200 4.17 8.00 3.0 9.17 23.83
201-300 14.33 15.67 10.4 31.67 28.33
301-400 21.50 23.50 19.9 20.17 18.83
401-500 24.67 21.17 25.0 16.33 12.33
501-750 28.83 24.17 30.1 12.67 8.00
751-1000 5.50 6.00 9.1 2.67 .33
1001-over 1.00 .83 2.2 .17 .17
No Report 0 0 0 4.00 3.67
Total 100.00% 100.00% 100.00% 100.00% 100.00%
Mean $468 $447 $466 $357 $306
Std. Dev. $192 $195 -— $163 $154

 

 

*Based on a 1963 National Consumer Finance Association sur-
vey as reported by Companies Operating over 1700 offices.

See:National Consumer Finance Association,

Yearbook 1965, Washington, D.C., p.

Finance Facts

113

 

 

 

 

 

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114
rejected applicants in Charleston have incomes of less than
$300 per month while only 44% of the rejected applicants in
Albany are in this income bracket. There is also a larger
percentage Of Albany rejected applicants in the higher income
brackets.

Consumer finance company experience across the nation
also indicates the great bulk of borrowers earn between $300
to $500 per month as both the two city and the NCFA data
show that 44%.of the borrowers are in this income classifi-
cation.18 The national experience differs slightly as there
is a higher number of borrowers in the higher income classi-
fications relative to the two city experience. A.comparison
of the income level of borrowers in Albany and Charleston with
that of the general population in these areas indicates a much
greater tendency for borrowers to be in the middle income
classifications. While 40%.Of borrowers have annual incomes
between $4,000 and $6,000, only 25% of Albany's pOpulation
and 20% of Charleston's pOpulation is within this income range.

The data on monthly and annual income indicates 1) a
slight tendency for borrowers in Albany to have higher incomes
since the mean income is $468 per month compared to $447 in

Charleston, 2) a similarity between the national experience

 

8 . . . . .
National Consumer Finance Assoc1ation, Finance Facts
Yearbook, 1965, Washington, D.C., p. 52.

115
and the two city sample experience, and 3) a much larger pro—
portion of borrowers within the middle income range compared
to the general pOpulation of the area. The income levels of
rejected applicants also tends to corroborate the findings
that lenders in Charleston accept a higher risk level when
considering this one variable alone. The tendency is a
little surprising since the median annual family income for

the general population in Charleston is $368 higher.

Personal Borrower Characteristics

Age, There appears to be little difference between the
average age of applicants in Albany and Charleston. In both
cases the mean borrower age is approximately 42 years. As
the frequency distributions in Table 4.8 show, the proportion
of borrowers in the various age classifications is about the
same. The heaviest concentration of borrowers is in the 35-
50 age group where approximately 40% Of the borrowers are
categorized. In contrast, the table also shows that only
27%w32% of the general pOpulation of each city is within this
age group. (This general population figure includes only the
relevant consumer finance market since those persons under 20
years of age are not included in the figures.) The mean age
of borrowers is only slightly higher in Charleston than in
Albany in contrast to the reverse tendency for the general

population. The approximate average age of the Albany

116

[population is 35 years and the Charleston population is 32
years, which in both cases is younger than the sample mean age.

These figures are a bit misleading, however, as the mean
age Of the general population is for the total community while
the frequency distributions refer to the eligible loan market
of 20 to 60 years and older. Therefore it is not quite accu-
rate to compare the average ages of the general pOpulation
'with the borrowers, but it is consistent to compare their
frequency distributions since they are classified on the same
basis. These figures indicate, however, that consumer finance
companies generally grant credit to the "typical resident" of
the community in terms of average age, but the distribution
of borrowers ages differs from that of the general community.
While 27% Of the population is 60 years or older in Albany,
consumer finance companies extend credit to only 7.5%.of
this population segment. There is little difference between
the proportion of borrowers under 30 years and the prOportion
of general pOpulation in this age group.

The average age of rejected applicants is 33.5 years in
Albany and 32.6 years in Charleston with a large percentage
of the turndowns in the lower age classifications. In both
cities, approximately 30% of all turndowns are less than 25
years of age. About 45% of all turndowns are less than 30

years of age.

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117

 

 

 

 

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118

Robbins'data, though somewhat different, show a greater
tendency for finance companies to make loans to younger bor-
rowers. While the sample data show approximately 30% of the
borrowers are over 50 years, Robbins study of 1950-1951 shows
only 13% of the borrowers over 50 years.19 It also reveals
that 38% of his sample was under 35 years while the Albany
and Charleston figures show only 29% of the borrowers are
under 35. If the Albany and Charleston figures are representa-
tive of the national experience, then there has been upward
shift of average borrower age. This is partially connected
with the greater tendency for present borrowers to Obtain
funds as they comprise 80% of the loan portfolios. Present
borrowers have a habit of getting Older and so does the aver-
age age of all consumer finance company borrowers. If this
trend continues, consumer finance companies face an Older
market which eventually will have less need for consumer credit
and finance companies. What are their alternatives then?
The figures on age classification indicate: 1) a tendency
for consumer finance companies to lend to borrowers between
35 and 50 years, 2) an increasing average borrower age,
3) a tendency for turndown applicants to be younger than

borrowers, and 4) that finance companies in both cities extend

 

9 .
Robbins, W. David, Consumer Instalment Loans, Ohio
State University, 1955, p. 74.

119
credit to the same age groups. After analyzing means and
distributions of age groups there is little basis to use this
criterion as indicating differing lending policies between

firms in the two cities.

Sex. One of the characteristics included in most credit

 

scoring systems is whether the applicant is male or female.
Statistical studies show that women borrowers are significantly
better risks than men, although the reason for this tendency

is, indeed, mystifying. Most credit managers, however, use
family characteristics when measuring consumer risk and deciding
whether or not to grant credit. While the woman Of the house-
hold may apply for a loan, most credit managers consider the
total family situation when evaluating risk.

Table 4.9 shows a slightly higher percentage of women
borrowers in Albany, although the difference between the ex-
periences in two cities is slight. Lenders in both cities
show approximately 80% Of their borrowers as male with a
similar percentage of rejected male applicants. This data
show a considerable increase in women borrowers compared to
the 1950—1951 data of Robbins which are based on a broader
sample of eight consumer finance companies and 775,000 bor-

20

rowers. His study shows a loan portfolio of 8% women, com-

pared to the 1965 two—city average of 19%. This indicates

 

201bid., p. 77.

120
TABLE 4.9

COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS BY
SEX, FOR BORROWERS AND REJECTED APPLICANTS

 

 

 

 

 

Borrowers Rejected accounts Robbins'
Sex Albany Charleston Albany Charleston data*
Male 82.3% 80.5% 79.3% 81.6% 91.9%
Female 17.7 19.5 20.7 18.4 8.1
Total 100.0% 100.0% 100.0% 100.0% 100.0%

 

*Robbins, W. David, Consumer Instalment Loans, Ohio State
University, 1955, p. 77.

finance companies are more active in cultivating this market
and are following the lead Of commercial banks which have
consistently granted more credit to women. Besides the efforts
of finance companies themselves, the increased affluence and
social independence of women over the past decade is partly
responsible for this greater tendency. Many female applicants
are members of familiesvflmxe both she and her husband work
and, therefore, have a higher family income and are more eli—
gible for consumer credit.

In the case of single men and women, there appears to be
a definite risk superiority of women over men, according to
the statistical studies. Part of this tendency can be explained
by the occupational characteristics of women who tend to be

employed in stable clerical and sales clerk positions.

121

Marital status. The evidence of risk experience varia—
tions among different marital status classifications is not
altogether conclusive. Smith's study shows married persons
to be better risks than single peOple, while a consumer finance
company scoring system shows an Opposite tendency. All scoring
systems indicate that divorced and separated persons are poorer
risks than single and married people, principally because of
their increased mobility and flexibility to move and default
on their loans. Most credit managers cast an wary eye, indeed,
to divorced and separated applicants. One major finance com-
pany has a general policy of not granting credit to separated
persons unless the applicant secures the note with a comaker's

signature.
TABLE 4.10

COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS BY
MARITAL STATUS, FOR BORROWERS AND REJECTED ACCOUNTS

 

 

 

 

 

 

 

General Rejected
, Borrowers Population* accounts

Marital
Status Charles- Charles- Charles-

Albany ton Albany ton Albany ton
Married 70.3% 73.6% 67.3% 71.3% 53.6% 62.3%
Single 11.2 9.7 25.2 22.4 23.9 20.2
Divorced 3.2 6.5 1.2 2.2 6.2 8.5
Separated 7.5 3.0 1.8 1.2 11.8 3.8
Widowed 7.8 7.2 4.5 2.9 4.5 5.2

 

 

 

Total 100.0% 100.0% 100.0% 100.0% 100.0%» 100.0%

* Source: Based on U.S. Bureau Of Census Reports.

122

A comparison of borrowers in each city reveals a close
similarity between the proportion Of loan applicants in each
marital status classification. There are approximately 2.5%
more married borrowers in Charleston, but since there is
little difference in a single and married person's risk, this
discrepency is not too revealing. The data in Table 4.10
indicate a substantially higher level of Albany borrowers who
are separated but a smaller percentage of divorced borrowers,
so that approximately 10% of the borrowers in each city are

21

either divorced or separated. While approximately 3% of
Albany's pOpulation is either divorced or separated, 15.3%
Of the borrower pOpulation comprise this group. There are
also prOportionately more widowed borrowers than are repre-
sented in the total pOpulation. These figures are a bit
deceiving, however, since the Albany and Charleston pOpula-
tion prOportions consider the total population and include
those under 20 years and who are not eligible for credit.
This is shown in the proportionately higher percentage of
single persons in the general pOpulation compared to borrower
population. While some discrepencies exist between the gen-

eral pOpulation and borrower pOpulations, there are very

little differences between the experiences of finance companies.

 

A partial explanation for the greater incidence of
separated persons in Albany is the unusually strict New York
State divorce law.

123

The Robbins 1950-1951 data show that single borrowers
comprise 17% of the total compared to about 10% today.22
While it is not within the purview of this study, it would
be interesting to see comparisons Of borrower trends at com-
mercial banks and credit unions.

The turndown data reveal that compared to the general
population a higher percentage of applicants are either sepa-
rated or divorced. About 18% of all rejected applicants in
Albany fall into this classification compared to 12% in
Charleston indicating that lenders in Albany are prone to
reject more applicants in the higher risk classifications.
In regard to differences in overall risk acceptance and re-
jection patterns, however, it is difficult to ascertain any
evidence of risk differences based on marital status infor—

mation.

Residential Characteristics of Applicants

 

Years in Residence and home ownership. One Of the most
important indicators of risk is the applicant's duration of
residence. The number of years a borrower has been at the
same residence is the 5th best index of risk according to

23,24
Smith's calculations and the 4th best index according to Durand.

 

22Robbins, op.cit., p. 77.
2
3Smith, Op.cit., p. 337.

24
Durand, op.cit., p. 80.

124
Strangely enough, however, credit managers have not recognized
this as rejected account analyses show managers consider
stability of residence as no more than the 15th most valid
indicator. (Table 4.2) A scoring system of one of the major
finance companies assigns a high positive value to residence
stability, thereby recognizing the high correlation between
stability and loan repayment. Using the Often cited 4 C's as
a measure of risk-—capacity, capital, character, and collateral,
--the stability of residence criterion easily fits into this
framework, for stability of residence tends to show character.
(At least to some people).

Table 4.11 shows a distinct difference of residence sta-
bility in each city. While almost half of the borrowers in
Albany have resided in their present dwelling less than three
years, only 42% of the borrowers in Charleston fall into this
category. While 21% of Albany borrowers have lived in their
residence 10 years or more, 31% of borrowers in Charleston have
lived in their address for this period of time. The mean resi-
dential duration is 7 years in Albany and 9.8 years in Charles-
ton. Based on these data, borrowers in Albany are poorer risks.

When comparing borrower residence duration with that of
the total pOpulation, the borrowensin Albany have less than
average stability while borrower stability in Charleston is

better than average. In Charleston, about 46% of the total

125
TABLE 4.11
COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS,

BY YEARS IN PRESENT RESIDENCE, FOR BORROWERS
AND REJECTED APPLICANTS

 

 

 

General Rejected
Years in Borrowers Population# accounts
residence Charles- Charles- Charles-
Albany ton Albany ton Albany ton
0-1.0 24.1% 20.8% 22.8% 26.5% 40.3% 38.3%
1.1-2.0 13.5 12.8 10.8 10.9 17.0 10.8
2.1-3.0 11.0 7.8 8.8 9.0 9.5 4.7
3.1-4.0 5.7 5.8 5.8 4.0
4.1-5.0 5.5 4.5 4 7 2 3
27. 27. ‘ °
5.1-7.5 9.0 7.7 9 8 3.7 3.8
7.6—10.0 8.7 8.6 4.8 6.3
10.1-over 21.5 30.8 29.7 25.8 12.5 22.3
NO Report 1.0 1.2 0 1.7 7.5

 

 

 

 

 

 

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Mean-Yrs. 7.0 9.8 -- -- 4.8 7.36

 

#Based on U.S. Bureau of Census Reports, 1959 data.

population has resided in their present addresses 3 years or
less while approximately 40% Of borrowers in Charleston have
lived in their residence for this time. Approximately 48%

of the borrowers in Albany have lived at their present address

3 years or less but only 42%.of the general pOpulation have
lived at their present abode for this time. At the other ex-
treme, 31% of Charleston's borrowers have lived at their present

address for 10 years or more, while only 26%.of the urban place

126
population is that stable.

These Observations are verified in the rejected applicant
data also, since 67% of the applicants in Albany have lived at
their present address for three years or less, while this is
true for only 52% of Charleston's applicants. In like manner,
12.5% Of Albany's rejected applicants have lived in their pre-
sent address for 10 years or more, but 10% more rejected appli—

cants in Charleston have been at their present address 10 or

run. I. msng nun": r- cg."
‘ s
.

more years. There is no doubt consumer finance company appli-

cants in Albany have less residence stability and therefore,

I‘ll“- noe-L- L- can v,

tend to be riskier.

Part Of the explanation for borrowers in Albany to be
less stable in terms of residence is the tendency for the
population as a whole to live in renter occupied housing
rather than owner occupied housing. Renters tend to be much
more mobile than non-renters. For both Albany and Charleston
borrowers this tends to be true. (Appendix G.5) If there
is any one characteristic which is different between the
pOpulation in the two cities, it is the incidence of home
ownership. As Table 4.12 shows, 29% of the population in
Albany live in owner occupied homes and 46% Of the pOpulation
in Charleston live in homes they own. This, in large measure,
accounts for the lower residence stability of borrowers in

Albany. Approximately 8% more borrowers rent homes in Albany

127
TABLE 4.12
COMPARISON OF PERCENTAGE DISTRIBUTIONS OF LOAN APPLICANTS,

BY OCCUPANCY OF OWNER OCCUPIED HOMES, FOR BORROWERS
AND REJECTED APPLICANTS

 

 

 

 

trvy

 

General Rejected
Borrowers POpulation* accounts

Occupancy
status Charles- Charles— Charles-

Albany ton Albany ton Albany ton
Rent home 66.3% 58.2% 70.9% 54.0% 77.8% 76.0% I
Own home 30.7 41.7 29.1 46.0 8.0 17.0 L
NO Report 3.0 .1 0 o 14.2 7.0 i

1.

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

 

*Source: U.S. Bureau of Census pOpulation reports.

accounting for the tendency of those borrowers to be less
stable.

The relative residence stability of borrowers who rent
compared to those who live in owner occupied dwellings is
shown in Appendix 6.5. The tendency for applicants to rent
rather than buy is also indicative of greater risk since all
score systems show a much greater loan repayment probability
for those living in owner occupied housing indicating, other
things being equal, an applicant owning his house is a pre-
ferred risk. Part of this tendency is explained by the higher
income of renters compared to non—renters as shown in the

data in Appendix G.4.

128

Telephone status. There is nothing in consumer finance
more mystifying and baffling than using an applicant's posses-
sion of a telephone as a criterion of creditworthiness. The
enigma is not limited to the general layman but extends to
most credit managers as well. It seems utterly ridiculous to
judge an applicant's merits on the basis Of whether or not he
has a telephone in his house. Statistical studies contradict
the general concensus, however, as they conclusively suggest
a strong positive correlation between telephone possession

and creditworthiness. Smith's data show that the possession

J’wrfipa.c_~ ...._‘.:. __ f—T.hu-.."
L

of a telephone is the single most important indication Of
2

applicant credibility. 5 If a credit manager is able to know
only one characteristic about a borrower and must make a credit
decision on this basis he logically would have to judge on the
basis of whether or not the borrower possesses a telephone,
given a minimum income level and a good credit history.
Credit scoring systems of finance companies place heavy weight
on telephone possession because the statistics showing the
correlation between default and telephone status are too con-
vincing to overlook.

The statistical significance of telephone status high-

lights a very important point concerning the reliance on any

single variable as a predictor of risk--one variable can be a

 

5
Smith, Op.cit., p. 337.

129
proxy for many other characteristics. Having a telephone in
and of itself is meaningless, but if a person possessing a
telephone tends to have a higher income, greater residence
and occupational stability, etc., then it is a reliable in-
dicator of consumer credit risk. Only when it is understood
that a single variable may represent several desirable or un-
26
desirable traits is it a useful credit guide.
TABLE 4.13
COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS,

BY TELEPHONE IN RESIDENCE, FOR BORROWERS
AND REJECTED ACCOUNTS

 

 

 

 

General Rejected
Borrowers POpulation* accounts

Telephone
status Charles- Charles- Charles-

Albany ton Albany ton Albany ton
No tele-
phone 14.8% 16.8% 18.5% 16.9% 34.7% 35.5%
Telephone 84.8 82.7 81.5 83.1 61.7 51.8
NO Report .4 .5 0 0 3.6 12.7

 

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

 

*Source: Based on U.S. Bureau of Census housing reports.

 

2
6Appendices 6.6 and G.7 show the tendency for tele-
phone possessors to have higher incomes and greater resi—
dence stability.

130

The samples of borrowers and the general population do
not differ in their tendencies for telephone possession.
Table 4.13 shows about 83-85% of borrowers in each city possess
a telephone compared to the total pOpulation possession of
about 82%. Not only is there a little discrepancy between
the two cities, but there is little difference between the
general telephone possession characteristics of the borrowers I“

and the general pOpulation.

Occupational characteristics

imam-d“ vacuum am. . 1...“.

Stability of Employment. Of all the intrinsic applicant -
financial and economic characteristics, stability of occupation
is one of the most important indicators of credit risk. A
study Of credit manager preferences shows a definite tendency
to use occupational stability as a guide for credit judgment.
Chapman's study shows stability Of employment as the most
important factor other than income for commercial bank mana-

27
gers. Plummer and Young also verify this Observation in
their 1940 study of the various factors affecting credit judg-

28
ment Of 688 retail establishments.
29 30
The statistical studies of both Smith and Durand

emphasize the credibility of occupational stability as Durand

 

27Chapman, Op.cit., p. 138.

28

29Plummer and Young, Op.cit., p. 137.
Smith, Op.cit., p. 337.

30
Durand, Op.cit., p. 80.

131

shows it to have an efficiency index of 20, slightly under
the efficiency index of 23 given to possession of a checking
account. Durand states "Of all the factors germane to all
fields of lending, stability of occupation and possession of
a bank account stand out as primarily important in the selec-

31
tion of risks." In fact, only on the relative importance
of occupational stability do both credit managers and statis—
ticians agree. Smiths' conclusions are not as strong as
Durand's since he assigns borrower occupation with a risk
index of 6. All consumer finance company scoring systems
rank occupation and occupational stability high on the list
of importance.

Of course, it is only logical that occupational stability
be an important clue Of credit stability since a stable em-
ployment record means: 1) employment, 2) regular income, and
3) a past record of agreeing to fulfill a contract--the con—
tract to work according to the rules of the employer. Ob-
viously, an ability and desire to fulfill a contract is a neces—
sary condition for assuming instalment debt.

The data in Table 4.14 show over 30% of borrowers held
their present jobs for three years or less and approximately
35%.have been at their present occupation for 10 years or more.

While 4% more Charleston borrowers are in the three year or

 

31
Ibid., p. 80.

. A‘b.‘o —. c c.“

‘Iu'A—A .2 hue-mp... 3. .-.‘-lru

132
TABLE 4.14
COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS,

BY YEARS IN PRESENT OCCUPATION, FOR BORROWERS
AND REJECTED LOAN APPLICANTS

 

 

 

 

 

 

 

 

 

Years:h1 Borrowers Rejected accounts
occupation Albany Charleston Albany Charleston
0-1J) 16.8% 20.8%» 47.5% 51.8%
1.1-2.0 9.2 7.3 13.3 13.5
2.1-3.0 6.3 8.3 8.8 6.5
3.1-4.0 6.7 6.5 5.7 6.0
4.1-5.0 5.2 4.2 3.7 3.7
5.1-7.5 8.5 9.8 4.2 6.2
7.6-10.0 10.8 8.7 5.5 4.3
10.1-over 36.2 34.0 9.0 6.8
No Report .3 .3 2.3 1.2
Total 100.0% 100.0% 100.0% 100.0%
Mean-Yrs. 9.23 9.15 3.5 3.1

 

less classification, it is difficult to use occupational
stability as a basis for implying any difference in risk
acceptance or rejection patterns of the firms in each city.
What.jJSInOSt interesting, however, is the very large pro-
‘portjxnn Of rejected applicants working at their present
occupation for three years or less--approximately 70%. Less
than 10% Of the turndowns have been at their present jobs
for 10 years or more compared to 35% of the borrowers. While

the discrepancy of occupational stability between borrowers

and rejected accounts is high, there is very close resemblence

 

l"- .1 “91‘-

lpwnvrv‘ m 1.!
.

L

 

133

between the stability in each of the two cities. There is

slitflitly more than a 2% difference between the prOportion of

rejexrted accounts in the three year or less category in each

citqn. The mean occupational duration of 9.23 years in Albany

anui 9.15 years in Charleston shows Albany with slightly higher

stability tendencies, but not enough to conclude a risk aver-

sion or rejection pattern. The mean occupational duration of

rejected applicants in Charleston is 3.1 years compared to

3.5 years in Albany indicating that the lenders in Charleston

reject poorer accounts. In summary, when using both borrower

and turndown data, there is only a slightly greater risk

acceptance tendency in Charleston.

Nature of borrower occupation

One of the most widely used criteria of consumer risk is

the nature of the applicant's occupation. While statistics

show it is not the most dependable and accurate measure Of
loan repayment or default, credit managers rely heavily on

occupation as an indication of creditworthiness. One major

finance company distributes to its employees a list of pro-\

tubited and restricted occupations to guide them in making

loans. Ironic as it may seem, a truck driver is considered

zatmtter risk than a minister or a general proprietor;

enghmmrs, firemen, and policemen are preferable to dentists,

jmhfim and lawyers. Barbers, bartenders, domestics, and taxi

Ji- 1‘!“- .‘L_"..‘.' ._" ‘J

In n1.

 

 

134

drixners are generally discriminated against because of occupa-

tion.

Indeed, the list of preferred and non-discriminated

cxmcupations is long, but it is an arbitrary classification,

at best” since many occupations overlap with one another and

Inany occupational groups exist today which did not exist five

years ago. Many decisions based on occupation are arbitrary

and out of data, but they are used. Of course, some of the

discrimination is rational since peOple employed in certain

occupations tend to be less stable and earn lower income, etc.

But credit managers allege, for instance, that ministers are

generally reluctant to repay their debts since "they feel the
world owes them a living," bartenders are poor risks because

they tend to become too engrossed with their job, and barbers

and actors tend to “skip town". While these allegations are

somewhat true, it is difficult to imply these tendencies for
all borrowers in a given occupational class.

Several studies show that unskilled laborers are gen-
erally poorer risks, while professional peOple tend to be
better than average risks. Durand's study shows general
proprietors, skilled and unskilled laborers with higher bad-
loan relative ratings than teachers, doctors, actors, artists,

32
cflerical workers and other "white collar" workers. Robbins'

 

32 _
Durand, Op.cit., p. 71.

7TH“ .‘upu—a-nal on -- -1 “1’... .

 

135
studqr<of the occupational characteristics of charged—off
ltnni accounts suggests that managers, officials, prOprietors,

anui clerical workers are poorer risks than unskilled and

skilled.1aborers,

33
vincing.

although this evidence is not fully con-

Consumer finance company studies show a preference

for teachers, engineers, doctors over cab drivers, laborers,

etc. One major finance company classifies borrowers into ten

occupational groupings with different risk weights assigned
to each group.

What we are able to conclude from studies on the correla—
tion of risk and occupation is that a good deal of bias and
misinterpretation exists as well as a large amount of rational
statistical analysis. Assuming, however, that finance com—

pany statistics are accurate, it is possible to study differ-

ences in borrower occupations and make judgments on the rela—
tive risk assumed by lenders in each city.

There appears to be a smaller percentage of poorer risk
"blue collar" workers in Albany than Charleston, since 33% of
Albany's borrowers are "blue collar" workers as compared to
eflnmst 40% in Charleston. In both cases, about 14% of the
lxurowers did not report their occupation or are considered
intim "miscellaneous" classification, as shown in Table 4.15.

Ushmga consumer finance company's risk index value system as

_

3
Robbins, Op.cit., p. 108.

n :,‘m

—.—..—..--..—s - a- --

 

136

 

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137
a guide, there appears to be greater risk acceptance patterns

in Charleston, although the difference is not substantial.

The tendency for lenders in Charleston to accept more

risk and also turndown more risk is not verified in the rejected

applicant statistics. In both cities approximately 26% of the

rejected applicants are "white collar" workers, while over 50%

The incidence of rejected applicants not

are "blue collar”.
reporting their occupations is over 20% in both cities, so

their analysis is not complete. The percentage in the mis-

cellaneous category includes those persons unemployed or

receiving unemployment compensation.

The Albany and Charleston occupational experiences differ

from those observed by Robbins in his 1950-1951 data since
only 24% of the borrowers in his sample were classified as

34
"white collar" compared to over 33% in the two city sample.

A 1958 survey by the National Consumer Finance Association

confirms Robbins' findings and shows little change in the
35

trend of borrower occupation.

The discrepancy between the national averages and the

Albany and Charleston experiences lies in the nature of occu-

pants found in each city. Since both Albany and Charleston

Ibid., p.58.

5
National Consumer Finance Assoc1ation, The Consumer

inance Industry, A monograph prepared for the Commission on
cney and Credit, Prentice-Hall, Inc., Englewood Cliffs, N.J.,

962, p. 67.

 

{‘U'L‘l‘fiv-I-..‘ 1\1'l ”A 5'

 

138

are the capitol cities of their states, there tends to be a

greater prOportion of clerical and professional workers com-

pared to those in an "average" industrial city. Approximately

44% of the general pOpulation in Albany are "white collar"

workers and 49% of the workers in Charleston are classified

into this category. This differs from the national average
36

of less than 40%. Since a greater percentage of population

\C'l'fl‘lfln’ful " ..’

is "white collar" it is only reasonable that a larger per-

centage would be found in the loan portfolios of consumer

{Min W' ' .‘I"I‘.4H {I

finance companies.

The significant part of this data is the similarity of the
experiences in the two cities, even if there is dissimilarity
between the national averages and the Albany and Charleston

experiences. The latter is explained by differences in their

loan markets relative to those in other cities. While there

is a 6% difference between the occupational status of borrowers

in the two cities, the difference is too small to be considered

as solid evidence of differences in aversion or tendency to

accept risk.

Puppose of the loan

Very few consumer finance company credit scoring systems

Dnsider the intended use of the loan as a means of predicting

36U.S. Bureau of Census Reports.

 

139
ride butnwst credit managers use this information in deter-
mhungvmedmr or not to grant credit. Smith analyzed the
lossgnprfllities of each loan purpose classification and
asmnts Hume applying for loans to use for debt consolida-

37
tioncntnwdical purposes are riskier applicants. Durand's
statistux;are not homogeneous enough for him to draw any
strongcxnmflusions regarding the risk aspects of each loan
purpose category.

The data gathered from the Albany and Charleston lenders
show approximately 18%.of the borrowers in each city obtained
credit to consolidate present debt, while 21% of the borrowers
in Albany obtained credit to pay for current expenses compared
to twice this amount in Charleston. Because of the arbitrary

assignment of loan purpose by the interviewer and researcher,
it is hazardous to place great reliance on the data. For

instance, "current expenses" can mean many things and can

applyrtxDInost of the other classifications. Table 4.16 shows

a fair similarity between the experiences in both cities except

for the current expense category.

of the data and the similarity of the data which is available

it is difficult to draw any conclusions regarding risk patterns

Dased on loan purpose.

It: is; iJiteresting to note the greater incidence of rejected

 

L37
Snuitli, pp.cit., p. 334.

Because of the unreliability

Inc—r- 'i-‘v'i.

 

140

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141

applicants who intend to use funds for debt consolidation and

automobile expense, since approximately 50% of the turndowns

needed funds for these purposes compared to 27% for the bor-

rowers. Part of the reason is because auto dealers probably

have first referred the prospective buyers to a commercial

bank where they have been rejected and when they apply at a
finance company they are rejected there also. Many credit

men are wary of a "merchant referrel".

Comparing the Albany and Charleston experiences with the
national average, we find fewer borrowers in these two cities

using funds for debt consolidation and more using the funds

for current expenses. The sample also includes a greater pro-

portion using funds for automobile purposes. The 1950-1951
study of Robbins using data from 14 consumer finance companies

38
reveal experiences similar to the NCFA data. The NCFA data
show 48% Of borrowers Obtain funds for debt consolidation, a

definite increase since 1958 and a distinct difference from

39
There is no doubt that

the Albany and Charleston findings.

a large part of this discrepancy can be explained by overlap—

ping groupings of data.

Analysis of Credit Factors Not Included in Score Systems

The Smith and Consumer Finance Company systems use only

38Robbins, op.cit. p. 87.
39National Consumer Finance Association, Finance Facts

'earbook, 1965, p. 51.

 

 

 

142
8 and 10 borrower characteristics and do not consider an

applicant's financial position nor the amount of credit a

borrower obtains. Since there is an $800 maximum loan size

limit in each state, the average loan size should be similar

if companies are equally restrictive. Other characteristics

as possession of a checking or savings bank account, race,

or new borrower, and the security

whether a present, former,

of the note are credit factors not included in the score sys-

tem. Therefore, in order to complete the study of relative

risk in each sample, it is necessary to compare these character-

istics.

Size of Loan. Table 4.17 indicates there is very little

difference in the number of loans according to loan size. A

firm charging a lower finance rate may be more reluctant to

grant smaller sized loans, (depending on the structure of rates,

of course), but there is little evidence of this being the

case. The average loan size is $587 in Albany and is somewhat

lower in Charleston-—-but the difference is too small to imply

different risk acceptance patterns. Table 4.17 indicates the

total loan size for each borrower while Table 4.18 shows the

additional credit extended to borrowers. Since a large pro-

>ortion of borrowers already have credit at the consumer firm
ompany and are classified as present borrowers (about 80%) ,

1e applicant is frequently requesting additional credit up

f... hLIU.‘ s- “a...“
."

 

'5' ”It, l-fi '

 

 

141.111.! '11..

143
TABLE 4. l7

COMPARISON OF PERCENT AND NUMBER OF LOANS
BY SIZE OF LOANS, FOR BORROWERS

 

 

'ng—m—f-‘NT-T‘ 449...... ;-.
1

 

 

 

m ====
Amount Albany Robbins* Charleston
of loan NO. % data NO. %
$0-$50 1 .2 7.6% 2 . 3
51-100 4 . 7 14.09 11 1.8
101-150 12 2.0 13.42 25 4.2
151-200 18 3.0 12.30 17 2.8
201-300 48 8.0 29.59 53 8.8
301-400 60 10.0 19_04 69 11.5
401-500 59 9.8 45 7. 5
501-750 142 23.7 2.33 101 16.8
751-800 256 42.7 1.36 277 46.2
NO report
& Misc. 0 0 .26 0 0
Total 600 100.0% 100.0% 600 100.0%
Mean $587 $577

 

*Source: Robbins, W. David, Consumer Instalment Loans, Ohio
State University, 1955, p. 41. It is assumed
Robbins' data refer to the total amount of credit.

to the $800 limit. Table 4.18 shows that in both Albany and
Charleston approximately 30% Of the borrowers obtained addi—
tional credit of $100 or less and approximately 10—12% of the
borrowers obtained new credit of $500-$800. The average loan

size of $587 in Albany is comparable to the $577 in Charleston.
It is interesting to compare the frequency distributions

If the sample with those derived by Robbins based on 1950—1951

 

CDMPARISON OF PERCENT OF LOANS BY SIZE OF

144

TABLE 4.18

ADDITIONAL CREDIT, FOR BORROWERS

 

 

 

 

 

State University,
Robbins' data refer to the total amount of credit.

lending experiences.

.portjrn1<3f larger loans are made today than 15 years ago when

I
Ammufi: Robbins' data*
of loan Albany (Total Credit) Charleston
$0-$fl3 18.5% 7.61% 6.8%
51-100 12.0 14.09 22.5
101-150 15.7 13.42 18.7
151-200 11.3 12.30 8.5
201-300 17.7 29.59 18.0
301-400 10.5 19.04 11.0
401—500 2.3 2.33 4.0
501-750 8.3 1.36 7.0
751-800 3.7 .26 3.5
No report
& Misc. 0 -- 0
Total 100.0% 100.00% 100.0%
*Source: Robbins, W. David, Consumer Instalment Loans, Ohio

 

1955, p.

41. It is assumed

It is clear that a much greater pro—

legal loan ceilings and general income levels were lower.

$500 in 1951.

and $750 in total credit,

While 60-70% of all loans in the 1964 sample are between $500

only 3.5% of all loans were over

Loan sizes are much larger today than before.

 

‘40

Robbins, op.cit., p. 41.
iata refer to the total amount of credit.

It is assumed that Robbins'

 

(t-‘u

 

IF" I. a» ‘ s

 

145
Based on total loan sizes and additional credit extended,
it is clear that very little differences in lending practices
between offices in these two cities and consequently there is
little justification for alleging risk acceptance differences

on the basis of loan size variations.

Amount of Debt

The score system does not measure the effect of present
financial consumer instalment obligations on a prospective
borrower's ability to repay a loan. While Smith and Durand
both conclude that financial characteristics are unreliable
predictors of loan repayment, it is still useful to compare
differences between borrowers in both communities for if
borrowers in Charleston, for instance, have a greater amount

of debt, it is fair to assume they are riskier than Albany
borrowers. Therefore, there would be a partial confirmation
of the hypothesis. As shown previously, credit managers con-
sider financial position very carefully when determining bor-
rower loan eligibility. (Table 4:2)
The data in Table 4.19 show some similarity between the

financial obligations of borrowers in Albany and Charleston.

In both cities, approximately 50% of the borrowers have instal-

ment debts exceeding $1000 and about 15% of borrowers have debts

less than $250. The mean debt level is somewhat higher in

Vharleston by about $375 and this amount is significant.

a ’_n ;'-' "d"

WnMuHA-‘wLafi‘ n In: (5..
i

 

 

146
TABLE 4.19
COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS

BY SIZE OF INSTALMENT DEBT, FOR BORROWERS
AND REJECTED ACCOUNTS

 

 

 

 

 

Debt Borrowers Rejected accounts
Albany Charleston Albany Charleston

$0 3.0% 2.5% 15.2% 14.0%
1-100 2.8 2.2 6.2 10.5
101-250 7.0 5.8 11.0 12.5
251-500 14.7 8.5 17.0 12.2
501-750 10.2 11.0 9.3 7.7
751-1000 10.8 9.3 11.2 6.7
1001-1500 17.8 14.2 10.7 9.0
1501-2000 13.3 14.3 6.5 5.8
2001-over 20.4 32.2 6.8 10.2
No Report 0 0 6.2 11.6

Total 100.0% 100.0% 100.0% 100.0%
Mean $1306 $1681 $782 $816

 

 

 

 

 

Not only is it significant when considered singularly but it
is meaningful when comparing it to the general population in-
come level of each city. The median annual income is $5616
for a borrower in Albany and $5364 for a borrower in Charles-
ton. (Table 4.7) Instalment debt as a percentage of annual
income is 31% in Charleston and 23% in Albany, implying a
”borrowing power premium" of 8% of annual income for Charleston
borrowers. If the 31% ratio is applied to the Albany income
:here would be an additional $430 of borrowing power available

or these borrowers. The data in Appendix G.l further show

r‘- MEL. "ran-u“ no".-c‘ L‘l v '...‘.u . - .
.

 

 

147
.hat in every income classification borrowers in Charleston
.ave more instalment debt. If borrower demand does not vary
oetween the two cities and there is no presumption there does,
then lenders are likely to assume more risk in Charleston
where the finance rate ceiling is higher.

This may be the result of lenders' overtly recognizing
that the rate differentials allow them more lending power,
but increased competitive pressure is the most likely explana—
tion. Assuming a fairly competitive market, each lender will

extend credit to those who will afford him a fair return.

TABLE 4.20

COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICATIONS
BY SIZE OF INSTALMENT DEBT INCLUDING PRESENT LOAN,
FOR BORROWERS

 

Debt Albany Charleston
$0 0% 0%
1—100 . 75 .25
101-250 4.00 3.25
251—500 9. 75 8.25
501-750 10. 75 7.25
751-1000 12.75 10.25
1001-1500 20. 50 19.00
1501-2000 15.00 14. 75
2001-0ver 26. 50 37.00
No Report 0 0

Total 100.00% 100.0%

 

Source: Based on a sample of 400 accounts in each city.

.._‘-__..‘_. ”1::9- 5.x 5. Sr

'5. 'T‘u‘ -

"IS-7'

 

 

148
The tendency for lenders in Charleston to extend credit
to applicants with higher debt levels is verified with the
data shown in Table 4.20 where 71% of Charleston borrowers had
debt of $1000 or more after the new loan was granted as com—
pared to 61% in Albany. The difference in the debt level of

rejected accounts confirms the observations of borrower debt

was:

level.

Monthlnyayments

HQLCAA‘QM. 1.; on‘. 1L". _ .
‘.

The average monthly payment for borrower in Charleston

1

is greater than in Albany, since the mean monthly payment be-
fore the new credit extension is $77 in Albany and $96 in
Charleston, compared to $89 and $102 when looking as the
"after new loan" monthly payment position. As Table 4.21
shows,approximately 32% of the borrowers make payments of

$100 or more in Albany, while 46% of the borrowers in Charles-
ton make this size of payment. The "before new loan" position
shown in Table 4.21 indicates there is a greater prOportion of
rejected applicants with high monthly payments in Charleston
than in Albany, since 9.5% of the applicants already have

$100 or more of monthly payments while approximately 10% more
Charleston applicants have this size of obligation. The same
tendencies are shown from the "after new loan" data in Table
4.22. In every income classification, borrowers in Charleston

have higher monthly payments. (Appendix Table G.2)

COMPARI

149
TABLE 4.21
SON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS

BY SIZE OF MONTHLY PAYMENTS, FOR BORROWERS
AND REJECTED ACCOUNTS

 

 

 

 

 

 

 

 

 

 

 

Monthly Borrowers Rejected accounts
Payment Albany Charleston Albany Charleston
$0 4.0% 3.0% 20.0% 12.0%
1-25 13.7 9.0 9.5 12.0
26-50 21.7 13.7 13.7 17.2
51—75 15.5 17.0 11.8 14.2
76-100 18.7 14.3 6.7 12.0
101-150 16.8 22.8 6.2 12.9
151-200 7.0 12.8 2.5 5.8
201-over 2.6 5.8 1.6 2.5
No Report 0 1.6 28.0 11.4
Total 100.0% 100.0% 100.0% 100.0%
Mean $77.39 $95.88 $42.73 $46.70
TABLE 4.22
COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS
BY SIZE OF MONTHLY PAYMENTS, INCLUDING PRESENT LOAN
FOR BORROWERS*
Monthly Albany Charleston
Payment
$0 .25% 0%
1—25 10.25 7.02
26—50 21.75 13.28
51—75 16.50 19.80
75-100 18.50 13.03
101-151 21.50 25.06
151-200 8.50 14.54
201-over 2.75 7.27
No Report 0 0
Total 100.00% 100.0%
Mean. $89 $102

 

*Based on a sample of 400 accounts in each city.

3 7 i...‘éJ

 

 

150
There is a greater proportion of rejected applicants who did
not report their obligation in Charleston, so it is difficult
to say what effect information derived from these applicants
would have. The monthly payment is also affected by the maxi-
mum legal maturity allowed in each state. A borrower may have

24 months to repay a loan in New York, while in West Virginia,

he is allowed 30 months. The longer the maturity of the note, g

the smaller will be the monthly payments. g
In any case, both the general debt and monthly payment é

levels are higher in Charleston than in Albany and tend to %

confirm the risk-rate hypothesis.

Security for Loan

Some credit managers allege that the type of security
offered by the lender is an important ingredient of risk since
a liquid and assessible type of security assures a larger prob-
ability of eventual loan repayment. While finance companies
do not like to repossess goods that are generally unsalable,
they sometimes do repossess items when there is no other re-
payment solution. During the past few years, the emphasis
has been for fewer tangible goods to be used as security items
and for more unsecured or signature notes. According to a
National Consumer Finance Association Study, only 15.7% of

loans granted in 1939 were unsecured compared to 28.9% in

151

 

 

 

41
1962. Robbins study of 13 consumer finance companies shows
42
that in 1950 and 1951 approximately 25% of loans were unsecured.
(Table 4.23) (It might be noted, in contrast, that 65% of com-
mercial bank loans and only 2.6% of loans from credit unions
were unsecured.)
TABLE 4.23
COMPARISON OF PERCENTAGE DISTRIBUTION OF LOANS fi
BY TYPE OF SECURITY, FOR BORROWERS ;
Robbins' i
Security Albany Charleston NCFA* study# 5
Motor Vehicle 18.0% 13.0% 12 .0% 14. 7% 1'
Household Goods 43.4 50.6 44.8 43.2
Unsecured Note 31.0 30.2 28.9 25.6
Comaker 7.0 5.7 2.2 4.2
Other .6 .5 12.1 12.3
Total 100.0% 100.0% 100.0% 100.0%

 

#Source: Robbth,W. David, Consumer Instalment Loans, Ohio
State University, 1955, p. 49.

*Source: Based on a 1962 study by the National Consumer
Finance Association. See: National Consumer

Finance Association, Finance Facts YearbookL41965,
Washington, D.C., p. 58.

The experience of the Albany and Charleston sample is
quite similar to national patterns. Approximately 31% of the

Albany loans were unsecured while 30.2% of the Charleston

 

l . . . . .
National Consumer Finance Assoc1ation, Finance Facts
Yearbook, 1965, p. 58.

 

2
Robbins, Op.cit., p. 49.

152
borrowers offer no tangible security. There is a slightly
higher number of loans secured by household goods in Charles-
ton, but the combined total of motor vehicle and household
good loans is similar in both cities. It is quite interesting
to note the similarity of: l) the eXperienceS<Ifthe offices
in the two cities, and 2) the similarity of the sample with
the national averages which includes more finance company
experience than the sample. Robbins' data also show similar
prOportions of security types.

While almost one-half of their loans are secured by
household goods, finance companies rarely repossess durable
goods. The most important reason for attaching a chattel
mortgage to household goods is to "encourage" the delinquent
customer to repay his loan. Its main asset is to psycho—
logically affect the borrower who usually recoils at the
thought of forsaking with his wife's refrigerator and oven
range. The utility of these goods to him is significantly
greater than their utility to the lender who will sacrifice
a high resale price for easy marketability.

Many credit men argue, however, that the customer is
aware of the lender's repossession reluctance and consequently
the existence of the chattel mortgage has little influence on
borrower attitudes. As a case point, one credit manager

relates a situation where he was experiencing a great deal

153

of delinquency from a group of borrowers in an outlying com-
munity. His cure for this delinquency problem was to repossess
a household good in full view of the whole neighborhood from a
borrower living in the central part of the area. Even though
he sold the good at a small price which did not cover the
amount of obligation, the payments from a rest of community
started to flow like never before and his delinquency problems
were solved.

While the type of security may affect the lender's risk,
the two city comparisons are so similar that there is practically

no differentiation of risk on the basis of security.

Present, Former, or New Borrowers

The great bulk of loan business in both Albany and Charles-
ton consists of lending to borrowers who presently hold some
credit with the finance company. As Table 4.24 shows, approxi-
'mately 80% of loans are with present borrowers with only about
9% to borrowers who have no previous credit experience with
the finance company. In contrast, about 85% of the applicants
rejected for credit have never had any credit at the firm. Not
alone does this highlight the necessity for finance companies
to cultivate their present customers, but it also indicates
the value lenders place on an adequate credit experience and
history. If a borrower has been faithful in his payments,

a finance company is almost sure to extend more credit to him,

154
TABLE 4.24
COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS

BY SOURCE--PRESENT, FORMER, OR NEW APPLICANT,
FOR BORROWERS AND REJECTED APPLICANTS

 

 

 

 

 

 

Borrowers Robbins' Rejected accounts
Source Albany Charleston data* Albany Charleston
Present borrower 80.2% 77.8% 65.4% 9.3%. 6.8%
Former borrower 10.8 14.7 14.0 7.6 8.5
New borrower 9.0 7.5 20.6 83.1 84.7
Total 100.0% 100.0% 100.0% 100.0% 100.0%

 

*Based on a 1950-1951 study of 15 consumer finance companies.
See: Robbins, W. David, Consumer Instalment Loans, Ohio
State University, 1955, p. 82.

 

even if other conditions change. Less than 10% of rejected
accounts are present borrowers. The scoring system of one
major finance company gives a substantial number of additional
points to a present borrower since he is considered a better
risk. If present borrowers are, in fact, better risks, then
a greater prOportion of new borrowers in the portfolio of a
particular company would indicate greater risk acceptance,
other things being equal. There is very little difference in
the experiences of lenders in Albany and Charleston, however,
as the data in table 4.24 show similar experienCes. Both the
borrowers and rejected accounts indicate a high degree of

similarity in portfolio management and in subsequent risk

155
acceptance and rejection patterns. It would be very useful
to analyze the loss rate of these groups as it would further
indicate the relative risk associated with them.

In total the proportion of present borrowers has increased,
since the 1950-1951 data of Robbins indicates only 65% of bor-
rowers are classified as present borrowers compared to 80% in
the two city sample.43 If the Albany and Charleston experiences
are representative of national trends, consumer finance com-
panies are either: 1) making better efforts to cultivate

present borrowers, or 2) are less successful in attracting new

clients to their firm.

Bess

It is frequently alleged that as a whole, the non—white
population tends to be more delinquent than the white pOpula—
tion, other things equal (and they never are, of course).
This is a statement subject to many legitimate questions and
some emotional ones, too, but repayment records of finance
companies show a higher probability of delinquency among non—
White borrowers. Of course, this is a dangerous position to
assert in an era of civil rights since many feel a private
enterprise should not discriminate on the basis of race, even
if the probabilities of default indicate it is is economi-

CBlLY‘justifiable to do so. While it is not the purpose of

 

43 _
Robbins, op.c1t., p. 82.

156
this discourse to discuss the social and moral ramifications
of this argument, it is fair to discriminate on the basis of
race when considering only the economic effects to the lender
if the repayment evidence is exhaustive and the analysis is
rational. Finance companies in Albany lend to more non-white
persons since 12% of their borrowers are non—white compared
to only 8% in Charleston. As Table 4.25 indicates, there is
a slightly smaller prOportion of non-whites in Albany but not
enough to be significant. The turndown information is not
conclusive, either, because a large percentage of borrowers
in Albany did not report their race. It is quite possible
that the economic and social attitudes in Albany are such
that more non—whites apply for credit, and therefore, are
better represented in the loan portfolios. Regardless of
this possibility, however, the similarity in the prOportions
are so close that one cannot make any conclusions about risk
acceptance or risk rejection on the basis of race.

TABLE 4.25

COMPARISON OF PERCENTAGE DISTRIBUTION OF LOAN APPLICANTS
BY RACE, FOR BORROWERS AND REJECTED ACCOUNTS

 

 

 

 

 

 

 

 

 

General Rejected
Borrowers POpulation* accounts
Charles- Charles— Charles-

Race Albany ton Albany ton Albany ton
White 88% 92% 91.5% 90.3% 47.0% 87.8%
Non-White 12 8 8.5 9.7 19.8 12.2
N0 report ___0 ___Q 0 O 33.2 0

Total 100% 100% 100.0% 100.0% 100.0% 100.0%

 

*merce: U.S. Bureau of Census Reports.

157

Possession of a checking account

 

Smith's studies show that knowledge of whether a loan
applicant has a checking or saving account at a commercial
bank is a useful indicator of bad account probability.

About 53% of the borrowers in Albany do not possess either
a checking or saving account while 57% of the borrowers in
Albany do not have a bank account. (Appendix F) While bank
account possession may be useful information, the difference
in borrower experiences is too slight to imply any difference

in risk acceptance patterns based on bank account possession.

Summary and Conclusions

A general and detailed comparison of risk elements in
both borrower and rejected accounts reveals there are small
but significant differences between lending policies of
lenders in Albany, N.Y. and Charleston, W.Va. Using two
develOped and tested credit scoring systems as instruments
of measuring overall risk, the mean scores of borrowers in
each city differ less than 1%, indicating the type of bor—
rowers in their loan portfolios is almost the same. A similar
comparison of rejected accounts also shows little difference
in the type of applicants who were refused credit.

A more introspective evaluation of loan elements shows

a tendency for borrowers in Charleston to be slightly more

 

44
Smith, op.cit., p. 337.

158

"risky". As Table 4.26 shows, the average monthly income is
$47 lower for borrowers in Charleston and their average debt
and monthly payment levels are significantly higher. The
relevant question is whether or not the financial differences
are large enough to warrant tentative conclusions about
greater acceptance tendencies for lenders Operating in
Charleston. Most credit managers agree that borrowers with
$375 more instalment debt and higher instalment payments of
$18 per month are, on the whole, poorer risks. This conclusion
is compatible with the credit score similarities since financial
characteristics are not included in the score system analysis.

Using the average instalment debt to annual income ratio
of Charleston borrowers as a measure of their credit eligibility,
it appears they have approximately $430 additional borrower
power than borrowers in Albany. This assertion assumes
1) similar total borrower demand in each city, 2) a similar
elasticity of borrower demand in each city, 3) generally
higher rates for all types of instalment credit extended to
Charleston borrowers, and 4) a similar market structure in
each city. The assumptions are examined in Chapter V.

The higher risk associated with the larger financial
obligations is somewhat balanced by the greater residence
stability and incidence of home ownership of applicants in

Charleston. Table 4.26 shows that four of the seven factors

159
not included in the score system computations indicate
greater risk concentration in Charleston.

In total, in spite of almost identical credit scores and
greater residence stability for Charleston borrowers their
increased debt position indicates lenders assume small but
significantly more risk in Charleston. The rejected account
data confirm borrower data and further support the risk-rate

hypothesis.

160

 

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CHAPTER V

EVALUATION OF THE EMPIRICAL EVIDENCE

Is the Hypothesis Confirmed?

The basic issue this study attempts to clarify is
whether or not there is a positive relationship between the
rates charged by consumer finance companies and the risk
accepted into their loan portfolios. The approach to re-
solving this issue was three fold: l) a study of management
policy as reported by company representatives, 2) a statistical
study of the correlation between chargeoff rates, turndown
rates, and gross yields, and 3) a detailed comparison of
borrower and rejected loan applicant characteristics in a
state where lenders charge a relatively high rate and in a
state where the rate is lower. The conclusions from the three
studies are: there is a positive relationship between risk
acceptance and rate levels, but further detailed study of
borrower characteristics must be done before this relationship
can be accurately measured. A study showing a moderate rela-
tionship between chargeoff rate and gross yield was more con-

clusive than a study comparing personal applicant characteristics

161

162
in Albany, New York and Charleston, West Virginia. The dis-

similarity of financial characteristics, however, supports the

 

validity of the hypothesis.

Since the evidence is predicated on risk measurement
assumptions, the conclusions are only as reliable as the
validity of these assumptions. This chapter presents the
conclusions of the study and examines their validity in terms
of the assumptions and risk measurements used to evaluate the
data.

Tests of the Hypothesis
Managerial Policy

Managerial policies toward risk acceptance flexibility
and rate differentials are somewhat inconsistent. Six of the
seven major finance company representatives interviewed assert
there is no change in credit availability because of rate
differentials. The implication is clear that risk standards
are determined on an average cost basis where profits from
larger loans compensate for the lossess on smaller loans and
where the favorable experiences from the "good" loans compen-
sate for the losses on the "bad" loans.1 There is no conscious
effort for management to determine output on the basis of

marginal cost and marginal revenue analysis. Rather, output

 

The term "losses" is nebulous, however, since these
amounts depend on the method of cost allocation and the
definition of direct and fixed costs.

163
is determined on the basis Of acceptable average returns and
costs.

Middle management views the relationship differently,
however, since loan supervisors adjust risk by altering
acceptable chargeoff rates. Rate differentials influence
credit standard criteria when loan supervisors evaluate branch
manager loan judgment on both active and rejected accounts.
Since the supervisor's Opinion Of branch manager judgment
influences loan patterns, he informally sets credit standards.
Most supervisors admit their loan judgment is somewhat flex-
ible, depending on whether or not they can "afford" to adjust
standards.

It is difficult to say which view is more correct, tOp
management's or middle management's, but loan supervisors
are closer to the actual Operation. There probably is a
tendency to adapt credit standards to the income produced by
finance charges. While tOp managers assert a rigid credit
standard policy the competition of other financial institutions
may force them to consciously or unconsciously alter their
standards in order to effectively compete within their area.

Management intentions may not vary, but local competition

 

may affect the implementation Of credit standard policies.

Test I

The statistical results from associating gross yield

164
levels with chargeoff rates confirmsthe hypothesis tolerably
‘well. A partial correlation analysis shows a moderate but
significant relationship between the variables for all three
firms. A summary of the results Of correlating chargeoff

rates with gross yield is shown in Table 5.1.

TABLE 5.1

SIMPLE AND PARTIAL CORRELATION COEFFICIENTS (r)
OF CHARGEOFF RATE AND GROSS YIELD,
FOR THREE FINANCE COMPANIES

 

 

m _1 w
Firm X Firm Y Firm Z

Simple (r) .38 .28 .41

Partial (r) .26 .31 .28

 

The relationship Of turndown rates and gross yield is
not conclusive and the coefficients Of correlation for all
three firms clearly are not significant. Rejection rates
appear to be unreliable and inaccurate proxies for risk
acceptance. A summary Of the results of correlating turn-
down rates and gross yield is shown in Table 5.2.

Validity of Test I as a Measure of Risk Acceptance
and Rate Relationship

To rely on chargeoff rates as a measure Of risk acceptance

and gross yield as a measure of finance charge level may be

165
TABLE 5.2
SIMPLE AND PARTIAL CORRELATION COEFFICIENTS (r)

OF TURNDOWN RATE AND GROSS YIELD,
FOR THREE FINANCE COMPANIES

 

 

m
Firm X Firm Y
Simple (r) -.03 -.05
Partial (r) -.12 —.10

 

assuming too much, however. While chargeoff rate is one mea-

sure Of risk assumption cost, it is inextricably associated

‘with other costs. A firm can substitute collection efforts

and investigation expenses to replace chargeoff expenses and

since these three expenses are interdependent, only their

total amount provides an accurate indication Of lender risk

assumption costs. The analysis of Test I does not consider

collection and investigation costs because it is very difficult

to allocate employee salaries and wages according to the various

functions performed. Therefore, chargeoff rates are used as

a proxy for risk acceptance. It is an inexact measure, to

be sure, but it does provide a rough measure Of borrower risk.
Gross yield percentages do not portray the whole story

on rates since revenue from insurance premiums is an additional

source of income not included in the gross yield amount. By

"suggesting" that borrowers purchase life, accident, and health

166
insurance policies to protect the borrower in the event Of
tragedy, finance companies secure additional revenue. The
amount of earnings from this source varies due to the various
regulations on insurance charges. Therefore, gross yield
presents a rather inaccurate portrayal of company earnings,

although it does provide an adequate "rough" measure.

Test II

The third measure Of risk and rate relationship is a
detailed comparison of borrower and rejected characteristics
in a low rate state with those in a high rate state. A com-
parison of applicants from three major finance companies in
Charleston, West Virginia where the rate is relatively high
with those from the same firms in Albany, New York where the
rate is relatively low indicates a small difference in their
personal characteristics. The close similarity of the "scores“
of applicants in each city is strong evidence of a constant
risk-rate relationship—-contrary to the hypothesis assertion.
Table 5.3 illustrates the comparison Of applicant character-
istics in each city. A more detailed analysis shows a
striking similarity between most applicant characteristics
except for 1) residence stability, 2) monthly income, 3) in-
stalment debt, and 4) monthly payments. There is greater
applicant resident stability in Charleston, indicating less

risk, but smaller applicant income and greater debt, indicating

167
TABLE 5.3
SUMMARY OF CHARACTERISTICS OF BORROWERS AND REJECTED

LoAN APPLICANTS IN ALBANY AND CHARLESTON,
1964—1965, by MEAN AVERAGE

 

Borrower Rejected
Accounts Accounts

 

Albany Charleston Albany Charleston

 

Borrower Characteristics
Personal Characteristics

 

Age - Yrs. 42.2 42.6 33.5 32.6
NO. in Family 3.2 3.2 2.7 2.9
Yrs. in Res. 7.0 9.9 4.8 7.4
Yrs. in Occup. 9.2 9.2 3.5 3.1
Financial Characteristics
Monthly Income $468 $448 $357 $306
Monthly Payment $77 $96 $43 $46
Instalment Debt $1306 $1682 $782 $816
Amount of Loan $587 $576 $389 $350
Payment on Loan $31 $29 -— —-
Debt to Annual Income

Ratio by Credit Rating

Tests .23 .31 .18 .22
Scores

Smith System Score 1200 1211 1591 1599

Consumer Finance Com—

pany System Score 773 777 526 613

 

A lower score on the Smith test indicates better credit.

A.higher score on the Consumer Finance Company test indi-
cates better credit.

168

more risk. In order to assess a final risk amount, the finan-

cial differences must be compared to the residential chara-
teristics. On balance, lenders in Charleston appear to
accept slightly more risk as the added financial burdens of
applicants appear to outweigh their tendency toward increased
home ownership and residential stability.
Validity Of Test II as a Measure of
Risk Acceptance and Rate Relationship

A serious limitation to a comparison Of this type is the
“haterggeneityoflegal regulations in each state. While pains-
taking effort was made to select economically similar cities,
there are interstate differences in the allowable collection
methods lenders are permitted to use. Since it is easier to
assign borrower wages in New York than in west Virginia col-
lection costs are apt to be lower in New York, allowing firms
to accept more risk. State regulations toward debtor and
creditor remedies vary and must also be considered when
assessing credit standards.

Another legal regulation affecting loan Operations is
the status Of "convenience and advantage" laws in each state.
Most states have laws requiring new licensees to show "con-

venience and advantage," but these laws are enforced with

 

2For a succinct summary of the advantages and disadvant—
ages Of convenience and advantage laws, see Neifeld, M.R.,
Neifeld's Manual on Consumer Credit, Mack Publishing Co.,
Easton, Penn., 1961, pp. 95-98.

{H : ‘. "hug-‘12:- _‘u- '91.- - an. a

169
varying degrees of severity. In New York, for example, the
law is more strictly enforced than West Virginia. While
casual Observation can be misleading, one has only to walk
on the main streets of Charleston to notice the greater num-
ber of loan Offices there than in Albany. Of course, this
could also be a reflection Of their rate level differences.
The relative merits and drawbacks Of convenience and advant-
age laws are too numerous to mention here, but where a strict
interpretation Of the law exists, branch Offices have greater
Opportunities to achieve economies of scale. A recent study
shows the Optimum number of accounts in a branch Office to
be between 2,000 and 2,500.3 In states where "C & A" laws
are strict, firms can afford to accept greater losses and

still maintain a "fair" rate Of return since larger sized

 

3Athorough analysis Of loan size and unit cost rela-
tzionships shows a decreasing cost relationship between branch
<>ffice size and unit costs. Other studies verify these
Jresults, but there are dissenting views which argue that loan
C>ffices have constant costs. Of course, a large part of this
.Elroblem reverts to a definition Of "costs". The nature Of
lfiinance company costs is not within the purview Of this study
1But the following sources discuss the problem in detail.

Comiskey, Eugene E., "A Study Of Loan Cost Behavior in
<2CDnsumer Finance Companies," unpublished Ph.D. dissertation,
1“’1—‘ii.chigan State University, 1965.

Farwell, Loring C., "Cost and Output Relationships of
c-‘-<>nsumer Instalment Credit Agencies, Based on Reports of
:[Ildiana Licensees," unpublished Ph.D. dissertation, North-
w’Gastern University, 1955.

Miller, Upton, "The Importance of Direct Costs in the
(31?anting Of Consumer Instalment Credit," unpublished Ph.D.
dlssertation, Northwestern University, 1948.

Anal-h." 4....I. v -g:.. 5!.

mum-c“ and
PF‘ ‘ l
7

170
branch offices tend to have lower unit costs. The Offices
where the sample data were secured are very similar in size
and average between 1,000 and 2,000 accounts per Office,
although one Office in Charleston is somewhat smaller. All
are considered mature downtown offices. Therefore, economies
or diseconomies of scale shouldn't affect the risk acceptance
level differences in the sample, although they may affect
Operations elsewhere. 1
I
Because of the more liberal convenience and advantage E
I

laws in West Virginia there is a greater tendency for consumer 1
finance companies to compete with each other, although the
amount Of increased competition because Of these laws is
uncertain. The greater number of Offices in Charleston makes
it somewhat tenuous to assert that the supply market Of con-
sumer credit is similar in both areas.

Competitive differences are also affected by other factors.
For instance, the greater concentration of employees in the
chemical industry in Charleston suggests company or labor
union credit COOperatives more actively compete with consumer
finance companies and affect the type Of applicants they are
likely to attract. On the other hand, this tendency is balanced
by the relatively greater number Of persons employed by the
State Of New York in Albany which also has credit unions. Since

this dissertation doesn't include a study Of market structure,

171
the strength and effect Of these differences is not measured.
However, loan market structure is influential in determining
loan quality and delinquency losses.

The amount of commercial bank branch banking and its
affect on competition with consumer finance companies is also
influential in ascertaining the nature of applicants securing
credit at finance companies. Inter—industry competition is
important but it was not explicitly considered in the study. E
There is no doubt, however, that in general commercial banks %

are now competing more aggressively for consumer credit accounts.

“a.” _—‘-. ".9.
I

It must also be remembered that the data from Test II
represents acceptance patterns from only three finance com-
panies, all large. It is quite conceivable "independent"
finance companies have a different set Of credit standards.
Major finance companies may tend to be more inflexible and
may not adapt to local loan markets as readily as the smaller
state and local Operations. It would be very useful to com—
pare borrower loan quality from the independent finance com-
panies with that from major finance companies. However,
there is no presumption the three large companies are not
representative of the whole industry. The 1950 data compiled
by Robbins shows little difference in loan size for the

4
various types Of firms granting consumer credit. His data

 

Robbins, W. David, Consumer Instalment Loans, Ohio
State University, 1955, p. 39.

172
from the Ohio area show "contrary to such belief (that chain
organization 'skim the cream' Of the loan market) no striking
difference is to be Observed between chain and independent
companies in regard to the amount Of money which they lend
to borrowers." Of course, other applicant characteristics

may differ between firms.

Significance of Hypothesis to Intra—Firm Risk Discrimination
The hypothesis asserts that output is determined on a

marginal cost and revenue basis where risk cost differentials

'1'; li'fl" IAIN Air Mm r.‘:~.u.r-.--o' _v u. .v r: -.

are matched with the revenue increments. The marginal analy—
sis application can be extended even further, however. By
varying rates to match the costs of borrowers according to
their risk class, firms can more effectively compete with
other lending institutions. The most efficient and accurate
method Of achieving this is with a credit scoring system
whereby those applicants scoring in the lower risk brackets
would receive a favorable rate while those scoring in a
higher risk bracket would pay a higher rate. Of course,
this mechanism is fraught with many administrative diffi-
culties and extra costs, but an experimental test seems
feasible. One major finance company executive indicates
his firm is presently considering this approach.

The advantage Of such a system is Obvious_-consumer

finance companies would be\better able to compete with

173

commercial banks and other financial institutions who charge
lower rates and make larger loans. Applicants falling into
the poorer risk classifications would have to "pay their own
way" since the expected returns from their risk class must
match the expected costs of credit losses, collection costs, etc.

The drawbacks are Obvious too--administration costs would
be higher, but this does not seem to be an insurmountable
problem. Though initial training and installation costs
would be higher, the variable costs Of day to day operation

would tend to diminish after some experience with the system.

‘Ugbiuvzn mrflc-htour ox» «no» .r -_

Many argue that competition would force firms to grant lower
rates to all applicants because those in the lower risk classi-
fications would "shOp around" until they secured a more favor-
able rate. Is is somewhat true that competition would be

keen, but if a competitor accepts a lower scored applicant

he also assumes more marginal cost than the revenue warrants
unless he has superior insight into the borrower's loan repay-
ment tendencies. Otherwise he will lose money on this account.
If the competitor is more efficient in securing loan repay-
ment, then resources should be allocated to this firm. In

the longer run, there would be more competition and vitality
in the whole consumer finance industry. Not alone would the
borrowing public be better Off because each person would be

paying a rate commensurate with his risk, but finance companies

174
would have a much larger market base and could more effectively
compete with other institutions.

This proposal is dependent on a substantial revision Of
state laws dealing with loan size ceilings.5 In order to
effectively compete with commercial banks and credit unions,
consumer finance companies must be allowed to better serve

the public with larger loans. In effect, a broader definition

. Uh P.fi§’

.~ P 11!" Q'I ".

of the consumer finance industry is needed to include all legal
institutions granting consumer credit rather than just Small

Loan Licensees.

I “”3 Runs “1:51“ «1a- n. a.
.

Significance of Findings to Public Policy

Deductive presumption indicates that Test II should have
disclosed a stronger positive relationship between applicant
personal characteristics and rate. The differences in annual
gross yields between the two states is substantial--about nine
percentage points. Why aren't more poorer risk borrowers
accepted by lenders in Charleston? There are two probable
answers: 1) finance company managers do not use marginal
analysis in determining output and, therefore, set credit

standards based on national averages Of revenues and costs,

 

5The need for larger loan ceilings has been proposed by
many sources. See Phelps, Clyde W., “Consumer Finance
Charges II, Journal of Marketing, July, 1952, pp. 27-32.

 

Kawaja, Michael, "On the Inflexibility of Small Loan
Rates and Loan Size Ceilings," Personal Finance Law Quarter-
ly Repgrt, Spring, 1965, pp. 57-59.

 

175
and 2) the acceptance of greater risk in a high rate state
would lead to a disprOportionate increase in marginal and
average costs.
Since loan supervisors tend to adjust risk standards
under various rates, there undoubtedly is greater considera-

tion Of marginal analysis than tOp managers assert.. It is

tors-14""

uncertain to what extent this is true because the mechanism
for risk standard adjustment is too informal and inexact.
Managers also assert that the acceptance of more lower

risk applicants would disprOportionately add to costs not

'mnnms-M us ‘1... .l . 5x”.”A 1

warranted by the marginal revenue. This may be valid, but
the evidence of Test II indicates a fairly large overlap
between borrower and rejected account scores and it appears
that a greater portion of accounts could be included into
the loan portfolio. (Charts 4.1, 4.2, 4.3 and Tables 4.4
and 4.5) 1

It is true that some states with higher rates may have,
some inherent characteristics causing higher costs. Lower
pOpulation density in some states makes investigation and
collection costs larger than in areas where population
density is high resulting in less physical effort and expense
to collect accounts. Population density also affects the

scale Of Operation since a firm serving a larger number of

accounts will achieve increased economies of scale. Factor

176

prices as wages and rents also vary geographically and affect
cost levels.

Even so, there is a real question of whether or not
higher rates are the result Of higher costs or the cause of
them. Finance companies as well as individuals have a tendency
to slacken their efficiency when "times are good". When in-
come is at a satisfactory level, the additional efforts to
"wring out" the last efficient dollar is not worth the addi-
tional revenue. Money has definite non-linear utility.

The decidedly higher level of net income in West Virginia

 

compared to New York suggests that the higher permissible
rate charged haless for risk assumption and passes through
to profits without performing its intended function. In 1964,
the average net yield after taxes but before interest was
7.15%»for West Virginia Licensees and 5.49%.for New York
Licensees. If the yield is adequate in New York, and there
is no reason to think otherwise, then the amount over the
adequate rate could be considered excess. Given the com-
plexities of determining net yield after considering fixed
and variable costs, interest, and taxes, the higher gross
yield in west Virginia results in firms earning higher than
adequate yields, if New York Licensees earnings are used as
an example of adequate yield. (An investigation Of the num—

ber of new loan Offices in low rate states would provide some

177
indication of whether finance companies consider their present
rate adequate.)

A useful type of analysis would be a correlation of state
gross yield and net yield experiences to indicate whether
those allowing higher finance charges also have lenders with
higher net earnings. If lenders earn more in states allowing
higher rates, there is a real question whether the higher
rates are justified. A recent study shows finance companies
charging higher rates with lower net yields, but the data is

not categorized by state.

_,.,—__._-_- .Am _u -x‘A‘ut-h..
W a ~ n I. . , v v
a . . .. . ~ ~ ._ A . n
..

If further research indicates that small loan companies‘
do not substantially increase risk assumption in return for
being allowed to charge higher rates, than there is real
reason for doubting the validity of allowing higher finance
charges, such as in West Virginia. The data from Test II are
somewhat conclusive in showing risk differentials, but the
west Virginia chargeoff rate is only slightly higher than the
rate in New York. It is also uncertain whether or not the
apparent larger borrowing power of Charleston borrowers is
large enough to justify the higher rates. If the pattern
disclosed in two states are representative Of the borrowing

public, then what is the rationale for permitting a rate higher

 

6Smith, Paul F., Consumer Credit Costs, 1949-1959,

National Bureau of Economic Research, New York, 1964, p. 25.

178
than the average 20.33%7 in New York if chargeoff rates and
risk acceptance standards do not rise accordingly? If the
finance rate is acceptable in New York and a similar type of
applicant is served in West Virginia where the average rate
is approximately 29%,8 then what are their reasons for allow-
ing this higher rate. Additional collection and investigation
expense may account for some of the differences, but it is
debateable whether or not it provides an adequate explanation.
- The traditional argument for higher rate ceilings rests

on the assumption that higher cost levels in a particular

'Wr.—n~-nu ‘1‘. . 1min .
I 1 ' .
A'

state make higher finance charges imperative if sufficient
capital is to be Obtained. The existence of cost differences
is somewhat valid since a recent study shows that firms
charging higher rates generally sustain higher overall costs
in almost all cost classifications and accounts.9
General Summary

The crucial policy question this study attempts to answer
is whether or not states are justified in permitting Small

Loan Licensees to charge borrowers higher finance charges.

The evidence suggests that lenders better serve the community

 

7Annual Report of the Superintendent Of Banks, State of
New York, 1964, p. 97.

8Consolidated Report of the Small Loan Licensees Of west
Virginia, 1964, p. 5.

9
Smith, Op.cit., p. 25.

179
and extend more credit to borrowers when allowed to charge
higher rates, but the evidence is too incomplete to draw
broad generalizations. Before this issue can be fully answered
there must be further research of borrower characteristics in
many states and from many finance companies. In addition,
the role Of other influences on risk acceptance--loan size
ceilings, convenience and advantage laws, and allowable 1
collection practices--must all be studied. If evidence 1
from additional research substantiates and confirms the im-

plication of the positive risk and rate relationship derived

 

from Tests I and II, then higher rate ceilings are justified.
Whether or not it is desirable for borrowers to be eli-
gible for more credit is not a matter Of economic science,

but a social value judgment.

APPENDICES

180

DOLLAR CHARGES ON DIFFERENT SIZE LOANS REPAID ACCORDING TO CONTRACT (12 MOS.)
AT LAWFUL MAXIMUM RATES OF CHARGE UNDER 44 SMALL LOAN LAWS ARRANGED
BY LOAN SIZE CEILING

181

APPENDIX A

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Rate Loan Size
4§100 $300 $500 $1000 $1500
CANADA 2-l-%%(300—1000) $13.46 $40.44 $60.76 $98.72 $126.60
UNITED STATES
$1500 Loan Ceiling or Over
California 2%-2-5/6%(200-500) $16.88 $49.32 $77.80 $123.08 $156.24
Colorado 3—15-l%(300-500) 20.48 61.56 92.08 139.40 177.12
Kansas 3—5/6%(300) 20.48 61.56 87.64 125.36 156.24
Maine 3-2%-1%%(150-300) 20.60 58.44 86.32 141.56 193.44
Massachusetts 2fi-2-1 3/4-3/4%(200-600-1000) 16.88 49.32 77.80 142.76 189.00
Missouri 2.218-2/3%(500) 14.96 44.88 74.92 119.24 148.20
Nebraska 28—2—15-1%(300-500—1000) 16.88 50.88 81.40 141.08 187.08
Nevada Add-on: 9-8%(1000) + fee 21.00 57.00 81.00 126.00 166.00
New Hampshire Add-on: 16-12%(600) 16.00 48.00 80.00 144.00 204.00
Ohio Add-on: 16-9-7%(500-1000) 16.00 48.00 80.00 125.00 160.00
Oregon 3-2—1%(300—500) 20.60 61.68 95.68 147.68 186.84
South Dakota 3-3/4%(300) 20.84 62.52 88.36 124.16 152.88
Texas Add-on: 19-16-13-11-9-7%(100-200-
300-500-1000) 19.00 48.00 70.00 115.00 150.00
$1000 Loan Ceiling $100 A§3OO $500 $1000
Alaska 4—2%-2%(300-600) 28.28 84.72 129.76 215.48
Arizona 3-2-1%(300-600) 20.48 61.56 95.56 154.16
Connecticut Add-on: l7-9%(300) 17.00 51.00 69.00 114.00
Idaho 3—2-1%(300—500) 20.48 61.56 95.56 147.68
Indiana 3-2-1%%(150-300) 20.84 56.16 81.52 135.80
Michigan 25-1%%(300) 17.24 51.72 77.44 125.60
Montana Add-on: 20-16-12%(300-500) 20.00 60.00 92.00 152.00
New Mexico 3-25-l%(150-300) 20.48 58.44 82.84 123.68
North Dakota 28-2—1 3/4-18%(250-500-750) 16.88 50.52 79.72 142.16
Washington 3-13-l%(300-500) 20.48 61.56 92.08 139.40
Wyoming 3%—28-l%(150—300) 24.20 66.00 91.24 132.44
$800 Loan Ceiling $100 $300 $500 $800
Illinois 3-2-1%(150-300) 20.48 55.32 77.08 101.44
Kentucky Add-on: 20-15-11%(150-600) 20.00 52.44 82.48 119.44
New York 2%-2-3/4%(100-300) 16.88 45.72 64.72 84.40
West Virginia Add-on: l9—16—12%(200—600) 19.00 54.00 86.00 126.00
$600 Loan Ceiling §100 $300 §500 ,_§600
Florida 3-2%(300) 20.84 62.52 97.00 112.20
Minnesota 2 3/4-1%%(300) 18.68 56.28 85.00 97.08
North Carolina Add-on: 20—18—15-6%(100-200-300) 20.00 53.00 65.00 71.00
Pennsylvania 3-2-l%(150-300) 20.60 55.32 77.08 85.80
Utah 3-l%(300) 20.84 62.52 90.04 99.96
Vermont 2%-2%—1%(125-300) 17.24 49.56 72.16 81.00
Virginia 2%-1%%(300) 17.24 51.72 79.12 90.96
$500 Loan Ccilipg $100 $300 $500
Iowa 3-2—1%%(150-300) 20.84 56.16 81.52
New Jersey 2%—%%(300) 17.00 51.00 71.44
$300 Loan Ceilipg $100 $300
Alabama 3-2%(200) 20.48 58.44
Hawaii 33-2%%(100) 24.56 62.64
Louisiana 3%-2%%(150) 24.56 66.96
Maryland 3% 20.60 61.68
Rhode Island 3% 20.84 62.52
Wisconsin 2%-2-1(100-200) 17.00 42.84
Source: A Major Consumer Finance Company.

182
APPENDIX B
CONSUMER FINANCE COMPANIES SUPPLYING INFORMATION
USED IN STUDY
American Investment Company of Illinois
Beneficial Finance Company
C. I. T. Financial Corporation
General Finance Corporation
Household Finance Corporation
Liberty Loan Corporation

Seaboard Finance Company

183
APPENDIX C

SIMPLE AND PARTIAL CORRELATION COEFFICIENT FORMULAS

Simple Correlation Coefficient:*

(23C 9
V 22.2 (2:) *5 3:11-0:02

Partial Correlation Coefficient:#

 

 

 

r12.3 = r12 ’ r13 r23

_ 2 _ 2
1Vr1 r13 1 r23

 

For 3 variables held constant:

 

 

r12.345 = r12.34 - r15.34 r25.34
I 2 2
Wi-r 15.34)V(1-r 25.34)
<a

Coefficient of Association:

(A) - 1 - 1-r2 = Standard deviationl - Standard deviation].2

 

__rfi

Standard deviationl

.4—

*Fruend, John E., Modern Elementary Statistics, Prentice-
Hall, Inc., Englewood Cliffs, N.J., 1952, p. 265.

#Ezekial, Mordecai, and Fox, Karl A., Methods of Correlation
and Regression Analysis, John Wiley and Sons, New York, p. 532.

@Ekeblad, Frederick A., The Statistical Methods in Business,
John Wiley & Sons, Inc., New York, 1962, p. 512.

184
APPENDIX D

RISK ELEMENT WEIGHTS, SMITH CREDIT SCORE SYSTEM

.Agg Score

1—25 years 147
26-30 184
31-35 145
36-40 104
41-45 110
46-55 91
56-65 77
66-over 71

Sex and MaritalfiStatus

 

 

Single
Male 200
Female 75
Married
Male 105
Female 56
Widowed
Male 105
Female 56
Divorced 289
Separated 289
Number in Family
1 159
2 89
3 121
4 108
5 118
6 131
7 106
8 261
9 and over 261
Telephone
Telephone 67
NO telephone 701

Home Ownership

Own home 69
Rent home 267

185

Time in last Residence

0.0-0.5 years 308
.6— 1.0 178
1.1-2.0 123
2. l- 3. 0 135
3. 1- 4. 0 133
4. l- 5.0 112
5. l- 10. 0 81
10.1- 15.0 72
lS.l-over 78

Time on last Job

0.0—00.5 320
0.6—01.0 156
1.1-02.0 167
2.1-03.0 138
3.1—04.0 167
4.1-05.0 126
5.1-10.0 104
10.1-15.0 91
lS.l-over 67

Monthly Income

$000-200 228
201-300 197
301-400 114
401-500 108
501-700 93
701—1000 92

1001-1500 66

lSOl-over 82

Purpose Of Loan
Automobile and Auto

Repair 81
Household Goods 60
Debt Consolidation 184
Medical Expenses 247
Clothing 181
Taxes and Insurance 68
Travel 130
Loans to Relatives 149
Other 89

 

f

* Adapted from: Smith, Paul F., "Measuring Risk on Consumer
Instalment Credit," Management Science, Nov—
ember, 1964, pp. 334-335.

Note the positive relationship between risk and risk points.

186

APPENDIX E

MEAN AND STANDARD DEVIATION VALUES OF STATE EXPERIENCES,
FOR VARIABLES USED IN CORRELATION ANALYSIS,

FOR THREE FIRMS

Gross Yield (%)

Percent of New Application
Turned down

Percent of Accounts Charged off
Average Loan Size ($)

Per Capita Income, by State

Gross Yield (%)

Percent Of New Application
Turned down

Percent of Accounts Charged off
Average Loan Size ($)

Per Capita Income, By State

Gross Yield (%)

Percent Of New Application
Turned down

Percent of Accounts Charged Off
Average Loan Size ($)

Per Capita Income, by State

_‘_'—======——

M

 

 

 

Firm X .

‘Mggp Std. Dev.
22.24% 2.74%
58.31% 5.57%

2.00%} .66%
$508 $ 91

$2372 $448
Firm Y

'Mpgp Std. Dev.
25.49%. 3.57%
57.24% 9.93%

3.49% 1.62%
$451 $ 75

$2299 $457
Firm Z

'Mggp Std. Dev.
22.38% 2.90%

2.42% 1.58%
$429 $100

$2303 $430

187

APPENDIX F

COMPARISON OF PERCENTAGE DISTRIBUTION OF BORROWERS,
BY BANK ACCOUNT*

NO Bank Account

Checking Account Only
Savings Account Only
Checking and Savings Account
No Report

Total

Albany

57.0%

27.3

 

100.0%

Charleston

52.7%

40.0

 

100.0%

 

* Includes only experiences from two of the three finance

companies.

APPENDIX G

SUPPLEMENTARY TABLES TO CHAPTER IV

188

189
TABLE G.1

COMPARISON OF BORROWER INSTALMENT DEBT IN VARIOUS INCOME
CLASSIFICATIONS, FOR ALBANY AND CHARLESTON

 

'.Albany Monthly Income

 

 

 

$101-250 $251-500 $501—750 $751-Over
$0-100 13.74% 5.33% 5.21%. 2.56%
101-250 11.76 9.20 2.89 0
Instalment 251-500 43.14 13.95 9.83 5.13
debt 501-750 11.76 12.76 5.78 5.13
751—1000 9.80 13.06 7.51 7.69
1001-1500 7.84 18.99 20.23 10.26
1501-over 1.96 26.71 48.55 69.23
Total 100.00% 100.00% 100.0% 100.0%
Charleston Monthly Income
$101—250 $251-500 $501-750 $751—Over
$0-100 14.95%» 2.48% 3.44% 2.43%
101-250 13.79 6.19 .69 4.88
Instalment 251-500 22.99 7.74 3.45 2.44
debt 501-750 16.09 11.76 7.59 4.88
751-1000 10.34 10.53 4.83 12.20
1001-1500 12.64 17.03 11.03 7.32
1501-Over 9.20 44.27 68.97 65.85
Total 100.00% 100.00%. 100.00% 100.00%

 

190

TABLE G.2

COMPARISON OF BORROWER MONTHLY PAYMENTS IN VARIOUS INCOME
CLASSIFICATIONS, FOR ALBANY AND CHARLESTON

 

 

Albaneronthly Income

 

 

 

 

$101—250 $251-500 $501-750 $750-Over
$0-50 82.35% 43.32%1 24.86% 12.83%
51-100 17.65 39.47 31.21 23.08
Monthly 101-150 0 13.35 28.90 15.38
Payments 151-200 0 2.67 11.56 33.33
201-over 0 1.19 3.47 15.38
NO Report 0 0 0 0
Total 100.00% 100.00% 100.00% 100.00%
Charleston Monthly Income
$101-250 $251-500 $501-750 $751-Over
$0-50 65.51% 23.21%. 10.35% 9.76%
51-100 27.59 39.01 21.38 14.63
Monthly 101-150 3.45 23.53 21.03 31.71
Payments 151-200 1.15 10.22 24.83 17.07
201-over 0 2.48 11.03 26.83
NO Report 2.30 1.55 1.38 0
Total 100.00% 100.00% 100.00% 100.00%

 

191
TABLE G.3

COMPARISON OF BORROWER RESIDENCE STABILITY FOR VARIOUS INCOME
CLASSIFICATIONS, FOR ALBANY AND CHARLESTON

 

 

 

 

 

m
Albany Monthly Income
$101-250 $251-500 $501-750 $751-Over
0-2.5)ms.39.21% 44.21% 37.57% 33.33%
2.6-5.0 19.61 18.40 18.50 20.51
Years in 5.1-7.5 13.73 6.53 13.29 5.13
Residence 7.6—10.0 3.92 8.01 12.14 5.13
10.0—over 21.57 21.01% 17.34 35.90
NO Report 1.96 1.84 1.16 0
Total 100.00%» 100.00% 100.00% 100.00%
Charleston Monthly Income
$101-250 $251-500 $501—750 $751—Over
0—2.5yrs.26.43% 38.08% 37.92% 36.58%
2. —5.0 18.39 13.31 17.93 19.51
Years in 5.1-7.5 6.90 6.81 8.97 12.20
Residence 7.6-10.0 8.05 8.36 8.28 12.20
10.0-over 37.93 32.20 26.21 19.51
NO Report 2.30 1.24 .69 0

Total 100.00% 100.00% 100.00% 100.00%

 

192

TABLE G.4

BORROWER MONTHLY INCOME FOR RENTERS AND NON-RENTERS,
FOR ALBANY AND CHARLESTON

 

 

 

 

 

m
Albany Monthly Income
$lOl-250 $251-500 $501-750 $751—over Total
Non-Renters 2.18% 47.28 39.67 10.87 100.00%
Renters 11.06% 60.05 24.12 4.77 100.00%
Charleston Monthly Income
y§lOl-250 $251-500 $501-750 $750-over Total
Non-Renters 8.80% 47.60 32.80 10.80 100.00%
Renters 18.62% 58.17 18.05 5.16 100.00%

 

193
TABLE G.5

BORROWER RESIDENT STABILITY FOR RENTERS AND NON-RENTERS,FOR
ALBANY AND CHARLESTON

 

Albany Residence Stability

0-2.5 2.6- 5.1- 7.6- 10.1- NO
years 5.0 7.5 10.0 over Report Total

 

Non-Renters 22.83% 19.02 10.33 11.41 34.78 1.63 100.00%

Renters 48.74% 19.10 8.54 7.04 15.83 .75 100.00%

No Report 61.11% 5.56 5.56 16.67 11.10 0 100.00%

Charleston Residence Stability

0-2.5 2.6- 5.1- 7.6- 10.1- No
yyears 5.0 7.5 10.0 over Report Total

Non-Renters 21.20% 12.80 8.00 7.20 49.20 1.60 100.00%
Renters 46.70% 17.77 7.45 9.46 17.77 .85 100.00%

NO Report 0 0 0 O 0 0 0

 

194

TABLE G.6

MONTHLY INCOME FOR THOSE WITH TELEPHONE IN RESIDENCE, FOR

ALBANY AND CHARLESTON

Albany Monthly Income

 

 

 

$101-250 $251-500 $501-750 $751-over Total
NO Telephone 10.11% 73.03 16.86 0 100.00%
Telephone 8.25% 53.05 31.04 7.66 100.00%
No Report 0 0 0 0 0%

Charleston Monthly Income

$101-250 $251-500 $501-750 $751-Over Total
NO Telephone 18.81% 63.37 16.83 .99 100.00%
Telephone 14.32% 51.81 25.81 8.06 100.00%
NO Report 0 0 0 0 0%

 

 

195
TABLE G.7

RESIDENCE STABILITY OF BORROWERS WITH TELEPHONE IN RESIDENCE,
FOR ALBANY AND CHARLESTON

 

Albany Residence Stability

 

0-2.5 7.6- 10.0-

years 2.6-5.0 5.1-7.5 10.0 over Total
No Telephone 57.30% 19.10 7.87 3.37 12.36 100.00%
Telephone 38.51% 18.66 9.23 9.63 23.97 100.00%

Charleston Residence Stability

 

0-2.5 7.6— 10.0-
years 2.6-5.0 5.1-7.5 10.0 over Total
No Telephone 42.57% 19.80 6.93 4.95 25.75 100.00%

Telephone 34.88% 14.92 7.86 9.07 33.27 100.00%

 

196
TABLE G.8

MONTHLY INCOME FOR PRESENT, FORMER, AND NEW BORROWERS,
FOR ALBANY AND CHARLESTON

 

Albany Monthly Income

 

$lOl-250 251-500 501-750 751-over Total

 

Present borrowers 8.73% 54.68 29.94 6.65 100.00%
Former borrowers 4.62% 53.85 32.31 9.22 100.00%
New borrowers 11.11%. 72.22 14.81 1.86 100.00%

Charleston Monthly Income
$101-250 251-500 501-750 751—over Total

 

Present borrowers 14.99% 52.68 25.70 6.63 100.00%
Former borrowers 9.09% 56.82 25.00 9.09 100.00%

New borrowers 8.89% 60.00 6.67 4.44 100.00%

 

197

TABLE G.9

BORROWER INCOME FOR WHITES AND NON-WHITES,

FOR ALBANY AND CHARLESTON

 

 

 

 

 

m
Albany Monthly Income

$101-250 251-500 501-750 751-over Total
Whites 8.54% 55.98 28.46 7.02 100.00%
Non-Whites 8.33% 58.33 31.95 1.39 100.00%

Charleston Monthly Income

$101-250 251-500 501-750 751—over Total
Whites 13.02% 54.97 24.41 7.60 100.00%
Non-Whites 32.61% 39.13 23.91 4.35 100.00%

 

 

198
BIBLIOGRAPHY

Books and Monographs

Barrett, Roger 8., Compilation of Consumer Finance Laws,
National Consumer Finance Association, Washington, D.C.,
1952.

Booth, S. Lees, Finance Facts Year Book 1965, National Consumer
Finance Association, Washington D.C.

Chapman, John M., Commercial Banks and Instalment Credit,
National Bureau of Economic Research, New York, 1940.

Cottle, Sidney, and Mickelwait, Kenneth, Composite Earnings
Performance of the Major Consumer Finance Companies,
Stanford Research Institute, Menlo Park, California, 1959.

 

Cox, R., The Economics of Instalment Buying, Ronald Press Co.,
New York, 1948.

Dauer, Ernst A., Comparative Operating Experience of Con-
sumer Instalment FinancingyAgencies and Commercial Banks,
1929-1941, National Bureau of Economic Research,New York, 1944.

Dauten, Carl A., The Consumer Finance Industry in a Dynamic
Economy, Consumer Credit Monograph No. 2, American In-
vestment Company of Illinois, St. Louis, 1959.

Durand, David, Risk Elements in Consumer Instalment Financing,
,National Bureau of Economic Research, New York, 1941.

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