• TABLE OF CONTENTS
HIDE
 Title Page
 Acknowledgement
 Table of Contents
 List of Tables
 List of Illustrations
 List of Appendix Tables
 Introduction
 Previous research relating to citrus...
 Purpose of present research and...
 Research methodology
 Characteristics of the test...
 An examination of the basic input...
 Characteristics of the demand for...
 The economic interaction among...
 Evaluation of findings
 Summary
 Appendices
 Bibliography
 Biographical sketch














Title: Demand and substitution relationships for Florida and California Valencia oranges produced for fresh market /
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Permanent Link: http://ufdc.ufl.edu/UF00091610/00001
 Material Information
Title: Demand and substitution relationships for Florida and California Valencia oranges produced for fresh market /
Physical Description: xiv, 258 leaves : ill. ; 28 cm.
Language: English
Creator: Chapman, William Fred, 1931-
Publication Date: 1963
Copyright Date: 1963
 Subjects
Subject: Citrus fruits   ( lcsh )
Fruit trade   ( lcsh )
Agricultural Economics thesis Ph. D
Dissertations, Academic -- Agricultural Economics -- UF
Genre: bibliography   ( marcgt )
non-fiction   ( marcgt )
 Notes
Thesis: Thesis (Ph. D.)--University of Florida, 1963.
Bibliography: Includes bibliographical references (leaves 255-258).
Additional Physical Form: Also available on World Wide Web
General Note: Vita.
Statement of Responsibility: by William Fred Chapman.
 Record Information
Bibliographic ID: UF00091610
Volume ID: VID00001
Source Institution: University of Florida
Holding Location: University of Florida
Rights Management: All rights reserved by the source institution and holding location.
Resource Identifier: alephbibnum - 000415054
notis - ACG2276
oclc - 37410841

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Table of Contents
    Title Page
        Page i
        Page i-a
    Acknowledgement
        Page ii
    Table of Contents
        Page iii
        Page iv
        Page v
        Page vi
        Page vii
    List of Tables
        Page viii
        Page ix
        Page x
        Page xi
    List of Illustrations
        Page xii
        Page xiii
    List of Appendix Tables
        Page xiv
    Introduction
        Page 1
        Page 2
        Page 3
        Page 4
        Page 5
        Page 6
        Page 7
        Page 8
        Page 9
        Page 10
        Page 11
        Page 12
        Page 13
        Page 14
        Page 15
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        Page 18
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        Page 21
        Page 22
        Page 23
        Page 24
        Page 25
        Page 26
        Page 27
        Page 28
        Page 29
        Page 30
        Page 31
        Page 32
        Page 33
        Page 34
        Page 35
        Page 36
        Page 37
        Page 38
        Page 39
        Page 40
        Page 41
        Page 42
        Page 43
    Previous research relating to citrus demand
        Page 44
        Page 45
        Page 46
        Page 47
        Page 48
        Page 49
        Page 50
        Page 51
    Purpose of present research and specific problem orientation
        Page 52
        Page 53
        Page 54
        Page 55
        Page 56
        Page 57
        Page 58
        Page 59
        Page 60
        Page 61
    Research methodology
        Page 62
        Page 63
        Page 64
        Page 65
        Page 66
        Page 67
        Page 68
        Page 69
        Page 70
        Page 71
        Page 72
        Page 73
        Page 74
        Page 75
        Page 76
        Page 77
        Page 78
        Page 79
        Page 80
        Page 81
        Page 82
        Page 83
        Page 84
        Page 85
        Page 86
        Page 87
        Page 88
        Page 89
        Page 90
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        Page 92
        Page 93
        Page 94
        Page 95
        Page 96
        Page 97
        Page 98
        Page 99
    Characteristics of the test stores
        Page 100
        Page 101
        Page 102
        Page 103
        Page 104
        Page 105
        Page 106
        Page 107
        Page 108
        Page 109
        Page 110
        Page 111
        Page 112
        Page 113
    An examination of the basic input data -- fresh orange sales
        Page 114
        Page 115
        Page 116
        Page 117
        Page 118
        Page 119
        Page 120
        Page 121
        Page 122
        Page 123
        Page 124
        Page 125
    Characteristics of the demand for Florida and California Valencia oranges
        Page 126
        Page 127
        Page 128
        Page 129
        Page 130
        Page 131
        Page 132
        Page 133
        Page 134
        Page 135
        Page 136
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        Page 138
        Page 139
        Page 140
        Page 141
        Page 142
        Page 143
        Page 144
        Page 145
        Page 146
        Page 147
        Page 148
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        Page 160
        Page 161
        Page 162
        Page 163
        Page 164
        Page 165
        Page 166
    The economic interaction among the three Valencia oranges
        Page 167
        Page 168
        Page 169
        Page 170
        Page 171
        Page 172
        Page 173
        Page 174
        Page 175
        Page 176
        Page 177
        Page 178
        Page 179
        Page 180
        Page 181
        Page 182
        Page 183
        Page 184
    Evaluation of findings
        Page 185
        Page 186
        Page 187
        Page 188
        Page 189
        Page 190
        Page 191
        Page 192
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        Page 194
        Page 195
        Page 196
        Page 197
        Page 198
        Page 199
        Page 200
        Page 201
    Summary
        Page 202
        Page 203
        Page 204
        Page 205
        Page 206
        Page 207
        Page 208
        Page 209
    Appendices
        Page 210
        Page 211
        Page 212
        Page 213
        Page 214
        Page 215
        Page 216
        Page 217
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        Page 252
        Page 253
        Page 254
    Bibliography
        Page 255
        Page 256
        Page 257
        Page 258
    Biographical sketch
        Page 259
        Page 260
        Page 261
Full Text










DEMAND AND SUBSTITUTION RELATIONSHIPS

FOR FLORIDA AND CALIFORNIA VALENCIA

ORANGES PRODUCED FOR FRESH MARKET











By
WILLIAM FRED CHAPMAN, JR.











A DISSERTATION PRESENTED TO THE GRADUATE COUNCIL OF
THE UNIVERSITY OF FLORIDA
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE
DEGREE OF DOCTOR OF PHILOSOPHY










UNIVERSITY OF FLORIDA
December, 1963










ACKNOWLEDGMENTS


The writer wishes to express sincere appreciation to his super-

visory committee chairman, M. R. Godwin, for his advice, council, and

encouragement throughout all phases of the graduate study program. Pro-

fessor Godwin spent many hours discussing, guiding, and developing the

research philosophy of the author, the culmination of which is expressed

in this thesis. For his honest and sincere concern in the development

of the student to a degree seldom found, an unrepayable debt of grati-

tude is due.

Appreciation is extended to the members of the author's

supervisory committee, composed of R. H. Blodgett, H. G. Hamilton,

W. T. Manley, and W. B. Riggan, whose contributions to the graduate

program have been of material benefit.

An especial note of thanks is also expressed to L. C. Martin

and W. T. Manley of Economic Research Service, United States Department

of Agriculture for providing the essential freedom and favorable

environment for conducting the research from which this thesis evolved.

Much valuable assistance in typing and in making necessary

statistical computations was provided by Mrs. Christine Ward, Mrs.

Irene Jolly, Mrs. Judy Cannington, Mrs. Earline Thompson, and Mr. T. L.

Brooks. The final manuscript was typed by Mrs. Carole Puller. For the

untiring efforts of L.W. Hicks in reproducing the final manuscript, and

to K. E. Ford for preparing the illustrations, the author is grateful.

Finally, the sacrifice, encouragement, and devotion of the

author's wife, Nancy, and children, Tony and Nancy Jean, is gratefully

acknowledged and sincerely appreciated.
ii















TABLE OF CONTENTS


ACKNOWLEDGEMENTS . . . . . . . . . .


LIST OF TABLES


LIST OF ILLUSTRATIONS . . . . . . . . . . .

LIST OF APPENDIX TABLES . . . . . . . . . .


Page
ii

viii

xii

xiv


Chapter
I.


INTRODUCTION . . . . . .


Statement of the General Problem

Florida orange production .
California orange production .
Production potential. . . .
Utilization trends and population


Position of Florida and California in
the fresh orange market . . .
Marketing period . . . .

Alternative Adjustment Available to the
Florida Orange Industry . . . .

The demand situation . . .
The importance of the sector analysis .
Promotional policy . . ...
Pricing policy . . . . .
Product policy . . . . . .
Optimum allocation . . . . ..

II. PREVIOUS RESEARCH RELATING TO CITRUS DEMAND


S. . 2
* . 2


trends


Examination of Data Sources . . . . .

Citrus Demand Work . . . . . . . .


III. PURPOSE OF PRESENT RESEARCH AND SPECIFIC PROBLEM
ORIENTATION . . . . . . .


The Specific Problem . . . ..

Variety . . . . . . . . . .
Fruit sizes . . . . . . . . .
Fruit grades . . . . . . . .


* . .


. . . . . . . . . . . . .


. . . .












* * *













TABLE OF CONTENTS--Continued


Specifications of the Research Problem..


Rationale Underlying Method Selection .

IV RESEARCH METHODOLOGY . . . . .

The Economic Model . . . . .

The Statistical Model . . . . .

Assumptions . . . .. .

The Experimental Model. . . . .

Limitations of the Model Formulation..

The Statistical Model Redefined . .

Specifications of Experimental Test ..

Size limitations . . . . .
Price differentials . . . . .
Experimental design layout. . . .

Requirements and Specifications of Expel

Selection of test site. . . . .
Selection of test stores. . . .
Orange pricing. . . . . .
Display control . . . . . .
Supply quality and storage. . .
Merchandising restrictions. . . .

Informational Requirements. . . .

Cooperative Arrangements. . . . .

V CHARACTERISTICS OF THE TEST STORES

General Description of Test Stores. .
Stores departmentalized . . . .
Degree of self service . . . .
Trading Stamp plan. . . . . .

Sales and Store Traffic . . . .
Customer count and sales. . . .
Daily distribution of store traffic .


Page
. . . 57


. . . . 58

. . . . 62

. . . . 63

. . . 64

. . . . 66

. . . . 68

S. . . 74

. . . . 75

. . . . 76

. . . 76
. . . . 78
. . . . 79

rimental Units 84

. . . . 84
. . . . 87
. . . . 88
S. . . 88
. . . . 95
S. . . 96

S. . . 97

. . 98

100

S. . . 100
S. . . 100
S. . . 101
. . 101

. . . 102
S. . . 102
S . . . 104









TABLE OF CONTENTS--Continued


Page
Daily distribution of store sales. . . . 105
Daily distribution of produce sales . . ... 108
Daily distribution of total sales per customer 110
Daily distribution of produce sales per customer 110

VI AN EXAMINATION OF THE BASIC INPUT DATA--FRESH
ORANGE SALES. . . . . . . . . ... ... 114

Aggregate Sales by Fruit Type . . . . . .. 114

Sales by store . . . . . . . . . 116
Sales by week. . . . . . . ... 117
Sales by day . . . . . . . 117

Sales per 100 Customers by Fruit Types. . . . 120

Sales by store . . . . . . . . . 120
Sales by week . . . . . . . . 122
Sales by day . . . . . . . . . 122

VII CHARACTERISTICS OF THE DEMAND FOR FLORIDA
AND CALIFORNIA VALENCIA ORANGES . . . . . . 126

Generalized Presentation of the Systems of
Demand Equations. . . . . . . 126

Requirements Necessary and Sufficient
for Economic Consistency. . . . . . 129

Method of Analysis. . . . . . . . 130

Coefficient estimation utilizing the method
of least squares . . . . . . . 131
Coefficient testing by students "t" test .... .132

Price and Substitution Effects. . . . .. 133

Tests Involving Florida Size 200 and California
Size 138. . . . . . . . . . 133

Direct price effects . . . . . ... 138
Differences among price elasticity estimates . 142
Cross-price effects. . . . .....145
Differences between cross elasticity estimates . 146
Summary of price effects . . . . . . 148

Tests Involving Florida size 163 and California
Size 138 . . . . . . . . ... . 149









TABLE OF CONTENTS--Continued
Page
Direct price effects. . . . . . . . 154
Differences between price elasticity estimates . 159
Cross-price effects . . . . . . . . 160
Summary of price effects. . . . . . .. 161

Differences in Demand Estimates Due to Size . . .. 161

VIII THE ECONOMIC INTERACTION AMONG THE THREE VALENCIA
ORANGES. . . . . . . . . . . . .. 167

Derivation of Price Estimating Equations from
Demand Equations. . . . . . . . .. 167

Generalized Presentation of Systems of Price
Estimating Equations. . . . . . . .. 169

Economic Consistency Requirements . . . . .. 171

The Effects of Supply Interactions. . . . . .. 172

Tests Involving Florida Size 200 and California
Size 138 . . . . . . . . . . 173

Prime product effect on price . . . . . .. 176
Competing product effects on price. . . . .. 177
Summary of product effects . . . . . ... 178

Tests Involving Florida Size 163 and California
Size 138. . . . . . . . . . . 178

Prime product effect on price .. . . . . 182
Competing product effects on price. . . . .. 182
Summary of product effects. . . . . . .. 183

IX EVALUATION OF FINDINGS . . . . . . . . 185

Effects of Major Changes in Price and Supply
Conditions for Florida Valencia Oranges . . .. 187

Effect of various price conditions on customers
purchases . . . . . . . . . . 188
Effect of various supply conditions on retail
prices. . . . . . . . . . . 193

General Implications to the Florida Orange Industry--
An Overview . . . . . . . . . . 198
vi












TABLE OF CONTENTS--Continued


X SUMMARY. . . . . . . . . . . .

Characteristics of the Test Stores. . . ..

Sales and Store Traffic ...........

Fresh Orange Sales . . . .

Total sales of fresh oranges. . . . .
Sales per 100 customers . .. . . .

Demand Relationships for Florida and California
Valencia Oranges . . . . . .

Component i--Florida size 200 and California
size 138. . . . . . . . . .
Component II--Florlda size 163 and California
size 138 . . . . . . . .
Differences In elasticities due to size .

The Economic interaction Among the Three
Valencia Oranges. . . . . . .
Component I--Florida size 200 and California
size 138 . . . . . . . .
Component Il--Flortda size 163 and California
size 138. . . . . . . . . .

Price and Supply Interactions . . . . .

Effect of price interaction on purchase rates
Effect of supply interaction on prices. . .

APPENDIXES . . . . . . . . . . . . .

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


. .


. .





. .




. C



. C


Page
202

203

203

203

204
204


204


204

205
206


206

206

207

207

208
209

210

255


. .














LIST OF TABLES


Table Page
1. Florida orange production, by type and area of production,
1952-53 through 1961-62 . . . . . . . . 4.

2. California orange production, by type, 1952-53 through
1961-62. . . . . . . . . . . 6

3. Florida Early-Midseason, Valencia and all oranges estimated
tree distribution, by age, 1961, 1966, and 1971 . . . 8

4. Estimated yields of orange trees, by orange type, and age
of tree. . . . . . . . . . . . . 11

5. Estimated production, Florida Early-Midseason oranges,
1961, 1966 and 1971. . . . . . . . . . 13

6. Estimated production, Florida Valencia oranges, 1961, 1966,
and 1971. . . . . . . . . . . . .. 15

7. Estimated production, all Florida oranges, 1961, 1966,
and 1971. . . . . . . . . . . . . 17

8. Florida Early-Midseason and Valencia orange utilization,
1951-52 through 1961-62. . . . . . . . ... .19

9. California Valencia and Navel orange utilization, 1951-52
through 1961-62. . . . . . . . .... 20

10. Per capital consumption of fresh, canned, chilled, and
frozen orange products, United States, 1950-60. . . ... 21

11. United States population, by years, 1951-62. . . .. 23

12. Orange unloads in selected U. S. cities, two-year
intervals, 1955-61. . . . . . . . .. . 25

13. Carlot shipments, California and Florida oranges, by
months, 1954 through 1962. . . .. . . . .. 29

14. Size distribution, Florida Indian River, Florida Interior,
and California Valencia oranges, 1960-61 season. . . 56

15. Basic demand relationships, Florida Indian River sizes
200 and 163, Florida Interior sizes 200 and 163, and
size 138 California Valencia oranges. . . . . . 58
viii










LIST OF TABLES--Continued


Table Page
16. Treatment price combinations, in terms of four cent
deviations, used in estimating demand relationships for
Florida and California Valencia oranges for fresh market. 80

17. Component I experimental price design for the study of the
competitive relationships among size 200 Florida Indian
River, size 200 Florida Interior and size 138 California
Valencia oranges, Grand Rapids, Michigan, April-May,
1962. . . . . . . ... .. . . . 81

18. Component II experimental price design for the study of the
competitive relationships among size 163 Florida Indian
River, size 163 Florida Interior, and size 138 California
Valencia oranges, Grand Rapids, Michigan, April-May, 1962. 85

19. Component I price design for the study of the competitive
relationships among size 200 Florida Indian River, size
200 Florida Interior, and size 138 California Valencia
oranges, Grand Rapids, Michigan, April-May, 1962. . . 89

20. Component II price design for the study of the competitive
relationships among size 163 Florida Indian River, size
163 Florida Interior, and size 138 California Valencia
oranges, Grand Rapids, Michigan, April-May,.1962 . 91

21. Arrangement of displays of Florida Indian River, Florida
Interior, and California Valencia oranges, Component I,
in a study of the competitive relationships between Florida
and California oranges.. . . . . . . . 95

22. Arrangement of displays of Florida Indian River, Florida
Interior, and California Valencia oranges, Component II,
in a study of the competitive relationships between
Florida and California oranges. . . . . . . . 96

23. Number of customers, produce sales, total sales, and
proportions of total sales in produce, by component
and store, experimental tests, Grand Rapids, Michigan,
April-May, 1962. . . . . . . . . . . 103

24. Customer traffic, by component, store, and day of week,
and daily percentage distribution by component,
experimental tests, Grand Rapids, Michigan, April-May,
1962. . . . . . . . . . . 106

25. Total sales, by component, store and day of week, and
daily percentage distribution by component, experimental
tests, Grand Rapids, Michigan, April-May, 1962. . . ... 107
ix












LIST OF TABLES--Continued


Table Page
26. Produce sales, by component, store and day of week, and
daily percentage distribution by component, experimental
tests, Grand Rapids, Michigan, April-May, 1962. . . ... 109

27. Total sales per customer, by component, store and day,
experimental tests, Grand Rapids, Michigan, April-May,
1962. . . . . . . . . . . .. . . . Ill

28. Produce sales per customer, by component, store and day,
experimental tests, Grand Rapids, Michigan, April-May,
1962. . . . . . . .. . . . . . . 113

29. Florida Indian River, Florida Interior, California, and
total Valencia orange sales, by component, by store,
experimental test, nine stores, 31 operational days,
Grand Rapids, Michigan, April-May, 1962. . . . ... 115

30. Florida Indian River, Florida Interior, California, and
total Valencia orange sales, by component, by week,
experimental test, 31 operational days, nine stores,
Grand Rapids, Michigan, April-May, 1962. . . . ... 118

31. Florida Indian River, Florida Interior, California, and
total Valencia orange sales, by component, by day,
experimental test, 31 operational days, nine stores,
Grand Rapids, Michigan, April-May, 1962. . . . ... 119

32. Florida Indian River, Florida Interior, California, and
total Valencia orange sales per 100 customers, by
component, by store, experimental test, nine stores,
31 operational days, Grand Rapids, Michigan, April-May,
1962. . . . . . . . . . . . . . 121

33. Florida Indian River, Florida Interior, California, and
total Valencia orange sales per 100 customers, by component,
by week, experimental test, 31 operational days, nine
stores, Grand Rapids, Michigan, April-May, 1962. . . ... 123

34. Florida Indian River, Florida Interior, California, and
total Valencia orange sales, per 100 customers, by com-
ponent, by day, experimental test, 31 operational days,
nine stores, Grand Rapids, Michigan, April-May, 1962.. ... .124

35. Measures of dispersion and tests of significance for rele-
vant coefficients in the demand equations for Florida
Indian River size 200, Florida Interior size 200, and
California size 138 Valencia oranges. . . . . .. 136
x









LIST OF TABLES--Continued


Table Page
36. Effects of price changes upon purchases of Florida
Indian River size 200, Florida Interior size 200, and
California size 138 Valencia oranges, Component I, ex-
perimental tests, Grand Rapids, Michigan, April-May,
1962. . . . . . . . . . . . . . 149

37. Measures of dispersion and tests of significance for
relevant coefficients in the demand equations for Florida
Indian River size 163, Florida Interior size 163, and
California size 138 Valencia oranges. . . . . ... 152

38. Effects of price changes upon purchases of Florida Indian
River size 163, Florida Interior size 163, and California
size 138 Valencia oranges, Component II, experimental
tests, Grand Rapids, Michigan, April-May, 1962. . . 163

39. Effects of quantity changes upon prices of Florida Indian
River size 200, Florida Interior size 200 and California
size 138 Valencia oranges. . . . . . . . . 179

40. Effects of quantity changes upon price of Florida Indian
River size 163, Florida Interior size 163, and California
size 138 Valencia oranges. . . . . . . . 184

41. Effects of various conditions of increased and decreased re-
tail prices of Florida Indian River and Florida Interior
Valencia oranges upon consumer purchases of Florida Indian
River Valencia oranges. . . . . . . . .. .. 189

42. Effects of various conditions of increased and decreased
retail prices of Florida Interior and Florida Indian River
Valencia oranges upon consumer purchases of Florida In-
terior Valencia oranges. . . . . . . . ... 192

43. Effects of various conditions of increased and decreased
supplies of Florida Indian River and Florida Interior
Valencia oranges upon prices of Florida Indian River Valen-
cia oranges. . . . . . . . . . . . 194

44. Effects of various conditions of increased and decreased
supplies of Florida Indian River and Florida Interior
Valencia oranges upon prices of Florida Interior Valencia
oranges. . . . . . . . . . . . . 197














LIST OF ILLUSTRATIONS


Figure Page
1. Hypothetical demand relationships for Florida fresh
oranges and processed orange products. . . . . ... 33

2. Component cubes of the Triple Cube Design. . . . ... 70

3. The Triple Cube Design. . . . . . . . . ... 72

4. Display and pricing placards used in the study of the
competitive relationships among Florida and California
Valencia oranges, Grand Rapids, Michigan, April-May,
1962. . . . . . . . .. . . . . . 93

5. Valencia orange display location on produce counter, in
the study of competitive relationships among Florida
and California Valencia oranges, Grand Rapids, Michigan,
April-May, 1962. .......... ......... . 94

6. The effect of price changes for Florida Indian River size
200 Valencia oranges upon retail sales of Florida Indian
River size 200 Valencia oranges, Florida 200-California
138 test. . . . . . . . . ... . . . 140

7. The effect of price changes for Florida Interior size
200 Valencia oranges upon retail sales of Florida Interior
size 200 Valencia oranges, Florida 200-California 138 test 141

8. The effect of price changes for California size 138
Valencia oranges upon retail sales of California size
138 Valencia oranges, Florida 200-California 138 test. . 143

9. The effect of price changes for Florida Indian River size
200 Valencia oranges upon retail sales of Florida
Interior size 200 Valencia oranges, and the effect of
price changes for Florida Interior size 200 Valencia
oranges upon retail sales of Florida Indian River size
200 Valencia oranges, Florida 200-California 138 test.. 147

10. The effect of price changes for Florida Indian River size
163 Valencia oranges upon retail sales of Florida
Indian River size 163 Valencia oranges, Florida 163-
California 138 test. . . . . . . . . . 156
xii











LIST OF ILLUSTRATIONS--Continued

Figure Page
11. The effect of price changes for Florida Interior size
163 Valencia oranges upon retail sales of Florida In-
terior size 163 Valencia oranges, Florida 163-
California 138 test. . . . . . . . . . 157

12. The effect of price changes for California size 138
Valencia oranges upon retail sales of California size
138 Valencia oranges, Florida 163-California 138 test. . 158

13. The effect of price changes for Florida Indian River
size 163 Valencia oranges upon retail sales of Florida
Interior size 163 Valencia oranges, Florida 163-
California 138 test. . . . . . . . 162


xii i














LIST OF APPENDIX TABLES


Table Page
1. Quantity of Florida Indian River size 200, Florida Interior
size 200, and California size 138 Valencia oranges sold
per 100 customers, and value of produce sales per 100
customers, by observation number, date, and price combi-
nation, Component I, experimental tests, six stores,
Grand Rapids, Michigan, April-May, 1962. . . .. . .. 219

2. Quantity of Florida Indian River size 163, Florida Interior
size 163, and California size 138 Valencia oranges sold
per 100 customers, and value of produce sales per 100
customers, by observation number, date, and price combi-
nation, Component II, experimental tests, three stores,
Grand Rapids, Michigan, April-May, 1962. ....... 226

3. Coding and transformation instructions for demand analyses,
Florida Indian River, Florida Interior and California
Valencia oranges. ... . .......... .. ... 231













CHAPTER I

INTRODUCTION



The fresh orange market is an important segment of the Florida

orange industry. Cash receipts to Florida growers from the sale of

oranges are in excess of $200 million annually. Although the fresh

orange segment amounts to only approximately 20 per cent of the total

market for oranges, it is of sufficient Importance to warrant attention

as to maintenance or expansion of its position. To maintain or improve

the position of this market, the industry has need of definitive in-

formation that describes the demand relationships faced in the fresh

orange market.

The major source of competition fresh Florida oranges face in the

marketplace is California's orange production. At present little is

known about the relative values consumers attach to oranges produced

in either state nor the magnitude of price change necessary to induce

them to vary or alter purchase habits.

Historically, the price competition between the two areas has

been quite favorable to California. Consumer preference is the only

basis upon which the California product can enter the market with a

price differential over the Florida product. If oranges from the two

states were, in fact, perfect substitutes, retail prices should be the

same. Yet by virtue of an advertiser-created preference, the California

product can command a higher price. Thus, consumer preference allows

California producers to compete effectively with Florida producers and,
I









thereby, defray cost differences resulting frcm the productive process

as well as differences in transportation charges. This retail price

differential has existed for such a long period of time that it is

difficult to determine how much of it arises from consumer preference

and how much stems from an institutional situation developed over the

years at the terminal market level.

SDuring the past two decades, technological changes and innovations

have revolutionized the orange industry, especially in Florida. The

introduction of frozen orange concentrate and chilled orange juice has

altered substantially the relative market outlet volumes for Florida

oranges. The amount of Florida fruit moving to fresh market has de-

clined with a corresponding increase in the amount moving through

processing channels./ This pattern occurred during a period of rapid

expansion in Florida production and a slight decline in California

production. With this succession of technological advances, it is

reasonable to assume a change in the competitive citrus marketing

situation between the two states. Yet comparatively little research

has been directed toward an assessment of the values consumers place

upon these fruits.


Statement of the General Problem


Florida orange production

Florida orange production is characterized by six major product

differentiations. The first major differentiation encompasses two

distinct areas of production: the Indian River district, comprised

of four counties along the east coast, and the interior district, made

up of the remaining citrus-producing counties in the state. It is an









accepted fact at the production and wholesale levels that fruits pro-

duced in the two areas are differentiated products, and some price

differential does exist between the two areas.

Within each of these producing areas three other major differenti-

ations result from a type-varietal complex. The fruits produced in

each area are generally classified as Early, Midseason, and Late. The

principal varieties of Early fruit are Hamlin and Parson Brown; Mid-

season fruit varieties are Pineapple, Homosassa, and Temple; and the

Late oranges are exclusively Valencias.

In the crop seasons 1952-53 through 1961-62, total Florida pro-

duction averaged 89.6 million boxes of oranges annually (Table 1).

Production of oranges in the Interior district averaged 79.9 million

boxes, or slightly over 89 per cent of the state total, while the

Indian River district produced an annual average of 9.7 million boxes.

Early and midseason fruit accounted for, on the average, 50.7 million

boxes compared with 38.9 million boxes of Late or Valencia oranges.

During this period, increased production was in evidence in all

the major product differentiations associated with Florida oranges.

In the 1952-53 season, Interior production amounted to 62.7 million

boxes, or 86.8 per cent of the state's total production, while in the

1961-62 season, Interior production accounted for 102.2 million boxes,

or 90 per cent of the total Florida production. Thus, the Interior

district increased production in both absolute and relative terms.

The Indian River district, on the other hand, declined in relative

terms but registered an increase in absolute terms, increasing from

5.8 million boxes in 1952-53 to 6.0 million boxes in 1961-62. Over

this same period Valencia," or Late oranges, also gained in absolute








Table l.--Florida orange production, by type and area of production,
1952-53 through 1961-62.


Indian River Districta Interior District
Crop Total
Season Early and Late Early and Late All
Midseason (Valencia) Midseason (Valencia) Oranges

----------------------------------(000 Boxes)b--- ---------------------

1952-53 5,790 3,665 36,510 26,235 72,200

1953-54 5,718 4,084 44,482 37,016 91,300

1954-55 5,853 3,622 46,147 32,778 88,400

1955-56 6,026 3,789 45,474 35,711 91,000

1956-57 6,771 4,086 47,529 34,614 93,000

1957-58 5,277 3,313 47,423 26,487 82,500

1958-59 4,786 3,716 42,314 35,184 86,000

1959-60 5,083 3,968 43,917 38,532 91,500

1960-61 5,852 4,051 45,148 31,649 86,700

1961-62 6,014 5,227 50,886 51,273 113,400

10-Year
Average 5,717 3,952 44,983 34,948 89,600

aThe figures for the Indian River district were derived by using
county estimated production. The counties included in the Indian
River district are Volusia, St. Lucie, Indian River, and Brevard.

bBoxes containing a net weight of 90 pounds each.

Source: These data were adapted from Florida Citrus Fruits, Annual
Summary, 1952-53 through 1961-62, Florida Crop and Livestock Reporting
Service, Orlando, Florida.


and relative terms. In the 1952-53 season, Valencia production amounted

to 29.9 million boxes, or 41 per cent, of Florida production. In the

1961-62 season, Valencia production accounted for 56.5 million boxes,

or 50 per cent of total orange production in the state. Early and









Midseason fruit increased from 42.3 million boxes in 1952-53 to 56.9

million boxes in 1961-62.

From this analysis, these major product differentiations evince

an imposing magnitude as they establish irrefutable lines of differences

within Florida orange production. Other differentiations in addition

to these cited above occur also within these delineated segments, such

as size of fruit, grade of fruit and, to a lesser degree, packinghouse

or grove brand names.


California orange production

California has three major orange-producing areas, but, unlike

Florida, they form no real differentiation based upon area of production.

The production areas are categorized as Central, Southern, and Desert

Valley districts. Orange production In California averaged 33.4 mil-

lion boxes during the ten-year period 1952-53 through 1961-62 (Table 2).

Only two major orange types are produced In the state, Valencia and the

Washington Navel. Valencia production dominates and during this period

averaged an annual production of 20.1 million boxes compared with 13.3

million boxes of Navels.

During this period total California production declined from 46

million boxes in 1952-53 to 22.5 million boxes In 1961-62. Valencia

and Navel shares have varied within these dates from a percentage ratio

of 55-45 In favor of Valenclas In the 1953-54 season to a 67-33 per-

centage ratio In favor of Valenclas In 1961-62.


Production potential

The Florida orange production base has expanded markedly during

the past decade. This expansion has been stimulated by the natural







Table 2.--California orange production, by type, 1952-53 through 1961-62.



Crop Navels and Valencia Total All
Season Miscellaneousa Oranges
-------------------------(000 boxes)---------------------

1952-53b 16,630 29,400 46,030

1953-54b 14,460 17,940 32,400

1954-55c 15,330 24,090 39,420

1955-56c 15,170 23,200 38,370

1956-57c 15,400 20,500 35,900

1957-58c 9,100 14,100 23,200

1958-59c 16,900 23,300 40,200

1959-60c 13,500 17,700 31,200

1960-61c 9,000 16,000 25,000

1961-62c 7,500 15,000 22,500

10-Year
Average 13,299 20,123 33,422

includes small quantities of tangerines.

bBoxes containing a net weight of 77 pounds each.

cBoxes containing a net weight of 75 pounds each.

Source: Citrus Fruits by States Production Use Value
.Statistical Bulletins 296, October 1961, and 201, January 1957, United
States Department of Agriculture, Statistical Reporting Service, Crop
Reporting Board, Washington, D. C.


growth of a dynamic industry, on the one hand, as well as a production

reaction to the advent and successful marketing of frozen orange con-

centrate and chilled juice, on the other. In the 1951-52 season, the

total acreage in Florida was 324.8 thousand acres. By the 1961-62

season, the total acreage had increased to 429.8 thousand acres, an








increase of 32.3 per cent.l

A citrus tree survey which was conducted in 1961 revealed that

Florida orange groves contained a total of 37.8 million trees (Table

3). Of this total, 17.9 million were Early-Midseason and 19.9 million

were Valencias. Also the 1961 survey revealed that a high proportion

of Florida orange trees were of nonbearing age. These trees, less than

four years of age, were more heavily distributed to the Valencia oranges

than to the Early-Midseason oranges, 6.9 and 5.1 million, respectively.

An additional 5.4 million trees were in the five-to-nine-year age group.

Thus, 17.4 million of 37.8 million trees were nine years of age or less.

The oldest trees in the state, categorized as 25 or more years, were

of an average age of 34 years. Of the 11.1 million trees in this

category, 5.3 million were Early-Midseason and 5.8 million were

Valencias.

Based upon the assumption that the percentage change in tree numbers

occurring during the 1951-52 through 1960-61 period is typical of the

changes to come in the next ten-year period, the estimated tree numbers

in 1970-71 will be 46.4 million. This represents a 19 per cent increase

in tree numbers. Under this assumption, however, there will be fewer

nonbearing trees than was true in 1961. Nonbearing or two-year average

age trees in 1971 are estimated to be 4.3 million, 7.7 million trees

fewer than in the nonbearing category in 1961. This results from the

assumed normality of the per-annum increase in tree setting estimates

based upon the period 1951-52 through 1960-61. During these years,

frozen orange concentrate and chilled juice emerged as major trends in


IFlorida Crop and Livestock Reporting Service, Florida Citrus
Fruit, Annual Summary, 1961, Orlando, Florida.







Table 3.--Florida Early-Midseason, Valencia and all oranges estimated tree distribution, by age,
1961,a 1966, and 1971.




Early-Midseason Valencia All Oranges
Average Age Production Year Production Year Production Year
of Treeb
(Years) 1961 1966 1971 1961 1966 1971 1961 1966 1971


----------------------------------Thousands of trees-----------------------------------

2.0 5,045.8 2,010.5c 2,010.5c 6,946.2 2,246.6c 2,246.6c 11,992.0 4,257.1 4,257.1

7.0 3,149.5 5,045.8 2,010.5c 2,290.8 6,946.2 2,246.6c 5,440.3 11,992.0 4,257.1

12.5 1,176.1 3,149.5 5,045.8 2,043.0 2,290.8 6,946.2 3,219.1 5,440.3 11,992.0

19.5 3,139.3 1,176.1 3,149.5 2,922.7 2,043.0 2,290.8 6,062.0 3,219.1 5,440.3

34.0 5,360.2 3,139.3 1,176.1 5,767.9 2,922.7 2,043.0 11,128.1 6,062.0 3,219.1

39.0 -- 5,360.2 3,139.3 -- 5,767.9 2,922.7 -- 11,128.1 6,062.0

42.0 -- -- 5,360.2 -- -- 5,767.9 -- -- 11,128.1

Total 17,870.9 19,881.4 21,891.9 19,970.6 22,217.2 24,463.8 37,841.5 42,098.6 46,355.7

aSource of the 1961 distribution, Florida Crop and Livestock Reporting Service, Orlando, Florida.

bThe age categories for the 1961 citrus tree survey were: 0-4, 5-9, 10-14, 15-24, 25+. These
were transformed to average ages for purposes of estimating tree numbers for 1966 and 1971.

CTwo-year-old trees for 1966 and 1971 estimated by applying the per-annum average increase in
total orange acreage for the years 1951-52 through 1960-61 to the total 1961 tree population.










the utilization of the Florida orange crop. Therefore, the early

portion of the period was characterized by a production reaction

based upon the recognized potential of the expanded processing market.

Logically then, this may be quite representative of the tree-setting

pattern of the 1960's, in that tree-setting may well continue at a

fairly rapid rate for a portion of the period before increased supplies

of oranges force a cessation of expansion.

During the period 1961-1971, Florida orange production is likely

to increase from three sources. One of these increases arises as

present nonbearing trees attain productive maturity. A second increase

springs from expanded bearing surface. As the trees age and become

larger, the per-tree bearing surface expands. Thus, an increase exists

due to the relationship between the age of the tree and the tree's pro-

ductive capacity. The third source of increased production will result

from new tree-settings. As new tree-settings occur in the 1960's and

reach bearing age, total productive capacity will increase.

To develop definitive estimates of orange production in future

years, a relationship must be established between age of tree and

production in addition to the Informational requirements concerning

tree numbers by age groups. Kelly developed such a relationship in

1953 from sample data from 15 thousand groves in Florida.2 Orange

varieties were grouped into Early, Midseason, and Late categories.

Utilizing regression analysis, a quadratic function was fitted to

describe the age-production relationship. From these regression


2Bruce W. Kelly, "A Method for Forecasting Citrus Production in
Florida", Ph.D. dissertation, University of Florida, August 1953.










equations, estimated yields were developed (Table 4). The estimated

yields derived from this study appear to overestimate production.

This discrepancy could easily be a result of the climatic conditions

prevailing during the period in which the primary data were secured.

To narrow this margin of error in estimating potential Florida pro-

duction, the percentage change between the estimated yields for given

tree ages as shown in Table 4 were developed and applied to a historical

series of production. The following assumptions were developed for

estimating production in 1966 and 1971:

(1) Estimated 1961 production is equal to the average
production for the years 1951-52 through 1960-61
multiplied by the estimated number of bearing trees
according to the 1961 tree survey.

(2) The percentage change in production of the 1961 bearing
surface in 1966 and 1971 can be estimated by calculating
the percentage change from the weighted average age of
bearing trees in 1961 to this age plus five and plus
ten based upon Kelly's relationship.

(3) Production addition due to 1961 nonbearing trees can be
estimated by deflating the 1961 per-tree production by the
percentage change in yield of Kelly's relationship from
the 1961 weighted average tree age to the average age of
new production trees in 1966 and 1971.

Another assumption more basic than those related to the mechanics

of estimation is the assumed normality of the basic per-tree production

estimate derived from the period 1951-52 through 1960-61. During these

years, Florida orange trees were exposed to adverse weather conditions

in at least three seasons. Two years the citrus belt was subjected to

freeze damage and in one other year to hurricane damage. Therefore,

recognizing the freeze damage incurred in January 1963, the per-

tree yield for this period may be quite realistic.









Table 4.--Estimated yields of orange trees, by orange type, and age of
tree.



Age of Orange Type
Tree
Early Midseason Late

----------------Boxes per tree---------------------


.479
.822
1.156
1.482
1.798
2.105
2.404
2.693
2.973
3.245
3.508
3.761
4.006
4.242
4.469
4.687
4.896
5.096
5.287
5.469
5.642
5.806
5.962
6.108
6.245
6.374
6.494
6.604
6.706
6.799
6.882
6.957
7.023
7.080
7.128
7.167
7.198
7.219
7.231
7.234
7.229


.317
.725
1.130
1.504
1.848
2.166
2.460
2.731
2.901
3.212 1
3.425
3.621
3.803
3.970
4.125
4.268
4.400
4.521
4.633
4.737
4.832
4.920
5.002
5.077
5.146
5.210
5.269
5.324
5.374
5.421
5.464
5.503
5.540
5.573
5.604
5.633
5.660
5.684
5.707


.141
.514
.876
1.226
1.564
1.891
2.206
2.509
2.801
3.081
3.349
3.606
3.851
4.084
4.306
4.516
4.714
4.901
5.076
5.239
5.391
5.531
5.659
5.776
5.881
5.986
6.068
6.126
6.185
6.232
6.267
6.290
6.302
6.302
6.290
6.267
6.232
6.186
6.128









Table 4.--Continued


Age of Orange Type
Tree
Early Midseason Late

-----------------Boxes per tree--------------------

42 7.214 5.727 6.058
43 7.191 5.747 5.965
44 7.158 5.764 5.872
45 7.117 5.781 5.778


Source: Bruce W. Kelly, "A Method for Forecasting Citrus Production
in Florida", Ph.D. dissertation, University of Florida, August 1953.


Based upon the foregoing assumptions, projected estimates were

made for 1966 and 1971 by a breakdown of Early-Midseason and Late,

or Valencia oranges. The bearing surface of Early-Midseason oranges

in 1961 was estimated to be 12,825,100 trees (Table 5). The non-

bearing surface in that same year was estimated to be 5,045,800 trees.

By applying the average per-tree yield of 3.83 boxes for Early-Mid-

season oranges from 1951-52 through 1960-61 to the 1961 bearing sur-

face, total production was estimated to be 49,120,100 boxes in 1961.

This bearing surface was estimated to be a weighted average age of

22 years. The percentage change due to age between 1961 and 1966,

based upon Kelly's age-production relationship, was found to be 14

per cent. Therefore, the 1961 bearing surface of 12.8 million trees

was estimated to yield 55,996,900 boxes in 1966. The 1961 nonbearing

trees in 1966 will be of an average age of seven years. Deflating

the base production per tree established at 3.83 boxes for 22-year-

old trees to seven-year-old trees based upon Kelly's relationship,

in 1966 an additional production of 8,880,600 boxes was estimated








Table 5.--Estimated production, Florida Early-Midseason oranges, 1961, 1966, and 1971.



Production
Weighted PAddtion Production
Average 1961 1961a Per Cent 1961 Addition Addition
Production Age 1961 Bearing BaseDue to Addition Total
Year Age 961 Bearing Base Due to Surface 1961 Non- Due to Production
Bearing Trees Production Production bearing New Tree
Age Production bearing
Surface Trees Setting

(Years) (000 Trees)(000 Boxes) (Per Cent) (000 Boxes) (000 Boxes) (000 Boxes) (000 Boxes)

1961 22 12,825.1 49,120.1 0 49,120.1 0 0 49,120.1

1966 29 12,825.1 49,120.1 +14b 55,996.9 8,880.6c 0 64,877.5

1971 35 12,825.1 49,120.1 +20d 58,944.1 14,885.1e 3,538.5 77,367.7

aDerived by using average per-tree production 1951-52 through 1960-61 multiplied by estimated
1961 bearing trees (3.83) (12,825.1).
bDerived from age-production relationship developed by Kelly by determining per cent change
between weighted average age 22 years and 29 years.

CDeflated average per-tree production, 1951-52 through 1960-61 from weighted average age 22 years
to 7 years by per cent change in Kelly's relationship (3.83) (.46) (5,045.8) = 8,880.6.
dDerived from age-production relationship developed by Kelly by determining per cent change
between weighted average age 22 years and 35 years.
eDeflated average per-tree production, 1951-52 through 1960-61 from weighted average age 22 to
12.5 years by per cent change in Kelly's relationship (3.83) (.77) (5,045.8) = 14,885.1.

fDeflated average per-tree production 1951-52 through 1960-61 from weighted average age 22
years to 7.0 years by per cent change in Kelly's relationship (3.83) (.46) (2,010.5) = 3,538.5.









from the 5,045,800 nonbearing trees of 1961. Thus, the total yield of

Early-Midseason oranges in 1966 is estimated to be 64,877,500 boxes.

In 1971 the bearing surface of 1961 is estimated to yield 20 per

cent more than in 1961 owing to differences in age of tree. Therefore,

the 1961 production base of 12,851,100 trees is estimated to yield

58,944,100 boxes in 1971. The 1961 nonbearing trees are estimated

to yield 14,885,100 boxes in 1971, while the 1966 nonbearing trees'

yield will be 3,538,500 boxes. This gives an estimated yield of

77,367,700 boxes of Early and Midseason oranges in 1971.
Ns
Turning now to the projection of Florida Valencia orange pro-

duction, the same basic assumptions were employed. In 1961, there

was an estimated 13,024,400 bearing Valencia orange trees in Florida.

Using the 1951-52 through 1960-61 period to establish the per-tree

production of 3.48 boxes, total production of Valencias in 1961 was

estimated at 45,324,900 boxes.

Differences due to age, derived from Kelly's age-production re-

lationship, were found to be a 14 per cent increase by 1966 and a

17 per cent increase by 1971. Therefore, the 1961 bearing surface is

estimated to yield 51,670,400 boxes in 1966 and 53,030,100 boxes in

1971.

The weighted average age of Valencia trees in 1961 was found to

be 23 years (Table 6). By deflating the established per-tree pro-

duction from 23 years to seven years, the 1966 production resulting

from 1961 nonbearing trees was estimated to be 7,015,700 boxes. The

total Valencia production for 1966 was estimated at 58,686,100 boxes.

Using the same deflation procedure, the 1961 nonbearing trees

were estimated to yield 14,725,900 boxes in 1971 and the 1966 non-







Table 6.--Estimated production, Florida Valencia oranges, 1961, 1966, and 1971.


.. Production
Weighted Production Production
Average 1961 1961a Per Cent 1961 Addition Addition
ProductionAge 1961 Bearing Base Change Bearing Due to Due Total
Age 1961 Bearing Base Due to
Year r r rDue to Surface 1961 Non- Production
Bearing Trees Production baig New Tree
Surface Age Production bearing Setting
Surface STrees tting

(Years) (000 Trees)(000 Boxes) (Per Cent)(000 Boxes)(000 Boxes) (000 Boxes)(000 Boxes)

1961 23 13,024.4 45,324.9 0 45,324.9 0 0 45,324.9

1966 30 13,024.4 45,324.9 +14b 51,670.4 7,015.7c 0 58,686.1

1971 36 13,024.4 45,324.9 -17d 53,030.1 14,725.9e 2,269.1f 70,025.1

Derived by using average per-tree production 1951-52 through 1960-61 multiplied by estimated
1961 bearing trees (3.48) (13,024.4) = 45,324.9.
bDerived from age-production relationship developed by Kelly by determining per cent change
between weighted average age 23 years and 30 years.

Deflated average per-tree production, 1951-52 through 1960-61 from weighted average age 23
years to 7 years by per cent change in Kelly's relationship (3.48) (.29) (6,946.2) = 7,015.7.
dDerived from age-production relationship developed by Kelly by determining per cent change
between weighted average age 23 years and 36 years.
e
Deflated average per-tree production, 1951-52 through 1960-61 from weighted average age 23
years to 12.5 years by per cent change in Kelly's relationship (3.48) (.61) (6,946.2) = 14,725.9.

fDeflated average per-tree production, 1951-52 through 1960-61 from weighted average age 23
years to 7 years by per cent change in Kelly's relationship (3.48) (.29) (2,246.6) = 2,269.1.










bearing trees, 2,269,100 boxes. Total Valencia production was esti-

mated to be 70,025,100 boxes in 1971.

To summarize these estimated yields, total Florida orange pro-

duction was estimated to be 94.4 million boxes in 1961, 123.6 million

boxes in 1966, and 147.4 million boxes in 1971 (Table 7).

No similar work has been done in the area of age-production re-

lationships for California oranges. However, research has progressed

in the projection of orange acreage to 1970 and 1980.3 Between 1960

and 1970, acreage of Valencia oranges is estimated to decline from

86,438 acres to 74,650 acres, while Navel orange acreage is estimated

to increase from 72,595 acres to 78,300 acres. The projections to 1980

indicate little change in Valencia acreage, but Navel acreage is esti-

mated to increase to 85,700 acres, approximately a seven thousand acre

increase between 1970 and 1980 compared with about a six thousand acre

increase from 1960 to 1970.

From these projections, undoubtedly Florida orange producers and

marketers must concern themselves further with the utilization of their

fruit during the 1960's. Valencia production in Florida, based upon

these projections, will increase by 17.0 million boxes or 32 per cent

by 1971. Early and Midseason production will increase 18.4 million

boxes or 31 per cent during the same period. In total, this represents

an increase in Florida production of 35.4 million boxes, an amount

equivalent to or exceeding the state's entire production in the 1942-

43 or any prior season.


R. C. Rock and R. G. Platt, Economic Trends in the California
Orange Industry. 1961, Agricultural Extension Service, University of
California, November, 1961.








Table 7.--Estimated production, all Florida oranges, 1961, 1966, and 1971.



Production Production
Pro- 1961 1961 1961 Addition Addition Total
duction Bearing Base Bearing Due To Due to Pro-
Year Trees Production Surface 1961 Non- New Tree duction
Production bearing Setting
Trees

(000 Trees)(000 Boxes)(000 Boxes)(000 Boxes) (000 Boxes)(000 Boxes)

1961 25,849.5 94,445.0 94,445.0 0 0 94,445.0

1966 25,849.5 94,445.0 107,667.3 15,896.3 0 123,563.6

1971 25,849.5 94,445.0 111,974.2 29,611.0 5,807.6 147,392.8

Source: Tables 5 and 6.


This enlarged production in Florida will be offset to some degree

by a reduction of orange acreage in California. Valencia acreage has

been projected to decline 13.6 per cent by 1970, but Washington Navel

acreage has been projected to increase by 7.9 per cent. The net change

in acreage for all California oranges, using these projections, will be

7,983 acres or 5.0 per cent.


Utilization trends and population trends

The amount of Florida oranges utilized in fresh market sales has

declined substantially in the past decade, while the number of oranges

used for processing has risen rapidly. The decline in fresh sales has

occurred notwithstanding increases in production. Florida Early-Mid-

season movement to fresh market has declined from 17.0 million boxes

in the 1951-52 season to 11.5 million boxes in 1961-62 (Table 8).

Valencia fresh sales declined from 13.6 million boxes in the 1951-52

season to the six million box level in 1957-59, gaining to the 9 mil-

lion box level in the 1959-60 season. The 1960-61 season again










registered a decline to 6.3 million boxes. Yet the 113 million box

crop of Florida oranges In the 1961-62 season led to higher fresh

sales In both Early-Midseason and Valencia categories, 11.5 and 9.4

million boxes, respectively.

Over the period 1951-52 through 1961-62, utilization ratios be-

tween fresh and processed Florida oranges were altered substantially.

In the 1951-52 season, 30.6 million boxes or 39 per cent of the crop

were utilized in fresh sales compared with a remaining 61 per cent or

47.5 million boxes used for processing. This emphasis on processing

has grown continuously since the early fifties. In the 1961-62 season,

which had a total sales utilization of 112.6 million boxes, 20.9 million

boxes or 19 per cent moved through fresh market outlets, while 91.7 mil-

lion boxes or 81 per cent were used for processing.

California fresh orange sales and processed orange sales have de-

clined during the past decade at a rather constant rate with respect

to shares. Fresh sales for the period 1951-52 through 1961-62 have

ranged between 72 and 79 per cent of total sales, with the exception

of the 1957-58 season when fresh sales accounted for 86 per cent. In

that season Florida incurred freeze damage and registered a total sales

volume of some eight million boxes below the decade average.

Sales of California oranges In the fresh market have declined from

27.2 million boxes In the 1951-52 season to 15.1 million boxes in the

1961-62 season (Table 9). Valencia fresh sales have declined from 19.7

million boxes in the 1952-53 season to 8.4 million boxes In the 1961-

62 season while Navel fresh sales have declined from a high of 14.8

million boxes in 1952-53 to a low of 6.7 million boxes in 1961-62.









Table 8.--Florida Early-Midseason and Valencia orange utilization,
1951-52 through 1961-62.



Orange Type

Season Early-Midseason Valencia All

Fresh Fresh Fresh
Fresh Processed Fresh Processed Fresh Processed
Sales Sales Sales

------------------------Thousands of boxes--------------------

1951-52 16,991 26,559 13,652 20,948 30,643 47,507

1952-53 15,212 26,838 10,637 19,063 25,849 45,901

1953-54 14,563 35,337 13,283 27,567 27,846 62,904

1954-55 16,320 35,380 10,837 25,313 27,157 60,693

1955-56 14,500 36,700 11,066 28,184 25,566 64,884

1956-57 13,984 39,966 10,132 28,268 24,116 68,234

1957-58 11,993 40,407 6,114 23,436 18,107 63,843

1958-59 10,574 36,176 6,263 32,337 16,837 68,513

1959-60 11,747 36,888 9,018 33,182 20,765 70,070

1960-61 10,441 40,199 6,359 29,041 16,770 69,270

1961-62 11,540 44,935 9,375 46,775 20,915 91,710


Source: Florida


Citrus Fruits, Annual Summary,


Crop and Livestock Reporting Service, Orlando, Florida.


(1952-1962), Florida


The decline in the fresh orange market can be traced primarily to

the successful marketing of frozen orange concentrate. In 1950, per

capital consumption of fresh oranges was 26.9 pounds (Table 10). By

1960, it had declined to 19.6 pounds, a decrease of 27 per cent. During

this same decade, per capital consumption of frozen orange concentrate

increased more than threefold, from 1.52 to 5.58 pounds. In the









Table 9.--California Valencia and Navel orange utilization, 1951-52
through 1961-62.



Orange Type

Valencia Navel All
Season
Fresh Processed Fresh Processed Fresh Processed
Sales Sales Sales

-----------------------Thousands of boxes----------------------

1951-52 16,895 8,499 10,338 1,783 27,233 10,282

1952-53 19,670 9,300 14,785 1,600 34,455 10,900

1953-54 13,028 4,557 11,945 2,135 24,973 6,692

1954-55 15,000 8,730 12,816 2,071 27,816 10,801

1955-56 14,330 8,550 13,070 1,623 27,400 10,173

1956-57 13,150 7,060 13,280 1,720 26,430 8,780

1957-58 10,978 2,880 8,485 375 19,463 3,255

1958-59 14,600 8,390 14,530 2,080 29,130 10,470

1959-60 10,980 6,060 11,550 1,650 22,530 7,710

1960-61 10,880 4,960 8,250 510 19,130 5,470

1961-62 8,400 4,210 6,660 700 15,060 4,910


Source: Florida


Citrus Fruit. Annual Summary


Crop and Livestock Reporting Service, Orlando, Florida.


(1952-1962), Florida


mid-fifties, chilled juice sales influenced in the market distribution of

orange products. The 1955 per capital consumption of chilled juice was

.94 pounds and by 1960 had increased to 2.11 pounds. Another market de-

cline registered in the 1950 decade was related to canned orange products.

In 1950, per capital consumption of these products amounted to 3.37 pounds,

but declined to only 2.13 pounds by 1960, a decrease of 37 per cent.


,









Table O0.--Per capital consumption of fresh, canned, chilled, and frozen
orange products, United States, 1950-1960.



Product Classification
Year
Fresh Canned Chilleda Frozenb

(lb.) (lb.) (lb.) Product Single
Weight Strength
(lb.) Bases
(lb.)c

1950 26.9 3.37 .. 1.52 5.12

1951 28.8 3.81 .. 2.19 7.22

1952 27.9 3.58 .. 3.53 11.44

1953 27.6 3.13 .. 4.08 12.85

1954 24.5 3.08 .. 4.40 13.93

1955 25.1 2.96 .94 4.94 15.81

1956 22.9 2.24 1.05 4.86 15.48

1957 21.9 2.45 1.71 5.32 16.99

1958 17.8 2.66 1.60 4.32 13.27

1959 20.1 1.91 1.87 5.42 16.64

1960 19.6 2.13 2.11 5.58 17.62


aChilled fruit juice
Florida; does not include
duced for local sale.


is produced commercially from fresh fruit in
reconstituted frozen juices or juice pro-


Includes single strength and concentrated juices of all citrus
products.
CConcentrated fruit juices converted to single strength on basis
of 3.525 pounds to 1.

Source: Supplement for 1961 to Consumption of Food in the United
States, Agricultural Handbook No. 62, Agricultural Marketing Service,
USDA.









The fluctuations in market shares between the various sectors of

the orange industry are especially vital in Florida, since a major pro-

portion, approximately 80 per cent, of its crop is utilized in the

processing market. The impact of the technological advances in frozen

concentrate and chilled products on the Florida industry is sufficient

to warrant study, notwithstanding the need for evaluation resulting

from increased supplies available for the national market.

California, on the other hand, has had a relatively constant market

share situation with regard to fresh and processing. Further, western

growers are facing a declining acreage in oranges, primarily from

continued urbanization in citrus producing areas.

The United States' population increased 20.9 per cent between 1951

and 1962. In 1951 the estimated population was 153.7 million and by

1962 it had mushroomed to an estimated 185.9 million (Table 11). The

average annual rate of increase from 1955 to 1961 was 2,931,454 per

annum, If this rate of increase is maintained, the estimated 1966

population will be 197.7 million persons, and in 1971 the census will

record 212.3 million.

Although United States' population is making rapid gains, this

increase in consumers will not solve the anticipated excess orange

production problem. Projected yields of Florida orange production

indicate 147.4 million boxes in 1971 and projected United States popu-

lation indicates 212.3 million persons in that same year.

This projection represents an increase over 1961 levels of 29.3

million persons and 52.9 million boxes of oranges, or 1.8 boxes per

additional person. However, consumption rates per capital tend to be

quite stable. The per capital consumption of all citrus fruits for the








Table ll.--United States population, by years, 1951-1962.


Year Persons Increase

1951 153,691

1952 156,421 2,730

1953 159,012 2,591

1954 161,761 2,749

1955 164,607 2,846

1956 167,509 2,902

1957 170,496 2,987

1958 173,367 2,871

1959 176,551 3,184

1960 180,007 3,456

1961 183,025 3,018

1962 185,937 2,912


Source:
Agriculture.


Agricultural


Statistics


1962, U. S. Department of


decade 1950-60 averaged 84.1 pounds. This represents less than one box

of citrus fruit to a consumer. Thus, to utilize the anticipated increase

in orange production, new uses for oranges must be found, or marketing

policies must be altered to effect a shift in consumption rates.


Position of Florida and California in
the fresh orange market

Aggregated over the various product differentiations, Florida and


Per capital consumption derived from Consumption of Food in the
United States, U. S. Department of Agriculture Handbook No. 62, August
1961.









California oranges compete to some degree in most major terminal market

areas east of the Rockies (Table 12). Of the 41 markets included in

this tabulation, Florida dominates 10 in terms of carlot unloads and

California predominates in 31. However, in several of the larger termi-

nal markets, the relative shares between Florida and California are

much closer to equality. In markets such as Cincinnati, Cleveland,

New York, Philadelphia, Pittsburgh, and Providence, the shares ranged

in a 40 60 division between California and Florida. In Cincinnati,

for alternate seasons from 1955 through 1961, Florida unloads accounted

for an average of 57 per cent of the total Florida-California oranges

coming into the market. On the other hand, in Cleveland during this

same period, an average of 56 per cent of the California-Florida oranges

were from California.

Over these same years, market shares have demonstrably changed in

several of the markets. For example, Florida shares have increased in

Albany and Columbia, while California shares have multiplied in Dallas,

Fort Worth, and Denver. The California share increase in these markets

can be attributed partially to increased Texas orange production. In

these six years Texas producers were recouping losses suffered in the

extensive freeze damage of 1949 and 1951.

It must be recognized, however, that these data do'possess limi-

tations relevant to an analysis of emphasis shifts within the fresh

orange market. Since the data are aggregated over several types and

varieties of oranges as well as intrastate production areas, an analysis

of shifts can be stated only in the most general fashion. Further,

such broad analyses make no allowance for transshipments. Although an

analysis of unloads may yield no appreciable changes within a given






Table 12.--Orange unloads in selected U. S. cities, two-year intervals, 1955-1961.


Calendar Year
Cities 1955 1957 1959 1961

Florida California Florida California Florida California Florida California

Albany, N. Y. 44 198 181 313 122 279 138 194
Atlanta, Ga. 955 45 956 38 745 72 725 41
Baltimore, Md. 1,085 356 961 312 744 433 801 254
Birmingham, Ala. 48 40 623 27 527 39 389 26
Boston, Mass. 1,530 1,616 1,146 1,761 802 2,081 755 1,264
Buffalo, N. Y. 103 522 347 502 210 568 186 235
Chicago, Ill. 1,835 2,003 1,577 1,776 1,049 2,347 1,033 1,303
Cinn., Ohio. 508 347 509 341 354 401 348 195
Cleveland, 0. 724 886 714 848 546 960 554 503
Columbia, S. C. 71 16 454 32 454 28 414 21
Dallas, Texas 267 277 248 266 0 345 32 188
Denver, Colo. 150 387 88 494 31 578 23 440
Detroit, Mich. 820 1,505 667 1,509 419 1,542 531 885
Ft. Worth, Tex. 98 83 45 93 19 108 5 62
Houston, Tex. 0 111 208 255 55 385 25 154
Indianapolis, Ind. 7 235 306 252 243 372 270 180
Kansas C., Mo. 175 397 188 442 100 569 107 281
L.A., Calif. 38 3,731 5 4,320 0 4,626 22 2,913
Louisville, Ky. 62 68 392 66 451 97 289 45
Memphis, Tenn. 91 37 337 94 229 111 134 66
Miami, Fla. O 0 596 15 769 63 498 65
Milwaukee, Wis. 65 493 170 518 99 541 145 273
Minneapolis, Minn. 6 743 132 801 45 878 52 365
Nashville, Tenn. 73 33 200 16 131 24 101 2
New Orleans, La. 566 93 524 101 298 112 246 64
New York, N. Y.b 4,994 4,036 5,033 3,978 3,262 5,042 3,250 3,294
Philadelphia, Pa. 2,321 1,505 2,230 1,521 1.537 1,805 1,844 1,151
Pittsburg, Pa. 844 1,111 709 1,321 456 1,301 505 768








Table 12.--Continued


Calendar Year

1955 1957 1959 1961
Cities

Florida California Florida California Florida California Florida California

Portland, Ore. 53 154 39 548 0 623 32 390
Providence, R. I. 142 195 205 166 129 195 121 81
St. Louis, Mo. 444 658 362 667 196 722 171 412
Salt Lake City, U. 1 21 13 423 0 447 13 275
San Antonio, Tex. 2 85 86 162 27 183 13 84
San Francisco, Cal.c 0 1,550 1 1,648 2 1,752 6 1,141
Seattle, Wash.d 98 707 51 321 3 459 71 520
Washington, D. C. 523 128 523 167 482 222 398 132
Wichita, Kans. O 15 18 122 5 138 12 119

aMinneapolis includes St. Paul, Minnesota.
New York includes Newark, N. J.

cSan Francisco includes Oakland, California.

Seattle includes Tacoma, Washington.

Source: Fresh Fruit and Vegetable Unloads, by Commodities, States and Months, USDA, AMS-428,
February 1962, and similar publications.









market, substantial changes within orange utilization patterns may

have been present from either Florida or California. For example,

a marked shift could have developed from California Valencias to

California Navels, or there may have been substantial changes among

Early, Midseason, and Late Florida oranges. In the same manner, major

production shifts may have emerged regarding fruit produced in the

Indian River and Interior sections of Florida.

As orange production rises, these variables will assume more im-

portance and an assessment of consumer preferences with regard to the

various orange products will become more crucial to the allocation of

supplies among market sectors.


Marketing periods

The Florida orange production year begins around the first of

October with Early oranges. Early orange production is most intense

in November and December and generally continues through February.

Midseason fruit harvest and shipment begins early in November and

continues through March. Heaviest production of Midseason fruit runs

from December through February. Temple oranges, often classified as

a Midseason fruit, are harvested from late November through mid-April,

with heaviest production in January and February. Late or Valencia

orange harvest begins about the first of February and continues to some

degree throughout the summer months. Heaviest production occurs in the

months of March through May.

California orange production is more of a year-around proposition

than is Florida's. California Washington Navel harvest and shipment

begins from early-to-mid-November and continues generally through










April. Valencia harvest in California usually overlaps Navel harvest

in early April and continues through October.

Consequently, California and Florida fruit meet in the marketplace

throughout most of the year. During the period 1954-62, May was the

heaviest shipment month for California oranges, averaging 4,822 carlots

or 11.1 per cent of annual shipments (Table 13). At this season, pri-

marily Valencias are available from either state, along with a negligible

amount of Florida Temples. In contrast, December is the heaviest orange

shipment month for Florida. An average of 5,114 carlots were shipped

from Florida in December during the period 1954-1962. In that month

Florida Early, Midseason and Temples are available for shipment. August

and September are lightest months for Florida orange shipment, averaging

in the period 1954-1962 only 211 carlots or 0.6 per cent of annual ship-

ments.

California, during the 1954-1962 period, shipped an average of more

than 2,000 carlots of oranges each month of the year, ranging from a

high of 4,822 carlots in May to a low of 2,385 carlots in November.

Florida, contrastingly, shipped an average of as low as 76 carlots

in September and as high as 5,114 in December during these identical

years. Throughout the five months, November through March, Florida

shipped more than 57 per cent of its total annual fresh shipments

compared to California shipments of 40 per cent during the same

five months.

The anticipated production increases in Florida will place larger

amounts of fruit on the national market in two critical periods. The

Early-Midseason and Temple increases will face keen competition frcm

California's Washington Navel fruit. The Navel season, starting in










Table 13.--Carlot shipments, California and Florida oranges, by months,
1954 through 1962.



Month
Year and
State
Jan. Feb. Mar. Apr. May June

--------------------------------------------Carlots---


1954
California
Florida
1955
California
Florida
1956
California
Florida
1957
California
Florida
1958
California
Florida
1959
California
Florida
1960
California
Florida
1961
California
Florida
1962
California
Florida


California
Total
Average

Florida
Total
Average


4,215
5,406

4,198
5,195

3,483
4,672

3,451
4,584

3,341
3,551

4,521
3,619

4,175
4,552

2,946
3,118

2,512
4,717


4,550
5,780

4,113
5,383

4,381
4,668

3,429
4,021

3,082
3,306

4,712
3,203

4,164
4,010

2,678
3,180

1,971
4,320


4,238 5,486
6,659 5,444


4,757
5,249

5,448
4,994

4,408
4,601

2,875
3,062

6,103
2,441

3,563
3,680

2,624
2,755


4,746
4,539

6,566
4,279

4,797
3,703

3,206
2,091

6,451
2,120

3,519
3,116

2,411
2,294


2,558 2,039
4,221 3,257


32,842 33,080 36,574
3,649 3,676 4,064


39,414
4,379


37,871
4,208


37,662
4,185


39,221
4,358


30,843
3,427


5,630 4,662
4,276 2,136


4,926
3,803


5,893
2,252


7,337 5,553
3,759 2,142


5,633
3,329


5,141
1,888


4,201 3,163
1,552 332


6,060
1,541

3,620
2,876

3,304
2,038

2,684
3,271


43,395
4,822


26,445
2,938


4,246
548

3,054
859

2,912
965

2,298
1,863


36,922
4,102


12,985
1,443










Table 13.--Extension


July Aug. Sept. Oct. Nov. Dec. Total


4,050
6,455


51,374
45,106


3,055 50,950
6,709 41,075


3,629
5,928

3,423
4,536

4,040
4,404

4,069
5,286


56,080
37,741

47,290
36,639

35,914
22,152

53,814
23,823


3,412 37,246
4,388 26,891


3,983
692

5,354
814

4,359
511

4,222
822

2,875
47

4,117
182

2,829
168

2,917
152

2,084
687


3,616
144

4,630
210

4,597
185

3,865
244

2,609
2

3,640
43

2,514
103

2,527
5

2,191
283


32,740 30.189 30,752 22,738 21,469 32,349 392,271
3,638 3,354 3,417 2,526 2,385 3,594 43,585


4,075 1,219 683 15,398 36,719 46,025 289,339
453 135 76 1,711 4,080 5,114 32,149


4,074
87

4,415
80

4,323
96

3,584
248

2,510


3,922
25

2,939
15

2,806
36

2,179
96


3,216
2,896

3,134
2,292

3,463
1,478

2,749
3,134

1,879
765

2,789
1,510

1,919
478

1,987
1,287

1,602
1,558


3,654
5,131

1,729
4,549

2,941
5,029

2,588
5,529

2,133
'3,040

3,184
3,305

1,538
2,646

1,609
3,205

2,093
4,285


31,205
23,528

28,398
32,384


2,484
4,493

4,187
3,826









November, will climax in December and January. During these same two

months, based upon current production and marketing schedules, Early

Florida fruit still will be strong, the Midseason fruit will be at

peak production, and Temple oranges will peak during January. Increased

supplies of Florida Valencia oranges will be met in the marketplace

during February, March, and April by some Florida Midseason and Temple

oranges, as well as by California Valencias harvested beginning around

the first of April.

As orange supplies increase, a comparable need will demand more

thorough knowledge of the market for oranges and orange products. The

allocation among market sectors and geographic markets based upon sounder

perception of the total orange market can refine the efficiency with

which the crops are marketed and consequently enhance the position of

the orange industry.


Alternative Adjustments Available to the
Florida Orange Industry


During the coming decade, per capital orange production is apparently

going to expand at a fairly rapid rate. The increase in production,

based upon projected yields, will definitely occur in Florida. Cali-

fornia production, meanwhile, is expected to be maintained at a rather

constant level. Therefore, the prime responsibility of merchandising

larger orange crops must rest with Florida producers and marketers.

To move effectively prodigious crops of oranges, shrewder attention

must be focused upon marketing policies and alternative adjustments

available to the industry. The effective utilization of alternative

adjustments to solve the dilemma of increased production depends,









beyond question, upon the accuracy with which the industry estimates

the demand relationships for its products.

Recognizing this adjustment to be the problem, it is necessary to

postulate the demand relationships existing in the orange market and

to examine possible alternative adjustments available to the orange

industry, in order to attain maximum effectiveness in marketing as

supply levels increase.


The demand situation

Florida oranges are marketed basically in four forms: (1) fresh

oranges, (2) chilled juice and products, (3) canned juice and products,

and (4) frozen concentrates. Each of these market sectors possesses

a separate aggregate demand relationship encompassing a family of

subsector demand curves relevant to the given sector. Within this

system of demand relationships, variations exist in levels and slopes

of the several demand functions, thus creating differences in price

and cross-price elasticities of demand at the sector and subsector

levels.

Graphically, these postulated sector demand relationships can be

depicted as in Figure 1. DI, D2, D3, and D4 represent, respectively,

chilled juice and products, canned juice and products, fresh oranges,

and frozen concentrates. Given the availability of these component

aggregate relationships, a composite function may be obtained by a

summation of the components, such as shown by DT. This composite is

an aggregated demand relationship over the various sectors and sub-

sectors making up the total orange market. Not only are these sector

relationships affected by the availability and prices of other orange










Price


D D D
Quantity

Figure 1.-Hypothetical demand relationships for Florida fresh oranges and processed orange products.









products, but also by the availability and prices of other substitute

citrus and noncitrus products.

To exploit fully the competitive situation, a detailed delineation

of demand relationships must be formulated to include the various sectors

of the industry. Since there are within each sector discernible product

differentiations, these characteristics must be accounted for. Recog-

nition of the existing product differentiations within a given sector

will allow any adjustment procedure to be applied with maximum ef-

ficiency.

The demand function for fresh oranges.--Within the Florida fresh

orange sector, differentiating characteristics which must be considered

include areas of consumption, areas of production, varietal-type dif-

ferences, sizes, and grades. It appears valid to assume that different

consuming areas possess distinct preferences regarding fresh oranges;

therefore, levels of demand and the respective functional relationships

are likely to differ between these areas. The Indian River and Interior

districts of production provide a second differentiation to be considered,

since fruits from the two areas are viewed as differentiated products,

at least at the grove and wholesale levels.

The varietal-type complex presupposes even further delineation.

Under this category, thought must be given to differences in consumer

preference with respect to Early, Midseason, and Late oranges, as well

as within these several varietal differences. Beyond these differen-

tiations are those resulting from grade and size of the common orange

types and varieties.

Certainly, then, the aggregate demand relationship for fresh oranges

is composed of countless differentiations. These differences must be










evaluated in the adjustment to increased supply levels of oranges

available for the national market.

The demand functions for processed oranges.--In the processed

sectors there are three basic forms of orange products--chilled, canned,

and frozen concentrates. Differentiations contained within these proc-

essed products must also be taken into account, and these are generally

the same without regard to form. To some degree, Florida processed

orange products maintain an identification as to area of production

within the state. Therefore, processed Indian River and Interior fruit

must be recognized as possibly differentiated products to the degree

that the area of production is identified with the product.

Evident differences also exist in the demand relationship based

upon consuming areas. For example, frozen orange concentrate accrues

differences arising from consumer preferences in unlike areas of the

market.

In all of the processed products another differentiation results

from brand names, normally classified into three categories: (1)

nationally advertised brands, (2) chain grocery store brands, and

(3) packer brands. Within brands further differences also result

from container sizes.

Thus, in developing the aggregate functional demand relationships

for each of the fresh and processed orange products, such a relation-

ship is an average over the various product differentiations. The

more complex the delineation within a particular market sector, the

sounder the knowledge for basing any adjustment to changing supply

conditions.










The importance of the sector analysis

The question arises, "What is the importance of the sector demand

relationships?" Knowledge of the component relationships provides the

basis for effective adjustment by the firms within the industry and the

industry itself. Additional Information from delineating the demand

relationships within the component or sector further refines facts

available for adjustments to changing levels of total orange output.

Consequently the reliability of any estimated demand relationships will

determine the success of the ensuing adjustment process.

The industry can avail itself of several adjustment alternatives

in coping with the problem of increasing supplies. These may be

categorized as adjustments in promotional activity, pricing policy,

product policy, and optimum allocation. Adjustments in each of these

categories require a knowledge of the demand structure and the func-

tional relationships therein.


Promotional policy

The prime concern of the Florida orange industry regarding pro-

motional activity is effectiveness in attaining the goals of any

promotional program. Basically, promotional activity of any specific

form is employed to effect a change in the demand relationship for

oranges. This change, if successful, is anticipated to initiate

shifts in the level and slope of the demand function whereby a more

favorable demand situation is created. Hence, effective promotional

activity results in some combination of increasing the level and

changing the elasticity of demand for oranges and orange products.

At the industry level, where much of the promotional activity










presently originates, two major considerations must be reckoned with. They

are (1) the allocation of promotional funds among market sectors, and (2)

the allocation of promotional funds among geographic market areas. As sup-

ply levels increase, the allocation becomes more important to the adjust-

ment process toward higher levels of output.

Recognizing the multi-use characteristics of the orange crop, the

firm, sector, or industry must decide wisely the allocation of promotional

funds. To allocate effectively these funds, management must forecast esti-

mates of the demand relationships for the products involved.

The allocation among sector markets depends upon the promotional

goals. Astute promotion initiating a shift in combination of level and

slope of the demand relation will result in higher prices or movement of

larger quantities at the same price, If the demand relation is relatively

elastic, effective promotional activity yields a more significant quantity

effect than price effect. Thus, in the matter of increasing supplies,

promotional activity could better assist movement of larger supplies if

applied in sectors of the greatest price elasticity for the demand relation.

However, another consideration in undertaking the allocation of pro-

motional funds is the substitution among products. Given equal degrees

of elasticity, a greater benefit would be derived if promotional funds

were allocated in the sector with the least degree of economic substi-

tution with respect to other orange products. In other words, to assist

adjustment to larger supplies, the greatest benefit would be derived from

promotional funds if allocated in the sector with the most elastic demand

relation and the least amount of substitution or smallest cross-price

elasticity of demand for other Florida orange products.









Pricinq policy

Another alternative available to the orange industry is adjustment

in pricing policy. Currently, on-tree prices are determined within the

framework of the purely competitive model notwithstanding an industry

market structure that departs noticeably from the competitive model.

If the industry were to engage further in vertical and horizontal in-

tegration to such an extent that a preponderance of the oranges produced

were marketed under a central authority, price policy would assume ut-

most significance in adjustment to increasing supply levels.

In a situation of rising supplies and decline in price, such de-

clines could be adjusted in a fashion to move toward a revenue maximizing

or revenue loss minimizing condition, whichever the case may be. If

the industry were operating in the elastic segment of the demand function,

then adjustments in price consistent with the demand situation within

given sectors would tend toward a maxima with regard to increasing revenue.

On the other hand, if the industry were operating in the inelastic segment

of the demand function, as supplies increased, the adjustment of prices

in the various sectors consistent with the sector demand relationships

would gravitate toward a minimization of revenue losses. Thus, a price

reduction in the sectors possessing the most elastic demand relationships

would increase revenue if the industry were operating in the elastic seg-

ment of the demand function. Contrarily, if the industry were operating

in the inelastic segment of the demand function, a price reduction in the

sectors with greatest elasticity would move toward a minimized revenue

loss.

Another consideration in pricing policy lies in the utilization of

demand relationships within a given sector. If, for example, there existed










within the fresh orange sector a functional price-quantity relationship

for Indian River fruit which was at a higher level and possessed a greater

elasticity than that for Interior fruit of like size and grade, a price

reduction for only Indian River fruit would increase revenue over an

equal price reduction for both fruits. A gain would ensue from the

elasticity character, since a price reduction for the Indian River fruit

under this hypothetical situation would yield a greater than proportionate

increase in the quantity marketed.

Further, notwithstanding increased supplies, the industry under

conditions of extreme inequality among the various sector demand func-

tions may raise prices in some sectors while lowering prices in other

sectors. If, for illustrative purposes, within the four sectors of

the orange market, two of the demand functions were highly inelastic

and two others were to a high degree elastic, price adjustments in

both directions may increase revenue. Upward movement of price in the

sectors with inelastic demands will lead to some quantity marketed

losses, but by an amount less than proportionate to the loss in price.

A decline in price in the elastic sectors will lead to an increase in

the quantity marketed by an amount more than proportionate to the price

decline. On balance, it is conceivable that such price adjustments may

increase revenue along with the increased supply levels.

The structure of the Florida industry is such that these adjust-

ments could be effected easily. The Citrus Exchange, along with similar

sales organizations representing growers' cooperatives, could organize

into a sales agency either through the organizational structure allowed

under the Capper Volstead Act or under the Marketing Orders and Agree-

ment Act.










Product policy

Another alternative similar to the pricing adjustment alternative is

product adjustment. Product adjustments among sectors of the industry could

be on a similar basis to those described under price adjustments. Any in-

creased supplies could be absorbed by adjustment of products to the various

sectors within the marketing system in the most favorable fashion, that

is, in the sector where increased quantities would have the least price

effects. Industry controlled allocation could seek to maximize revenue

along with increasing supplies.

If the industry were producing in the inelastic segment of the de-

mand relationship by allocating among the four product markets based

upon the relative elasticities, it could seek to minimize revenue losses.

For example, if the industry were producing in the inelastic portion,

but only one or two of the sector demand functions were inelastic in

nature, revenue losses would tend to be minimized by allocation of

greater quantities to the sectors possessing the elastic demands. This

follows since such an allocation would result in a less than proportionate

decline in price. Therefore, the loss incurred wculd be less than if

equal increments of the increased supplies were applied to the various

market sectors.

If, on the other hand, the industry were producing in the elastic

portion of the demand relationship, greater revenue gains would be found

by allocation of increased supplies to the sectors possessing elastic

demand functions. In this case, revenue losses resulting from quantity

increases would be less than a proportionate amount of the actual quan-

tity increase, and, thus, greater revenue.









Optimum allocation

Either of the two alternatives, price adjustment or product adjust-

ment, may be looked upon as intermediary steps to complete and optimum

allocation from the revenue standpoint. To maximize net revenue at the

industry level, complete knowledge of two economic forces, costs and

revenue, must be sought. The equation of the marginal quantities of

these two functional relationships will result in a maximization of net

revenue. The unique point of complete maximization occurs where industry

marginal costs are minimized and equated with industry marginal revenues

for the several orange products involved.

From the cost side of the equation, knowledge of the industry marginal

cost function is obligatory for each of the four orange product markets.

To minimize marginal costs for the industry, firm costs are required at

each possible level of production, since cost minimization requires that

the individual member firms' marginal costs be equated. This process could

be accomplished by allocating quotas to the individual member firms in

such a manner as to equate the marginal cost for each firm to the marginal

cost of every other firm for their respective quotas. If this allocative

procedure is followed, industry costs for any given level of output will

be minimized.

The foregoing analysis indicates the ideal situation from the stand-

point of minimizing industry costs. It requires that the firms within

the industry must yield to some central authority the ultimate decisions

relative to rates of output by individual firms. Although this procedure

attains an optimum condition in the industry cost structure, it is not a

necessary requirement for the utilization of the maximization principle.

As the industry chooses to deviate from the minimization of costs, total










net revenue declines. However, within the confines of any given system

of output allocation among firms and any industry cost level, net revenue

maximization can be attained by equation of the marginal cost and revenue

functions.

From the revenue side of this equality, full maximization requires

complete knowledge of the demand for oranges within sectors of the industry.

For a program of supply management, there must be information to guide

allocation of supplies among the various market sectors. A method must

be devised to show the marginal revenue for the industry's entire volume.

The availability of the sector demand functions would allow a summation

yielding a composite demand function for all oranges. From the sector

and composite demand relationships, marginal revenue functions can be

derived.

To determine optimum allocation among sectors, the industry output

must be allocated to equate the sector marginal revenue functions with the

marginal cost functions. In this allocative procedure, it may well be

determined that total industry output may be beyond the amount required

for maximization of revenue. Thus, to attain maximization of net revenue,

in addition to the allocation among market sectors, the need could exist

for limiting the total output to less than available supplies. In such

an event, economic abandonment of a portion of present supplies would be

a logical procedure to follow.

In contrast, if available supplies were less than the optimum out-

put, further expansion of the productive capacity of the industry would

increase revenue. Present supplies under this situation should be al-

located in such a manner as to minimize industry marginal cost.











It is recognized that total knowledge of the cost and revenue aspects

of an industry are rarely secured on a simultaneous basis. The utilization

of complete information concerning the demand relationships for the products

of the industry can be a practical intermediate step toward net revenue

maximization. Gross revenue maximization can be attained without cost

information. From the various sector demand relationships, the industry

demand function for all oranges can be obtained and, subsequently, a

total revenue function can be derived.

These functional relationships can be used to maximize gross revenue

by equating the marginal revenue functions for the four sectors. The

point of marginal revenue equation would be the quantity dictated by the

high point on the total revenue function, which also would be, of course,

the point of unit elasticity on the industry demand curve. Maximum

revenue would be received if supplies in each of the sector categories

were so allocated that the quantities of each would yield a marginal

revenue of zero. This is true only if the total supply offerings are

equal to or in excess of the high point on the total revenue function.

On the other hand, if total supplies are less than the quantity

dictated by the peak of the total revenue function, then maximizing

gross revenue would be a process of equating the sector revenues at

a point which would absorb all available supplies. This would, in

turn, allocate quantities and dictate prices among the various market

sectors.













CHAPTER II

PREVIOUS RESEARCH RELATING TO CITRUS DEMAND


Examination of Data Sources


The study of demand relationships has become an increasingly im-

portant field of research over the past several decades. A major dif-

ficulty the demand analyst has encountered in the past, and presently

faces to a lesser degree, is the source of data for estimating demand

relationships. Data from many sectors of the economy have been col-

lected in such a manner that the researcher has reason to question their

reliability as basic input data for demand estimation. Basically, this

situation stems from the fact that these data were not collected for

the purpose of demand estimation and consequently are not generally in

the form desired for such utilization. Further complications arise

from the analytical tools available to cope with and parcel out the

sources of variability existing in the basic data. Much of the data

available for demand estimation possess aggregation problems which

limit their usefulness. For commodities differentiated on the basis

of grade and size, much of the available data are presented in ranges

or averages. When these types of data are used for the estimation of

demand parameters, the results must be accompanied with many restrictive

qualifications.

Initially, estimates of demand relationships were derived from

yearly series of national aggregates. The input data used for this

44










early demand work were plagued, quite naturally, with many inadequacies.

Over the course of time these inadequacies have been remedied to some

extent. The severe problems associated with actual reporting of sta-

tistics two or three decades ago are presently of no major consequence,

with the technological advances which have occurred. The agencies whose

responsibility it is to gather statistics on production and utilization

have constantly sought to improve sampling and estimation procedures. In

addition, they have substantially broadened their base of reporting to

include further breakdown concerning grade, size, and other differenti-

ations.

However, from the standpoint of demand estimation, there still exists

many problems with time series data. During the past two decades, tech-

nological advances have taken place with extreme rapidity along with

changes in the consumption habits of the population. In many cases these

changes have caused difficulties in utilizing time series data. The in-

creased introduction of convenience into food and fiber processes has

operated to change the demand relationships over time, and compensation

for these factors appears to be quite difficult. Not only has the process

included innovations such as reduction of food preparation time but also

the introduction of old products in new forms. For example, orange con-

centrate came into existence in the middle 1940's. Compensation for the

effects of orange concentrate on other orange and citrus sales would be

extremely difficult.

Another question with regard to time series data lies in the identi-

fication of variables. While demand theory dictates the dependent variable

to be quantities taken and the independent or explanatory variables to be









prices, there is some debate as to the validity of time series data con-

forming to these theoretical requisites.

The use of consumption statistics and quoted prices from a series

to estimate elasticity coefficients appears to overstate price effects.

This is a result of exogenous variables upon which no quantitative measure

can be placed or at least, given the present state of the arts, has not

been quantitatively measured. For example, with regard to most commodities,

society is subjected to considerable merchandising and promotional activi-

ties. The extent to which these activities are effectual is included in

consumption disappearance. Demand work has generally taken on the charac-

teristic of estimating coefficients of elasticity rather than the creation

of demand surfaces recognizing price and other variables as explanatory

changes in consumption. Thus, when time series data are used for the esti-

mation of demand parameters and further defined in terms of price elasti-

city coefficients, the effects of price are overstated.

Disregarding the accuracy of estimating coefficients of elasticity

from time series data, a severe limitation does exist from the standpoint

of the range of variability in the proposed independent variables. The

range of prices existing over time resulting from the normal situation

is very limited. It therefore, follows that measurement of demand

relationships is acutely limited with regard to the scope of the function.

Another source of data for demand estimation that has gained much

favor in recent years is that generated from consumer panels. These data

are held by some to be superior to national and regional data collected

by the various data procurement agencies of state and national govern-

ment. However, these cross-sectional data are not without inherent

faults. They are to some extent subject to some of the difficulties

encountered in a general time series. Notable is the limitation in the








range of price or explanatory variables. Also, there exists the same dif-

ficulty as in time series with regard to the over estimation of price ef-

fects when these data are used for elasticity coefficient estimation. The

method of data collection, recall through personal interview or mailed

questionnaire, makes the data questionable from the standpoint of ac-

curacy, while yet another difficulty lies in the possibility of a non-

representative sample.

As a result of the difficulties found in these data sources and with

the evolution and advancement of research methodology, much consideration

has been given to the possible generation of basic input data from contr6l-

led pricing experiments. This technique has gained, in some quarters,

great impetus in forming the basis for estimating functional demand re-

lationships. The assets and liabilities of this method of procedure will

be discussed fully in a later section, since it was selected as the method

of procurement of the data for the empirical demand work upon which this

dissertation is based.


Citrus Demand Work


Over the years, more demand work has been directed to aggregation

of commodity groups than to individual commodities. Relatively little

research attention has been devoted toward an assessment of demand re-

lationships for individual commodities. This void is especially true

with regard to citrus products. Further, there has not been as much

work utilizing time series analyses as might be suspected.

Time series demand estimation has been more nearly confined to

basic agronomic commodities and meat products. However, some work has

advanced in specific fruit and vegetable areas and over fruit and vege-









table commodity groups. Brandow estimated demand relationships for

apples, recognizing orange production as an independent variable asso-

ciated with apple purchases.

More directly in the citrus field, Hoos and Boles reported a func-

tional relationship between California f.o.b. orange prices and four

independent variables.2 The variables included were (1) California fresh

shipments; (2) fresh shipments from other areas; (3) index of U. S. non-

agricultural income (1935 39 = 100); and (4) time. They found that

for the period 1925 through 1950, omitting the war years, 1942-45, that

some 89 per cent of the variation in California f.o.b. prices for summer

oranges was explained by these variables.

Powell and Godwin, using short term observed price-quantity data,

estimated demand relationships for fresh oranges. The purpose of this

study was to analyze demand relationships for certain citrus and non-

citrus products using data obtained under normal retail store performance

in Jacksonville, Florida and Memphis, Tennessee. To obtain information

with respect to income levels, each city was divided into low, medium,

and high income areas.

The data collection process included weekly visits to each of the

stores, an inventory being taken and recorded for each product included

in the study. All additions to stock received subsequent to the previous


G. E. Brandow, A Statistical Analysis of Apple Supply and Demand
(University Park, Pennsylvania, Agricultural Experiment Station, Penn-
sylvania State University, AE and RS No. 2, January 1956).
S. Hoos and J. N. Boles, Oranges and Oranqe Products Chanqing
Economic Relationships (Berkeley, California: California Agricultural
Experiment Station Bulletin 731, 1952).

3L. A. Powell and M. R. Godwin, Economic Relationships Involved in
Retailing Citrus Products (Gainesville, Florida: Florida Agricultural
Experiment Stations Bulletin 567, August 1955).










visit were also listed. From this information sales volume was determined

by subtracting the ending inventory for the current period from the sum

of stock receipts and the ending inventory of the preceding period. To

maintain exacting price-quantity data, inventories were taken each time

prices for the concerned commodities were changed.

Principal fruits included in the study were oranges, apples, grape-

fruit, and tangerines. In addition, five processed citrus juices and four

noncitrus juices were included.

Of the four fresh fruits, oranges ranked first in terms of quantity

sold accounting for in excess of 40 per cent of the volume. From the

standpoint of value, apples were the leading fruit of the four in

Jacksonville. In Memphis, however, in the low income strata, the value

of apples and oranges were of about equal magnitude, while the total value

of oranges exceeded that of apples in the other two income strata.

Estimated price elasticity coefficients in low, medium, and high

income areas of Jacksonville were 1.206, 1,329, and 0.860, respectively.

In a similar analysis utilizing the data gathered in Memphis, price

elasticity coefficients estimated for low, medium, and high income areas

were 1.782, 1.665, and 1.143, respectively. It was further noted that

orange concentrate was increasingly regarded as a fair substitute for

fresh oranges. This consumer preference may partially account for the

slightly higher coefficients derived from the Memphis study, conducted

approximately nine months later than the Jacksonville study.

In 1952, Godwin, using controlled pricing techniques, developed a

functional quantity-price relationship utilizing controlled prices per








4
dozen of oranges. Seven levels of price were included in the study,

three 5 cent deviations on either side of the mean price. The methodo-

logical procedure followed was the artificial inducement of price vari-

ation above and below the established market price over a relatively

short period of time. This study was done at the retail level of distri-

bution utilizing seven supermarkets in central Kentucky. As higher prices

were induced the total volume sold in the stores tended to decline.

A functional relationship was derived by fitting an orthogonal

polynomial. The coefficient of price elasticity for the curve as a

whole was 1.160. The degree of elasticity varied considerably over the

range of prices tested, with greatest elasticity near the established

market level. An elasticity approximating unity was found at a discount

of 15 cents per dozen. At prices representing an increase of 10 and

15 cents above the normal market, the demand function becomes inelastic.

Using similar methodology, Godwin and Powell studied the effects

of price on frozen orange concentrate. This study was conducted in 10

supermarkets in the Lower Delaware Valley area of Pennsylvania, and

New Jersey. The test prices differentials included in the study were

as follows: the price in effect at the time the study was initiated;

prices 3, 6, and 8 cents per 6-ounce can below the market price; and

one, 4 cents higher. By this method, consumers were subjected to re-

tail prices varying over a range of 12 cents per can. Although the

retail units carried three types of orange concentrate--namely, a

nationally advertised brand, a private label, and a packer label--no


M. R. Godwin, Customer Response to Varying Prices for Florida
Oranges (Gainesville, Florida: Florida Agricultural Experiment Stations
Bulletin 508, December 1952).










distinction was made with regard to the appropriate deviation dictated by

the pricing differential. For example, when the six cent deviation was

applied, each type of concentrate was reduced by six cents.

The prices employed in the study were 8.5, 10.5, 13.5, 16.5 and

20.5 cents per 6-ounce can. Analysis of the results of the pricing

experiment indicated marked variation in concentrate purchases over the

range of prices tested. Variations in purchase rates in response to

the induced prices indicated that customer sensitivity to price change

declined continuously as price was varied from the lowest to the highest

test price. The theoretical price of 12.5 cents per can was found to

be the price at which unit elasticity was found. At test prices below

this level, the demand function grew more elastic, and, conversely, at

higher levels the demand function became more inelastic.

An interesting aspect of this study was the provision in the ex-

perimental design to compensate for possible price carryover effects

attributable to storability of orange concentrate. This possibility

was treated by the continuance of the same price treatment in each

store for a period of several weeks.

In summary, a relatively small amount of work has been done in the

area of demand estimation for citrus fruits. This gap is especially

true prior to 1950, and since that time most consistent attention has

been devoted to this economic area by the Florida Agricultural Experiment

Station, specifically by Professors Godwin and Powell. A further area

long neglected in all commodity demand work is that of economic substi-

tution among products. As methodological advances progress, it appears

that the substitution effects of price changes will move to the fore-

front as a significant area of economic research.













CHAPTER III

PURPOSE OF PRESENT RESEARCH AND SPECIFIC PROBLEM ORIENTATION



The present study was designed to investigate the competitive re-

lationships among Florida Indian River, Florida Interior and California

Valencia oranges for fresh market. This project constitutes one phase

of a broad research program, the objective of which is to determine the

nature of economic competition among citrus producing areas of the United

States by obtaining estimates of demand and substitution relationships for

principal citrus products.

The production of citrus fruits provides much of the farm income

in Arizona, California, Florida, and Texas. These citrus crops are

marketed in many forms. At present little is known about the economic

interrelationships among these products in the marketplace. The multi-

plicity of the products produced in the citrus industry makes the question

of the interrelationships among them highly complex. However, the dynamics

of the citrus industry are such that there is a need to identify and assess

the importance of these relationships.


The Specific Problem


Interregional competition within the orange industry is primarily

in the fresh fruit sector. In the United States the majority of oranges

for fresh market is produced in Florida and California. On the basis

of the ratio of fresh fruit sales to processing sales the former is more









vital to California than Florida. However, recognizing that Florida oranges

are marketed along with California oranges throughout the orange season,

the economic interrelationships in the fresh market are of utmost importance

to Florida. Another pressing question is the degree of economic competition

between oranges produced in the Indian River and Interior districts of

Florida. Disregarding differences resulting from variety, grade, size,

and other distinguishing characteristics, within Florida and California

in reality three differentiated producing areas produce and market oranges.

Further, there exists three separate demand functions for oranges produced

in these three areas, and to some degree economic substitution rates among

them.

The purpose of this research was to measure the degree to which price

changes could alter the consumption patterns of the three types of oranges.

The measures sought were defined in terms of price and cross-price elas-

ticities of demand. Stated formally the specific objectives were these:

(1) Estimate price elasticity of demand coefficients for Florida
Indian River, Florida Interior, and California oranges and,

(2) Estimate cross-price elasticity of demand coefficients for
Florida Indian River, Florida Interior, and California
oranges with respect to the prices of the other two oranges.

Since there were within each of the three producing areas differences

in oranges as to varieties, grades, and sizes, determinations were neces-

sary to ascertain which of the characteristics within these variables to

use in the measurement of the demand relationships.


Variety

In the determination of the variety over which the demand relation-

ships were to be measured, two major considerations evolved. One of

these was related to the relative volumes produced and the second to the









degree of competition in the marketplace between the Florida and California

varieties.

The major variety produced in Florida is the Valencia. This variety

is also predominant in California production. Valencia oranges from the

Indian River and Interior districts of Florida are marketed along with

California Valencias throughout the season. Few other orange varieties

are available for the national market during the spring and early summer

months when the Valencias are in peak production. During the 10 year

period 1952 through 1961, Valencia oranges accounted for an average of

43.4 per cent and 60.2 per cent of total orange production, respectively,

for Florida and California. Thus, the Valencia fruit from each of the

three areas was selected as the most important fruit for which to esti-

mate demand relationships.


Fruit sizes

Based upon information relative to the distribution of sizes of

fruit produced in the three areas, consideration was given as to what

size or sizes must be included in the study to realize an effective

measure of demand relationships for Florida and California Valencia

oranges. The sizes predominant in Florida Valencia production are from

size 163 to size 252 (Table 14). The average diameter of this fruit

ranges from 3.063 inches for size 163 to 2.625 inches for size 252.

During the season immediately preceding the study, 21.3 per cent

of the Indian River Valencia oranges moving through interstate commerce


Percentages derived from the report of the Growers Administrative
Committee, Lakeland, Florida.









was of the size 163, and 36.22 per cent of the Interior Valencia fell

into this category. In this same season 41.02 per cent of the Indian

River Valencia oranges moving into interstate commerce was of size 200,

and 40.52 per cent of the Interior fruit was of this size.

California Valencia oranges, characteristically smaller than Florida

Valencias, also have two dominant sizes: size 113 and size 138, which

have an average diameter of 2.600 inches and 2.240 inches, respectively

(Table 14).

During the season immediately preceding the study, 33.0 per cent

of the Valencia oranges marketed by a leading California citrus marketing

firm was of size 138. In this same season 31.7 per cent of the Valencia

oranges produced in the central California area, approximately 90 per cent

of the State's production, was of size 113, and 32.9 per cent was of

size 138.3

Historically the sizes of predominance indicated above have been

the norm. This pattern, therefore, evolved consideration of two sizes

from each state over which the demand estimate should be made.

Based upon this information and recognizing the limitations placed

upon the study from the standpoint of capital and management, the Cali-

fornia size 138 was selected as the representative size California Valencia

for the study. An ordering of Florida Valencia oranges was made, with

the first choice being the size 200 and the second choice the size 163.


2 Ibid.

Derived from data of the Valencia Orange Administrative Committee,
Los Angeles 15, California.









Table 14.--Size distribution, Florida Indian River,a Florida Interior, and California Valencia
oranges, 1960-61 season.



Area of Production

Florida California

Indian River District Interior District

Container Average Per cent of Container Average Per cent of Container Average Per cent of
Size Diameter Total Size Diameter Total Size Diameter Total

Number Inches Per cent Number Inches Per cent Number Inches Per cent

96 3.527 .1 96 3.527 .1 72 3.040 2.0
125 3.313 5.5 125 3.313 7.7 88 2.840 11.0
163 3.063 21.3 163 3.063 36.2 113 2.600 31.0
200 2.875 41.0 200 2.875 40.5 138 2.420 33.0
252 2.625 30.2 252 2.625 15.0 163 2.290 15.0
324 2.313 1.9 324 2.313 .5 180 2.220 7.0
-- -- -- -- ---- 245 1.980 1.0

aDerived from Interstate shipments; source: Growers Administrative Committee and Shippers
Advisory Committee Report, Lakeland, Florida.

Derived from data furnished by one of the leading California citrus marketing firms. These
data represent the entire 1960-61 season's volume of this firm.










This decision led, as further explanation will indicate, to form the

basis for two major experiments.


Fruit grades

Dominance of the U. S. No. 1 grade in all three areas of production

established it as the grade over which to measure the demand relationships.


Specifications of the Research Problem


By virtue of the size distribution for Florida Valencia oranges,

the desirable measure of competitive relationships between Florida and

California fruit would include both of the modal sizes of Florida Valencia

oranges. The inclusion of sizes 200 and 163 from the Indian River and

Interior Districts of Florida and size 138 from California evolves into

six basic relationships for study (Table 15). These relationships pro-

vide quantitative measures of the nature of competition among these fruits

in terms of price and cross-price elasticity of demand. Within each re-

lationship the effect of the price of one specific orange type upon the

quantity taken of the same orange type defines the price elasticity of

demand. On the other hand, the effect of the price of one specific orange

type upon the quantity taken of another orange type defines the cross-price

elasticity of demand.

The basic procedure selected to obtain data of the type required to

measure the competitive relationships between Florida and California

Valencia oranges consisted of a series of experimental tests conducted

at the retail level of distribution under controlled conditions.









Table 15.--Basic demand relationships, Florida Indian River sizes 200 and
163, Florida Interior sizes 200 and 163, and size 138 California Valencia
oranges



Quantities taken of As a Function of the
Valencia oranges Prices of

Area of Production
and Size Area of Production

Florida: Florida Indian River Florida Interior California
Indian River: Fruit Size
Size 200 200 200 138
Size 163 163 163 138
Interior:
Size 200 200 200 138
Size 163 163 163 138
California:
Size 138 200 200 138
Size 138 163 163 138


Rationale Underlyina Method Selection


Controlled experimentation offer an opportunity to overcome some of

the inherent difficulties found in secondary data. The researcher can

adequately describe, with precision, the demand side of the market for

a specific commodity. By creating the controlled situation, he can cope

successfully with such variables as advertising, quality levels, display

size, and location, plus commodity characteristics such as size and grade.

During the reasonably short time required for generating adequate data,

he can comfortably assume constancy of consumer income, prices of other

goods, general level of prices, and consumer tastes and preferences.

One advantage of controlled experimentation to estimate demand re-

lationship lies in the fact that the researcher can obtain parametric









estimates of demand for a dynamic industry. The relative rapidity with

which data can be collected by experimentation makes the approach quite

suitable for studying the demand for products under the stress of final

product changes and innovations.

The major advantage of the controlled experiment is the ability to

manipulate price and thereby describe a wide range of price-quantity re-

lationships beyond the experience of the market. When an industry is

undergoing rapid production changes, demand relationships estimated from

data generated in controlled pricing experiments can yield results which

have application to the changing supply situation. For example, Florida

orange production increased 30 per cent from the 1960-61 to the 1961-62

season, a heretofore unheard of increase. It is not reasonable to assume

that demand estimates from a historical production base and the uncontrolled

experience of the market could yield demand information which would cover

this great a change. By utilizing a controlled pricing scheme and deviating

price above and below the normal market situation, estimates of price elas-

ticity can provide a basis for determining direct price effects of larger

changes in supply conditions.

In addition to obtaining direct price effects in terms of price

elasticities, the controlled experiment is conducive to the exploration

of economic substitution rates. By inclusion of two or more major com-

modities or ccimodity characteristics and manipulating price over them,

one can obtain estimates of the cross elasticity of demand. Price manip-

ulation, coupled with adequate control, provides for observation of the

decision-making process in an atmosphere of varying price differentials

between a good and assumed substitute goods. For example, in the present

research problem, there is, in addition to the concern over the effects










of Florida Indian River price upon Florida Indian River sales, an especial

interest in the effects of California price upon Florida Indian River

sales. The controlled pricing experiment allows the pursuit of information

to answer such questions.

These characteristics render controlled experimentation one of the

most powerful generative devices the economist has today for providing

input data to estimate demand and substitution relationships.

Notable among the disadvantages of the experimental approach is the

high cost involved in the effective generation of data for the estimation

of demand relationships. There must be a system of adequate mechanics to

facilitate the data collection. This requires a number of personnel as

well as a system to compensate for losses directly relating to the study.

The costs involved in creating the controlled situation can be classified

into three categories: administrative, logistical, and operational losses.

The extent these vary with respect to shares is dependent primarily on

the nature of the study. Administrative costs include all outlays for

physical procurement of the data, such as enumerator, delivery and super-

visory personnel. Logistical costs can be incurred as a result of personal

delivery to the stores, outlays for physical storage of the commodity

or commodities, and other costs involved with the physical handling

of the product. Operational losses arise as a result of payments for

price differentials below the normal market, payments for quality main-

tenance in the case of a perishable commodity, and other preagreed pay-

ments.

The disadvantage most often mentioned is that in such a study the

researcher gains knowledge of great substance but relatively limited

generality. The validity of this alleged disadvantage is really a










question of scale, stemming from the limited population reached by a given

experiment and the difficulty in obtaining a localized representative popu-

lation. Conceptually, if a researcher could divide a commodity market into

regions which would provide a cross section of the market population, and

if representative cities were selected in each region to conduct experi-

ments, he could gain generality and still maintain substance of unquestion-

able validity. Once the cities were selected, a cross section of the city

population based on census tracts and other known information could be ob-

tained. If, for example, the midwestern and eastern United States' market

for fresh Florida oranges were divided into 5 or 6 regions, with appropriate

cities selected to represent these regions, estimates of demand relation-

ships would yield the composite demand for the entire market area.















CHAPTER IV

RESEARCH METHODOLOGY



In an analysis of the demand for fresh oranges, economic logic

must provide the framework within which the statistical computations

are made in obtaining estimates of demand and substitution relation-

ships. The theoretical framework, within which the demand relation-

ships for fresh Valencia oranges were developed, was regarded as

lying within the body of neoclassical demand theory doctrines. This

theory is based upon the premise that each consumer possesses a given

set of preferences. Further, it is assumed that each consumer chooses

from among alternative combinations of goods and services. This selec-

tion is done in such a manner as to maximize satisfaction within the

confines of a given set of market prices and is subject to the level of

income available for expenditure on consumption of these goods and ser-

vices. Thus, the quantity of a given orange type purchased by an ag-

gregation of consuming units, per unit of time, as of a particular time,

was assumed to be a function of (1) the prices of that particular orange

type; (2) the prices of other orange types; (3) the prices of closely

related substitutable products; (4) tastes, preferences, and real income;

and (5) the attitude of the group concerning future prices of oranges

and substitute products.









The Economic Model


A model conceptualizing the demand relationship for fresh Valencia

oranges may be written as follows:

ijk = f(Xli, X2i' X3i; XlXl' Xj' X an,' a2' an+l, (4.1)

where:

Yijk = quantity disappearance in the k-th observation of
the i-th Valencia orange type as the j-th level
of prices of all goods and services, consumer in-
come, and other preference factors.

Xli = price of a particular Valencia orange type.

X2i = price of a second Valencia orange type.

X31 = price of a third Valencia orange type.

X.j = the general level of prices of all goods and services.

X2j = consumer income.

X 3j...X = other preference factors.

al a2...an+l = a set of parameters that connect Yi with all
factors X., and X..
n nj

The economic model attempts to portray the relationships believed

to exist in the real world situation between the preference of consumers

for Valencia oranges and the monetary values placed upon these pref-

erences. The model, therefore, has many explanatory variables, variations

which, either separately or in combination, affect quantity disappear-

ance. In demand analyses, quantitative measurements have been placed

directly on a portion of these variables, while the remainder are passive

elements affecting the quantitatively measurable variables. The effect

of price of a given Y. on purchases of the same Y. is measured in terms
I I
of the price elasticity of demand. Changes in the purchase rates of

a given Y., resulting frcm changes in the price of a second or third










Y., are measured as the cross elasticity of demand. A change in purchases

of a given Y. resultant of a change in consumer income is measured defin-

itively in terms of income elasticity of demand. Other explanatory vari-

ables, the general level of prices of all goods and services, and other

preference factors operate to modify the measurements attainable in the

quantitative measures of the elasticity coefficients.

Specifically, this model specifies the quantity-price relationships

for the three Valencia oranges at a given level of other variables. It

is designed within this framework to explain the behavior patterns of

consumers as well as modifications resulting from a changing orange

price structure.


The Statistical Model


In the generation of data reflecting consumer decisions regarding

the purchase of fresh oranges, two fundamental assumptions are (1) in

the aggregate, consumers possess a basis for discrimination in their

decision-making process relative to purchases, and (2) in the aggre-

gate, consumers possess sufficient information to render their basis

for discrimination operational. With these assumptions and the basic

objectives of estimating coefficients of price and cross-price elasti-

city, the statistical model was developed.

The economic model depicted in equation 4.1 was conceptual and

no attempt was made to estimate all the parameters involved. Rather,

data were generated to estimate the effects of price and changing price

structure upon quantity disappearance of each of the three Valencia

orange types, other variables remaining constant. Consistent with these

requirements, the statistical model formulated to describe the functional









demand relationships for fresh market Valencia oranges produced in the

three areas is as follows:


Y' =
Y-ijk

Y2-ijk

Y3-ijk


1' 0

13~


+ 3X-i + j312X2-j + 13X3-k + l-ijk

* P21XI-i + 322X2-j + 3233-k + 62-ijk

+ 331X-i + -332X2-j + P33X-k + 63-ijk


(4.2)

(4.3)

(4.4)


Yi-ijk


Y2-ijk


Y3-ijk


N 0'

/3,1'


320'

A12


30

P13


A21' P22' A23


331' P32' 133



ijk' X2-ij


l-ijk' 2-iJ.i


= log of the quantity of Florida Indian River Valencia
oranges purchased.

= log of the quantity of Florida Interior Valencia
oranges purchased.

= log of the quantity of California Valencia oranges
purchased.

= log of regression constants (Y intercepts).

= regression coefficients associated with Florida
Indian River Valencia oranges (price elasticity
and two cross elasticity estimates).

= regression coefficients associated with Florida
Interior Valencia oranges (price elasticity and
two cross elasticity estimates).

= regression coefficients associated with California
Valencia oranges (price elasticity and two cross
elasticity estimates).

, X' -. = log of prices of Florida Indian River,
S-ijk Florida Interior, and California Valencia
oranges.

, E. = random disturbance associated with Y,
S Y and Y;.
23


This system of equations allows the simultaneous consideration of

the direct and cross-price effects on quantity disappearance at the

retail level for the three orange types. Verbally, the expressions

stipulate that the quantity taken of any specific orange type is a

function of a constant ("Y" intercept), the price of the orange type


where:










in question, the prices of the other types of oranges available, and a

random disturbance which is assumed to be normally, and independently,

distributed with a mean of zero and constant variance.


Assumptions

There are several assumptions both explicit and implicit to the

model formulation. Explicit assumptions relate to the statistical

formulation, while the implicit assumptions'are those which must be

made in order to couch the model in the theory of demand.

In constructing the model as a linear logarithmic function, two

basic considerations must be made. One of these relates to the neces-

sity for transformation of the variables to logarithms, and the other re-

lates to the logic of the utilization of constant elasticity coefficients

derived from the logarithmic equations.

The use of the logarithmic function provides a corrective measure

to insure against nonadditivity when the treatment effects are of a

multiplicative nature. Common is the assumption of multiplicative ef-

fects in economic data. If the operations producing the data are of

such a nature that the effects are really not additive, then the sums

of squares attributable to such effects do not represent the true effects.

Effects which are multiplicative on the original scale of measurement be-

come additive on the logarithmic scale. By transforming to the log-

arithmic scale, additivity is introduced. This introduction of addi-

tivity rules out interaction effects between prices and the error term

associated with quantity disappearance. In effect, this conclusion means

that regardless of what the level of prices P. may be, a random term of

a given magnitude always has the same effect on quantity disappearance.










In regards to the logic of the assumption of constancy, with respect

to the price elasticity, it appears to be a feasible assumption, especially

as the first approximation. Although the elasticity of demand with respect

to price may change from one price to another, it is desirable to obtain

an average elasticity over some specified range of prices. Such an esti-

mate of price elasticity may be quite sufficient for guidance in some of

the preliminary adjustments outlined in Chapter I. It is recognized that

a more sophisticated estimate relating to given price levels would be re-

quired as the adjustment process moves close to the maximizing position.

However, since such a position is not eminently foreseeable, the constant

elasticity function produced by the logarithmic regression is quantitatively

appropriate at the present stage of adjustment.

Although the parameters in equations 4.2-4.4 are assumed to be multi-

plicative, they are further assumed to be independent. The controlled

manipulation of price according to a predetermined plan forces conformity

to this assumption. In addition, controlled price manipulation coupled

with managed unlimited supplies clearly identifies the dependent variable

as quantities taken and the independent variables as prices.

Assumptions necessary and sufficient for the application of the

statistical model to test the postulated economic model include the

assumptions underlying consumer demand. These assumptions must remain

constant as price is varied in order to determine the direct and cross

effects of price.

Of major importance is the movement incurred in the general level of

prices. In the relatively short period of time required for generating

data in a controlled experiment at the retail level of distribution,

the assumption of constancy of the general level of price appears quite










feasible.

Another variable eliciting much concern in the estimation of demand

relationships is that of consumer income. Here again, during the short

time required to generate the data through retail store experimentation,

It can be safely assumed that no significant variation occurred.

To control a source of variation which limited time periods do not

preclude, much attention was devoted to the control of advertising and

merchandising promotional activities. Since these forces form the basis

for short run changes in consumer tastes, preferences, and expectations,

It becomes Imperative to impose restrictions on these activities. To

accomplish this objective, advertisement and merchandising promotional

programs for fresh oranges were eliminated during the test period. With

regard to longer-run changes In tastes and preferences, the time element

in controlled experimentation is sufficiently short to preclude any basic

changes in these factors.


The Experimental Model


The experimental model selected for generating the data required

to estimate the parameters of equations 4.2-4.4 was the Triple Cube

design. This design, an outgrowth of the central composite designs

developed by G.E.P. Box and Associates, was developed by T. E. Tramel



For a description of the Box design see: Box, G.E.P. and Hunter,
J.S., "Experimental Designs for Exploration and Exploitation of Response
Surfaces," Proceedings of Symposium on Design of Industrial Experiments
(Nov. 5-9, 1956) pp. 138-192, Institute of Statistics of the Consolidated
University of North Carolina, and Box, G.E.P., Haden, R.J., and Hunter,
J.S., Experimental Designs for Multifactor Experiments, Institute of
Statistics Mimeo No. 71, Raleigh, North Carolina, 1953.









and suggested for use in Agronomic-Economic fertilizer experiments.2

The original Box design is considered quite efficient in estimating

parameters of a quadratic function. In the development of the basic design,

Box and his associates were interested primarily in industrial experiments,

and consequently the requirements for such work were well adapted to a very

limited number of observations dictated by the single cube.

Generally, more variables can be controlled in industrial work than

in economic work. Therefore, for an equal level of precision, a greater

number of observations is needed in the latter than in the former. Repli-

cation is one solution to this problem. An alternate solution is to modify

the basic design to accommodate a wider range of measurement. Such a pro-

cedure gave rise to the Triple Cube.

A total of 15 treatment combinations are derived from the original

Box design. With respect to the cube, the treatment combinations may

be divided into three categories: (a) those forming the corners of the

cube, (b) those on the three major axes, and (c) the one at the center

of the cube. Thus, the cube plotted in three dimensional space reflects

eight treatment combinations from the corners of the cube, six treatment

combinations on the major axes equidistant off each face, and one combi-

nation located in center of the cube (Figure 2C).3



Tramel, T.E., "A Suggested Procedure for Agronomic-Economic
Fertilizer Experiments,".' Chapter 15, Economic and Technical Analysis
of Fertilizer Innovations and Resource Use, edited by: Baum, E.L.,
Heady, E.O., Pesek, J.T., and Hildreth, C.G., The Iowa State College
Press, Ames, Iowa, 1957.

The original Box design also had the observation in the center
of the cube such as is shown in Figure 2A.

















X (.1, 1, .1)
(-1, -1, 1)-
(-1, -1, -1),

X/
X3
(-2, 2,


I /


Figure 2.--Component cubes of the Triple Cube Design.


X3


S(1, -1)
- (o, 0, 0)


-Xl


X3

(3, 3, 3)










- -X,



(3, -3, 3)


(-3, 3,


Xi-










(-3, 3, -









The modification developed by Tramel was the addition of two more

cubes, increasing the number of treatment combinations by 16 (Figure 2B,

Figure 2C). Hence, total treatment combinations were increased to 31,

24 of which are formed by the corners of the three cubes, while the

remaining are those on the major axes plus the one in the center of the

system as in the original design (Figure 3).

Tramel in his work measured the relative efficiency of the Triple

Cube as compared with the original Box design and found a considerable

increase in efficiency. The greatest increase in precision was found

in the estimation of the intercept and in the interaction terms. How-

ever, a worthwhile increase in precision was brought about in the esti-

mation of the quadratic terms.

The utilization of this design for the allocation of price treat-

ments to generate input data for demand estimation is particularly ap-

propriate. Aside from the efficiency gains resulting from the use of

the Triple Cube, it allows a wide range of price levels. Application

of the design permits the use of nine price levels in combinations dic-

tated by the Triple Cube. Using the major axes as a focal point and

mean level, four deviations on either side of the mean are available.

The availability of nine price levels was considered quite adequate

for the measurement of demand relationships for the three Valencia

orange types.

Another question of significance related to the likelihood of dif-

ferent base prices for the three Valencia oranges used in the study.



Tramel, op. cit.


























X20---_
S -4
X2 -


Figure 3.--The Triple Cube Design.










The Triple Cube accommodated this requirement in that the base prices

could be different for all three orange types if this were the case

at the time of market entry.

A last important influence on the selection of the Triple Cube was

that it adapts quite well to the experimental approach to demand esti-

mation. The fact that this approach has high capital requirements has

placed many researchers in this area in a position of estimating functional

relationships on a much smaller scale, either in terms of number of com-

modities considered or in terms of price levels over which to estimate.

On the one hand, a more orthodox design which accommodates three or more

commodities may also require a large number of retail stores, an impos-

sibility from the standpoint of management and resources. On the other

hand, increases in the number of price levels utilizing some of the more

conventional experimental models lengthens the time required to generate

the data to an unbearable financial extent. More frequently than not,

the more conventional experimental models impose some combination of the

aforementioned problems, so the researcher is forced to choose among

fewer price levels or fewer commodities or both.

The Triple Cube alleviates these problems to a degree. It provides

a fractional replication with respect to treatment combinations which

have semiorthogonal properties. Therefore, it requires fewer observational

periods to generate adequate data to estimate the demand parameters, for

a given number of price levels, and allows estimation of these parameters

for three commodities or commodity characteristics. These properties make

it a highly desirable experimental model for demand estimation.











Limitations of the Model Formulation


Unlike some of the more orthodox experimental approaches, the

model formulation had no inherent facility to account for or parcel

out extraneous variation. Retail stores in a metropolitan area will

vary considerably. This variation can be classified basically into

two categories: (1) differences in store volumes and (2) differences

in clientele. These variations are generally a result of socio-economic

differences as well as differences in the population base the store serves.

Since volume of the individual store is affected by the population

base the store serves, a correction for difference in traffic flows

would tend to eliminate this source of variation. Thus, to compensate

for these differences, a transformation, in the form of a reduction of

sales to a per 100 customer basis, was planned.

Clientele differences are basically in the realm of socio-economic

considerations, in that these could be due to ancestral differences,

economic differences, and social differences. This category presupposes

an adequate cross section of these population characteristics over which

to measure demand relationships. Careful selection of stores can insure

coverage of the heterogeneity of the market population. Although it is

desirable to have a measure across this heterogeneous population, it

is also desirable to reduce the heterogeneity to more homogeneous popu-

lation by removing differences in purchase habits among the various store

populations. Since the product with which the research was concerned

was in the produce line, fresh orange purchases were assumed to be a

function of produce purchases. Therefore, to remove differences in pur-

chase habits of the clientele of the various stores, a measurable variable,









value of produce sales, was planned for inclusion.


The Statistical Model Redefined


Consistent with the transformation of sales to a per 100 customer

base and the addition of the variable, value of produce sales, the

statistical model redefined is as follows:

Yj .. =30 + plXl i + + X' I+3X3 + I X + (4.5)
-ijkm = 10 P -i- + 122-j 13X-k 14Xm -ijkm

2-ijkm = 20 + 21X-i + 22X2-j + P23X3-k + 24m + 2-ijkm (4.6)

Y3-ijkm = P30 + -31XI-i + I32X2-j + P33-Xk + '34Xm + 3-ijkm (47)

where:

Y-ijkm = log of the quantity of Florida Indian River Valencia oranges
purchased per 100 customers.

Y'-ijkm log of the quantity of Florida Interior Valencia oranges
purchased per 100 customers.

Y-.km = log of the quantity of California Valencia oranges pur-
chased per 100 customers.

P;1, 20', 30 = log of regression constants ("Y" intercepts).
P11, P12' 13 = regression coefficients associated with Florida
Indian River Valencia oranges (price elasticity
and 2 cross elasticity coefficients).

P21' P22' P23 = regression coefficients associated with Florida
Interior Valencia oranges (price elasticity and
2 cross elasticity coefficients).

P31' 32' 133 = regression coefficients associated with California
Valencia oranges (price elasticity and 2 cross
elasticity coefficients).

P14' P24' 134 = regression coefficients associated with value of
produce sales per 100 customers with respect to
YV, Y2, and Y3.

X' ., Xj' '-k= log of prices of Florida Indian River, Florida
Interior, and California Valencia oranges.










E6' ..m' E ..ijk' ,ii = random disturbances associated with
1i-ijkm' 2-ijkm 3-ijkm Yi, Y' and Y'.
1' 2 and


Specifications of Experimental Test


Upon completion of the delineation of variables, construction of

the economic model, and formulation of a statistical model to test the

economic model, evaluations were made concerning the specifications of

the tests to be conducted.


Size limitations

The size of the tests was limited by three major factors: (1)

dictates of the experimental design, (2) management, and (3) resources.

Attention was given to each of these in formulating the specifications

of the tests.

The Triple Cube design used for the data generating model dictated

a requirement of 31 pricing periods or observations per replicate. A

further consideration was the fact that there was no accounting for the

differences in time periods inherent in the model. To compensate for

time, a system of balance must be built into the design layout.

The first element was the length of the observational period.

The alternatives considered were one-half week periods, two-day periods,

and one-day periods. The process of logical determination of an adequate

time period was not only a function of consumer habits in relation to

frequency of grocery purchase but also a function of the habits sur-

rounding the commodity of interest, fresh oranges.

The decision, recognizing the needed control of variation due to

differences in days as well as in weeks, was to use a one-day observational

period. It was recognized that normally the distribution of shoppers is










more heavily concentrated in the latter part of the week. However, consumers

shopping in the early portion of the week may be quite unlike those in the

latter part of the week. In fact, it appears reasonable that when looking

at the aggregation of consumers shopping in a given week, one might well

have a different population on each day. The time for grocery shopping

in a given household tends toward an institutional arrangement by habit.

Further, credence is added to the daily observational period upon exami-

nation of consumer habits with regard to the purchase of fresh oranges.

Basically the grocery shopper enters a grocery store for one of two pur-

poses, the purchase of a full grocery order or the selection of a few

items such as milk, bread, or occasionally meats to fill in between

grocery orders. In general, most items in the grocery budget are pur-

chased at one time during the week. It was considered that, in the

main, fresh oranges would be an unlikely item to be purchased between

grocery orders. Therefore, daily observational periods would not create

undesirable distortion in consumer purchase rates.

The removal of time period variation as indicated above had to be

built into the design layout. Since the generating model dictated 31

price combinations to appear in each store used in the study, 31 ob-

servational periods were also required. Identification of an obser-

vational period as one day further required 31 operational days. Pro-

jecting a six-day operational week, two alternatives were considered

to compensate for time period variation by a system of balancing treat-

ments over days:

1. Using three stores and randomly assigning the 31 pricing treat-
ments to ore store and then balancing the treatments over two-
day periods in the two remaining stores. This would result in
every pricing treatment appearing once on a Monday-Tuesday,
a Wednesday-Thursday, and a Friday-Saturday at some time during
the study.









2. A second alternative, and the optimum, was to use six stores
in which the 31 pricing treatments were randomly assigned in
one store and balanced over days for the remaining five stores.
This plan would result in a complete balancing of pricing
treatments over days, since each treatment would appear once
on each day in some store included in the study.

Upon examination of capital resources available for this work and

the human resources considered essential for the conduct of the study,

it was evident that the inclusion of the two experiments of six stores

was prohibitive. However, the resource outlay could support the utili-

zation of nine stores. In conformance with the restrictions from the

standpoint of resources, two simultaneous experiments were conducted,

utilizing both alternatives of balancing pricing treatments over time.

From one of these experiments data were generated for the estimation of

demand relationships for California size 138, Florida Indian River size

200, and Florida Interior size 200 Valencia oranges. This particular

experiment was conducted in six stores and thus contained complete

balance in the compensation for time period variation. The second

experiment was for the generation of data to measure the demand re-

lationships for Florida Indian River size 163, Florida Interior size

163, and California size 138 Valencia oranges. This experiment was

conducted in three stores and contained a pricing treatment balance

over two-day periods, a partial compensation for variation due to

differences in time periods. The selection of Monday-Tuesday, Wednesday-

Thursday, and Friday-Saturday for the three sets of two periods was

based upon the assumption that these pairs of days would be the most

comparable from the standpoint of the consumers patronizing the stores.


Price differentials

The generating model utilized in the study allowed for nine levels









of price. To insure a range of prices which would be relevant under

foreseeable changes in the quantities available for the fresh orange

market, much thought was given to the size of the differential to be

used. On the basis of prices per dozen for fresh oranges, a four cent

differential was selected. Among the factors relating to the differen-

tials was the need for conformity to conventional pricing procedures.

Accordingly, the differentials had to be even integers greater than one

to produce odd cents per dozen pricing, starting from a base stated

in odd cents. Further, from the desire to cover the relevant range

of prices foreseeable and to force substitution within the range, the

four cent differential was selected. Thus, from a given base price

there would be deviations of -16, -12, -8, -4, +4, +8, +12 and +16

cents. The 31 treatment price combinations in terms of four cent

differentials are shown in Table 16.


Experimental design layout

The allocation of pricing treatments to stores was of crucial

concern, since the system of balance over time periods had to be

built into the design layout. In the experiment involving Florida

Indian River size 200, Florida Interior size 200, and California

size 138 Valencia oranges, the 31 pricing treatments were randomly

assigned to one store and balanced over the other five stores.5 This

assigning was done so that each treatment appeared in a store on each

day of the week at some time during the test (Table 17). For example,



Hereafter, the experiment involving Florida Indian River and
Interior size 200 and California size 138 will be referred to as
Component I.










Table 16.--Treatment price combinations, in terms of four cent devia-
tions, used in estimating demand relationships for Florida
and California Valencia oranges for fresh market.



Florida Florida California
Indian River Interior Oranges
Oranges Oranges


-16
0
0
-12
-12
-12
-12
-8
-8
-8
-8
4
-4
-4
-4
0
+4
+4
+4
+4
+8
+8
+8
+8
+12
+12
+12
+12
0
0
+16


0
-16
0
-12
-12
+12
+12
-8
-8
+8
+8
-4
-4
+4
+4
0
+4
+4
-4
-4
+8
+8
-8
-8
+12
+12
-12
-12
0
+16
0


0
0
-16
-12
+-12
-12
+12
-8
- 8
-8
+8
-4
+4
-4
+4
0
+4
-4
+4
-4
+8
-8
+8
-8
+12
-12
+12
-12
+16
0
0







Table 17.--Component I experimental price design for the study of the competitive relationships among
size 200 Florida Indian River, size 200 Florida Interior, and size 138 California Valencia
oranges, Grand Rapids, Michigan, April-May, 1962.



Store Number
Day of
Week 1 2 3 4 5 6
Week
IR Int. Cal. IR Int. Cal. IR Int. Cal. IR Int. Cal. IR Int. Cal:. IR Int. Cal.

- - - - - - - - - -Price differential- - - - - - - - - -

Monday -12 +12 -12 0 0 +16 + 4 4 4 + 8 + 8 8 4 4 -4 + 4 + 4
Tuesday -12 -12 +12 + 4+ 4 + 4 + 8+ 8 8 0 0 +16 + 4 4 4 +4 4 + 4
Wednesday -4 + 4 +4 + 4 4 + 4 0 Q +16 + 4+ 4 +4 + 8 + 8 8 + 4+ 4 4
Thursday 8 8 8 + 4 + 4 -4 + 4 + 4 +4 + 4 4 +4 0 0 +16 +12 -12 -12
Friday + 8 8 8 +12 -12 -12 + 4 4 + 4 + 4 + 4 4 + 4 + 4 + 4 0 +16 0
Saturday 0 -16 ;0 0 0 0 + 4 + 4 4 +12 -12 -12 + 4 4 + 4 -12 -12 -12

Monday -12 +12 +12 -12 -12 -12 +12 -12 -12 0 +16 0 + 4 + 4 4 -16 0 0
Tuesday 4 4 + 4 -16 0 0 0 +16 0 -12 -12 -12 +12 -12 -12 -12 +12 -12
Wednesday 8 + 8 8 -12 +12 -12 -12 -12 -12 -16 0 0 0 +16 0 -12 -12 +12
Thursday 4 + 4 4 -12 +12 +12 -16 0 0 -12 +12 -12 -12 -12 -12 4 + 4 + 4
Friday +16 0 0 4+ 4 + 4 -12 +12 -12 -12 -12 +12 -16 0 0 8 8 8
Saturday +12 +12 +12 8 8 8 -12 -12 +12 4+ 4 + 4 -12 +12 -12 + 8 8 8

Monday 8 + 8 + 8 + 8 8 8 4 + 4 + 4 8 8 8 -12 -12 +12 0 -16 0
Tuesday 0 0 0 0 -16 0 8 8 8 + 8 8 8 4 + 4 + 4 -12 +12 +12
Wednesday 0 0 -16 -12 +12 +12 + 8 8 8 0 -16 0 8 8 8 4 4 + 4
Thursday +12 -12 +12 4 4 + 4 0 -16 0 -12 +12 +12 + 8 8 8 8 + 8 8
Friday + 8 8 + 8 8 + 8 8 -12 +12 +12 4 4 +4 0 -16 0 4 + 4 4
Saturday 8 8 + 8 4 + 4 4 4 4 + 4 8 + 8 8 -12 +12 +12 +16 0 0









Table 17.--Continued


Store Number
Day of
Week 1 2 3 4 5 6

IR Int. Cal. IR Int. Cal. IR Int. Cal. IR Int. Cal. IR Int. Cal. IR Int. Cal.

- - - - - -- - - - - --Price differential- - - - - - - - - -

Monday + 8 + 8 + 8 +16 0 0 8 + 8 8 4 + 4 4 4 4 + 4 +12 +12 +12
Tuesday 4 4 4 +12 +12 +12 4 + 4 4 +16 0 0 8 + 8 -8 -8 +8 + 8
Wednesday + 4 4 4 8 + 8 + 8 +16 0 0 +12 +1+1212 4 + 4 4 0 0 0
Thursday + 8 + 8 8 0 0 0 +12 +12 +12 -8 + 8 + 8 +16 0 0 0 0 -16
Friday 0 0 +16 0 0 -16 8 + 8 + 8 0 0 0 +12 +12 +12 +12 -12 +12
Saturday + 4 + 4 +4 +12 -12 +12 0 0 0 0 0 -16 -8 +8 +8 + 8 8 +8

Monday + 4 4 + 4 + 8 8 + 8 0 0 -16 +12 -12 +12 0 0 0 8 8 + 8
Tuesday + 4+ 4 4 8 8 + 8 +12 -12 +12 + 8 8 + 8 0 0 -16 + 8 + 8 + 8
Wednesday +12 -12 -12 + 8 + 8 + 8 + 8 8 + 8 8 8 + 8 +12 -12 +12 4 4 4
Thursday 0+16 0 4 4 4 8 8 + 8 + 8 + 8 + 8 + 8 8 + 8 +4 4 4
Friday -12 -12 -12 + 4 4 4 + 8 + 8 + 8 4 -4 4 8 8 + 8 + 8 + 8 8
Saturday -16 0 0 + 8 + 8 8 4 4 4 + 4 4 4 + 8 + 8 + 8 0 0 +16


Monday
Tuesday
Wednesday
Thursday
Friday
Saturday


+12 +12 -12 a a a a a a a a a a a a a a a
a a a a a a a a a +12 +12 -12 a a a a a a
a a a +12 +12 -12 a a a a a a a a a a a a
a a a a a a a a a a a a +12 +12 -12 a a a
a a a a a a +12 +12 -12 a a a a a a a a a
a a a a a a a a a a a a a a a +12 +12 -12











price differentials of -12+12-12 appeared on Monday of week one in store

one, on Tuesday of week two in store six, on Wednesday of week two in

store two, on Thursday of week two in store four, on Friday of week two

in store three, and on Saturday of week two in store five.

Only one treatment combination, +12+12-12, remained for allocation

in the sixth week. This arrangement provided some flexibility in that

a missed observation could be secured by repeating the price treatment

associated with it during the final week. The one restriction upon this

was if a missing observation occurred on the day of the week that the

+12+12-12 treatment was to be applied, then the appropriate day of the

following week must be added to secure the missing observation. The

letters a, a, a, indicate the days in the final week available for such

a procedure (Table 17).

In the experimental test including Florida Indian River size 163,

Florida Interior size 163, and California size 138 Valencia oranges,

the price treatment allocative procedure was essentially the same with

the exception of the balance concept. With only three stores, balance

was reduced to two day periods. Each treatment appeared in a store on

a Monday-Tuesday, Wednesday-Thursday, or Friday-Saturday at some time

during the study. As in the allocation procedure in Component I, the

31 pricing treatments were randomly assigned to days in store 7 and



Hereafter, the experiment involving Florida Indian River and
Interior size 163 and California size 138 will be referred to as
Component II.










balanced over the two day-periods in the remaining two stores (Table

18).


Requirements and Specifications of Experimental Units


The selection of a test site and test stores within the test city

required careful consideration. With the realization that the validity

of the estimated relationships depended upon the limited population

reached by a given experiment, much effort was devoted to a delineation

of the factors affecting selection and to determination of the most

effective selection.


Selection of test site

In the marketing of fresh oranges, Florida and California fruit

meet in competition from the Rocky Mountains to the Eastern seaboard.

The competition between the fruit of the two areas is especially heavy

in the midwest. Therefore, the area west of Pittsburg, Pennsylvania,

and east of Chicago, Illinois, was designated as the area in which the

study would be conducted.

Within the specified area other factors affected the selection of

the test city. It was recognized that the population base over which

the measurement of demand relationships were to be made could be ex-

panded greatly by the selection of a high population density trading

area. This led to the selection of a relatively large and heavily

populated metropolitan area. To insure further a representative sample

population, the area was to be characterized by moderate industrialization

and an adequate cross section of social, ancestral, and income strata.

To meet these prerequisites, metropolitan areas in excess of





85


Table 18.--Component II experimental price design for the study of the
competitive relationships among size 163 Florida Indian River,
size 163 Florida Interior, and size 138 California Valencia
oranges, Grand Rapids, Michigan, April-May, 1962.



Store Number
Day of
Week 7 8 9

IR Int. Cal. IR Int. Cal. IR Int. Cal.

--------------------Price differential-------------------

Monday -12 +12 -12 0 0 +16 + 4 4 4
Tuesday -12 -12 +12 + 4 + 4 + 4 + 8 + 8 8
Wednesday 4 +4 +4 +4 4 +4 0 0 +16
Thursday 8 8 8 + 4 + 4 4 + 4 + 4 + 4
Friday + 8 8 8 +12 -12 -12 + 4 4 + 4
Saturday 0 -16 0 0 +16 0 + 4 + 4 4

Monday -12 +12 +12 -12 -12 -12 +12 -12 -12
Tuesday 4 4 + 4 -16 0 0 0 +16 0
Wednesday 8 + 8 8 -12 +12 -12 -12 -12 -12
Thursday 4 + 4 4 -12 -12 +12 -16 0 0
Friday +16 0 0 4 + 4 + 4 -12 +12 -12
Saturday +12 +12 +12 8 8 8 -12 -12 +12

Monday 8 + 8 + 8 + 8 8 8 4 + 4 + 4
Tuesday 0 0 0 0 -16 0 8 8 8
Wednesday 0 0 -16 -12 +12 +12 + 8 8 8
Thursday +12 -12 +12 4 4 + 4 0 -16 0
Friday + 8 8 + 8 8 + 8 8 -12 +12 +12
Saturday 8 8 + 8 4 + 4 4 4 4 + 4

Monday + 8 + 8 + 8 +16 0 0 8 + 8 8
Tuesday 4 4 4 +12 +12 +12 4 + 4 4
Wednesday +4 4 4 8 + 8 + 8 +16 0 0
Thursday + 8 + 8 8 0 0 0 +12 +12 +12
Friday 0 0 +16 0 0 -16 8 + 8 + 8
Saturday + 4 + 4 + 4 +12 -12 +12 0 0 0

Monday + 4 4 + 4 + 8 8 + 8 0 0 -16
Tuesday + 4 + 4 4 8 8 + 8 +12 -12 +12
Wednesday +12 -12 -12 + 8 + 8 + 8 + 8 8 + 8
Thursday 0 +16 0 4 4 4 8 8 + 8
Friday -12 -12 -12 + 4 4 4 + 8 + 8 + 8
Saturday -16 0 0 + 8 + 8 8 4 4 4




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