THE APPLICATION OF MULTIPLE FACTOR
ANALYSIS TO THE AGGREGATE DEMAND
FOR DAIRY PRODUCTS

by
HARRIET SCHULMAN SIEGEL

A THESIS
Submitted to the School of Graduate Studies
Michigan State College of Agriculture and
Applied Science in partial fulfillment
of the requirements for the degree of
DOCTOR OF PHILOSOPHY
Department of Agricultural Economics

1951

B urtot I* Siegel
AB8T1ACT
*

T B AFPLICATIOM OF MULTIPLE VXeTOB H A L T S 18 TO THE
AOflBSOATE DSMUn> FOX DA 1ST PXODDCTS
Tha * j N t | v « l tf Sill ( M r i w # »
1«

To m l j n * M o a a d i litft saw urnlng

m o

» o b 1« Mniltiaia la

g«B«rtlt and daisy pradaata la p u i d w l t r i by aaama of a ilH plo
fastor

o m

Ij i Ii la ordor to laroatlgata what firtiri wort spaw

otlio daring tha two tlas periods of 1 9 8 ^ 1 ^ 1 aad
t«

Ta determine har wail thaaa footers seal* esplala aad yrodlot
tha dlaappsaraass of vorlooa daisy prodaets* by sattlag ap anltlpla regression ayatawa based aa tha lhatar analyses*

Aa aaalaattaa of tha eppllsablllty of tha aathad of naltiple footer aa*
olysls to aaaaaala data wos alae nods*
Thara wora 7 6 warlable laaladad la thla stady*
aggregate 8* 8* data aa a monthly basis.
dlfforoat ways*

Thaaa wore osapesed of

Thaaa variables aara graapad la aaworal

Oaa groap aaaMaod all variables relating ta goaeral aaaaaala

conditions wh&la ottor groups severed prised* flald ailk aad arson* batter *
oheeeo* condensed* day aad aaaparatad allk, tha pradmotioa aad diaappaaraaaa
of daisy prodaets* aad oourhat&Bg margins*
of

8operate fhetor analyses were node

aaah of thaaa groapa far tha taa ttao period*.
The lhatar aaalyala of goaorol eeonowia aandltlaaa ohaaad taa hotiaol

faatora*
f00 tor.

Thaaa wora a goaaral pr&aa laral lhatar aad aa ladaatrlal aatlwlty
Tha aaalyaaa of daisy pradaata showed that tha goaaral prlaa laoal

footer explained aoat of tha variations la tha whalaaala aad, ratall prlaoa af
tha dairy pradaata*

Sowo of tha raal whalaaala prlaaa wora aaplalaadby tha

lndaatrlal aatlwlty footer* tat ooraral real whalaaala aad raal ratall prlaaa

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ACKNOWLEDGEMENT
The author expresses her sincere appreciation to all
who helped in the preparation of this manuscript.

Valuable

assistance was given by Dr. Leo Katz of the Department of
Mathematics in the statistical portions of this research.
Dr. Katz and Dr. L. W. Witt of the Department of Agricultural
Economics read the manuscript and made many suggestions which
were gratefully appreciated.
The author especially wishes to thank Dr. G. G. Quackenbush
of the Department of Agricultural Economics.

His guidance and

background experience and the generous amount of time which
he expended on this manuscript helped to make it a better and
more integrated piece of work.
The statistical assistance given by Mrs. Mary Hudson and
Mrs. Frances Root of the clerical staff of the Department of
Agricultural Economics was an invaluable aid in the completion
of this work.

The author, of course, assumes complete

responsibility for any errors which may be present in the
manuscript.

TABLE OF CONTENTS
Page
List of Illustrations
List of Text Tables
List of Appendix Tables
I.

.............................
Introduction
Review of Literature
....................
Purpose and Method of Procedure .........
Source and Adequacy of D a t a .............

1
2
4
6

II.

M e t h o d o l o g y .................................
General Aspects ...........................
Theory of Multiple Factor Analysis
...
Computation of Factors
..................
Interpretation of Factors - Two Factor
E x a m p l e ...................................
Extension to More Than Two Factors
...
Correlated Factors
......................
Inclusion of D a t a ........................
Strengths and Weaknesses of the Method of
Factor Analysis ..........................

6
6
12
16

III.

IV.

P r o c e d u r e ...................................
Basic Data U s e d ...........................
Description of D a t a ......................
Processing of D a t a ......................
Comparison of Coded and Uncoded Data
. .
Comparison of the Correlation Coefficients
of the Two Time P e r i o d s ..................
Computation Methods Used in Factor
A n a l y s e s .................................
Individual Factor Analyses
.............
Analyses of Economic D a t a .............
General Economic Conditions .............
Analysis I 1929-1946
. . . . . . . .
Analysis II 1929-1941 ...............
Comparison of Analyses ...............
P r i c e s ........................
Analysis
I 1929-1946
Analysis
II 1929-1941 ................
Comparison of Analyses ...............
Fluid Milk and C r e a m ....................
Analysis
I 1929-1946
Estimation of Fluid Milk and Cream
Disappearance ......................
Analysis
II 1929-1941 ................
Estimation of Fluid Milk and Cream
Disappearance ......................

19
25
26
28

30
34
34
36
46
52
54
55
57
59
59
59
65
69
76
76
67
92
95
95
99
102
10#

Page
Comparison of Analyses ..................
B u t t e r ......................................
Analysis I
1929-194$
Estimation of Butter Disappearance.
Analysis II 1929-1941 ..................
Estimation of Butter Disappearance.
Comparison of Analyses ..................
C h e e s e ......................................
Analysis I
1929-194$
Estimation of Cheese Disappearance.
Analysis II 1929-1941 ..................
Estimation of Cheese Disappearance.
Comparison of Analyses ..................
Condensed, Dry and Evaporated Milk
...
Analysis I 1929-194$
Estimation of Evaporated Milk
Disappearance .........................
Analysis II 1929-1941 ..................
Estimation of Evaporated Milk
Disappearance .........................
Comparison of Analyses ..................
Production and Disappearance of Dairy
P r o d u c t s .............................
169
Analysis I 1929-194$
Analysis II 1929-1941 ..................
Comparison of Analyses ..................
Marketing Margins ...........................
Analysis I 1929-194$
Analysis II 1929-1941 ..................

110
115

116
121
123
12$
129
133

133
141
142
147
14$
153
153
159
160
164
166
169
174
ISO
l£2
1$3
1$$

V.

Summary of Results of Analyses of Economic
D a t a ......................................
190
Results of Factor Structures
..............
190
Relationships within Factor Structures
.
196
Results of Multiple Regression Systems
.
199

VI.

Summary and Evaluation of the Method of Fac­
tor Analysis as Applied to Economic Data . .

VII.

Appendix A:
Orthogonal and Rotated Factor
S t r u c t u r e s ...............................

VIII. Appendix B:
IX.

Regression Systems

211

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

Appendix C:
Monthly Disappearance of Fluid
Milk and C r e a m ...........................
Correlation Matrices

204

242

247

X.

Appendix D:

...........

XI.

B i b l i o g r a p h y ..............................
250
Literature Cited
...........................
Background Information
....................

249
250
251

LIST OF ILLUSTRATIONS
Figure
No.
1
2
3
4
3
6
7
#
9
10
11

Page
Illustration of Complete Correlation
M a t r i x ......................................
13
Illustration of Complete Factor Matrix . . .
14
Illustration of Reduced Factor Matrix
. . .
16
Example of Factor Structure obtained from
Factor Matrix in Table I ..................
21
Example of a Variable Configuration
. . . .
21
Examples of Different Possible Factor
Structures from one Configuration
. . . .
22
Example of a Rotated Factor Structure
...
27
Orthogonal Factor
Structure of General
Economic Conditions
Analysis I, 1929-194#
63
Orthogonal Factor
Structure of General
Economic Conditions
Analysis II, 1929-1941 ....................
67
Orthogonal Factor
Structure of Marketing
Margins
Analysis I, 1929-194#
1#6
Orthogonal Factor
Structure of Marketing
Margins
Analysis II, 1929-1941 .........
1S9

LIST OF TEXT TABLES
Table
No.
I
II
III
IV
V
VI
VII
VIII
IX
X
XI
XII
XIII
XIV
XV
XVI
XVII
XVIII
XIX
XX
XXI
XXII
XXIII
XXIV
XXV

Page
Example of a Factor M a t r i x ..................
Example of a Complete Correlation Matrix . .
Variables Included in this Study, giving
Description and Source
of D a t a ..............
Illustration of Coding Used for I.B.M.
P r o c e s s ......................................
Comparison of Some Randomly Selected Corre­
lation Coefficients, showing Differences
between Coded and Uncoded
D a t a ..............
Variables Used in Analysis of General
Economic Conditions
......................
Summary of Factor Structure of General
Economic Conditions
Analysis 1,1929-1943.
Summary of Factor Structure of General
Economic Conditions
Analysis 11,1929-1941
Variables Used in Analysis of Prices . . . .
Summary of Factor Structure of Prices
Analysis I, 1929-1943
Summary of Factor Structure of Prices
Analysis II, 1929-1941 ....................
Variables Used in Analysis of Fluid Milk . .
Summary of Factor Structure of Fluid Milk
Analysis I, 1929-1943
Summary of Factor Structure of Fluid Milk
Analysis II, 1929-1941 ....................
Actual and Estimated Disappearance of
Fluid Milk in 1949 and 1950
Variables Used in Analysis of Butter . . . .
Summary of Factor Structure of Butter
Analysis I, 1929-1943
Summary of Factor Structure of Butter
Analysis II, 1929-1941 ....................
Actual and Estimated Disappearance of
Butter in 1949
. . . . . . . . .
Variables Used in Analysis of Cheese . . . .
Summary of Factor Structure of Cheese
Analysis I, 1929-1943
. . . . .
Summary of Factor Structure of Cheese
Analysis II, 1929-1941 ....................
Actual and Estimated Disappearance of
Cheese in 1949 .............................
Variables Used in Analysis of Condensed,
Dry and Evaporated M i l k ..................
Summary of Factor Structure of Condensed,
Dry and Evaporated Milk
Analysis I, 1929-1943

21
21
33
51
53
61
62
66
77
79
39
97
93
104
113
117
11B
125
132
136
137
144
152
154
155

Table
No.
XXVI
XXVII

XXVIII
XXIX
XXX
XXXI

XXXII

Page
Summary of Factor Structure of Condensed,
Dry and Evaporated Milk
Analysis II, 1929-1941 ....................
Actual and Estimated Disappearance of
..................
Evaporated Milk in 1949
Variables Used in Analysis of Production
and Disappearance of Dairy Products
. . .
Summary of Factor Structure of Production
and Disappearance of Dairy Products
Analysis I, 1929-1943
....................
Summary of Factor Structure of Production
and Disappearance of Dairy Products
Analysis II, 1929-1941 ....................
Variables Used in the Analysis of Marketing
M a r g i n s ...................................
Summary of Factor Structure of Marketing
Margins Analysis I, 1929-1943 ...........

161
163
170
171
175
134
135

LIST OF APPENDIX TABLES
Table
No.

I a
II a
III a
IV a
V a
VI a
VII a
VIII a
IX a
X a
XI a
XII a
XIII a
XIV a
XV a
XVI a
XVII a
XVIII a
XIX a
XX a
XXI a
XXII a
XXIII a
XXIV a
XXV a
XXVI a
XXVII a
XXVIII a
XXIX a
XXX a
XXXI a

Page
Appendix A: Orthogonal and Rotated Factor
Structures ....................
General Economic Conditions:
Analysis I Orthogonal Factor Matrix .
. .
Analysis I Rotated Factor Matrix
Analysis II Orthogonal Factor Matrix .
. .
Analysis II Rotated Factor Matrix
Prices:
Analysis I Orthogonal Factor Matrix •
Analysis I Rotated Factor Matrix
. .
Analysis II Orthogonal Factor Matrix .
. .
Analysis II Rotated Factor Matrix
Fluid Milk and Cream:
Analysis I Orthogonal Factor Matrix .
Analysis I Rotated Factor Matrix
. .
Analysis II Orthogonal Factor Matrix .
Analysis II Rotated Factor Matrix
. .
Butter:
Analysis I Orthogonal Factor Matrix .
Analysis I Rotated Factor Matrix
. .
Analysis II Orthogonal Factor Matrix .
. .
Analysis II Rotated Factor Matrix
Cheese:
Analysis I Orthogonal Factor Matrix •
Analysis I Rotated Factor Matrix
• .
Analysis II Orthogonal Factor Matrix .
Analysis II Rotated Factor Matrix
. .
Condensed, Dry and Evaporated Milk:
Analysis I Orthogonal Factor Matrix .
Analysis I Rotated Factor Matrix
. .
Analysis II Orthogonal Factor Matrix .
Analysis II Rotated Factor Matrix
. .
Production and Disappearance of Dairy
Products:
Analysis I Orthogonal Factor Matrix .
Analysis I Rotated Factor Matrix
. .
Analysis II Orthogonal Factor Matrix .
Analysis II Rotated Factor Matrix
. .
Marketing Margins:
Analysis I Orthogonal Factor Matrix .
Analysis I Rotated Factor Matrix
. .
Analysis II Orthogonal Factor Matrix .

Orthogonal Factor Matrices give factor loadings and
communalities
Rotated Factor Matrices give rotated factor loadings
and cosines of angles between factors

211
212
213
214
215
216
217
213
219
220
221
222
223
224
22 5
226
227
223
229

230
231

232
233
234
235
236
237
233

239
240
240
241

Table
No.

I b
II b
III b
IV b
I c

I d
II d

Page
Appendix B: Regression Systems for
Individual Dairy Products . .
Fluid Hilk and C r e a m ..................
C h e e s e .............................
B u t t e r ..................
Evaporated M i l k .........................

242
243
244
245
246

Appendix C: Original Disappearance Data .
Monthly Disappearance of Fluid Milk
and C r e a m ...............................

247

Appendix D: Basic Intercorrelation Tables
Analysis I Table of Simple Correla­
tions between Variables 1929-194#
Analysis II Table of Simple Correla­
tions between Variables 1929-1941

249

24#

I. INTRODUCTION
Dairy products are the largest single source of agri­
cultural income in the United States.

They also account for

almost one - fifth of the total amount spent for food by
civilian consumers.

Thus dairy products comprise an extremely

important segment of the economy for both producers and con­
sumers .
The total milk production on farms is utilized in the
production of many products, and this total production is
relatively fixed over short periods of time.

It is desirable

to allocate this production among the various products in
order to obtain the maximum satisfaction for consumers and
the maximum revenue for producers.

It is also desirable to

determine what prices should exist in order to bring about
these optimum positions.
The problems of allocation of milk supplies and pricing
of dairy products have been intensified within the past few
years by the institution of federal milk marketing orders in
many areas of the United States.

These milk marketing orders

have been for the purpose of providing orderly and dependable
milk supplies for these areas, and have principally taken the
form of establishing pricing formulas for various classes of
milk.

The pricing formulas have generally been based on

various economic indices without too careful an analysis of
the relationships involved.

Most of them have been empirically

determined and, although it might suffice in the short run,

this type of analysis leaves much to be desired on theoret­
ical grounds and for long run considerations as well.

The

ability to forecast the demand for dairy products would be
a significant contribution toward the problem of allocation
of raw milk supplies.

The knowledge of what factors affect

dairy product prices, and how these prices affect the demand
for these products, would aid not only in the establishment
of federal order regulations but also would aid individual
organizations in the policy questions of price formulation.
Review of Literature
Many studies have been made concerning demand and price
relationships for various dairy products.

Most of these have

concerned specific regions or markets, although a few have
been on an aggregative basis.

The studies relating to specific

markets have relied mainly on information obtained from surveys
of consumers and/or milk dealers and many have been reported
as simple compilations of the data.

A study of this type is

the one by Blanford (2) concerning consumer purchases of fluid
milk in New York City.

His results are based on tabular

analyses, using simple differences in purchases of fluid milk
in relation to prices in different areas and different sources
of these purchases.
Some regional and market studies have estimated the demand
and supply for dairy products by means of multiple correlation
analyses.

An early study by Ross (&) used multiple correlations

to show the effects of various selected factors on the sales of

- 3 -

fluid milk in New York City.

Waite and Howe (15) used

correlations in estimating the demand for fluid milk in six
New Jersey townships, while Gaus (5) used multiple correlation
analysis to estimate milk deliveries in Vermont.

A recent

study by Pritchard (7) estimated the demand for fluid milk in
Indianapolis.

The system giving the highest coefficient of

correlation in this latter study included indices of the
average retail price of milk in Indianapolis, the B.L.S index
of retail commodities, an index of per capita disposable
income, and an index of lagged income.

The selection of these

indices was based primarily on the judgment of the investigator.
Relatively little has been done on the analysis of aggre­
gate demand for dairy products.

The most important recent work

has been done by the Bureau of Agricultural Economics of the
U. S. D. A. (1,6) where a simple correlation between annual
per capita expenditures for fluid milk and cream and annual
per capita disposable income has yielded good results.
rationale for this system is stated as follows:

The

"Expenditures

by consumers for fluid milk and cream are directly related to
consumers' earnings.

Consumer purchase studies have also

demonstrated the close association between expenditures for
fluid milk and cream, as well as quantities purchased, and
consumers incomes" (6).

Similar studies for butter, cheese,

and evaporated milk have also been made (1).
These studies involve expenditures for dairy products,
which consist of both quantities consumed and prices.
Shepherd (10) has estimated the demand for butter using
these variables specifically.

In one system he has shown tlM

- 4 -

regression of retail butter prices on disposable income,
butter consumption and time, and in another he has shown
the regression of retail butter prices on retail expenditures
for food, consumption and time.

All of these aggregate studies

have used annual data.
Purpose and Method of Procedure
The purpose of this study was to determine what the
factors are that affect the aggregate demand for individual
dairy products and to utilize these factors to estimate the
demand for these products.

Heretofore the important factors

affecting demand have been selected by investigators on the
basis of theory, previous knowledge, and intuition.

This

study investigated the possibility of putting this process of
selection on a sounder foundation, by applying the method of
multiple factor analysis to the data.
The method of multiple factor analysis makes it possible
to analyze large bodies of data and determine what relation­
ships exist among these data.

Therefore it was decided to

apply it to the large body of data available for dairy products
in order to investigate the similarities and differences which
exist among these data.

Although some of the data included

herein have been analyzed and compared in previous studies,
this has never been done on a similar scale.
There were 76 individual indices included in this study.
These were composed of aggregate U.S. data on a monthly basis,
covering in one case the period 1929-1941 and in another, the

period 1929-194#.

These data included prices (retail, whole­

sale, and real ) production and disappearance of individual
dairy products, total milk production, the milk-feed and
butterfat-feed ratios, marketing margins on dairy products,
and various indices of general economic conditions including
among others the consumers price index, employment and pay­
rolls in manufacturing industries, industrial production, and
consumer credit.

(A complete list is given in Table III.)

These data were grouped in several ways in this study.
One grouping consisted of all indices relating to general
economic conditions.

Another combined all the price series.

Still others referred specifically to Fluid Milk and Cream,
Butter, Cheese, Evaporated Milk, Production and Disappearance
of Dairy Products, and Marketing Margins on Dairy Products.
Bach of these groupings contained from 20 to 40 series of data.
A factor analysis was then applied to each to investigate the
underlying factors which were present in each grouping.

An

interpretation of each group was made, analyzing the factors
and discussing the relationships of the variables to the
factors and to each other.
The method of multiple regression was used to estimate
the demand for the individual dairy products.

The number of

variables to be used in each regression was determihed by the
number of factors present in the group under consideration.
The selection of the variables in the regression also depended
on the factors present.
In summary, this study investigated the factors which

- 6 -

influence the demand for dairy products.

The method of

multiple factor analysis was used to determine what these
factors are.

The results of the factor analyses for the

individual dairy products then became the basis for multiple
regression systems for estimating the demand for these dairy
products.

In addition, an evaluation was made of the

application of multiple factor analysis to data of this
particular type and to economic data in general.
Source and Adequacy of Data
The data used in this study were gathered from various
governmental agencies such as the Department of Agriculture,
the Department of Commerce and the Department of Labor.

The

degree of accuracy of much of this data is questionable, as
many approximate measures have been used in deriving these
series due to the unavailability of the exact data.

However,

these were the best data that were available for this work
and it was assumed herein that, although the exact figures
might be inaccurate, the directions of change (increases or
decreases) were consistent with actual changes.

This should

have been sufficient for the determination of the factors
influencing demand, even though the actual estimates of demand
would obviously be dependent upon the size of the changes as
well as the directions of change.
Several compromises were necessary in this study in the
practical aspects of dealing with such a large body of data.

- 7 -

First,

compromises were made in the selection of data,

since

many of the desired series were not available in the necessary
form or for the necessary period of time.

For example, the

index of disposable income could not be included as it is
only published quarterly.

Therefore, the monthly index of

payrolls in manufacturing industries was included instead.
A second compromise was necessary in the statistical aspects
of this study.

Since the manual labor involved in obtaining

statistics from the basic data would be prohibitive,
were coded for I.B.M. tabulations.
of the labor involved.

the data

This reduced a large part

A complete description of the indices

included in this study and the way these indices were processed
is given in section III.

-

3

-

II.METHODOLOGY
General Aspects
The validity of economic studies utilizing simple and
multiple correlation analysis has depended primarily upon
the selection of the variables and the manner in which these
variables have been treated.
The selection of variables has usually been based on
theoretical economic concepts,

information already available

on the problem from various sources, and on the knowledge
and judgment of the investigator.

The inclusion or exclusion

of data has had a crucial effect on the results obtained, with
the decisions in this respect depending entirely upon the
discretion of the investigator.
In a correlation analysis, one variable is considered as
being dependent upon the remaining variables, or as being
determined by the remainder.

The selection of the dependent

variable has usually been governed by the nature of the problem,
with the discretion of the investigator again being of major
importance.

It can be noted here that some studies (9) have

been made with first one, and then another, variable considered
as dependent (i.e. consumption considered as dependent with
price as the independent variable and then price considered
as dependent with consumption as the independent variable)
and the result selected which yielded the highest coefficient
of correlation.

Although this procedure

provides a more

- 9 -

significant result statistically, it is questionable how much
economic significance it contains.
It has also been shown (4) that the independent variables
must be uncorrelated amongst themselves and be truly independent
of each other in a multiple correlation analysis.

Otherwise,

spurious results arise where it is impossible to tell how much
of the correlation is due to the relationship between the
dependent and independent variables, and how much is due to
the relationship between the independent variables.
An investigation of the relationships between variables
using economic data was developed by Frisch (4) known as the
technique of confluence analysis.

This technique starts with

two variables and successively adds further variables, inves­
tigating the resulting relationships after each addition.
These relationships indicate whether the added variable is
essential to the relationship, unessential or immaterial to
the relationship, or actually detrimental to it.

The technique

of confluence analysis has been used by Tinbergen in business
cycle analysis (13), and by Stone (11) in the analysis of
price-consumption relationships.
Confluence analysis and multiple factor analysis are
similar in that they both seek to determine significance
among variables; however, whereas confluence analysis builds
up significant variables by analyzing them one at a time,
factor analysis analyzes all variables at the same time, shows
the various relationships between them, and also provides a
method for estimation purposes.

Factor analysis should prove

- 10 -

to be a more powerful technique than that of confluence analysis,
since it provides more information concerning the variables.
The method of multiple factor analysis avoids some of the
pitfalls of correlation analysis and confluence analysis.
No one variable is chosen as dependent upon the remaining
variables; all of the variables are considered simultaneously.
Simple correlations are found between each pair of variables;
these correlations are then analyzed to discover any under­
lying relationships which exist.

The object is to be able

to explain the correlations between these variables by a
smaller number of variates,

or factors, which can be considered

as clusters or combination of the original v a r i a b l e s .

These

clusters can show which variables are related in each factor,
how the factors are related to each other (i.e. whether they
are independent of each other or closely connected) and how
important each factor is in the explanation of the various
original variables.

It is then possible to combine the

factors obtained into a regression system for estimation
purposes•
For example, a number of measurements might be taken
from several different people.

These measurements could include

such things as height, weight, length of arm,

chest expansion,

size of head, upper forearm, thigh, pulse rate, etc.

A factor

analysis of all these measurements would probably show that
two underlying factors were involved, a body build factor and
a metabolic rate factor.

Thus all of the measurements might

have been explained in terms of these two factors, and it

- 11 -

would be possible to determine which of these factors had
the largest influence in any one variable of body measurement*
It could also be seen whether the two factors of body build
and metabolic rate were independent of each other or to what
extent they depended on each other.

After the two factors

were determined, a regression system could be set up for
estimating the various body measurements.
The problem of selection of data is present in factor
analysis as well as in correlation analysis; however,
not as important in the former as in the latter.

it is

This is

mainly because more variables can be included in a factor
analysis than are usually included in a correlation analysis;
the techniques of factor analysis can more easily accomodate
a larger number of variables than a correlation analysis.

In

a factor analysis twenty to thirty variables can be included
without too much difficulty; factor analyses have been made
using from fifty to one hundred variables, although these
have resulted in very cumbersome procedures.

In a multiple

correlation, on the other hand, four or five variables can
be included without too much difficulty, but any larger number
results in long and involved manipulations.

Since more

variables can be utilized in a factor analysis, the importance
of each is correspondingly decreased.

The significance of

including extraneous variables in a factor analysis, or
conversely of excluding essential variables, will be discussed
later.

- 12 -

Theory of Multiple Factor Analysis
The techniques of multiple factor analysis were first
applied in the field of psychology.

They were applied to

large batteries of tests, in an attempt to determine basic
human abilities or traits.

However, the techniques are

applicable to almost any body of related information if the
theoretical criteria discussed below can be satisfied.
Factor analysis assumes that there is an underlying
order in any collection of data relating to a specified field,
and that this order can be expressed,

at least in part, by a

relatively smaller number of variates than the original number.
These variates have been called ’factors*,

’causes*,

’functional

unities’, or ’independent measurements’, depending on the
context and on the preferences and philosophies of the inves­
tigators in the studies under consideration.

They will be

called ’factors’ hereafter in this study, since this term
carries fewer connotations than the others.
The assumption of an underlying order is essential to the
theory of factor analysis.

This order is not one which would

enable one x.o distinguish between dependent or independent
variables; it is, instead, an order which gives one the ability
to describe a large number of variables by a lesser number.
This could not be done if it were assumed that all the
original variables were completely independent of the others,
or that they were independently determined.

This lesser

number of variates, or factors, is assumed to be able to

- 13 -

account Tor the variations in the entire number of original
variables•
Before the variables are analyzed, a table of inter­
correlations is set up.

Each intercorrelation is a simple,

linear correlation between two variables; the table of inter­
correlations shows the simple correlation between each pair
of variables.

This table of intercorrelations is called the

correlation mat r i x .

The correlation matrix is the basis for

all further analysis in determining the desired factors.

A

correlation matrix is shown in Figure l a .
la

Variables

12

13

21
31

r32

nl

n2

Figure 1.

lb

In

Variables

12

In

2n

2n

3n

3n

n3

Illustration of a Complete Correlation Matrix

This table is symmetric, as the intercorrelation between
variables 1 and 2 (z*^) is the same as that between variables
2 and 1 ( z ^ K

Using a general notation, r ^

• r^

; thus it

is only necessary to present half of the table of intercorre­
lations to have the complete set of information.

In general,

- 14 -

the correlations above the diagonal will be presented with the
lower half left blank, as in Figure lb.
that the diagonal elements are all unity.

It will be noticed
This is true because

there is perfect correlation in any variable with itself.
The assumption of an underlying order within the corre­
lation matrix leads to the further assumption that the
variations in any variable can be explained by means of under­
lying factors.

Each variable is assumed to consist of three

types of component factor parts:

common factors, which are

found in several, or perhaps all, of the variables; a specific
factor, which is unique in the individual variable; an error
factor, to account for errors in measurement of the variable.
Since the specific factors and the error factors are not
measurable individually they are combined into a unique f a c t o r ,
which is not shared with any other variable.

A complete factor

matrix is shown in Figure 2.
Common Factors

Unique Factors

I

II

III

1

all

a12

a 13

un

2

a21

a22

a23

3

a31

a32

a33

1

•

e

•

•

•

•

*

♦

n

anl

Figure 2.

an2

<

•

u33

e

•

0

•

•

3 ..... n

u 22

O

•

2

an3

Illustration of Complete Factor Matrix

unn

- 15 -

The value a-^ shows the weight of common factor I in variable
1, a^2 shcrws the weight of common factor II in variable 1,
a13 shows the weight of common factor III in variable 1, etc.
The significance of these factor weights will be discussed in
a subsequent section.
The sum of squares of the weights of the common factors
for any one variable, plus the square of the weight of the
unique factor, comprise the total variation of that variable.
It is also the equivalent of the unit variance to be found
in the diagonal of the complete correlation matrix; i.e.
(a1 1 )2* ( a i 2 )2.(ai3 )2+ ( u n ) 2 - 1
or, in general notation:
(

(

a i2

ai3

u ii ^

^

where i is an y variable from 1 to n.
The sum of squares of the factor weights,

or loadings,

of

the common factors alone for any single variable is known

as

the communalitv ofthat variable.

of

the variance of each variable which is explained by these

common

factors alone.

This gives the amount

The communality of any variable 1 is

denoted h|, where:
h^ m

^a i3 ^

( - 1)

The communality is less than or equal to one since the unique
factors are omitted.
Actually,
u2
2
hl + Uji = 1

.

As the unique factors become smaller, the communalities
approach unity.
The matrix multiplication of the complete factor matrix

- 16 -

(the matrix of common factors and unique factors) by its
transpose^ exactly reproduces the complete correlation matrix
of Figure 1.

This corresponds to the theoretical assumption

that the underlying factors are sufficient to explain and
reproduce the variations of all the original variables.
Using the common factor matrix alone,

the original corre­

lation matrix can be reproduced with the exception of the
diagonal elements.

The diagonal elements which do appear

are minus the effects of the unique f a c t o r s ; they are the
communalities mentioned above.

The product of the factor

matrix and its transpose is called the reduced factor m a t r i x .
It is shown in Figure
1

j•

hi

r12

r21

h2
2

r31

r32

•

•

•

• • • •

•

•

•

• • • •

rnl
Figure 3•

.... n

3

rn2

• • •

rln

r2n
r 23
h^
2 •• • •
r3n

rn3

•

k

Illustration of Reduced Factor Matrix

Computation of Factors
Theoretically the complete factor matrix exactly repro­
duces the correlation matrix?

However, there is no need for

^The transpose of a matrix A, denoted A * , is a matrix in which
the successive rows of A have become the successive columns
of A 1 . For a more complete description, see Thurstone (l|jp^

- 17 -

the complete factor matrix; interest centers on the common
factor matrix, which involves all variables, rather than
on the unique factor matrix which shows the unique factor
for each variable.

Actually, it would be sufficient to

consider the common factor matrix alone,

since it does

reproduce the original correlation matrix with the excep­
tion of the diagonal elements (see Figure 3)However,

the matrix which exists at the beginning of the

analysis is the correlation matrix without adjusted diag­
onal elements.

The problem is to estimate these adjusted

diagonal elements, the communalities, and to derive a fac­
tor matrix which can reproduce the given matrix.
There are several possible methods for estimating the
communalities of the variables.

One of the simplest meth­

ods is to adopt the highest correlation in any row

(or

column) as the communality for the variable represented
by that row

(or column).

This selects the highest corre­

lation among all the correlations for any one variable as
the communality for that variable.
The estimation of comraunalities is quite important,
since if the estimate is too high, some of the unique
factors are included and thus too many factors are in­
cluded in the analysis.
communalities likewise

Too low an estimate for the

- 1$ -

yields incorrect results.

However, the method described

above has been found satisfactory for most studies.
One method of estimating factors is the centroid m e t h o d .
The centroid method for determining one factor is as follows:
if several variables seem to form a similar group, all having
high intercorrelations amongst themselves and low intercorre­
lations with the rest of the variables, this group can be
considered as a factor.
may be purely arbitrary.
grouping process.)

(The actual grouping of variables
The results do not depend on this

This factor can be computed, and its

effect eliminated from the original correlation matrix.

The

remainder is called a residual m a t r i x , which is the same type
of matrix as the original correlation matrix,

except that the

correlations shown are usually smaller, since the effects of
one of the factors contained in the original correlation
matrix have been removed.

In this residual matrix, the

communalities can be reestimated; this increases the precision
of estimation.

If the first factor has explained all the

variation in the original correlation matrix, the correlations
remaining will approximate zero; however, if high intercorre­
lations still remain, the residual matrix is analyzed in the
same way as the original correlation matrix, to investigate
whether any further groupings can be found signifying further
factors.

This process continues until the residual matrix

approximates zero.

r

- 19 -

Interpretation of Factors - Two Factor Example
A geometric interpretation of factors gives the clearest
conception of the relationships which exist among the
variables and the factors.
Independent factors are assumed to be orthogonal to each
other, having no influence upon each other.

The factor

matrix which is determined by the previously described
centroid method is composed of these orthogonal factors,
since the effect of each factor is removed individually.
In the multiple centroid method, the factors determined
simultaneously are later transformed into orthogonal factors.
The following example from Thurstone (12, pp. 67-39)
will illustrate some of the geometric concepts involved.

A

sample factor matrix of two factors is shown in Table I.
These two factors are assumed to be derived from an intercorre
lation table from eight variables, with communalities along
the diagonal.

This intercorrelation table is given in Table

II.
Since the factor matrix is orthogonal,
ings can be graphed directly.

the factor load­

The graph of these factors

is a factor structure. as shown in Figure 4-

As can be seen,

Factor A is in one direction while Factor B is orthogonal to
it; variables 2 and 5 involve only Factor A, variables 6 and
6 involve only Factor B, and variables 1, 3, 4 and 7 are
combinations of both A and B.
explained by Factor A.

Variable 2 is almost totally

If variable 2 were completely

explained by Factor A its factor loading on A would be 1.00:
«;* *

-

20

since its factor loading on A is only .90, there is still
some part of its variation not explained by that factor.
However, Factor B cannot explain any of the variation in
variable 2; the factor loading of variable 2 is .00 for
Factor B.

Variable 5 is likewise only explained by Factor

A and not influenced at all by Factor B; however, less of
its variation is explained by Factor A than is variable 2.
Variables 1 and 4 are explained by both Factors A and
B.

They are more explained by A than B, however,

since

their projections in the A direction are greater than those
in the B. direction.
by

Variables 3 and 7 are also explained

both A and B, but more
Actually, A and B are

reference vectors.

by B than by A.
not factors;

they are called

They provide the axes, or the orientation,

for the configuration of variables.

This configuration

could be drawn without axes, as in Figure 5.

The configuration

remains fixed; the variables retain the same relationship to
each other in this same pattern.
axes is purely arbitrary.

The selection of reference

For example, the axes could be

rotated in various directions and located as in Figure 6.
In

each of these rotations

a new factor matrix is derived.

The reference axes are

selected so as to make the struct­

ure as simple as possible.

If several variables are colinear,

or cluster in the same direction,

it is desirable to place

one reference axis in that direction,

since all these variables

could then be explained by the one reference vector rather
than by several.

This can be illustrated by Figure 6.

Table I.

Example of a Factor Matrix

8
.ables

A

B

.70
.90
.40
.60
.50
.00
.30
.00

.30
.00
.60
.30
.00
.60
.B0
.B0

i

1
2
3
4
5
6
7
B

i

Table II.

Example of a Complete Correlation
Matrix with estimated communalities

Variab les

1

2

3

4

5

6

7

B

1
2
3
4
5
6
7
B

.5$
.63
*46
.51
.35
.IB
.45
.24

.63
.Bl
.36
.54
.45
.00
.27
.00

.46
.36
.52
.42
.20
.36
.60
.4B

.51
.54
.42
.45
.30
.IB
.42
.24

.35
.45
.20
.30
.25
.00
.15
.00

.IB
.00
.36
.IB
.00
.36
•4B
•4B

.45
.27
.60
.42
.15
.4B
.73
.64

.24
.00
*4B
.24
.00
.4B
• 64
.64

Figure 4

Example of Factor Structure
obtained from Factor Matrix
in Table I.

Figure 5.

Example of a Variable
Configuration

ro
H
1

a. Factor Structure I

b. Factor Structure II

S

z
c. Factor Structure III

Figure 6. Examples of Different Possible Factor
Structures from O n e Configuration

- 23 -

In Figure 6a variables 2 and 5 can be explained in terms of
A alone, while variables 6 and £ can be explained in terms
of B. alone.

In Figure 6b all of these variables can only

be explained in terms of both A and B.
This difference in the location of reference axes is
very important.
purely arbitrary,

Since the selection of reference axes is
there is no absolute way of determining

what factor is represented by reference vector A.

However,

if reference vector A alone is found in several variables
and explains a large part of the variation in those variables,
they can be analyzed to see what similarities they possess
and what single explanatory concept connects them all.
concept becomes the basis of Factor I.

This

Thus the identification

of the factor depends on the investigator; it is his job to
analyze the variables which cluster together and determine
what the unifying concept is, and hence what the factor is.
There is another consideration in the location of the
reference vectors, especially in regard to economic data.
In addition to identifying the unifying concept comprising
any single factor, it is desirable to select one variable
which best typifies that factor, for further investigation
and for estimation purposes.

This

variable is the one which

lies closest to the reference axis

and has the longest

pro­

jection on that axis (hence having

most of its variation

explained by that reference vector

alone).

The amount of explained variation of any variable equals
the sura of squares of the factor loading of that variable*

- 24 -

Thus the explained variation for variable 1 in the above
example (see Table I) is (.70)^

which equals .53*

Since the total variance for any variable equals unity, the
unexplained variance for variable 1 is .42.
be put in terms of percentages; thus,

These may also

of the variation

in variable 1 is explained by Factors 1 and II, while 42^
is not.

The unexplained variance may be caused by specific

factors occuring only in this variable or by errors in the
measurement of this variable.
The amount of explained variation remains the same no
matter how the reference axes are rotated.

This is true

since the configuration between the variables remains the
same.

It can also be noted that the amount of explained

variance of each variable is equal to the communality of
that variable.
In summary, the factor matrix is an orthogonal matrix.
It can be graphed, with the graph giving the fixed config­
uration existing between the variables.

The first graph

depends on a pair of reference axes which are determined by
the method used to derive the factor matrix (which in most
cases arbitrarily determines the location of the axes); these
reference axes can be rotated to obtain the best factor
structure, with as many variables as possible located on
these axes and with as large projections as possible on
these axes.

The amount of explained variation for any one

variable is equal to the sum of squares of the factor load­
ings of that variable; the unexplained amount equals one

- 25 -

minus the explained amount.

The unexplained amount may be

due to specific factors or errors in measurement of the
variable.

It is up to the investigator to determine what

the unifying concept is for any one factor and to select
the variable which best typifies that factor for use in
estimating equations.
Extension to More Than Two Factors
The above example has dealt with a factor structure in
two dimensions, signifying two factors.

This may also be

extended to three factors, or, geometrically, a threedimensional structure.

Although it is more difficult to

illustrate geometrically, all the concepts which apply to
two factors are also applicable to three.

Independent

factors are orthogonal, the axes must still be rotated to
obtain the best factor structure, the explained variance is
the sum of squares of the factor loadings, and the investi­
gator must still decide what the factors represented
actually are.
When the number of factors goes beyond three, the geo­
metric analysis breaks down completely.

However, theoret­

ically four factors span a four-dimensional space, five
factors span a five-dimensional space, while n factors span
an n-dimensional space.

The same factor concepts are still

applicable as in the case of fewer numbers of factors.
The graphic technique described above shows how the refer-

- 26 -

ence axes are located to get the best structure possible
for two factors.

(See Figure 6.

shown by Figure 6a.)

The best structure is

When three or more factors are

present this same technique may be used with but a slight
modification.

Each pair of common factors are graphed, and

the rotation of axes accomplished in a series of rotations,
adjusting first one reference axis and then another until
the best structure is found for all the factors.
Correlated Factors
Until this time only completely independent factors
have been considered, represented by orthogonal reference
axes.

However, this restriction is unnecessarily severe,

especially in regard to economic data.

Basic factors might

be, and probably are, correlated in economic data.

By remov

ing the restriction of strictly orthogonal reference vectors
it is possible to discover whether or not the basic factors
are correlated and, if so, how closely they are correlated.
This may be done by deriving the best structure possible
for the factors without the restriction that the reference
axes are orthogonal to each other.

If it appears that a

cluster of variables determines a reference axis at an acute
or obtuse angle to another axis, this is investigated.

If

the new structure derived is better than the structure
derived with orthogonal axes, it is accepted.

The angle

between the axes signifies the strength of the correlation
between the factors.

7a.

Rotation 1

1Tf

/

/

/

/

/

,
^
o
ZiS,__________ LZ 41
/

7b.

Figure 7.

Rotation 2

Example of a Rotated Factor
Structure

-

26

-

A hypothetical example is set up in Figure 7.

This

shows that one cluster of variables may locate reference
axis I, but an axis orthogonal to I will have little meaning
as far as the second cluster of tests is concerned.

Rotat­

ing reference axis II to I I ’ will improve the structure; it
also shows that the two factors located by these two refer­
ence axes are correlated with each other.
It does not matter whether the orthogonal axes are
located as in Figure 7a or as in 7b.

In Figure 7a, reference

axis I goes through the cluster of 1, 3, 4 and II must be
rotated to go through the cluster 2, 5, 6.

In Figure 7b,

reference axis II goes through the cluster 2, 5, 6 and I
must be rotated to go through 1, 3, 4.

The end result, how­

ever, gives the two axes with the same angle of separation.
This also illustrates the point that it does not matter in
what order the reference axes are rotated.

Since the config­

uration of variables remains fixed, the end results are the
same no matter what order of rotation is used.
Inclusion of Data
The original variables are explained in terms of a
smaller number of common factors which are found in several,
if not all, of these original variables.

It can be seen if

an extraneous variable is included, since the common factors
will explain very little of the variance of this variable
and most of its variance will be left unexplained.

This

- 29 -

unexplained variance will be due to an unique factor in
this variable.
However, if several new variables which contain the
same, but previously unique, factor are added to the matrix,
this factor would then become a common factor found in the
otherwise extraneous variable and the newly added variables.
It would be added to the common factors derived prior to the
inclusion of the new variables.

The importance of this new

common factor could then be investigated.
Thus, if extraneous variables are included in the analysis,
they can be identified as such and eliminated.

However, the

inclusion of new variables might show this extraneous
variable to contain a common factor after all.

Also, the

inclusion of new factors might change the importance of
previously determined factors in relation to the variables
in the analysis.
One further point is pertinent here.

If it is found

that very little of the variance of a variable has been
explained by the factors, there is nothing to indicate what
further factors will explain this variance.

The investigator

might set up his study anew with revised variables, but this
depends on his judgment alone.

There is nothing in the

method of factor analysis to aid him here.

- 30 -

Strengths and Weaknesses of the Method of Factor Analysis
The main strength of factor analysis is its ability to
reduce a large number of variables to a smaller number, with­
out losing any of the essential information.

This smaller

number of variables enables one to recognize the fundamental
factors present in the large body of variables, since they
are the basic component parts of the larger body.

It is

then possible to work with the factors rather than the com­
plete number of variables.
There is value in being able to recognize what these
basic factors are, and in analyzing how they fit into a
theoretical framework.
estimation purposes.

These factors can also be used for
The factor analysis aids in the selec­

tion of variables which best represent the various factors,
and these variables can be used in standard regression
systems.

This method of selection of variables should be

more reliable than that based solely upon the judgment of
the investigator in setting up regression systems.
There are some weaknesses in the method of factor analysis
itself.

The most important of these are the estimation of the

communalities, the rotation of the reference axes to obtain
the best factor structure, and the identification of the
factors.

These are places where the investigator makes

independent decisions which affect the over-all results.
However, experience in the methods and techniques of factor
analysis can reduce these weaknesses to a minimum.

- 31 -

More serious are the weaknesses which result from
theoretical concepts.

Factor analysis cannot be used to

distinguish causes from effects.

It shows what the primary

factors are, and whether they are independent of each other
or correlated in some way, but it is not designed to show
casual relationships.

Also, the method gives no indication

of what variables should be included in the total number of
variables in the analysis.

These must be selected on the

basis of theoretical concepts, independent information,
and the judgment of the investigator.

If a number of impor­

tant variables are excluded from the analysis, the results
obtained may or may not indicate that some variables are
lacking.

If they are subsequently included, and they are

important to the analysis, the previously obtained results
may be completely revised.
Factor analysis is based on a table of intercorrelations.
Whenever time series are used in correlations a theoretical
weakness exists since successive items are not independent
of each other and serial correlations occur in the data.

In

other words, a price of one month depends on the prices of
preceding months, in addition to other causal factors.

This

serial correlation in individual variables is assumed to be
less than the intercorrelation between variables, however,
so that time series can be used in correlations.
In using time series data for factor analysis, a factor
structure is obtained for any given time period.

In order

- 32 -

to obtain the same factor structures for two time periods,
similar conditions have to exist for both periods.

For

example, a shift in demand could not occur without causing
changes in the factor structure.

Factor analysis over two

time periods might be used to examine whether these changes
had taken place and how they had affected different
variables.
When a variable has factor loadings on more than one
factor it is possible to describe the variable in terras of
the factor which appears to influence that variable to the
greatest extent.

In terms of the example given earlier,

where body build and metabolic rate factors were determined
from body measurements, the chest expansion measurement
might be said to be influenced primarily by the body build
of an individual.

However, knowing the metabolic rate of

the individual would also be necessary before the chest
expansion could be accurately determined.

Thus the affects

of all the factors would have to be considered before the
total net effect could be determined.
It should be emphasized that the naming of the factors
in a factor analysis is not a part of the factor analysis
itself.

The factor clusters are determined by the analysis,

but the naming of the factors depends primarily on the
investigator.

The naming of the factors is most important

in fitting these factors into a theoretical framework.
There are many ways of describing a factor structure.
A cluster of variables determines a factor, which in turn

can be said to explain these variables.

This is a kind of

circular process, where the variables locate the factor and
the factor explains the variables.

Occasionally some

variable might appear to be principally influenced by a
factor in the factor structure when there is no logical
correspondence between the variable and the factor.

This

could be due to a relationship which further study might
uncover; it could also be due to an accidental relationship
where the factor and the variable apparently followed the
same trend due to different causes.

Mis-naming a factor

might also lead to a contradiction between factors and
variables in the factor cluster.

The judgment of the inves­

tigator plays an important part in deciding whether a factor
•influences* or ’explains* the variation in a variable or
whether the factor and the variable in question follow the
same trend due to an incidental relationship.

This depends

primarily on background knowledge concerning the area being
investigated.

- 34 -

IIX.PROCEDURE
Basic Data Used
The data used in this study were aggregate statistics
of the United States, published by the U. S. Department of
Agriculture, Department of Commerce, and Department of
Labor.

Data on supply, disappearance and prices (both whole­

sale and retail) for the individual dairy products were
included.
conditions,

Information was also included on general economic
such as wholesale and retail price levels, depart

ment store sales, purchasing power, employment,

etc.

These

will be enumerated in detail in a subsequent section.
Since so many variables were included,

it was necessary

to get a large number of observations for each variable.

Due

to the analysis used, it was also necessary to have these
observations for comparable time periods.

Annual data were

not adequate, since many time series are only complete back
to 1929; this would provide only twenty or so observations
for each variable, and these would be too few to obtain
consistent results for the number of variables involved.
Consequently monthly data were employed for each variable.
The effects of different time periods in such a problem
were considered, especially in regard to the war years.

To

investigate this, two different sets of analyses were made,
utilizing the same variables but comprising two different
time periods.

The first covered the period from 1929 to

1941; the second covered the period from 1929 to 194&.

Hen##

- 35 -

the first begins with the boom year 1929, includes the
depression period of the 1930*s and the start of the up­
swing at the end of the 1930*s and the early 1 9 4 0 fs, ending
with the U. S. entry into World War II.

The second includes

all of the information of the first plus the World War II
period and some of the period of post-war adjustments as
well.

These analyses will be discussed individually and

then compared to discover the similarities and differences
between the two.
There has been some criticism concerning the accuracy of
the basic data used in this study.

Much of this criticism

is justified,

since it is recognized that these data contain

many errors.

Some of these errors are due to interpretation

concerning what should be included in or excluded from the
data.

Others are due to the unavailability of data due to

a lack of records or the presence of non-comparable data.
Still others are due to the attempt to cover the entire
United States, thus raising the problems of sampling from
such a large population and of covering the entire population
satisfactorily.
These errors in the data are certain to affect the results
of this study.

However, if the errors are small relative to

the reported statistics, the effects of these errors can be
assumed to be at a minimum especially in regard to the factor
analyses.

This is true since small relative errors would

have little effect on the correlations which are the basis

- 36 -

of the factor analyses.

iSven if these errors were relatively

large, it would still be possible to test the applicability
of the method of factor analysis to economic data, although
the results of the factor analyses might change with revisions
in the basic data.
The errors in the basic data become of greater importance
when an attempt is made to forecast future events.

Using

incomplete or incorrect data cannot yield accurate r e s u l t s ;
the results will be in terms of these data.
bias exists in the data,

If a systemmatic

i.e. a constant overestimation or

underestimation, this would not seriously affect the results;
however, this would not be the case if these errors are not
systemmatic•
Description of Data
There are 76 variables included in this study.

A complete

description of these, including the sources from which they
were obtained, is given in Table III.

(The variables are

numbered to 79, but 67 and 66 are combined in the one variable
67A while 69, 70 and 71 are the single variable 69A.

This is

due to the I.Q.M. set-up.)
PRODUCTION:

The total U. S. monthly milk production consti­

tutes variable #1.

Total monthly factory production of the

various dairy products, butter, cheese, evaporated milk,
condensed milk, dry whole milk, and dry or powdered skim milk
constitute #2-7 and #42.

Oleomargarine production, #£, has

- 37 -

also been included for comparison with butter.
PRICES:

The average price per pound of butterfat, #9, and

the average wholesale price per hundred pounds of milk, #10,
have been included as general,
prices.

over-all indicators of milk

The butterfat-feed price ratio, #11, and the milk-

feed price ratio, #12, are included to investigate what
relationships they might have with the other variables.
The average price to producers for fluid milk constitutes
# 13 , while the average price to producers for manufactured
products is #14.

The average wholesale prices for the six

previously listed dairy products are #15-lS, #41 and 42,
while the average retail prices for dairy products are #1922.

The average price per pound of uncolored oleomargarine

is variable # 23 .
The general Consumers Price Index (i.e. Cost of Living
Index), the Consumers Price Index for Foods, the Wholesale
Price Index, and the Wholesale Price of Foods Index have
been included in #24-27 to investigate their relationships
with other variables.
Real wholesale and retail prices of the various dairy
products comprise variables #43-51, 54-57.

These were derived

by dividing each actual monthly retail price by the corre­
sponding monthly Retail Food Price Index (#25) and each
actual monthly wholesale price by the corresponding Index of
Wholesale Food Prices (#26).

Table III.
Variable
Number

Variables Included in this Study, giving Description and Source of Data

Source

Description

B .A.E.,

U,>S.D

1

Milk, Production on Farms

2

Creamery Butter Production

it

it

3

Cheese, Total Factory Production

n

IT

4

Evaporated Milk, unskimmed case goods production

rt

II

5

Condensed Milk, skimmed, sweetened and unsweetened
case goods and bulk goods production

n

II

6

Dry Whole Milk Production

tt

n

7

Ice Cream Factory Production

ti

tt

*

Oleomargarine Production

n

ti

9

Butterfat, average price per pound received by farmers

ti

it

Milk, average wholesale price per 100 pounds received
by farmers

tt

n

11

Butterfat-feed price ratio

if

n

12

Milk-feed price ratio

n

n

13

Milk, average dealers buying price per cwt. for
standard grade milk

it

tt

10

Table III.

Variable
Number

Variables Included in this Study, giving Description and Source of Data
(Continued)

Description

Source
B.A.E.,

U,.S.D.A.

14

Milk, average price per cwt. paid producers for 3»5
milk delivered at condenseries

15

Cheese, fresh single daisies, average wholesale
price per pound at Chicago

ti

ti

16

Cheese, American twins, wholesale price on
Wisconsin Cheese Exchange

it

n

17

Creamery Butter, 92 score, average wholesale price
per pound

n

it

IS

Evaporated Milk, unsweetened, average wholesale
selling price per case

n

it

19

Milk, fresh, delivered, average retail price per
quart in leading cities

ti

ti

20

Evaporated milk, average retail price per 14i oz.
can in leading cities

it

n

Cheese, average retail price per pound in leading
cities

ti

ti

Butter, average retail price per pound in leading
cities

ti

it

Oleomargarine, uncolored, average retail price per
pound in leading cities

ti

n

21

22

CM

Table III.

Variable
Number
24

Variables Included in this Study, giving Description and Source of Data
(Continued)

Description
Consumers Price Index for moderate income families
in large cities

25

Retail Food Prices in large cities

26

Index Numbers of Wholesale Prices in Foods
(1 9 2 6 = 1 0 0 )

27

Wholesale Price Index of All Commodities ( 1 9 2 6 : 1 0 0 )

2S

All Manufacturing Industries, Employment Index
(1 9 3 9 = 1 0 0 )

29

Source
B «L *S *,

U.S.D.L.

it

tt

n

tt

n

tt

it

tt

ft

n

All Manufacturing Industries, Payroll Index
(1 9 3 9 = 1 0 0 )

30

Retail Cost of the Market Basket

31

Marketing Margin on the Market Basket

tt

ft

32

Retail Cost of Dairy Products in the Market Basket

tt

tt

33

Marketing Margin on Dairy Products

tt

tt

34

Fluid Milk Marketing Margin

tt

ft

35

Butter Marketing Margin

tt

tt

36

American Cheese Marketing Margin

tt

tt

B.A.E.,

U.S.D.A.

Table III.

Variable
Number

Variables Included in this Study, giving Description and Source of Data
(Continued)

Source

Description

B.A.E.,

37

Evaporated Milk Marketing Margin

3$

Estimated Population of the U.S.-

39

Department Store Sales

Federal Reserve Bulletin

40

Milk, dry or powdered whole, average wholesale
selling price per pound

B.A.E.,

U.S.D.A

tt

tt

U.S.D.A.

41

Dry Skim Milk, average wholesale price per pound

n

Tt

42

Dried or Powdered Skim Milk, production

ti

tt

43

Cheese, real wholesale price per pound at Chicago

44

Cheese, real wholesale price on Wisconsin Cheese
Exchange

16 *

26

45

Creamery Butter, real wholesale price per pound

17 -•

26

46

Evaporated Milk, real wholesale price per case

18 :*

26

47

Delivered Milk, real retail price per quart

19 -•

25

48

Evaporated Milk, real retail price per 14i oz. can

20 1

25

49

Cheese, real retail price per pound

21 1

25

50

Butter, real retail price per pound

22 :

25

#15 * # 26

Table III.

Variable
Number

Variables Included in this Study, giving Description and Source of Data
(Continued)

Description

Source

51

Oleomargarine, real retail price per pound

52

Market Basket, real retail cost

30 7

25

53

Dairy Products in the Market Basket, real retail :ost 32 7

25

54

Dry Whole Milk, real wholesale price per pound

40 -• 26

55

Dry Skim Milk, real wholesale price per pound

41 7

56

Butterfat, real wholesale price per pound

57

Milk, real wholesale price per cwt. received by
farmers

10 ^t 26

53

Real Purchasing Power in Manufacturing Industries

29 7

59

Manufacturing Industries Employment, advanced one
month

60

#23 i #25

26

9 ^• 26

#23 advanced one month

Manufacturing Industries Employment, advanced two
months

2$ advanced two months

61

Retail Food Price Index, advanced one month

25

it

one

w

62

Retail Food Price Index, advanced two months

25

it

two

ti

63

Real Purchasing Power, advanced one month

53

n

one

it

64

Real Purchasing Power, advanced two months

53

ti

two

it

Table III.

Variable
Number

Variables Included in this Study, giving Description and Source of Data
(Continued)

Description

Source

65

Consumers Price Index, advanced one month

#24 advanced one month

66

Consumers Price Index, advanced two months

24

67-A

Time Variable, months

1,2,3,...,11,12

69-A

Time Variable, consecutive

1,2,3,....,156,....240

72

Time Variable, seasonal

73

Creamery Butter, disappearance

74

Cheese, factory made, whole milk and part skim,
disappearance

tt

tt

75

Evaporated Milk, disappearance

tt

rt

76

Fluid Milk and Cream, disappearance

tt

tt

77

Total Freight Car Loadings Index (1935-39 = 100)

7*

Short Term Consumers Debt

n

tt

n

79

Industrial Production, Index of Physical Volume
(1935-39 = 100)

tt

It

Tt

M

two months

1

B.A.E.,

U.S.D.A.

£
1

Federal Reserve Bulletin

- 44 -

In order to investigate the effects of one m o n t h ’s
prices on a subsequent m o n t h ’s production or prices, four
indicators are used.

The Retail Food Price Index is ad­

vanced one month and thus compared with the other variables
for the following month (for example,

the Retail Food Price

Index for January advanced and analyzed against February
data) in #61; it is advanced two months and compared with
the variables two months afterwards in #62.

The general

Consumers Price Index is advanced one month and compared
with the variables in the following month in #65; it is
advanced two months and compared with the variables two
months afterwards in #66.
MARKETING M A R G I N S :

The Retail Cost of the Market Basket

and the Retail Cost of Dairy Products in the Market Basket
comprise #30 and 32.

(The Market Basket of Farm Food Products

is a total of 93 food items, in proportions determined by the
1935-39 annual average purchases per family of three average
consumers.)

The marketing margins on the market basket, on

dairy products as a whole, and on various individual dairy
products comprise #31 and #33-37.

(A marketing margin is

defined by the B.A.E. as the difference between the retail
price paid by consumers for a commodity and the payment to
farmers for an equivalent amount of that commodity.)
DISAPPEARANCE DATA:

The monthly disappearance of butter,

cheese and evaporated milk appear as #73-75.

The data for

January, 1941 to February, 1947 have been approximated, since

- 45 -

the war years were not included in the regular published
series.

The approximations were made by using the annual

disappearance data (which were available) for the individual
products, and applying a monthly seasonal index for each
which was derived from the monthly data of 1920-1940 and
1947-1949•

The method of link relatives was used to derive

these monthly seasonal indexes (3).

These consumption data

were for civilian consumption alone during the war years.
The monthly disappearance of fluid milk and cream,#76,
was computed as a residual of the total monthly milk supply,
after subtracting the milk equivalent of butter,

cheese,

evaporated milk and ice cream produced each month.

This was

necessary because the disappearance data for fluid milk and
cream have not as yet been published.

The four products

listed above cover most of the dairy products consumed out­
side of fluid milk and cream and were the only ones available
for use.

The determination of the disappearance of fluid

milk and cream by this residual method should approximate
the actual disappearance relatively closely.

A table showing

the monthly disappearance of fluid milk and cream which was
determined by this method is given in Appendix C.
There are no disappearance data shown for ice cream,
since it is assumed that the production figures reproduce
the disappearance figures closely.

The milk equivalent for

monthly ice cream production (used to derive the residual
disappearance data of fluid milk and cream) was derived by
taking the milk equivalent of the annual ice cream production

- 46 -

(which is available) and allocating it among the months by
the amount of monthly ice cream production.

This is not

completely accurate, since some storage stocks of fluid
cream are used for ice cream production during the off-season;
however, it should be sufficiently accurate for use here.
GENERAL ECONOMIC INDICATORS:

The Index of Employment for

All Manufacturing Industries comprises variable #23.

This

is used as an indicator of total employment since the actual
data for total U. S. employment is not available for a
sufficient period of time.

It has been assumed, however,

that employment in manufacturing industries is a major element
of total employment, and thus can be substituted for it.

This

employment variable is advanced.one month in #59, to note the
effect on other variables (i.e. January employment compared
with February data); it is advanced two months in #60.
The Index of Payrolls in All Manufacturing Industries
comprises #29.
income.

This is used as an indicator of national

Actual national income statistics published on a

monthly basis are adjusted for seasonal variation and it is
almost impossible to derive the unadjusted data.

This Index

of Payrolls for manufacturing industries serves as an
indicator of relative movement of the national income, rather
than as an indicator of its absolute level.

It omits such

things as pensions, interest, and dividends; however, the
series does provide comparable information on a relative
basis even if somewhat understating the absolute levels.
An estimate of real purchasing power in manufacturing

- 47 -

industries (#5#) was derived by dividing each m o n t h ’s pay­
roll figure by the corresponding m o n t h ’s Consumer Price
Index for All Commodities.

This estimate of real purchasing

power advanced one month is variable #63 $ it is advanced two
months as #64.
The estimated population of the U. S. constitutes #3#*
Estimates have been made for the population at six months
intervals, for January 1 and July 1, since 1909.

These

figures were adapted to monthly estimates by a straight
line interpolation for each six months period.
period 1941-194#,

During the

the estimates of the civilian population

were used, rather than those of the total population, thus
eliminating from the estimates the members of the armed
forces.

This estimate of the population is included as a

separate variable, rather than taking account of it in the
other variables (i.e. instead of using per capita production
and consumption data the totals are used, and the population
estimate included separately).
Total U. S. Department Store Sales are included as
variable #39.

Total freight car loadings comprise #77,

short term consumers debt is #7#, and the physical volume
of industrial production is #79.
TIME VARIABLES:
included.

Three estimates of time trends have been

A monthly time trend comprises #67-A (which in

the original numbering of the variables included both 67
and 66); this was set up by a consecutive numbering of the

- 46 -

months from January to December.

Thus a cyclical pattern

is set up, which is repeated for each year.

A consecutive,

straight line trend comprises #69-A (which in the original
numbering of the variables included 69, 70 and 71)•

This

was to investigate any steady increase which exists in the
included variables.

A special seasonal trend comprises #72 ;

this was set up to approximate the seasonal pattern of monthly
milk production, in order to

investigate its relationship

with dairy products data.
Processing of Data
Since a factor analysis cannot begin until a table of
intercorrelations is set up, the first step was to compute
the simple correlations between each pair of variables for
this table.

With 76 independent variables in this study,

there were 2,650 distinct correlations to be computed for
each intercorrelation table and, since two different time
periods were used, two intercorrelation tables were necessa­
ry.

This made a total of 5*700 simple correlations to be

computed.

Each correlation in the first table contained 240

observations (the 240 months in the period 1929-1946), while
each correlation in the second table contained 156 observa­
tions (the 156 months in the period 1929-1941).
Using the raw data for these computations would have
involved very long and cumbersome processes.

Using first

differences between the observations, logarithms, or link
relatives would have been just as cumbersome, if not more so.

- 49 -

Also, making all the necessary computations by hand calcu­
lating machines would have meant not only innumerable hours
of labor but also innumerable possibilities of errors, due
to the number of computations necessary.
In order to cut down on the number of computations and
the possibilities of error, an I.B.M. tabulation set-up was
used.

Bach variable was coded by taking the complete range

of data and dividing it into equal parts,

corresponding to

the ten numerals (0,1,2,•..,3,9) in each column of an I.B.M.
card.

Each variable was then assigned to a column of the

I.B.M. card, i.e. variable #42 was also column 42 on the
card.

(The reason why some numbers were skipped in the

numbering of the variables was due to this correspondence
between the number of the variable and the number of the
column on the I.B.M. card.

The monthly seasonal time

variable, #67-A, actually was placed in two columns,

67 and

66, since the seasonal was coded to correspond to the months,
i.e. as 01,02,••.10,11,12.

The straight line trend, #69-A,

actually covered three columns, 69, 70 and 71, to correspond
to the consecutive numbering of the observations as 001,002,...,011,012,013,•••,239,240 for the first intercorrelation
matrix and 001,002,...,011,012,013,...,135,156 for the second
intercorrelation matrix.

These numbers correspond to the

total number of months in each time period.

The variables

#67-A and 69-A were the only ones to cover more than one
column on the I.B.M. card; all the other variables were
coded to include all the data in a single column.

- 50 -

Since the range of data usually differed for the two
time periods under consideration, with higher values usually
occurring in the period from 1941 to 1946 than had occurred
during the period 1929-1941» two different codes were neces­
sary for the two intercorrelation tables.

For example, in

variable # 7, Monthly Ice Cream Production, the range of
values for the period 1929-1946 was from 5,631,000 gallons
to 91,956,000 gallons per month.

A large part of this

increase occurred during the latter part of this period,
however.

During the period 1929-1941 the range of values

was from 5,631,000 gallons to 54,661,000 gallons per month.
The coding had to take into consideration this difference in
the two time periods.

The two complete codes for variable

7 are reproduced in full in Table IV.

Each variable was coded in this same fashion.

Each

code was different, depending on the range of values for
the time period under consideration.

The coding interval

for each individual variable was kept constant, but this
interval varied between variables and also varied for the
same variable in the two correlation matrices due to the
difference in the range of values.

For example, in the code

for "Ice Cream Production 1929-1946" (see Table IVa) the
coding interval was 10,000,000 gallons, and this stayed
constant for each code number.

In the code for "Ice Cream

Production 1929-1941” (Table IVb) the coding interval was
5,000,000 gallons and this stayed constant for each code
number.

However, the code interval obviously changed for

Table IV.
IVa.

Illustration of Coding Used for I.B.M. Process1
IVb.

Ice Cream Production
Analysis I

Code
Number

Analysis II

1929-1943

Interval (in thousands)

0

below 10,000

1

10,000

- 19,999

2

20,000

-

3

30,000

4

Ice Cream Production

Code
Number

1929-1941

Interval (in thousands)

gallons

0

below 10,000

11

1

10,000

-

14,999

"

"

2

15,000

-

19,999

"

- 39,999

"

3

20,000

-

24,999

"

40,000

- 49,999

"

4

25,000 - 29,999

n

5

50,000

- 59,999

11

5

30,000 - 34,999

n

6

60,000

-

"

6

35,000 - 39,999

"

7

70,000

- 79,999

"

7

40,000 - 44,999

"

d

SO,000

- up

d

45,000 - up

29,999

69,999

'Codes given are for Variable jf7, Monthly Ice Cream Production

gallons

- 52 -

the same variable in the two time periods, and the code
interval also changed between different variables since
the range of values was different.
Each I.B.M. card contained the coded information for
all variables for any given month.

Thus the coded data

for all variables for January, 1929 were on card #1, the
data for February, 1929, on card ^2, etc.

By appropriately

sorting and tabulating these cards, the sums, sums of
squares, and cross-products for each variable were obtained.
These were used to obtain the necessary correlations, which
were computed by hand calculating machines rather than by
I.B.M. machines.

However,

the use of the I.B.M. tabulations

greatly reduced the number of hand computations and also
reduced the number of possible errors in the calculations.
Comparison of Coded and Uncoded Data
It was recognized that coding the variables for the
I.B.M. process might change the value of the correlation
coefficients.

A small number of variables were selected

and correlations were computed for them using the uncoded
data.

Comparisons of the correlation coefficients derived

from coded and uncoded data from both intercorrelation
tables are given in Table V.

These variables were selected

at random from the complete correlation table.
The correlations for the coded data are generally lower
than those for the uncoded data.

Thus it can be said that

- 53 -

Table V.

Comparison of Some Randomly Selected Correlation
Coefficients showing Differences between Coded
and Uncoded Data
A. Matrix I

Variables
being
Correlated
1,
1,
1,
1,
25,
3,
12,
19,
24,
28,
31,
39,
47,
59,
3,

1,
1,
1,
1,
25,
3,
12,
19,
24,
28,
31,
39,
47,
59,
3,

10
25
28
29
28
12
19
24
28
31
39
47
59
61
61

10
25
28
29
28
12
19
24
28
31
39
47
59
61
61

1929-1943

Correlation Coefficients
Coded
Uncoded
.1971
.2379
.3492
.3414
.7008
-.1086
.1586
.9461
.7141
.4396
.6398
-.5813
-.4861
.6989
.4765

.2009
.2605
.3725
.3744
.7330
-.1030
.1834
.9776
.7178
.4511
.6873
-.5718
-.4885
.7340
.4957

B.

1929-1941

-.3029
-.0862
.0862
.0918
.4234
-.1987
.2055
.8753
.3009
.0394
.1013
.1199
.1564
.3970
-.1600

Matrix II

-.3005
-.0933
.1166
.1326
.4267
-.2159
.1965
.8726
.3044
.0569
.1106
.1058
.1100
.4348
-.1266

Uncoded-Coded
.0038
.0226
.0233
.0330
.0322
.0056#
.0248
.0315
.0037
.0115
.0475
.0095#
-.0024
.0351
.0192

.0024#
-.0071
.0304

.0408
.0033

-.0172
- . 0090#
-.0027#
.0035
.0175
.0093
-.0141#
-.0464#
.0378

.033^#

# Denotes poorer correlation coefficient in uncoded data
than in coded data

- 54

-

the correlation coefficients for the coded data are in
general underestimated rather than overestimated.

These

effects of the coding of the data might be reflected in the
factor analysis

but, if so, they would tend to under­

estimate the common factors and attribute more of the varia­
tion to unique factors.

This underestimation of common factors

is less serious than an overestimation of common factors.
Comparison of the Correlation Coefficients of the Two Time
Periods
From Table V it can be seen that the correlation
coefficients from Matrix I, 1929-194#, were generally higher
than those concerning the same variables from Matrix II,
1929-1941.

For example, the coded correlation between

variables #25 and #2# was .700# in Matrix I and .4234 in
Matrix II.

This was true for the majority of the coded

correlation coefficients.
It can be seen how the correlation coefficients were
lower during 1929-1941 than during 1929-194# when the effects
of the business cycle are considered in relation to these
data.

During 1929-1941 business conditions were in a very

unsettled state, and the variables under consideration here
were not all affected in the same fashion or at the same
time by these business conditions.

However, over the longer

period of time, with the addition of the inflationary trends
of the period 1941-194# which affected practically all of
these data in a like manner, the correlations between

- 55 -

variables Tor the period 1929-194# were generally higher.
Even though this discrepancy exists between these two
time periods, factor analyses can be applied to the individ­
ual time periods.

As subsequent sections show, the same

factors may be found existing in both time periods even
though differences exist in the correlation coefficients.
Computation Methods Used in Factor Analyses
A multiple group centroid method of factorization was
used in all analyses in this study.

Thus several factors in

any one analysis could be determined simultaneously.
residual matrix showed many large values,

If the

the factorization

process was continued until the residual matrix contained
values which were not significant.
was judged to be non-significant.

Any value below 0.16
Any variables showing

non-significant residuals were removed from the residual
matrix before the factorization process was continued with
the variables still showing significant residuals.
The communalities were estimated by selecting the high­
est coefficients appearing for the individual variables.
These communalities were reestimated in the residual matrix
before the factorization process was continued; this was done
to increase the precision of the estimate of the communalities.
However, in a few cases this reestimation of the communalities
resulted in over-determination, as the total communality
resulting from the factor structure was over 1.00.

Some of

- 56 ~

these cases have been mentioned in the text where they are
an important part or the analysis.

The complete number of

variables whose communalities have been overestimated can
be found in Appendix A, where the orthogonal structures and
communalities for all variables in the individual analyses
have been given.
An orthogonal structure was derived for each analysis•
The axes were then rotated to obtain the best simple
structure, and in most cases oblique, rather than orthogonal,
structures resulted.

The rotated factor structures, along

with the cosines of the angles between the factors, have also
been given in Appendix A.

In a few cases distortions arose

from the rotation process, where a variable which showed a
fairly clear structure in the orthogonal matrix showed an
indeterminate structure in the rotated matrix.

In these cases

both the orthogonal and the rotated matrices were used in the
analysis of the factor structure.

Most of these cases of

distortions due to the rotation process have been noted in
text wherever they were important in the analysis.
The absolute sizes of the factor loadings in the rotated
matrices were not of major significance.

The relative sizes

of the factor loadings within each factor were of importance
and the directions of the loadings, i.e. whether they were
positive or negative, were also of importance.

A comparison

of the sizes of factor loadings between factors of different
analyses or between factors within the same analysis would
have little meaning, as the sizes of these loadings depended

- 57 -

upon the rotation process which differed between analyses.
Individual Factor Analyses
The 76 variables included in this study were not analyzed
as a complete group.

Instead,

separate analyses were made of

various groups of these variables.

These groups included:

General Economic Conditions; Prices; Fluid Milk and Cream;
Butter; Cheese; Condensed, Dry and Evaporated Milk; Production
and Disappearance of Dairy Products; Marketing Margins.

Each

variable was used at least once in these analyses.
Each group consisted of two analyses, one covering the
period 1929-194# and the other the period 1929-1941.

A

comparison was then made of these two analyses, discussing
the similarities and differences which existed.

For the

individual commodities estimates of the monthly disappearance
in 1949 and the first part of 1950 were made using multiple
regression systems based on the factor analyses.

These regres­

sion systems were set up using the same number of variables
as there were factors in the commodity analysis and the
variables for these systems were selected from the point clus­
ters of the factors.
The analysis of General Economic Conditions included
graphs of the factor structure to illustrate the point clusters
and the factor configurations.

The graphic structure has been

omitted from all other analyses, however,

since these struct­

ures become more cumbersome when more than two factors are
present.

Each analysis contains a summary of the factor

- 5S -

structure, giving those variables showing simple structure,
those showing complex structure, and those showing an
indeterminate structure in the analysis.

The complete

orthogonal matrices and rotated matrices with the cosines
of the angular separations between the factors for all
analyses will be found in Appendix A.

-

59

-

IV. ANALYSES OF ECONOMIC DATA
GENERAL ECONOMIC CONDITIONS
All of the variables included in this study which
pertain to general economic conditions were included in
this group.

These included,among others, the consumers

price index, the food price index, indices of employment
and payrolls in manufacturing industries, real purchas­
ing power,

short term consumer debt, and industrial

production.
in Table VI.

A complete list of these variables is given
A discission of the factor analyses of

these variables follows.
Analysis I

1929-194&

Two factors were evident among these variables.
These factors were not independent; there was some rela­
tionship between them.

A graphic presentation of the

factor structure is given in Figure £.
structure is given in Table VII.

A summary of this

This latter table gives

the variables where the principal component was Factor I
or Factor II and the variables which were influenced
by neither Factor I nor Factor II (denoted as Indeterminate
in this structure).
The variables influenced by Factor I were wholesale and

- 60 -

retail prices, both actual and advanced through time, the
estimated population, and short term debt.

Almost all of

these are consistent with the hypothesis that Factor I was
an indicator of the general price level.

The general price

level factor had a great influence on the indexes of whole­
sale and retail prices and also on the amount of short term
consumer debt.

The appearance of the Estimated U. S. Popu­

lation in this group was probably an accidental relationship.
This will be discussed again later.
The variables influenced by Factor II were industrial
employment, both actual and advanced through time, real
purchasing power, both actual and advanced through time,
and an index of industrial production.

All of these are

consistent with the hypothesis that Factor II was an indica­
tor of general industrial activity.
Department Store Sales, #39, were primarily influenced
by Factor I, the general price level factor, although Factor
II, the industrial activity factor, appeared to have a
slight influence on this variable also.

Industrial Payrolls,

if2 9 , were mostly influenced by the industrial activity

factor, although the general price level factor influenced
them somewhat.
The Marketing Margin on the Market Basket, #31, appeared
to be largely influenced by the general price level factor
and somewhat adversely affected by the industrial activity
factor.

In other words, as industrial activity increased,

marketing margins on the market basket decreased; as industrial

Table VI.

Variable
No.
1.
2.
3.
4.
5.
6.
7.
d.
9.
10.
11.
12.
13.
14*
15*
16.
17.
Id.
19*
20.
21.
22.
23.
24.
25.

24
25
26
27
2d
29
30
31
3$
39
52
5#
59
60
61
62

63
64
65
66
67
69
77
7#
79

Variables Used in Analysis of General Economic Conditions

Description
Consumers Price Index for Moderate Income Families in Large Cities
Retail Food Price Index for Large Cities
Index of Wholesale Food Prices
Index of Wholesale Prices of All Commodities
Employment Index for All Manufacturing Industries
Payroll Index for All Manufacturing Industries
Retail Cost of the Market Basket
Marketing Margin on the Market Basket
Estimated Population of the U. 3.
Department Store Sales
Real Retail Cost of the Market Basket
Real Purchasing Power in Manufacturing Industries
Employment advanced 1 month
Employment advanced 2 months
Retail Food Prices advanced 1 month
Retail Food Prices advanced 2 months
Real Purchasing Power advanced 1 month
Real Purchasing Power advanced 2 months
Consumers Price Index advanced 1 month
Consumers Price Index advanced 2 months
Monthly Time Trend
Consecutive Time Trend
Index of Total Freight Car Loadings
Short Term Consumer Debt
Industrial Production (Index of Physical Volume)

•
o
h
»

- 62 -

Table VII.

Summary of Factor Structure of General
Economic Conditions
Analysis I

1929-194#

(2 factors)

Factor I
Variable
No.

Factor Loading
(Rotated Matrix)

24
25
26
27

.726
.741
.690
.65#

30
31
3#
39
61
62
65
66
7#

.697
.##6
.552
.4#4
.755
.760
.733
.737
.641

Description
Consumer Price Index in Large Cities
Index of Retail Food Prices
Wholesale Price Index for Foods
Wholesale Price Index for All
Commodities
Retail Cost of the Market Basket
Marketing Margin on Market Basket
Estimated Population of the U. S.
Department Store Sales
Retail Food Prices advanced 1 month
Retail Food Prices advanced 2 months
Consumer Price Index advanced 1 month
Consumer Price Index advanced 2 months
Short Term Consumer Debt
Factor II

2#

.690

29

.543

5#

.753

59
60
63
64

.6#6
.679
.747
.73#

69
79

.545
.743

Index of Employment in All Manu­
facturing Industries
Index of Payrolls in All Manufac­
turing Industries
Real Purchasing Power in Manufac­
turing Industries
Index of Employment advanced 1 month
Index of Employment advanced 2 months
Real Purchasing Power advanced 1 month
Real Purchasing Power advanced 2
months
Consecutive Time Trend
Index of Industrial Production
Indeterminate

52
67
77

Real Retail Cost of the Market Basket
Monthly Time Variable
Index of Total Freight Car Loadings

- 63 -

Figure 8.

Orthogonal Factor Structure of General Economic Conditions,
Analysis I, 1929-19i*8.
'

\

\

\

\

\

1.00) .. . . .

t

9gk

—

**’■
'
.75 . ....*

\

59

*

•29

\

i

... N

\
\

.50

4'
•77
,....

L

.25

y

•39
27*

38

Lev<il Factor

26. *
I

•
vn
ro

j
I

t"
'O.

f'
/
x. Generi 1 Price

¥

5

i

i
ii
i
i
i
i

i

.

CO
Is -

-,25

•31

-.50

-.75

-.25

o

_

.25

.50

.75

. . . ____ 1

1.00

- 64 -

activity decreased, marketing margins increased.

This is

consistent with the observation that marketing margins are
♦sticky*, and that they tend to respond slowly to economic
changes.

However, the high loading on Factor I shows they

were mostly influenced by the general price level factor.
The Real Retail Cost of the Market Basket, ^52, was not
influenced by either Factor I or Factor II.

VJith the effects

of the price level removed from this variable,

it should not

have been affected by the general price level factor.

This

signifies that real retail food prices were not influenced
by the level of industrial activity.
There was no evidence of a monthly seasonal pattern in
either of these factors, as the monthly seasonal trend
variable, # 6 7 , had insignificant factor loadings on both
Factor I and Factor II.
The Index of Freight Car Loadings was about equally
influenced by these two factors, although together they
explained only about 60^ of the variations in this variable.
Since neither factor was predominant and the factor loadings
of both were small in the rotated matrix, this variable has
been classified as Indeterminate in structure.
In summary, two factors were determined in this analysis
of indices of general economic conditions.

These were a

general price level factor and an industrial activity factor.
These two factors explained over SO percent of the variations
i*1 1& out of the 25 variables included in this analysis*

- 65 -

Analysis II

1929-1941

Two distinct factors were evident in this analysis? how­
ever, these factors were not independent.

A graphic presen­

tation of the factor structure is given in Figure 9.
summary of this structure is given in Table VIII.

A

The latter

gives the variables where the principal component was Factor
I or Factor II, the variables which were influenced by both
I and II (hence denoted as Complex in structure) and the
variables which were influenced by neither Factor I nor
Factor II (denoted as Indeterminate in this structure).
The first factor contained indices of retail prices, and
appears to be consistent with the hypothesis that Factor I
was an indicator of the general price level.

The Index of

Retail Food Prices was influenced by this factor, as was
this same variable advanced through time.

The same holds

true for the Consumer Price Index.
Factor II contained indexes of employment, payrolls,
purchasing power, short term consumer debt, and industrial
production.

These are consistent with the hypothesis that

Factor II was an indicator of industrial activity.

Variable

#73, Short Term Consumer Debt, was almost entirely influenced
by Factor II, even though this factor explained a somewhat
smaller proportion of the variance within this variable than
it explained in the other variables in this cluster.

Depart­

ment Store Sales, #39, also seemed to be influenced solely
by Factor II, even though this factor only explained a small
part of the variation within this variable.

This might b#

Table VIII.

Summary of Factor Structure of General
Economic Conditions
Analysis II

1929-1941

(2 factors)

Factor I
Variable
No.

Factor Loading
(Rotated Matrix)

24
25
26
30
31
61
62
65

.909
.895
.768
.896
.961
.916
.927
.912

66

.917

Description
Consumer Price Index in Large Cities
Index of Retail Food Prices
Wholesale Price Index for Foods
Retail Cost of the Market Basket
Marketing Margin on Market Basket
Retail Food Prices advanced 1 month
Retail Food Prices advanced 2 months
Consumers Price Index advanced 1
month
Consumers Price Index advanced 2
months
Factor II

28

.927

29

.873

58

.964

39
59
60

63

.492
.915
.895
.955

64

.938

78
79

.797
.931

Index of Employment in All Manu­
facturing Industries
Index of Payrolls in All Manufac­
turing Industries
Real Purchasing Power in Manufac­
turing Industries
Department Store Sales
Index of Employment advanced 1 month
Index of Employment advanced 2 months
Real Purchasing Power advanced 1
month
Real Purchasing Power advanced 2
months
Short Term Consumer Debt
Index of Industrial Production
Complex

27

(I) .628 (II ).464

38
69
77

(I)-.706 (II).762
(I)-.690 (II).816
(I) .624 (II).415

Wholesale Price Index for all
Commodities
Estimated U. S. Population
Consecutive Time Variable
Index of Total Freight Car Loadings
Indeterminate

52
67

Real Retail Cost of the Market Basket
Monthly Time Trend

Figure 9 .

Orthogonal Factor Structure of General Economic Conditions,
Analysis II, 1929-1951*

.69
•38

/
/

eo ?9
^

4
p/
*7/
v/

i?
•27
*77

S/
&
&
/

•26

w/
,*>2
~~ —

— US
<evg^. _£actor
i

l

31*

- 6B -

due in part to more violent cyclical fluctuations in Depart­
ment Store Sales than those in the other variables, and also
to more violent seasonal fluctuations in this variable.
The Index of Wholesale Food Prices, #26, appeared to be
influenced primarily by Factor I, the general price level
factor, with Factor II, the general industrial activity
factor, having only a slight influence on it.

The Wholesale

Price Index for All Commodities, however, showed a decrease
in the influence of the general price level factor and an
increase in the influence of the industrial activity factor.
Freight Car Loadings, #77, were affected by the two factors
about the same as the Wholesale Price Index for All Commod­
ities, #27.

The effect of the general price level factor

was slightly more important than the industrial activity
factor in both ^27 and ^77*
The Marketing Margin on the Market Basket, #31, showed
a strong influence by Factor I, the general price level
factor, and a negative influence of Factor II, the industrial
activity factor.

This same result was obtained in Analysis I.

The similarity between the Estimated U. S. Population,
#38, and the straight line time trend, #69, signifies that
the population estimates increased linearly over this period.
A comparison between this linear trend, #69, and the remain­
ing variables shows that there was a positive relationship
with Factor II and a negative relationship with Factor I.
This signifies that, although there was a linear change
evident in the industrial activity factor, the trend in th*

- 69 -

general price level was essentially non - linear.
The Real Retail Cost of the Market Basket, ^52, and
the monthly seasonal trend, ^67» were not affected by either
of these two factors, and hence were considered Indeterminate
in this structure.
In summary, two factors were determined in this analysis,
a general price level factor and an industrial activity fac­
tor.

These two explained over BO percent of the variations

in 21 out of the 25 variables included in this analysis.
Comparison of the Two Analyses
The two analyses discussed above produced two factors
involving approximately the same variables.

The factor

structures (Figures B and 9) showed many similarities and a
few sharp differences.
A closer relationship existed between a general price
level factor and an industrial activity factor during the
period 1929-194B than during the shorter period 1929-1941.
The two factors were relatively more independent of each
other during the shorter period of time.

This was probably

due to the differences in cyclical variations which occurred
during these periods.

The cyclical effects of the depression

during the period 1929-1941 affected both industrial activity
and the general price level, but these factors were not
affected simultaneously and did not react to these cyclical
variations in exactly the same manner.

Thus the relationship

between the two factors was not very large.

These same

- 70 -

cyclical variations were included in the period 1929-194#,
but the variations occurring during 1941 to 194# were also
added.

During this period conditions resulting from World

War II caused increases in both industrial activity
the general price level simultaneously.

and

The close relation­

ship between these two during the period 1942-194# helps to
explain why the over-all relationship between the two fac­
tors increased from that existing during the period from
1929-1941 to that existing over the entire period 1929-194#.
Approximately the same variaoles determined the two
factors in these two analyses.

Variables # 24, 25, 30, 61,

62, 65, and 66 determined Factor I in both cases.

In Analysis

I the Wholesale Food Price Index, ?f26, and the Wholesale
Price Index for All Commodities, yf27, also fit into the point
cluster of Factor I.

However, in Analysis II these two

variables showed signs of being influenced by both factors,
rather than by one factor alone.

Variable ^26 appeared to

be primarily influenced by Factor I and secondarily by
Factor II, while this situation was exactly reversed for
^27.

Wholesale prices followed the same pattern as retail

prices in Analysis I and showed no influence of industrial
activity; during the shorter period existing in Analysis II
industrial activity appeared to have some affect upon whole­
sale prices.
The Marketing Margin on the Market Basket, #31* retained
its same position in relation to the other variables in both

- 71 -

analyses.

Variables #52, the Real Retail Cost or the Market

Basket, and #67 the monthly time trend, shifted their posi­
tions slightly in the factor structures of the two analyses,
but still showed no effects at all of the two factors.
The Index of Payrolls in Manufacturing Industries, #29,
appeared as part of the factor cluster in Analysis II, sig­
nifying that these payrolls depended primarily upon indus­
trial activity in 1929-1941*

However, #29 moved from the

cluster of points in Analysis I, signifying that although
industrial activity had a strong influence on payrolls in
manufacturing industries in this longer period, the general
price level also was of importance.

This was probably due

to the fact that during 1929-1941 there was less than full
employment and payrolls depended on the amount of industrial
activity.

Once full employment was reached the general price

level became of greater importance in the level of wages and
payrolls.
Variable 3#> Estimated U. S. Civilian Population, was
located in two entirely different positions in the two fac­
tor structures.

In Analysis I, it followed the same pattern

as the general price level.

In Analysis II it followed a

pattern comparable to industrial activity and a pattern
opposite to that of the general price level.

The estimated

U. S. Population did not follow either of these factors too
closely during 1929-1941,

since the population was constantly

increasing during this time while cyclical fluctuations
occurred in the other data.

The civilian population decreased

- 72 -

somewhat from 1 9 4 2 - 1 9 4 5 ,

due to the induction of civilians

into the arm ed forces, but increased again in 1 9 4 6 with the
end of World War II and the demobilization of forces.

The

pattern of prices shows that they fluctuated during 1 9 2 9 1934,

increased during 1 9 3 5 - 1 9 4 1 ,

during 1 9 4 2 - 1 9 4 5

remained fairly stable

(due to price controls), and rose rapidly

in 1 9 4 6 when price controls were removed.

Thus the pattern

of civilian population and the pattern of prices were
similar over the period 1 9 2 9 - 1 9 4 S .

This might explain in

part the location of variable 3& in the factor structure of
Analysis I.
Department Store Sales, #39, showed the influence of
industrial activity to be stronger than that of the general
price level during the period 1929-1941, while the influence
of the general price level appeared stronger over the longer
period 1 9 2 9 - 1 9 4 & .

This might be due to the fact that depart­

ment store sales depended on the physical volume of goods
sold and the dollar volume of sales.

Thus during 1 9 2 9 - 1 9 4 1

the effect of the physical volume of goods sold was predomi­
nant, while during the entire period 1929-194& the dollar
volume, which depends on the price level, was of greater im­
portance.

However, both factors explained only a small part

of the variations in either time period.

The remaining

variations might be due to a very pronounced seasonal pattern
of sales which was unique in this variable.
The variable showing straight line time trend, #69, did

not retain the same relationship with the rest of the vari­
ables during the two time periods being discussed here.
This was primarily due to the cyclical fluctuations which
affected the economic data.

This variable can be considered

immaterial to the explanation of the other variables in the
analysis of General Economic Conditions.
Freight Car Loadings, #77, retained approximately the
same relationship with the rest of the variables during
these two time periods.

However, the general price level

and industrial activity accounted for more of the variation
in Freight Car Loadings during 1929-1941 than during 19291943.

This was probably due to extraneous factors which

occurred during the war years, such as the allocation of
freight cars to military uses.
Short Term Consumer Debt, #73, showed a complete rever­
sal between factors during the two time periods.

During 1929-

1941 , industrial activity had a large effect on consumer debt,
while the general price level showed almost no effect at all
upon it.

This result is plausible.

However, over the entire

period 1929-1943 it appears that the general price level had
the most important influence while industrial activity had a
slight negative effect on short terra consumer debt.

In

general, the patterns of prices and consumers debt were
similar over this period.

The slight negative effect of indus­

trial activity upon consumer debt might be due to the credit
restrictions which were in effect during the war; at least
the volume of consumer debt decreased from 1942-1945 while

- 74 -

industrial production increased greatly over this same
period.

This might explain the slight negative relationship

between Short Term Consumer Debt and industrial activity.
In subsequent analyses it will be desirable to include
these two factors of general economic conditions.

It is

sufficient to include just one variable from the point
cluster which determines the factor as representative of
the factor if that factor is the major component of the vari­
able selected.

In other words, in Analysis I any one variable

from the point cluster of # 24 , 25 , 26 , 27 , 30 , 61, 62 , 65 , 66
could be selected as representative of Factor I, while any
variable from the point cluster of #23,

53, 59 , 60 , 63 , 64 ,

79 could be selected as representative of Factor II.
The selection of these representative variables has been
made as follows:

the purpose of including the factors com­

prising general economic conditions in other analyses is to
investigate what effects they might have in these analyses.
Since many important changes come about first in some series
and are later reflected in others it is desirable to select
variables advanced in time, to see how these general indices
affect the specific commodity indices of a month or two
later (these being the time lags included in this study).
It is also desirable to select the same representative vari­
ables for the two time periods since the factors are approx­
imately the same.
In Factor I both the Consumer Price Index for All Com­
modities and the Retail Food Price Index are found advanced

- 75 -

through time in both analyses.

The Consumer Price Index

Tor All Commodities can be considered as more indicative
of the general price level than the Retail Food Price Index,
and the Consumer Price Index advanced one month, #65 > has
been selected as the representative of this general price
level factor.
In Factor II the Index of Real Purchasing Power is the
only variable present advanced through time.

It has been

decided to select # 64 » Real Purchasing Power advanced two
months, as representative of the industrial activity factor,
since this variable appears to be nearest the center of the
point cluster in both analyses.
Summary
Two similar factors were derived from the variables in
the two analyses described above.

These were a general

price level factor and an industrial activity factor.

These

two factors explained a major part of the variations occur­
ring in the variables included in the analysis of general
economic conditions.

For subsequent analyses the Consumer

Price Index for All Commodities advanced one month, #65>
will be used as representative of the general price level
factor while Real Purchasing Power advanced two months, # 64 ,
will be used as representative of the industrial activity
factor.
# # # # # # # # #

- 76 -

PRICES
All of the indexes of prices included in this study
were included in this group.

Among these were retail, whole­

sale, and real prices of butterfat, milk, cheese, butter,
evaporated milk, dry skim and dry whole milk, and oleomar­
garine.

Also included were the milk - feed and butterfat -

feed ratios, marketing margins, and various time trends.
The purpose of this analysis was to investigate whether
there were any general patterns which prices followed, i.e.
to determine how many factors were present within the many
price series and related series included.

Real Purchasing

Power advanced two months, #64, and the Consumer Price Index
advanced one month, #65, were also included as representative
of the industrial activity factor and the general price level
factor determined in the analysis on General Economic Condi­
tions, to investigate how these factors influenced prices.
A complete list of the variables included in this group
is given in Table IX.
Analysis I

1929-194#

There were three distinct factors in this analysis.

A

summary of the variables in relation to the factor structure
is given in Table X.

This table shows the variables influ­

enced by more than one factor (i.e. of Complex structure)
and those showing no significant influence of any factor
(hence classified as Indeterminate in structure).

Table IX.
■ial
No
9

10
11
12
13
14
15
16
17

16
19
20
21
22

23
32
33
34
35
36
37
40
41
43
44
45
46
47
46

Variables Used in Analysis of Prices
Description

Butterfat, Av. price per lb. received by farmers
Milk, Av. wholesale price per 100 lb. received by farmers
Butterfat - feed price ratio
Milk - feed price ratio
Milk - av. dealers buying price per cwt. for city distribution
Milk - av. price per cwt. paid producers delivered at condenseries
Cheese: Av. wholesale price per lb. at Chicago
Cheese: Wholesale price on Wise. Cheese Exchange
Creamery Butter: Av. wholesale price per lb.
Evaporated Milk: Av. wholesale selling price per case
Milk, fresh, delivered: Av. retail price per qt. in leading cities
Evaporated Milk: Av. retail price per 14i oz. can in leading cities
Cheese: Av. retail price per lb. in leading cities
Butter: Av. retail price per lb. in leading cities
Oleo, uncolored: Av. retail price per lb. in leading cities
Retail cost of dairy products in market basket
Margin on Dairy Products
Fluid Milk Marketing Margin
Butter Marketing Margin
American Cheese Marketing Margin
Evaporated Milk Marketing Margin
Milk, dry or powdered whole: Av. wholesale selling price per lb.
Dry skim milk: Av. wholesale price per lb.
Cheese - real wholesale price per lb. at Chicago (Av. wholesale price
at Chicago •? Wholesale prices of Foods)
Cheese: real wholesale price on Wisconsin Exchange
Butter: real wholesale price
Evaporated milk: real wholesale price
Real retail price of delivered milk
Real retail price of evaporated milk

1
'

Table II.

ia
No
49
50
51
53
54
55
56
57
64
65
67
69
72

Variables Used in Analysis of Prices
(Continued)

Description
Real retail price of cheese
Real retail price of butter
Real retail price of oleo
Real retail cost of dairy products in the Market Basket
Real wholesale price of dry whole milk
Real wholesale price of dry skimmilk
Real wholesale price of butterfat
Real wholesale price of milk
Real purchasing power advanced two months
Consumer price index advancedone month
Monthly time trend
Consecutive time trend
Seasonal milk production trend

,

o&
i

Table X.

Summary of Factor Structure of Prices
Analysis I

1929-194# (3 factors)

Factor I
No
9

10
13
14
15
16
17
1$

19

20
21
22
23
32
33
34
40
46
47
50
51
65

Factor Loading
(Rotated Matrix)
.505
.474
.6##
.673
•679
.665
•675
•6#6
.670
.694
.679
.63#
.#15
.6#5
.670
•650
•502
-.502
-.537
.516
.575
.674

Deacription
Butterfat, Av. price per lb. received by farmers
Milk: Av. wholesale price per cwt. received byfarmers
Milk: Av. dealers buying price per cwt. for city distribution
Milk: Av. price per cwt. at condenseries
Cheese: Av. wholesale price at Chicago
Cheese: Wholesale price on Wise. Cheese Exchange
Butter: Av. wholesale price per lb.
Evaporated Milk: Av. wholesale price per case
Milk, delivered: Av. retail price in leading cities
Evaporated Milk: Av. retail price per can
Cheese: Av. retail price per lb.
Butter: Av. retail price per lb.
Oleomargarine: Av. retail price per lb.
Retail Cost of Dairy Products in Market Basket
Margin on Dairy Products
Fluid Milk Marketing Margin
Milk, dry or powdered whole: Av. wholesale selling price
Real wholesale price of Evaporated Milk
Real Retail price of Delivered Milk
Real Retail price of Butter
Real Retail price of Oleomargarine
Consumers Price Index advanced one month

»
-si
*

Table X.

Summary of Factor Structure of Prices
Analysis I 1929-194#

(Continued)

(3 factors)

Factor II
Variable
No.

Factor Loading
(Rotated Matrix)

11
12

.854
.796
-.491

72

Description
Butterfat - feed price ratio
Milk - feed price ratio
Seasonal Milk Production Trend

Factor III
41
54
55
56
57
64

.497
*603
.745
.559
.619
.571

Dry Skim Milk: Av. wholesale price per lb.
Real Wholesale price of dry whole milk
Real Wholesale price of dry skim milk
Real Wholesale price of butterfat
Real Wholesale price of milk
Real purchasing power advanced 2months
Complex

35
36
37
49

(I).936
(I).§33
(I).707
(I).751

(III)-.696
(III)-.473
(III)-.376
(III)-.521

Butter Marketing Margin
American Cheese Marketing Margin
Evaporated Milk Marketing Margin
Real Retail Price of Cheese

Table X.

Summary of Factor Structure of Prices

Analysis I

(Continued)

1929-194$ (3 factors)

Indeterminate
Variable
No.
43
44
45
46
53
67
69

Factor Loading
(Rotated Matrix)

Description
Cheese: Real wholesale price per lb. at Chicago
Cheese: Real wholesale price on Wise. Cheese Exchange
Butter: Real wholesale price
Evaporated Milk: Real retail
price
Real Retail Cost of DairyProducts in Market Basket
Monthly Seasonal Variable
Consecutive Time Variable

- 32 -

The first factor was the general price level factor
determined in the analysis of General Economic Conditions.
This was evidenced by the presence of #65, the Consumer
Price Index advanced one month, which represented the gener­
al price level factor in this analysis.

The variables in­

fluenced by this factor were mainly wholesale and retail
prices of individual products.

The retail cost of dairy

products in the market basket was also influenced primarily
by this factor.

The Marketing Margin on Dairy Products and

the Marketing Margin on Fluid Milk appeared to be influenced
solely by Factor I, although the other marketing margins
showed a complex structure.

This signifies that the market­

ing margin on dairy products was heavily weighted by the
marketing margin on fluid milk rather than by the margins
on the other dairy products.
The Real Retail Price of Butter, #50, and the Real Re­
tail Price of Oleomargarine, #51, were the only real prices
influenced positively by this general price level factor.
This signifies that these real prices, with the effects of
the price level removed from them, still followed a pattern
similar to the general price level.

Thus it appears that

the price of butter and the price of oleo responded in a
kind of geometric fashion to changes in the general price
level over a long period of time.

For example, if the general

price level were to double, the prices of butter and oleomar­
garine would increase by approximately four times.

However,

the relatively small factor loadings indicate that other,

- S3 -

perhaps unique, factors would also have to be considered
before the net change could be predicted.
The Heal Wholesale Price of Evaporated Milk, #46, and
the Real Retail Price of Delivered Milk, #47 > had negative
factor loadings on Factor I in the rotated matrix.

These

two were the only variables which showed negative loadings
for this factor.

The negative loadings indicate that the

real prices of delivered milk and of wholesale evaporated
milk decreased as the general price level increased.
Factor II was composed of three variables: the Butter­
fat - Feed Price Ratio, the Milk - Feed Price Ratio, and the
trend of seasonal milk production.

This factor was almost

independent of the other two factors.
The Butterfat - Feed Price Ratio shows the amount of
feed you can buy with one pound of butterfat; the Milk Feed Price Ratio shows the amount of feed you can buy with
one pound of milk.

All of these are based on the current

prices prevailing for the individual products.

The general

price level would affect the individual prices for these
products but, assuming that the general price level affects
them all in approximately the same manner, taking the ratio
of the two prices would cancel out this effect.

Since the

quantity of milk produced over short periods of time is
relatively constant, this factor appears to be a Feed Factor,
reflecting the demand for and supplies of feed.

The fact

that the seasonal milk production trend had a negative load­
ing on this factor indicates that this demand for feed was

contra - seasonal to the milk production trend.

This may be

explained by the fact that the peak in milk production occurs
during the early summer when natural feed and pasture are
readily available; the demand for feed is highest during the
winter months when seasonal milk production is low.

This

inverse relationship is not exact, however, as some feed is
used constantly over the year.
structure also,

This is shown by the factor

since the negative loading for the seasonal

milk production variable was only -.491.

If this contra -

seasonal tendency were very pronounced, this negative load­
ing would have been much larger.
Factor III contained variable # 64 , Real Purchasing
Power advanced two months, which represented the industrial
activity factor determined in the analysis of General Economic
Conditions.

Thus Factor III could be considered an indicator

of industrial activity.

It is interesting that the real

wholesale prices of dry whole milk, dry skim milk, butterfat,
and milk (an average price for all milk sold by farmers)
were influenced principally by this one factor.

The wholesale

price of dry skim milk (the others are real wholesale prices)
was also influenced by this factor, although to a lesser
extent than the others listed above.
Several variables had a complex factor structure.

The

Butter Marketing Margin, the American Cheese Marketing Margin
and the Evaporated Milk Marketing Margin all had positive
factor loadings on Factor I, the general price level factor,

- 85 -

and negative factor loadings on Factor III, the industrial
activity factor.

This indicates the fstickiness’ of market­

ing margins, which moved in general in the same direction
as the price level but opposite in direction to industrial
activity.

In other words, if the general price level were

to increase while industrial activity remained constant,
marketing margins would increase.

If industrial activity

were to decrease while the general price level remained
constant, marketing margins would increase.

(A situation

similar to this latter case occurred during the 1 9 3 0 Ts).
If both the general price level and industrial activity were
to change simultaneously, the net effects would depend on
both the sizes and directions of change.
The Heal Retail Price of Cheese, #49, also had

acomplex

structure with a positive loading on Factor I and a negative
loading on factor III.

Although the actual price of cheese

was influenced principally by the general price level
factor, when this price level factor was removed it appears
that the series still followed the same pattern as the general
price level and a pattern opposite to that of industrial
activity.

Thus it seems that the price of cheese was affected

geometrically by changes in the general price level

and in an

inverse ratio to changes in industrial activity.
Several variables were of indeterminate structure in
this analysis.

The real wholesale price of cheese, both at

Chicago and on the Wisconsin Cheese Exchange, the real whole-

- S6 -

sale price of butter, the real retail price of evaporated
i

milk, and the real retail cost of dairy products in the
market basket all showed insignificant loadings in the rota­
ted factor matrix.

Of these, the real wholesale price of

cheese was best explained as a combination of the three fac­
tors derived here, as about 70 percent of the variations in
this real wholesale price (both at Chicago and on the Wiscon
sin Cheese Exchange) could be explained by these factors.
The other variables mentioned above had a much lower percent
age of their variations explained by these factors.

It is

significant, however, that these did not form a separate fac
tor which would signify a similarity in their variations.
Each must have had a unique form of variation.
There was no monthly seasonal trend or consecutive time
trend evident in the price series included in this analysis,
as these two variables both showed an indeterminate
structure.
In summary,

there were three factors evident in this

analysis of prices.

One was a general price level factor,

the second was a feed factor which is important to only two
variables and insignificant to the remainder, and the third
was an industrial activity factor.

These three factors

accounted for over SO percent of the variations in 25 out of
the 4-2 variables included in this group.

- S7 -

Analysis II

1929-19/4.1

There were five factors determined in this analysis*
A summary of the factor structure is given in Table XI*
There were no variables which showed a simple structure
for Factor IV, and only one variable showed a simple struct­
ure for Factor V*

These will be discussed later in this

analysis•
Factor I contained the variables of wholesale and retail
prices of individual dairy products and also the consumer
price index advanced one month.

This latter variable repre­

sented the general price level factor in this analysis.

The

other variables in this factor cluster are consistent with
the hypothesis that Factor I was the general price level
factor determined in the analysis of General Economic Condi­
tions.

It is interesting that the marketing margin on dairy

products and the butter marketing margin followed the same
trend as the general price level during this period.
real prices,

Various

such as the real wholesale and real retail

prices of butter,

the real retail price of oleomargarine and

the real wholesale price of butterfat also were a part of the
factor cluster of Factor I.

This indicates that the general

price level had a multiple effect on these prices.

However,

the sizes of the factor loadings for these variables indicates
that other factors would have to be considered 3ince these
variables were not influenced solely by the general price
level factor.

/ a c t o r I I co n ta in e d the v a r i a o l e s o f the b u t t e r f a t f'eed p r i c e r a t i o , the m i l k - f e e d p r i c e r a t i o , and the
s e a s o n a l milk production t ren d .

l’h i s f a c t o r corresponded

very c l o s e l y t o / a c t o r I I in A n a l y s is I o f P r i c e s .

Thus

i s was p o s s i b l e to c o n s id e r t h i s f a c t o r t h e feed f a c t o r
waicn was de t e r m i n e d i n Analysis I .
/ a c t o r I I I contained the v a r i a b l e s o f t h e r e a l whole­
s a l e p r ic e o f c h e e s e , the r e a l r e t a i l c o s t o f d a i r y products,
in the market b a s k e t, and the r e a l v m o le s a l e p r i c e o f dry
skim m ilk .

This might have been a r e a l d a i r y p r i c e f a c t o r ,

/ a c t o r V appears t o have been a r e a l d a ir y p r i c e f a c t o r
a l s o ; the f a c t o r s t r u c t u r e i n d i c a t e s t h a t there i s a r e l a ­
t i v e l y stro n g r e l a t i o n s h i p between the / a c t o r I I I and F actor
V.
The v a r i a b l e o f Real Purchasing Power advanced two
months, ydA, had l o a d i n g s on both / a c t o r I I I and F actor IV.
This i n d i c a t e s t h a t t h e i n d u s t r i a l a c t i v i t y f a c t o r ( r e p r e ­
se n ted by t h i s v a r i a b l e ) had some e f f e c t on F actor I I I , the
r e a l d a iry p r i c e f a c t o r , and a l s o on Factor IV.

The f a c t o r

s t r u c t u r e did not show the i n d u s t r i a l a c t i v i t y f a c t o r as an
independent f a c t o r in t h i s a n a l y s i s , however.
A c t u a l ly , the f a c t o r s t r u c t u r e f o r F a cto rs I I I , IV, and
V was not too w e l l d e f i n e d .

Although Factor IV was a d i s t i n c t

f a c t o r in t h i s a n a l y s i s which was r e l a t i v e l y independent o f
the o th e r f a c t o r s , th e re was no v a r i a b l e which showed simple
s t r u c t u r e f o r Factor IV.

The c l o s e r e l a t i o n s h i p between

Table XI.

Summary of factor structure of Prices

Analysis II

1929-1941 (5 factors)

Factor I
•ia
No

Factor Loading
(Rotated Matrix)

9

.733
.791

10

13
14
15
16

17

13
19

.377
.777
.707
.733
.796
.375
.332

20
21
22

.961

23
32
33
35
40

.922

41
45
50
51
56
65

.970
.349
.905
.936
.336
.922

.533
.452
.463
.661

.554
.933

description
Butterfat: Av. price per pound received by farmers
Milk: Average wholesale price per 100 pounds received by
farmers
Milk: Average dealers buying price for city distribution
Milk: Average price per cwt. at condenseries
Cheese: Av. wholesale price per lb. at Cnicago
Cheese: Wholesale price on Wisconsin Cheese Exchange
Butter: Av. wholesale price per pound
Evaporated Milk: av. wholesale selling price per case
Milk, delivered: Av. retail price per quart in leading cities
Evaporated Milk: Av. retail price per 14ioz. can
Cheese: Av. retail price per pound
Butter: Av. retail price per pound
Oleomargarine: Av. retail price per pound
Retail Cost of Dairy Products in the Market Basket
Marketing Margin on Dairy Products in the Market Basket
Butter Marketing Margin
Milk, dry or powdered whole: Av. wholesale selling price
per pound
Dry skim milk: Av. wholesale price per pound
Real wholesale price of Creamery Butter
Real retail price of butter
Real retail price of oleomargarine
Real wholesale price of butterfat
Consumer price index advanced one month

Table XI.

-Summary of factor Structure of Prices
(Continued)

Analysis II

1929-1941 (5 factors)

factor II
Variable
No.

Factor Loading
(Rotated Matrix)

11
12
72

.£24
.866
-.490

Description
Butterfat-feed price ratio
Milk-feed price ratio
Seasonal Milk Production Trend
Factor III

43
44
53
55

*582
.555
.736
.701

Cheese: real wholesale price per lb. at Chicago
Cheese: real wholesale price on Wisconsin Cheese Exchange
Real retail cost of dairy products in the Market Basket
Real wholesale price of dry skim milk
Factor V

48

.707

Real retail price of evaporated milk
Complex

34
36
37
46

47
49

(I) .584
(I) .728
(I) .652
(III).519
(V)
(I)-.593
(IV)
(I) .645

(IV) .615
(III)-.500
(III)-,306
(IV) .448
.675
(III) .492

Fluid Milk Marketing Margin
American Cheese Marketing Margin
Fvaporated Milk Marketing Margin
Real wholesale price of evaporated milk
Real retail price of delivered milk

.585

(V) .468

Real retail price of cheese

Table XI.

Summary of factor Structure of Prices
(Continued)

Analysis II

1929-1941 (5 factors)

Complex
Variable
No.
54
57

Factor Loading
(Rotated Matrix)

(I) .532
(V) .613
(I) .417 (III) .432
64 (III) .452 (IV) .53$
69(I)-.459
(III) .530
(IV) .585

Description
Real wholesale price of dry whole milk
Real wholesale price of milk
Real purchasing power advanced two months
Consecutive Time Trend
Indeterminate

45
67

Real wholesale price of creamery butter
Monthly Time Trend

- 92 -

factor III and Factor V indicates that they might have been
one factor which was over-determined in this analysis.
Another possibility is that the unsettled conditions of the
depression years caused erratic changes in these variables
rather than uniform changes.

If they had all reacted in a

similar fashion to conditions over this period a more defi­
nite structure would probably have been obtained.
Several variables showed a complex structure in this
analysis.

The number and the complexity of these variables

are another indication of the indeterminate nature of the
factor structure.
In summary, there were five factors determined in this
analysis.

The first two factors, the general price level

factor and the feed factor, were fairly well determined.
The other three factors were not well determined and there
was not enough information in the factor structure to identify
them satisfactorily.

The complete factor structure explained

over 80 percent of the variations in 31 out of the 42 vari­
ables included in this group.
Comparison of Analyses
There were three factors determined in Analysis I and
five factors determined in Analysis II.

Two factors were

the same in these two analysis; a general price level fac­
tor and a feed factor were fairly well determined in both
analyses.

An industrial activity factor was determined in

- 93 -

A n a ly sis
th is

I;

however,

a lth o u g n t h e r e were i n d i c a t i o n s

f a c t o r was a p a r t o f t w o o f t h e

m in e d i n A n a l y s i s

II,

the

other fa c to r s d e te r ­

in d u stria l

a c t i v i t y f a c t o r did

not appear as a se p a r a te f a c t o r in A n a ly sis
the

general p rice le v e l

above,

A n a ly sis II

sa tisfa cto ry

reta il

II

o f A n a l y s i s I and A n a l y s i s

the g e n e r a l p r ic e
p rices;

p r ic e s fo llo w ed

the

a n a ly ses.

in d u str ia l

on t h e p r i c e s

a c tiv ity

other
feel
in

The f e e d f a c ­

I and A n a l y s i s

factors

in

w h o lesa le

p rices

p ric es,

II

both

f a c t o r had l i t t l e
th is

group.

f a c t o r d eterm in ed in A n a ly s is

o f r e a l w h o lesa le

194&.

g e n e r a l w h o l e s a l e and

botn A n a l y s i s

in clu d ed

II

in v o lv e d both

general tr e n d .

th a t the

v a ria b les

a ctiv ity

thus in

in depend en t o f th e

T h is i n d i c a t e d

no i n f l u e n c e

le v e l factor

same

t o r w h i c h was d e t e r m i n e d i n
was r e l a t i v e l y

fiv e fa c to r s

c o u l d n o t be g i v e n .

stru ctures

and r e t a i l

A sid e from

s t r u c t u r e and a

ex p la n a tio n fo r th r ee out o f the

The f a c t o r

w h o lesa le

II.

and f e e d f a c t o r m e n t i o n e d

s h o w e d an i n d e t e r m i n a t e

d eterm in ed in A n a ly s is

sh o w ed t h a t

factor

that

or

The
I in v o lv ed

sh ow ing t h a t t h e s e

real

f o l l o w e d t h e same t r e n d a s i n d u s t r i a l

and r e a l p u r c h a s i n g p o w e r d u r i n g t h e p e r i o d 1 9 2 9 -

T h i s f a c t o r w as n o t e v i d e n t d u r i n g t h e p e r i o d 1929-

1941, p r o b a b l y a s a r e s u l t o f t h e u n s e t t l e d e c o n o m i c
co n d itio n s

ex istin g

d uring t h a t t im e .

Although the factor structure of Analysis II was largely
indeterminate, a few general observations may be made from
the factor structure of Analysis I.

During the period covered

by Analysis I, 1929-194#, marketing margins on fluid milk
followed the trend of the general price level.

Marketing

- 94 -

margins on dairy products in the market basket also followed
the trend of the general price level, signifying that fluid
milk had the largest influence in tne combined group of
dairy products.

Other dairy products, such as outter,

cheese, and evaporated milk, showed that they followed tne
trend of the general price level but were inverse to the
trend of industrial activity.

In other words, if the general

price level were to rise, the marketing margins on butter,
cheese, and evaporated milk would rise.

If industrial activ­

ity were to rise, the marketing margins on these products
would fall.

3otn of these reactions assume that all other

factors remained tne same.

If both the general price level

and industrial activity were to change, the net effect of
these cnanges would have to be considered.
During this period the real retail price of delivered
railK and the real wholesale price of evaporated milk were
opposite to the trend of the general price level.

Thus as

the general price level increased the real retail price of
delivered milk and the real wholesale price of evaporated
milk went down, while as the general price level decreased
these real prices went up.

This indicated that the prices

of delivered milk and evaporated milk were not as flexible
as the general price level during 1929-194&*
Since the feed factor was relatively independent of the
other factors in the analyses discussed above, the butterfatfeed price ratio and the milk-feed price ratio (the variables
which determined the feed factor) snowed no relationship with

- 95 -

the price variables included in this group.
# # # #

$ .¥ $ !t # $

r &

FLUID MILK AND CRLAM
All of the variables pertaining to fluid milk in this
study were included in this group.

Also included were vari­

ables which were used as a part of the pricing formulas in
federal milk marketing orders, such as Department otore
Sales and prices for dry and powdered skim and whole milk.
Three time trends were included, and so was the Index of
Payrolls in Manufacturing Industries.

A complete list of

the variables used in this group is given in Table XII.
Analysis I

1929-1943

Although there were just three distinct factors in
this analysis, the factor structure indicated that there
were actually four factors present.

A summary of the factor

structure is given in Table XIII.
The variables contained in Factor I and the sizes of the
factor loadings indicate that two factors were present in
Factor I.

These were the two factors of General Economic

Conditions, the general price level factor and the industrial
activity factor.

This is shown by the fact that price vari­

ables from the general price level factor cluster were con­
tained in Factor I in addition to the payroll and department
store sales variables which were a part of the industrial
activity factor cluster.

The factor loadings were also

- 96 -

similar to those in the General Economic Conditions analysis.
Thus the first two factors in this analysis were

a general

price level factor and an industrial activity factor.
Factor III contained the variables of milk production
on farms, the seasonal milk production trend, and the monthly
disappearance of fluid milk and cream.
probably a production factor.

This factor was

The sizes of the factor load­

ings indicate that the seasonal milk production trend vari­
able followed the actual milk production and also that the
disappearance of fluid milk and cream followed a similar
pattern to the production pattern.
Factor IV contained the variables of the real wholesale
price of milk and the real wholesale price of dry skim milk.
This indicates that these two followed approximately the same
pattern.

This factor might be called a real wholesale dry

skim milk price factor.
Two variables had a complex structure in this analysis.
The rnilk-feed price ratio showed a positive loading on Factor
III, the milk production factor and a positive loading on
Factor IV, the real wholesale dry skim milk price factor. The
positive loading on Factor III was opposite in direction to
the other variables in this factor, indicating that the milkfeed price ratio had an inverse relation to milk production;
the positive loading on Factor IV indicates that it had a
direct relationsnip with the real wholesale dry skim milk
price.

The Average Wholesale Price of Dry Skim Milk, ^41, had

Table XII.

Variable
No.
1.
2.
3.
4.
5.
6.
7*

3.

9.
10.
11.
12.
13.
14.
15.
16.
17.

13.

19.
20.

1
10
12
13
19
25
29
32
33
34
39
40
41
47
55
57
67
69
72
76

Variables Used in Analysis of fluid Milk

Description
Milk Production on farms
Milk: Av. wholesale price per 100 lb. received by farmers
Milk - feed Price Ratio
Milk: Av. dealer buying price for city distribution
Milk: Av. retail price per quart
Retail food Prices in Large Cities
All Manufacturing Industries - Payroll
Retail Cost of Dairy Products
Margin on Dairy Products
fluid Milk Marketing Margin
Department Store Sales
milk: Dry or powdered whole: Av. wholesale selling price per
Dry Skim Milk: Av. wholesale price per lb.
Real Retail Price of Delivered Milk
Real Wholesale Price of Dry Skim Milk
Real Wholesale Price of Milk
Monthly Time Trend
Consecutive Time Trend
Seasonal Milk Production Trend
Monthly Disappearance of Fluid Milk and Cream

vO
-0

Table XIII.

Summary of Factor Structure of Fluid Milk

Analysis I

1929-194$ (4 factors)

Factor I, II
Variable
No,

Factor Loading
(Rotated Matrix)

10
13
19.
25
29
32
33
34
39
40
47
69

,$2S
.939
.923
.925
*725
.934
*91$
.900
.777
.$4$
-.750
.595

Description
Milk: av. wholesale price per 100 lb. received byfanners
Milk: Av. dealers buying price per cwt. for city distribution
Milk: Fresh delivered; av. retail price per qt. in cities
Retail Food Prices in Large Cities
Payrolls in Manufacturing Inaustries
Retail Cost of Dairy Products in Market Basket
Margin on Dairy Products
Fluid Milk Marketing Margin
Department Store Sales
Milk: dry or powdered whole; Av. wholesale selling
price per lh
Real Retail Price of Delivered Milk
Consecutive Time Trend
Factor III

1
72
76

-.$66
-.$31
-.7$0

Milk, Production on Farms
Seasonal Milk Production Trend
Monthly Disappearance of Fluid Milk and Cream
Factor IV

55
57

.792
.754

Real Wholesale Price of Dry skim Milk
Real wholesale Price of Milk
Complex Structure

12 (III) .437
41
(I) .579

(IV) .570
(IV) .606

Milk - feed Price Ratio
Dry Skim Milk: av. wholesale price per lb.
Indeterminate

67

monthly Time Trend

a positive loading on factor I and factor IV.

The analysis

of prices indicates that the positive loading of factor I
was a positive effect of the general price level factor.
The positive loading on factor IV shows that the wholesale
price of dry skim milk was influenced by the real wholesale
price of dry skim milk and this result is consistent.
The monthly seasonal trend had an indeterminate struct­
ure in this analysis, showing very little influence of any
of these factors.
In summary, there were actually xour factors determined
in this analysis: a general price level factor, an industrial
activity factor, a milk production factor, and a real whole­
sale dry skim milk price factor.

These factors accounted for

over 80 percent of the variations in 13 out of the 20 vari­
ables in this group.
Estimation of fluid milk and Oream Disappearance
One variable was selected to represent eacn of the above
factors in the regression system on the disappearance of fluid
milk and cream.

#65,

The Consumer Price Index advanced one month,

was used to represent the general price level factor even

though this variable was not included in this analysis, since
it was used uniformly to represent the general price level
factor wherever possible in this study.

The variable of Real

Purchasing Power advanced two months, ^64, was selected to
represent the industrial activity factor for the same reason.
Milk Production on Farms, ^1, was selected to represent the

- 100 -

milk production factor, and the Real Wholesale Price of Milk,
73*57, was used to represent the real wholesale dry skim milk
price factor.

All of these variables were used as independ­

ent variables in this regression system with fluid Milk and
Cream disappearance, ,j*76, as the dependent variable.
The coefficient of multiple regression between these
variables was r = .3642.
was r^ = .7463.

The coefficient of determination

This indicates that approximately 75 per­

cent of the variation in fluid milk and cream disappearance
was explained by these four factors.

Also, the coefficients

of all of these variables were significant to the analysis.
A second regression system was set up with fluid milk
and cream disappearance as the dependent variable and the
consumer price index advanced one month, real purchasing
power advanced two months, and the real wholesale price of
milk as independent variables.

This system investigated

how mucn influence these variables had on the disappearance
of fluid milk and cream when the production factor was
eliminated.

It yielded a coefficient of multiple regression

of r * .5733 and a coefficient of determination of r^ = .3345.
This signifies that these three variables, two of general
economic conditions and the third of the real wholesale price
of milk, explained approximately 33 percent of the variation
in the disappearance of fluid railic and cream.

Thus the

addition of the production factor to this system explained an
additional 42 percent of the variation in disappearance, since
the complete system of four variables explained 75 percent of

- 101 -

these variations.
A third regression system was set up using the Retail
Price I'or Delivered Milk, /19» as the representative of* fac­
tor I, with Milk Production on /arms, #1, Real Wholesale
Price of Milk, #-57, and Real Purchasing Power advanced two
months,

rfb4,

as the other independent variables and Fluid

Milk and Cream Disappearance, ^76, as the dependent variable.
Hence this system was similar to the first system described
above, with the retail price of milk replacing the consumer
price index advanced one month as the representative of Fac­
tor I, the general price level factor.
The coefficient of multiple regression in the latter
system was r = .8412.
r2 ■ .7076.

The coefficient of determination was

These results w egr very similar to the first

regression system, where the coefficient of multiple corre­
lation was r = .8642 and the coefficient of determination was
r2

m

.7468.

Thus it appears that these two systems provided

essentially the same results, and that the substitution of
one variable for another (where the two variables were from
the same factor cluster) made no difference in the coefficient
of multiple correlation.

The substitution of the retail milk

price for the consumer price index in the regression system
made very little difference in the regression coefficient.
However, it should be noted in these systems that the
simple correlation between total milk production and fluid
milk and cream disappearance was r = .7890 and the coefficient
of determination was r2

*

.6225.

The large degree of corre-

- 102 -

spondence between these two variables might be due to the
fact that fluid milk and cream disappearance was computed
as a residual after the milk equivalents of several manu­
factured dairy products were deducted from total milk pro­
duction.

A more precise measurement of disappearance would

probably make the above regression systems more meaningful.
The regression systems discussed above will be found in
detail in Appendix B.
Analysis II

1929-1941

There were six factors derived from this analysis.

A

summary of the factor structure is given in Table XIV.
Factor I appeared to be the general price level factor
discussed earlier in this study.

The variaoles included in

this factor were average dealers buying price for milk,
retail food price index, retail cost of dairy products in the
market basket, marketing margin on dairy products, and aver­
age wholesale selling price for dry or powdered whole milk.
Tnese variables were all found in the general price level
factor in either the analysis of General Economic Conditions
or the analysis of Prices.
Factor II contained the variables of seasonal milk
production trend and the disappearance of fluid milk and
cream.

The variable showing milk production on farms had

a complex structure with this factor and Factor IV, but from
the composition of the factor cluster, it can be assumed that
Factor II was a milk production factor.

Factor III contained only one variable showing simple
structure.

This variable was ^29, the Index of Payrolls in

Manufacturing Industries.

Since this variable was found in

the cluster denoting the industrial activity factor in the
analysis of General Economic Conditions, this factor might
be assumed to be the same industrial activity factor.
Factor IV contained only one variable with simple struct­
ure.

Tais was variable 7/19j the Average Retail Price of

Delivered .'.ilk.

It is interesting that this factor was rela­

tively independent of all others in this analysis.

This

factor could oe considered a retail milk price factor wnich
was unaffected by the other factors.
Factor V contained the variables of department store
sales and the monthly seasonal time trend.

This illustrates

that department store sales had a pronounced seasonal trend
which was not shared by the other variables in this analysis.
Since this seasonal trend factor had a minor relationship
with the other factors, it was considered to be immaterial
to this analysis.
Factor VI contained only one variable with simple struct­
ure.

This was variable v55, theReal Wholesale Price of

Skim Milk.

This factor might be termed areal wholesale

Dry
dry

skim milk factor; it was relatively independent of all other
factors in this analysis with the exception of Factor III,
the industrial activity

factor. However, even this relation­

ship between Factor III and Factor VI wasminor.

Table XIV.

Summary of factor Structure of fluid milk
Analysis II

1929-1941 (6 factors)

factor I
Variable
No.

Factor Loading
(Rotated Matrix)

13
25
32
33
40

.644
.7^8
.657
.734
.615

description
Milk: Av. dealers buying price for citydistribution
Retail food Prices in Large Cities
Retail Cost of dairy Products in Market Basket
Margin on dairy Products
Milk, dry or powdered whole: Av. wholesale selling price per lb

104

Factor II
72
76

-.693
-.753

Seasonal milk Production Trend
disappearance of fluid Milk and Cream
factor III

29

.619

All Mfg. Industries - Payroll
factor IV

19

-.570

Milk, fresh, delivered:

av

.

retail price per

factor V
39
67

.661
.629

department Store Sales
Montnly Time Trend
Factor VI

55

.6#5

»

Real Wholesale Price of dry Skim Milk

qt.

in leading
cities

Table XIV,

Summary of factor Structure of fluid Milk

Analysis II
Variable
No.

Factor Loading
(Rotated Matrix)

(Continued)

1929-1941 (6 factors)
Description

Complex
1
41
47
69

(II)-.$13
(I) .513
(I)-.790
(I)-.632

(IV).553
(VI).6#7
(III).635
(III).630

Milk, Production on farms
Dry Skim Milk: Av. wholesale price per lb.
Real Retail Price of Delivered
Milk
Consecutive Time Trend

10
12
34
57

Milk: Av. wholesale price per 100 lb. received by farmers
Milk - feed price ratio
Fluid Milk Marketing Margin
Real Wholesale Price of Milk

105

Indeterminate

1

Several variables had a complex structure in this analysis.
Variable £1, Milk Production on farms, had factor loadings on
factor II, the milk production factor, and on Factor IV, the
retail milk price factor.

From the directions of the factor

loadings of the other variables in these factors, it can be
seen that this milk production variable conformed directly
with the milk production factor and inversely to the retail
milk price factor.

This indicates that the retail price of

milk fluctuated somewhat inversely to milk production during
this period.
The average price for dry skim milk had a positive fac­
tor loading on Factor I and on Factor VI.

This shows that

the dry skim milk wholesale price was influenced directly
by both the general price level factor and the real wholesale
dry skim milk price factor.
The complex factor structure for the Real Retail Price
of Delivered Milk, j^47, indicates that this variable
fluctuated inversely to the general price level factor and
directly with the industrial activity factor.

It is inter­

esting that this variable showed no influence of the retail
milk price factor, Factor IV, but instead showed the influ­
ence of these other two factors.

This structure indicates

that as the general price level factor increased, the real
retail price of milk decreased during this period, while as
the industrial activity factor increased, the real retail
price of milk also increased.
The Consecutive Time Trend,

69, showed negative loadings

- 107 -

on Factor I and positive loadings on Factor III.

This

relationship to the general price level factor and the
industrial activity factor is similar to that obtained for
this variable in the analysis of General Economic Conditions.
Several variables had an indeterminate structure in
this analysis.

The Average Wholesale Price of Milk,

t/10,

showed an indeterminate structure in the rotated matrix even
though it had quite a high factor loading on Factor I in the
original orthogonal matrix.

Thus it appears that there was

some effect of the general price level on this variable
even though other factors were also to be considered in the
explanation of the variations which occurred.
The Milk-Feed Price Ratio showed an indeterminate
structure in this analysis, having minor factor loadings on
Factors I, II and III.

This is consistent with the findings

in previous analyses that this variable, representing the
feed factor discussed earlier, was relatively independent
of other factors.
The fluid milk marketing margin and the real wholesale
price of milk both had inconclusive structures, although
both showed positive loadings on Factor III, the industrial
activity factor.

This indicates a direct relationship exist­

ed between the industrial activity factor and these vari­
ables, as the relative changes which occurred in them were
in the same direction.

However, the orthogonal matrix shows

that Factor I, the general price level factor, and Factor II,
the milk production factor also influenced these variables

somewhat, even though none o±' these factors have truly
significant factor loadings in the rotated matrix.
In summary, there were six factors determined in this
analysis.

These were a general price level factor, a milk

production factor, an industrial activity factor, a retail
milk price factor, a monthly seasonal trend factor, and a
real wholesale dry skim milk price factor.
factors accounted for over

SO

Together these

percent of the variations in

16 out of the 20 variaoles included in tnis analysis.

How­

ever, the last three factors appear to be overdetermined
in this analysis and cannot be considered too reliable.
Estimation of Disappearance of Fluid Milk and Cream
One variable was selected to represent eacn of the fac­
tors described above in the regression system for the
estimation of the disappearance of fluid milk and cream.
There were tv;o exceptions made to tnis.

The monthly time

trend factor was eliminated, since it was felt that this
applied solely to the Department Store Sales variable and
was extraneous to the rest of the analysis.

The real whole­

sale dry skim milk factor was also eliminated, since it
applied primarily to dry skim milk prices and was extraneous
to the rest of the analysis.
Variable ^65, the Consumer Price Index advanced one
month, was selected to represent the general price level fac­
tor.

Even though this variable was not included in the

- 109 -

factor analysis described above, it was used in the regression
system since it was considered a good indicator of the general
price level factor in the analysis of General Economic Condi­
tions.

Variable >#64, Real Purchasing Power advanced two

months, was selected to represent tne industrial activity
factor even though this variable was not included in this anal­
ysis.

This selection was on the basis of the representative

nature of variable //-b4 in the analysis of General Economic
Conditions, also.

Variable yl, Milk Production on farms, was

selected to represent the milk production factor, and the
Average Retail Price of delivered Milk, ^19, was used to rep­
resent the retail milk price factor.

The disappearance of

fluid milk and cream was the dependent variable in the regres­
sion system; the variables listed above were the independent
variables.
The coefficient of multiple correlation in this system
was r = .8768, while the coefficient of determination was
r^ z .7688.

Thus these variables explained approximately

77 percent of the variations which occurred in fluid milk
and cream disappearance during the period

1929-1941.

The

coefficient of real purchasing power advanced two months
proved to be not significant in the regression system, but
the coefficients of the other three variables, consumer pur­
chasing power advanced one month, retail milk prices, and
milk production, were all highly significant in this system.
A second regression system was set up using only the

- 110 -

consumer price index advanced one month and retail milk
prices as independent variables and fluid milk and cream
disappearance as the dependent variable.

This system

eliminated the variable which was not significant in the
above system; it also eliminated the production variable in
order to see how much effect this production variable had on
the system.
In this second regression system, the coefficient of
multiple correlation was r = .3749, and the coefficient of
determination was r^ = .1405.

This shows that the consumer

price index advanced one month and the price of milk only
explained 14 percent of the variations in fluid milk and
cream disappearance.

Since the previous regression system

explained 77 percent of these variations, approximately 63
percent of the variations in fluid milk and cream disappear­
ance were accounted for by fluid milk production during this
period.
Comparison of Analyses
There were four factors determined in the analysis
which covered 1929-194& and six factors determined in the
analysis which covered 1929-1941.

The four factors of

Analysis I had counterparts in Analysis II, while the two
remaining factors of Analysis II fitted into the factor
structure of Analysis I without appearing as separate factors.
Factors I and II, the general price level factor and the

industrial activity factor, in Analysis I had counterparts
in Factor I and Factor XII of Analysis II.

However, more

variables showed the influence of these factors in Analysis
I than in Analysis II.

There was a milk production factor

in each analysis which contained the variables of seasonal
milk production trend and fluid milk and cream disappearance.
The milk production variable also appeared in this factor
cluster in Analysis I although it showed a complex structure
in Analysis II.
The real wholesale dry skim milk price factor appeared
in both Analysis I and Analysis II.

The variable of real

wholesale milk prices had an indeterminate structure in the
period 1929-1941, but it appeared to be largely influenced
by this factor during the period 1929-1943.
A retail milk price factor appeared in Analysis II
although it did not appear in Analysis I.

In Analysis I

the retail milk price variable appeared as part of the
general price level factor.

From the orthogonal and rotated

structures of Analysis II, it can be seen that the general
price level factor also influenced the retail milk price
variable in Analysis II even though other factors were
operative, also.

Actually, this retail milk price factor

probably was not too significant in the analysis, since
indications are that it was overdetermined in the factor
analysis structure.
A seasonal trend factor appeared in Analysis II which
involved the Department Store Sales variable.

Since this

- 112 -

factor did not affect any of the variables involving fluid
milk and cream, it was considered extraneous in this analysis.
The factor structures for the two analyses discussed
above were quite similar.

A general price level factor, an

industrial activity factor, a milk production factor, and a
real wholesale dry skim milk price factor were determined in
both analyses.

The retail milk price factor in Analysis II

was perhaps overdetermined; hence, it probably should not
have appeared as a separate factor.

The monthly seasonal

trend factor determined in Analysis II was considered extra­
neous to this analysis.
Regression systems were set up using the results of
these analyses.

In each the coefficient of multiple corre­

lation was approximately r = .87, although different inde­
pendent variables were used in these systems.

To investigate

how well these systems would fit present-day conditions, they
were extrapolated to 1949 and 1950 and the estimated disap­
pearance of fluid milk and cream for the two years was com­
puted using first the equation derived from Analysis I, 1929194#> and then the equation derived from Analysis II, 19291941.

These estimates of the disappearance figures, plus the

actual disappearance figures,appear in Table XV.
It can be seen that, although the estimates from Analysis
II are mucn lower than the actual disappearance, the estimates
from Analysis I approximate the disappearance figures quite
closely.

This indicates that a shift in demand might have

taken place from 1941 to 1943 so that the estimates derived

- 113 -

Table XV.

Actual and Estimated disappearance ox
Milk in 1949 and 1950

'luid

(Million Pounds)
I

II

Actual
Disappearance

Estimated
Disappearance

Estimated
Disappearance

5,176
4,399
5,270
5,394
5,752
6,090
5,906
5,292
5,009

5,175
4,900
5.375
5,300
5.375
5.375
5.375
5,675
5,150

1949
January
February
March
April
May
June
July
August
September
October
November
December
Total

5,200

5,200

5,105
5,131

4,950
5,200

4,030
4,040
4.420
4,760
5,2 60
5,230
5,160
4,900
4,520
4.420
4,160
4,300

94,224

64,550

55,300

5,330
5,075
5,417
5,593
5,730
6,056
6,073
5, 507
5,213
5,351
5,316
5,449

5,200
5,150
5.400
5,600
5.300
6,050
5,775
5,500
5.300
5,325
5,050
5.400

4,440
4,300
4,640
4,930
5.240
5.530

66,214

65,550

56,740

1950
January
February
March
April
May
June
July
August
September
October
November
December
Total

5,220

4,340
4.530
4,460
4,120
4.240

- 114 -

from t h e p e r i o d 1 9 2 9 - 1 9 4 1 w ould no l o n g e r f i t

1949 or 1950,

w h e re a s t h e e s t i m a t e s d e r i v e d from t h e e n t i r e

p eriod 1929-

1946 co u ld

c lo sely

a p p r o x i m a t e 1 9 4 9 and 1 9 5 0 .

The factor structure for Analysis I indicated that the
real wholesale prices of milk and dry skim milk were rela­
tively independent of the other prices in the group, while
wholesale milk prices, dealers rnilk prices for city distri­
bution, retail milk prices and dry or powdered v/hole milk
prices all followed one trend of the general price level
factor.

Approximately the same results were obtained in

Analysis II, although in the latter case the factor structure
was not as well defined as in Analysis I.
The real retail price of milk was opposite in direction
to the general price level factor, indicating that as the
general price level increased the real retail price of milk
decreased.

This also indicated that the retail price of milk

was not as flexible as the general price level and did not
fluctuate as readily as the general price level.
A lthou gh t h e r e g r e s s i o n
a n a ly ses y ie ld e d

co rrela tio n

s y s t e m s b a s e d on t h e
c o e ffic ien ts

.6 7 in each system the sim p le c o r r e l a t i o n

factor

o f a p p ro x im a tely
betw een f l u i d

m i l k an d c r ea m d i s a p p e a r a n c e an d t o t a l m i l k p r o d u c t i o n was
over

.75,

s i g n i f y i n g th a t the p ro d u ctio n v a r ia b le accoun ted

f o r o v e r 60 p e r c e n t o f
w h ile

a ll

o f the

the v a r i a t i o n s

other v a ria b les

10 p e r c e n t o f th e v a r i a t i o n s
m ilk and cream .

There i s

in th e d isa p p ea ra n ce

accoun ted f o r a p p ro x im a tely

in th e d isap p eara n ce o f f l u i d

a ls o a q u e s tio n as t o w hether th e

- 115 -

production variable should be included at all, since disap­
pearance (as a measure or demand) should theoretically be
explainable in terms of prices or general conditions, but
not in terms of the supply.

Even though supply, in this

case, refers to total milk production, and disappearance
refers to that portion which is used for fluid milk and
cream, there is still the question whether this supply vari­
able ought to have been included at all.

As was shown above,

excluding the supply variable from the regression systems
greatly reduced the strengths of the correlations.
The close correspondence which occurred between produc­
tion and disappearance might be explained in another way,
however.

The disappearance of fluid milk and cream was cal­

culated as a residual, deducting the milk equivalent of
several manufactured dairy products from total milk produc­
tion.

A more accurate method of calculating these disap­

pearance figures might help to improve these regression
systems.
t

"

/

#

4 ¥ .¥ .¥ it i

BUTTER
All of the variables included in this study which
pertained to butter were included in this group.

Among

these were butter production, outter disappearance, oleo­
margarine production, the butterfat-feed price ratio, butter
and oleo prices, the index of retail food prices, marketing
margins on dairy products, department store sales and time
trend variables.

A complete list of the variables in this

- 116 -

group is given in Table XVI.

Analysis 1

1929-1948

There were four distinct factors in this analysis.

A

summary of the factor structure is given in Table XVII.
Factor I contained variables of wholesale and retail
prices of butter, the retail food price index, and the re­
tail cost of the market basket.

These indicate that Fac­

tor I was the general price level factor discussed previous­
ly.

The presence of Payrolls in Manufacturing Industries,

V29, and Department Store Sales, ^39, in this factor indi­
cates that an industrial activity factor might also be
present here; however, this industrial activity factor did
not appear as an independent factor.
It appears that Oleo Production was directly influenced
by the general price level factor while Butter Disappearance,

Jr73 >

was inversely influenced by this general price level

factor.

This latter variable was the only one which showed

a negative factor loading on Factor I, indicating that as
the price level decreased butter disappearance increased,
while as the price level increased butter disappearance
decreased.

(These changes would be in relative, rather than

absolute, terms.)
Factor II contained the variables of butter production,
the monthly time trend and the seasonal trend of milk produc40

tion. This was probably a butter production factor. The
factor loadings showed that butter production followed the

Table AVI.

Variable
No.

6.
7.
S.
9.
10.
11.
12.
13.
14.
15.
16.
17.
IS.
19.
20.
21.

17
22
23
25
29
32
33
35
39
50
51
56
67
69
72
73

Description
Creamery Butter Production
Oleomargarine Production
Butterfat - Av. Price per lb. received by Farmers
Butterfat - feed price ratio
Milk - Av. price per cwt. paid producers for 3*5 milk
condenseries
Butter - Av. wholesale price per lb.
Butter - a v . retail price per lb.
Oleomargarine - Av. retail price per lb.
Retail Food Prices in Large Cities
All Manufacturing Industries - Payrolls
Retail Cost of Dairy Products
Margin on Dairy Products
Butter Marketing Margin
Department Store Sales
Real Retail Price of Butter
Real Retail Price of Oleomargarine
Real Wholesale Price of Butterfat
Monthly Time Trend
Consecutive Time Trend
Seasonal Milk Production Trend
Monthly Disappearance of Butter

117

2.
3.
4.
5.

2
a
9
11
14

1.

Variables Used in Analysis of Butter

Table XVII.

Summary of factor Structure of Butter

Analysis I

1929-194# (4 factors)

factor I

£
9
14
17
22
25
29
32
33
39
73

Factor Loading
(Rotated Matrix)
.611
.669
.679
.679
.63#
.675
.#66
,6#3
*659
.611
-.607

Description
Oleomargarine: Production
Butterfat: Av. price per lb, received by farmers
Milk: Av. price per cwt. at condenseries
Butter: Av. wholesale price per lb.
Butter: Av. retail price per lb.
Retail food Prices in Large Cities
Payrolls in Manufacturing Industries
Retail Cost of Dairy Products in Market Basket
Marketing Margin on Dairy Products
Department Store Sales
Butter: Disappearance
Factor II

2
67
72

-.711
.673
-.756

Butter: Production
Monthly Time Trend
Seasonal Milk Production Trend
Factor III

35
51

-.605
-.552

Butter Marketing Margin
Real Retail Price of Oleomargarine
factor IV

11

.551

Butterfat - feed Price Ratio

i
8TT

Variable
No.

Table XVII.

Summary of factor Structure of Butter (Continued)

Analysis I
Variable
No.

Factor Loading
(Rotated Matrix)

1929-1943 (4 factors)
Description

Complex
56
69

(I).557 (IV).523
(l).&95 (III).573

Real Vdiolesale Price of Butterfat
Consecutive Time Trend
Indeterminate
Oleomargarine: Av. retail price per lb.
Real Retail Price of Butter

119

23
50

- 120 -

seasonal trend of milk production and was inverse to the
monthly time trend.
Factor III contained the variables of the butter mar­
keting margin and the real retail price of oleo.

From the

factor loadings in the rotated matrix this might have been
a real retail oleo price factor; however, this was difficult
to determine from the limited information available.
The Butterfat-Feed Price Ratio variable, /fll, was the
only variable with simple structure in Factor IV.

Factor

IV might thus have been the feed factor discussed earlier,
as this was the only factor containing variaole ^11 in this
study.
Two factors showed complex structure in this analysis.
The Real wholesale Price of Butterfat, ^56, had positive
loadings in Factor I and Factor IV.

Tnis indicates that

the real wholesale price of butterfat was directly influ­
enced by the general price level factor and also by the feed
factor.

The consecutive time trend variable also had a

complex structure.

The factor loadings indicated a direct

relationship with the general price level factor and an
inverse relationship witn the real retail oleo price factor.
This latter relationship indicates that the real retail oleo
price decreased over time.
Two variables showed an indeterminate structure in this
analysis.

The Average Retail Price of Oleo, ^23, had an

indeterminate structure in the rotated matrix; inspection
of the orthogonal matrix, however, shows that this variable

- 121 -

was influenced by Factor I, the general price level, and
i<’actor III, the real retail oleo price factor.
Retail Price of Butter,

,f50,

The Heal

showed some influence of the

general price level factor and the x'eed factor, but these
were minor factor loadings; hence this variable was classed
as indeterminate in structure.
In summary, there were four factors determined in this
study.

These were a price level factor, a butter production

factor, a real retail oleo price factor, and a feed factor.
There was also some indication of an industrial activity
factor.

These factors accounted for over &0 percent of the

variations in 11 out of the 21 factors included in this
group.

Other, perhaps unique, factors would have to be

considered in order to explain the variations in the other
ten variables.
Estimation of Butter Disappearance
One variable was selected to represent eacr of the
above factors in a regression system for the estimation
of butter disappearance.

/ariable ^65, the Consumer Price

Index advanced one month, was chosen to represent the general
price level factor; Butter Production, #2, represented the
butter production factor; the Real Retail Price of Oleo, jf51,
represented the retail oleo price factor; the Butterfat-Feed
Price Ratio, #0.1> represented the feed factor.

These were

all independent variables in the regression system.

Butter

Disappearance, ^73, was the dependent variable in this system.

- 122 -

The coefficient of multiple regression obtained from this
system was r z .772?; the coefficient of determination was
r2 = .5970.
There was some question as to wnether there was an
industrial activity factor included in this group.

A second

regression system was set up including all of the variables
of the first system and adding Real Purcnasing Power advanced
two months,

$64,

representing the industrial activity factor.

This system yielded a coefficient of multiple correlation of
r = .3326, with a coefficient of determination of r2 = .6933.
Thus the addition of the industrial activity factor explained
an additional 10 percent of the variation in butter disap pearance.

In this second regression system the real retail

price of oleo and the butterfat-feed price ratio proved to
be not significant to the relationship; the consumer price
index, real purchasing power, and butter production variables
proved to be highly significant in the system.
A third regression system was set up using the variables
found to be significant in the above system but eliminating
the butter production variable.

This system thus included

butter disappearance as the dependent variable with the
consumer price index advanced one month and real purchasing
power advanced two months as the independent variables.
This system yielded a coefficient of multiple regression of
r = .7654 with a coefficient of determination of r2 3 .6l6£.
Thus the factors of general economic conditions explained
61 percent of the variations in butter disappearance while

- 123 -

butter production explained another 5 percent of these vari­
ations .
In order to investigate the influence of the retail
price of butter in these regression systems, bnis variable
was substituted for the Consumer Price Index advanced one
month while all of the other variables of the second regres­
sion system discussed above were retained.

This yielded a

coefficient of multiple correlation of r = .5306, while the
previous correlation was r = .5326.

Thus the same results

were obtained whether the consumer price index or the retail
price of butter was used in the regression system.
Analysis II

1929-1941

There were four factors determined in this analysis.

A

summary of the factor structure is given in Table XVIII.
factor I appeared to be uhe general price level factor
described previously, since it contained variables of \vholesale and retail prices.

The fact that variable /f29, Pay­

rolls in Manufacturing Industries, appeared in tnis factor
also is evidence that an industrial activity factor might
also have been present; however, this industrial activity
factor did not appear as a separate factor.
factor II contained the variables of butter production
and the seasonal trend of milk production.

It might be

considered a butter production factor.
factor III contained the variables of retail oleo prices,
real retail oleo prices, the butter marketing margin, and the

- 124 -

consecutive time trend.

The factor loadings indicate that

this tni,;ht have been a real retail oleo price factor.

The

negative factor loading for the consecutive time variable
indicated that the relative sizes of the other variables
in this factor decreased over time.
Factor IV contained the variables of department store
sales and monthly seasonal trend.

This was probably a

monthly seasonal trend factor; this same factor appeared
in the analysis of Fluid v.ilk and Cream.
The Butter disappearance variable, ^73, showed a com­
plex structure witn factor loadings on Factor II, the butter
production factor, and Factor IV, the monthly seasonal
trend factor.

The influence of the butter production factor

is understandable; however, the effect of the monthly season­
al trend factor is more difficult to explain.

Perhaps the

loading on Factor IV reflects a similar pattern to Depart­
ment Jtore Sales.

In this case the factor actually operating

might have been the industrial activity factor, since the
analysis of General economic Conditions showed that Depart­
ment Store Sales were influenced by the industrial activity
factor during this period.
The Butterfat-Feed Price Ratio, //ll, had an indetermin­
ate structure in this analysis.

Previous analyses have

shown that this variaole was part of a feed factor which was
relatively independent of the other factors.

The indetermin­

ate structure of this variable in this analysis showed that
the feed factor was of minor significance.

Table

a VIII.

Summary of factor otructure of Butter

Analysis II

1929-1941

(4 factors)

factor I
Factor Loading
(Rotated Matrix)

3
9
14
17
22
29
32
50
56

.626
.707
.6B7
*704
.697
.S29
.669
.740
.7$4

Description
Oleo: Production
Butterfat: av. price per lb.
I-Iilk: av. price per cwt. at condenseries
Butter: Av. wholesale price per lb.
Butter: Av. retail price per lb.
Payrolls in Manufacturing Industries
Retail Cost of Dairy Products in Market Basket
Real Retail Price of Butter
Real Wholesale Price of Butterfat
Factor II

2
72

.752
.740

Butter: Production
Beasonal Milk Production Trend
Factor III

23
35
51
69

.542
.663

.514
-.745

Oleo: Av. retail price per lb.
Butter Marketing Margin
Real Retail Price of Oleo.
Consecutive Time Trend
Factor IV

39
67

-.651
-.630

Department Store Sales
Monthly Time Trend

»
125

Variable
Mo.

Table XVIII.

Variable
No.

Summary of factor Structure of Butter

factor Loading
(Rotated Matrix)

Description
Complex

73

(II).506

(IV)-.343

Butter Disappearance
Indeterminate

11
25
33

Butterfat - feed price ratio
Retail Food Prices in Large Cities
Marketing Margin on Dairy Products

(Continued)

- 127 -

Retail Food Prices in Large Cities, /f25, showed an
indeterminate structure in the rotated matrix, but inspec­
tion of the orthogonal matrix shows that the general price
level had a large influence on this variable which the
rotation process covered up.
Products,

t5
t33>

The Marketing Margin on Dairy

also showed an indeterminate structure. Both

of these variables showed factor loadings on Factor I, the
general price level factor, and Factor III, the real oleo
price factor, but these loadings were not high enough to
determine what factors had predominately influenced these
variables in this analysis.

Since a real oleo price factor

should not have influenced these variables to any extent,
this might indicate that the real retail oleo price factor
actually represents some other factor.

It was,however,

impossible to determine this oLher factor from the information
at hand.
In summary, there were four distinct factors in this
analysis.

These were a general price level factor, a

butter production factor, a real retail oleo price factor,
and a monthly seasonal trend factor.

There was also some

indication that an industrial activity factor might be
present, although this was not a distinct factor.

The fac­

tor structure also indicated that the real retail oleo price
factor was actually an indicator of some other factor, but
this other factor could not be determined from the information
available.
The four distinct factors determined in this analysis

- 125 -

explained over 50 percent of the variations in 11 out of
the 21 variables included in this group.
Estimation of Butter Disappearance
One variable was selected from each of the above fac­
tors for a regression system estimating butter disappear­
ance.

The Consumer Price Index advanced one month, #65,

represented the general price level factor, Butter Produc­
tion, #2, represented the butter production factor, and the
Real Retail Price of Oleo,
oleo price factor.

#51,

represented the real retail

The monthly time trend factor was

considered extraneous to the analysis, but the industrial
activity factor was substituted to account for the relation­
ship between butter disappearance and department store sales;
Real Purchasing Power advanced two months, 7/64, was used to
represent this industrial activity factor.

These variables

were the independent variables in the regression system;
Butter Disappearance, #73» was the dependent variable in the
analysis•
The coefficient of multiple correlation in this system
was r * .5120, with a coefficient of determination of
r^ * .2621.

The coefficients of the consumer price index

and real purchasing power variables were not significant
in this system; only the coefficients of butter production
and real retail oleo price proved significant in the regres­
sion equation.
In order to investigate the effect of the retail price of

- 129 -

butter in this system, this variable was substituted in
place of the Consumer Price Index advanced one month.

This

system yielded a coefficient of multiple correlation of
r » .5494* whereas the earlier system had a coefficient of
multiple correlation of r z .5120.

Thus this substitution

of variables made little difference in the correlation
coefficient.
Gomparlaon of Analyses
There were four distinct factors determined in each of
the analyses discussed above.

A general price level factor,

a butter production factor and a real retail oleo price fac­
tor were derived in each analysis.

Also, evidences of an

industrial activity factor were found in each analysis,
although this was not a distinct factor.

A feed factor was

determined in Analysis I, whereas a monthly seasonal trend
factor was derived in Analysis II.
In Analysis I Butter Disappearance was a part of the
general price level factor, although it was influenced
inversely by this factor.

Thus as the general price level

increased, Butter Disappearance decreased.

In Analysis II

Butter Disappearance had a complex structure, showing a
relationship with the butter production factor and the
monthly seasonal trend factor.

This difference might be

explained by the shift in the demand for butter which
apparently took place in recent years.
The regression system set up from Analysis I explained

- 130 -

69 percent of the variation in butter disappearance over
the period 1929-194&.

The regression system set up from

Analysis II explained only 26 percent of the variation in
butter disappearance over the period 1929-1941 although
essentially the same variables were used in both analyses.
In Analysis I the consumer price index, real purchasing
power, and butter production variables proved significant
in the analysis.

The consumer price index and real purchas­

ing power explained approximately 61 percent of the varia­
tions in butter disappearance, while butter production
explained another & percent of these variations.
These results directly contradict a statement made by
C. .d. Vial (14) in 1940.

He said: "Cnanges in industrial

production, employment, and payrolls have little or no
effect on the consumption of outter.
by production"

(14, pp. 6).

Consumption is governed

The results of the factor

structure and the recession systems in Analysis I indicate
that consumption was not governed primarily by production in
this period and that the factors of industrial production
and the general price level explained at least ol percent of
the variations in outter disappearance.
In Analysis II, however, the consumer price index and
real purchasing power were not significant in the regression
system; the only significant variaoles were those of butter
production and the real retail price of oleomargarine.
variables only explained 26 percent of the variations in
butter disappearance.

These

- 131 -

Thus it appears that the statement quoted above by
9ial may have been true prior to 1940, although there was
not too great a correspondence between production and
consumption even at that time.

during the period 1929-

194& his statement was contradicted completely.
In order to investigate how well the regression
systems would approximate present-day conditions they were
extrapolated for 1949 and the estimated disappearance com­
puted for eacn system.

The estimated butter disappearance

as estimated by regression systems I and II and the actual
disappearance figures are given in Table XIX.

It can be

seen that I estimated the disappearance fairly closely,
while II yielded estimates which were much higher than the
actual figures.

This is another indication of the downward

shift in the demand for butter between these two periods.

- 132 -

Table XIX.

Actual and Estimated Disappearance of Butter
in 1949
(Million Pounds)

Actual
Disappearance

I

II

Estimated
Disappearance

Estimated
Disappearance

1949
January
February
March
April
May
June
July
August
September
October
November
December
Total

106
100
112
110
121
101
99
112
105
107
100
97

93
95
101
101
113
113
107
107
104
102
99
102

143
143
147
150
157
157
154
153
149
149
14#
149

1,270

1,239

1,799

119
10#
125

101
102
114

149
149
152

1950
January
February
March

- 133 -

CHEESE
All of the indices relating to cheese including pro­
duction, prices and monthly disappearance have been included
in this group.

In addition, indices of the average price of

betterfat, the average wholesale price of milk, the butter­
fat feed and milk-feed price ratios, the buying price for
milk for city distribution, and various time trends have
also been included.

The two indices of General Economic

Conditions, Real Purchasing Power advanced two months,
and the Consumer Price Index advanced one month,

ft65,

were

included to investigate how they fit into the factor struct­
ure.

A complete list of the variables in this group is

given in Table XX.
Analysis I

1929-1943

There were five factors in this analysis.

A summary

of the factor structure is given in Table XXI.
Factor I was the general price level factor described
previously.

This was determined by the presence of the

Consumers Price Index advanced one month,

ft65, which

repre­

sented the general price level factor in this analysis.

The

variables which were influenced primarily by this factor
were the average buying price for milk for city distribution,
the average price paid at condenseries, the average whole­
sale price and average retail price of cheese, the retail cost
of dairy products in the market basket and the marketing

- 134 -

margin on dairy products.

These are all consistent with

the hypothesis that Factor I was the general price level
factor.
Factor II was composed of two variaoles, the Heal /Whole­
sale Price of Butterfat, ^56, and the Real /Wholesale Price of
Milk, ^57.

This factor was somewhat related to Factor I but

was relatively independent of the other factors in this
analysis.

This factor could be considered to be an indicator

of the real wholesale price of milk.
Factor III consisted of cheese production, cheese disap­
pearance, and the seasonal trend of milk production.

This

factor showed a slight relationship with Factor IV, but it
was relatively independent of the other factors.
was probably an indicator of cheese production.

Tnis factor
The factor

loadings indicate that the production of cheese closely followed
the seasonal pattern of milk production.

The fact that cheese

disappearance was part of this same factor but with a smaller
factor loading shows that cheese disappearance was influenced
by cheese production, but that there was not an exact corre­
spondence between the two.
Closer examination of the rotated matrix shows that the
price level factor had some slight effect on cheese disap­
pearance but that the cheese production factor had the high­
est factor loading on cheese disappearance.

This signifies

that cheese disappearance depended more upon cheese production
than on the price of cheese.

This indicates that the demand

for cheese is inelastic with respect to the price of cheese.

- 135 -

However, the factors determined here only explained about
63 percent of the variations occurring in cheese disappear­
ance; hence other, perhaps unique, factors must be considered
before the net effects of the factors could be predicted.
Factor IV consisted of the Butterfat-Feed Price Ratio,

a-11,

and the Ililk-feed Price Ratio, //12.

These constitute

the Feed Factor described in the previous analysis on Prices.
As was stated before, this factor was somewhat related to
Factor III, the Cheese Production factor.

The Feed factor

was relatively independent of all other factors in this
analysis.
Factor V contained the variables of the real wholesale
price of cheese at Chicago and on the Wisconsin Cheese Ex­
change.

It is interesting that these real wholesale prices,

although related to each other, were independent of every
other factor in this analysis including the factor containing
the real wholesale prices of milk and butterfat (Factor II).
Thus this Real Wholesale Cheese Price factor was not influ­
enced by the Real Wholesale Milk Price factor.
A complex structure was obtained for three variables in
this analysis.

The American Cheese Marketing Margin was

associated with Factors I and II.

The positive loading on

Factor I shows that it was influenced directly by the general
price level; the negative loading on Factor II shows that it
had an inverse relationship with the real wholesale milk
price.

The high loading on Factor I appears to be a distor­

tion due to the rotation process; reference to the orthogonal

Table

Variable
No.

1.
2.
3.
4.
5.
6.
7.

15
16
21
32
33
36
43
44
49
56
57
64

65
67
6S
72
74

7ariaoles Used in analysis of Cheese
Description

Cheese, Total factory Production
Butterfat, Av. price per lb. received by farmers
Milk, Av. wholesale price per 100 lb. received by farmers
Butterfat - feed price ratio
Milk - feed price ratio
Milk - Av. dealer’s buying price per cwt. for city distribution
Milk - Av. price per cwt. paid producers for 3.5 milk delivered at
condenseries
Cheese - fresh, single daisies, Av. wholesale price per lb. at Chicago
Cheese - Am. twins, Wholesale price on .Msconsin Cheese exchange
Cheese, Av. retail price per lb. in leading cities
Retail Cost of Dairy Products
Marketing Kargin on Dairy Products
American Cheese Marketing Margin
Cheese, Real wholesale price per lb. at Chicago
Cheese, Real wholesale price per lb. on Vjisconsin Cheese exchange
Real retail price of cheese
Real wholesale price of butterfat
Real wholesale price of milk
Real Purchasing Power advanced 2 months
Consumer Price Index advanced 1 month
Monthly Time Trend
Consecutive Time Trend
Seasonal Milk Production Trend
Monthly Disappearance of Cheese

13 6

d.
9.
10.
11.
12.
13.
14.
15.
16.
17.
IS.
19.
20.
21.
22.
23.
24.

3
9
10
11
12
13
14

a X.

Table All.

Summary of factor Structure of Cheese
Analysis I

1929-194& (5 factors)

factor I
Variable
No,

factor Loading
(Rotated Matrix)

13
11*
15
16
21
32
33
65

.533
.525
.530
.514
.526
.531
.676
.521

Description
Milk, Av. buying price per cwt. for city distribution
Milk, Av. price per cwt. at condenseries
Cheese, Av. wholesale price per lb. atChicago
Cheese, Av. wholesale price on Wisconsin Cheese exchange
Cheese, Retail price per lb.
Retail Cost of Dairy Products in Market Basket
Marketing Margin on Dairy Products
Consumers Price Index advanced one month
factor II

56
57

.494
.523

Real wholesale price of Butterfat
Real wholesale price of Milk
Factor III

3
72
74

-.613
-.613
-.494

Cheese Production
Seasonal Milk Production Trend
Cheese Disappearance
Factor IV

11
12

.637
.619

Butterfat - feed price ratio
Milk - feed price ratio

Table

a x I.

Summary of factor Structure of cheese (Continued)

Analysis I
Variable
No.

Factor Loading
(Rotated Matrix)

19^9-1943 (5 factors)
description

Factor V
43
44

*500
*490

Cheese, Real wholesale price at Chicago
Cheese, Real wholesale price on Wisconsin Exchange
Complex

36 (I).319 (II)-.494
49 (I).611 (II)-.612
64 (II).651 (III) .433

American Cheese Margin
Real retail price of cheese
Real purchasing power advanced two months
Indeterminate

9
10
67
69

Butterfat, Av. price per lb. received by farmers
Milk, Av. wholesale price per cwt. received by farmers
Monthly Time Trend
Consecutive Time Trend

- 139 -

matrix shows that the influence of the general price level
in this variable was less than the influence in the vari­
ables influenced principally by this factor.

The loading

on factor II, however, remains relatively the same in both
the orthogonal and rotated matrices.
The Real Retail Price of Cheese,
structure involving three factors.

/fh9,

showed a complex

The loading for the

first factor was positive, indicating that the general price
level had a direct influence on the real retail cheese price.
The negative loading for factor II indicates that the real
retail price of cheese fluctuated in an opposite manner to
that of the real wholesale price of milk.

This is contra­

dictory to the expected result; it might be due to lags in
price changes whereby changes in the real wholesale price of
milk and butterfat are only reflected in real retail cheese
prices after some time.
the basic data.

This might also reflect flaws In

It is interesting that there was also a

positive factor loading on factor V, the real wholesale
cheese price factor.

However, this loading was not very

large, indicating that the influence of this factor on the
real retail price of cheese was not very strong.
The variable indicating the industrial activity factor,

rftk-y had

a complex structure in tnis analysis, having positive

loadings on factor II, the real wholesale milk price factor,
and factor III, the cheese production factor.

This complex

structure could be interpreted as indicating that no single
variable in this analysis was solely influenced by the

- 140 -

industrial activity factor, but that those variables in
factor II and Factor III were influenced to some extent
by this factor.

The positive loading of variable

ft64

on

factor III was opposite to the loadings of the other vari­
ables in this factor.

This indicates that cheese produc­

tion had an inverse relationship with industrial activity.
Thus when industrial activity was low cheese production
increased,

while when industrial activity increased, the

production of cheese decreased relative to the proportionate
changes occurring.
The average butterfat price received by farmers, ,/9,
and the average wholesale price for milk received by farmers,
zflO, appeared Indeterminate in structure in the rotated ma­
trix.

However, this may have been due to distortions occur­

ring in the rotation process.

The original orthogonal

structure shows that these two variables had a high loading
on Factor I, the general price level factor, and a slight
loading on Factor II, the real wholesale milk price factor,
dince these results are consistent with the rest of the fac­
tor structure, variables ,/9 and /r~10 could be considered part
of Factor I rather than Indeterminate in structure.
There was no consecutive time trend or monthly time
trend evident in this analysis, as both these variables were
Indeterminate in the rotated structure and in the orthogonal
structure as well.
In summary, there were five factors determined in this
analysis of Cheese.

These were a general price level factor,

- 141 -

a real wholesale milk price factor, a cheese production
factor, a feed factor, and a real wholesale cheese price
factor.

These five factors explained over 80 percent of

the variations in 19 out of the 24 variables included in
this analysis.
Estimation of Cheese Disappearance
One variable out of each one of these factors was
selected for a multiple regression system.

Cheese disap­

pearance was the dependent variable in this system; the
five variables chosen as independent variables in this sys­
tem were the Consumer Price Index advanced one month, /65,
Total Factory Production of Cheese, /3 , Real Purchasing
Power advanced two months,

rfol+,

Butterfat-Peed Price Ratio,

z/11, and Real Wholesale Cheese Price at Chicago, ?f43 • These
variables were selected from the factor structure as repre­
sentatives of the individual factors.
The coefficient of multiple correlation was r = .7603.
This signifies that these five variables explained
of the variation in cheese disappearance.

5&

percent

However, there was

still 42 percent of the variation in cheese disappearance
left unexplained by this system.

Variables /II and /43 may

be eliminated since they were not significant, and thus did
not affect the coefficient of multiple correlation.
A regression system using cheese disappearance as the
dependent variable with the consumer price index advanced
one month and real purchasing power advanced two months as

- 142 -

independent variables (thus eliminatin ; the cheese produc­
tion variable) yielded a multiple correlation coefficient
of r » .5493.

This signifies that these variables explained

approximately 30 percent of the variation in cheese disap­
pearance and cheese production explained approximately 30
percent of the variation also.
Analysis II

1929-1941

There were five factors present in this analysis.

A

summary of the factor structure is given in Table X4II.
Factor I was an indicator of the general price level.
Tnis was shown by the presence of the Consumer Price Index
advanced one month, //65, wnich represented this factor from
the analysis of General Economic Conditions.

The other vari­

ables present in this factor were prices of butterfat, milk,
and cheese, the retail cost of dairy products in the market
basket, and the marketing margin on dairy products.

This

general price level factor was relatively independent of all
other factors in this analysis.
Factor II consisted of the variables of cheese production
and cheese disappearance.

This factor could be considered a

cheese production factor.

This factor was relatively inde­

pendent of every factor but Factor V, which was a time trend
factor.
Factor III consisted of the variables of the butterfatfeed price ratio and the milk-feed price ratio.

This was

the feed factor discussed previously.

It was slightly

related to factor IV, but relatively independent of all
other factors in this analysis.
factor IV involved the real wholesale price of cheese
at Chicago and on the Wisconsin Cheese Exchange and the real
wholesale price of butterfat.

This factor was probably a

real wholesale cheese price factor, since the factor loadings
for the real wholesale price of cheese were larger than the
factor loading of the real wholesale price of butterfat.

As

was mentioned before, this factor was somewhat related to
Factor III, the feed factor.

It was relatively independent

of all other factors in this analysis.
Factor V consisted of the monthly seasonal time trend
and the seasonal milk production trend.

It was somewhat

related to Factor II, the cheese production factor, but
otherwise was completely independent of the other factors.
This time factor was probably extraneous to this analysis,
since it involved only two time variables and appeared to
be independent of the other factors.
A few variables were of complex structure in this analysis
The average price of milk at condenseries and the average
wholesale cheese price at Chicago and on tne Wisconsin Cheese
Exchange all showed approximately the same factor loadings on
Factor I, the general price level factor, and Factor IV, the
real wholesale cheese price factor.

This indicates that the

average price of milk at condenseries was influenced by the
general price level and by the real wholesale price of cheese.

Table XXII.

Summary of factor Structure of Cheese

Analysis II

1929-1941 (5 factors)

Factor I
Variable
No.

Factor Loading
(Rotated Matrix)

9
10
13
21
32
33
6$

.611
.621
.S40
.868
.86)

.366
.#bl

Description
Butterfat, Av. price per lb. received byfarmers
Milk, Av. wholesale price per cwt. received by farmers
Milk, Av. buying price per cwt. for city distribution
Cheese, Av. retail price per lb.
Retail cost of Dairy Products in market Basket
Marketing Margin on Dairy Products
Consumers Price Index advanced one month
Factor II

3
74

.585

.615

Cheese Production
Cheese Disappearance
Factor I I I

11
12

.782

.743

Butterfat - feed price ratio
Milk - feed price ratio
Factor IV

43
44
56

.623
.642
.509

Cheese, Real wholesale price at Cnicago
Cheese, Real wholesale price on Wisconsin Cheese exchange
Real wholesale price of Butterfat
Factor V

67
72

.471
.551

Monthly Time Trend
Seasonal Milk Production Trend

Table XXII.

Summary of Factor Structure of Cheese (Continued)

Analysis II

1929-1941 (5 factors)

Complex
Variable
No.
14
15
16
36

Factor Loading
(Rotated Matrix)
(I)
(I)
(I)
(I)

.597
.527
.552
.$$5

(IV) .402
(IV) .421
(IV) .402
(IV)-.631

description
Milk, Av. price per cwt. at condenseries
Cheese, Av. wholesale price at Chicago
Cheese, Av. wholesale price on Wisconsin Cheese Exchange
American Cheese Marketing Margin
Indeterminate

64
69

Real Retail Price of Cheese
Real wholesale Price of milk
Real purchasing power advanced 2 montns
Consecutive Time Trend

145

49
57

- 146 -

The average wholesale cheese prices at Chicago and on the
Wisconsin Cheese Exchange were also influenced principally
by the general price level and the real wholesale price of
cheese.

The sizes of the factor loadings indicate that in

each of these cases the general price level factor was
somewhat more important than the real wholesale cheese
price factor.
The American Cheese Marketing Margin, /f36, had a com­
plex structure with a positive loading on factor I and a
negative loading on factor IV.

This indicates that the

cheese marketing margin was directly influenced by the gen­
eral price level and inversely influenced by the real whole­
sale price of cheese.
Several variables were of indeterminate structure in
this analysis.

The heal Retail Price 01 Cheese, /f49> showed

a positive loading on factor I, the general price level fac­
tor, and a negative loading on factor V, the seasonal milk
production trend factor.

The Real Wholesale Price of Milk,

7^57, showed a positive loading on factor IV, the real whole­
sale cheese price factor.

However, in both of these cases

these factor loadings were minor.
Variable ^64» Real Purchasing Power advanced two months,
showed positive factor loadings on Factors I, II, and IV,
although none of these loadings was large enougn to be
significant.

This indicates that none of these factors was

affected primarily by an industrial activity factor, since

- 147 -

variable #64 represented this factor from the analysis of
General Economic Conditions.

The first four factors in

this analysis explained approximately 93 percent in the
variation of variable #64, however, signifying that the fac­
tor represented by this variable (industrial activity) was
present to some extent in each of these factors even though
it was not dominant in any one of them.
The consecutive time trend had a negative factor load­
ing on factor I and positive factor loadings on the other
factors, but all of these were minor in size.

Hence this

time trend could be considered indeterminate in structure.
In summary, there were five factors determined in this
analysis.

factor I was the general price level factor; Fac­

tor II was a cheese production factor; Factor III was a
feed factor; Factor IV was a real wholesale cheese price
factor; Factor V was a seasonal milk production trend factor.
These five factors accounted for over SO percent of the varia
tion in IS out of the 24 variables included in this analysis.
Estimation of Cheese Disappearance
Although five factors appeared in the above analysis,
only four were used for estimation purposes.

The time fac­

tor was eliminated as it appeared to be independent of the
other factors in this analysis.

Cheese disappearance was

the dependent variable in the regression system, while the
Consumer Price Index advanced one month represented Factor
I, factory Cheese Production represented Factor II, the

- 14S -

Butterfat-Feed Price Ratio represented Factor III, and the
Real Wholesale Cheese Price at Chicago represented Factor
IV.
The coefficient ol‘ multiple correlation using these
variables was r = .7602.

However, only the coefficient of

cheese production proved to be significant.

The simple

correlation between cheese production and cheese disappear­
ance was r = .750; therefore the addition of the three other
variables added very little to the estimate of cheese disap­
pearance .
Comparison of the Two Analyses
Although there were five factors determined in each
analysis, many differences existed in the two factor struct­
ures described above.
Factor I, the general price level factor, had the same
general characteristics in both analyses.

However, the

Average Price of Butterfat, 7^9 > and the Average Wholesale
Price of Milk, /flO, appeared to have simple structure in
Analysis II, whereas in Analysis I they were indeterminate
in structure.

In the latter case, however, it appears that

these two variables belonged in the structure of Factor I.
The fact that these two variables had smaller loadings than
the other variables on Factor I in Analysis II also demon­
strates that some other elements should be considered when
analyzing variables //9 and

#10,

In Analysis I, the Average Price for Milk at Conden-

- 149 -

series, j-14, the Average Wholesale Price lor Cheese at
Chicago, /rl5, and. tne Average Wholesale Price for Cheese
on the Wisconsin Cheese jixchany;e, //16, all showed simple
structure and appeared to be influenced solely by factor
I.

However, in Analysis II these same three variables had

complex structures, being composed of both [’actor I, the
general price level factor, and factor IV, the real whole­
sale cheese price factor.

It is interesting that, although

there was a comparable real wholesale cheese price factor in
Analysis I, the variables mentioned above were not influ­
enced by it and depended solely upon the general price level
factor.

Thus it appears that in the shorter period of time

the actual wholesale prices of cheese and the price of milk
at condenseries depended on both the general price level
and the real wholesale price of cheese, whereas in the longer
period of time the major influence on these variables became
the general price level.
Variables //13» 21, 32, 33 > 65 all retained approximately
the same positions in the two time periods.
factor II, the real wholesale milk price factor, and
factor V, the real wholesale cheese price factor, in Analysis
I were merged as factor IV, the real wholesale cheese price
factor, in Analysis II.

This demonstrates that, whereas real

wholesale cheese prices and the real wholesale price of
butterfat followed a similar trend in the period 1929-1941
while the real wholesale price of milk did not follow this
trend, over the longer period 1929-1948 the real wholesale

price of cheese followed one trend while the real wholesale
price of milk and the real wholesale price of butterfat
followed a different trexid.

This was probably due to the

fact that during the shorter period of time the real whole­
sale price of cheese helped determine the real price of
butterfat, while in the longer period other products became
of increasing importance and the real wholesale price of
butterfat and the real wholesale price of rail-k reflected
these changes.
factor III, the cheese production factor in Analysis
V, was equivalent to factor IV in Analysis II.

However,

while the seasonal milk production trend variable was a part
of the factor cluster in Analysis I it appeared as a part
of a separate factor, factor V, in Analysis II.
factor IV, the feed factor in Analysis I, was equivalent
to factor III in Analysis II.

The sane variables were in­

volved in these factors and they had approximately the same
relationship with the other factors.
As mentioned earlier, factor V together with factor II
in Analysis I were comparable to factor IV in Analysis II.
Factor V in Analysis II, however, did not have a counterpart
in Analysis I.

factor V, a time trend factor, was probably

a separate factor in Analysis II due to the non-linear
aspects of the business cycles affecting the data in the
shorter period of time; during the longer period of time
the seasonal milk production trend was found to correspond
closely to cheese production and became a part of this fact

cluster.
The American Cheese Marketing Margin,

$36,

a complex structure in both of these analyses.

exhibited
The Real

Retail Price of Cheese, ;f49> and Real Purchasing Power
advanced two months, ,f64, both had a complex structure in
Analysis I while they were indeterminate in structure in
Analysis II.

This was probably due to the fact that the

influences of the factors oecame more evident and pronounced
over the longer period of time.

rf69,

The Consecutive Time Trend,

was indeterminate in structure in both these analyses,

signifying that there was no evident linear growth pattern
present in either of them.
Two regression systems were set up based on the above
analyses.

In both systems the coefficient of multiple corre­

lation was approximately r = .75 although different variables
were used in each.

Also, the only variable witn a significant

coefficient in both these systems was that of cheese produc­
tion.
In order to investigate how well these systems v/ould
estimate recent disappearance, these systems were extrapolated
to 1949-

The estimated cheese disappearance for 1949 by

system I and system II and the actual 1949 disappearance is
given in Table XXIII.

The estimated disappearance for 1949

based on system II was much lower than the actual disappear­
ance; the estimated cheese disappearance based on system I
approximated the actual disappearance more closely even though
it was still somewhat lower than the actual figure.

Table

Actual and Estimated disappearance
in 1949

of

Cheese

(Million Pounds)

Actual
disappearance

I

II

estimated
disappearance

Estimated
disappearance

1949
January
February
March
April
May
June
July
August
September
October
November
December
Total

88

80
78
81
88
88
85
88

72.0
71.5
7b.0
81.0
89.0
89.0
81.0
80.0
77.5
72.5
68.5
68.5

1,071

926.5

734.4

92
92
104

72.5
73.5
77.5

55.6
55-6
63.2

83

99
100
113

55.2
54 *8
62.4
66.0
66.0
66.4
66.0
66.4
62.8
58.8
54.8
54.&

1950
January
February
March

- 153

COlMLLNdfD,

DdY

-

AIJD EtfAPORATfD MILK

Since there was not enough information available to
make a complete analysis of these individual products,
condensed, dry and evaporated milk products were analyzed
as a single group.

This group included all the variables

in this study relating to these products such as produc­
tion, wholesale prices, and retail prices of condensed
milk, evaporated milk, and dry and powdered whole and skim
milk.

The only disappearance figures available for this

group were those for evaporated milk and they were included*
In addition, other variables sucn as total milk production
on farms, real purchasing power advanced two months, the
consumers price index advanced one month, and indicators of
the various time trends were used in this analysis.

A com­

plete list of the variables included in this group is given
in Table JuCIV.
Analysis I

1929-194#

There were four distinct factors determined in this
analysis.

A summary of the factor structure is given in

Table XXV.
factor I contained variables of retail and wholesale
prices and the consumer price index advanced one month.
This factor was probably the general price level factor
discussed previously.
factor II contained the variables of the real wholesale

Table IMIV.

Variable
No.
1.
2.
3.
4.
5.
6.

1
4
5
6
10
14

7.
d.
9.
10.
11.
12.
13.
14.
15.
16.
17.
id.
19.
20.
21.
22.

id
20
37
40
41
42
46
4d
54
55
64
65
67
69
72
75

Variables used in analysis of Condensed, Jry and Evaporated Milk

Description
Milk, Production on P a m s
Evaporated Milk, unskimmed,case goods production
Condensed Milk, skimmed, Production sweetened and unsweetened
Dry Whole Milk Production
Milk, Av. wholesale price per 100 lb. received by farmers
Milk, Av. price per cwt. peid producers for 3.5 milk delivered at
condenseries
Evaporated Milk, unsweetened, Av. wholesale selling price per case
Evaporated Milk, Av. retail price per Iki oz. can in leading cities
Evaporated milk marketing margin
Milk, dry or powdered whole, av. wholesale selling pr ic e per lb.
Dry dkirn Milk, Av. 'wholesale selling price per lb.
Dried or Powdered okim Milk, Production
Meal Wholesale Price of Evaporated Milk
Real Retail Price of Evaporated Milk
Real Wholesale Price of Dry Whole Milk
Real Wholesale Price of Dry Ekim Milk
Real Purchasing Power advanced Two Months
Consumers Price Index advance One month
Monthly Time Trend
Consecutive Time Trend
Eeasonal Milk Production Trend
Monthly Disappearance of Evaporated Milk

Table XX/.

Summary of factor structure of Condensed, dry and evaporated i.ilk

Analysis I

1929-1948 (4 factors)

factor I
lal
No_

Factor Loading
(Rotated Matrix)

10

,682
.725
.783
.321
,725
.826

14

IS
20

40
65

description
Milk, Av. wholesale price per cwt received by farmers
Milk, Av. price per cwt. at condenseries
evaporated Milk, Av. wholesale selling price
evaporated milk, Av. retail price
Milk, dry or Powdered whole, Av. wholesale selling price
Consumers Price Index advanced one month
factor II

55
64
69

.819
.644
.585

Real wholesale price of dry Skim Milk
Real Purchasing power advanced 2 months
Consecutive Time Trend
factor III

1
72

.804
.830

Milk Production
Seasonal Milk Production Trend
Factor IV

46
48

-.761
-.747

Real wholesale price of evaporated Milk
Real retail price of evaporated Milk

Table XXV.

Summary of factor Structure of Condensed, dry and ihraporated milk
(Continued)

Analysis I 1929-194# (4 factors)
Variable
No.

Factor Loading
(Rotated Matrix)

Description
Complex
Evaporated kilk production
Condensed kilk production
Dry whole milk production
Evaporated milk marketing margin
Dry skim milk, Av. wholesale price per lb,
Dried or powdered skim milk production
Real wholesale price of dry whole milk

Indeterminate
67
75

Monthly Time Trend
Evaporated Milk Disappearance

156

4 (II) .576 (III) .610
5 (I) .409 (III) .667
6
(II) .507
(I) .436
37
(II)- .545
(I) .#51
(I) .460
41
(II) .500
42 (II) .503 ( H I ) .5##
54 (I) .444
(II) .444
(IV)-.529

price of dry skim rnilk, real purchasing power advanced two
months and the consecutive time trend.

This factor appeared

to be the industrial activity factor determined in previous
analyses.
Factor III contained the variables of inilk production
and the seasonal milk production trend.

From the appear­

ance of the factor structure, this was probably a milk
production factor.
Factor IV contained the variables of the real wholesale
price of evaporated milk and the real retail price of evap­
orated milk.
factor.

This was probably a real evaporated milk price

It is interesting that it is virtually independent

of Factor II, the industrial activity factor, wnich contained
the variables of the real wholesale price of dry skim milk
and real purchasing power.
Several variables showed a complex structure in this
analysis.

Dry whole milk production and dry skim rnilk aver­

age wholesale prices were influenced by Factor I, the general
price level factor, and Factor II, the industrial activity
factor.

The Fvaporated Milk Marketing Margin, ^37, showed a

direct influence by the general price level factor and an
inverse influence by the industrial activity factor.

This

indicates that as industrial activity increased, this market­
ing margin decreased, while as industrial activity decreased
this margin increased.

This is consistent with the behavior

of other marketing margins discussed previously.
Condensed Milk Production,

showed a complex struct­

ure with factor loadings on Factor I, the general price level

factor, and factor III, the milk proauction factor.

Evar-

oratea i.ilK I'roauction, ^4, and Dried or Pondered okim Kilk

ffi+2,

Proauction,

showed the influence of factor II, the

industrial activity factor, and factor III, the milk produc­
tion factor.

Thus, while all tnree of these products were

influenced by a milk production factor with condensed milk
proauction followed the trend of the general price level,
evaporated milk and dry skim milk production followed the
trend of industrial activity.
The Real Wholesale Price of Dry V.hole milk was influ­
enced by three factors in this analysis.

These were the

genera^ price level factor, the industrial activity factor,
and the real evaporated milk factor.
Two variables showed an indeterminate structure in this
analysis.

The monthly time trend was not present to any

large extent in any of the factors, although it was somewhat
contra-seasonal to the milk production factor.

Evaporated

Kilk Disappearance, ,
t 75, had minor factor loadings on Factors
II, III, and IV, altnough none of these was large enough to
exert a significant influence.
In summary, there were four distinct factors determined
in this analysis.

These were a general price level factor,

an industrial activity factor, a milk production factor, and
a real evaporated milk price factor.

These factors accounted

for over 80 percent of the variations in 14 out of the 22
variables included in this group.

- 159 -

Estimation of Evaporated Milk Disappearance

Since the only actual disappearance figures available
for this group were for evaporated milk, a regression system
was set up using the results of the above analysis to esti­
mate evaporated milk disappearance.

One variable was select­

ed to represent each factor determined in the analysis. The
Consumer Price Index advanced one month, #65, represented
tne general price level factor, Heal Purchasing Power advanced
two months, /fb4, represented the industrial activity factor,
Milk Production on farms, /f1, represented the milk production
factor, and Real '..holesale Price of Evaporated Milk,
represented the real evaporated rnilk price factor.

tfk6,
These

variables were the independent variables in the regression
system; Evaporaben Milk Disappearance, #75, was the dependent
variable.
The coefficient of multiple regression obtained from
this system was r = .6342 and the coefficient of determina­
tion was r^ = .4022.

Thus 40 percent of the variation in

evaporated milk disappearance was explained by this system.
In this system the coefficient of the consumer price index
was not significant, out all the other coefficients were
significant.
A second regression system was set up using the real
purchasing power variable and real wholesale price of evap­
orated milk as independent variables and evaporated milk
disappearance as the dependent variable.

This system gave

- 160 -

a coefficient of multiple correlation of r = .
coefficient of determination of r2 =

.2333.

6 with a

Thus the total

milk production on farms accounted for approximately 17
percent of the variation in the disappearance of evaporated
milk, since the above system explained 40 percent of the
variation.
Analysis II

1929-1941

There were five distinct factors determined in this
analysis.

A summary of the factor structure is given in

Table AXVI.
Factor I contained indices of wholesale and retail
prices and also the consumer price index advanced one month.
These indicate that this factor was the -general price level
factor discussed previously.

It is interesting that the

Evaporated Hilk marketing margin,

jf37,

also followed the

same pattern as this price level factor.
Factor II contained varia'Dle /f64, Real Purchasing Power
advanced two months.

This variable represented the indus­

trial activity factor in tnis analysis; therefore Factor II
was probably this same industrial activity factor.

The fac­

tor loadings indicate that dry whole milk production was
influenced principally by this factor during this period.
Factor III was probably a milk production factor,
since the factor cluster contained the variables of milk
production on farms and the seasonal milk production.

Evap­

orated milk production was influenced by this milk production

Table XXVI.

Summary of factor St rue tare of Condensed, Dry and Evaporated milk

Analysis II

1929-1941 (5 factors)

Factor I
Factor Loading
(Rotated Matrix)

10
14
1$
20
37
40
65

.$52
.$$7
.927
.936
.569
.929
.$97

Description
Milk, av. wholesale price per cwt. received byfarmers
Milk, av. price per cwt. at condenseries
Evaporated Milk, av. wholesale selling price
Evaporated Milk, av. retail price
Evaporated Milk Marketing Margin
Milk, dry or powdered whole; av. wholesale selling price
Consumers Price Index advanced one month
Factor II

6
64

69

.47#
.512
.4$2

Dry whole milk production
Real purchasing power advanced two months
Consecutive Time Trend
Factor III

1
4
72

.7b5
.573
.765

Milk production on farms
Evaporated Milk production
Seasonal milk production trend
Factor IV

55

-.510

Real wholesale price of dry skim milk

161

Variable
Mo.

Table XXVI.

Summary of factor Structure of Condensed, Dry and evaporated milk
(Continued)

Analysis II
Variable
No.

Factor Loading
(Rotated Matrix)

1929-1941 (5 factors)
Description

factor V
46
4#

.852
.760

Real wholesale price of evaporated Kilk
Real retail price of evaporated milk

5 (I) .643
41 (I) .6 36
54 (I) .501

(III) .654
(IV)-.6#9
(V) .670

Condensed milk production
Dry Skim Kilk, av. wholesale price per lb,
Real wholesale price of dry whole milk
Indeterminate

42
67
75

Dry or Powdered Skim Kilk Production
Monthly Time Trend
evaporated Kilk Disappearance

162

Complex

- 163 -

factor during this period also.
Factor IV contained only one variable showing simple
structure.

This was variable ^55, the Real Wholesale Price

of Dry 3kim Kilk.

This factor was somewhat closely related

to Factor II, the industrial activity factor, in the com­
plete factor structure but was relatively independent of
the other factors.

There was not enougn information here

to identify this factor so it was called by the name of the
variable, a real wholesale dry skim milk factor.

Although

there was not enough information to identify this factor
more completely tnis does signify that the real wholesale dry
skim milk price was relatively unique in this analysis and
did not follow the same general trend as the other variables.
Factor V contained the variables of the real wholesale
price of evaporated milk and the real retail price of evap­
orated milk.

This might be called a real evaporated milk

price factor, although there was not enough information here
to identify this factor completely.
A few variables showed a complex structure in this
analysis.

Condensed Kilk Production, //$, showed the influ­

ence of the general price level factor and the milk production
factor.

The wholesale price of dry skim milk showed the influ

ence of the general price level factor and the real wholesale
dry skim milk factor.

The complex structure of the real whole

sale price of dry whole milk involved Factor I, the general
price level factor, and Factor V, the real evaporated milk
price factor.

This indicates that the real wholesale price

- 164 -

followed a pattern similar to the general price level even
though the price level had been removed and also a pattern
similar to that of evaporated milk prices.
Three variables snowed an indeterminate structure in
this analysis.

Dry or powdered Skim Milk Production, #42,

and Evaporated Milk Disappearance, //75, showed minor factor
loadings on a few of the factors, but these loadings were
not large enough to be significant.

The monthly time trend

variable showed insignificant loadings on all factors,
signifying that no monthly time trend was evident in all fac­
tors .
In summary, there were five factors determined in this
analysis.

These were a general price level factor, an indus­

trial activity factor, a milk production factor, a real whole
sale dry skim milk price factor, and a real evaporated milk
price factor.

These factors explained over BO percent of the

variations in 15 out of tne 22 variables included in this
group.

The real wholesale dry skim milk price factor and the

real evaporated milk price factor were not completely deter­
mined in tnis analysis, however.

further information is

necessary to determine what these factors actually were.
Estimation of Evaporated Milk Disappearance
A regression system was set up using the results of the
above analysis to estimate evaporated milk disappearance.
One variable was selected to represent each factor in the
analysis.

The Consumer Price Index advanced one month, #65»

- 165 -

represented the general price level factor; deal Purchasing
Power advanced two months, //o4, represented the industrial
activity factor; Milk Production on farms, yl, represented
the milk proauction factor; deal Wholesale Price of dry Skim
i.ilk, z/55, represented the real wholesale dry skim milk price
factor; deal Wholesale Price of Evaporated Milk,
sented the real evaporated milk price factor.

/fl+6,

repre­

These variables

v/er • the independent variables in the regression system; the
dependent variable in this system was Evaporated Ifilk disap­
pearance, ,/7 5 •
The coefficient of multiple regression in this system was
r = .7150 and the coefficient of determination was r2 = .5153.
Thus approximately 52 percent of the variation in evaporated
milk disappearance was explained by this regression system.
In this system, the coef'dcients of the real wholesale price
of dry skim milk and the real wholesale price of evaporated
milk proved to be not significant.
A second system was set up asirg the consumer price index
and real purchasing power variables as independent variables,
wit-, evaporated milk disappearance as the dependent variable.
This system yielded a coefficient of multiple correlation of
r = .5943 and a coefficient of determination of r^ - .3532.
Thus these two indicators of

;eneral economic conditions

explained 3 5 percent of the variation in evaporated milk
disappearance.

- 166 -

Comparison of Analyses

There were four factors determined in the first analysis
and five factors determined in the second.

The four factors

of the first analysis were all present in the second analysis;
one additional factor appeared in the second analysis which
apparently merged into the factor structure of the first
analysis over the longer period of time.
The four factors present in both analyses were a
general price level factor, an industrial activity factor, a
milk proauction factor, and a real evaporated milk price fac­
tor.

The real wholesale dry skim milk price factor wnich

appeared in Analysis II became a part of the industrial activ­
ity factor in Analysis I.
The variables showing simple structure had approximately
the same relationships to each other in both analyses.

evap­

orated milk production, widen had a complex structure in
Analysis I between the industrial activity factor and the milk
proauction factor, exhibited simple structure in the shorter
period of time of Analysis II.

In this latter analysis it

showed only the influence of milk production.
Dry whole milk production, which showed tue influence of
both the general price level factor and the industrial activ­
ity factor in Analysis I, was influenced only by the indus­
trial activity factor in Analysis II.

This indicates that

the general price level factor became of increasing importance
over the longer period of time in the production of dry whole
milk.

Dry

or powdered skim milk production had an indeterminate

structure in Analysis IX, but it snowed a complex structure
between the industrial activity factor and the milk production
factor in the longer period covered in Analysis I.
The evaporated milk marketing margin was influenced sole­
ly by the general price level factor in Analysis II, but was
influenced also by the industrial activity factor in Analysis
I.
Condensed rnilk production showed the same type of complex
structure in both analyses.

Both the monthly time trend and

evaporated milk disappearance had an indeterminate structure
in these analyses.
Regression system I explained 40 percent of the variation
in evaporated milk disappearance while regression system II
explained 50 percent of this variation.
variables were used in both systems.

essentially the same

However, while the

coefficient of the consumer price index was highly significant
in regression system II, it was not significant in regression
system I.

Exactly the reverse of this situation was true for

the coefficient of the real wholesale price of evaporated
milk; it was highly significant in regression system I, but it
was not significant in regression system II.
In order to investigate how well the regression systems
set up on the basis of these analyses would fit present day
conditions, they were extrapolated to 1949 and the estimated
disappearance from the two series compared with the actual
disappearance.

These figures are given in Table XXVII.

The

estimates from regression system II were much lower than the
actual disappearance figures; this indicates that an upward
shift in demand might have taken place during 1942-1949.
The disappearance estimates from regression system I more
nearly approximated the actual disappearance figures; however,
the 1949 total was underestimated by almost 10 percent even in
regression system I.
Table XXVII.

Actual and Estimated disappearance of
Evaporated Milk in 1949
(Million Pounds)

Actual
Disappearance

.Estimated
Disappearance

242
222

136
174

224
213

202
212
232
232
232
220

II
Estimated
Disappearance

1949
January
February
March
April
May
June
July
August
September
October
November
December
Total

220

237
204
234
190

196

130

126
124
126
142
129
126
117
105
105

214

196
130

222

192

111
102

2,629

2,454

1,444

245
217
241

192

114
123
132

202

1950
January
February
March

192
204

PRODUCTION AND DI o h PPDARh NCD Of DAIRI PRODUCTS
All of the data concerning the production and disappear­
ance of dairy products were included in this group.

Total

milk production on farms, factory production of seven manu­
factured dairy products, and the proauction of oleomargarine
were included along with the disappearance of four dairy pro­
ducts; in addition representatives of the general price level
factor and industrial activity factor were used to investi­
gate how production and disappearance were related to these
factors.

A complete list of the variables included in this

group is given in Table AXVIII.
Analysis I

1929-194#

There were three factors determined in this analysis.
A sum ary of the factor structure is given in Table XXIX.
factor I included variables of milk production, cheese
production, evaporaoed milk production, and dried or powdered
skim milk production.

This factor was probably a milk pro­

duction factor since these products followed the pattern of
seasonal milk production.

It is interesting that fluid milk

and cream disappearance also was a part of this factor
structure.

This signifies tnat fluid milk and cream disap­

pearance followed tne trend of milk production during this
period.
factor II contained the variables of butter production,
oleo production, and butter disappearance.

The factor struct­

ure indicated that this was probably a butter production facto

Table XXVIII.

Variable
No.
1
2
3
4
5
6
7
8
42
64
65
73
74
75
76

Description

Milk, Production on Farms
Creamery Butter Production
Cheese, Total Factory Production
evaporated Milk, unskimmed case goods production
Condensed Milk, skimmed; Proauction sweetened and unsweetened
Dry Whole Milk Production
Ice Cream Factory Production
Oleomargarine Production
Dried or Powdered Skim Milk Production
Real Purchasing Power advanced two months
Consumers Price Index advanced one month
Monthly Disappearance of Creamery Butter
Monthly Disappearance of Cheese, Whole i-dlk and Part Skim
Monthly Disappearance of Evaporated Milk
monthly Disappearance of Fluid Milk and Cream

170

1.
2.
3.
4*
5.
b.
7.
8.
9.
10.
11.
12.
13.
14.
15.

Variables Used in Analysis of Production and Disappearance of Jairy
Products

Table XXIX.

Summary of factor Structure of Production and Disappearance of Dairy
Products

Analysis I

1929-1948 (3 factors)

factor I
Factor Loading
(Rotated Matrix)

1
3
4
42
76

.554
.563
.652
.534
.4^2

Description
milk production on farms
Cheese production
Evaporated milk production
Dried or powdered skim milk production
Fluid milk and cream disappearance

Factor II
2
8
73

-.328
.746
-.764

Butter Production
Oleo Production
Butter Disappearance
Factor III

74
75

.504
*328

Cheese Disappearance
Evaporated milk Disappearance
Complex

6 (I) .439
64 (I) .507
65 (II) .538

(II) .495
(ll) .652
(III) .425

Dry Whole Milk Production
Real purchasing power advanced 2 months
Consumers price index advanced one month
Indeterminate

5

7

Condensed milk production
Ice cream production

171

Variable
No.

- 172 -

The f a c t o r l o a d i n g f o r

o l e o p r o d u c t i o n was o p p o s i t e

t o t h o s e o f b u t t e r p r o d u c t i o n and d i s a p p e a r a n c e ,
th at o leo

in sig n

in d ica tin g

p ro d u ctio n v a r ie d in v e r s e ly w ith b u tte r p rod u ction

and d i s a p p e a r a n c e .
Factor I I I

con ta in ed th e v a r ia b le s

a n c e and e v a p o r a t e d m i l k d i s a p p e a r a n c e .
t h e s e tw o v a r i a b l e s d i d n o t f o l l o w
v a ria b les
clo sely

in t h is

a n a ly sis.

in the r o ta te d f a c t o r

stru cture.

T h is i n d i c a t e s

that

the tren d o f the o th er

However,

a s s o c ia t e d w ith F actor I ,

of ch eese d isap p ear­

th is

f a c t o r was r a t h e r

th e m ilk p r o d u c tio n f a c t o r ,
Thus,

a lth o u g h ch eese d isa p ­

p e a r a n c e and e v a p o r a t e d m i l k d i s a p p e a r a n c e w e r e r e l a t e d t o
th e m ilk p r o a u c tio n f a c t o r ,

t h e r e were o t h e r e le m e n t s o p e r a t ­

i n g t o p r o d u c e trie v a r i a t i o n s

in th e se v a r ia b le s .

be c a l l e d a c h e e s e d is a p p e a r a n c e f a c t o r ,
was a s e p a r a t e f a c t o r
Three v a r i a b l e s
m a trix .

sig n ify in g

that th is

in the a n a ly s is .
sn o w e d a c o m p l e x s t r u c t u r e

Dry W hole m i l k P r o d u c t i o n ,

F actor I ,

T h is m ight

//6,

th e m ilk p ro d u c tio n f a c t o r ,

p rod u ction f a c t o r .

T n is i n d i c a t e s

t io n d id n ot f o llo w

a p attern

v id u a l fa c to r p a tte r n s,

in tne ro ta ted

h a d f a c t o r l o a d i n g s on

and F a c t o r I I ,

the b u tter

t h a t dry w hole m ilk produc­

sim ila r to e ith e r o f the in d i­

b u t o n e w h i c h w as a c o m b i n a t i o n o f

th e tw o.
It

is

tw o m o n t h s ,
month,

s i g n i f i c a n t t h a t l i e a l P u r c h a s i n g Po w er a d v a n c e d
//-z64» an d t h e

Consumer P r i c e I n d e x a d v a n c e d o n e

^t>5» b o t h h a d c o m p l e x s t r u c t u r e s

S in ce th e s e

r e p r e s e n t e d t h e two f a c t o r s

econom ic c o n d i t i o n s ,

it

in t h i s

a n a ly sis.

d e r i v e d f r o m general

appears th a t the f a c t o r s

rep resen tin g

- 173 -

t h e p r o d u c t i o n and d i s a p p e a r a n c e
fo llo w

t h e x’a c t o r s o f

g e n e r a l econom ic

b u t w e r e s o m ew h a t i n f l u e n c e d

by t h e s e

i n g P ow er a d v a n c e d tw o m o n t h s ,
factor I,

factor

co n d itio n s e x a c tly
factors.

and f a c t o r I I ,

T h is i n d i c a t e s

(represented

Real P urch as­

,/6/f, h a d f a c t o r l o a d i n g s on

t h e m ilk p r o d u c tio n f a c t o r ,

b u tte r p rod u ction f a c t o r .
a ctiv ity

o f d a ir y p roducts d id not

the

that the in d u s tr ia l

by //b 4 ) had some i n f l u e n c e

on

t h e m i l k p r o d u c t i o n f a c t o r and t h e b u t t e r p r o d u c t i o n f a c t o r ,
even though th e i n d u s t r i a l a c t i v i t y
p attern s o f th ese

other fa c to r s

f a c t o r d id not f o llo w

ex a ctly .

sum er P r i c e I n d e x a d v a n c e d one m o n t h ,
on f a c t o r I I ,

general p rice le v e l fa c to r
in

the

Con­

, / 65, had f a c t o r l o a d i n g s

the b u tte r p ro d u ctio n f a c t o r ,

the ch eese d isap pearan ce f a c t o r .

tia l

L ik ew ise,

the

and f a c t o r I I I ,

T h is i n d i c a t e s

th a t the

( r e p r e s e n t e d by if£>5) was i n f l u e n ­

t h e b u t t e r p r o d u c t i o n f a c t o r and t h e c h e e s e d i s a p ­

pearance f a c t o r

out d id n o t e x p l a i n a l l

o f the v a r ia tio n s

in

those f a c t o r s .
Two v a r i a b l e s w e r e i n d e t e r m i n a t e i n s t r u c t u r e i n
a n a ly sis.

C o n d e n s e d m i l k p r o d u c t i o n and i c e

th is

c r ea m p r o d u c t i o n

had m i n o r f a c t o r l o a d i n g s i n t h e r o t a t e d f a c t o r s t r u c t u r e .
However,

the orthogonal str u c tu r e

in d ica tes

v a r i a b l e s were l a r g e l y

in flu en ced

t o r even though o t h e r ,

perhaps u n iq u e,

that

both t h e s e

by t h e m i l k p r o d u c t i o n f a c ­
f a c t o r s were n e c e s s a r y

to e x p la in th e rem ainder o f t h e i r v a r i a t i o n s .
I n summary, t h e r e w e r e t h r e e f a c t o r s
a n a ly sis.

d eterm in ed in t h i s

These were a m ilk p r o a u c t io n f a c t o r ,

d u ction f a c t o r ,

a b u tte r pro­

and a c n e e s e d i s a p p e a r a n c e f a c t o r .

T ogether

- 174 -

these variables explained over 80 percent oi‘ the variations
in 9 out of the 15 variables included in this group.

These

factors explained the variations in the production of dairy
products rather fully, but did not explain a large part of
the variations occurring in the other variables included
here.

This indicates that the milk production factor was

well defined in this analysis but that the other two factors
were not as well defined in structure.
Analysis II

1929-1941

There were t h r e e f a c t o r s d eterm in ed in
A summary o f t n e f a c t o r s t r u c t u r e
factor

I con ta in ed v a r ia b le s

evaporated m ilk ,

dry w h ole m ilk ,

p o w d e r e d s k im m i l k and v a r i a b l e s
m ilk d isa p p e a r a n c e .

d u c tio n p layed a la r g e

tor

-iv en

in

a n a ly sis.

T a b l e XXX.

o f th e p rod u ction o f c h e e se ,
ice

cream ,

and d r i e d o r

o f c n e e s e and e v a p o r a t e d

fro m t h e f a c t o r s t r u c t u r e

a b ly a m ilk p r o d u c tio n f a c t o r ,

p rod ucts.

is

th is

t h i s was p r o b ­

s in c e th e p a tt e r n o f m ilk p ro­

p a r t i n t h e p r o d u c t i o n of t h e s e

The a p p e a r a n c e o f i c e

cream p r o d u c t i o n i n t h i s

fac­

c l u s t e r may h a v e b e e n au e t o a t e m p e r a t u r e f a c t o r v e r y

s i m i l a r in p a t t e r n o f v a r i a t i o n t o m ilk p r o d u c tio n ,

sin ce

the

s e a s o n a l demand f o r I c e

Cream f o l l o w e d t h e same g e n e r a l t r e n d

of m i l k p r o d u c t i o n .

is

It

in te re stin g

that

e v a p o r a te d m ilk

d i s a p p e a r a n c e ana c n e e s e d i s a p p e a r a n c e f o l l o w e d t n e
g e n e r a l p a tte r n as m ilk p ro d u c tio n ,
where a l s o

same

even though o t h e r f a c t o r s

o p era tiv e in th e se v a r ia b le s .

factor II

c o n t a i n e d o n l y one v a r i a b l e w it h s i m p l e s t r u c t -

Table XIX.

Summary of factor Structure of Production and disappearance of Jairy
Products

Analysis II 1929-1941
(3 factors)
Factor I
Factor Loading
(Rotated Matrix)

Description

3
4

.914
.895
.729
.681
.782

Cheese Production
evaporated milk Proauction
Dry whole milk production
Ice Cream production
Drieu or powdered skim milk production
Cheese disappearance
Lvaporated milk uisaooearance

6

7
42
74
75

.652

.525

Factor II
-.784

65

Consumers Price Index advanced one month
Factor III

.768
.651

8

64

Oleo Production
ueal purchasing power advanced 2 months
Complex

1
2

5
76

(I)
(I)
(I)
(I)

.937 (III)-.506
.910 (III)-.505
.751 (II)-.701
.618 (III)-.742

Total Milk production on farms
Butter production
Condensed milk production
Fluid Milk and Crea^ disappearance
Indeterminate

73

Butter Disappearance

175

Variable
do.

- 176 -

ure.

This was the Consumer Price Index advanced one month,

,?65.

Since this variable represented the general price level

factor in this analysis, factor II may be considered this
general price level factor.
Factor III cont ained the variables of Oleomargarine Pro­
duction and heal Purchasing Power advanced two months, ,/:64.
The factor structure indicates that the variable of oleo pro­
duction had its chief factor loading on this factor.

From the

appearance of the factor structure this night be considered an
oleo production factor.

The large factor loading of the vari­

able of real purchasing power- indicates that the industrial
activity factor (represented by this variable) had a pattern
similar to that of the oleo production factor.

Factor III is

not considered to be the industrial activity factor as vari­
able

if04,

the representative of this latter factor, had a

loading on Factor I wnicln was at least of minor significance.
Thus, althou.gn Factor III was similar to the industrial activ­
ity factor it could not oe considered tne same factor.
Several variables had a complex structure in this analysis.
Total Hilk Production on Farms,

Tf± ,

had a large positive load­

ing on Factor I, the milk production factor, and a negative
loading on Factor III, tne oleo production factor.

This

signid'ies that total milk production on farms and the various
dairy production variables in Factor I were closely associ­
ated, while milk production had an inverse relationship with
oleo production and real purchasing power.

Butter production

had similar factor loadings on these two variables, showing a

Pago:

17/ find. 173 lacking in numbering only.

UNIVERSITY MICROFILMS

- 179 -

positive relationship with the milk production factor and an
inverse relationship with the oleo production factor.

fluid

milk and cream disappearance also dad ni gn factor loadings
on these two factors; however, the size of tnese loadings
indicated that the direct relationship witn the milk produc­
tion factor was not as great as in the other variables men­
tioned above, while the inverse relationship between fluid
milk and cream disappearance and tne oleo production factor
was larger than in the other variables.
Condensed milk production, /r5, snowed a complex struct­
ure indicating a direct relationship witn factor I, the milk
production factor, and a direct relationsnip with factor II,
the general price level factor.
Butter disappearance, /r73 > siiowed an indeterminate
structure in this analysis, with minor factor loadings on all
three factors.

The three factors only explained about 21 per

cent of tne variations occurring in butter disappearance dur­
ing this period.
In summary, there were three -factors determined in this
analysis.

These were a milk production factor, a general

price level factor, and an oleomargarine production factor.
In the factor structure the production factor was fairly
well determined; however, the other two factors were not
well defined and indications are that, with the inclusion
of additional variables, tnese factors might have changed in
composition and importance.
The three variables determined above explained over BO

- 180 -

percent of tne variations in only five out of the fifteen
variables included in this group.

The five variables which

were oest explained by this analysis were production vari­
ables.

This is another iuuication that the milk production

factor was './ell defined while the other factors were not.
from tnis it may be concluded that the grouping of produc­
tion and disappearance vari.holes did not give enougn infor­
mation for a clearly defined factor analysis during tnis
period•
Comparison of Analyses
there were three factors determined in each of the
analyses described above.

a milk proauction factor was

found in eacr analysis; nowever, the structure of the other
two factors changed completely from one analysis to the other.
In Analysis I, factor II was a outter proauction factor.
The variables comprising; this factor in Analysis I changed in
the structure os' Analysis II; in tne latter analysis the
ouster production variaole showea a complex structure, the
butter disappearance variable snowed an indeterminate struct­
ure, and tne oleo production variable was considered a part
of an oleo proauction factor which was associated with the
variable of real purchasing power.

In Analysis I the oleo

production variable was also associated with the variable of
real purchasing power, but onis latter variable snowed more
of a complex structure in Analysis I than it did in Analysis
II.

- 181 -

Factor III in Analysis I was a cheese disappearance
factor.

The two variables in tnis factor merged into Fac­

tor I, the milk production factor, in Analysis II.

Thus,

wnereas the variables of cheese disappearance and evaporated milk
disappearance appeared as a separate factor during the period
1929-1948, they followed the same general trend as tne milk
production factor auring the period 1929-1941*

Their appear­

ance as a separate factor during the longer period of time
might have been caused

oy a change in the demand for cheese

and evaporated milk.
The variable of the consumer price index appeared as a
distinct factor in Analysis II, whereas it snowed a complex
structure involving the butter production factor and the
cheese disappearance factor in Analysis I.

This difference

might have been caused by tne indeterminate nature of the fac­
tor structures in these analyses.
In general, it can be said of both these analyses that
the milk production factor was fairly well determined in
each.

The remainder ox" the variations in each analysis were

explained by two rurtner factors, but in each case the factor
structure was poorly defined and would probably change on the
addition of further variables to this group.
The factor structure indicated that milk production was
a significant factor affecting the production of cheese,
evaporated milk, fluid milk, and dried or powdered skim milk.
This milk production factor was influenced oy the industrial
activity factor but it was not influenced by the general price

-

level factor.

132

-

Thus industrial activity affected the production

of milk and the production of tnese other products, but the
general price 1. vei had little
duction.

or no

influence on this pro­

milk production on farms was negatively affected by

industrial activity in the period 1929-1941, but was positive­
ly affected by this industrial activity factor in the period
1929 -19 4 3 .

Butter production did not follow the trend
of the other products listed above.

This mi-hi

of production
oe due to the

fact that butter was a residual dairy product; after the recuirerents for other dairy products were met, the excess
milk and cream was made into outter.
There was no general pattern in dairy product
ance such as appeared in production.

disappear­

Although une general

price level factor and the industrial activity x'actor were
of importance in the disappearance variables, there were
still other factors which influenced tne disappearance of
these dairy products.

These other factors could not oe

identified from the nature of the factor structure.

ifa $ a & a a >¥& a $ $ a
MARKCTIhG mAkGIiMo

All of the variables relating to the market basket and
th e marketing margins on the market basket were included in
this group.

Also included were deal Purchasing Power ad­

vanced two months,

fbl+>

7

and the Consumer Price Index advanced

one month, //65> as representatives of the factors of general
economic conditions wrhich were derived in a previous analysis.

- 133 -

A complete list ox Che variables in this group is given
in Table XXXI.
Analysis I

1929-1943

There were two factors determined in this analysis.
Summary of the factor structure is given in Table Xa XII.
A diagram of the orthogonal factor structure is given in
figure 10.
factor I contained variables of the retail cost of the
market basket, tne retail cost of dairy products in the mar­
ket basket, the marketing margin on dairy products, the
fluid milk laarketin^; margin, and the consumer price index
advanced one month.

This last variaole represented the

general price level factor in this analysis, and all of tne
other variables in the factor cluster are consistent with
the hypothesis that factor I was tnis general price level
factor.
It is interesting that the marketing Margin on Dairy
Products, ;/33 i followed the same general trend as this
general price level factor.

The fluid milk marketing margin,

t/34, had a similar factor loading, although the marketing
margins for the other dairy products were not a part of this
factor cluster.

This indicates that the fluid milk market­

ing margin was a predominant part of the marketing margin
on dairy products since they botn followed similar patterns.
This same result was obtained in che analysis of prices.

Table X jUI.

Variable
No.
1.
2.
3.
4.
5.
6.
7.
S.
9.
10.
11.
12.

30
31
32
33
34
35
36
37
52
53
64
65

Variaoles Jsed in analysis or marketing Margins

Description
Retail Cost of the Market Basket
Marketing Margin on Market Basket
Retail Cost 01 Dairy Products in Market Basket
Marketing Margin on Dairy Products
Fluid Milk Marketing Margin
Butter Marketing Margin
American Cheese Marketing Margin
Evaporated Milk Marketing Margin
Real Retail Cost of the Market Basket
Real Retail Cost of Dairy Products in the Market Basket
Real Purchasing Power advanced twomonths
Consumer Price Index advanced one month

Table XXXII.

Nummary of factor Btructure of Marketing Margins
Analysis I

1929-194$ (2 factors)

factor I
Variable
No.

factor Loading
(Rotated Matrix)

30
32
33
34

.672
.640
*559
.743
.616

65

Description
Retail Cost of Market Basket
Retail Cost of Dairy Products in Market Basket
Marketing Margin on Dairy Products
fluid Milk Marketing Margin
Consumers Price Index advanced one month
factor II

33
36
37

.533
.604
.621

Butter Marketing Margin
American Cheese Marketing Margin
fvaporated Milk Marketing Margin
Complex

64

(I) .906

(II)-612

Real Purchasing Power advanced one month
Indeterminate

31
52
53

Marketing Margin on market Basket
Real Retail Cost of Market Basket
Real Retail Cost of Dairy Products in Market Basket

-

Figure 10.

186

-

Orthogonal Factor Structure of Marketing Margins, Analysis I,

1929-191*8.
00

G - j e n e r a l

■ r i

F a c t o r

152

c e

L e v e l

32-

1.00

- IS 7 -

Factor II contained the variables of marketing margins
on butter, American cheese, and evaporated milk.

This might

be considered a manufactured dairy products marketing margin
factor.

However, the factor structure showed that Factor I

and Factor II were rather closely associated and were not
independent of each other.
Real Purchasing Power advanced one month, /r64, showed
a complex structure in this analysis.
structural relationship with

tf65

It had the same

as it showed in previous

analyses, but it was opposite in direction to Factor II.
Three variables showed an indeterminate structure in
this analysis.

rf52,

The Real Retail Cost of the Market Basket,

and the Real Retail Cost

ket Basket,

o x '

Dairy Products in the Mar­

53 > showed insignificant factor loadings in

both the orthogonal and rotated matrices and could be con­
sidered as unaffected
analysis.

b y

the factors determined in this

However, the Marketing Margin on the Market Basket

^31, had minor factor loadings in the rotated matrix but
significant factor loadings in the orthogonal matrix.

The

orthogonal matrix showed that tnis variable was primarily
influenced by the general price level factor; this was
probably covered up

b y

the rotation process.

Actually, the orthogonal matrix gave a much better pic­
ture of the factor structure than the rotated matrix did for
this group.

The orthogonal matrix showed that there was a

definite general price level factor and a second factor on
which the marketing margins for manufactured dairy products

,k--

- lgg -

had positive loadings and tne industrial activity factor
(represented by ,;-b4) had a negative loading.

However,

there was not enough information in this analysis to identify
this second factor.

The use ox' the rotated matrix in this

case did not simplify the factor structure bub made it more
obscure.
Analysis II

1929-1941

There were two factors determined in this analysis.
ever, the factor structure was very indeterminate.

How­

The over­

all structure was similar to that in analysis I, but the
structure was more scattered in appearance without any defi­
nite factor clusters.

A diagram ox the orthogonal structure

is given in Hi ;ure 11.
A comparison of figure 10 ana figure 11 show^s that,
whereas varixoles 33, 03, 32, 30 can be considered a factor
cluster in figure 10 tney are more widespread in figure 11,
without any definite indication of wnere the factor plane
should be located.

It could be hypothesized that one of

these factors was tne general price level factor (along the
horizontal) while the second factor (vertical) was such that
marketing margins had positive facoor loadin 's on it and the
industrial activity factor (represented by
loading upon it.

;f6t+) had

a negative

There was not enough information in this

analysis bo determine what this second factor was.

-

Figure 11.

189

-

Orthogonal Factor Structure of Marketing Margins, Analysis II,

1929-19U1.

-

f

•

0 l -

I

l j

O .

j

X

j

I O

190

v_ m

-

^ " 1 W - a l T l J O

L'i-1

a G L

This study had two main objectives.

:\! C - ' i . i l l

T h e

J A i ' A

’irst objective

was to analyse economic data cone e m i n ■
, economic conditions
in general, and dairy proarcts in particular, by means of
multiple factor analysis in order to invest! yate what fac­
tors were operative during the; two time periods of 1929194-3 and 1929-1941.

The second objective was to determine

how well these factors coula explain and predict the aisappearance of various dairy pro ducts, by setting up multiple
repression s>stems based on the factor analyses.
In order to investigate what factors were operative
during the two time periods under consideration in this
study, the variables were grouped in several different ways.
O n e

yroup combined all variables relating to jcneral economic

conaitions while otner groups covered prices, fluid ililk and
crea.ii, butter, cneese, condensed, dry and evaporated milk,
the production and disappearance of dairy products, and
marketing margins.
ilesuits of factor Structures
The analysis of General Economic Conditions produced
two factors, a general price level factor and an industrial
activity factor.

These two factors were evident in both

time periods, and explained most of the variations occurring
in the variables included in the group.

The appearance of

these two factors indicated that they were not independent;

- 191 -

h o w e v e r ,
w e r e

i n d u s t r i a l

m o r e

n e a r l y

a c t i v i t y

i n d e p e n d e n t

1 9 2 9 — 1 9 4 3 .

T h e

i n f l u e n c e d

s u c a

v a r i a b l e s

t h e

f o o d

p r i c e

r e t a i l

f a c t o r
i n

g e n e r a l

i n f l u e n c e d

d u c t i o n

t h e

t h e

l e v e l

f a c t o r
o f

a n d

r a t i o

a n d

p r i c e

l e v e l

f a c t o r

r e t a i l

w h o l e s a l e

t h e
a s

d u r i n g

p r i m a r i l y
i n d e x

i n d u s t r i a l

i n d e x

l e v e l

t h a n

p r i c e

r e a l
o f

a n d

a c t i v i t y

p u r c n a s i n g

i n d e x

p r i c e s

w e r e

i n f l u e n c e d

t h a t

P r i c e s

p o w e r

e m p l o y m e n t

o f

i n

i n d u s t r i a l

s a m e

t h e

i n

b y

p r o ­

t h e

f a c t o r

o f

f o l l o w
o t h e r

p e r i o d

a s

t h e s e

o f

w h o l e s a l e
a n d

r e a l

t h a t

l e s s
T h e

a n d

w i t h

w e r e

a n d

r e t a i l

t h e

r e a l

p r i c e s ,

T h e

w a s

p e r i o d .

w h o l e s a l e

a c t i v i t y

w e l l .

r e t a i l

p r i m a r i l y

w h o l e s a l e

t i m e .

t h e

f a c t o r

t h i s

w h o l e s a l e

1929-1941

p r i c e

g e n e r a l

o o t h

d u r i n g

s h o w e d

r e a l

f a c t o r s

p e r i o d

l o n g e r

a l s o

t h e

T h e

a s s o c i a t e d

a c t u a l

i n

f e e d

w h o l e s a l e

t r e n d

t h a t

g e n e r a l

b u t t e r f a t - f e e d

o f

i n d u s t r i a l

t h e

a

r a t i o .

t n e s e

w a s

s h o w i n g

o f

t n e

e v i d e n t

d e r i v e d

a n d

v a r i a o l e s

g e n e r a l

t h e

f a c t o r

p r i c e

t h a t

w e r e

w e r e

c o n d i t i o n s

s t r u c t u r e

t h e

T h e s e

v a r i a o l e s ,

t r e n d

n o t

f a c t o r s

a c t i v i t y

s h o w i n g

p r i c e s ,

i n

t i m e .

t w o

a c t i v i t y

d i d

t h r e e

i n f l u e n c e d

f a c t o r

t a i l

f o r

o f

i n d e p e n d e n t

T h e

P r i c e s

m i l x - f e e d

t h e

f o l l o w e d

p r i c e s .

t h a n

f a c t o r

c o n s u m e r

t h e

e c o n o m i c

t n e

f o l l o w e d

r e l a t i v e l y

u r e

a n d

o f

o n l y

p r i c e s ,

i n d u s t r i a l

p r i c e s

t h e

g e n e r a l

p r i c e

r e a l

i n d u s t r i e s ,

i n d u s t r i a l

i n f l u e n c e d

T h e

1 9 2 9 - 1 9 4 1

w h i l e
- l e s

p e r i o d

w h i c h

p r i c e s

i n d e x ,
v a r i a

a n a l y s i s

l o n g e r

a n a l y s i s

a n d

t h e

p r i c e

.

I n
o v e r

g e n e r a l

l e v e l

a s

i n d u s t r i e s ,

t h e

d u r i n g

p r i c e

s u c n

m a n u f a c t u r i n g

m a n u f a c t u r i n g

a n d

f a c t o r
w e l l

g e n e r a l

r e ­

b u t
s t r u c t ­
d e f i n e d
p r i c e

level
was

factor

also

well

in

this

to

identify

f a c t o r s

t h e

l e v e l

f a c t o r .
p r i c e s

f a r m s

a n c e

o f

r e l a t e d

t o

d a t a ,

a s

t n e

w h o l e s a l e

o f

f a c t o r

m i l k

d i s t i n c t i v e
o f

o t h e r

factors

feed

were

factor

determined

made

a n d

d e t e r m i n e d

t i m e .

T h e s e

a c t i v i t y

it

difficult

s t r u c t u r e

i n d i c a t e d

t h a t

m i l k

a n d

e c o n o m i c

s e a s o n a l
a n a

f r o m

p r i c e

a p p e a r e d
t h a t
t h i s

a l s o

b e e n

f l u i d

o f
a s

a

t h e s e

a n d

d u e
a n d

t o t a l
s k i m

t o

s e p a r a t e

p e r i o d

a n d
r e a l

w h o l e s a l e
a n d

d i d

t n e

t n i s

n o t

m i l k

o f

p r i c e
t h e

t r e n d

a i s a p p e a r -

s e a s o n a l
i n

t h e

d i s a p p e a r a n c e

t h e

o f

p r o d u c t i o n

m o n t h l y

p r o d u c t i o n .
m i l k

m o s t

w e a k n e s s

c r e a m

g e n e r a l
a

M i l k

t h e

a

a

f o u r

m i l k

f o l l o w e d

f o l l o w e d

m i l k

r e a l

s k i m

c o n d i t i o n s .

t r e n d ,

c r e a m

n a v e

c r e a m

w e r e
f a c t o r ,

d r y

t n e

d u r i n g

o f

C r e a m

- w h o l e s a l e

r e s i a u a l

i n d i c a t e d

the

r e a l

m i g h t

a s

p r i c e

a

m i l k

c o m p u t e d
r e a l

a

a

g e n e r a l

h o w e v e r ,

and

structure

i n d u s t r i a l

f l u i d

f o l l o w e d

M i l k

p e r i o d

a n d

f a c t o r

T h i s

other

factor

F l u i d

a n

T h e

p a t t e r n .

the

o f

f a c t o r ,

f l u i d

Three

l o n g e r

o f

defined,

satisfactorily.

f a c t o r ,

f a c t o r s

o n

but

them

i n

well

defined.

a n a l y s i s

p r o a u c t i o n

t h e

fairly

analysis

T h e

p r i c e

was

T h e

f a c t

r e a l

b a s i c
w a s
t h a t

w h o l e s a l e

w h o l e s a l e

p r i c e

p r i c e s

w e r e

f o l l o w

t h e

t r e n d

p r i c e s .

During the period 1929-19^-1 tne analysis of Fluid Milk
and Dream showed tne same factors as were evident during the
period 1929-194&.

However, one further factor was evident:

a retail milk price factor.
t h a t

The factor structure indicated

the retail price of delivered milk was relatively

- 193 -

i n d e p e n d e n t
n o t

o f

i o l l o w

t r i a l

t h e

t h e

I t

m i l k

p r o d u c t i o n

m i l k

w a s

n o t

T h e

a n a l y s i s

w e r e

f a c t o r ,
t a i l

w a s

a

o f

f e e d

s e a s o n a l

t r e n d

t h a t

b u t t e r f a t - f e e d

o t h e r

v a r i a b l e s

m a r g a r i n e
o f

f a c t o r s
T h e

a

w h o l e s a l e

o t h e r

l e v e l

a n d

w h o l e s a l e

a

f a c t o r s ,
t h e

s a l e

c h e e s e

p r i c e
o n e

l o n g e r

a n d

d u r i n g

p r i c e s

f a c t o r

p r o d u c t i o n

w a _

a

r e a l

o x '

b u t t e r

f a c t o r

c h e
s h o w e d

i n d e p e n d e n t
t h e
t h i s

t h e

r e a l

o t h e r

o f

t h e

r e t a i l

r e a l

r e ­

l e v e l

f o l l o w e d

f e e d

r e a l

f i v e

c h e e s e

o l e o ­

r e t a i l

p r i c e s

f a c t o r s .

p r i c e
a n d

t h e

T h e s e

p r o d u c t i o n

t h e

f e e d

c h e e s e

f a c t o r

w a s

f a c t o r .

t i m e

w h i l e

t h e

r e a l

m i l k

s h o r t e r

m i l k
t h e

p e r i o d

T h e

o f

t h e

p r i c e
r e a l

p r i c e

f a c ­

w h o l e ­

f a c t o r
o f

a

d i s c u s s e d

i n d e p e n d e n t

w n o l e s a l e

w e r e

f a c t o r ,

f a c t o r

p r i c e

o f

t h e

o f

a n a l y s i s .

a

p e r i o d

f a c t o r

f a c t o r

t h e

b u t t e r

p r i c e

o f

f a c t o r ,

p r i c e

a

g e n e r a l

t h a t

w h o l e s a l e

i n c l u d i n g

d u r i n g

a s

r e a l

c h e e s e

t o r ,

m e r g e d

p r i c e

t h e

f a c t o r s ,

T h e

a n d

s n o w e d

f a c t o r ,

d i s t i n c t

t h e

i n d i c a t e d

C h e e s e

o f

p r i c e

a n d

r a t i o

t n i s

d i d

i n d u s ­

p r e v i o u s l y ,

p r o d u c t i o n

i n

r e t a i l

r e t a i l

i n d e p e n d e n t

o f

m i l k

a n d

a n a l y s i s ,

i n c l u d e d

p r i c e

p r e v i o u s l y ,
r e a l

w a s

a n a l y s i s

g e n e r a l

r e a l

t h i s

t h e

f a c t o r ,

f a c t o r .

p r i c e

o r

i n d e p e n d e n t

f o u r

p r o d u c t i o n ,

f a c t o r

o l e o m a r g a r i n e

o t h e r

m i l k

i n

p r i c e

l e v e l

o u t t e r

l e v e l

i t

p r o a u c t i o n .

s n o w e d

w h o l e s a l e

i h e
o f

m i l k

a n a l y s i s ;

p r i c e

t h a t

d i s c u s s e d

p r i c e

t h e

b u t t e r f a t ,

w i t h

f a c t o r

i n v o l v e d

t h e

s n o w i n g

p r i c e

t h e

r e l a t i v e l y

B u t t e r

g e n e r a l

i n

g e n e r a l

a l s o

f a c t o r ,

o l e o m a r g a r i n e

a n d

t h e

a s s o c i a t e d

t h e

f a c t o r

v a r i a o l e s

01

t r e n d

a c t i v i t y .

f r i e s e

o t h e r

t i m e .

w e r e
T h i s

- 194 -

i n d i c a t e d
t h e

t h a t

s a m e

p r i c e s
s a l e

t h e

t r e n d

a s

d u r i n g
c h e e s e

t h e

p r i c e

w h o l e s a l e

t h e

f a c t

t h a t
p r i c e

o f

o f

t h e s e

d r y

a n d

a p p e a r e d

f a c t o r s

a n
a n d

w h o l e s a l e

a

p r i c e

o f

d u r i n g

i n t o

t h e

f a c t o r

s t r u c t u r e

a c t i v i t y

f a c t o r

d u r i n g

t h e

w h o l e s a l e

i n d e p e n d e n t

f o l l o w e d

t h e

c o m p l e t e

p e r i o a

r a i l k

f o l l o w e d
T h r e e

g e n e r a l

f a c t o r s

o f

d u e

t h e

t o

r e a l
p r i c e

o r o d u c t s

w h o l e s a l e

o f

m i l k

p r i c e

r e f l e c t e d

t n e

t r e n d

i n d e p e n d e n t

a s

t h e

i n

o f

o f

a l l

s k i r a

a

d u r i n g

t h e

s h o r t e r

i n d e p e n d e n t
w e r e

m i l k

r e a l

t r e n d

e v i d e n t

i n

i n
t h e

b o t h

a

m i l k
f a c t o r .

a s

a

m e r g e d

T h u s ,

a p p e a r e d
o f

a c t i v i t y

p r i c e

: e n e r a l

i n d u s t r i a l

p e r i o d

i n d u s t r i a l

T h e

b u t

1 9 2 9 - 1 9 4 8 .

s k i m

o f

p r i c e

a p p e a r e d

t h e

d r y

o f

m i l k

m i l k

o f

a

f a c t o r ,

1 9 2 9 - 1 9 4 1

p a r t

p e r i o d

a n a l y s i s
w e r e

a c t i v i t y

o f

t r e n d

t h e

T h e s e

p e r i o d

1 9 2 9 - 1 9 4 8 .
a n

r e a l

e v a p o r a t e d
d r y

t h e

p r i c e

t r e n d

b e

t h a t

r e a l

f o l l o w e d

m i l k .

r e a l

f a c t o r

a n

t o

i n d u s t r i a l

s e p a r a t e

r e a l

t h e

p r i c e

p r e s e n t

e v a p o r a t e d

f a c t o r ,

t i m e

o c h e r

t h e

w h o l e s a l e

f r o m

w h o l e ­

a n a l y s i s .

f o u r

f a c t o r ,

p r o d u c t i o n
r e a l

t n i s

a n d

d i s a p p e a r a n c e

a n d

i n

w e r e

l e v e l

r e a l

o f

p e r i o d

r e a l

p r o b a b l y

d e t e r m i n e

l o n g e r

f o l l o w e d

b u t t e r f a t
t h e

w a s

p e r i o d

h e l p e d

a n d

t r e n d

T h i s

s h o r t e r

c h e e s e

m i l k

u i i f e r e n t

i m p o r t a n c e

s h e

o f

1 9 2 9 - 1 9 4 8

m i l k .

t h e

C h e e s e

f a c t o r s

C o n d e n s e d ,

T h e

i n

p r o d u c t i o n ,

T h e r e

p r i c e

a n d

a

c h e e s e

i n c r e a s i n g

c h a n g e s .

c h e e s e
o t h e r

o f

w h i l e

b u t t e r f a t

d u r i n g

o f

t h e

p r i c e

w h o l e s a l e

b u t

p r i c e

d u r i n g

b u t t e r f a t ,

b e c a m e

r e a l

f o l l o w e d

r e a l

o f

w n o l e s a l e

1 9 2 9 - 1 9 4 1

t h e

w h o l e s a l e

r e a l

o f
t i m e

a n a l y s i s

a l t h o u g h
t o

f o l l o w

t i m e ,
o v e r

e v a p o r a t e d
p e r i o d s .
o f

i t
t h e

- 195 -

P r o d u c t i o n
g r o u p e d

a n d

t o g e t h e r

a p p e a r a n c e
t o r s

D i s a p p e a r a n c e

o f

a n d

g e n e r a l

a c t i v i t y

f a c t o r

i n

d a i r y

t h e
w a s

p r o d u c t i o n

o f

i n

t h e

p r o d u c t i o n

e v a p o r a t e d

p o w d e r e d

w a s

n o t

e v i d e n t

i n

t h e

p r o d u c t i o n

g e n e r a l

p a t t e r n

i n

t h e

d i s a p p e a r a n c e

a c t i v i t y

f a c t o r

I n

t h e

s t r u c t u r e
i l y
a

o t h e r

n o t

s h o w e d

a n d

b u t t e r ,
e n o u g h

m a r k e t i n g

a

a l s o

w a s

n o t

o f

p r i c e

m a r g i n s

c l e a r l y

m i l k

i n

a n d

f a c t o r

b u t t e r .

T h e r e

o f

p r o d u c t s ,

a n d

t h e

w a s

n o

i n d u s t r i a l

a n d

a l o n e

l e v e l
d a i r y

t h e

p r i m a r ­

p r o d u c t s
b u t

t h a t

m a r k e t i n g

e v a p o r a t e d

f a c t o r

f a c t o r

m a r g i n ,

i n

t h e

m i l k .

m a r g i n s
T h e r e

i d e n t i f y

t h i s

o t h e r

w e r e

i n d i c a t i o n s

t h a t

i t

s o m e

a c t i v i t y
d e f i n e d .

f a c t o r .

a s
s o m e

t o

w e l l

t h e

c r e a m ,

a v a i l a b l e

i n d u s t r i a l
v e r y

i n f l u ­

e v i d e n t

d a i r y

t h a t ,

d i s a p ­

w a s

p r o d u c t i o n

o n

m a r k e t i n g

c h e e s e ,

t h e r e

a n d
a l s o

w h i c h
w a s

s h o w e d

i n d u s t r i a l

w e r e

m a r g i n s

i n f l u e n t i a l

i n f o r m a t i o n

a n

g e n e r a l

m i l k

t h e

f l u i d

f a c t o r

o f

a n a l y s i s

i m p o r t a n c e .

m a r k e t i n g

A m e r i c a n

w i t h

l e v e l

f a c ­

p r o d u c t i o n

T h i s

s o m e

f l u i d

w a s

a l t h o u g h

a s s o c i a t e d
s t r u c t u r e

t h e

m i l k .

o f

t h a t

t h e

f a c t o r

f a c t o r ,

w e r e

p r i c e

a n a l y s i s

i n f l u e n c e d

i v h o l e

o f

g e n e r a l

t h e

a n d

m i l k ,

d r i e d

t h e

r e p r e s e n t i n g

w h i c h

a n d

a l t h o u g h

s k i m

d i s -

f a c t o r

f a c t o r

a n a l y s i s
a n d

f a c t o r s

o n l y

T h i s

p r o d u c t i o n

T n e

f a c t o r

o t h e r

T h e

c h e e s e ,

o r

l e v e l

p r o d u c t s ,

a

o f

v a r i a b l e s

o p e r a t i v e

a n a l y s i s .

i d e n t i f i e d

P r o d u c t s .

c o n d i t i o n s .

p r i c e

w e r e

D a i r y

v a r i a b l e s

i n c l u d e d

g e n e r a l

o f

t h e

e c o n o m i c

t h e

p e a r a n c e

o i '

a l s o

a l t h o u g h

e n t i a l

a l l

o f

T h i s

w a s

w a s
f a c t o r

Relationships within factor structures
S o m e

o i

s t r u c t u r e s
a .

m a y

s e p a r a t e

i n g

g e n e r a l

g e n e r a l

t h e

C o n s u m e r

o r
c .

I t

t w o

m o n t h

t h e

I n d e x
i n

a c t i v i t y
R e a l

t r i a l

P r o a u c t i o n .

a b l e s

w e r e

I t

u s e d

T h e

I n d e x

o f

d e p a r t m e n t

t h e
w i s e

t h e

i n

g e n e r a l
W h o l e s a l e

t i m e

f a c t o r .
b e s t

R e t a i l

b y

f o o d

v a r i a o l e s
b y

a

o n e

w e r e

m o n t h

e c o n o m i c

r e p r e s e n t e d
t h e
t h e

o f

I n d e x

b y

e m p l o y ­
o f

w h e t h e r

t h e s e

a d v a n c e d

t h r o u g h

t i m e

d a l e s

w a s

c o n d i t i o n s
p r i m a r i l y
w h i l e
f a c t o r

f r e i g h t
D e b t

a n d

I n d e x

b e s t

m a t t e r

o t o r e

1 9 2 9 - 1 9 4 &

o f

w a s

n o t

e c o n o m i c

C o n s u m e r

a n d

g e n e r a l

I n d u s ­
v a r i ­
b y

a

i n t e r v a l .

a c t i v i t y

I n d e x

T e r m

o f

t h e s e

P o w e r ,

o r

i n f l u e n c e d

i n d u s t r i a l

S h o r t

m o n t h

g e n e r a l

f a c t o r

f a c t o r

d i r e c t l y

t w o

l e v e l

a

a c t i v i t y

I n d e x

t h r o u g h

d i d

o r

w a s

w e r e

i n d i c a t ­

r e p r e s e n t e d

I n d u s t r i e s ,

m o n t h

I t

w a s

t n e

P u r c h a s i n g

o n e

t i m e .

f a c t o r

v a r i a b l e s

T h e s e

w h e t h e r

a d v a n c e d

h a n u f a c t u r i n g

o f

a n a

m a t t e r

t h e

i n t e r v a l .

i n d u s t r i a l
o f

i n

i n d u s t r i a l

f a c t o r

I n d e x

n o t

o r

T h e

c a t o r

e .

d i d

e v i d e n t

a n

w i t h i n

f o l l o w s :

c o n d i t i o n s .

l e v e l

P r i c e

e x i s t i n g

a s

w e r e

a n d

p r i c e

d i r e c t l y

m e n t

d .

f a c t o r s

f a c t o r

T h e

u s e a

d e s c r i b e d

e c o n o m i c

l e v e l

P r i c e s .

r e l a t i o n s h i p s
b e

T w o

p r i c e
b .

t h e

C a r
w e r e

i t

n o t

a s
b y

w a s

d u r i n g

i t

t h e

a

g o o d

v a r i e d
g e n e r a l

i n f l u e n c e d

1929-1941.

L o a d i n g s

a n d

n o t

i n d i c a t o r s

g o o d

t h e

i n d i ­
t h r o u g h
p r i c e
r a o r e
L i k e ­

I n d e x

o f

o f

c o n d i t i o n s *

r e t a i l

p r i c e s

o f

m o s t

d a i x ' y

p r o d u c t s

w e r e

b y

- 197 -

i n f l u e n c e d

p r i m a r i l y

i n

t i m e

t h e

t w o

t h o u g h

u n i q u e

v a r i a b l e s ,

a

v a r i a b l e s

b y

t h e

p e r i o d s

f a c t o r s
l a r g e

g e n e r a l

u n d e r

m i $ h t

p r i c e

c o n s i d e r a t i o n

h a v e

a l s o

f a c t o r

h e r e .

A l ­

i n f l u e n c e d

p a r t

o f

t h e

v a r i a t i o n s

e x p l a i n e d

b y

t h e

g e n e r a l

m a r g i n

f l u i d

w a s

l e v e l

t n e s e

i n

t h e s e

p r i c e

p r i c e

l e v e l

f a c t o r

c r e a m

w a s

a l o n e .
f .

T h e

m a r k e t i n g

e n c e d

g .

p r i m a r i l y

a s

t h e

o n

f l u i d

T h e

g e n e r a l
m i l k

t h a t

cream

t h e

w a s

k e t i n g

f o r

f a c t o r

a s

i .

d a i r y

p r i c e

T h u s

m a r k e t i n g

w e r e

w a s

l e v e l
o n

m a r g i n

i n f l u e n c e d

i n f l u ­
T h i s

m i l k

t h e

s i n c e

a l s o

f a c t o r .

f l u i d

i n

p r o d u c t s ,

p r o d u c t s

m a r g i n s

m i l k

p r i c e

t h e

w o u l d

t o

f a c t o r ,

b e

T h u s
m a r g i n s

m a r k e t i n g

p r i c e

n e t

t o

l e v e l

e f f e c t

o n

A m e r i c a n

b e

f a c t o r

f a c t o r .

g e n e r a l

b u t t e r ,

s h o w n

l e v e l

m a r k e t i n g

i n c r e a s e d ,

f o r

w e r e

b o t h

f e e d

g e n e r a l

p r o d u c e

i n c r e a s e d ,

A

t n e

f a c t o r .

p r o d u c t s

i m p o r t a n t

a c t i v i t y

h a v e

d a i r y

m o s t

a c t i v i t y

t h e

a l l

m a r g i n

o t h e r

g e n e r a l

i n c r e a s e d ,

i n c r e a s e d

m a r k e t i n g

a l l

l e v e l

a n d

c o m b i n e d
t h e
o y

m a r ­

m a r k e t i n g
a

s e c o n d

w e l l .

e v a p o r a t e d

t r i a l

p r i c e

i n f l u ­

i n c r e a s e d .

f o r
t h e

a n d

t h e

o x ’

m a r k e t i n g

t h e

c r e a m

b y

m i l k

g e n e r a l

l e v e l

m a r g i n

m a r g i n

m a r g i n s

T h e

a n d

p r i m a r i l y

i n d i c a t e d

t h e

p r i c e

m a r k e t i n g

e n c e d

h .

b y

o n

i n f l u e n c e d

a n d

a s

i n v e r s e l y

t h e

g e n e r a l

i n c r e a s e d .
m a r g i n s
a n d
t h e s e

A s

c h e e s e ,
d i r e c t l y
b y
p r i c e

m a r k e t i n g

t h e

b y
i n d u s ­

l e v e l

i n d u s t r i a l

d e c r e a s e d .

i n d u s t r i a l

a n d

I f

a c t i v i t y
m a r g i n s

c o n s i d e r e d .
i n v o l v i n g

t h e

b u t t e r f a t - f e e d

p r i c e

r a t i o

193

-

a n d .

t h e

m i l k - f e e d

o t h e r

f a c t o r s

t h e s e

v a r i a b l e s

a b l e s

i n

a f f e c t
t h e
j .

f a i r l y

T h e s e

v o l v e d

m o s t

t h i s

m a n y

t r e n d

w h o l e s a l e
f r o m

t n e

p l a i n e d
t i o n

k .

T h e

m

o t h e r

t o

c o m p u t e d
m o r e

w o u l d

s h o w e d

w a s

a c t i v i t y

i n c l u d e d

i n

t h i s

v a r i a b l e s

f a c t o r ,
p r i c e s

I n

w n i c h

i n ­

p r i c e s ,

a n d

n a t u r e
;

t o

t h e

c a s e s

w n i c h

w a s

d i f f e r e n t

t h e

s t u d y
n o t

a

o f

f e w

T h e s e

i n ­

i n c l u d e d

a

i n

w e r e

c o m m o d ­

w h i c h

c o r r e s p o n d i n

;

o r

a p p e a r e d

f a c t o r ,

f a c t o r .

o c c u r r i n

n o t

h e r e i n ,

s e a s o n a l

e v i d e n t

d i d

p r i c e s ,

r e t a i l

t h e

v a r i ­

a

r e a l

f a c t o r s

e x ­

a n d

p r o d u c ­

p r i c e

f a i r l y

w e l l ,

e x p l a i n e d

a s

b u t
f u l l y

v a r i a b l e s ,
o f

f o l l o w

h a v e

r e a l

p r o d u c t i o n .

v a r i a t i o n s

v a r i a b l e

.t.y

A

i n d u s t r i a l

d i s a p p e a r a n c e
t h e

e d

m i l k

l e v e l

a c t i v i t y
a n d

t h a t

i n d i v i d u a l

r e t a i l

p r o d u c t s

f a c t o r

v a r i a b l e s

t n e

d a i r y

o f

p r i c e
a n d

r a t i o s

w n i c h

a n a l y s e s

a l l

o t h e r

i n c l u d e d

f a c t o r s

g e n e r a l

w h i c n

o f

p r i c e

t n e

t h e

w h o l e s a l e

m a n u f a c t u r e d

s e a s o n a l

t h r e e

t h e

o t h e r

p r o d u c t s

i n d u s t r i a l

f a c t o r ,

f e e d
s h e

o f

i n d i c a t e d

w i t h

t h e s e

w h o l e s a l e

a n

r e a l

p r o d u c t i o n
m o s t

t h e

s t u d y ,

v o l v e d

a

T h i s

p r i c e s ,

d a i r y

i n

w e r e
o f

t h a t

w e r e

c o n s i s t e n t l y

i t i e s .

i n

a n d

t n e

t h e r e

i n d e p e n d e n t

a s s o c i a t e d

p r o d u c t
o f

w a n

a n a l y s e s .

n o t

s t u d y

d a i r y

g e n e r a l ,

r a t i o ,

t h e s e
v . - e r e

p r o d u c t i o n

I n

a s

i n

t h i s

t h e

p r i c e

-

f l u i d

t h e

t r e n d

b e e n

d u e

a s

r e s i d u a l

a

a c c u r a t e
p r o b a b l y

m i l k

t o

o f
t h e

r n e t n o d
m a k e

a

a n d

s e a s o n a l
f a c t

f r o m
o f

C r e a m

m i l k

t h a t
t h e

t h e

t o t a l

m e a s u r i n

d i f f e r e n c e

d i s a p p e a r a n c e

g
i n

a p p e a r ­

p r o d u c t i o n .
d i s a p p e a r a n c e

m i l k
t h i s
t h e

T h i s
w a s

p r o d u c t i o n .

d i s a p p e a r a n c e
f a c t o r

s t r u c t u r e

199

-

-

o b t a i n e d .
1 .

T h e
t o

a

l a r p e

i n d i c a t e d
c h e e s e

i n

e x t e n t
t h a t

t h e

p r i c e

f o r

d i s a p p e a r a n c e
p r i c e

l e v e l

b u t t e r

m a y

h a v e

i n g

t h e

T h e

d i s a p p e a r a n c e

b e e n
p e r i o d

t o

T h i s

l a r g e l y

s u c h
m a j o r

s i g n i f i e d

f o r

Results of x-iulti

a n

a s

u p o n

t h e

i m p o r t a n c e
i n e l a s t i c

h o w

p r e d i c t

f a c t o r

a n a l y s e s .
a s

v a r i a b l e

w h i l e

t n e

b a s i s

t h e

f a c t o r

t h e

g e n e r a l

d e m a n d

w i t h

t h e

p r i c e

d e c r e a s e d .

s h i f t i n g

T h i s
f o r

l e v e l
r e s u l t

b u t t e r

d u r ­

h o w e v e r ,
m i l k

d u r i n g

a g a i n ,

t h e

r u e y

e v a p o r a t e d

w e l l

t h e

d e m a n d

r e g r e s s i o n

d i s a p p e a r a n c e ,

p r i m a r i l y

a p p e a r e d
t w o

h a v e

m i l k

t i m e
b e e n

d u r i n

t o

h a v e

under

p e r i o d s
d u e

t o

t h e s e

a n

t h e
p e r i o d s .

Regression by stems

p l e

d e t e r m i n e

A s

e v a p o r a t e d

s t r u c t u r e

d e m a n d

m u l t i p l e

t h e

o f

c h a n

o f

o f

a s s o c i a t e d

1 9 2 9 - 1 9 A - B ,

T h i s ,

d u c t s ,

w a s

f a c t o r .

c o n s i d e r a t i o n .

a n d

n o t

T h i s

f a c t o r .

d e p e n d e d

" a c t o r s ,

w e r e

d i s a p p e a r a n c e

d u e

i n d e t e r m i n a t e

e s t i m a t e

o t h e r

i n f l u e n c e d

c h e e s e ,

i n c r e a s e d ,

T o

t h a t

w a s

p r o d u c t i o n

d i s a p p e a r a n c e

c h e e s e .

B u t t e r

d i s a p p e a r a n c e

c h e e s e

f a c t o r ,

f o r

;

t h e

a n d

d e m a n d

g i n

c h e e s e

c h e e s e

l e v e l

d e m a n d

g e n e r a l

n .

;

b y

p r o d u c t i o n

g e n e r a l

r n .

3 n o w i n

v a r i a b l e

f o r

f a c t o r s
t h e

s y s t e m s

I n

e a c h

a n

i n d i c a t o r

i n d e p e n d e n t
s t r u c t u r e s .

i n d i v i d u a l
w e r e

m u l t i p l e
o f

d i s c u s s e d

s e t

u p

r e g r e s s i o n
d e m a n d ,

v a r i a b l e s
T h e

w a s

p r o ­

b a s e d

o n

s y s t e m

t h e

t h e

d e p e n d e n t

s e l e c t e d
o f

c o u l d

d a i r y

t h e

w e r e

n u m b e r

a b o v e

o n

i n d e p e n d e n t

t h e

200

-

v a r i a b l e s
i n

t h e

w a s

d e t e r m i n e d

b y

f a c t o r

s t r u c t u r e ;

o n e

v a r i a b l e

t h e

r e g r e s s i o n

r e p r e s e n t

e a c u

f a c t o r

t e m s

t h e n

e x t r a p o l a t e d

t o

w e r e
s e e

t h e

h o w

w e l l

a c t u a l
T h e

f o r

t h e

m u l t i p l e
p e r i o d

p r i c e

o f

t h e

m i l k
o f

o f

d i s t i n c t

f a c t o r s

w a s

s e l e c t e d

t o

s y s t e m s .

1 9 4 9

a n d

d i s a p p e a r a n c e

T h e s e

p a r t

o f

c o m p a r e d

f o r

m u l t i p l e
i n

p u r c h a s i n g

t a i l

p r i c e

o f

p o w e r ,

p o w e r

a l l

t h e

t h e

o f

s y s ­
1 9 5 0 ,

w i t h

t o

r

=

t h e

I n

p r o v e d
o t h e r

b e

o n

a s

i n d e p e n d ­

w h o l e s a l e

y i e l d e d
T h e

i n
T h e
c o e f f i ­

i n d e p e n d e n t

f a r m s ,

n o t

a

p r i c e

s y s t e m

v a r i a b l e s

m u l t i p l e

s i g n i f i c a n t .

. 8 7 6 8 .

t h i s

c r e a m

v a r i a b l e s

c o n s u m e r

t o

o f

r e a l

b e

a n d

p u r c h a s i n g

t h e s e

p r o d u c t i o n

m i l k .

r e a l

1 9 2 9 - 1 9 4 1

o f

w e r e

m i l k

d e l i v e r e d

o f

a n d

p e r i o d

a n a l y s i s

p u r c h a s i n g

c o e f f i c i e n t s

t h e

m i l k

i n c l u d e d

i n d e x . ,

p r o v e d

c o r r e l a t i o n

t h i s

s y s t e m

f a r m s ,

a l l

f l u i d

c o e f f i c i e n t

c o e f f i c i e n t s

e q u a t i o n

s y s t e m

f o r

a

p r i c e

o n

t h e

s y s t e m

T n i s

c o n s u m e r

a n d

r e a l

r e a l

c o v e r

y i e l d e d

. 0 6 4 2 .

t h e

m i l k ,

v a r i a b l e s

f o r

*

p r o d u c t i o n

r e g r e s s i o n
o f

r e g r e s s i o n

r

r e g r e s s i o n

c i e n t

n u m b e r

t o

e s t i m a t e d

1 9 2 9 - I 9 h 8

v a r i a b l e s

p o w e r ,

t h e

i n

t h e

d i s a p p e a r a n c e .

c o r r e l a t i o n
e n t

-

i n d e x ,
a n d

t h e

t h e

c o e f f i c i e n t

s i g n i f i c a n t .

w e r e

r e ­

T h e

s i g n i f i c a n t ,

h o w e v e r .
T h e

e s t i m a t e d

c o m p u t e d

f r o m

w a s

c l o s e

v e r y

m a t e d

t h e
t o

d i s a p p e a r a n c e

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e x a m p l e ,

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f a c t o r ,

t h e

F o r

b u t t e r f a t

n o n - o r t h o g o n a l

c o n s i s t e n t :

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i n

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m i g h t

p r e v i o u s l y

k n o w l e d g e .

m i l k

p a r t

c o n s i s t e n t

h o w e v e r ,

d i s a p p e a r a n c e

a

a

t i m e

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s e p a r a t e

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p r o v i d e d

s t u d y .

f a c t o r

o f

v a r i a b l e s

r e s u l t s .

t n i s

p r i c e

s e p a r a t i o n

s t a t e d

r e s u l t s

a

c o n f l i c t s

t h e

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t o

s h o w e d

c a s e s ,

p r e v i o u s

d u e

a n g u l a r

a n a l y s i s .

a s

s e l e c t i o n

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o b t a i n e d

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a s

o f

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i n

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e v a p o r a t e d

w h o l e s a l e

T h e s e

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d i f f e r e n t

p r i c e

a p p e a r e d

P r i c e s ,

< b l e s

m a d e
o f

d u e

s i n c e

r e s u l t s

p r i c e

D r y

b e e n

p r o v i d e d

a n a l y s e s

g e n e r a l

w h i l e

h a v e

a n a l y s e s ,

c h o i c e

w h o l e s a l e

P r i c e s ,
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t h e

-

m i g h t

c o n f l i c t i n g

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m i l k

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a b o u t

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s t r u c t u r e .

m a y

a l s o

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t h e

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o f

t h e

d a t a

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o f

i c e

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m i l k

a n d

d i s a p p e a r ­

c r e a i u

p r o d u c t i o n

i t

g e n e r a l l y

s t r u c t u r e ,

a l t n o u g h

p r o d u c t i o n

f o l l o w s

t h e

s e a s o n a l

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r o t a t i o n

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o f

p r o c e s s

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u p

s o m e

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t h e

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t h u s

s e v e r a l

f r o m

t h e

d u e

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p r o c e s s ,
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i s

c o m e

o r t h o g o n a l
t h e

a s

c o d i n g

t h i s

c h a n g e s

f a c t o r
o f

c o d i n g
w h i c h

t h e
m i g h t

t o o k

p l a c e .
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n e s s e s

o f

n o m i c

m u l t i p l e

d a t a

i n

o f

v a r i a o l e s

o i '

t h e

d a t a ,
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t o

t i m e
t h e

t h i s
o e

f a c t o r

d o m e

i n c l u d e d
t o

b e

n o n - o r t h o g o n a l
w h i c h

a n a l y s i s

s t u d y .

p e r i o d s

c o d i n g

p o s s i o l e

u s e d ,

a p p l i e d

t h e s e

t h e

w e r e

a c c u r a c y

c o v e r e d

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t h e

w e a k ­

t o

t h e

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t h e

s t r u c t u r e s

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t h e

f a c t o r

m i g h t

a s

o f

i n

c a u s e s

o f

s e l e c t i o n
s e l e c t i o n

t h e

u s e d ,

b a s i c

a n d

t h e

e s s e n t i a l

i m f o r m a -

t i o n .
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i s

i n h e r e n t

t h a t

t h e

a n a l y s i s

o f

t h e s e

s i m p l e

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s e a s o n a l ,
d r a w b a c k .
a l s o

c y c l i c a l ,
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i n v o l v e s

u s e

w e a k n e s s
i s

o f

b a s e d

o n

o f

r a n d o m

w i t h

t h a t

t h e

s e r i e s

d a t a

e v e n

s a m e

c o r r e l a t i o n s

f a c t o r s

t h o u g h

f a c t o r

d a t a

c o u l d

d i f f e r e n t

w i t h

w i t h i n
b e
t i m e

a n a l y s i s

c o r r e l a t i o n s .

i s

t i m e
t h e

T h e

c o n t a i n i n g

f l u c t u a t i o n s

a u t o - c o r r e l a t i o n s

s h o w e d

s i m p l e

c o r r e l a t i o n s
a n d

m u l t i p l e

a

s e r i o u s

s e r i e s

d a t a .

d e r i v e d
p e r i o d s

d a t a
T h i s

f r o m
w e r e

s t u d y

t i m e
u s e d .

Vvci3 tioz t r u a

J V i i s

i;i a l l

cusas,

-clh,l . o it* ■
.iii 1y c,’os a o
or
s

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p e r c e n t

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accuracy of
the

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rected
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revision.

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d

e

n

t

s

u s e d

s i v e

a n d

e n o u g h

i n

t n i s

t i m e - c o n s u m i n g

s i g n i f i c a n t

t i o n s

t o

b e

s t u d y .

d a t a

t u r e s

f o r

p r i c e

r a t i o s

g a r i n e ,

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g a t e

s u c h

b e

m e a t ,

a n d

m i

b e
w o u l d

h a v e

t o

a

m o n t h s

t h e y

m i

o n

p r i c e

_ ; h t

o r

This

t h e

t n o s e

t i m e

p e r i o d s

c o m b i n a t i o n s
t o

o b t a i n

study

o f

m o r e

t n e

o f

w e r e

d e t e r m i n a t e

investigated

the

b e

p r i c e s
t o

b e

1946-1951,

m i g h t
f a c t o r

various

i n v e s t i ­

a d v a n c e d

h o g

t h e s e

d e t e r m i n e d

v a r i a o l e s

t o

i n v e s t i ­

p r o d u c t i o n ,

a n d

t o

s t r u c t u r e s ,

m i g h t

1929-1946

a s

r e l a t i o n s h i p s .

t i m e

d u r i n g

a n d

s u c h

a t t e m p t

m o n t h s ,

p e r i o d s

o p e r a t i v e

a n

o r

d a i r y

m i l k

p r o d u c t s
i n

b e e f

o l e o m a r ­

f l u i d

m i g h t

o r

a n d

f a c t o r

t i m e

w h i c h

a n d

d e m a n d

o n

f a c t o r s
f r o m

e m p l o y m e n t ,

t o r

h a v e

w h e t h e r

w e r e

t o t a l

e i g h t e e n

t i g a t e

d i f f e r e d

d i f f e r e n t

y e a r ,

f u r t h e r
e x p e n d i ­

t h e

r a t i o s

t o

i ' o r

t h r o u g h

1929-1939, 1941-1951,

a t t e m p t

t a b u l a ­

m o n t h l y

\ S ,

c o n c e r n i n '

p e r i o d s

t o

e x p e n ­

p r o v i d e

I . b . m .

i n c l u d e d

a d v a n c e d

f e e d

s i x

be

n t

t h e

d i f f e r e n t

; h t

a n

b u t t e r

d a t a

a s

o f

n o t

a s
e g

d i s a p p e a r a n c e .

a n a l y s e s

m i

a n d

s u c h

c h e e s e

m o n t h s

e f f e c t s

p r o d u c t s ,

r e s u l t s

v a r i o u s
s i x

i n c o m e ,

o r

w h i c h

b e

i n c l u d e d

d i s a p p e a r a n c e

t h e y

a d v a n c e d

w h a t

w o u l d

c o d e d

o e

c o m m o d i t i e s

m i g h t

e f f e c t s

a l s o

d a i r y

d e t e r m i n a t e

t i m e

^ h t

c e r e a l s ,

v a r i a b l e s

t h r o u g h

w a i c n

f r o m

d i s p o s a o l e

a n d

a n d

s p e c i f i c a l l y ,

g a t e

a n d

m i l k ,

w h a t

m i g h t

a s

b e t w e e n

■; ; s ,

m o r e

o p e r a t i o n

m i

i n d i v i d u a l

e v a p o r a t e d

e

,n t h i s

c h o u

d i f f e r e n c e s

d a t a

c h e e s e

m e a t ,

a l

w o r t h w h i l e .

A d d i t i o n a l

g e t

w o r k ,

i n

p r i c e s ,

u s e d ,
t o

s u c h

i n v e s ­

p e r i o d s
t h e

1929-1941.
a l s o

b e

u s e d ,

s t r u c t u r e s .

factors

which

-

e x i s t e d

d u r i n g

H o w e v e r ,
f a c t o r s
f o r

o r

n o

e x i s t e d

o r

w h a t

a

o f

o p e r a t i o n ,

i n

l e v e l ,
a n d

e n t

c o m m o d i t i e s .

r e a l

w h o l e s a l e

t o r ,

w o u l d

a t t e m p t e d
t h e

t o

d a t a .

v a r i o u s

b e

m a d e

e i g h t
o n

t h e

i n t e r ­

d e v e l o p m e n t s ,
d i f f e r ­

s u c h

a s

a

p r o d u c t i o n

T h i s

t o

b y

a n d

b e t w e e n

o n l y

e x i s t e d

g r o u p e d
a

f a c ­

e l e m e n t s

s t u d y

w h i c h

f a c t o r s ,

f a c t o r s

i n

p o l i c i e s

d i s t i n c t

v a r i a b l e s

d i s t i n c t

t h e s e

f i s c a l

r n i l k

m a n y

f a c t o r s ,

i n f l u e n c e d

f a c t o r ,

f a c t o r s

7 6

o t h e r

f u t u r e

a

t h e s e

i n

i n t o

m o r e

d e t e r m i n e

e x h a u s t i v e

w h a t

m a d e

d i s t i n c t .
f a c t o r

l a r g e

a n a l y s i s

m a s s

o f

w h i c h

u s e f u l

w h i c h

o r

r o u g h

m e t h o d

■ w h i c h

c o n t a i n

e x a c t

i n x ' o r m a t i o n

o r

s h o w n

t h a t

a n a l y s i s

p r o v i d e

a b o u t

d a t a

i s

g a t e d

f a c t o r

b y

w h y

d e t e r m i n e d

e x i s t i n g

o r

1 9 2 9 - 1 9 4 1 .

n a t i o n a l

m e a s u r a b l e .

o r i g i n a l
o r

a

o t h e r

d e t e r m i n e d

t h e

t h e

b o t h

f a c c o r

i s o l a t e

w a s

a n d

c o n c e r n i n g

b e

s t u d y

t o

o n

n o t

s e v e n

a

m o n e t a r y

m i g h t

e s s e n t i a l l y

t h e m

o e

f r o m

p r o b a b l y

a n y

p r i c e

a n d

a n a l y z e

f a c t o r

w a r

e v e n t s

. S i m i l a r l y ,

W i t h

m i g h t

f a c t o r

to

d i s t i n c t

r e l a t i o n s h i p s

c h e e s e

o r

male

l e v e l

c i r c u l a t i o n ,

p r o b a b l y

m i g h t

p r i c e

s u p p l y

1 9 2 9 - 1 9 4 5

t h e m

e x p e c t a t i o n s

d e m a n d

o f

w a s

m a d e

p o l i t i c a l

a n d

w h i c h

a t t e m p t

T h i s

m o n e y

n a t i o n a l

p e r i o d s

g e n e r a l

a n a l y s e s .

a m o u n t
i n

t i m e

l i t t l e

e x a m p l e ,

s e v e r a l

t h e

2(39 -

o f

s o r t i n g
s i m i l a r

f a c t o r

d e t e r m i n a t e

a n a l y s e s

n o t

c o u l d

h a s
v e r y

a

a s
n o t

c a n

b e

I t

a p p r o x i m a t i o n
t h o r o u g h l y

k n o w n .
o f

d a t a

c a n n o t

b e

m e a s u r e m e n t s .

f a c t o r
n o t

i s

b o d y

e l e m e n t s .

f i r s t
b e e n

m u c h

l a r g e

p r e c i s e

a

b e

a p p l i e d

s t r u c t u r e s
u s e d

t o

i n

e s t i m a t e

i n v e s t i ­

I t

p r o v i d e s

i n t o

g r o u p s

u s e d

T h i s
t o

f o r

t o

p r o v i d e

s t u d y

e c o n o m i c
s o m e

h a s
d a t a

c a s e s .

d e m a n d

a

b u t

T h e

r e g r e s s i o n
u s e d

t o

e s t i m a t e

d u c t s .
t h a t
f o r

T h e
t h e

t h e

c a s e s

f a c t o r

w e r e

t o

o e
a

i s

a

f i r s t

o n

v e r y

u s e f u l

t h e

o f

t h e s e

o x '

r e l a t i o n s h i p s

a n a l y s e s

o f

v a r i o u s

r e g r e s s i o n
p r o v i d e d

d e m a n d

i n

a

h a v e
d a i r y

s y s t e m s

c a s e s ,

p r o ­

i n d i c a t e

s a t i s f a c t o r y

s o m e

b e e n

w h i l e

b a s i s
i n

o t h e r

u n s a t i s f a c t o r y .
i t

i s

f e l t

t o o l

i n

a p n r o x i m a t i o n

n e c e s s a r y

f a c t o r

d i s a p p e a r a n c e

s t r u c t u r e s

c o n c l u s i o n ,

p r o v i d e

d e m a n d

t h e

r e s u l t s

t h e y

p r o v e

b a s e d

e s t i m a t i o n

I n

s t u d y

s y s t e m s

b e f o r e
c a n

i t s
b e

t h a t

f a c t o r

e m p i r i c a l
f o r

e c o n o m i c

u n e x p l o r e d

v a l u e
p r o p e r l y

a n a l y s i s

i n

t h e

a n a l j ^ s i s

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35
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f : r *.

Fac t or;;

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. 2l»*
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.569
• 71:3

36
37
5?
U
oh

66

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.9(0'
.9'6'
.590
-.1 0 1
-.210
-.6 5 ?

a
...atr IX.
’O f ' n e s c

33
3?
35

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.93 7
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.623
- . 309
•-w• ' i •' tJ
~
1•^
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-.036
-.0 6 0
• u9o
.6 1 6

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.-1
Ptvre<iu Factors
.iit?

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1.000

d

'-'■If’

X•
-

Anal
Mr t.h*.''jj.'>n a l

i

i'nr*.o;r t/'ti ri;-:,

corr.n: i i r u i l i t 1 *■ l .

. ±-4.

..■'!■
iv.,

i

e

xic.:r—

*11 ia

-

T a b i c

*1

*

t o t a l

?U3

-

l b .

F L U I D

m i l k

p r o d u c t i o n

I L K

..

D

^19=

retail

x c=)jZ

I’o a l

x c4 x6 5 =

r e a l 'urc^asliv'; rov/er advar. ced t v /
c o n s u m e r -rice index a d v anced one

x

Y 6-

u pres cion

price

?

of m i l k

v/tolesale

disappearance

.rice

of

c f

fluid

milk

mill: a n d

^ 7 6 . 6 5 , 1 , 6 4 , 5 7 =
P Z ( .r f u y I

f ^

cream

- 5 7 6 3

—

7 6 . 1 9 , 1 , 6 4 , 5 7

.257**

- 2 6 4 2

-

V»

- A 1 *">

"

Hec rr ess lo n uystern II

XY6-

-onths
conth

.System I____ 1 ° 2 ° - 19 4 9

:':76= .303**'xp,5 ♦ 1 .061**x1 - .145*;h;x64 ♦

r 7 J, p-• •. 7 0

o

1929-1941

.448-,**-x,cs5 - .111x^2, - .474**x1<? +• .625***^
nr-

nrin —
•
-'70'S
"
*

7 6 . 6 5 , 6 4 , 1 9 , 1

r76.65.19 = -3749

* « T w o
o f

a s t e r i s k s
b e s i d e
a
n u m b e r
n e a r
t h a t
i n
a n
i n f i n i t e
n u m b e r
s a m n l e s
s i m i l a r
t o
t h i s
o n e ,
w h e r e
t o e
r e p r e s s i o n
c o e f f i c ­

i e n t
c o n c e r n e d
c o n c e r n e d
h e r e

v : a s
a c t u a l l y
z e r o ,
v.’o u l d
b e
f o u n d
i n

a
1

v a l u e
p e r c e n t

l a r p e r
o f
t h e

t h a n
t h e
c a s e s .

o n e

Table

lib

GHEESE

r cheese production
X^T - milk-feed rice ratio
X2 i - re'tail price of cheese
x
= real wholesale price of cheese at Chicago
x
= real wholesale price of milk
5 7

X6b ~ consuraer pt*ice index advanced one month
= cheese disappearance

. - ( . e g r e s s i o n

S y s t e m

I

1 9 2 9 - 1 9 4 7

x?4 = .2S3x 65 - .190x 57 +
r 7 A . 5 5 , 5 7 , 3 , 1 1 , 4 3

=

- 7 * o 8

r 7 4 . 2 1 , 5 7 , 3 , ’

=

* 7 5 1 1

" 7 4 . 3

--

R e p r e s s i o n

, V ;

-7°6
S y s t e m

.761** x 5 - . 0 2 5 * ^ +• . 2 8 9 * ^

•

I I

1 9 2 9 - 1 9 4 1

x^^ = -.l42Xg

-4-

- .026x11 4- . 0 5 4 x ^

* 7 4 . 6 5 , 3 , 1 1 , 4 3

=

, 7 6 ° 2

r 7 4 . 2 1 , 3 , H , 4 3

=

* 7 6 4 5

r74.3 = -75°

Two asterisks beside a number mean that in an infinite num­
ber of samples similar to this one, where the regression
coefficient concerned was actually zero, a value larger
than the one concerned here would be found in 1 percent of
the cases.

M

Table

Illb.

BUTTER

xo
x^
x_
c—<c
Xj_^

r butter nroduction
r butterfat-feed -rice ratio
= retail price of butter
= real retail price ofoleomargarine
=:real purchasing power
advanced two months
x
= consumer orice index advanced one month
65
x
= butter disappearance
73
Repression System I
a.

x
73

r -.6Sl**x

65

r
73.65,2,51,11
b.

x
73

= -,276**x

1929-1948

+ .262**x

2

+ .I82x - .046x
51
11

= .7727
65

-I- ,287**x - •008x
2
51

+ ,032x
- .340**x
11
64

r73.65,2,51,H,64 = *633°
r73.22, 2,51,11,64 = -'"3°6
r73.65,64 = ,7654

Regression System II
x
= .032x
73
65

+

1929-1941

.037x^, -4- .I34**x - .175**x
64
2
51

r73.65,S4,2,51 = ■512°
r73.22,64,2,51 ’ •5^94
r73.51 = -3S3
Kit

Two asterisks beside a number mean that in an infinite number
of samples similar to this one, where the regression coeffic­
ient concerned was actually zero, a value larger than the one
concerned here would be found in 1 oercent of the cases.

tavj

T a b l e

I V b .

- milk

T V A P O R . / O ' I i D

'm or lu ct io n
nrlce

on

of

R I L K

far^s

x oq

-

retail

evaporated

milk

X 2^q

-

real

wholesale

price

of

evaporated

x

=

real

wholesale

rrice

of

dry

=

real

purchasing

=

consumer

skim

milk

milk

5 5
0 4
x qc)

x

S y s t e m

= *056x

75

4

.155^x

65

64

*6 ^ 4 2

* 7 5 . 2 0 , 5 4 , 1 , 4 6

=

*°325

r

36

i o n
=

-

. 4 5

3 y s t e m

-

I I

. 5 3 2 * " * x _
6 5

id

-4- .8

=

*7 2 4 9

O n e
o f
i e n t

=

46

-#•

-

. 2 6 2 - - « - x

-4

. 4 4 9 x

1

, 0 4 1 x

55

_
4 6

. 5 9 4 3

a s t e r i s k
b e s i d e
s a m p l e s
s i m i l a r
c o n c e r n e d

.Tpl^-^x,

6 4

* 7 5 . 2 0 , 6 4 , 1 , 5 5 , 4 5
/-»,

month

disappearance

1

7 9 ~ :," K ~ x

- 7 1 5 °

^

one

months

1 9 2 9 - 1 9 4 1

=

7 o *o d ,o 4

advanced

<4 .596**x -

r 7 5 . 6 5 , o 4 , l , 5 5 , 4 6

r ^ r -

a a v a n c -d t w o

1 9 2 9 - 1 9 4 8

=

R e p r e s s

u

I

* 7 5 . 6 5 , 6 ' , 1 , 4 6

,
7 5 . 6 5 , 6 4

index

e v a P o r s.t'ed. m i l k

X 'j<~) ~

R e p r e s s i o n

nrice

oower

w a s

c o n c e r n e d

h e r e

w o u l d

a s t e r i s k s

m e a n

t h e y

a

n u m b e r
m e a n s
t h a t
i n
a n
i n f i n i t e
n u m b e r
t h i s
o n e ,
w h e r e
t h e
r e g r e s s i o n
c o e f f i c ­

t o

a c t u a l l y
b e
w o u l d

f o u n d
b e

z e r o ,
i n

a
5

f o u n o

v a l u e
p e r c e n t
i n

1

l a r g e r
o f

p e r c e n t

t h a n

t h e
o f

t h e

c a s e s .
t h e

o n e
T w o

c a s e s .

I X . APPENDIX

0

ORIGINAL DISAPPEARANCE DATA

Table Ie

1929
1930
1931
1932
1933
1934
1935
1936
193?
1938
1939

lSfeO
19W
191(2
191(3
1944
191(5
1946
191(7
191(8
191(9
1950

MONTHLY DISAPPEARANCE OF FLUID MILK AND CREAM 1
(Million pounds)

Jan.

Feb.

Mar*

Apr*

May

4,420
U.U72
U.71U
4,797
4,64?
4.536
1(,23B
4,260
U.561
4.207
4,263
U,226
4.436
4,543
I+.901
5,074
5,213
5,559
5,107
5.11(2
5,176
5,380

1*»16!(
4,271
4.497
4,595
U.396
4,207
3,91(5
U,206
3,967
3,913
3,975
U.031
4,146
4,159
4.547
U.9U1
U.979
5.255
4.719
l(.96l
4,899
5,075

4,889
5,037
5,102
5.151
5,054
4,817
4,705
4,806
4,558
4,668
4,659
4.742
4.793
4,766
5.127
5,353
5,652
6,080
5.286
5.293
5,270
5.417

4,773
4,971
5,092
5,060
5,208
4.975
4.757
4,974
4,662
4,730
4,858
4 ,7 io
4,824
4,357
5,175
5,352
5,670
6,021
5,379
5,387
5.394
5,593

5.084
5.214
5,544
5.480
5,498
5.195
5.113
5,279
5.094
4,964
5.053
5,003
4,927
5,034
5,527
5.586
6,017
6,739
5,855
5,825
5,752
5,780

June

July

Aug.

4,950
5,335
5,304
5,483
5,069
5,177
5,243
5,254
5,051
5,102
5,168
5,293
5,302
5.488
5,681
5,787
6,174
6,811
6,225
6,142
6,090
6,056

4,645
4.873
5,134
5,286
5,069
4,785
5,064
5,106
5,126
4,985
4,984
4,857
4,913
5,277
5.499
5,716
6,009
6,122
5,934
5,902
5.906
6,073

4,404
4,489
4,805
4,672
4,622
4,409
4,757
4,397
4,719
4.484
4,493
4,482
4,606
4,949
5,236
5,286
5,681
5,673
5,435
5,313
5,292
5,507

Sept*
4,131
4,137
4,483
4,529
4,385
4,211
4.220
4,046
4,268
4.157
4,349
4,284
4,244
4,720
4,767
4,917
5,334
4,961
4,880
4,871
5,009
5,218

Oot,

Nov*

Dec.

Total

4,116
4,256
4,358
4,595
4.495
4,199
4,307
4.126
4.299
4,194
4,312
4.281
4,237
4,690
4,914
5,136
5,356
4,912
4,900
5,015
5,200
5,351

4,177
4,380
4,404
4,514
4,505
4,199
4,303
k, 160
lj.,081
U.163
U.229
U.285
k,309
k,689
k,772
5,036
5,301
k,9k0
5,023
M 95
5,105
5,316

i+,381
U.l|88
U.613
b,6k3
U,658
U,379
k,335
U,383
k,2b7
i+,239
k,33k
k,3l6
U,398
k,798
k ,9lh
5,263
5,570
5,05k
5,012
5,011
5,131
5M 9

5k,l3k
55,923
58,050
58,805
57,60k
55,089
5k,987
5k,997
5k,k33
53,806
5k,677
5k,510
55,135
57,975
61,060
63,kk7
66,956
68,127
63,660
63,755
6k,22k
66,21k

1 Disappearance equals total production minus milk equivalent of manufactured products of butter,
cheese, evaporated milk, condensed milk, dried whole milk, and ice cream.

TA 3L E3 CE SIMPLE 3CRRELATICN3 BETY/EE'T VARIABLES

(Tables enclosed In attached pocket)

- 250 -

XT.

BIBLIOGRAPHY

Literature Cited:
1.

Anonymous. Retail values of dairy products and
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1946.

2.

Blanford, G. J. The Demand for Milk and Cream as
Revealed by Consumer Purchases at -Retail Food
Stores in New York City. Cornell University
Agricultural Experiment Station June, 1941.

3.

Croxton, F. 3. and Cowden, D. J. Applied General
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4.

Frisch, R. Statistical Confluence Analysis by
Means of Complete Regression Systems. Economic
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1934.

5.

Gaus, A. R. elasticity of Supply of Milk from
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6.

Kriesel, H. C. Expenditures for fluid milk and
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7.

Pritchard, N. T. The Indianapolis I ilk Market.
Purdue University Agricultural Experiment
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4 August, 1949.

B.

Ross, H. A. Some Factors Affecting the Demand for
Milk and Cream in the Metropolitan Area of New
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9.

Schultz, H.
Chicago:

The Theory and Measurement of Demand.
University of Chicago Press 193$.

10.

Shepherd, G. Changes in the Demand for Meat and
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Iowa State Agricultural Experiment Station
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11.

Stone, R.

12.

Thurstone, L. L. Multiple - Factor Analysis.
Chicago: University of Chicago Press 1949*

The Analysis of Market Demand.

1945.

- 251 -

13*

Tinbergen, J. Statistical Testing of BusinessGycle Theories I. A Method and its Application
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14*

Vial, S. S.
Production and Consumption of Manu­
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15.

Waite, W. C. and Howe, C. B. The Consumption of
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New Jersey Agricultural Experiment Station
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Background Information
16.

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17.

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IB.

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19.

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20.

Fox, K. A. Relations between Prices, Consumption
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21.

Gaurnnitz, E. V/. and Reed, 0. 11. Some Problems
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22.

Hoel, P. G.
New York:

23.

Holzinger, K. J. and Harman, H. H. Factor Analysis.
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24.

Hopper, W. C. and Boucher, G. P. Department of
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Introduction to Mathematical Statistics.
John Wiley and Sons, Inc. 1947.

- 252 -

25.

Smith, B. B. Factors Affecting the Price of Cotton.
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26.

Thomsen, F. L. Measuring changes in the demand for
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27.

Thomson, G. H. The Factorial Analysis of Human
Ability. New York: Houghton Mifflin Co. 1948.

28.

Woefle, D. Factor Analysis to 1940. Psychometric
Monograph ^3 Chicago: University of Chicago
Press 1940.

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