AN ABSTRPCT OF THE THESIS OF
Takainasa Okutsu for the degree of Master of Science in
Agricultural and Resource Economics presented on March 5,
1991.
Title:
Food Demand During the Stage of Rapid Economic
Development: The Cases of Japan. Korea, and
Taiwan.
Redacted for Privacy
Abstract approved:
Richard S. Johnston
The purpose of this study is to comprehend food demand
structure and its changes under rapid economic development
theoretically and statistically.
The recently developed Far
East Asian countries, Japan, Korea, and Taiwan were chosen
to obtain implications for the food demand patterns in the
future industrialized countries around the world.
Food
demands for nine food commodities, rice, bread/wheat,
barley, beef, pork, chicken, fish, eggs, and milk were
analyzed.
The first three commodities are plant origin, the
rest are animal origin.
Study periods are from the early
1910's to the end of 1980's for Japan (the end of 1930's to
the early 1950's were excluded because of the war period),
and from the early 1960's to the end of the 1980's for both
Korea and Taiwan.
The importance of income growth on food
demand changes in developing countries has been stressed.
Many studies have been done based on a simple model using
per capita income as the only explanatory variable, or at
most including the prices of own and closely related
commodities.
This study employed a more versatile
analytical framework, incorporating a wider range of cross
price effects.
This study has two main objectives; the
first is to reconsider the effect of income growth on food
demands, particularly to examine whether income elasticities
change between various stages of economic development.
The
other is to evaluate non-economic factors that cause changes
in food consumption patterns under economic development.
Age-population composition and household size were two of
the explanatory variables.
A complete demand system by
adding dynamic and demographic features to DEATON and
MtJELLBAUER's (1980a, b) LA/AIDS model.
from various secondary sources.
Data were complied
Price and quantity data
sets passed nonparametric tests of stability of preferences.
Two different estimation techniques; an iterative STiR
(maximum likelihood) estimation and a single equation
estimation using the homogeneity condition and first order
autocorrelation were applied for the demand system.
Assuming weak separability for the group of foods in the
study, the expenditure elasticities calculated by the demand
system were converted to the ones equivalent to income
elasticities.
Major findings were:
the impact of the "pure" income effect was not
significant.
However, effects of age-population composition
changes and own and cross price effects were significant.
The impacts from changes in own price level and/or agepopulation composition exceeded the impacts from changes in
expenditure level most frequently for animal origin foods.
Significant cross price effects between animal and plant
origin foods were observed.
Various patterns of changes in income elasticities
were observed.
Unexpectedly, some animal origin foods such
as beef, chicken, and eggs showed negative expenditure
elasticities at low income level.
This phenomenon was
observed across countries and across time periods.
C
Copyright by Takamasa Okutsu
March 5, 1991
All Rights Reserved
FOOD DEMAND DURING THE STAGE OF RAPID ECONOMIC DEVELOPMENT:
THE CASES OF JAPAN, KOREA, AND TAIWAN
by
Takamasa Okutsu
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Master of Science
Completed March 5, 1991
Commencement
June 1991
APPROVED:
Redacted for Privacy
Professor of Agricultural and Resource Economics in charge of
major
Redacted for Privacy
Head of Department of Agricultural and Resource Economics
Redacted for Privacy
Dean of Grade School
Date Thesis is Presented:
March 5, 1991
Typed by Takamasa Okutsu for
Takamasa Okutsu
ACKNOWLEDGEMENT
I have learned a lot during my time at Oregon State
University.
Every moment has been precious and
unforgettable.
I wish to express my sincere gratitude to
the entire community which provide a terrific learning
environment.
I have been fortunate to have met so many nice people,
many have seen like family to me.
Dr. Richard S. Johnston,
my major professor, was like a father to me.
He devoted
substantial time and energy to encourage me in every
respect.
Without his patient and inspirational guidance,
this thesis would not have been completed.
I would also
like to thank Mrs. Johnston for her heartful hospitality.
Ms. Kathy Carpenter has been like my mother and
provided substantial help in many ways.
I had many generous uncles and big brothers also.
Dr.
Michael V. Martin suggested my thesis topic and provided
constant encouragement.
Dr. Gene Nelson gave me a chance to
start my career at Oregon State and provided much
appropriate advice.
I learned what it means to be rigorous
from Dr. Steven T. Bucolla; without having his excellent
lectures in microeconomic theory, I would have experienced
many more difficulties in completing my thesis.
I also had many great brothers and sisters:
I deeply
appreciate the efforts of Mr. Kevin Kinvig and Ms. Mona
Fisher in proofing and editing my thesis.
Many thanks go to
my three econometric instructors, Dr. David Gryer, Dr. Alan
Love, and Dr. Joe Kerkvliet, who taught me that econometrics
is fun.
I received a great deal of help from my office
mates, Mr. Deqin Cai, Mr. Berhanu Anteneh, and Mr. Elliot
Rosenberg.
I would like to especially thank Mr. Toyokazu
Naito for his great care of me.
Also, I would like to thank
Ms. Mary Brock for her help and beautiful smiles.
I would like to mention my gratitude to Dr. Miyohei
Shinohara for his generosity in providing his valuable
article.
Special thanks goes to Mr. Noritaka Suzuki, who
provided me with financial support.
Finally, I deeply appreciate my three ladies, my girl
friend Mihoko, my mother Taiko, and my little sister Akiko
for their patience in waiting for me so long.
I wish to
dedicate this thesis to them and my father in heaven.
TABLE OF CONTENTS
Paqe
Chapter
INTRODUCTION
1.1. Food Demands in the Context of Economic
Development
1.2. A General Perspective
1.3. Food Demand and Real Income
1.4. Study Motivation
1.5. Characteristics of Japan, Korea, and Taiwan
1.6. Study Objectives
1.7. Outline of the Thesis
LITERATURE REVIEW
2.1.
2.2.
2.3.
2.4.
2.5.
3.6.
3.7.
5
8
10
12
13
14
Introduction
Part I: Cross Country Studies
2.2.1. Empirical Study I: MARKS and YETLEY
2.2.2. Empirical Study II: ITO, PETERSON and
GRANT
Part II: Studies of Individual Countries
2.3.1. Case I: Japan
2.3.2. Case II: Korea
2.3.3. Case III: Taiwan
Discussion on the Previous Results
Critique of Engel Analysis
MODELLING FOOD DEMAND IN THE CONTEXT OF ECONOMIC
DEVELOPMENT: FACTORS, ASSUMPTIONS, AND MODEL
FORMULATION
3.1.
3.2.
3.3.
3.4.
3.5.
1
2
21
26
26
34
41
44
47
53
Introduction
Food Diversification
Habit Formation and Custom Formation
The Neoclassical Formulation
Demographic Variables
3.5.1. Age-Population Structure
3.5.2. Family Size
Separability: Assumption and Modelling
From Group Expenditure Elasticities to Total
Expenditure Elasticities
.
14
15
15
.
53
57
59
67
69
70
71
73
78
TABLE OF CONTENTS (Cont.)
Chapter
Paqe
4. MODEL SPECIFICATION
83
41. The Structure of the Model: Summary of
4.2.
4.3.
Chapter 3
The Econometric Model
Calculating Elasticities
4.3.1. Elasticity in Part I
4.3.2. Elasticities in Part II
4.3.3. Elasticities in Part III
83
86
93
93
94
98
5. VARIABLES AND DATA
100
5.1.
5.2.
5.3.
5.4.
Introduction
Study Period
Variables and Data for Part I
Variables and Data for Part II
5.4.1. Choice of the Commodities
5.4.2. Price and Quantity Data Construction
5.4.3. Quantity Data
5.4.3.1. Food Consumption Trends Japan
5.4.3.2. Food Consumption Trends Korea
5.4.3.3. Food Consumption Trends Taiwan
5.4.3.4. International Comparison of
Food Consumption Level
5.4.4. Price Data
5.4.4.1. Comments on the Japan Beef and
Fish Price Data
5.4.5. Demographic Trends in the Three
Countries
5.4.6. Group Expenditure Data
5.4.7. Correlation Among the Variables in
Part II
5.5. Variables and Data for Part III
5.6. Evaluation of the Data Sets by the General
Hypothesis
5.7. Evaluation of the Data Sets by the Tests of
Revealed Preference
.
.
.
100
101
103
110
110
111
112
115
117
117
120
122
128
130
135
136
140
146
150
TABLE OF CONTENTS (Cont.)
Page
Chapter
ESTIMATION: METHODS, PROCEDURES, AND RESULTS
.
.
.
6.1. Introduction
6.2. Estimation Method and Procedure for Part I
6.3. Estimation Method and Procedure for Part II
6.3.1. System Estimation Approach
6.3.2. The Model Specification Test
6.3.3. System Estimation of LA/S/H/AIDS
6.3.4. Non-system Estimation of LA/S/H/AIDS
6.4. Estimation Method and Procedure for Part III
.
.
.
.
7.1. Introduction
7.2. Group Expenditure Elasticity Estimates
7.3. Price Elasticities
7.3.1. Own Price Elasticities
7.3.2. Cross Price Elasticities
7.4. Age-Population Composition
7.5. Household Size
7.6. Habit and Custom Effects
7.7. Total Expenditure Elasticities and Allocation
Factors
7.8. Comparison of Factors: Is Expenditure So
Important?
7.9. Movements of Expenditure Elasticities
7.10. Possible Explanations of the Negative
Expenditure Elasticities for Foods of Animal
Origin
.
.
8.1.
8.2.
8.3.
8.4.
8.5.
8.6.
Study Motivation
Main Objectives
Study Characteristics
Model and Estimation Procedures
Empirical Findings
Implication for Future Study
BIBLIOGRAPHY
153
154
166
167
168
177
186
199
207
ANALYSIS OF ESTIMATION RESULTS
SUMMARY AND CONCLUSION
153
.
207
227
231
246
250
260
267
271
276
287
294
311
316
316
317
317
318
321
324
326
TABLE OF CONTENTS (Cont.)
Chapter
Page
APPENDIX A:
LIST OF SIGNS AND ABBREVIATIONS
.
.
.
.
APPENDIX B:
CHOICE OF THE DEMAND SYSTEM
Bi. Basic Requirements
338
338
The AIDS Model
AIDS vs. QES: A Curvature Problem
AIDS vs. TTL
APPENDIX C:
EXTENSION OF THE LA/AIDS MODEL
C.l. LA/AIDS with Habit Formation
C.2. LA/AIDS with Demographic Variables
C.2.l. The Methodological Alternatives
C.2.2. The BARTEN's Scaling Method
C.2.3. Further Restrictions
336
344
345
.
.
348
348
353
353
355
360
.
APPENDIX D:
A DATA COMPILING METHOD: THE RATIO METHOD
362
APPENDIX E:
FARMER'S HOME CONSUMPTION IN JAPAN
364
APPENDIX F:
THE CHOICE OF RETAIL PRICE DATA:
.
RPS VS.
366
HES
APPENDIX G:
WARP AND SARP TESTING PROCEDURES
APPENDIX H:
DATA SOURCES
APPENDIX I:
DATA FOR JAPAN, KOREA, AND TAIWAN
APPENDIX J:
JAPAN RETAIL PRICE DATA SERIES
391
APPENDIX K:
JAPAN TOTAL DOMESTIC CONSUMPTION DATA
SERIES
413
APPENDIX L:
JAPAN SOCIOECONOMIC DATA SERIES
445
APPENDIX M:
KOREA RETAIL PRICE DATA SERIES
449
.
.
.
.
370
378
TABLE OF CONTENTS (Cont.)
Page
Chapter
APPENDIX N:
KOREA DOMESTIC TOTAL CONSUMPTION DATA
SERIES
461
APPENDIX 0:
KOREA SOCIOECONOMIC DATA SERIES
.
.
.
469
APPENDIX P:
TAIWAN RETAIL PRICE DATA SERIES
.
.
.
473
APPENDIX Q:
TAIWAN PER CAPITA CONSUMPTION DATA SERIES
484
APPENDIX R:
TAIWAN SOCIOECONOMIC DATA SERIES
485
APPENDIX S:
ALLOCATION FACTORS AND TOTAL EXPENDITURE
ELASTICITIES
.
.
.
486
LIST OF FIGURES
Page
Title
No.
1-1:
The GOREUX Engel Curve
2-1:
The Estimated Engel Curves for Wheat and Rice,
Coarse Grains, and Meat in MARKS-YETLEY Study
6
.
19
The Plots of Income Elasticities for Wheat and
Rice, Coarse Grains, and Meat in MARKS-YETLEY
Study
19
The Estimated Engel Curves for Meat In MARKSYETLEY Study
20
The Plots of Income Elasticities for Meat in
MARKS-YETLEY Study
20
4-1:
Assumed Structure of Utility Tree
81
5-1:
Variable for Part I
106
5-2:
Japan Food Consumption Trends
116
5-3:
Korea Food Consumption Trends
118
5-4:
Taiwan Food Consumption Trends
119
5-5:
Japan Food Real Price Trends
124
5-6:
Korea Food Real Price Trends
126
5-7:
Taiwan Food Real Price Trends
127
5-8:
Japan Food Real Price During the War Period
129
5-9:
Demographic Data Trends
131
5-10:
Population (Share) Growth Rates
132
5-11:
Per Capita Real Group Expenditures vs. Time
141
5-12:
Per Capita Real Total Expenditures vs. Time
141
2-2:
2-3:
2-4:
LIST OF FIGURES (Cont.)
5-13:
Page
Title
No.
Per Capita Nominal Group Expenditures vs.
Per Capita Nominal Total Expenditures
.
5-14:
.
.
.
Per Capita Real Group Expenditures vs.
Per Capita Real Total Expenditures
142
143
171
6-1:
Model Specification Tests for Part II
6-2:
Model Specification Search for the Non-System
Estimation
188
7-1:
Allocation Factors
279
7-2:
Allocation Factors vs. Per Capita Real Total
Expenditure
285
Total Expenditure Elasticity vs. Real Per Capita
Total Expenditure
295
Abstraction of the Trend of Total Expenditure
Elasticity (Yi) against Real Per Capita Total
Expenditure (XT*)
305
7-3:
7-4:
.
.
.
LIST OF TABLES (Cont.)
Title
Paqe
Unreasonable Marshailian Own Price Elasticity
Estimates
246
Reliable Marshallian Own Price Elasticity
Estimates
249
Hicksian Cross Price Elasticities Consistent
and Significant Results of Substitutionary
Relationships
252
Hicksian Cross Price Elasticities Other
Significant Results of Substitutionary
Relationships
254
Hicksian Cross Price Elasticities Consistent
and Significant Results of Complementary
Relationships
255
Hicksian Cross Price Elasticities Other
Significant Results of Complementary
Relationships
257
Summary Table of Population Share Elasticities
From System Estimation Results
261
Summary Table of Population Share Elasticities
From Non-System Estimation Results
262
7-16:
Population Share Average Growth Rates
264
7-17:
Net Population Effect at the Mean from System
and Non-system Estimation
265
Coefficients of Economies of Household Size at
the Mean
268
Consistency and Statistical Significance Check
for Habit Effects Between System and
Non-System Estimation Results
273
7-20:
Summary of Total Expenditure Elasticities
277
7-21:
Average Growth Rates of Nominal Group
Expenditure For Each Sample (%)
288
Average Growth Rates of Nominal Retail Prices
For Each Sample (%)
288
No.
7-8:
7-9:
7-10:
7-il:
7-12:
7-13:
7-14:
7-15:
7-18:
7-19:
7-22:
.
.
.
LIST OF TABLES (Cant.)
No.
Paqe
Title
290
7-23:
Impacts of Group Expenditures on Food Demand
7-24:
Impacts of Own Price Levels on Food Demand
7-25:
Comparison of Impacts on Food Demand Among
Group Expenditure, Own Price, and AgePopulation Effects
292
Short- and Medium-run Trends in Total
Expenditure Elasticities
308
Changes in Direction of Total Expenditure
Elasticities
309
7-26:
7-27:
.
291
LIST OF APPENDIX FIGURES
Page
Title
No.
B-i:
The AIDS Engel Curve
343
D-l:
Illustration of the Ratio Method
362
F-i:
A Hypothesized Situation of Change in Own Price
and Corresponding Change in Quantity Demanded
for A Commodity Consists of Several Products
368
J-l:
Japan Barley Price Data Converting Procedure
.
.
399
3-2:
Japan Fish Price Data Converting Procedure
.
.
409
.
LIST OF APPENDIX TABLES
No.
Title
Page
Applications of The Three Demand Systems
with Additional Features
339
Farmer's Home Consumption Rates of Farm
Products in Japan(%)
365
I-i:
Japan Retail Price Data Series
379
1-2:
Japan Consumption Data Series
381
1-3:
Japan Socioeconomic Data Series
383
1-4:
Korea Retail Price Data Series
385
1-5:
Korea Consumption Data Series
386
1-6:
Korea Socioeconomic Data Series
387
1-7:
Taiwan Retail Price Data Series
388
1-8:
Taiwan Consumption Data Series
389
1-9:
Taiwan Socioeconomic Data Series
390
J-1:
Japan Price Data Original Sources
391
J-2:
Japan Price Data Publications
392
J-3:
List of Japan Rice Price Data
393
J-4:
List of Japan Bread Price Data
395
J-5:
List of Japan Barley Price Data
397
J-6:
The Bank of Japan Barley Price Data
398
J-7:
List of Japan Beef Price Data
401
J-8:
List of Japan Pork Price Data
402
J-9:
List of Japan Chicken Price Data
404
B-l:
E-l:
LIST OF APPENDIX TABLES (Cont.)
No.
Paqe
Title
J-10:
List of Japan Fish Price Data
407
J-l1:
List of Japan Eggs Price Data
410
J-12:
List of Japan Milk Price Data
411
K-i:
Japanese Way of Eating Staple Grains
415
Wheat Flour Utilization Rates in Japan (%)
Change in The Use of Barley in Japan
.
.
.
419
.
422
Fish Data in Japan Statistical Yearbook
434
(J4)
Discrepancies in Japan Fish Trade Data in
1938 between SHINOHARA (J8) and FAO (F7)
L-l:
.
434
.
Basic Assumptions in Food Balance Sheet for
Eggs in Different Sources
438
Basic Assumptions in Food Balance Sheet for
Milk in Different Sources
443
List of Japan Household Size Data
445
List of Japan Consumer Price Index Data
.
446
.
List of Japan Private Final Consumption
Expenditure Data
447
List of Japan Automobiles Data
448
Basic Source of Korea Retail Price Data
Data List of Korea Rice Retail Price
.
.
.
.
449
.
.
.
451
Data List of Korea Wheat Flour Retail Price
452
Data List of Korea Wheat Flour Retail Price:
Data Combination and overlapping Period
.
.
453
LIST OF APPENDIX TABLES (Cont.)
No.
Page
Title
Data List of Korea Barley Retail Price
Data List of Korea Beef Retail Price
453
.
.
.
.
.
455
Data List of Korea Pork Retail Price
Data List of Korea Chicken Retail Price
Data List of Korea Fish Retail Price
454
.
.
.
456
.
.
457
M-l0:
Weights for Fish Items in Consumer Price Index
458
M-ll:
Data List of Korea Eggs Retail Price
.
460
N-i:
Korea Fish Production and Trade Data Sources
.
467
0-1:
Household Size in Korea - Comparison of
Different Data
P-l:
.
.
.
470
List of Taiwan Wheat Products Price Data
.
475
476
List of Taiwan Beef Price Data
Structural Change in Taiwan Beef Supply
.
.
.
477
List of Taiwan Pork Price Data
478
List of Taiwan Chicken Price Data
479
List of Taiwan Fish Price Data
481
List of Taiwan Eggs Price Data
482
List of Taiwan Milk Price Data
483
S-i:
Allocation Factors Year by Year
487
S-2:
Total Expenditure Elasticities Year by Year
.
488
LIST OF TABLES
No.
Page
Title
Changes in Rice Consumption and Income Level
in Selected Asian Countries
23
Changes in Income Elasticity for Rice in
Selected Asian Countries: 1961-84
24
Income Elasticities for Pre-war Japanese Urban
Workers' Households
28
2-4:
Income Elasticities for Pre-war Japan
28
2-5:
Income Elasticities for Post-war Japanese
Urban Workers' Households and Farm Households
29
Official Estimates of Income Elasticities
of Various Foods in Japan: 1965-77
32
Income Elasticities for Rice in Japan:
1965-87
33
Korea Income and Own Price Elasticities for
Selected Commodities
36
2-1:
2-2:
2-3:
2-6:
2-7:
2-8:
.
.
5-1:
Summary of the Study Periods
103
5-2:
Correlation Matrices of Variables for Part I:
Choice of Independent Variables
109
5-3:
Commodities Included in Part II By Country
111
5-4:
International Comparison of Food Consumption
Levels
120
5-5:
Real Price Trends summary
123
5-6:
Variable Names Used in Correlation Matrix for
Part II
136
5-7:
Correlation Matrix of Variables for Part II
137
5-8:
Per Capita Proteins Intake Per Day by Major
Food Groups in Japan: 1903-65
148
LIST OF TABLES (Cont.)
No.
Page
Title
155
6-i:
Summary of Critical T-Statistics
6-2:
Notations Used in the Tables of Part I Estimation
158
Results
6-3:
Estimation Results for Part I
160
6-4:
Notation Used in the Tables of Estimated
Coefficients for Part II
178
6-5:
Commodities Included in Part II By Country
179
6-6:
Demographic Variables Included in Part II
By Country
179
Estimated Coefficients for Part II from System
Estimation: LA/S/H/AIDS Specification
180
Summary of Estimation Method for Non-System
Estimation
191
Estimated Coefficients for Part II from
Non-System Estimation: LA/S/H/AIDS
Specification
193
6-10:
Estimated Coefficients for Part I]1
201
7-1:
Summary Sheet of Calculated Elasticities From
System Estimation
209
Summary Sheet of Calculated Elasticities From
Non-System Estimation
218
7-3:
Group Expenditure Elasticity Estimates Summary
228
7-4:
Reliable Group Expenditure Elasticity Estimates
230
7-5:
Price Elasticity Matrix From System Estimation
232
7-6:
Price Elasticity Matrix From Non-System
Estimation
238
Marshallian Own Price Elasticity Estimates
Summary
244
6-7:
.
6-8:
6-9:
7-2:
7-7:
.
.
FOOD DEMAND DURING THE STAGE OF RAPID ECONOMIC DEVELOPMENT:
THE CASES OP JAPAN, KOREA, AND TAIWAN
CHAPTER
1
INTRODUCTION
1.1. Food Demands in the Context of Economic Development
The causes of economic development have been discussed
widely in various contexts.
However, the impacts of
economic development are an equally important concern.
The
potential for rapid economic growth has been realized by the
group of nations called Newly Industrialized Countries
(NIC5).
History may or may not repeat; however it is
possible that other currently less developed countries
(LDCs) will follow development patterns similar to the
current NICs.
Food demands are one area that has been affected by the
rapid economic development experienced in the NICs.
The
goal of this study is to analyze the food demand patterns of
three Newly Industrialized Countries, Japan, Korea, and
Taiwan, and to reassess the relative importance of income
with respect to other socioeconomic factors.
2
1.2. A General Perspective
Interest in these three East Asian countries of Japan,
Korea, and Taiwan stems from their successful transformation
from agrarian to industrial countries.
When compared to the
more developed Western nations, this growth rate can best be
described as "rapid".
As non-Western nations they share
many cultural characteristics.
At the same time, there
exist differences among them In how demographic and
socioeconomic characteristics have changed.
They seem an
ideal laboratory in which to examine how food consumption
patterns more in move in countries that experienced rapid
economic development.
The relationship between the demand for food and
economic development has been examined in countries that
have developed at a less rapid pace than those of Japan,
Korea, and Taiwan.
According to
MELLOR (1983, p. 241),
changes in the aggregate food demand-supply relationship are
most dramatic between the initial and medium stages of
development approximately corresponding to the "taking-off"
phase of economic development.
He described the food demand
cycle with the following three-stage model:
3
Stage I:
Per capita income is very low and growing slowly.
Death rates are high.
Growth rates in food demand are
approximately 3 percent or less.
Domestic production
satisfies domestic consumption during this stage.
Stage II:
Development causes rapid population and income growth;
the demand for food increases by approximately 30
percent over that of stage I.
A natural upper-limit in
agricultural production, in terms of land expansion
and/or technological progress, prevents domestic supply
from catching up with the growth of domestic food
demand.
imports.
This is a cause of increasing agricultural
"It is only countries with unusual potential
to expand onto high productivity land areas that can
avoid this phenomenon" (ibid., p. 241).
Stage III:
Population growth slows and the income effect on food
is reduced; high growth rates of food production are
realized with a lag; agricultural imports become less
necessary and there may be an agricultural surplus.
4
This argument has been extended by PINSTRUP-ANDERSEN
(1986, PP. 2-4), who argued that, as economic development
unfolds, there is a shift from foods of vegetable origin to
animal products.
Thus the composition of food demand is
different in each stage of economic development.
With this perception of the general relationship it is
appropriate to enquire if the relationship holds for those
countries that are experiencing
rapid economic growth.
There is the subject of enquiry of this study.
5
1.3. Food Demand and Real. Income
It is generally assumed that the economic development
of a country is best reflected in the rate of growth of the
real per capita income.
Thus, it is argued, the
relationship between food demand and economic development is
reflected quantitatively in how food consumption changes
with changes in real income, often referred to as the Engel
curve.
GOREtJX (1978)
(among others) provides one of the most
explicit views on the relationship between per capita income
and food demand using a specific Engel curve, where quantity
demanded for good i per period (Qi) is stated as a function
of single variable, per capita income per period (X)
Figure 1-1):
(see
6
Figure 1-1:
Note:
The GOREUX Engel Curve
Curve Representing the Function
logQi = a - b X1 - c logX
a = 11
Source:
b = 200
C
= 1
GOREtJX, 1978, P. 391.
The first segment AB of the curve represents the
consumption of a luxury commodity which increases
rapidly with income; segment BC represents the
consumption of necessities, for which the rate of
increase in consumption diminishes progressively as
income rises; segment CD represents the composition of
an inferior good, which diminishes as income rises (pp.
390-391).
7
This hypothesis can be restated as follows:
As income
grows, from the standpoint of a consumer, an agricultural
commodity will be perceived first as a luxury good, then as
a necessity and finally as a inferior good.
Thus, in the
various stages of income growth, the income elasticity of
demand for an agricultural commodity will not be constant;
rather, it will vary with each level of income and be
represented by some curvilinear function (MARKS and YETLEY,
1987).
The decline of single commodity demand is a result of
the substitution effect, which is described in the context
of the more general multi-commodity case as follows:
As
income grows, not only will the total quantity of food
consumed change, but also the composition of demands;
consumers continuously substitute higher priced/more
preferred/higher quality/more nutritious agricultural
commodities for lower priced/less preferred/lower
quality/less nutritious ones.
This will be referred to in
the present study as "the general hypothesis on the
development cycle of food demand," or more briefly, as "the
general hypothesis."
8
1.4. study Motivation
The general hypothesis is intuitively appealing and
there are many supportive findings (which will be reviewed
in Chapter 2).
However, the simplicity of the model may
mask some of the important aspects of changes in food
demands.
Recent developments in economic theory and
quantitative methods, together with progress in
computational facilities, enable an approach to the problem
that is more comprehensive with less restrictive formats.
Food demand analysis is one of the most popular fields
of economic study.
There are two primary focuses of food
demand analyses currently taking place.
One focus of study
is the rigorous application and investigation of economic
theory, with less emphasis on specification of data samples
and non-economic phenomena.
The other focus of study also
uses economic theory, but primarily aims to explain economic
phenomena for a specific population, for a specific period,
considering the various socioeconomic factors present.
The
formal application of theoretically rigorous models, such as
a complete demand system whose parameters can be estimated
econometrically, to specific economies are somewhat limited
to the economies of advanced Western markets or the
economies of rural areas in underdeveloped non-Western
9
markets.
Studies of the NICs are few (at least in
English)
There is no study has been done satisfying the
following points simultaneously:
demand theory is formally applied.
potentially important non-economic factors are
incorporated.
applied to the recently developing/developed
countries.
To consider the economic implications for future food
supply-demand conditions in developing nations, the study
covering the above three requirements is needed to be done.
1
Examples of the economies of advanced Western markets
are the United States (e.g., BLANCIFORTI, GREEN, and KING,
1986; CHALFANT, 1987), the United Kingdom (e.g., STONE, 1954;
POLLPJK and WALES, 1978), and Canada (e.g., SAFYURTLU, JOHNSON,
and HASSAN, 1986; NICOL, 1989). Examples of the economies of
rural areas in underdeveloped non-Western markets are India
(e.g., RAY, 1980), Brazil (e.g., THOMAS, STRAUSS, and BARBOSA,
1989), Sierra Leone (e.g., STRAUSS, 1986), and Senegal (e.g.,
The studies using complete
BRAVERHAN and HAMMER, 1986).
demand system analyses for Japan are YOSHIHARA, 1969; HAYES,
WAHL, and WILLIAMS, 1990.
10
1.5. Characteristics of Japan, Korea, and Taiwan
As indicated above, Japan, Korea, and Taiwan are chosen
for this investigation because of their rapid and resent
economic development.
These countries have both
similarities and difference.
Among the common
characteristics are the following:
Sustained rapid economic growth.
Transformation from an agrarian society to an
industrial society.
Non-Western nations.
These three points are considered to be central
characteristics of "latecomers"2 to the development
process, and carry important meanings in connection with
food demands:
First, standards of living in these nations
have improved over time.
This induced drastic changes in
per capita food demands.
Second, the limitation of natural
endowments encouraged the nations to emphasize nonagricultural manufacturing.
2
This term
suggested that:
is
This also contributed to
originated
with
GERSCHENKRON,
who
the industrial history of Europe appears not as a
series of mere repetitions of the "first"
industrialization but as an orderly system of graduated
deviations from that industrialization ... the more
delayed the industrial development of a country, the
more explosive was the great spurt of its
industrialization (1962, p. 44).
11
radical changes in the socioeconomic structure which is
expected to affect consumption behavior.
Third, as
non-Western nations, there is a possibility that different
types of demand patterns may exist, and more importantly,
demands for Western foods (non-native or non-traditional
types of agricultural commodities) may increase
substantially.
It is likely that these countries will provide
interesting study samples for food demand analysis.
One of
the factors creating much of the dissimilarities in
historical development process patterns is "time", i.e., the
timing of development may bring other exogenous factors into
each development path.
KUZNETS (1988, p. S35) noted,
Japan is much more economically advanced than Taiwan
and Korea and, as an independent nation-state, has had
over a century to develop, compared with less than 40
years for the other two.
Japan has achieved significant levels of economic
development prior to Korea and Taiwan.
Assuming the three
countries have similar food demand structures, it may be
possible to compare the patterns of food demand changes in
Korea and/or Taiwan to the longer-run changes that have
occurred in Japan.
12
1.6. Study Objectives
The specific objectives of this thesis are:
1. To develop a theoretical econometric model for Japan,
Korea, and Taiwan, with special emphasis on:
disaggregateci food commodities, rather than using one
total food measure.
a long time-series analysis that reveals the
historical process of development, particularly for
Japan (spanning the pre- and post-WWII periods).
Japan's development in the pre-war period may
correspond to the earlier post-war stages of
development for the other two countries.
non-economic variables that illuminate socioeconomic
effects on food consumption.
2. To examine the validity of the general hypothesis of
income elasticity, i.e., to investigate:
how significant is the effect of income growth on food
demand relative to other factors?
how significant are the changes in income elasticities
- are they changing?
If so, how are they changing?
whether there is a general pattern in the behavior of
income elasticities.
13
1.7. Outline of the Thesis
This thesis proceeds in the following order:
empirical studies are reviewed in Chapter 2.
Previous
The
methodological issues in the previous studies are discussed
at the end of Chapter 2.
To accomplish the study
objectives, a more versatile and theoretical analytical
framework is developed.
The framework and its development
are contained in Chapter 3.
developed in Chapter 4.
are explained.
An econometric model is
In chapter 5, variables and data
The data sets are presented in various
configurations and brief analyses are conducted as a
preliminary examination
Also, the price and quantity data
sets are examined by the non-parametric tests of the weak
and strong axioms of revealed preference.
Chapter 6
describes estimation methods and procedures.
The estimation
results are summarized and interpreted in Chapter 7.
Summary and conclusions are Chapter 8.
To improve
readability, technical details, data, and documents of data
compilation are located in appendices.
14
CHAPTER
2
LITERATURE REVIEW
2.1. Introduction
In part I, two
This chapter consists of three parts.
cross-country studies of change in food consumption patterns
with respect to income growth are reviewed.
In part II,
various studies analyzing change in food consumption
patterns for Japan, Korea, and Taiwan individually are
reviewed.
Part III presents discussion of the empirical
findings and critiques of methodologies.
The objectives of
this chapter are:
To confirm the existence of dramatic changes in food
consumption patterns in developing nations, particularly in
Japan, Korea, and Taiwan.
To present information about possible factors
affecting food demands, particularly in Japan, Korea, and
Taiwan.
The information will be reflected in model
development procedures in the subsequent sections and also
help interpretation of the results.
To determine the empirically appropriate functional
form for Engel curves that will capture the effect of income
on food consumption under substantial income growth.
15
2.2.
Part I: Cross Country Studies
2.2.1.
Empirical Study I: MARKS and YETLEY
Suzanne Marie MARKS and Marvin J. YETLEY (1987)
conducted pooled cross-sectional and time series analysis
with 105 countries over the 1961-81 period "to investigate
general global patterns of consumption as a function of
economic development"
12).
Essentially the research was
an Engel curve analysis.
"least
They divided food into three categories:
preferred" (coarse grains - maize, millet, barley, and
sorghum), "preferred" (wheat and rice), and "most preferred"
(meat - beef and buffalo, pork, poultry, and sheep and
goat).
They assumed weak separability and a two-stage
budgeting procedure:
In the first stage income is allocated
into two broad groups of "food" and "non-food".
In the
second stage of the budgeting procedure, income for food is
allocated into the three subgroups of food categories.
Each
of the three food subgroups were taken as single composite
commodity (further disaggregation was considered for meats).
They assumed disposable income as a "proxy measure for
economic development" (p. 2) and used per capita Gross
Domestic Product (GDP) adjusted for purchasing power parity
in constant 1975 international dollars (X).
They commented
that "to the extent disposable income remains a constant
portion of GDP, justification exists for substituting GDP
for disposable income in consumption analysis" (p. 9).
16
Another variable, quantity demanded (Q), was derived by a
"food balance sheet method" using data from FAQ's Food
Balance Data Tapes, covering the period 1961-81 for 105
countries.3
Two kinds of quantity measures were considered
for quantity demanded Q, which are "percent of diet" and
"annual kilograms per capita."
Their basic approach was to search for the
statistically best fit specification of Engel curves for
each commodity out of the given set of plausible functional
forms suggested by GORETJX's general hypothesis.
Following
is the list of functional forms considered:
Q = f(X)
Q = f(X, X2)
Q = f(X, X2, X3)
logQ = f(logX)
logQ = f(logX, (logX)2)
logQ = f(logX, (logX)2, (logX)3)
The MARKS-YETLEY study did not include Taiwan.
It is
likely that the FAQ data set also does not contain data
regarding Taiwan either.
17
The resulting best-fitted functional forms for each group
were:4
<Coarse grain equation>
logQ = log(a) - b*logX
<Wheat and rice equation>
logQ = log(a) - b*logX + c*(logX)2 - d*(logX)3
<Meat equation>
Q = a + b*X + c*X2 - d*X3
Figure 2-2-1 through 2-2-4 provide graphical presentation of
the functional forms and for the corresponding income
elasticities.
The functional forms for the wheat and rice
(preferred foods) equation and the meat (most preferred)
equation both have three phases:
luxury, necessity, and
In their estimation of wheat and rice equation, they
excluded 25 countries out of 105 countries and utilized data
from the rest of the 80 countries. The excluded 25 countries
Algeria, Bangladesh, Mainland China, Egypt, Guyana,
were:
Liberia,
Jordan,
Japan,
India,
Indonesia,
Iran,
Iraq,
Pakistan,
Morocco,
Mauritius,
Madagascar,
Malaysia,
Philippines, Republic of Korea, Sierra Leone, Sri Lanka,
Nine countries in
Syria, Thailand, Tunisia, and Turkey.
Southeast Asia are typed in bold face. Only three Southeast
Asian countries were included in the 80 utilized countries,
they were Burma, Hong Kong, and Singapore. Again, Taiwan was
This was due to the fact that the
excluded from the study.
data from these two groups seemed to behave in different ways
for changes in income level. They noted that
'
The "25" countries consumed much larger proportions of
their diet as wheat and rice than countries in (other
Many of these countries subsidize
80 countries).
consumption of either wheat or rice, causing
consumption to be artificially higher than normally
would be (p. 14).
However, the specific criteria for the separation of the data
set was not clearly mentioned in the article.
18
inferiority as income grows.
This is consistent with
GOREUX's general hypothesis.
The coarse grain (least
preferred) equation produced results that were contrary to
GOREUX's hypothesis in two ways:
the equation has only the
inferior good phase and the income elasticity remains
constant even for a very wide range of incomes.5
In
addition, while beef and pork roughly follow the hypothesis
for commodities in the meat group, the two commodities sheep
& goat and poultry have shown inconsistencies.
Poultry is
the only commodity with increasing demand at the highest
income levels; it shows a unique pattern with a functional
form that goes from luxury to necessity, skipping the
inferior phase, then returning to luxury phase at the
highest income levels.
GOREUX accepted such results for those commodities with
a "sufficiently narrow" range of income.
19
Figure 2-1: The Estimated Engel Curves for Wheat and Rice,
Coarse Grains, and Meat in MARKS-YETLEY Study
Figure 13 - SLages 01 Economic Dnvelopmenl Seen in Shares 01 Wheat and Rice.
Meat, and Coarse Grsii in the Diet
Sq.
State
Stqe
0
VI
4
2
IS
9
(ThOa..nd daltara)
PER CAPITA INCOMI
COARSE StAINS
HEAT AND RICE
MEAT
Source: MARKS and YETLEY, 1987, p. 25, Figure 13.
Figure 2-2: The Plots of Income Elasticities for Wheat and
Rice, Coarse Grains, and Meat in MARKS-YETLEY Study
Figure 16 Food Commodlt income ElastIcItIes ol Demand
2
a
a
a
Stag. I
taN
Stag Stag. VI
Stag. IV
Stag. III
V
4
I'll
I
4
(Th,a..nd
I0
6
PER CAPITA INCOME
a
WHEAT AND RICE
-
COARSE GRAINS
o
MEAT
Source: MARKS and YETLEY, 1987, p. 31, Figure 16.
20
Figure 2-3: The Estimated Engel Curves for Meat
YETLEY Study
In
MARKS-
FIgurE 17 SutListical Best Fft Lines Kor Meat Consumption
0
0
S
6
4
rThegiana flhl.r.)
'PITA NCQME
PER
-
PCULTR
BEEP
.
PORK
SHEEP
Source: MARXS and YETLEY, 1987, p. 32, Figure 17.
The Plots
Figure 2-4:
MARKS-YETLEY Study
of Income
Elasticities for Meat in
FSn,e 19Income EIaaUc*Ues o( Demand (or Meat
(Th.u.a,4 4.111,,)
PER CAPITA I8COUC
ALl.
SW
A
POULTRY
PORE
SMEEP a SOAT
Source: MARKS and YETLEY, 1987, p. 34, Figure 19.
21
2.2.2. Empirical Study II: ITO, PETERSON and GRANT
Another recent study concerning consumption patterns is
titled "Rice in Asia: Is It Becoming an Inferior Good?" by
ITO, PETERSON, and GRANT (1989).
The objective of the ITO
et al. study was to investigate whether the declining trend
of per capita rice consumption as income increases observed
in some Asian countries such as Japan, is present in other
Asian countries.
Their study was a pooled cross-section and
time-series analysis where changes in income elasticities,
signs and magnitudes for each country were explicitly
analyzed.
Fourteen countries were considered in their study
for the period 1961-85.
The countries were:
Bangladesh,
Burma, People's Republic of China (PRC), India, Indonesia,
Japan, South Korea, Malaysia, Nepal, Philippines, Singapore,
Sri Lanka, Thailand, and Taiwan.
The countries were divided into three groups based on
the rate of change in per capita rice consumption.
Estimation was then done for each group (see Table 2-1).
The employed methodology was an Engel curve analysis
with the following modifications.
Starting with a
functional form proposed by GOREUX, they added own and cross
price terms, and used intercept and slope dummy variables to
distinguish estimated coefficients for each country.
The
price variable was the ratio of the price of rice to the
price of wheat since wheat is a major substitute for rice in
Asia.
Their model is reproduced below without including
22
dummies for simplicity:
inQ
= a
- b*Xt - c*lnX
where
lnP.
+ d*1nPt + e
1 ==ln(PRt/PWt)
l,2,...,m (countries)
t
Q
X
PR
PW
e
in
=
=
=
=
=
=
=
l,2,...,T (years)
per capita rice consumption
per capita real income
world price of rice
world price of wheat
error term
natural logarithm
Data for rice consumption was taken from Foreign
Agriculture Circular, Grains, a publication of USDA Foreign
Agricultural Service, which is based on the "food balance
sheet" method.
GDP was used as the income variable, and
figures were from the IMF publication of International
Financial Statistics, together with population and price
data.
Rice prices at Bangkok, and wheat prices at U.S. Gulf
ports were used as representatives of world prices.
To mitigate the multicollinearity problem between
X-inverse and lnX variables, the ridge regression technique
was employed.
23
Table 2-1: changes in Rice Consumption and Income Level
in Selected Asian Countries
Per Capita Real GDP
and its Growth Rate
(comparison between
1961-65 average real
GDP and 1981-85
Annual Per Capita Rice Consumption
(five-year average, milled, kg)
average GDP) *
Change(%)
GDP($)
(1984-5) ***
1961-65
1981-85
161
124
103
132
126
191
98
88
74
109
105
164
-39.1
-29.0
-28.2
-17.4
-16.7
-14.1
3033
10456
7206
2237
251
139
278
135
138
752
121
India
77
Bangladesh 154
Sri Lanka
109
South Korea 129
75
156
113
136
-2.6
1.3
3.7
5.4
252
144
371
2052
102
108
157
222
12.1
33.3
46.7
66.9
603
222
519
171
Change(%)
Group I
Taiwan
Japan
Singapore
Malaysia
Nepal**
Thailand
0
Group II
31
19
162
417
Group III
Philippines 91
PRC
81
Indonesia
107
Burma
133
Note:
*
52
140
152
8
For Bangladesh between 1971-75 and 1981-85
period and for Indonesia between 1966-70 and
1981-85.
**
Rice consumption data include stocks for
Nepal.
***
Some of GDP are 1984 figures while others are
1985 figures.
Source: ITO, PETERSON and GRANT, 1989, p.33,
Table 1 and 2.
24
The results of estimated income elasticities are partly
reproduced from the ITO et al. (see Table 2-2).
In the
table, base countries are Taiwan in Group I, India in Group
II, and Burma in Group 111.6
The coefficients of the
income variables for these countries were all significant
except for the coefficient of the mx variable for Burma.
Table 2-2: Changes in Income Elasticity for Rice in
Selected Asian Countries: 1961-84
1961
1965
1970
1975
1980
1984
0.015
0.165
0.211
0.328
--0.237
-0.192
-0.141
0.128
0.110
-0.335
0.042
-0.356
-0.546
-0.267
-0.064
-0.352
-0.123
-0.455
-0.603
-0.392
-0.200
-0.392
-0.250
-0.562
-0.671
-0.525
-0.625
-0.351
-0.396
-0.594
-0.708
-0.599
-0.671
-0.346
-0.431
India
0.163
Bangladesh
--Sri Lanka
0.022
South Korea 0.095
0.157
--0.023
0.081
0.148
0.026
0.064
0.153
-0.016
0.028
0.053
0.126
-0.017
0.031
0.047
0.125
-0.016
0.032
0.046
0.179
0.327
0.151
0.266
0.266
0.032
0.131
0.226
0.195
0.036
0.110
0.183
0.122
0.030
0.121
0.133
0.108
0.028
Group I
Taiwan
Japan
Singapore
Malaysia
Nepal
Thailand
Group II
Group III
Philippines 0.201
PRC
0.418
Indonesia
--Burma
0.030
0.033
Source: ITO, PETERSON and GRANT, 1989, p.39.
6
A total coefficient for a dummy-country is the sum of
the base-country coefficient and the slope-dummy coefficient.
On the base country, only the base-country coefficient is
applied.
25
The coefficients of the slope dummies for the X-inverse
variables were mostly significant, implying that demand
responses to income levels in these countries are different
from the respective base countries, except for Thailand and
South Korea.
The coefficients of the slope dummies for the
mx variables were not significant in Group I but were
significant in Group II and III, except for Indonesia.
In
all countries studied, except for Bangladesh and Sri Lanka,
the X-inverse variables had negative total coefficients.
This suggested that income elasticities were generally
decreasing as incomes increased (p. 37).
According to the results, the historical turning points
of income elasticities for rice from positive to negative
were 1961/62 for Taiwan, 1963/64 for Japan, 1967/68 for
Singapore, 1968/69 for Malaysia, and 1965/66 for Thailand.
In Nepal and Bangladesh, rice had always been an inferior
good during the study period.
For other countries, income
elasticities were all positive, but showed declining trends
generally.
around 0.3.
Burma had a relatively stable income elasticity
Sri Lanka was an exception; income elasticity
was increasing slightly.
26
2.3. Part II: Studies of Individual Countries
2.3.1. Case I: Japan
For discussions of food consumption patterns in the
pre-war period in Japan, OHKAWA (1972) and KANEDA (1970)
were reviewed.
Note that their conclusions crucially
depended on the quality of the data used in their studies
rather than the methodologies.7
Both scholars supported
the hypothesis that people continuously substitute animal
proteins for starchy staples as per capita real income
grows.8
KANEDA applied single regression analysis using
household expenditure survey data for urban household from
the 1920's to 1930's period.
The model was log-log form,
having per capita household total expenditure as an
explanatory variable and expenditures for each item as
dependent variables.
KANEDA's results are presented in
Hereafter, "war" stands for the Second World War. Many
researchers' efforts to improve the data sets of earlier
periods allows later studies to utilize more reliable
statistics providing more reasonable explanations for Japan's
historical development process.
The results of these
improvements and revisions
for historical statistics are
contained in the Estimates of Long-term Economic Statistics of
Japan Since 1868 (called LTES), which consists of 14 volumes
and is presently the most organized and reliable data source
for pre-war Japan.
8
The starchy staples group in the argument include
cereals such as rice, barley, naked barley, and wheat; roots
such as sweet potatoes and white potatoes; and processed foods
such as wheat flour, noodles, and starch. The animal proteins
group in the argument include meat, milk, eggs, fish,
shellfish, and other marine products.
27
Table 2-3.
KANEDA introduced other researchers' estimates
of income elasticities for food items, which are in Table
2-4.
Interestingly, staple foods turned out to be an
inferior good in pre-war Japan.
Besides, rice, the most
important staple in Japan, was an inferior good in the
1930's in urban areas, while a necessity in rural areas.
Animal proteins were luxury goods in early 1920's urban
Japan, they slowly became necessities.
KANEDA commented:
Because of the progress in industrialization after
World War I, the patterns of food consumption in the
urban areas underwent some considerable changes. The
aggregate patterns, however, remained rather stable,
and the changes that took place were moderate and
gradual (p. 426).
KANEDA referred to the change in other non-economic
factors occurring in the economic development process.
The
change in the dietary patterns was more drastic in the
post-war period owing to:
(1) massive exposure of Japanese people to the
influences of "foreign consumption patterns"; (2) the
rapid acculturation of these influences through mass
communication media; and (3) the inauguration in 1947
of a school lunch program (with emphasis on bread and
milk)
(p. 416).
28
Income Elasticities for Pre-war Japanese Urban
Table 2-3:
Workers' Households
Food
Total
Year
0.494
0.386
0.347
0.329
1921
1926-27
1931-32
1936-36
Aniival
Cereals
0.216
_0.021*
-0.105
-0.016*
Others
Proteins
0.477
0.657
0.582
0.545
1.182
0.943
0.753
0.824
* Not statistically significant at 5 % level of
significance.
Source: KANEDA, 1970, p.413.
Table 2-4:
Income Elasticities for Pre-war Japan
Source
Products
Years Covered
Income E1asticity
NODA (1956)
Agricultural
Food Products
1922-1937
0.23
NODA (1963)
Agricultural
Food Products
1915-1937
0.18
Starchy
Staple Food
NAKAYAMA (1958) 1918-1942
-0.27
Rice
1931-1939
OHKAWA (1945)
Urban Area: Salaried workers
Wage workers
Rice
1936
OHKAWA (1945)
Rural Area: Owner-cultivators
Tenant farmers
Source: KANEDA, p.405 and p.426
-0.2 to -0.4
0.0 to -0.2
0.3
0.6 to 0.7
29
KANEDA also pointed out:
rapid urbanization of Japanese life, not only in the
usual sense of the shift of population from rural to
urban areas, but in the sense of all that modern urban
life and technology connote, has helped in shaping new
food consumption patterns (p. 416).
For the post-war period, KANEDA conducted regression
analysis in the same manner as above using household
expenditure data on both urban and rural households for the
1953-61 period.
The results are presented in Table 2-5:
Table 2-5: Income Elasticities for Post-war Japanese
Urban Workers' Households and Farm Households
Year
Food
1953
1957
1961
Urban Workers' Households
0.481
0.750
0.196
0.456
0.062
0.773
0.472
0.075
0.700
1953
1957
1961
0.529
0.531
0.529
Starchy
Staples*
Farm Households
0.466
0.363
0.159**
*
Animal
Proteins
1.117
1.156
1.087
Other
0.590
0.602
0.585
0.412
0.507
0.720
For workers' households including cereals only.
** Not statistically significant at 5 % level of
significance.
Source: KANEDA, 1970, p.424.
30
The author interpreted the result as follows:
(G)iven that the service (processing and marketing)
components of food expenditure are higher in the
postwar years, ... (still) the higher income elasticity
of food demand should be interpreted as indicating that
the Japanese are not content to eat the same kinds of
foods as they used to before World War II.
Their food
consumption patterns are changing together with the
rapid income growth (p.
(T)he geographic
shifts of the population and the changes in the
technological and institutional framework of food
consumption played vital roles in determining Japanese
food consumption patterns in the postwar years '(p.
428).
This urban-rural comparison strongly
suggests a tendency for farm households to emulate the
consumption patterns of urban households (p. 425).
(T)he urban consumption habits seem to be moving more
rapidly toward the Western pattern than in any other
period in Japan's economic development (p. 428).
420). ...
...
In terms of total caloric intakes, the pre-war peak
levels recorded at the end of the
during the 1954-56 period.
1930's
were attained
Judging from consumption levels
of food with high protein, pre-war levels were recovered
around the end of the
1940's;
in terms of starchy food
consumption levels, pre-war levels were regained around
1951-53.
According to the author, these gaps in the speed
of recovery among the food group indicates "clear evidence
of the change in food consumption patterns from the prewar
to the postwar period"
(p.
419).
For the subsequent period of the
1960's
and the
1970's,
Yoshimi KURODA (1982) conducted a study on Japan's food
consumption patterns.
Major findings were as follows:
During the 1955-73 period, Japan experienced robust per
31
capita income growth, at the same time per capita
consumption of staple (starchy) food (rice and others)
declined greatly and that of "subsidiary" or luxury foods
(meat, milk, eggs, vegetables and fruits) increased
constantly.
Particularly, per capita consumption of pork,
chicken, milk and milk products increased substantially.
On
the other hand, during 1973-77 period, the composition of
food consumption did not show any remarkable changes.
Besides, during the 1955-73 period, the per capita
consumption of beef increased very slowly and is still
growing, while the consumption of eggs seems to have reached
a plateau (pp. 91-95).
The official estimates of income elasticities for
various commodities for the post-war period found in KURODA
are partly reproduced in Table 2-6.
For the most recent period, NAKAGAWA (1990) reviewed
some studies on rice consumption in Japan.
Rice consumption
has been decreasing since 1963, and rice became an inferior
good after 1965.
The most recent estimates of income and
price elasticities for rice are reproduced in Table 2-7.
32
Table 2-6:
Official Estimates of Income Elasticities
of Various Foods in Japan: 1965-77
Item
Year
1965
1970
1973
1975
1977
0.73
0.66
0.65
0.51
0.58
Staple Food
0.41
Rice
0.31
Wheat, Barley,
& Other Cereals -0.56
Bread
0.88
Noodles & Others 0.52
0.41
0.35
0.44
0.39
0.39
0.35
0.42
0.38
-0.06
0.74
0.47
-0.19
0.65
0.47
0.28
0.55
0.36
0.28
0.59
0.39
M.A.
0.68
0.66
0.46
0.58
0.82
0.70
0.66
0.46
0.64
0.77
1.12
0.64
0.94
0.68
0.87
0.52
0.60
0.66
0.78
0.87
0.65
0.56
0.42
0.39
\
Food Total
Subsidiary Food
Fresh Fish &
Shell Fish
Dried Fish &
Shell Fish
Meats
Milk &
Milk Products
and Eggs
Notes
:
For 1965, figures are for all cities with
more than 50000 persons; whereas, figures are
for all Japan for 1970-77.
Source: KURODA, p.118, Table 4-A-8.
Original Source: Annual Report on the Family and
Expenditure Survey., Bureau of Statistics, Office of
Prime Minister.
33
Table 2-7:
Income Elasticities for Rice in Japan: 1965-87
Source
Period
Income
Elasticities
for Rice
Own Price
Elasticities
for Rice
YOSHIDA*
(1990)
1965-87
-0.448
-0.2 18
MAFFJ**
(1988)
1965-73
1975-87
-0.393
-0.591
-0.120
-0.025
Note:
* YOSHIDA used log-log specification.
** Ministry of Agriculture, Forestry, and
Fishery, Japan.
Source:
NAKAGAWA, 1990.
According to the estimates from Ministry of Agriculture,
Forestry, and Fishery, Japan, rice became a less price
responsive food and its inferiority increased.
According to
YOSHIDA (1990), rice had a substitutionary relationship with
"perishable meat" and "edible oils and fats".
HIRAO (1990),
MORISHIMA (1988), and INOUE (1990) found that the consumers'
age had a substantial effect on rice consumption in Japan.
According to INOtJE, males had two age peaks for rice
consumption in their 20's and 60's.
peaks in their 30's and 60's.
Females had two age
NAKAGAWA noted that "recently
Japanese traditional dietary habits (are) dramatically
changing,
...
(and) serious production adjustment problems
are occurring" (p. 7)
34
2.3.2. Case II: Korea
According to JtJ, YOO, and MEUNG (l985),
a basic trend
after the niid-1960's indicated substantial change in food
demand composition shifting from staple grains to livestock
products, fish, vegetables, and fruits.
Within staples, switching from barley to rice or wheat
products was observed as accompanying income growth.
For
the period 1965-70, rice consumption was increasing.
After
this period, it then steadily declined.
The Korean
government strongly promoted mixing rice with barley during
1972-77 to discourage further increases in rice consumption.
As rice production increased, this policy was discarded.
Consumption of wheat flour increased rapidly during the
1965-75 period.
increase.
Wheat consumption still continues to
Per capita consumption of wheat flour exceeded
that of barley after 1977, and the former was fivefold of
the latter in 1984.
Domestic production could be increased
fairly easily for barley but not for wheat.
In the face of
rising demand for wheat and declining barley demand, wheat
imports were expected to increase (pp. 6-9).
Between 1960 and 1984, per capita consumption of beef
increased 5.2 times, pork 3.7 times, chicken 4.1 times, and
milk 10 times.
The growth rate for per capita consumption
of beef was the greatest among meat products, which implied
Many thanks go to Mr. Yongsam LEE for helping us with
translating the article.
35
beef was the most preferred meat commodity in Korea (p. 11).
Income and price elasticities were estimated for each
of 36 commodities.
The resulting income and own price
elasticities for some commodities were compared with the
results from other previous studies (see Table 2-8).
All estimated equations were in log-log form.
Many
different types of variables were tried to choose the
statistically best fit specification.
As a result,
variables were different from equation to equation.
The
variables included in the estimated equations were per
capita real income, own and cross real prices, and dummy
variables for the structural changes in consumption.
all equations contained income and own price.
per capita GNP was used for income variables.10
Almost
Generally,
For price
variables, in general, wholesale prices were used for
staples and retail prices were used for animal origin
products.
Per capita real income of farm households was used f or
the barley equations and for the rice equations for farm
households; per capita real income of urban wage workers was
used for the rice equations for urban households.
36
Table 2-B: Korea Income and Own Price Elasticities for
Selected Commodities
Own Price
Elasticity
I tern
Source
Period
RICE
JU et al.
1970-84
1965-80
1962-76
1959-74
1960-71
1958-68
-0.1971
1967-8 1
-0.0377
-0.7187
-0.2054
A
B
C
D
I
J
K
K
WHEAT JU et al.
FLOUR
A
BARLEY
BEEF
197 1-81
1965-80
B
C
D
1962-7 6
K
1962-78
JU et al.
197 0-84
A
1966-79
B
C
D
1962-7 6
1959-74
1960-7 1
1959-74
1960-7 1
-1.1701
0.2197
0. 1480
0.0965
-1. 23 60
0. 2200
0. 0600
-0.2000
-0.41
-1.3598
-0.33
-0.7899
-2.5590
-0.7501
-0.1493
-0.0852
-0.3010
-0. 3152
-0. 8750
-0. 5500
-0. 10
0.4963
0.0039
0.09
0.5960
0. 4820
0.2279
1. 3590
0. 2593
-2. 0094
-1.8773
-0.0610
-0. 4664
0. 1710
I
1958-68
J
K
1968-8 1
1962-78
-0. 7911
JU et al.
A
19 65-8 3
19 65-8 0
B
C
1962-7 6
G
196 1-80
1969-8 0
1965-8 1
-0.5927
-0.5064
-0.8407
-0.8068
-0.3567
-1.34
-0.81
19 65-8 3
-0. 1723
1.3229
1.3020
1.1742
0.8058
0.6076
1.38
1.48
1.7036
197 0-8 3
-0. 9071
0. 7141
19 65-80
1962-7 6
1959 -74
196 1-80
1959-8 0
1968-8 1
-1.0729
-0.3086
-0.0727
-0.5778
-1.53
-0.89
-0.8787
-0.5284
1.2714
0.6492
H
J-1
P
PORK
1962-78
1956-69
-0. 2250
-0. 1840
-0. 3831
In come
Elasticity
JU et al.
A
B
C
G
H
J- 1
N
P
19 59-7 4
1968 -8 1
1968 -8 3
0.15
-0.1600
-1.28
-0.4896
1. 9621
0.4841
1.19
0.96
1.0279
1.0481
37
Table 2-6:
Korea Income and Own Price Elasticities
for Selected Commodities (Cont.)
Item
Source
POULTRY JU et al.
A
G
H
J-1
P
1968-8 3
0. 2947
1965-83
-1. 0091
1962-7 6
-1.1461
-0.7669
C
MILK
JU et al.
B
C
1959-74
G
J-1
1961-8 0
1968-8 1
P
1965 -8 3
JU et al.
B
C
G
J-1
P
FISH
JU et al.
(SHELLB
FISH
H
EXCLUDED)
J-1
Source:
Own Price
Elasticity
1970-83
1965-80
1962-76
1959-74
1961-80
1969-80
1971-81
B
EGGS
Period
-0. 1590
-0.7448
-0. 7577
-0. 6769
N.A.
-1.64
-0.29
N.A.
-0.40
-1.3597
197 0-8 3
1962 -7 6
0.1948
1959-74
-5.0345
1969-8 0
1968-8 1
-1.75
19 65-8 3
-0. 9587
N.A.
N.A.
Income
Elasticity
0. 8742
0. 7842
1. 0550
1.3346
0.4640
1.54
0.59
0.6574
0.3144
0.9333
0.7321
0.4391
0.71
0.4834
2.5748
0.3633
3.5914
1.0337
2.15
4.4060
1968-8 3
-0.7223
1962-76
-0. 3313
1.0976
0.5323
1969-8 0
0.87
-1.03
0.83
1.09
197 1-8 1
JU, 100, and MEUNG, 1985, pp. 64-69.
Description of the sources A to P is in
the following page.
38
Table 2-8: Korea Income and Own Price Elasticities
for Selected Commodities (Cont.)
"JuYo NongSanMul SuYo BanEung
(1982)
HUH, Shin-Haeng.
BunSeog <Demand Analysis of Major Agricultural
Commodities>." NongChon GyeongJe <Rural Economy>.
Vol.5, No.1. Mar., 1982.
"NongEob YeCheug Model SeulJeong
LEE, Sang-Won.
(1978)
<Construction of Agricultural Forecasting Model>."
YeonGu BoGo 98 <Reseach Report 98>. GugRib NongEob
GyeongJe YeonGuSo <National Institute of Agricultural
Mar., 1978.
Economics>.
"SigRyang GyeongJe MunJe
(1975)
SEONG, Yeong-Bae.
JongHabJeog BunSeog <General Analysis of Problems in
Food Economy>." YeonGu BoGo 73 <Reseach Report 73>.
GugRib NongEob GyeongJe YeonGuSo <National Institute of
Dec., 1975.
Agricultural Economics>.
SaRyo SuIb AnJeongHwa
(1981)
KIM, Hyeong-Hwa.
HyoYu].Seong JeGo <Reconsideration on Stabilization and
HanGug NongChon GyeongJe
Efficiency of Peed Import>.
YeonGuWon <Korea Rural Economic Institute>.
A Study on Consumption Pattern of
(1982)
CHO, Suk-Jin.
Livestock Products. HanGug NongEob GyeongJe HagHoe,
ChuGe HagSul BalPoeHoe, BalPoe NonNun <Thesis Presented
at Summer Conference of Korea Agricultural Economics
Society>.
"SigRyang Sub ChuJeong HanGeJeog
(1983)
J-l. JU, Yong-Je.
JeobGeun BangBeob <Limitation and Methodology of Food
Demand Estimation>." NongChon GyeongJe <Rural
Economy>. Vol.6, No.2. Jun., 1983.
"YangDonEob YugSeong GaGyeog
(1982)
N. LEE, Chul-Hyun.
AnJeong JeDo <Price Stabilization System for the
Development of Hog Breeding Industry>." NongChon
Dec., 1982.
Vol.5, No.4.
GyeongJe <Rural Economy>.
P. HanGug NongChon GyeongJe YeonGuWon <Korea Rural Economic
NongSusanMul GaGyeog AnJeong
(1984)
Institute>.
JeongChaeg GaeBal JoSa YeonGu <Study on the Development
of Price Stabilization Policy for Livestock Products>.
Dec., 1984: pp. 153-6.
C-84-13.
Note:
Titles, subtitles, and article and institution names
in < > parenthesis are our translation.
39
In the estimation of income elasticity, results were
sensitive to the choice of time period and specification.
For some particular commodities, income elasticities were
unstable.
For example, estimated income elasticities for
rice were 0.4963 for 1962-78 period whereas it was 0.0039
for 1956-69 period according to JU et al.
(p. 60 and p. 64).
A priori expectation was that income elasticities would
steadily decline as income grow.
Market distortions due to
the government intervention in the rice market may have
caused this contradictory results (p. 60).
Income elasticities for rice were calculated separately
for farm and non-farm households.
In addition, the national
average income elasticity for rice was calculated by
weighting farm and non-farm populations.
The estimated
income elasticities were 0.2418, -0.1701, and -0.0786 for
farm households, non-farm households, and national average
respectively (p. 62).
Per capita consumption of rice in
farm households was expected to decrease; then, the non-farm
household result of -0.1701 was taken as a projected income
elasticity of rice for all Korean household for 1991,
assuming constant preferences and relative prices.
40
Supplemental information as provided by SILLERS (1984)
found in the East Asia: Outlook and Situation Report,
publication of the United States Department of Agriculture
(USDA) states that:
Per capita consumption of wheat and rice has probably
peaked.
Current trends suggest that wheat consumption per
person is unlikely to increase in the 1980's and may
decline.
On the other hand, demand for animal products will
increase rapidly in response to growth in real income and to
changes in tastes resulting from urbanization and other
demographic trends.
Urbanization is increasing rapidly as industrial growth
draws workers into cities, where they are exposed to new
food products rare or absent in the rural diet, including
meats, milk, and sugar products.
Korean consumers have traditionally prized beef over
pork, chicken meat, and eggs, and in the absence of
constraints, would channel higher real income strongly
toward increased beef consumption.
Demand for milk has grown very rapidly since the
1960's, and dairy production will be increasingly important
in the total demand for feedstuff s.
Income growth in South Korea will shift consumer demand
The
from cereals toward meats and milk in the 1980's.
feed
grains
result will be strong growth in imports of U.S.
rice
and
and soybeans, and slower growth in imports of U.S.
wheat (pp. 30-31).
41
2.3.3. Case 111: Taiwan
A study by Shun-Yi SHEI (1983a) is reviewed in this
section.
His study covered the period from 1955 to the
early 1980's.
The major source of per capita consumption
data was Taiwan Food Balance Sheet.11
The Taiwan economy experienced rapid growth beginning
in the late 1960's.
Prior to this period, food consumption
patterns were stable.
However, once rapid economic growth
started, "very substantial changes in both the total
quantity and structure of consumption demand for food
products" occurred (p. 14).
Analysis by each commodity is
as follows (pp. 14-15):
Rice - When per capita rice consumption reached a
maximum of 141 kg in 1969, it began to decrease constantly
until 1980 when consumption was 105 kg.
This suggests "the
fact that income elasticity of demand for rice became
negative in the early 1970's" (p. 14).
The large reduction
in per capita rice consumption after 1974 was primarily due
to the rapid increase in the retail price of rice.
Pork and Poultry - Pork and poultry are the two highly
consumed meats in Taiwan.
The consumption level of each of
these was related to both income levels and to production in
the following manner:
rising income promoted poultry and
pork consumption which then encouraged producers to adopt
This is also the data source for the estimation done
in this paper.
42
modern larger scale production method using imported feed
grains.
As a result, pork and poultry gained relatively
higher price competitiveness than other meats and fish,
demands for pork and poultry were further expanded.
The
retail prices of pork, poultry, eggs and powdered milk
increased only modestly in the 1955-1980 period.
There was
a shift in meat consumption towards a higher composition of
poultry meat, which "implied that there were a substantial
substitution effect between poultry and other meats" (p.
15).
Beef - Beef consumption has increased recently but the
rate of growth is relatively low.
This is because "a high
proportion of the population refuse to eat beef because of
the wide-spread traditional belief that most beef comes from
water-buffalo and draft cattle slaughtered at the end of
their working lives" (p. 14).
One factor considered to
contribute to the recent increase in beef consumption is the
substantial increase in Australian beef imports after 1975,
which is hypothesized to have affected Taiwanese tastes and
preferences toward beef.
Before 1975, a relatively small
amount of beef, approximately 1,000 metric tons, was
imported each year.
tons of beef.
In 1975 Taiwan imported 26,000 metric
This was due to low world market beef price
caused by excess world supply.
During most of the years
after 1975, Taiwan imported approximately 10,000 metric tons
of beef annually.
43
Fish - Between 1955-75, per capita fish consumption
There was a sharp increase in the price of fishery
products in 1974, and subsequently fishery connnodity prices
rose faster than the price of meat coxtunodities; as a
consequence, per capita fish consumption remained relatively
doubled.
unchanged since the mid-1970's.
Dairy - "Per capita increases in the consumption of
dairy products are a result of large increases in the demand
for milk mixed with fruit juices, a beverage consumed mainly
during the summer months" (p. 14).12
Fruits - Per capita fruit consumption increased more
than fourfold between 1955 and 1980, meanwhile per capita
real income increased threefold. "Most of the increased
demand for fruit was the result of rising real income" (p.
14). Therefore, fruit can be considered luxury goods for
that period, i.e., the income elasticity for fruit was more
than unity.
SHEI concluded with the following comments:
The above analysis suggests that the consumption
patterns for major categories of agricultural and food
products in Taiwan during the past quarter century were
more responsive to the general income level and to
relative price changes than to noneconomic factors such
as demographic change and changes in tastes and
preferences" (p. 15).
12
No more information was found in SHEI's article on
this point.
44
2.4. Discussion on the Previous Results
Japan:
With respect to the income elasticity estimates
of urban workers' households, it is found that cereals
changed from necessity goods to inferior goods in the 1920's
(Table 2-3), however, cereals again became necessity goods
in the 1950's (Table 2-5).
The income elasticities for
total food in the early 1920's (Table 2-3) are about the
same level as the 1953-61 period (Table 2-5).
The income
elasticities for animal proteins in the early 1930's (Table
2-3) are corresponding to these in the 1953-61 period (Table
As a simple interpretation, the Japanese people after
2-5).
the WWII returned to their former food consumption patterns
of three to four decades past.
In the official estimates (Table 2-6), "wheat, barley,
and other cereals" shows the reverse trend - the income
elasticity of this group has turned from negative to
positive in the official estimates, while the income
elasticities for the other items show stable or decreasing
trend.
The inferiority of rice is not reported officially for
the 19 65-77 period (Table 2-6), whereas it appears in the
official estimates for the 1965-87 period (Table 2-7).
Korea:
As seen in Table 2-8, the estimates of own
price and income elasticities in previous studies show large
deviations from study to study.
45
Taiwan:
Inferiority of rice in terms of income
elasticity was also pointed out.
However, it is not clear
whether the income or price effect is greater for most of
the other conunodjties.
The effects of other non-economic
factors on food demands were rejected in Taiwan by SHEI, but
it is not clear why the non-economic factors were not
important in Taiwan; their importance were admitted by the
researchers for Japan (eg. KANEDA) and Korea (eg. SILLERS).
The rice income elasticities of ITO et al. study were
Negative
consistent with various other country studies.
income elasticity for rice was reported for Japan and Taiwan
in all cases for recent periods.
However, negative rice
income elasticity was not observed for Korea.
More
interestingly, there is no clear relationships between real
per capita income (GDP) growth and per capita rice
consumption growth to be found across the countries (Table
2-1).
For instance, Nepal and Burma show the lowest level
of per capita real income growth between 1961-65 and 1981-85
(0% and 8%, respectively) and their absolute per capita real
income levels are also among the lowest level, whereas their
per capita rice consumption growth rates differ
substantially between two countries, -16.7% and 66.9%,
respectively.
Although there is a tendency shown in the
table that higher income countries show declining per capita
rice consumption trends, it seems that per capita real
income level is not the only factor shaping rice demands in
46
Asia.
These results indicate further study with a more
comprehensive approach is needed to fully understand the
patterns of the changes in food demands under economic
development; it will be desirable to consider more variation
of goods, more potential factors affecting demands, use
longer time periods, and apply more theoretical treatments.
47
2.5. Critique of Engel Analysis
The studies reviewed thus far primarily rely on Engel
curve analysis or an extension of Engel curves, such as best
fit criteria.
In principle, the genuine Engel curves with
single independent variable of income (or expenditure13)
and having quantity demanded for a good as a dependent
variable are consistent with economic theory of utility
13
The term "income" is sometimes used for convenience,
however, the term "expenditure" is more appropriate than
"income" in the demand analysis based on the assumption that
each individual is a utility maximizer. Total expenditure XT
is defined as
XT = Pl*Ql + P2*Q2 + ... + Pn*Qn
where Pi and Qi represent price and quantity demanded of good
respectively, and n is total number of commodity in
i
(In some cases, the entire consumer's
consumer's bundle.
Suppose the entire
bundle is split into many sub-bundles.
bundle is divided into two sub-bundles A and B, and each
contains k and m goods, respectively (i.e., k+m = n). Then,
total expenditure XT may be written as
XT = (P1Q1 + ... + PkQk) + (Pk+lQk+l + ... + PnQn)
+
XB
=
XA
In some articles, expenditures for sub-bundles such as XA or
To avoid confusion,
XB are referred as total expenditure.
expenditure for the sub-bundle will be referred to "group
expenditure" in this study.)
On the other hand, income is defined as,
Income = XT + Savings + Others
others have included such variables as taxes and donations.
The reason why the term "income" in demand analysis is not
appropriate is because all income is not usually spent for
Theoretically, a
consumption in any particular period.
utility maximizing consumer reaches her optimal choice at the
point where her indifference curve tangent to the given budget
line. That is, when a consumer is in optimum, her budget has
to be exhausted; if not so, she does not maximize her utility
for the given level of XT, which is contrary to the basic
assumption.
48
maximization behavior.
The use of Engel curves is more
appropriate than some ad hoc specification based on best fit
criteria in the sense that the method does not lose contact
with the theory.
However, Engel curves also have some
limitation as an analytic model.
This often has been
overlooked, and it may be a serious disadvantage for certain
study objectives.
In the chapter, properties of Engel
curves will be reviewed briefly and problems related to
Engel curve analysis will be discussed.
Engel curves are derived from the constrained utility
maximization procedure.
Constrained utility maximization
problem of an n commodities case is
max U = U(Q)
st.
P'Q = X
(2-3--i)
where P and Q are vectors of price and quantity demanded for
n goods, respectively, and X is expenditure for the n goods.
Forming the Lagrangean as
(2-3-2)
max L = U(Q) - A (P'Q - X)
where A is a Lagrange multiplier.
The corresponding first
order conditions are
aL/aQi = DU(Q)i - A P1 = 0
aL/ak=P'Q-X=o
for i=l,...,n
(2-3-3)
(2-3-4)
49
where DtJ(Q)i is the i-th term in the gradient vector DU(Q).
Marshallian demand function Mi is obtained by solving (2-33) and (2-3-4) for Qi in terms of P and X:
(2-3-5)
Qmi = Mi(Pl,...,Pn,X) = Mi(P,X)
where Qini denotes the optimum quantity demanded of the good
i as a solution of the Marshallian demand function for the
given level of P and X.
Engel curve Ei(X) for good i is
then derived by fixing P at some level, say P°, as
Mi(X;P0)
(2-3-6)
Ei(X) = Q0mi = Qei
where Qei and Q0mi denote the optimum quantities demanded of
good i as a solution of the Engel curve and of the
Marshallian demand function, respectively, for various level
of X with the given P0.
The following can be concluded:
1. The Marshallian demand function and the Engel curve
are derived from the same direct utility function through
constrained utility maximization procedure.
Therefore, both
the Marshallian demand function and the Engel curve reflect
consumer's utility maximization behavior.
The optimum
quantity demanded derived from the Marshallian demand
function is equal to that from the corresponding Engel
curve, which is always true for each level of X for given
50
level of P.
2. Recovering the underlying Marshallian demand
function from the Engel curve is not possible since
information about the relationships between prices and
quantity have vanished.
At the given level of P, the Engel
curve can be specified uniquely; however, there exist a
infinite number of Marshallian demand functions for each
Engel curve.
The following limitations are immediately implied:
First, the possibility of time-series analysis with Engel
curves is very limited since price variation usually exists
in time-series even for short time periods.'4
Second, the
intractability from any existing Engel curves to the
underlying utility functions provides limited theoretical
justification for the use of Engel curves; since it is not
known whether a well behaved underlying preference structure
14 To avoid this consequence, many Engel curve analyses
employ cross-sectional data, rather than time-series data;
because under some circumstances, prices can be assumed to be
constant. Without price variation, it may be reasonable to
model consumer behavior with only total expenditure. Given an
assumption that the observed consumers are utility maximizers,
also that all consumers reacts the same to expenditure
total
observed
the
that
said
is
it
changes,
expenditure-consumption relationship should be derived from
the corresponding unobservable Marshallian demand function;
then the underlying utility function can be claimed to exist.
A simple Engel curve with one argument of income (total
expenditure) is inappropriate particularly for the long-term
GOREUX (1978, p.399) noted that "the elasticity
analysis.
estimated from time series corresponds to the combined
influence of income and of the other factors correlated with
it."
51
is modelled or not.15
A recent study by GOPMAN (1981) develops a formal proof
that for some specific Engel curves there will always be a
corresponding cost functions.16 As long as an observed
Engel curve has the specifications outlined by the author,
it is possible to assert that the person or group in
question exhibits cost minimizing behavior, which is
analogous to utility maximizing behavior.
GORMAN's study
opens a way to incorporate information other than income or
expenditure into Engel curves.
First, finding an
appropriate cost function implied by the class of Engel
curves specified by GORMAN, then specifying the cost
function so as to incorporate variables such as prices and
socioeconomic factors, finally it is possible to present the
corresponding Marshallian demand function by invoking ROY's
identity.
Unfortunately, this technique is costly.
15
This process
Applying the duality theorem, it may be possible to
specify theoretical plausible utility functions from the given
Once the existence of any
Marshallian demand function.
underlying utility function is confirmed, we can proceed with
the analysis based on economic theory; since, now we know the
observed demand function represents optimization behavior.
However, obtaining any implications about the corresponding
utility function from the existing Engel curve is not
Marshallian
straightforward since the passage back to the
As long as the underlying
demand function has vanished.
structure of the observed consumption behavior is not
confirmed, any further analysis nested on that observation
could not be claimed as a neoclassical economic analysis.
16
study.
See also DEATON (198Th, 1986) for the review of the
52
was actually taken by POLLAK and WALES (1978, 1980) in their
development of the Quadratic Expenditure System (QES).
The
resulting model was shown to be complex and highly nonlinear
even though they started with a simple quadratic Engel
curve.
In summary, the most significant shortcoming of Engel
curves is the lack of flexibility in incorporating any other
factors (eg. prices) affecting demands.
Engel curves are
consistent with the neoclassical economic theory.
However,
the extension of the model requires certain steps; which are
often neglected.
A consequence, the modified Engel curves,
featuring other variables such as prices, has no theoretical
justification.
Engel curves are a simple tool but should be
manipulated cautiously.
Still it cannot be denied that the
information extracted out of the analyses is useful;
particularly, a priori information about the shape of the
Engel curve is helpful in avoiding potential
xnisspecification on income (expenditure) - demand
relationships.
53
CHAPTER
3
MODELLING FOOD DEMAND IN THE CONTEXT OF ECONOMIC
DEVELOPMENT: FACTORS, ASSUMPTIONS, AND MODEL FORMULATION
3.1. Introduction
Many studies have been done to seek for income and
price elasticities for various food commodities as reviewed
in Chapter 2.
However, many discrepancies are observed
among income and price elasticity estimates for the same
commodities over the same time periods in the same country:
research methods affect the results.
Thus, when the close
numerical results are generated in different studies, it is
hard to judge whether they are the outcome of mere
coincidence or the scientific evidence of the truth.
Some
common aspects of these studies are:
The model specifications were not consistent with
economic theory.
The substitutional/complementary relationships among
commodities were limited.
The changes in socioeconomic environment likely
affecting food demands were not properly accounted for.
54
Therefore, the "income effects" reported in these studies
may have been the combination of income and other unknown
factors and should not be taken as the income effect
introduced in standard economics text books.
Thus the
general trends of income and price elasticities in these
studies of the three countries cannot be understood with
surety.
There is a clear need for a more consistent approach to
comprehend the historical perspective of food demands in
these three nations.
A theoretically consistent and
practically versatile analytic tool must be developed first.
To capture the full range of historical changes, it is
desirable to use long time-series data (at least two to
three decades).
The use of long time-series has been
avoided by many researchers partially because it has been
thought that it involves too many factors to be considered.
In fact, data is generally available for only the recent
periods in the more recently developed countries for limited
kinds.
However, by taking into account of
dynamic/endogenous aspects in food demands, also by paying
attention to change in exogenous factors induced by economic
development, the potential danger of misspecification errors
may be reduced.
55
Taking Japan as an example, its history in this century
may be symbolized as a continuous process of
modernization.17
In the context of food consumption,
modernization has been proceeded through introducing nontraditional food items and non-traditional eating styles to
the Japanese diet.
It may be reasonably admitted that one
of the most fundamental changes in food
consumption
in Japan
during this century is the diversification of food items.
Particularly, the increasing number of choices is considered
to be more important than the qualitative changes in food
items in the context of food demand analysis.'8
The
problem related to this phenomenon will be discussed
shortly.
17
of
the model
The Western nations have been
modernization for Japan, and it has received substantial
amount of technological and cultural influences; then, the
process is sometimes called Westernization.
18
Since the nature of agricultural production requires
fairly long time to change its structure,
the speed of
fundamental changes in agricultural products is usually slow.
Besides, agricultural goods are not durable in general.
Therefore, it may be safe to say that the change in the
quality is so gradual that each individual may not be able to
Even when some consumers recognize changes in
recognize it.
fundamental quality of a good (not such a case of defection),
it may be too costly for them to change the agricultural
production and marketing procedure for their own sake. Then,
consumers tend to admit the constant value of satisfaction for
each good over time (for the same commodity from the same
brand, for example).
Therefore, the qualitative change of a
good on consumption behavior is considered to be safely
ignored.
56
It turns out that, the notion of habit formation is
very useful in conceptualizing food consumption behavior
under food diversification.
It then will be considered how
to synthesize the effect of habit formation and the effect
of the exogenous changes associated with economic
development at market level and at individual level.
How to
incorporate this process into the neoclassical demand model
will then be considered.
Additionally, the issues on separability assumption
will be reviewed.
Weak separability is found to be most
attractive for this study, still the model under the
assumption of weak separability alone may place a limit on
deriving expenditure elasticities.
Then, the additional
step for calculating expenditure elasticity will be
considered as a final step of the model formulation.
57
3.2. Food Diversification
Increasing diversification of food items might be
viewed at two different ways:
The food consumption bundle is expanded by
introduction of new items.
The food consumption bundle itself is fixed, and
never expanded.
Although the first view seems natural, its application
will be beyond the conventional theoretical framework.
Then, it is convenient to work on the second view.19
It
turns out that the second view seems not to place severe
restrictions on analysis when one deals with the market
level data, such as per capita national average consumption.
Suppose an individual happens to eat a new food item
which he has never tasted before.
In this individual's
life, he has discovered a new item and actually he has
expanded his own consumption bundle; thus, the first view
above is supportive.
Meanwhile, others having the same experience may
coexist in the society.
From the stand point of the
society, on the other hand, the consumption bundle has been
19
The author owes to Professor Steven T. BUCCOLA of the
Department of Agricultural and Resource Economics at Oregon
State University for this valuable suggestion.
58
rather unchanged, i.e., the item has been there regardless
of how much and how many people have been consumed per
period of time.
In this sense, he is the marginal increase
of the "taste career" in the society for that period.
This suggests a habit formation procedure for the
entire society.
The "custom" (in contrast to "habit" for
individual level) of eating the good is formed through
increasing number of population constantly consuming it
again and again, no matter who is eating how much.
The
number of persons who just has been introduced and accepted
the new good in a period is equivalent to the marginal
increment of "custom formation" for the good.
In other
words, the phenomenon can be taken as "market expansion".
It should be clarified that the custom formation of
society level would not be perceived by an individual
accurately; rather individual may feel it as a gradual
change in the atmosphere or mood in one's living place.
It
is not like one's own past consumption level which can be
quantified by oneself.
As a summary, both the first and second view on the
food consumption bundle - expanded or fixed - can be taken
at different stand point of individual or market,
respectively.
When the market level data are used to
calculate individual consumption level, the "representative"
individual bundle is considered to be equivalent to the
aggregate (market) bundle; i.e., the "representative"
59
individual's bundle may be assumed to be fixed.
3.3. Habit Formation and Custom Formation
DEATON and MtJELLBAUER (1980a) wrote
it seems unrealistic to suppose that preferences are
Rather they
exogenous, God-given, and unchangeable.
are socially inherited and conditioned and are governed
by the conventions of technology and social
Individuals need to define themselves
institutions.
a consumption life-style is thus
vis-a-vis others ...
part of this definition of identity. ... It is
inevitable that, to some extent, households will
pattern their consumption and market behavior on that
of other households. ... This patterning of behavior
takes time, particularly since there are
recognition lags in perceiving what is the behavior of
the group with which the individual identifies. These
lags in interaction result, when aggregated, in current
purchase level being dependent on past ones (p. 330).
;
Also, POLLAK (1970) mentioned that "an individual
utility function depend on other people's past consumption"
(p. 760)
In the view of DEATON and MUELLBAUER, the inertia of
consumption behavior at aggregate level20 is a result of
many interactions of socioeconomic factors in the society.
20
This is an important distinction. At individual level,
the lag period may be a day or two to response any changes in
the society which is exogenous for him/her; from the stand
point of society, however, it may take a year or even a decade
to fully respond to the changes which is endogenous for the
therefore,
a
consequences,
society.
Combining these
"representative" individual for the society may have a
lag-period of a year or a decade in response to any
socioeconomic changes, on average.
60
Note that the "pure" habit formation procedure is
self-evolutionary and endogenous; which means consumption of
a good in this period is only explained by the historical
achievement in the prior period(s),21 with everything else
being held constant.
Then, extending the view of DEATON and
MUELLBAUER, if one deals with aggregated level data,
interactions of socioeconomic changes may be summarized in
the "pure" habit formation procedure.
Although the market
level data are frequently used in empirical analyses, what
is modelled by the economic theory is an individual
behavior, usually.
As an individual behavioral assumption,
it may be too strong that the inertia is "purely"
endogenous.
For individual level, it is more natural to
consider that the inertia has two aspects - one is the
effect of "pure" habit formation, and the other is the
effect of environmental changes; the former is endogenous
effect, while the latter is exogenous effect to the
individual consumption.
The changes in socioeconomic
environment (exogenous effects) is considered to condition
the habit formation procedure (endogenous effects)
positively or negatively.
21
A unique and important aspect of habit formation is
that it does not take into account of future consumption
(POLLAK,
1970, p.
761) .This is a fundamental difference
between habit formation model and durable goods consumption
model although the former has stemmed from the latter and both
have a similar structure (POLLAK, 1970, p.761).
61
The variable explaining the "purely" endogenous habit
formation within the individual utility inaxiinization
framework is individual's past consumption level.
In the
present case, individual consumption lagged one period is
considered to be reasonable for that variable, which is
referred to Qi1 for good j.
The exogenous effects may be classified into the
following two broad cases:
Information effects:
Driving an individual toward the consumption of a
good, psychologically.
Availability effects:
Providing an environment which allows an
individual to consume a good physically and
economically.
62
Among many, here are some examples of possible factors
for each effect:
Information on tastes - conditioned by urbanization
and technological progress in telecommunication.
Access to the market22 - conditioned by
technological progress, particularly in agricultural
production and marketing.
The variables representing the information and
availability effects, which indirectly condition the habit
formation, will be referred as the socioeconomic variables.
Define a vector Z consisting of m socioeconomic variables Zi
as
Z' = (Zl,Z2,...,Zm)
(3-1)
The possible choice of Zi's are such as number of
restaurants, books or TV programs showing foods, or
population density for information effects and road and rail
road extension, number of retail store, or capacity of
freezing facilities for availability effects.
22
Changes in economic factors, such as price and income
changes,
are
also
for
an
conditioning market access
individual, which are included as constraint of utility
maximization problem.
63
To combine these endogenous and exogenous effects,
consider the following formulation for good j:
Qi.1
= h*j (TQj,2,
Z2}
(3-2)
where TQj2 is a consumption amount of good j in the entire
society at the t-2 period, and Z
is socioeconomic factors
affecting an individual consumption of good j at the t-2
period.
It is assumed that the past trend of the aggregate
consumption of good j prior to the t-2 period (all t-k
periods, k = 3,... ,) is summarized in the realized
aggregate consumption level at the t-2 period (TQj2) as the
end result of the historical process.
The function
h*j
represents the "true" systematic effects of the historical
trend in the environmental changes on the individual
consumption level of good j.
Since Qj
is considered to be
the most important factor for the inertia of the individual
consumption behavior, measuring how socioeconomic changes
add systematic variation around this variable may be a
reasonable way to capture the effects of socioeconomic
changes on the individual consumption.
The factors in the
left hand side and in the right hand side are assumed to be
determined in the totally different contexts; the left hand
side variable
is determined by the individual
optimization procedure at the t-1 period, and TQj2 is
determined by the market demand-supply relationships at the
64
t-2 period.
Therefore, the individual is assumed not to
affect the market relationship and the environmental
changes, which is a reasonable assumption.
The aggregate
consumption level is assumed to does affect the individual
consumption level, but only as one of the factors forming
the gradual changes in the environment.
Moreover, it is
assumed that only the systematic trends in her environment
affect the individual optimization but any short-run
exogenous shocks:
this is a reasonable assumption when
inertia is considered.
Besides, it is assumed that the
individual behaves as if she ignored the socioeconomic
environment, and is myopic - only considers the short-run
conditions such as the given price and expenditure levels as
the constraints of her optimization problem.
That is, the
individual is assumed to be unconscious about the fact that
her behavior is influenced by the underlying socioeconomic
trends.
The relationship (3-2) is not measured by the
individual; it is only observed by the third person.
It is natural to think that the direction of the
environmental effects is from past to future.
If the above
relationship is modified to be
Qi..1
= l*j(TQjt, Z)
(3-3)
then the l*j function could not be considered as the
representation of the consequence of natural movements.
It
65
rather represents some sort of the artificial evaluation
procedure by the individual.
This is not compatible with
the concept of inertia; rather, it may be compatible with
the notion of dynamic optimization, which is not considered
in this study.
Further, if the both of the right hand side
and left hand side variables are in the same period, this is
the same to assume some direct relationships between the
individual optimization behavior and the market demandsupply relationships, for example.
This view is conflicting
with the assumption that the individual optimization problem
is independent from the market optimization problem.
Thus,
it may be reasonable to have socioeconomic variables lagged
one more period.
The individual past consumption level
including
the prior socioeconomic trends is referred to Q*jtl, which
is derived by the "truet' forecasting model (3-2)
(by the
third person), i.e.,
Qi1
where
= h*j {TQj2, Z.2)
+ e1
is a pure random disturbance at t-l.
Q Jr-i =
= h j {TQj..2,
Z2)
(3-4)
Therefore,
(3-5)
66
However, most probably the forecasting is not perfect; for
example, there may be some variables are missing.
hj {TQj2,
Z2)
+
Therefore
+ e1
is at best one can observe, where
(3-6)
represents the
combined effects of some missing factors, and hj is the
function representing the approximate relationships among
the variables.
The predicted value for the t-1 period
obtained from this model is denoted as
referred as the "habit variable".
which will be
Inclusion of this
variable into the model may provide more stability to the
model by eliminating any systematic fluctuations in food
demands due to the inertia of consumption behavior and/or
the socioeconomic trends.
67
3.4. The Neoclassical Formulation
It is possible to incorporate the formulation above
nicely into the neoclassical economic model of consumer
behavior.
A utility function may be written as
(3-7)
U = U(Q,A)
where Q is a vector of quantity of commodities and A is "a
list of factors or characteristics conditioning tastes"
(DEATON, 1981, p. 45).
"attributes".
The latter will be referred as
In this case, an individual maximizes her
utility subject to price vector P and total expenditure X
with the vector A taken as fixed.
The corresponding cost
function can be written as
C = C(P,U,A)
(3-8)
Budget share for the i-th commodity (WI) can be derived as
(see PHLIPS, 1983, p. 134)
Wi = 8logC(P,U,A) / älogPi
(3-9)
Since U is not observable, inverting the cost function and
solving for U (assuming X = C by the cost minimizing
principle)
68
U = v(X,P,A)
(3-10)
Substituting (3-10) into (3-9), a Marshallian demand
function in share form is derived as23
Wi = fi(X,P,A)
(3-11)
This generalization is very useful, since one can model
any kind of variables possibly "conditioning tastes" in the
framework of estimable Marshallian demand function.
A broad choice of A variables for an individual can be
classified into two parts: one is information associated
with an individual's characteristic and another is
information associated with characteristic of environment
surrounding an individual.
Then, A can be written as
A = (I1,...,In, Sl,...,Sm}
(3-12)
where Ii is the i-th individual condition indicator, and Sj
23
The dependent variable is written as Wi = PiQi/X.
Notice that only the Qi is endogenous; Pi and X are assumed to
be given.
69
is the j-th socioeconomic condition indicator.24
The habit variable Q*jtl may be included as a part of
the vector A theoretically.
It is indirect but this
variable is considered to contain every information of
socioeconomic trends; S's may be represented by this single
variable.
For I's, demographic characteristics are
considered to be appropriate.
3.5. Demographic Variables
Among many possible demographic factors, age-population
structure and family size are widely recognized as important
factors affecting food consumption level.
In the following,
the relationship between age-population structure and
economic development, also family size and economic
development will be briefly considered.
24
DEATON (1981) called attention to the choice of
variables in A giving a case of number of children in the
context of household study. The utility function specified as
(4-3) must imply that children are "gifts from the gods, over
which the family has no control" (p. 45) but the number of
children could be controlled by households by their seeking
for
thus
the
the
optimum
children,
number
of
the
The "given" nature of A,
specification has a limitation.
however, seems not so troublesome; considering an individual
rather than a household, many factors can be taken as
For I's,
predetermined or exogenous for a utility maximizer.
factors such as age or race are taken as exogenous since
hardly to be controlled; moreover, factors conditioned at
family level, such as number of children, are thought to be
exogenous for some individual family members, particularly to
children. For S's, there is little problem since an ordinary
individual can hardly control over the whole society's
consumption anyhow. Moreover, some of the factors in A may be
related to the matter of the past, thus predetermined.
70
3.5.1. Age-Population Structure
Taking Japan as an example again, the relatively rapid
change in age-population structure has been observed there
(some calls this as "the miracle of demographic science"),
where drastic decrease in younger age population and large
increase in elderly population have occurred.
Causality of this phenomenon may be roughly described
as follows:
Lower mortality rate has achieved by higher
health standard.
been achieved.
At the same time, lower fertility rate has
People have been motivated to have less
children, partially because social security system has been
well developed so that people become less dependent on their
children in their retirement life.
Also because the cost of
maintaining large family has increased, particularly of the
child education.
Combination of lower mortality and lower
fertility rates have made the Japanese society more and more
aged.
In terms of food demand, the expected consequences of
aging population are:
Per capita food consumption generally increases due
to increase in physiological requirement and per
capita income.
Preference shifts to reflect more of the elderly
people' s favorites.
71
In the context of long-run analysis, an additional
assumption may be required that the spectrum of preference
according to age remains unchanged during the study period.
This means, for example, if beef was preferred to pork by a
typical person of age 30 but pork was superior for a typical
person of age 50 in the beginning of the study period, these
relationships are assumed to persist for the entire study
period.
3.5.2.
Family Size
The effect of family size (number of people in a
household) on food demands has been pointed out by many
researchers (DAVIS, 1982; DAVIS et al., 1983; GOREUX, 1978;
and WEST and PRICE, 1976).
The hypothesis is that the
smaller the family size become the less efficiently foods
will be consumed, that is, the more waste the household will
make; thus demand of a food item per household member is
expected to increase as family size shrinks, with holding
everything else constant.
In other words, there is so
called "economies of family size".
In the phase of economic development, family size tends
to be smaller.
This can be explained by the following
hypothesis based on the assumption that the economic
development is mainly achieved by industrial growth.
To
achieve industrial growth most effectively in the shortest
period of time, accumulation of monetary and human capital
72
in some specific field of industries and areas is strongly
required.
This leads to "urbanization"; which is thought to
be highly responsible for reduction in family size.
There
seems two reasons for the reduction in family size in urban
areas; one is more cultural and the other is more economical
reasons.
Industrialization attracts many rural dwellers to
city places.
Rural people move into urban areas and start
families away from their families and relatives.
The
traditional family ties, as symbolized by large family with
several generations living together, fade away as time goes
by.
Traditional large families break up into sub-units
consisting of husband, wife, and a few children.
The other reason, mainly economic, is related to the
increasing cost of living in the urban areas, particularly
in housing cost.
It becomes harder to maintain large family
in a single place in the urban areas; thus large family
tends to split into sub-parts.
Also the higher housing cost
discourages whole family emigration and encourages small
family or single emigration.
Urbanized areas become larger and larger as economic
development proceeds, therefore, the average family size for
the entire country would continue to become smaller.
73
These two demographic variables have the property that
they are closely related to food demands and also to the
progression of economic development.
Therefore, they are
relevant variables to be included as a part of A.
Let the vector of demographic variables D.
Including
the demographic variables into A together with the habit
variable considered in the prior section, then A looks like
= {Q
it-i'
D)
(3-13)
The resulting demand function in share form for good i is
written as
Wi = fi(X,
A*.
Q
'
D)
(3-14)
where
*it_1 = hi(TQi..2,
3.6. Separability:
Z2}
Assumption and Modelling
There is another practical issue for food demand
analysis using time-series data.
In general, time-series
data provides limited number of observations, which is
particularly true for annually surveyed data.
This directly
constrains the number of commodities to be included as
individual entries in statistical models.
Too many entries
makes statistical estimation impossible due to lack of
degrees of freedom.
There are two possibilities to mitigate
74
the problem:
one is to aggregate commodities into some
broader categories such as food and clothing.
The other is
to exclude commodities thought to be less important for
study objectives.
The former approach is not appropriate
for this study since the change in each commodity demands
will be investigated.
The latter approach is more
appropriate for this study, which requires another
assumption that the preferences toward the commodities under
consideration is "separable" from that for the excluded
commodities.25
Among different forms of separability, the one has been
most frequently used is called "weak separability".
It is
relatively less restrictive thus more realistic than others
such as "strong separability" or "PEARCE separability" of
GOLDMAN and UZAWA (1964).
suppose the whole consumer's
bundle is partitioned into N different groups, where each
commodity belongs to only one group.
Then preference is
said to be separable if total utility function U can be
written as
U(Q) = f{vl(Ql),v2(Q2),...,vF(QF),...,vN(QN)}
25
(3-15)
The following arguments are based on the articles which
provide excellent summary of the previous studies on
separability:
DEATON and MUELLBAUER (1980a), chapter 5;
JOHNSON, HASSAN, and GREEN (1984), chapter 3; PHLIPS (1983),
chapter 3; and POLLAK (1971).
75
where Q stands for quantity vector of all commodities; f
stands for some monotonically increasing function; vi,
i=1,2,...,F,...N, stands for sub-utility function for each
partitioned group i; and Qi stands for quantity vector for
commodities belonging exclusively to each group 1.
It was
proven by LEONTIEF (1947) that such a form of utility
function (4-11) will result if and only if marginal rate of
substitution between two commodities x and y with respect to
U is not influenced by change in the level of consumption of
the third commodity z (PHLIPS, 1983, pp. 68-9).
Among many,
three major and plausible specifications for x, y, and z are
compared by JOHNSON et al.
(1984, pp. 48-9) and each leads
to quite different situations:
x and y are in the same group but z is in different
group:
The case of weak separability.
x, y, z are in all different groups:
The case of
strong separability.
x and y are in the same group F; z is in any group
including F:
The case of PEARCE separability.
The second case of strong separability implies that "the
marginal utility of a commodity in one group is independent
of the consumption of any good in any other group, which is
a very strong condition indeed" (PHLIPS, 1983, P. 49).
In
the third case of PEARCE separability, the marginal rate of
76
substitution between any two goods (x and y) in the same
group is perfectly independent from the quantity of any
other goods (such as z).
If there are only two goods in
every group, the case reduces to weak separability (JOHNSON
et al, 1984, p. 50); other than that, it is more restrictive
than strong separability.
Weak separability has another attractive property.
There is a hypothesis called "two-stage budgeting procedure"
coined by STROTZ using a concept of a utility tree, which is
intuitively "appealing and highly plausible" (PHLIPS, 1983,
p. 71).
Weak separability "is both necessary and sufficient
for the second stage of two-stage budgeting" (DEATON and
MUELLBAUER, 1980a, p. 124; see also PHLIPS, 1983, p. 71,
foot note 6).
26
According to this hypothesis, a consumer
optimizes her utility in the multi-stage fashion:
Stage I (Top Stage):
A consumer allocates total income (= total expenditure)
for each different group of commodities based on "given
knowledge of total expenditure and appropriately
defined group prices" (DEATON and NUELLBAUER, 1980a, p.
123)
Stage II (Bottom Stage):
A consumer optimizes "quantities purchased within the
group ... (based on) group expenditure and prices
within the group alone" (p. 124).
26
Weak separability is not the only form being consistent
with two-stage budgeting procedure. (DEATON and MUELLBAUER,
1980a, p. 126)
77
One important rule behind this procedure is that, once
a consumer allocates her total disposable income to many
sub-groups, it is impossible to reallocate them without any
changes in prices or income.
In other words, a consumer in
the second stage must admit that the expenditure for that
group has been preallocated thus unchangeable.
In the following, preference is assumed to be weakly
separable.
By this assumption, one can estimate demand
functions for the goods in any one of the groups using
prices of those goods and expenditure for the group27.
As
long as separability assumption is not very misleading and
equations used in estimation are not misspecified severely,
one can obtain reasonable estimates of expenditure
elasticities for each good as a result of the second stage
optimization.
27
This will be referred as "group expenditure".
78
3.7. From Group Expenditure Elasticities to Total
Expenditure Elasticities
Further, an important notice was given by POLLAK (1971)
that the resulting second stage optimization is by no means
"independent of the prices of goods in the other groups or
of total expenditures" (PHLIPS, 1983, pp. 73); the change in
total expenditures or the price of the goods in the other
groups does affect the quantities demanded in that group but
"only through their effect on" the expenditure for that
group (POLLAK, p. 426-7).
Importantly, implications obtained from the second
stage results may differ from implications from the first
stage results.
The expenditure elasticities at the first
stage, i.e. the response of quantities demanded in each
group against the change in total expenditure (will be
referred as total expenditure elasticities; Yi for good 1)
are considered to be practically more useful than the
expenditure elasticities estimated at the second stage (will
be referred as group expenditure elasticities; Xi for good
79
Then, consider the possibility to recover Yi from Xi
for good i belonging to group F.
Define the followings
(everything is in time period t):
Yi
Xi
Qi
XT
XF
PIT
PIF
PlO
-
total expenditure elasticity for good i
group expenditure elasticity for good i
quantity demanded for good i
total expenditure
group expenditure
price index for all goods
price index for the goods in group F
price index for all the other goods not in group F
By definition, the total expenditure elasticity for good i
is
Yi = aQi/axT x XT/Qi
(3-16)
Assuming weak separability, the first term on the right hand
side may be broken down into two parts (PHLIPS, 1983, p. 74)
Yi = (3Qi/3XF x aXF/aXT) x XT/Qi
(3-17)
First, total expenditure is allocated to each group.
The
effect of change in XT (which is assumed to be exogenous) on
XF is captured by 3XF/3XT.
Second, with the given level of
XF, Qi is determined in each group.
The response of Qi for
the change in XF is captured by aQi/axF.
In the above
mathematical expression, this procedure is exactly reversed;
which cause no problem since no information has been lost.
80
Next, by definition, the group expenditure elasticity
for good i is
(3-18)
Xi = aQi/aXF x XF/Qi
Also, the response (elasticity) of XF for the change in XT
is
(3-19)
alnXF/31nXT = &XF/3XT x XT/XF
Multiplying the both sides of (3-18) and (3-19) gives
(320)
Xi x 31nXF/01nXT = Yi
31nXF/31nXT will be referred as "allocation factor", or
simply "AF".
Next question is how to obtain this factor in practice.
Under weak separability, price change of any goods outside
of the group F can only affect the demands of the group F
through allocation of expenditure to the group F.
Suppose
there are two goods x and y in F, also there are two goods w
and z in G. When the price of w and/or z changes, with
holding everything else constant, the quantity of x and/or z
may change.
It does not matter which price of commodity w
or z has changed; what does matter is how much the group
expenditure for the group G has changed due to the price
81
change of w and/or z.
That is, the individual relationships
among the commodities in different groups (such as the
substitutionary/compleinentary relationship between x and z)
are not the factor affecting demands under weak
separability.
Therefore, if price indices for each group
are available, given total expenditure, the effect of change
in prices outside of the group for the all goods in the
group may be derived.
The following structure of two stage budgeting is
assumed:
total expenditure is allocated into two groups;
the group F ("foods" covered by the study )
else.
and everything
The corresponding utility tree looks like:
Figure 4-1:
Assumed Structure of Utility Tree
Group
/
F
/
(Some foods)
/
/I\ \
\
Others
(Including other foods
and non-food items)
Given this, it is essential to consider the following
relationship to estimate allocation factor:
XF = f( XT, PIF, PlO
}
(3-21)
82
Further, when XF is relatively small fraction of XT, PIT may
give a reasonable approximation of PlO.
Then, the following
specification can be the best alternative for (3-17):
XF = g{ XT, PIF, PIT
}
(3-22)
Taking the log-derivative against (3-21) or (3-22) with
respect to XT, allocation factor will be derived.
Using the
estimated allocation factor, Xi will be converted into Yi,
finally.
This is a meaningful exercise since many previous
studies provided "income elasticities" based on the Engel
curve analysis, which is only comparable with the total
expenditure elasticities, not with the group expenditure
elasticities.
83
CHAPTER
4
MODEL SPECIFICATION
In this chapter, an estimable econometric model is
developed following the basic structure outlined in Chapter
3.
Before proceeding further, a brief summary of the model
structure is first reviewed.
The econometric model used in
the analysis is presented next.
The formulas for the
elasticities of the various factors are summarized in the
subsequent section.
4.1. The Structure of the Model: Summary of Chapter
3
Our model consists of three parts featuring a
short-run/dynamic complete demand system:
Part I:
= hi{ TQ±2, z2 } +
or
= hi{ TQi2,
Part II:
Wi = fi{
where
'
z.2
XF, Q
i.1,
D
XF =
Part III: XF = g( XT, PIF, PIT
)
(4-3)
)
(4-4)
84
where Z.2 is a 1 by
in
vector (in socioeconomic factors),
and Q are 1 by n vectors (n commodities), and D is a 1 by
k vector (k demographic variables).
A brief summary of each part follows.
In Part I, the
habit variable is derived by estimating the relationship in
(4-1-i).
Equation (4-1-i) is a forecasting model to capture
the systematic effects of socioeconomic changes on the
individual consumption levels using total consumption of
good i in the whole society at t-2, TQi2, and in
socioeconomic factors at t-2, Z,2
habit variable
i1
{Z1..2,...,
Zm2).
The
is defined as the predicted value
derived by the forecasting model (4-1-i).
In Part II, the n goods belonging to the group F are
exclusively considered based on the weak separability
assumption.
The model for Part II is a complete demand
system consisting of n demand equations.
Per capita optimal
quantity demanded for good i at time t (Q1) is determined
by the habit variable predetermined in Part I
prices of n goods at t
=
budget (=
expenditure) at t (XF), and m demographic factors at t (D
{Dl,...,Dk)).28
always as XF
=
XF
is called group expenditure defined
within the group F.
Part II presents
28
The differences between the D and Z variables are the
following: D variables represent a consumer's characteristics
such as age, race, or composition of the family the consumer
belongs to; whereas, Z variables represent characteristics of
the consumer's environment such as road extention, electric
facilities, and number of TV stations.
85
important information to consider the relative significance
of expenditure in food demands.
In Part III, the "allocation factor" is estimated to
convert group expenditure elasticities obtained from Part II
into approximate total expenditure elasticities.
The
resulting total expenditure elasticities may be comparable
with "income elasticity" estimates in previous studies.
With the utility tree assumption, XF
is determined by XT,
a price index for goods in the group, PIF, and a price
index for all commodities outside of the group, PIO.
XF
When
is a fairly small fraction of the total expenditure XT,
PIO can be substituted by a price index for all
commodities, PITS.
The elasticity of XT
allocation factor.
Total expenditure elasticities are
for XF
is the
calculated as group expenditure elasticity multiplied by the
allocation factor.
Note that the allocation factor is
common to all goods in F.
86
4.2. The Econometric Model
Following the model structure outlined above, an
estimable econometric model is developed corresponding to
each part.
In this section, the features of Part I through
Part III will be explained briefly.
For Part I, no theoretical formulation is available.
Since the most appropriate type of functional form is
unknown, a functional form called translog (TL) is applied
for this part.
TL gives a second order local linear
approximation to any arbitrary (unknown) function, using the
TAYLOR series expansion technique.
One disadvantage of this
functional form is that it requires a large number of
parameters to be estimated.
Therefore, the number of
factors in Z2 is considerably limited.
The model for Part II is developed based on the Almost
Ideal Demand System (AIDS) of DEATON and MUELLBAUER (1980a,
l980b).
The model is modified to incorporate non-economic
variables such as
i1 and Dr's.
The advantages of the AIDS
model over other existing demand systems are the
following:29
The model can deal with inferior goods.
It has a simpler version which can be estimated
1)
with a smaller numbers of parameters.
ii) by a linear regression technique.
29
For details, see Appendix B.
87
It has relatively less theoretical restrictions
embedded in the model.
It has been applied widely, and good examples of
modification are readily available.
One of the distinct feature of the AIDS model is that
it achieves so called "non-linear exact aggregation," i.e.,
aggregation over individuals while permitting non-linearity
in Engel curves (WORKING and LESER'S semi-log form).
Next, the points of modification on the AIDS model are
the following:30
Using STONE's price index, the AIDS is simplified
into the linear approximate AIDS (LA/AIDS) of
DEATON and MtJELLBAUER (1980a, 1980b).
The habit variable
i1 is incorporated via the
Linear Habit Formation scheme of POLLAK and WALES
following BLANCIFORTI, GREEN, and KING (1986).
The demographic variables Dr's are incorporated by
BARTEN's Demographic Scaling "type" method
following POLLAK and WALES (1981) and DEATON and
MUELLBAUER (1980a).
30
See Appendix C for details.
88
Four versions of the modified AIDS model from the most
restricted to the least restricted are considered as
alternative model specifications:
LA/AIDS
LA/H/AIDS:
LA/AIDS +
LA/S/AIDS:
LA/AIDS + Dr's
LA/S/H/AIDS:
LA/AIDS + Dr's +
For Part III, no theoretical format exists with respect
to the appropriate functional forms.
Based on reasoning
similar to Part I, the translog (TL) form is also applied in
this part.
For the variable PIF, the STONE's index
calculated in Part II is utilized.
The resulting econometric model featuring the
LA/S/H/AIDS specification with n goods, m socioeconomic
variables, and k demographic variables is presented below:
89
<Part I: TL>
log Qi
= aiti + bi1 1ogTQi2
+ E1 cij1 1ogZj..2
+ E
dij.., 1ogTQi..2 1ogZj2
+ 1/2 ei..1
(logTQi..2)2
+ 1/2 EJEk fijk1 logZj..2 logZk2
+
(4-5)
where
i1 = Hypothesized Error Terms
i=l,...,n; j =1,...,m
<Part II: LP/S/H/AIDS>
Wi = a*it
+ Pi
+
logX
± E
logPj
rij
Ek Oik logDk
+ £2
*
A*
(4-6)
Q
where
1og(X/p*)
X
=
logp*
= 1ogX
- logP
j
= E3 Wj
1ogPj
= exp( Predicted Value of log Qi
i,j = l,...,n; k =
90
<Part III: TL>
log XF
= a + b logXT + E1 ci 1ogPIi
+ E1 di logXT 1ogPIi
+ 1/2 e
(logXT)2
logPIi logPIj
+ 1/2 E1E1
(4-7)
where
i,j = 1,2 and { PIl, PI2
XF = X =
=
{
PIT, PIF
)
i = 1,...,n
j
PIF = exp{ E
)
Wj
logPj
A demand system is said to be "complete" when an
adding-up condition is satisfied.
This condition guarantees
that the given budget is always exhausted within the system,
i.e. the given income is exactly equal to the total
expenditures in the system.
For a set of n demand equations
having budget shares (Wi's) as dependent variables, the
adding-up condition essentially requires
Wi = 1
i = ls,...,n
(4-8)
To satisfy (4-8) always, for the LA/S/H/AIDS specification,
the following set of restrictions is required:
<Adding-Up Conditions for The LA/S/H/AIDS>
E
a*i
= 1,
E1 Oik = 0
= 0, E
ri
= 0,
(4-9)
91
The total number of parameters to be estimated in each
demand equation of the LA/S/H/AIDS model is (n+2)+1+k for
the case of n goods and k demographic variables with the
adding-up restrictions; (n+2) is required for the LA/AIDS (n
prices, 1 expenditure, and 1 constant), 1 for the habit
variable, and k for the demographic variables.31
It is possible to reduce the number of parameters to be
estimated further by imposing the following theoretical
restrictions.
Homogeneity restrictions are32'33
31
In our study, (n,k) = (9,4) is maximum, therefore a
maximum of 16 parameters has to be estimated in each equation.
32
The homogeneity condition requires each demand equation
That is, if
to be homogeneous of degree zero in P's and X.
P's and X are multiplied by any non-zero scalar X, Qi will not
be affected and will remain at the same level. To see this,
solving (4-6) for Qi,
= X/Pi
[ ai
+ 18i
+ Ek
ogX*
+
ik logDk +
'Ti
iogPj
Q*
]
(4-6-u)
In this setting, since XX/APi = X/Pi, the coefficients a*it,
will be not affected; thus no restriction is
Oik, and
required for them. No restriction is required for it either;
are homogeneous of degree one in P'S, XX/XP
since X and
= Xt/P*t.
Only rij's need restriction since
1ogPj, and E rij
E rij logPj) = E Tijt logX + E rij
logX = 0 is required.
fi
Note that Pi's are replaced by PiMi(D) 'S ifl the process
of scaling, where Mi is a function of demographic factor D's
(see Appendix C). RAY (1980, p. 596) imposed the homogeneous
restriction in terms of the transformed prices PiMi, which is
Note that Mi was not the
theoretically more rigorous.
function of other variables in his case, which differs from
our case.
The specification of Mi in the present case
prevents us
from specifying the restriction as RAY did.
Therefore, this specification of homogeneity restriction
should be understood in the more practical sense.
92
Ej rii
= 0
(4-10)
Syirrrnetry restrictions require
rut =
7-ji
(4-11)
These restrictions are testable; homogeneity restrictions
can be tested equation by equation, whereas symmetry
restrictions can only be tested jointly for the entire
demand system.
93
4.3. Calculating Elasticities34
4.3.2.. Elasticity in Part I
From the Part I estimation, an elasticity refer to as
"custom effect" is calculated.
This is essentially
measuring the effect of marginal change in the total
domestic consumption of good i in t-2 (TQi..2) on the per
capita consumption level of the good i in the subsequent
period t-1 (Qi1).
Applying the ordinary elasticity formula
for the equation (4-5), the custom effect for good i
(Ci)
is
shown as
Ci = bi1 + E dij1 logZj2 + eiti 1ogTQi2
(4-12)
where bi1, dij1's, and ei1 are the estimated coefficients
using the model (4-5) and Z's are socioeconomic variables.
In the following, elasticities are specified for the
time period t, reflecting the nature of the formulas that
depend on variables (or predicted values of variables).
are obtained by
Elasticities evaluated at the mean(s)
replacing every variable having time subscript with the
arithmetic means of those variables for the sample period.
94
4.3.2. Elasticities in Part II
The following explanation is based on the Part II
model.
<Part II: LA/S/H/AIDS>
Wi
= a*i + /3i
lQg*
+ E
Tij
logPj
+ Ek Oik logDk
+ c * a.
A*
Q
1i
(4-13)
The term elasticity is generally referring to the percentage
change in the per capita demand for good i due to the
percentage change in the level of each variable.
Group expenditure-elasticity (Xi)
is calculated by the
following formula (BLANCIFORTI, GREEN, and KING, 1986, p.
10; CHALFANT, 1987, p. 238)
Xi
where Wi
= 1 + /3i / wit
(4-14)
is the predicted budget share of good i for the
time period t (the same notation is maintained below).
This formula is common to the AIDS model and the
LA/AIDS model using STONE's index.
95
Further, standard error of the estimated Xi, SE(Xi) is
approximated by CHALFANT's (1987,
P.
238) formula:
SE(Xi) = SE(1 + /3i / Wit) = 1 + SE(f3i)/Wi
(4-15)
where the predicted budget share Wi is treated as fixed.
Marshallian own price elasticities (for a single good
i, denoted as Eu) and cross price elasticities (between
good i and j, denoted as Eij) are calculated by the formula
for the LA/AIDS model in GREEN and ALSTON (1990).
Using
matrix notation, the Marshallian price elasticity matrix E
for the time period t is
x c +
=
+ ij -
x
I
(4-16)
where the typical elements of each matrix are in the
parentheses for n goods case (i,j = 1,
(n x n) =
=
B
(
(
-6ij + (nj
x n) =
I (n x n) =
(
{
}
. ,n):
.
aij
(n x 1) = ( bi
Ct (1
.
{
cj
Sij
/ Wit) - /3i
) = {
Pi / Wi
) =
Wj
}
(
1ogPj
(Wj
/ Wit)
(4-17)
(4-18)
}
}
= Identity Matrix
(4-19)
(4-20)
96
where Sij is the KRONECKER delta (Sij = 0 for i
j and 6ij
= 1 for i = i).36
The corresponding Hicksian price elasticities (E*ij)
are calculated by JOHNSON et al.
E*ijt = Eij
(1984, p. 32)
+ Wit x Xj
(4-21)
Elasticities for demographic variables D's, namely those for
age population compositions and household size, denoted as
Dk are37
Dk = Oi]c / Wit
(4-22)
Following CHALFANT's treatment shown above, the
corresponding standard errors are
SE(Dk) = SE(Oik) / wit
(4-23)
36
Note that the A matrix (4-17) is equivalent to the
Marshallian price elasticity matrix reported in many previous
studies.
Unlike the GREEN-ALSTON formula (4-16), it can be
calculated in a much simpler way. When (B x C,) in (4-16) is
How well E is approximated by A largely
null, E = A.
depends on the value of b1 in B matrix.
In practice,
checking briefly whether the ratios of f3i and WI are close to
zero or not, and if the ratios are substantially different
from zero, the use of the formula
strongly
(4-16)
is
recommended.
The same formula was obtained by RAY, 1980, p. 597.
97
Note that the elasticities for household size are not
the measure of economies of household size.
The economies
of household size (EHS) on the demand for good i is defined
for a demand equation having Qi as dependent variable as
(DAVIS et al., 1983, p. 194):
EHSi =
3(3Qi/3Hs)
(4-24)
/ 3}IS
where HS stands for household size.38
Let Oih be the
estimated coefficient for the household size variable.
Then
EHSi for the time period t in the model is shown as
EHS1 = - Oih x
38
{
(XF / Pit)
x HS2 }
(4-25)
According to the definition given by DAVIS et al.
(1983) as (4-24), economies of household size do not always
mean that consumption amount per household member (per capita
in this study) declines as household size becomes larger.
Rather, it represents the fact that the per household member
(capita) consumption amount of good i (Qi) is determined by a
concave function of household size (HS) with a global maximum,
holding everything else constant (diseconomies of size
represents a convex function with global minimum). Then, in
the case of economies of size, as HS becomes smaller (which
has been observed for Japan and Korea), Qi increases at a
decreasing rate, then Qi decreases at an increasing rate after
On the other
passing the maximum point, ceteris paribus.
hand, in the case of diseconoinies of size, as HS becomes
smaller Qi decreases at decreasing rate; then Qi increases at
an increasing rate after passing the minimum point, ceteris
paribus. Also, constant size (the coefficient of economies of
size is zero) shows that the marginal rate of change in Qi
with respect to HS is constant, which does not mean Qi remains
unchanged as HS increases or decreases; Qi may increase or
decrease at a constant rate as HS changes.
98
Elasticity for the habit variable
(referred to
Hi) is
Hi =
c2*i
X
Q i1
/
Wi
(4-26)
Again, following CHALFANT (1987), the corresponding standard
errors are
SE(Hi)
SE(c2*i)
x
(4-27)
/ Wj
4.3.3. Elasticities in Part III
The allocation factor (AF)
is calculated based on the
model (4-7), which is an elasticity of group expenditure for
the group F (XF) for total expenditure (XT) in each time
period t:
AF = b + E di logPIi
+ e
logXT
(4-28)
i = 1,2
where b, dir's, and e
are estimated coefficients using the
model (4-7) and PIi's are two kinds of price indices, one
for the group F and the other for all commodities.
99
Then, using the results from Part II, the total
expenditure elasticity for good i (referred to Yi) is
calculated for the time period t as
Yit = Xi
x AF
(4-29)
100
CHAPTER
5
VARIABLES AND DATA
5.1. Introduction
In this chapter, selection of the study period for each
country, procedures and criteria for variables and data
selection are explained for each part of the model.
were compiled from various sources.
Data
The "ratio method" was
used to combine several time series data sets.39
As a
preliminary examination of the resulting data sets, the data
plots for each variable are provided and brief analyses
follow.
Formal tests for the stability of preferences are
conducted based on the revealed preference theorem.
These
pre-tests are useful in check deficiencies in the data sets,
and in providing background for estimation results.40
See Appendix D for an explanation of the ratio
method.
40 The data procurement and construction procedures are
outlined in detail in Appendices J to R.
are listed in Appendix H.
The data sources
101
5.2. Study Period
To capture the full process of changes in food demand
patterns, the use of longer study period is desirable.
Japan's development process has preceded Korea and Taiwan's.
The study period for Japan is longer than that of Korea and
Taiwan as the changes that have taken place over a longer
period.
It is well known that Japan experienced substantial
economic development during World War I prior to the drastic
growth after World War II.
Korea and Taiwan industrialized
rapidly after World War II.
Data was desired for Japan from
1900 to 1987, and from 1950 to 1987 for Korea and
Taiwan.41
The effect of WWII on Japan's economy
necessitated the exclusion of the data covering the years
1938-52.
Pre-war Japan (prior to 1938) quantity and price
data were primarily from SHINOHARA (1967, J8), who estimated
the two data sets separately (e.g. before and after the year
of 1909).
The post 1909 data is considered more accurate.
Some data, such as marine products are not compatible
between the two periods.
pre-WWII data after 1909.
1909-37 and 1953-87.
It was decided to adopt the
The study periods for Japan are
Three sub-periods were also considered
for Japan (see Table 5-1).
41
Taiwan data reported in the Appendices cover the
1952-87 period except price data sets for eggs and milk.
102
Korea experienced the Korean War in the early 1950's.
It was therefore advised by Korean researchers42 that
before 1960 the quality of economic data may not be
reliable.
Thus, the study period for Korea was set for
1960-87.
For Taiwan, complete retail price data were not
available before 1961.
To maintain conformity in the model
specification among the three countries, only the data after
1962 were used for Part II.
Considering the lagged
variables in Part I, the study period for Taiwan was set for
1960-87.
The study periods for the three countries are
summarized in Table 5-1.
The study periods for Part I and
for Part II and III are different since Part I contains
variables lagged two periods.
The war break is not considered as is interruption in
This
data continuity for Japan all-period and mid-period.
requires the following assumptions:
First, in the Part I
estimation, the level of general economic activities in the
pre-war period of 1937 is assumed to be approximately
equivalent to that in the post-war period of 1953.
For
example, (lagged) habit variables for for the Japan
all-period, 1911-37:1955-87, are generated in the Part I
42
Thanks goes to Mr. Seonghoon HONG and Mr. Kook SHIN
of the Department of Agricultural and Resource Economics,
Oregon State University for this valuable suggestion.
103
estimation using the data for the 1909-37:1953-87 period.
Similarly, in Part II and III, the level of general economic
activities between the pre-war period and the post-war
period are assumed to be approximately equivalent.
Table 5-1:
Summary of the Study Periods
Period Name
Japan
Part I
All-period
1909-37
1953-87
:
Pre-war
period
Mid-period
1909-37
1923-37
1953-70
Post-war
period
(n)
Part II & III
(n)
(64)
1911-37
1955-87
(60)
:
1911-37
(29)
(33)
1925-37
1955-70
:
(27)
(29)
1953-87
(35)
1955-87
(33)
Korea
1960-87
(28)
1962-87
(26)
Taiwan
1961-87
(27)
1963-87
(25)
Note:
Numbers of observations are in parenthesis.
':' indicates that the two periods are
considered directly connected.
5.3. Variables and Data for Part I
Choice of socioeconomic variables is the main concern
in this section.
Choice criteria for commodities will be
explained in the next section.
Various types of
socioeconomic data are available for each country.
Out of
all available data sets, combinations satisfying the
104
following points were selected:
to all three countries.
First, variables are common
Second, at least one variable
represents one of the two broad categories of socioeconomic
effects, either the information effects or the availability
effects.
The following four variables met these
criteria:43
percentage of population in the agricultural
sector (AGPOP)44; numbers of automobiles per capita
(AUTO); numbers of telephone lines per capita (PHONE); and
general consumer price index (CPI)
These variables are
plotted against the time periods in Figure 5-1.
For AUTO,
PHONE, and CPI variables, Japan pre-war data are plotted
These variables (except CPI) have been obtained by
dividing original figures by total population (multiplying
100).
Specification of the variable AGPOP was slightly
different among the countries due to data constraints:
Country
Japan:
Specification of AGPOP
Percentage of labor force in agriculture,
forestry, and fishery in total employed labor
force.
Korea:
Percentage of farm population in total population.
Taiwan:
Percentage of employment in Primary Industry.
CPI was added as an important socioeconomic factor,
particularly for consumption behavior. For instance,
inflation may be considered as a kind of environmental
changes.
The information contained in CPI may be considered
to be related to availability effects. The author owes his
gratitude to Professor Richard S. JOHNSTON of the Department
of Agricultural and Resource Economics, Oregon State
University for bringing my attention to this variable.
105
against the left-hand side y-axis, and Japan entire period,
Korea and Taiwan data are plotted against the right-hand
side y-axis.
Notice that there are clear lags of economic
development among the three countries;
the development
level is highest in Japan, then Taiwan, and Korea for AGPOP,
AUTO, and PHONE.
In terms of AGPOP, the pre-war Japan
pattern of a modestly declining trend for high levels is
also observed for the mid-1960's in Taiwan and for the early
1970's in Korea.
Thereafter, AGPOP for Korea and Taiwan
decline at faster rate than that of post-war Japan.
In the
post-war period, decreases in AGPOP in Taiwan and Korea lag
approximately ten to fifteen years and fifteen to twenty
years respectively from that of Japan.
Notice that the
differences become smaller and smaller.
Similarly, the
increase in PHONE in Taiwan lags approximately ten to
fifteen years from Japan, while Korea lags approximately
fifteen to twenty years from Japan post-war period.
In
terms of AUTO, seven to eight years of lags between Japan
and Taiwan are observed in the first half of the post-war
period; which vanishes in the early 1980's.
In general, the socioeconomic changes realized in Japan
through the pre- and the early post-war periods have
occurred in Korea and Taiwan at faster rate.
106
Figure 5-la:
Variable for Part I: AGPOP
AGPOP: Agricultural Population
70
60
50
40
0
L
20
10
0
iiii iii.
1900
1910
I
1920
1930
I ri rn-i UT
UT
1940
ri I
I
iI
1950
III II III
I
1960
1970
I
II
I
I I
I
I
II
I
1980
Year
--- Japan
Figure 5-ib:
-p Korea
Taiwan
Variable for Part I: AUTO
AUTO: Motor Vehicles Per Capita
0.003 5
C
a
C
t 0.45
a
0.003
0.4
H
'1
j0.0025
0.3
0.002
0.25
0.0015
02
0
'I,
L
a-
N
a
0.35
C
0
a-
N
0.15 (I
0.1
0.0005
0
0.05
IQn
U0 191fl
1Q4.fl
1Ofl 1Rfl 107fl
0
Year
Japan
'
Korea
Taiwan
-a-- Pre-war
C
107
Figure 5-ic: Variable for Part I: CPI
CPI: General Consumer Price Index
120
100
..0.08
0.07
80
II
0
.4
a
0.06
60
0.05
0
40
0.04
-20
0.03
0
0.02
1900
1910 1920
1930 1940 1950 1960 1970 1980
Year
Japan
4-- Korea
Prewar
-4-- Taiwan
Figure 5-id: Variable for Part I: PHONE
FHONJE: Telephone Lines Per Capita
0.4
0.016
C
0.35
a 0.014
0.3
0.012
I
0.01
,0.008
0.25
0.2
0.15
0.006
:4'
0.004
0.1
0.002
0.05
0
0
1900 1910 1920 1930 1940 1950 1960
1970
1980
Year
Japan
Korea
--4 Taiwan
e-- Prewar
0
108
These variables were highly correlated with each other.
Furthermore, no more than six variables could be included
with the translog functional form for some cases, such as
Korea and Taiwan having less than twenty eight
observations.46
Deleting one more variable and thus
saving six degrees of freedom in small samples was
considered critical.
It was decided to always include CPI
and omit one of the other three variables.
Using the
correlation matrix of variables, the one showing the highest
correlation with others was deleted for each case (see Table
5-2).
The variable PHONE was dropped in every case.
In
this way, AGPOP, AUTO, CPI and total domestic consumption of
each item i (TQi) were selected as the independent variables
in Part I.
46 The translog function has (n2 + 3n + 2)
/
2
parameters including a constant term for the n independent
variables case.
109
Table 5-2:
Correlation Matrices of Variables for Part I:
Choice of Independent Variables
GeneraL Description of The VariabLes:
tr%AGPOP
InCAR
LnPHONE
LnCPI
tog of the share (or percentage) of the labor force in the primary inó.istry
Log of the ntjiter of stor vehicles per capita
tog of the ni.ag,er of telephone subscribers per capita
Log of the gener.l consuner price index
MOTE: AU the variabLes is tagged one period.
JAPAN:
ALL-PERIOD
LnAGPOP
InCAR
InPHONE
LnCPI
LnPHOIIE
InCPI
trGPOP
InCAR
IrPHONE
LnCPI
JAPAN:
0.92376
1
InCAR
LnPHONE
lriCPI
1
1910-37
0.98191
InCAR
MID-PERIOD
1
lrHONE
1
LnCPI
1924-75
1
-0.94953
-0.9921
-0.8551.6
tnA
1
0.97719
0.92939
0.88257
InCAR
LriPHONE
POST-WAR PERIOD
1
1
LnCPI
1954-87
LnAGP
1
InCAR
-0.97799
-0.99144
-0.98073
0.99234
0.93009
0.96309
1
LMOP
InCAR
LrPHOIIE
LnCPI
tnPHONE
InCPI
-098334
1
InPHONE
-0.98083
-0.97348
0.99137
0.98649
0.99475
1
InAGPOP
IriCAR
1rHONE
(nCPI
LnCPI
IMGPOP
1
1
1
1
InCAR
TAIWAN:
069826 0.72239 0.66293
tnA
JAPAN:
0.94736
0.90617
I
-0.97022
-0.98321
1961-87
IMGPOP
1
PREWAR PERIOD
IMGPOP
InCAR
KOREA:
1
-0.87531
0.98186
-0.88902
tr
JAPAN:
1910-87
1
1962-87
1
InCAR
-0.95341
InPHONE
-0.9938
-0.98053
0.96296
0.92242
0.98747
1
LnAGPOP
InCAR
InPHONE
LnCP!
LnCP!
1
1
110
5.4. Variables and Data for Part II
5.4.1.. Choice of the Commodities
As many researchers have pointed out (KANEDA, KURODA,
MELLOR, OHKAWA, and PINSTRUP-ANDERSEN), the shifts from
plant origin foods to animal origin foods is one of the
fundamental change in food consumption patterns due to
economic development.
GOREUX and MARKS and YETLEY have
suggest that income growth from a very low level may cause
shifts within grain consumption.
In the low to middle
income levels, the most important food commodities are
considered to be staple grains and those foods being the
major source of protein.
The following nine commodities
were selected: rice, wheat, barley, beef, pork, chicken,
fish, eggs, milk.
Note that rice and barley are traditional
food items for Japan and Korea, and that barley is an
inferior substitute of rice.47
Rice is also a traditional
food item in Taiwan; data for barley was not available for
Taiwan.
There are a variety of ways to consume wheat
traditionally and non-traditionally; therefore a finer
classification is desired for wheat.
Due to data
limitation, bread was used in stead of wheat f or Japan but
'
"Barley and naked barley are pressed flat and mixed
in rice and then boiled, when the latter is not available in
sufficient quantities" (KANEDA, 1970, p. 410).
111
wheat flour was used for Korea and Taiwan.48
Data for
milk was not available for Korea.
Table 5-3:
Commodities Included in Part II By Country
Japan
Commodity
Rice
Bread
Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Note:
*
*
*
---
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Country
Korea
Taiwan
*
-*
-*
*
*
*
*
*
stands for "included".
stands for "not included".
5.4.2. Price and Quantity Data Construction
Except for the consumption data sets for Taiwan, price
and quantity data sets used in Part II were largely
constructed by the author.
The data representing final
stage of marketing, i.e., purchases of an average consumer
at an average retail price were compiled.
It is assumed
that the market is always in equilibrium, and that one price
48 For details, see the consumption data document for
Japan in Appendix K.
112
prevails for each commodity in each period of time.49
5.4.3. Quantity Data
There are two popular types of published quantity data
for
consumption.
One is Food Balance Sheet (FBS) data or
(domestic) disappearance data, the other is Household
Expenditure Survey (HES) data.
The former measures "the
quantities of food available for human consumption (and)
reaching the consumer" (FAQ, F6, 1984, p. Viii).
It
includes all consumption within the boundaries of a nation.
The calculation procedure is similar to that of Gross
National Product.5°
RAUNIKAR and HUANG note:
If the market in question Is in equilibrium and
all the variables on the right hand side of the
equation are measured without error, then disappearance
in period t is conceptually equivalent to the quantity
demanded in that period. ... However, many possible
random measurement errors and bias potentials exist
in disappearance data (1987, p. 55).
Consumption of food away from home is included in
the quantity data; this assumption means that there is no
price difference between food consumed away from home and at
home.
This may be a reasonable assumption f or the lower
income stage when food away from home is negligibly small on
average.
50
The quality of the food balance sheet data depends
on the following: the reliability of the given data sets
for each component, such as production and trade figures,
and the plausibility of the assumptions made in the food
balance sheet calculation, such as waste rate and extraction
rate.
Note that original figures such as production data
are also estimated based on many assumptions such as average
yield per hectare.
Therefore, it should be emphasized that
there is no perfect food balance sheet compiled.
113
On the other hand, liES data reports consumption amounts of
an average household.51
Even though there are some
possible errors in the sampling procedures (RAUNIKAR and
HTJANG, 1987, p. 35), the resulting data is based on direct
observation; assumptions play a lesser role than in
disappearance data calculation.
HES data would therefore be desirable.
Unfortunately,
it does not exist as continuously surveyed time series data
for Japan's pre-war period and for Taiwan.52
Therefore,
FBS data were utilized for all cases.
The following equation is a typical formula for
disappearance data (RAUNIKAR and HUANG, 1987, p. 55):
Disappearance
=
Production + Change in Observable Stock
+ Imports - Exports - Nonfood Consumption
- Nonmarket Food Consumption
Nonfood consumption includes feed use, seed (or
hatching) use for reproductive purpose, manufacturing use
for nonfood products, and wastes.
Wastes can be divided
51
HES data do not include "food away from home" for
each commodity, therefore the consumption figures for each
item will be less than the domestic disappearance figures.
52
liES data exists for Korea starting from 1960's,
however it reports expenditures for various commodities only
and does not report quantities.
Time subscripts t for time period are dropped (every
component is in time t; which may change case by case).
114
into two types:
wastes accrued in the marketing or
distribution processes (e.g., such as loss in
transportation).
The other is loss in processing, such as
the husking process for grains.
Conventionally the former
is called "waste," and the latter process is called
"extraction."
In other words, extraction is excluding the
inedible portion of each raw commodity.
Inputs used in food processing (sometimes referred as
"manufacturing use") and farmer's home consumption may be
classified as nonmarket food consumption.
Nonmarket food
consumption was generally excluded from this study.54
"Post-harvest losses in most of the countries are considered
to be substantial due to the fact that most of the grain
production is retained on the farm so as to provide
sufficient quantities to last from one harvest to the next
(F6, 1984, p. Viii)."
developing economy.55
This problem may be important f or the
However, according to the
Agricultural Rousehold Models: Extensions, Applications, and
Policy, edited by SINGH, SQUIRE, and STRAUSS
problem requires special treatment.
(1986), this
The treatment is beyond
the scope of the study.
The resulting domestic disappearance data sets are
converted into per capita figures and plotted against time
The case of bread is exceptional.
See Appendix G for some evidences in Japan.
115
periods as a preliminary examination of the trends in the
data sets.
The data sets are briefly inspected for
individual countries.
5.4.3.1. Food Consumption Trends - Japan
The data plots cover the 1900-40 and 1950-87
periods.
Figure 5-2 shows sharp contrasts between the
pre-war and the post-war periods.
The pace and
characteristics of the changes in food consumption patterns
are quite different between the two periods.
In the pre-war
period, the change is moderate except for fish, which
increases about threefold during the 1910-36 period.
The
switch from coarse grain to higher grade grain, namely from
barley to rice and/or wheat flour is the only notable change
in composition.
period.
The change is drastic in the post-war
Per capita consumption of barley declines largely
after the mid-1950's, then rice consumption declines after
the mid-1960's.
Per capita consumption of all other
commodities increases; milk, meats, and fish increase
notably and are still on increasing trends.
Wheat flour and
bread/eggs seem to reach a plateau around the mid-l960's and
around the early 1970's, respectively.
Among meats,
increases in chicken and pork are most pronounced.
The increase in barley consumption right after the war
period is hypothesized to be a result of a food supply
shortages caused by the war time disruptions.
116
Japan Food Consumption Trends - I
Figure 5-2a:
Grains & Dairy Products 1900-40:1 950-88
160
40
150
CD
C)
140
30
130
a)
C)
x
w
120
20
110
100
90
80
p900
1920
1910
Rice
1940 1950
Year
1930
-4--- Wheat Flour
--- Barley
1970
1960
1980
70
Bread
-A- Milk
Eggs
Japan Food ConsumptiOn Trends
Figure 5-2b:
Meats & Fish: 1900-40:1950-88
28
12
a)
a,
t 26
1
>.
10
24
0
C-,
a)
C
0
22
8
20
18
6
- 16
aE
14
U,
C
0
0
U)
- 12
ii ii
2
U
__
y.
a)
-
1
900
-
1910
,-.Z.
'10
-
rr1rr1-,TTrTT,3JL1
1920
.--- Beef
1930
8
F
1940 1950
Year
-f-- Pork
1960
1970
--- Chicken e-- Fish
1980
117
5.4.3.2. Food Consumption Trends - Korea
The data plotted in Figure 5-3 cover the 1955-87
period.
According to Figure 5-3, per capita consumption of
wheat, beef, pork, chicken, fish, and eggs steadily increase
over time.
Rice consumption levels fluctuates for this
period but seems to be on slightly increasing trend.
Barley
consumption begins to decline rapidly after the mid-1970's.
5.4.3.3. Food Consumption Trends - Taiwan
The data plotted in Figure 5-4 cover the 1945-88
period.
Although the data plots start at 1945,
attention
is mainly paid to the period after the 1960's since the
study period has been set for the 1962-87 period.
According
to Figure 5-4, wheat consumption quickly increases at first
but levels off after the 1970's.
grow at the end of 1960's.
the early 1970's.
mid-l970's.
Egg consumption starts to
Milk consumption increases in
Rice consumption largely falls after the
Fish and pork consumption have increased
constantly since 1945.
Chicken consumption increases
suddenly in 1965 as if it was just introduced and continues
to steadily increase thereafter.
Beef consumption levels
are low but are slightly increasing after the mid-1970's.
118
Korea Food Consumption Trends - I
Figure 5-3a:
Grains & Dairy Product 1955-87
180
60
50
cx:
a
a,
ci
w
ICa
a,
C',
20
C',
C)
.10
I
II
I
1960
I
I
I Ill1970
V
1965
Year
-- Rice
'---- Wheat
Eggs
Barley
Korea Food Consumption Trends - II
Figure 5-3I:
Meats & Fish 1955-87
30
Ca
>
-25
Ca
Ca
0
20
0)
0)
0
-15
a
E
10
Ci,
E
U,
C
0
o2
0
0
Ci,
Cal
.U)
I
1955
I
I
1960
-- Beef
11
I
I 11111 1
1965
1970
Year
+---- Pork
I
I
11 I
1975
---- Chicken
I
I
I
I
0
I
1985
1980
Fish
119
Taiwan Food Consumption Trends
Figure 5-4a:
Grains & Dairy Products 1945-87
150
30
140
i.\Ila:N
tei.
5-
ci
110
a
100 E
a,
'J
C
0
0
Cu
0
70
S945
1950
1955
1960
1965 1970
Year
Wheat
Rice
1975
1980
1985
Eggs -e- Milk
Taiwan Food consumption Trends
Figure 5-4b:
Meats & Fish 1945-87
45
40
35
cci
>s
cci
a.
(ci
U
30
25
20
0) 15
10
5
rr.
0
1945
-
-
1950
Beef
1955
1960
Pork
1970
1965
Year
-
1975
1980
---- Chicken -s-- Fish
1985
120
5.4.3.4.
International Comparison of Food Consumption
Level
Table 5-4 presents the prior expectation that Japan's
food consumption pattern is being repeated by Taiwan and
Korea.
For rice, wheat flour, and barley, the year of
change in consumption trends are compared.
For the other
commodities, the year certain consumption levels were
reached are compared.
Table 5-4:
Levels
Commodity
Rice
Wheat Flour
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
International Comparison of Food Consumption
Indicator
1.5kg
8kg
4kg
15kg
7kg
5kg
Year in Each Country
Korea
Taiwan
Japan
1979
1968
1963
1971
1974
1967
1975
1914,1954
1964
1983
1964
1984
1948
1977
1983
1966
1971
1973
1955
1913,1969
1963
1955
1983
1978
1986
Note: Indicators represent the following:
- The approximate beginning of declining trend
-* - The approximate beginning of plateau
x kg - The year when the consumption level of
x kg/capita/year is recorded at the first time
in the study period (the pre-war and the post
-war are separately considered for Japan)
Source: Appendix I.
121
Korea's development pattern repeats Japan's except for
the case of beef where no lag in increased consumption is
observed between Japan and Korea.
Taiwan's development
patterns also follows Japan's except for the cases of pork,
chicken, and fish.
Pork, chicken, and fish per capita
consumption levels in Taiwan are relatively higher for
equivalent income levels than in Japan and Korea.
There are
no apparent reasons that Taiwan's consumption levels of
pork, chicken, and fish differ from Japan's and Korea's.
One reason may be cultural differences.
In general, time lags in food consumption patterns
between Japan and Korea/Taiwan for the post WWII period are
ten to twenty years.
122
5.4.4. Price Data
Price data should represent the commodities described
by the corresponding quantity data:
for the quantity data
derived by the FBS approach, national average retail prices
are considered to be appropriate.56
There are two types of retail price data.
The first is
price data for narrowly defined commodities from the stand
point of retailers.
This type of data is referred as to
Retail Price Survey (RPS) data.
The other price data is
found in Household Expenditure Surveys (HES).
It is
calculated by dividing the expenditure spent on a good per
period by the quantity purchased per period.
This is
sometimes referred as to HES price data or "implied retail
price" data.
HES price data is commodity prices viewed from
the stand point of consumers.
HES price data is more
favorable than RPS price data in the demand analysis using
FBS quantity data, particularly for the commodities having
relatively large product variations, such as beef, pork, and
fish (see Appendix F for discussion).
Unfortunately, HES
price data was only available for the Japan post-war period.
HES price data were only used for the Japan post-war period.
RPS price data were applied for all the other cases.
56
As mentioned above, the farmer's home consumption
should be taken into account in the early phase of economic
development. What sort of price should be the most
appropriate was considered, however, I could neither find a
Therefore, this
good solution nor satisfactory data sets.
problem was excluded from the scope of our study.
123
The compiled price data sets are deflated by the
general consumer price index (CPI)51 and plotted against
time (see Figure 5-5, 5-6, and 5_7).58
Table 5-5 summarizes the real price trends observed in
the three countries.
In general, real prices are declining
as economic development proceeds, except for rice, beef, and
Egg real prices are declining in all three countries.
fish.
Table 5-5:
Real Price Trends Summary
Commodity
Rice
Bread/Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Increasing
Decreasing
JT, K
JR, K
JT, K
K
JT, K, T
JT
JT,
JR,
JR
JR,
JR,
T
JT, T
JT, K, T
JT
Note: JR - Japan pre-war period
JT - Japan post-war period
K - Korea
T - Taiwan
Blank - Almost constant trends
Beef in JT sample shows both of increasing
and decreasing trends.
For Japan, CPI reported is from the Estimates of
Long-Term Economic Statistics of Japan since 1868 (LTES;
Jil).
58
Units are yen/kg, won/kg, and NT$/kg for Japan,
Korea, and Taiwan, respectively, for all commodities except
for milk for which yen/liter (Japan) and NT$/liter (Taiwan)
Further, it is assumed that 1 liter of milk = 1
are used.
kg of milk.
124
Figure 5-5a:
Japan Food Real Price Trends - I
Grains 1900-38:1950-88 (Bread 1904-)
1100
1000
900
800
700
600
500
400
300
200
100
1900
1920
1910
1930
-.-- Rice
Figure 5-5b:
1940 1950
Year
1960
1970
1980
4--- Bread -- Barley
Japan Food Real Price Trends - II
Meats & Fish 1900-38:1950-88
6000
0)
5000
U.
4000
>-
3000
C
2000
1000
0 111111! ,rJrr,ljTTfljtl I1IT1 lT1I,I1LlTIlIIIIYIlTTflhI T!ILClllTIIiflhLllIlllI!hllhhuhhlj
1900
1910
-- Beef
1920
1930
4-- Pork
1950
Year
1940
1960
-- Chicken
1970
Fish
1980
125
Figure 5-5c:
Japan Food Real Price Trends - III
Dairy Products 1900-38:1 950-88
1800
1600
1400
1200
C
1000
LI)
800
600
U
400
ITJILITTJ!11T lillill Ti J!TI&ITIIliflhIlhTIIIIII
200
1900
1910
1920
1930
1940 1950
Year
- Eggs -4- Milk
1960
1970
1980
126
Figure 5-6a:
Korea Food Real Price Trends - I
Grains 1955-88
1000
900
800
700
600
500
'if
400
,w.
300
200
1955
1960
1965
--- Rice
Figure 5-6b:
1970
Year
1975
1980
1985
-'---- Wheat -'-- Barley
Korea Food Real Price Trends - II
Meats, Fish, & Eggs 1955-88
0)
C
0
9000
8000
7000
C
0 6000
to
C)
5000
I
4000
C
U,
U)
3000
0 2000
a-
U,
a)
1000
0
1111111
1955
1960
I
I
I
1965
I
I
I
I
1970
1975
Year
- Beef
-4-- Pork
-- Fish
-4---- Eggs
1980
-a'- Chicken
1985
127
Taiwan Food Real Price Trends - I
Figure 5-7a:
Grains & Dairy Products 1962-87
14
I
1982
1977
1972
1967
1962
1987
Year
-a-- Rice
Figure 5-7b:
-+-- Wheat -a'- Eggs -- Milk
Taiwan Food Real Price Trends - II
Meats & Fish 1962-87
350
C)
300
z
z
250
/\_____
I
200
-t-
150
S
4
.
0
50
1962
1967
1977
1972
1982
Year
-- Beef
-k-- Pork
----- Chicken -- Fish
1987
128
5.4.4.1. Comments on the Japan Beef and Fish Price Data
The price gaps between the Japan pre-war and post-war
periods for beef and fish are due to the nature of the data
sets reported by the Bank of Japan (BJ)
(see Figure 5-8;
also see Appendix J for the data compiling procedure).
This
data set is used to connect the pre-war and post-war price
series.
Tuna and mackerel prices were used for Japan fish
retail price compilation.
The real retail prices for these
commodities during the war period are calculated by using a
different CPI series reported by BJ (J2, p. 222).
According to Figure 5-8, it is clear that beef and tuna
real prices declined constantly during the war period.
For the mackerel price, no data was reported for 1938
and there was a revision in measuring unit before and after
1938.
However, nothing was reported about the change in the
survey method or definition of the good.
The BJ-CPI (1934-36=1.0) series is known as "The
Pre-war Standard Tokyo Retail Price Index" (monthly data).
This series was adjusted to the other CPI series (1985=100)
used in the prior sections to improve the compatibility of
The ratio method was employed and the conversion
the plots.
rate was the average ratio of the 1937-38 and 1950-51
periods.
129
Figure 5-8a:
Beef
Japan Food Real Price During the War Period -
RPS data and OH from The Bank of Japan
8000
7000
6000
U,
cx
0)
'
5000
I\/'.
0
4000
/
'a
a)
3000
a)
2000
1924
1929
1934
1939
1944
949
1954
1959
Year
Figure 5-Bb:
Fish
Japan Food Real Price During the War Period -
RPS data and CPI from The Bank of Japan
3500
a)
3000
>-
2500
U,
2000
0
1500
1000
CU
(I)
500
(U
a)
0
1924
..JuIIII[II II III LI ITT III TI
1929
1934
1939
1944
1949
Year
--- Tuna
---+-- Mackerel
1954
1959
1964
130
5.4.5.
Demographic Trends in the Three Countries
Age-population composition and household size were
chosen as demographic variables.
Household size was only
available for Japan and Korea; it was not available for
Taiwan.
Age-population composition consists of the three age
classes:
population below age 15 is referred to "child
population", population between age 15 and age 64 is
referred to "working population", and population over age 65
is referred to "old population".
The age-population
compositions are specified in share form derived by dividing
the population of each age class by the total population in
each time period.
As a preliminary examination, to see the nature of the
data trends, these data sets are plotted against time (see
Figures 5-9).
In Figures 5-9a and 5-9b, the population
shares are measured by the left scale and household sizes
are measured by the right scale.6°
60
In Figure 5-9, the values of household size for the
1900-12 period are constant; the original data were only
available for the limited period of 1908, 1911, and 1912,
which were constant values, and the other blank periods
between 1900 and 1912 were filled with interpolation method
Since only the data from 1911 are used in the
before 1912.
estimation, this data trend causes no problem.
13].
Demographic Data Trends - Japan
Figure 5-9a:
1900-40:1950-87
Japan
0.7
0
-C
ID
0.6
.J..J
I
0
0.5
N
' 0.4
C
a
0
(F)
C
0
a-
4.5'
0.3
'
a.
0
0.1
rItiuhIrrI., hhhhghil r1,u,,hhhlthtl 3.5
0
1920
1910
1900
1950
1940
1930
1960
1970
1980
Year
-- Child Pop.
-'*- Old Pop.
-4-- Working Pop.
Household Size
Demographic Data Trends - Korea
Figure 5gb:
1955-87
Korea
0.7
6.5
0.6
L'
0
6
0.5
5.5
-c
0.4
C
0
5
0.3
a.
1,
a
ID
4.5
0.2
V)
0.1
0
I
r
I
1955
I
I
1960
I
I
I
I
1965
r
I
1970
1975
1980
1985
3.5
Year
-*- Child Pop.
Working Pop.
-4-- Old Pop.
--- Household Size
I
132
Demographic Data Trends - Taiwan
Figure 5-9c:
Taiwan
1952-88
0.7
0.6
I..
0.5
C
U)
0.4
C
0
d 0.2
0.1
0
I
1952
1957
1962
Ii II ii FIJI,. T1977
r
1967
I
1982
1972
1987
Year
-- Child Pop.
Figure 5-ba:
-4- Working Pop. -- Old Pop.
Population (Share) Growth Rates - Japan
Japan 1901-40:1951-87
4
a
-C
3
2
0
0
C
.2 -3
34
a
a.
0
O_ -5
1901
,,...,,
1911
1921
1931
1951
1941
Year
-.- Child -4-- Work
Old
1951
=
Total
1971
1981
133
Population (Share) Growth Rates - Korea
Figure 5-lOb:
Korea 1962-87
3
02
1
0
L
L
0
-c
U,
C
0
0
-3
I
1
1
I
1962
I
I
I
I
1967
V
V
1
1
1
r
1
1
1977
1972
I
I
I
I
I
1982
1987
Year
-- Child -4-- Work --- Old
-B- Total
Population (Share) Growth Rates - Taiwan
Figure 5-bc:
Taiwan 1953-87
6
0
(ii
a,
Ui
C_
0
0 -3
0
1953
1958
1963
1968
1973
l978
Year
-
Child
Work --- Old
-a--- Total
1983
134
It is observed in the three countries that as economic
development accelerates, the child age population share
declines and the working age population share increases.
Meanwhile, the old population share increases consistently.
Also, according to the cases of Japan and Korea, household
size consistently declines as economic development proceeds.
The growth rates of population shares and total
population are also plotted against time for each country
(see Figures 5-10) 61,62,63
It is observed in the three countries that the old age
population share growth rates are accelerated as economic
development proceeds, and increase from around -1% to 2% 4%.
The working age population share growth rates increase
from small negative rates or zero percent to a 1.5%, then
settle at around 1%.
The child population share growth
61 In Figure 5-ba for Japan, abnormal values around
1918-20 may be reflecting a discrepancy in the original
population census data between 1918 and 1920. Estimation is
done with this state of the data since no relevant
The
information is available to adjust the data sets.
following analysis is proceeded ignoring this gap, also.
62 It was found that Korea population (share) growth
This study does
rates before 1961 are quite unreasonable.
not employ this portion; however, the reported data sets
include this portion, therefore readers should be careful
when use the data sets. Some portion of the graphs look
"bumpy", which are due to the interpolation employed in the
data compiling procedure.
63 In Figure 5-bc for Taiwan, the unusual values at
1969 are due to the original data, which may have been
caused by the change in survey method. The details is not
readily known, therefore the data are not corrected and the
gaps are ignored in the subsequent discussion.
135
decrease from the 0% - 1% to -2%
rates follow two patterns:
and become stable (Korea and Taiwan); or oscillate in the
negative range and fall down to -3% with an indication of
further decline (Japan).
two patterns:
Total population growth also shows
a stable growth path at a almost constant
rate of 1% (Japan); or slows down as economic development
proceeds and the growth rate drops sharply from 3% or 4% to
1% level (Korea and Taiwan).
The demographic changes are most drastic in Taiwan.
The changes in the other two countries are also notable.
It
can be concluded that economic development is likely to be
associated with substantial demographic changes,
particularly during the middle stage of development or the
"taking off" stage from the low level of income.
The
typical consequence observed in the three countries is aging
of the whole nation with the shrinking child population
share and a growing old population share.
Also the working
population share increases rapidly at the very beginning of
the rapid economic development.
This corresponds with the
rapid reduction in the child population share.
5.4.6. Group Expenditure Data
Group expenditures are defined by the price and
quantity data.
The trend in the group expenditures will be
examined shortly in the section 55.
136
5.4.7. Correlation mong the Variables in Part II
The partial correlation of per capita real group
expenditure with other factors is reported in Table 5-7 (see
Table 5-6 for variable names).
The correlation coefficients
between per capita real group expenditures and other
variables are fairly low (0.1 to 0.6) for every samples
except for post-war Japan where they range from 0.8 to 0.9
(see the first column of Tables 5_7)64
Therefore, except
for the post-war Japan case, it is potentially possible that
the effects of almost "pure" variations of the real group
expenditure on food demands are properly captured in Part II
estimation, with less trouble of multicollinearity.
Table 5-6:
Part II
Variable Names Used in Correlation Matrix for
LOGXS = log of per capita real group expenditure
LOGPS = log of retail price of good $
LOGCHILD = log of child population share (age 0-14)
LOGWORK = log of working population share (age 15-64)
= log of old population share (age 65-)
LOGOLD
= log of household size
LOGHS
= habit variable for good $ (lagged 1 period)
LQ$
$: R = rice, BR = bread, W = wheat, B = barley,
BF = beef, P = pork, C = chicken, F = fish,
E = eggs, M = milk
64 As will be seen shortly, the reason behind this is
that the real group expenditures are linearly correlated
with neither price trends nor time trends in general. Note
that the demographic factors have fairly strong linear
relationships with time trends; then, the variations in
group expenditures are in general independent from the
demographic changes.
137
II Table 5-7a: Correlation Matrix of variables for Part
Japan All-Period Sample
JAPAN:
t0S
100xS
1.8
100908
1.0090
100909
10099
10090
10099
LOOPS
100PM
LOGPBF
10099
0.99832
1
0.99186 0.99512
0.99605 0.99629 0.99568
0.99585 0.99595 0.99333
0.99563 0.99677 0.99775
0.99118 0.99242 0.98645
0.99618 0.99483 0.99645
0.99871
10098
100981
LOOPS
1911-87
Al.L-P88100
IOGPC
10099
0.98914
0.99824
0.9979
0.98173
0.99103
10098
100PM IOOCHILO 10Gm
10001.0
LOOMS
I
0.35038
0.33543
0.35521
0.32352
0.35379
0.3738
0.28556
0.39333
0.36858
1
0.99889
0.99919
0.99784
0.99778
0.99698
0.99589
0.99283
0.99755
1
0.99308
0.99564
0.99182
1
1
0.99425
I
1088
108
I
-0.911 -0.90162 -0.94504 -0.08134 -0.90817
0.9418 0.95384 0.94303 0.94117 0.95461 0.92765 0.94119 -0.97036
0.94555 0.95068
0.81294 0.82088 0.51541 082878 0.79201 0.77514 0.85214 0.75083 0.78176 -0.93951 0.86071
1
-0.877 0.93506 -0,90332 -0.55821
-0.8856 -0.89118 -088233 -0.90361 -0.87573 -0.86638 -0.90664 -0.84221
0.9182
-0.09726 -0.90482 -090961 -0.90507 0.91568 -0.88471 -087412 -0,9219 -0.85755 -0.85657 0.93902 -0.57306 0.94093
0.87679 -0.92649
09763 0.97938 0.9728 098643 097139 096497 0.98774 0.91245 0.97274 -0.95969 095375
0.23114
003879 -060596 -0.61314 -0.59223 064944 -0.59595 -0.57486 -0.65248 -0.5364 -0.60591 0.79717 -0.73331 -0J4837 0.78066
1009
-002367 0.76867 0.79479
109
-0.02861
bC
0.50977
-0.09796 0.72041
0.81586
0.72631
109
-0.12217 -034336
-03427 -0.35289 -0J0262 -0J6097 -0.38378 -0.31047 -0.41236 -0.33585 0.18111
I060NIID .0.1291
1O01lK 0.23908
-0.06221
100010
-0.16567
tOGAS
101
0.92229 -0.92859 -0.91997 -0.93637
tOE
006886 087677 0.88187
101
3.05061
01896 0.80737 0.76355 076482 0.82489 0.71949 0.76215 '0.90265 078178 096962 -0.56995
0807 0.83383 0.78826 0J6858 0.84957 074061 078835 -0.92506 082307 0.97058 -0.90289
0.8705
0.87735
088733
0.88154
067016
0.69285
0.74464
0.71965
0.82396
0.82601
0.90969
0.91456
0.86452 0.84915
0.86636 0.85088
0.9009
0.90343
0.64043
0.76455
0.6922 -0.56784
0.7255
-0.843
0,95854
-0.3242 -0.08339 0.06406
0.912" 092907
0.86725 -0,96761
0.8669 '0,91249
3.90618
-0.9355
0.95913 -0.93869
Part II Table 5-7b: Correlation Matrix of variables for
Japan Pre-war Period Sample
988-61*8
JAPAN:
L0S
1.0098
0.25692
1
100908
10098
100989
1009!
988100
10090
191137
100PF
IOGPE
100PM I000HIID L001
100010
LOOMS
IOGXS
10690
100908
0.17962 084552
0.25035 0.68937
0.29407 0.86487
100989
IOGPP
0.06286 0.67479
IOGPC
0.10808 0.91239
-0.09812 08T734
10699
0.03617
0.8304
IOGPE
0.28347 0.76547
106PM
0.6325
IOGCNIL0 0.36349
1OGPI
L0G0ADR
106010
LOCNS
100
6088
100
1069
lOP
ICC
109
ICE
tOM
1
0.86892
0.74159
0.70198
3.75902
0.60734
0.70411
0.60204
0.45905
1
0.64048
0.12002
1
0.84315
1
0.74667 0.70774
0.95705
0.88106
0.89564
096061
0J5124
0J9447 0.85993
0.86783
0.46709
0.71022
I
0.88571
0.90252
0.76593
031555 089509 0.60496 060006
¶
0.56633
0.58212
0.40191
0.60304
0.46963
0,94241
I
'0.3484 -0.62923 -0,58051 -0.37294 -0,68646 -0.64954 -0.62626 -0.65667 .3.52266 -0.59616 '0.93494
-0.7317 '0.66011 0,73275
-0.43198 -0.28187 -0.19006 0.02199 -0.60266 -0.17111 -017326 0.02364 -0.04553
0.1139 0.20746 -0.02589 -0,03937 0.03253 0.06761
-0.0051
0.0262
0.04242
-0.07121
'0.09463 0.0564
0.0627
0.39737 0.47979 0.45657 '0,43578 -0.28853
0.1734 045298 3.50257 040216 052613 0,49913 0.49141 0.42773
057433 -0.90293 -0.65425
1
0.68636 047187 0.87467 012502 071142 0.57985
0.66179
0.86325
I
0.02235
0.06432
0.719
0.21656
07421 0.95114 016343
-0.48002 -0.32068 -031615 -0.07655 -063018 -0.20225 -0.19856 '0.04536 -1L05603 -0.71068 -0.83895
0.16431 -0.09223 -0.07519 '3,0581 0.11435 0.57086
-0.01893 -005716 0.04424 3.01051 -0.0692 0.09032 -0.06229 0.02501
-3.09637
0.05576 0.07353 0,70408 0.83055 -0.78065 '8.93548
0.46025 0.31734 033077 0.07579 3.62828 0.22749 015518
0.14983 -0,06127 0.03064 0.66066 3.78249 .066767 0.94293 -0.10394
0.59283 0.26766 0.19488 0.01851 0.54849 0.12285
0.49645 065t19 .0.63361 -0.77677 -0.19207
037068 0.12953 019594 -000525 0.44796 0.0707 0.04505 -0,13293 .0,07763
0,0621 0,68544 0.86763 -0.74577 -0.95814 -0.07325
0.156 -0,00414
0.0184 0.60502 0.18439
0.43668 0.28908 0.24669
0,05963 -0.05542 .0,00766 0.60053 0.76541 '0.67681. -0,95398 -0,04903
0.49961 0.21366 0.22202 -0.01494 0.50239 0,07347
138
Table 5-7c: Correlation Matrix of Variables for Part II Japan Mid-Period Sample
930-988308
JAPAN:
L0R
LOGXS
tO8N
LOOPS
LOOPS
0.60786
0.60029
0.63203
100969
0.58131
LOCPP
0.60239
0.60749
0.57272
0.62511
0.60323
10098
LOGPBR
LOGPC
LOGPF
10098
100PM
10001089
LOB
1088
LOB
LOBE
LOP
ICC
LOP
108
1CM
0.99516
0.9955 0.99097
0.99931 0.9958%
0.99676
10099 L000HILD L0GlK
LOOKS
LOGOLD
1
0.99905
0.99937
0.997'.8
0.99899
0.99898
0.99106
0.99726
1
O.9985-.
1
0.99528
0.99806 0.99724
0.99658 0.99644 0.99931
0.99557 0.99789 0.99708
0.99805 0.99114 0.99717
0.99797 0.99793 0.99888
0.9980%
1
0.99835
0.99877
0.99627
0.99601
0.999%? 0.99915
1
0.39043
0.38156
I
1
I
-0.9947
0.89423 -0.98776
0.90935
0.90215 0.92919 0.55071
0.88595 0.92236 0.86958
0.90977
0.8993
0.93116
0.91807
0.89635
0.85537
0.91096
0.9065
1
-0.9076 .0.85298 -0.87356 -0.90738 -0.8492% -0.85171
LOGONILD -035729 -0.87919 -08536 -086853
LOCKS
10098
I
LOGXS
LOGOLO
10099
10090
10099
LOGP8F
19257'O
1
0.93039
0.89527 0.89504
-0.30692 -0.78627 -0.19515 -0.77398 -0.81546 -0.78497 -0.78265 -0.79922 -0.75307 -0.79688 0.87509 -0.87974 -0.82228
-0.54315 09333S -0.93583 -0.93593 -0.94017 -0.93392 -0.9304 -0.93576 -0.92593 -093677 0.84716 -0.85454 -0.86544 0.77649
0.957 -0.84674
09655 0.9713% 0.96364 0.95029 0.97087 0.96554 0.91971 0.95321 0.96947 -0.95143 0.96276
0.49079
0.3562
0.36802 0.03159 0.01686 0.05053 -0.02521 0.02306 0.03956 -0.02845 0.08502 0.02492 0.41734 -0.36681 -0.34716
0.98315 0.97401 -0.84616
0.45112 0.91355 0.91553 090499 0.93356 0.91422 090774 0.93492 0.88931 0.91315 -0.91903
0.9326 -081801
0.6973 0.1533? 0.66483 0.71172 -0.94406 0.90911
0.19194 0.71216 0.71365 0.69615 0.75165 0.71345
0.5476 0.50302 0.48251 0.55293 0.44755 0.50368 -0.80344 0.74438 0.79965 -0.68919
0.02854 030331 0.50088 048437
-039918 -0.91895 -0.91842 -0.91861 -0.9115 -092752 -0.93154 -0.90621 -0.9344% -0.92391 0.75329 -0.79366 -0.77992 0.66225
0.9222 094065 -0.84123
0.697 0.14301 -0.95335
0.22686 0.74281 0.74414 0.72155 0.78042 0.74312 0.72329 0.78088
0.7951 0.84098 0.76646 080664 -0.98159 0.9595% 0.97192 -0.86147
0.8064
0.21570 0.80608 0.80864 0.79238 0.83993
1
Table 5-7d: Correlation Matrix of Variables for Part II Japan Post-war Period Sample
JAPAN:
LOGOS
LOOPS
LOGPBB
LOOPS
100989
P057WAR
10099
10090
989308
10099
195581
LOGPO
100914 LOCCOILD 1000108K
100010
LOCKS
LOGXS
LOGPR
1.00988
1.0098
100989
40099
LOGPC
LOGPP
10098
100994
LOGCHILO
10091080
LOGOLD
LOCKS
taR
1088
108
LOBE
100
ICC
109
108
108
-0.86541
-0.89947
-082347
-092226
-0.93133
-0.92398
-0.91705
-0.71701
-0.92997
1
0.98715
0.98533
0.97613
1
0.97719
0.95313
1
094251
0.96718 0.97864 0.93185
0.94779 0.91144 0.91202
0.98352 0.98691 0.95426
0.77024 0.79061 0.77685
¶
0.9916
0.98071
0.99635
0.75478
092266 0.98921
0.98863
0.98904
0.77057
0.93924
0.97321
0.7527
08175
1
0.95157 093885 0.7925
0.96219 0.97352
0.81855 -0.67244 -0.57864 -0.83119 .0.91823 -0.89784 -0.86049 -0.91423 .0.48342 -0.87485
1
1
0.65723 0.67144 0.20348 0.65202 -0.8787
09497k 0.92116 0.91545 0.69663 0.93697 -0.91748 0.63083
090218 -0.93768 -0.95231 -0.89665 -0.97903 -0.96748 -0.94809 -0.97782 -0.65359 -0.96057 0.96322 -0.78552 -095292
0.83771 -0.98166 -0.93627 -0.96178 -0.92452 -0.91041 -0.87144 -0.93563 -0J3002 -0.91127 0.80232 -0.45727 -094824 0.8738
0J522 0.94162 -0.97195
-0.935
0.9753
-0.91232 094945 09477 0.90157 0.98689 0.91699 0.95643 0.98278 0.69271
0.92239
0.80665 -0.76429 -0.80299 -0.69604 -0.86247 -0.85524 -0.84455 -084504 -0.4558 -0.83505 0.94008 -0.93492 -0.79926
0.51396 0.93099 -0.8919
-0.81087 095991 0.95518 0.97856 0.91713 0.89501 0.86142 0.93255 0.69309 0.88625 -0.85196
0.9672 0.94284 0.98889 0J3157 0.96225 -0.91266 0.63535 039259 -0.96123
-087317 0.99143 0.96463 097089 0.93346
0.0679 0.73219 0.93178 -08766 0.55586 0.99343 -0.92845
-0.84784 09$696 0.97678 0.95949 0.95523 0.93546 0.90693
0.8346 -0.78941 031662 084637 -0.84115
0.8358 0.82361 0.79868 0.55052 0.60343
0.8233 0.83339 0.81215
-0.80299
07901 0.90992 -0.97302
0.94351 -0.94501
0.8993 0.90296 0.84538 035571 0.94153 0.91087 0.95543 0.61423
-0.93109
0.9427 0.98755 0.66677 0.96371 -0.95541 0.74791 0.97082 -038183
0.9713
0.9859
-0.63307
-0.85409
-0.8972
0.57692 0.60217
0.98517 0.98001
0.49479
0.98068
0.98124
0.92711
0.96164
0.6948
0.96728
0.67923
1
139
Table 5-7e: Correlation Matrix of Variables for Part II Korea Sample
1962-87
0585A:
1OS
tOGPV
IOGPR
LOGPB
1
LOOPS
-0.27381
LOOPy
-0.33404
0.98319
5
10095
-0.28688
0.99658
0.9822
1
-0.2757
0.9399
0.95897
0.98116
10099
-0.26541
0.98394
0.96244
0.93244
0.99497
10090
-0.2126%
0.97541
0.95186
0.96647
0.98498
0.99027
10099
-0.35466
0.99073
0.96737 0.98654
0.99335
0.9936
100939
10095
LOGPP
100989
IOGXS
L00PF
1009 10008110 10K
10G.D
100110
1
1
1
0.98701
1
0.98678
0.9883 0.99311
-0.21257 0.93254 0.96603 0.97795 0.98394
1
0.40601 -0.96987 0.96652 -0.97786 .0.95572 -0.94892 -0.93067 -0.96869 -0.93922
1
L0t4 -0.33746 0.98638 0.9144 0.98892 0.97697 0.97109 Q.952.L 0.98278 0.95869 -0.99464
1
106010
-0.52566 0.91183 0.93112 0.92887 0.68654 0.87877 0.86123 0.91257 0.57466 -0.96005 0.95558
0.41535 -0.94799 -0.94621 -0.9587 -0.93404 -0.92575 -0.91508 -0.9553 -0.92006 0.99255 -0.98205 -098016
106145
10093
10603510
108
0.07634
0.56222
0.56246
0.56583
1044
0.19132
0.75921
0.66449
0.73007
108
0.64319 -0.71744 -0.76377 -0.75636
1
0.59466
0.60613
0.58203
0.59634
0.58198 -0.50-452
0.55152
0.42666 -0.49819
0.7704
0.75728
0.72638
0.72654
0.73682 -0.66604
0.70503
0.49998 -0.60331
-0.6805 -0.67426
-0.658 -0.72872 -0.68468
0.63696 -0.78496 -0.91439
0.85627
0.95383 -0.93623
0.83075 -0.94066
0.92466
0.83115
-0.9428
0.90924
0.97218 -0.94316
0.89642
-0.9731.
0.95972
0.96126 -0.96694
1069
-0.46716
0.90319
0.91573
0.91336
0.88494
0.87534
0.86996
0.91178
lOP
-0.60324
0.86458
0.88386
0.88326
0.84505
0.83766
0.81973
0.8718
100
-0.45316
0.93126
0.92709
0.93779
0.95415
0.90212
0.88825
0.927
109
-014026
0.96973
0.94634
0.96825
0.96121
0.96183
0.95049
0.96305
095285 -0.94659
0.9685
0.87863 -0.93398
103
-0.29092
0.98226
0.93591
0.98499
0.9158
0.97189
0.95385
0.95264
0.96156 -0.98003
0.98868
0.92856 -0.97057
Table 5-7f: Correlation Matrix of Variables for Part II Taiwan Sample
10S
t.06xs
1069*
1069*9
100944
TAIWAN:
1983-81
10699
10090
LOGPE
10099
10693
0.02589
1
100944
-0.10137
0.93055
1
100959
0.95604
0.9191
10699
0.00933
0.00241
0.97879
0.95527
095641
10090
-0.01593
0.91393
0.87633
0.93512
10699
-0.12316
0.97574
10693
-0.09443
0.9021
0.97337 0.96741 0.96466 0.94105
0.91456 0.63299 0.86937 0.71778
154
-0.1158
0.40221
0.53848
LOP
-0.29886
-0.048
0.77088
0.31778
bC
-0.13818
109
0.10101
106
L011
LOSE
106010
100014510 10G4
1
0.92091
¶
0.8596
0.98369 0.98999 0.93893 0.93915 0.95715 0.95743
10008510 0.01549 -0.96008 -0.94494 -0.98099 -0.95061 -0.95965 -0.95436
0.05839 0.91.753 0.90535 0.96502 0.95266 0.97058 0.96395
-0.10557 0.95133 0.96612 0.95732 0.93018 0.92632 0.93554
100610
10*
0.32128 -0.8233 -0.89802 -0.78109 -0.76045 -0.16629 -0.87956
1044
LOGPS4
1
0.62016
0.50039
0.83468 -0.84135
0.7658
0.86652
0.67015
0.85996 -089345
0.84445
0.89872
0.9216
0.90305
0.94079
0.73117
0.93247 -0.95395
0.90795
0.97332
0.83393
0.38933
0.7841
0.85291 -0.89419
0.9063
0.86228
0.81822
0.91111
0.96373
0.76882
0.95373 -0.96903
0.92642
0.98817
0.86162
0.81247
0.86337
0.72305
0.88196 -0.90973
0.8654
0.93006
0.78288
0.86892
0.8501
0.93786
0.91303
0.93843
0.90054
0.90085
0.84382
0.81635
0.89152
0.91725
0.83548
0.83806
-0.1463
0.91718
0.09098
0.8798
1
-0.75735 -089367 0.85239 -0.76707 -0.95996
038359 0.48121 -0.51945
0.73287
0.85565
0.84785
I
0.95062
0.65504
0.534%
0.59463
1
-0.79172 0.96437
0.15615 0.93441 0.98622
0.81655 0.97396 -0.98647
0.50393
0.84632
0.52314
0.73388 o.rsaos
0.44258
0.84769
1
0.89428
140
5.5. Variables and Data for Part III
Per capita total expenditure is defined as the private
final consumption expenditure in the GNP account divided by
the total population.
The STONE's index calculated in Part II was applied as
the price index for the group of commodities in the study.
The following four kinds of graphs are presented:
Per capita real group expenditures (deflated by
the STONE's index) plotted against time for the three
countries.
(Figure 5-11)
Per capita real total expenditures (deflated by
CPI) plotted against time for the three countries.
(Figure 5-12)
Per capita nominal group expenditures plotted
against per capita nominal total expenditures in log
form for each country.
(Figure 5-13)
Per capita real group expenditures (deflated by
the STONE's index) plotted against per capita real
total expenditures (deflated by CPI) in log form for
each country.
(Figure 5-14)
141
Figure 5-11:
Per Capita Real Group Expenditures vs. Time
Japan, Korea, and Taiwan
5.4
Ui
a-
5.3
5.2
5.1
C!,
Ui
4.9
(D
C
-J
4.8
I UT TI 11111TVT111TUT1
4.
1
11
1921
1111
1931
I
T T11FU1UFTIIT UTTU..I I TI I I
1951
1941
I
I I I
1961
IIIIIIIII!IIII III
1971
1981
Year
-.- Japan -4-- Korea
Figure 5-12:
Taiwan
Per Capita Real Total Expenditures vs. Time
Japan, Korea,
and Taiwan
14.5
14
1 3.5
13
x 1 2.5
C
a
12
11.5
a
10.5
C
10
9.5
1911
1921
1931
1941
rrflr1IITrF1;IrY1LIIIrTT1IIT111flI
1981
1971
1961
1951
Year
Japan
Korea
Toiwa
142
Figure 5-13a: Per Capita Nominal Group Expenditures vs. Per
Capita Nominal Total Expenditures - Japan
Group vs. Total: 1911-87
12
11
ci:
xLu
10
0
9
0
z
2
z
w
Ii
0
4
3
0
2
4
10
8
6
12
14
16
LOG(NOMINAL TOTAL EXP.)
Figure 5-13b: Per Capita Nominal Group Expenditures vs. Per
Capita Nominal Total Expenditures - Korea
Group vs. Total: 1962-87
13
12.5
0.:
x
Ui
12
/W
/R
0
8.
i
ii
i)
1
ii c
12S
LOG(NOMNAL TOTAL EXP.)
15
14
14 .5
143
Per Capita Nominal Group Expenditures vs. Per
Capita Nominal Total Expenditures - Taiwan
Figure 5-i.3c:
Group vs. Total: 1963-87
10
9.5
w
a-
0
V
-J
8.5
IFJI
7.5
8
10.5
10
9.5
LOG(NOMINAL TOTAL EXP.)
8.5
11
9
11.5
Per Capita Real Group Expenditures VS. Per
Figure 5-14a:
Capita Real Total Expenditures - Japan
Group vs. Total: 1911-87
5.05
5
a-
/N
t.0
a- 4.95
0
4.9
-J
jfK
4.85
0
-J
4.8
4.75
12
12
11
13.5
LOG(REAL TOTAL EXP.)
14
14 .5
144
Figure 5-14b: Per Capita Real Group Expenditures vs. Per
Capita Real Total Expenditures - Korea
Group vs. Total: 1962-87
5.35
5.3
5.25
w
5.2
-J
5.15
0
-J
5.1
7---
5.05
12.4
T
12.6
12.8
F
-;
I
13.4
LOG(REAL TOTAL EXP.)
13
13.2
I
13.6
13.8
14
Figure 5-14c: Per Capita Real Group Expenditures vs. Per
Capita Real Total Expenditures - Taiwan
Group vs. Total: 1963-87
5.15
5.1
a:
5.05
7
0
cc
(1:,
5
-J
w
4.9
4.85
98
10
10.2
10.4
10.6
10.8
LOG (REAL TOTAL EXP.)
11
11.2
11.4
145
The contrast between Figures 5-13 and 5-14 for each
country is interesting.
Figure 5-13 shows very clear linear
relationships (in logarithm form) between the group and the
total expenditures in nominal terms in every country
throughout the sample periods.
On the other hand, as shown
in Figure 5-14, the relationships between the group and the
total expenditures in real terms are not linear but roughly
quadratic (in logarithm) and the graphs are concave to the
x-axis.
Japan.
This trend is particularly apparent for Korea and
This suggests that when the general price trends are
taken into account, the responses of the group expenditure
level against the changes in the total expenditure level may
not be constant but changing with the level of the total
expenditure.
The real expenditures for staples and animal
protein foods are rising in the low real income phase, but
eventually decline in the high real income phase.65
65
Note that Figure 5-18c for Taiwan shows a sudden
increase in the real group expenditure in the middle of the
This may be caused by the first oil shock in 1973;
diagram.
cost-push inflation may have raised the real commodity
prices for this group higher while the real income
(approximately equivalent to the real total expenditure) did
not increase due to slow down of the national economic
According to the Taiwan real price plots of
activities.
Figure 5-7, rice, beef, and fish real prices increased
largely in 1973.
146
5.6. Evaluation of the Data Sets by the General Hypothesis
According to Figure 5-12, the real per capita total
expenditures increase constantly over time for all three
countries.
Therefore, the plots of food consumption trends
against time in Figures 5-2, 5-3, and 5-4 cani be viewed as
approximate plots of food consumption trends against real
per capita total expenditures.
Then, using Figures 5-2, 5-
3, and 5-4, the validity of the general hypothesis is
briefly considered.
It seems that the hypothesis holds for
Japan, Korea, and Taiwan for almost all commodities with the
following exceptions:
as per capita real income grows in
the three countries, plant origin foods (for direct food
use) such as rice and barley have been continuously
substituted for animal origin foods such as meats, fish, and
dairy products.
For Japan, Korea, and Taiwan, rice and
barley may be regarded as relatively lower priced/less
preferred/lower quality/less nutritious and bread/wheat,
beef, pork, chicken, fish, eggs, and milk as relatively
higher priced/more preferred/higher quality/more nutritious
agricultural commodities."
The demands for wheat items seem to be satiated in the
three countries; also eggs indicates satiation in Japan and
Taiwan.
Every other commodities (except the cases listed
below) follows the monotonic growth patterns with respect to
66
Rice in Korea does not indicate the declining trend
that is the pattern in Japan and Taiwan.
147
real income growth.
Exceptional cases observed in Japan (see Figure 5-2)
are 1) a sudden increase in wheat flour consumption just
after the war period in Japan; 2) a sudden increase in
barley consumption just after the war period; and 3) fish
consumption patterns between the pre- and post-war periods.
The sudden increase in the wheat flour consumption in
Japan after the war period may be due to the influence of
the emergency food imports from the United States during the
war occupation.
The high demand for barley just after the war period
may have been caused by an actual food shortage or the after
effects of the shortage, or both.
In that period, inferior
grains (e.g., barley) substituted for rice or other staple
foods.
The fish consumption trend in Japan is a unique case,
where the pre-war and the post-war consumption patterns seem
to belong to the two separate demand patterns.
Note that
the fish quantity data were taken from two different sources
for the pre- and post-war periods.
This questionable result
may have been generated in the data compiling procedure (see
Appendix K for details on the procedure).
information supporting this result.
following table in his 1972 article:
However, there is
OHKAWA reported the
148
Table 5-8:
Per Capita Proteins Intake Per Day by Major Food
Groups in Japan: 19 03-65
Unit: Grams
Year
1903-07
1908-12
1913-17
1918-22
1923-27
1928-32
1933-37
1938-42
1951-53
1954-56
1957-59
1960-62
1963-65
Starchy Others
Staples
51.8
52.8
50.3
51.7
51.1
46.4
44.4
45.9
41.7
45.2
46.0
47.2
47.3
4.2
4.4
4.4
4.6
4.3
4.3
4.3
4.1
3.2
3.1
3.6
6.5
7.6
Sum Livestock Marine
Products Products
56.0
57.2
54.7
56.3
55.4
50.7
48.7
50.0
44.9
48.3
49.6
53.7
54.9
1.5
1.7
1.8
2.3
2.8
3.1
3.6
3.1
2.5
3.5
4.6
6.3
9.4
5.9
6.1
9.6
12.2
15.0
15.5
17.8
19.1
10.5
11.5
13.9
15.1
14.4
Sum
Total
7.4
7.8
11.4
14.5
17.8
18.6
21.4
22.2
13.0
15.0
18.5
21.4
23.8
63.4
65.0
66.1
70.8
73.2
69.3
70.1
72.2
57.9
63.3
68.1
75.1
78.7
11.7
12.0
17.2
20.5
24.3
26.8
30.5
30.7
22.5
23.7
27.2
28.5
30.2
100
100
100
100
100
100
100
100
100
100
100
100
100
Percentage Distribution
1903-07
1908-12
1913-17
1918-22
1923-27
1928-32
1933-37
1938-42
1951-53
1954-56
1957-59
1960-62
1963-65
81.7
81.2
76.1
73.0
69.8
67.0
63.3
63.6
72.0
71.4
67.5
62.8
60.1
Source:
6.6
6.8
6.7
6.5
5.9
6.2
6.1
5.7
5.5
4.9
5.3
8.7
9.7
88.3
88.0
82.8
80.0
75.7
73.2
69.5
69.3
77.5
76.3
72.8
71.5
69.8
2.4
2.6
2.7
3.2
3.8
4.5
5.1
4.3
4.3
5.5
6.8
8.4
11.9
9.3
9.4
14.5
17.2
20.5
22.4
25.4
26.5
18.1
18.2
20.4
20.1
18.3
OHKAWA, 1972, P. 213.
OHKAWA provided these comments about the table:
As is widely known, marine products play a major role
in supplying protein sources in Japan and the
statistics in the table reflects the fact. ... An
appreciable increase in the animal protein intake from
non-marine sources is only a recent phenomenon (OHKAWA,
1972, p. 214).
149
Although the definition of each commodity group is not
clearly mentioned, it is shown that the per capita protein
intake from the marine products were larger in the pre-war
period than in the post-war period.
Therefore, the validity
of the fresh fish consumption data trend is not rejected.
The rate of change is greater in this fish data than the
OHKAWA's marine products data, however the basic trend is
quite similar.
It may be that the difference is due to the
difference in the level of aggregation.
assessment for the situation:
The following is an
fish was a major source of
protein in the pre-war period, therefore, fish consumption
was relatively responsive to economic growth.
When the
economy began to grow in the post-war period, food
diversification proceeded, as fish had many substitutes,
such as meats and dairy products (see Figure 5-2).
Therefore, the growth of per capita fish consumption slowed
in the post-war period.
Following this line of reasoning,
the trend in the fresh fish consumption data is taken as
reasonable. 67
67
The estimated Hicksian cross price elasticities also
provide some evidence supporting this assessment.
See
section 7.3.2. for details.
150
5.7. Evaluation of the Data Sets by the Tests of Revealed
Preference
In this section, following CHALFANT and ALSTON
(1988)68 and VARIAN (1987), the formal analysis called
"nonparanietric tests" is applied to examine stability in
preferences represented by the price and quantity data sets.
The null hypothesis in the nonparametric tests is
that observed data conform to the restrictions implied
by a stable set of well-behaved preferences, under the
necessary additional restrictions that (the goods under
consideration) constitutes a weakly separable group and
that it appropriate to analyze per capita consumption
data as having been generated by maximization of a
utility function by a representative consumer (CHALFANT
and ALSTON, 1988, pp. 397-8).
The "nonparametric" approach is based on the revealed
preference analysis established by SANUELSON and HOUTHAKKER,
and further developed by AFRIAT and VARIAN.
There are two
well known axioms of revealed preference, namely SARP
(Strong Axiom of Revealed Preference) and WARP (Weak Axiom
of Revealed Preference).
"The strong axiom is equivalent to
the existence of a well-behaved utility function"
68
(p. 393).
CHALFANT and ALSTON (1988) applied the nonparainetric
approach to examine an existence of a structural shift in
meat demands and found that preferences on demand for meats
(and fish) had been stable and no shifts had occurred in the
U.S. for the 1967-84 and 1947-83 periods, or in Australia
for the 1967-84 period.
Their findings are a formal
objection against a common belief that the demand structure
for red meats has changed in these countries during the last
few decades due to growing health concerns among consumers.
151
The potential problems with the data sets such as
"aggregation or erroneous separability assumptions" can be
confirmed to be insignificant if the data passes the SARP
test (pp. 396-7).
Therefore, checking the given data sets
with respect to the SARP is attractive as a pre-test before
applying a theoretical "parametric" model.
According to THURMAN (1987), the power of the tests may
be limited for time-series data.
Citing from CHALFANT and
ALSTON (1988):
the method might have low power ... (w)hen budget lines
shift steadily outward over time so that they rarely
cross, there will be little chance of finding
Each year's
observations inconsistent with the axioms.
consumption bundle is revealed to be preferred to all
previous ones. ... the finding was therefore necessary,
but not sufficient, to conclude that preferences (have)
remained stable (p. 394).
CHALFANT and ALSTON argued against this point that
It seems reasonable to expect the power of
nonparametric tests to be higher for disaggrerated
goods such as these than for more aggregated goods.
Quantities consumed do not all rise uniformly with
time, and relative price variation is likely to be
greater, relative to variation in real expenditure,
than for more aggregated bundles of goods (p.494).
152
Deqin CAl (1990) developed a computer program for
testing the WARP and the SARP consecutively.69
Using this
program, the six data samples, four from Japan (pre-war,
mid-period, post-war, and all-period) and one each from
Korea and Taiwan were tested.
Somewhat surprisingly, all
the data passed both of the WARP and the SARP tests.
Although the results may have limited reliability, several
conclusions are:
First, the data are consistent with the
assumption that preferences for the food group were stable
in Japan for almost the entire century, and in Korea and
Taiwan for the period after the 1960's.
Second, it is
reasonable to apply neoclassical econometric models on the
data sets.
Third, the assumption of the weak separability
is reasonable for all samples.
Although the price and quantity data passed SARP tests,
it may be reasonable to include some non-economic variables
in the demand equations since, as CHALFANT and ALSTON noted,
it is difficult to specify the functional form correctly in
the "parametric" analysis.
Inclusion of other non-economic
variables may provide greater flexibility to the model and
may reduce possible problems in the model related to the
functional forms, such as limited degrees of curvature.70
69
The basic ideas and procedures are summarized in
Appendix G.
70 See Appendix B for the discussion about the
functional form of the AIDS Engel curve.
153
CHAPTER
6
ESTIMATION: METHODS, PROCEDURES, AND RESULTS
6.1. Introduction
In this chapter, statistical methods and estimation
procedures along with assumptions on the error structure are
explained for Part I, II, and III separately.
Also,
estimated coefficients and other statistics are presented in
this chapter.
Elasticity estimates will be presented in
Chapter 7.
A common assumption is that the error terms in each
part of the model are not correlated with the error terms in
the other parts.71 Based on this assumption, each part of
the model was estimated separately.
The six different data samples (four for Japan with
different time periods, and one each for Taiwan and Korea)
were estimated separately.
71
This assumption is reasonable when there is no
significant autocorrelation among the error terms in Part
II.
In this case, Part I and Part II as a whole have a
recursive structure in which Part I receives no feed back
from Part II; since the events in Part I belong to the
previous time periods for Part II. As a consequence, Part I
and Part II can be estimated separately. The error
structure between Part II and Part III may be assumed to be
independent in the sense that each data generating procedure
belongs to the different stages in the utility tree; thus,
the major omitted factors may be different in each case.
154
The critical t-statistics to statistically test how
significantly the estimated coefficient is different from
zero are summarized in Table 6-1.
6.2. Estimation Method and Procedure for Part I
For the purpose of estimation, additive random
disturbances for each equation were assumed.
The model has
a structure described by
Part I:
Qi..1 = hi( Tqi..2,
Zi:..2
+ Citi +
where vi..1 is the newly added random error term.
(6-1)
The
equation (6-1) can be rewritten as
= hi{ Tqi,2,
where
eiti =
Z2 }
+ ei1
+ vit_1
i=l,...,n;t=3,...,T
(6-2)
155
Table 6-1:
Summary of Critical T-Statistics
Critical T-Statistics For Part I
Data Sample Title
Japan:
n
k
df
All-period
64
15
49
Pre-war period
29
15
14
Mid-period
33
15
18
Post-war period
35
15
20
Korea
28
15
13
Taiwan
27
15
12
* - Tc for 50 degrees of freedom.
To (5%)
2.008*
2.145
2.101
2.086
2.160
2.179
Tc (10%)
1.676*
1.761
1.734
1.725
1.771
1.782
Critical T-Statistics For Part II
Data Sample Title
n
k
df
Tc (5%)
Tc (10%)
Japan:
All-period
60
16
44
Pre-war period
27
16
11
Mid-period
29
16
13
Post-war period
33
16
17
Korea
26
15
11
Taiwan
14
11
25
* - Tc for 45 degrees of freedom.
2.014*
2.201
2.160
2.110
2.201
2.201
1. 680*
1.796
1.771
1.740
1.796
1.796
Critical T-Statistics For Part III
Data Sample Title
i-i
k
df
Tc (5%)
Tc (10%)
10
10
10
10
10
10
50
17
19
23
16
15
2.008
2.110
2.093
2.069
2.120
2.131
1.676
1.740
1.729
1.714
1.746
1.753
Japan:
All-period
Pre-war period
Mid-period
Post-war period
Korea
Taiwan
Note:
60
27
2,
33
26
25
n = Number of observation.
k = Number of independent variables including a
constant term.
df = Degrees of freedom.
Tc (p%) = Critical T-Statistics with p% level of
statistical significance for two-tail test.
Source: OSTLE and MALONE, 1988, p. 583.
156
The following error structure was assumed for
ei_1 assuiiiing the independent variables are nonstochastic
and bounded:
ei_1 is normally distributed with mean
E(ei_1) = 0, i = l,...,n
(6-3)
variance of the errors in i-th equation is
E(ei_1 ei_1) = a2ii, i = 1,
(6-4)
. . .
the errors are contemporaneously correlated among
equations, i.e.
E(ei_1 ej_1) = aij, i,j = l,...,n
(6-5)
there is no autocorrelation, i.e.
E(eit_a
= 0 for a * b, i,j = 1,...,n
(6-6)
A system estimation approach called the Seemingly
Unrelated Regressions (SUR) technique, coined by Arnold
ZELLNER (1962), was applied for the model.
SUR is a
generalization of the Least Squares method with regard to a
particular error structure under which errors across
different equations are possibly correlated with each other
within a time period.
Contemporaneous correlation among
error terms may occur from "some common unmeasurable or
omitted factors" (JUDGE et al., 1988, p. 443) among the
equations.
If there is contemporaneous correlation and an
appropriate method such as SUR is applied, efficiency is
157
gained in the estimation since some additional information
contained in the error structure will be reflected in the
resulting estimator.
The SUR estimation requires the variance-covariance
matrix for the system as a whole.
thus needs to be estimated.
This is rarely known, and
Technically, there are two
kinds of SUR, depending on the estimation procedure for the
unknown system variance-covariance matrix:
one uses only
first round estimation of the variance-covariance matrix,
known as ZELLNER's method.
The estimated coefficients are
equivalent to those from OLS estimates; only the variance of
the coefficient estimates are changed.
to here as the "non-iterative SUR".
It will be referred
The other SUR employs
an iteration procedure to estimate the unknown
variance-covarjance matrix; it will be referred as the
"iterative SUR" (for details, see JUDGE et al., 1988, pp.
451-2).
The iterative STiR estimator is equivalent to the
maximum likelihood estimator assuming "the random errors
follow a multivariate normal distribution" (p. 452).
A computer program SHAZAM Version 5.13 developed by
K.J. WHITE (1987) was used for this part of the analysis.
The iterative SUR was initially tried but failed in the
iteration procedure, probably due to high correlation among
independent variables.
As a best alternative, the
non-iterative STiR method was applied.
The estimated
coefficients are reported in Table 6-3 and the notation for
158
Table 6-3 is summarized in Table 6-2:
Table 6-2: Notations Used in the Tables of Part I
Estimation Results
Parameter Name
Corresponding Variable
TQ
logTQ
TQX#
logTQ x logX#
TQTQ
(logTQ)2
X#
logX#
X#X$
logX# x logX$
where:
TQ = Total domestic consumption of each commodity
#,
$ = 1,2,3 and
Xl = AGPOP
X2 = AUTO
X3 = CPI
Due to high correlation among the independent variables,
many t-statistics were not significant but the value for
each R2 was high72; this situation indicates a
multicollinearity problem (see, for example, GUJARATI, 1988,
72 SHAZAN automatically calculates R2 statistics for
each equation automatically even when the system estimation
technique is used. In the system estimation, R2 statistics
are not applicable to judge the model performance. However,
they may be taken as an approximation for the R2 statistics
in the single equation estimation.
The R2 statistics are
generally high except for fish in Japan all-period and prewar period samples, rice in Japan pre-war period and Korea
samples, and beef in Japan pre-war period sample.
159
pp. 283-309).
Alternative methods or specifications to
correct the multicollinearity were not attempted, in part,
because the estimator is still unbiased and consistent.
More importantly, the main purpose of Part I is to obtain
the predicted values.
As long as "the purpose of regression
analysis is prediction or forecasting, then
multicollinearity is not a serious problem" given the high
R2 statistics (GUJARATI, 1988, pp. 307).
Another motivation
for "following a course of inaction" (KENNEDY, 1986, p. 151)
is that the model was specified based on the mathematically
well developed form of the TAYLOR series expansion.
INTRILIGATOR (1978) noted that
if the model as specified is based on a well-developed
underlying theory, then there is no justification for
changing the specification. Such a change, such as
omitting variables, would induce a specification error
into the analysis, ... creating biases in all of the
estimated coefficients. This specification error could
lead to more problems, so that the "cure" might be
worse than the "disease" (p. 155).
CONSTANT
PARAMETER
MILK
EGOS
FISh
1.7380
T-S1ATISTICS
-S'IA'I'IS"l 1(9
ESIIMATES
STANDARD ERROR
-0.49649
0.12476
030701
0.93641
-021037
11371
021409
87424
'017839
0.20448
TQXI
.0.043405
03308
0.12813
0)046
.038336
011661
0,20687
172)72
0.061091
0.22773
.14015
1.9738
TQX3
1,7498
0088636
0.2146)
1.452
0,024)85
0,4735
0,02823)
TOX2
0.83796
107(3
.0.30087
2,4391
TISXI
172172
0.77275
0.27088
0.21153
0.064942
-0.38340
105172
0.049859
-11178
0.019938
037)066
033477
I.3249
437309
0.048733
0.053808
0,29328
0,14483
0,66243
037046
0,037)06
.030605
00491338
033398
0,45902
.0068002
2,0882
039307
47742
.033494
11,943
0077092
0.90681
2(27(3
XIX3
0342474
037789
XIX2
2(37(3
-0.018777
098497
031287
X2X2
0.045828
035753
0,003046)
-1.6104
0.043)49
XIX)
2,0763
X2X3
0.080571
1300)
XIX)
018571
-0.0312)5
0,82081
7(3
-1,4046
0.9)90
XI
7(2
13034
-1.705
-13042
0,81354
0028514
7(37(3
0,0086803
2(22(2
0.35607
35372
17.678
XIX)
1,8368
4.68521
8,32.49
7(3
XIX2
499)49
-0.11926
08435)
-0,7)399
-0.04592
0.75221
0.097309
3,10985
-0.034577
0.066271
0323)3
033503
0.043348
0.085502
7(22(3
0.15695
XIX)
14313
0381568
0.11675
-0.039967
0.016)77
0.011549
0.018459
097212
432)4
3,453)
21,191
Xix)
-0.036607
X2X2
0.47)76
0.13)75
XIXI
2(3
XIX2
080149
096866
2.08
059908
-1,7061
0.62112
0.71)91
0360264
0.0097386
2.0337
037373
0.61841
X2X2
X1X2
-0.4595)
X2
091938
-14672
0.10287
>3
-31556
0.077227
0.3974
X2X3
0.071016
XIX3
X2X3
038305
13104
X1X3
13106
0.045517
039086
Xix.)
1
0.8419
19
XIX2
048682
0.055095
7(27(3
0.26395
0,026256
1.5515
XIX)
-037949
XI
X3X3
1.1581
.0.73261
Xl
-1.409
14337
-050219
0.75726
2.0503
107(3
4,1432
4.2855
40579
038882
0,1939
0,033793
172X2
.5.8376
Xi
7.0913
X2
13439
-15392
-59316
Xl
042.37
2.4854
33593
039444
T)JIU
032831
57,791
036418
4,6)93
0370734
7(22(2
XIXI
-044373
032284
0330176
X1X2
XIX3
-19991
3.73)3
-2.732
0.11335
)C3X3
0.10343
0.85974
0.78728
-2.1508
030684
X2X3
035551
0.14768
0.037438
33979
XIX3
079021
031247
XIX2
035581
04392
7(22(3
13300
-1.1916
XIX3
0.0)907
0319079
-039805
0382285
X2X3
X1X3
043592
0.19748
XIX)
0319625
03014316
-095)04
032793
0.77457
X3
0.0085033
0.9705)
23058
3340)
2(2
Xl
2(22(2
4.009987
-03212
XIX)
-19641
2(3
.33083
-1.0439
084158
11.775
13631
24391
0,1832.3
1(210
-1.6218
X2
3.1469
1023
038661
-2.6202
0396682
0314149
2.4736
2.8597
2.2149
1391
030425
X3X3
0337072
-03063
23109
X3
1.5698
0.10878
0.17977
Xix)
29255
X2
59,613
Xl
2.0642
12824
26.47
XI
-0.075133
25.268
039349
'lOX.)
-1.0935
0.11324
0.13349
1(23(3
107(2
.0.26048
1.1)8
0.22415
2.4345
TO
.1.7302
16,457
-28.406
TO
0.48309
23,964
11517
lOX)
-2.1926
055707
-0.40303
TO
0.18267
2.0998
1536
-15.596
CONSTANT
128.77
STANI)ARD ERROR
PAI4AME1ER
225,2
CONSTANT
PARAME'ISiR
ES1'IMATES
.0.42)95
190.74
STANDARD ERROR
T-STATISTICS
-99,401
CONSTANT
)(.SISMA1'l'.S
PAR.AML-rI(K
0.490)7
1.1497
'I9)X2
0.13245
0.25752
0.16862
1(23(2
-94408
'1133(1
-0,47439
0391)5
-037783
'1)3
1.947
19.071
151.63
492365
lOX)
STANDARD ERROR
7.4595
-57393
ESTIMATES
TO
CONSTANT
PARAMETER
-1,0448
0.470)8
13424
-1,0591
0.79913
0.14755
0392461
030496
172113
0.069359
103(3
2.321
0.12412
70>3
-2.804
-1.7091
lOX)
13596
25.039
10
2.0363
-1925
-2.3046
044381
039218
031917
1.8596
6.0714
-15714
0.48141
XIX)
10301
TQTQ
-13997
0.64992
lOX)
107(2
-35797
lOX)
-13992
TO
T-STA11SISC'S
T-STATISTICS
X2X2
045367
030884
0.77157
-09966
-0.047085
0.069950
-0.79937
14755
T.STA11STICS
.0.71286
091258
LISM
43628
1.1109
0.70393
035517
23.63
STANDARD ERROR
032827
-041401
73335
0.14882
X2X2
-0350153
XIX)
Xi
084928
2.7776
-1.6638
-0343413
X2
-1.7482
1.5426
XI
030345
1(1193
-0,70)54
'lOX)
-0.016722
'lOX)
-2.4422
-15131
-13874
-13957
23079
0.95049
0.15902
031)626
03074905
0.242
13396
34195
8956
14880
0.078616
XIX2
033337
X3X3
-0319349
0.21181
2(22(2
-03003232
037337
.3.3505
XIX)
7(3
-44902
Xl
7(2
TQTQ
9.7391
TQX3
.2375)
0303Z3
0.22564
107(2
2.0561
0.14384
ISJXI
0.05724
039284
039964
TOXI
28883
-22.8
193
18021
46.25
74.099
TO
173.3
-17874
15566
T-STA11SmCS
ES11MA11OS
28.117
STANDARD ERROR
( (3NSSAN'l
1)11K) N IA) MI HI
74.031
LStIMAII'.S
153.79
STANDARD ERROR
PORK
REEF
43.768
CONSI'ANT
OAKlEY PARAMLIS39
ES11MATES
0.79825
180.78
STANDARD ERROR
CONSTANT
PAI(AML(lEI(
15)49
.1574
TEIA1SSTICS
ESTIMATES
35882
5648
CONSTANT
STANIMRD ERROR
IO,1SMATES
PARAMETER
Estimation Results for Part I - Japan All-period Sample
T-STATIS11CS
141(1(A))
RICE
Table 6-3a:
0.9974
R-SQUARE
0.997)
R-SQUARE
0.7)63
K-SQUARE
0,9932
KSOIJAKII
09928
K-SQUARE
09063
K-SQUARE
09752
K-SQUARE
09803
K-SQUARE
09718
K-SQUARE
034764
040329
CONSTANT
T-STA11S11CS
PARAMIOTiR
6111 K
EGGS
PuSH
T-STATI03ICS
IisrIMATL2i
STANDARD ERROR
-0.73534
64,272
13753
0,77072
-47 275
TO
3039.9
CONSTANT
XI
7(3
37.474
42.749
0.87061
-8,2237
13.943
.050968
56258
446305
3.0072
-0.2472
1.332
028151
0,45576
3.4036
Xl
7(2
10277
.373.02
12493
107(3
.0,24890
107(2
0.40342
58.353
23.46
XIXI
2.7115
-0073574
046392
037457
0,033099
-048.348
4.3995
032642
.0.026542
7(27(2
XIX2
-2.6405
037628
85902
.1.149
-0.62023
X2X3
-5.6545
023672
.33305
7(273
047463
1.1009
052254
2(27(3
143
035417
087072
7(2)3
.9.8703
XIX3
-2.462
4.6809
X3X3
5.8626
3888
-33723
030806
-13883
2.0367
0.026461
28241
-2.0299
26.262
038093
22.171
93305
270.74
0.95903
0,67358
0,44743
-14.434
XIX3
.0.030235
24.18
-024749
-63699
-0036735
1.980
036453
0.63
XIX2
8.7953
13647
2(37(3
40651
XIX3
3.061
1397
XOX2
10914
53307
1.0654
7(37(3
33407
9.6686
14-12
X2X2
-57336
XIXI
53540
7(3
33922
7(2
25981
0.24063
Xl
0.12613
0.3863
0.024057
0.88648
172197
0.23623
0.42743
-0,77828
0,000096
114.2
1014
34.951
979.76
29.271
-27.332
7.6321
7(27(2
33734
XIXI
0.62763
0.06256
-0.94899
102.86
-2.1709
-1.7391
737(3
X1X3
-080346
-092285
098487
13026
-0.093399
X2X2
1.2528
XIX2
10379
25.644
0.33933
-083423
X2X3
86156
X1)0
1.1748
14833
-2446
XIX2
-13546
0.47947
84452
7(3)3
0.17435
X2X2
-092634
1342
43206
346.97
XIXI
0.47432
14837
70.47
XIXI
43.425
23
7(2
Xl
037497
0.18125
0.2.7063
034905
107(2
1727(3
-2.1614
-1.2302
0.74906
3.79
2.3642
039396
-3.806
15119.3
-2.6191
48.625
-3.7639
28.403
-303.02
Xi
-2.4626
-99.945
13955
107
11722
Xi
22303
1.1401
039476
12.754
33585
0.48617
0079441
43319
58.632
-0.74538
3(2)3
048655
.01376
X1X3
040075
0.19499
X2X3
88563
-12186
XIX3
-11815
XIX2
2.1881
44424
9.7232
XIX2
54881
3(37(3
2.1105
34884
0.11718
XIXI
X2X2
19309
23465
4939
3(3
.0.10146
0.12285
046793
3748
0057833
151.42
73993
X3X3
-34897
4443
0.43614
-23651
0011985
X2X2
046804
13.01$
4067
0.75626
-1.1288
X2X3
32571
023014
0.28943
X2X3
-1994
XIX3
33996
49334
6.757
XIX3
3.7738
-19399
XIX2
-12544
447509
X3X3
1.5873
.1.991
XIX2
027993
.0.18898
X3X3
0.11167
XIXI
-38028
-3.7701
10032
1.7004
34241
0.66833
1Q73
3.3334
33656
TQX2
33495
13151
-3782
2.4227
19JR7
7(2
42109
TQ)3
Xl
3.4106
TQX2
-095498
-037421
-33362
-30,68.3
.490.17
-096423
7(2
Xl
0.34638
-0.6823
.035234
38.413
12.055
31401
-82347
13113
-1393
192397
2.7818
12624
35136
TQTQ
13443
0.63968
1.1008
7(2
-19144
034644
-373.44
-1.1489
322.25
42299
12611
-1243
1727(3
19281
1.3515
2.2261
17373
-13326
-63693
11351
3(2
XI
TQTQ
23
0.1528
09211$
-0.7435
-0.22435
14654
-1.1427
0.40395
0077303
60439
X2X2
31147
522.53
13797
.12.399
37372
-122.46
0.033264
XIXI
-1.7308
0033288
92179
X2
Xl
-38058
-057723
X2X2
.0.057614
48068
XI
-12338
TQTQ
20419
22.m
7.8999
023131
-37.174
XIXI
.13.147
X3
1.7811
X2
0.7905
0.11372
33,027
TQXL
3.7059
PARAMETER
7,0736
39262
-13617
68603
-0,16781
22.633
TQXI
-0.83876
29.387
-16.932
TQXI
STANDARD ERROR
T.STAIISTICS
-53,639
TO
1.6573
101.12
16737
TO
4.8357
23542
11394
TQXI
-0.63852
33.108
-32308
-115.13
CONSTANT
FARAMLOER
30239
.4.7252
-3.7004
-2349
7977(2
03928
9263
707X1
-1.4225
1177(2
.22.464
ESTIMATES
-049523
T.SI'ATISJlCS
22788
STANDARD ERROR
CONSTANT
PARAMEtER
-31283
4.0353
1-STATISTiCS
ES11MATES
23063
STANDARD ERROR
-484.17
TO
CONSTANT
CIIICKIiN PARAME1ER
96233
0.78577
025322
T.STA11S11CS
ESTIMATES
96385
29112
STANDARD ERROR
75.736
737.39
TO
44942
637.13
STANDARD ERROR
24.612
227.14
ESTIMATES
TQXI
CONSTANT
PARAMETER
117
030303
-13603
-13497
14191
045807
T-STA11STICS
ESTIMAThS
P034K
BEEF
1.4551
3.33384
32302
0.73486
172)3
9408
-3.8854
728.22
-434
172X1
TQX2
1.1891
040992
-0.13721
3.428
0.48893
93433
1808
1177(3
1.717
-12751
-3.0946
028041
1177(2
1.035
2.4094
433.7
Xl
4.1216
-5.2S79
172717
094614
.0.92615
TQX3
-3282
1(7X1
-0.5815
0.41188
0.42629
197X2
STANDARD ERROR
13337
717
0.66415
44306
38287
TQ
1.1772
33.22
-6.52.43
TQXI
33358
CONSTANT
BARLEY PARAMETER
390
1634
197
ESTIMATES
0.090586
3360.4
CONSTANT
PARAMEIUO
STANDARD ERROR
-1.4275
T-STA11SflCS
105.11
36343
STANDARD ERROR
ESTIMATES
-2333
CONSTANT
ES11MAThS
PARAMIE lift
Estimation Results for Part I - Japan Pre-war Period Sample
T-STATWflCS
BREAD
RICE
Table 6-3b:
0.9961
ft-SQUARE
R-SQUARE
00968
0.7803
R-SQUARE
R-SQUARE
00612
09264
ft-SQUARE
08034
ft-SQUARE
0966
R-SQUARE
0.947
R-SQUARE
0.6139
ft-SQUARE
Table 6-3c:
I
Estimation Results for Part I - Japan Mid-period Sample
lIAR.! FY
ORE
>12
X3
X2X2
X3X3
-2 247.1
-.47938
-12.765
-28458
(.0501
-0,019017
11.02338.!
2.6842
0,07905!
0.27652
110! 34
l0 22
31050
11272
0 29075
3 (5I
(I1.35!
21.331
5.257
692.75
0.11914
0.05952
2.680!
037509
012(3)9
'.15(15111 7,
-0,542(9
77 07103
011,03(91
11.13.8
023139
II
09124
(1.59550
(4 30876
('7475
(115991
03)288
0.96808
0.21070
2 2(15-1
'ARAME! LII
CONSiAtol
Xl
X2
>13
MILK
'lOX!
1'QX2
1'QX3
TQ>3
1'YIQ
101(3
XIX!
X2X2
X3X3
X!X2
XIX2
XIX3
XIXS
>12>13
X2X3
-68,148
4,3707
053384
-1.1322
202(9
59,688
12.295
20938
-13.8(3)
0.40393
-0.21777
-5.1805
-1,2266
-0.99409
651.68
76459
6.3727
2.104
1.4772
2.8021
178.48
44.999
20.615
6349
03820
031780
6.7346
2.465!
0.80799
I YI'A'IlSii('S
0.4724
0.80129
0,6871
0.25373
-0.76646
0(691
033397
0.27322
1.0911
-0.83806
1.0549
-038307
-0.77058
.0.49158
-11303
I'ARAMI(! ER
CONSTANT
XI
X2
TO
392X1
TQX2
l-SIIMASI'.S
-2929
-3! 053
84302
1.7479
SIANDARD ERROR
307.71
23.222
9.1452
1.5737
-0.75547
-13372
0,92247
1.1107
CoNSTANT
IARAME1ER
214.25
ES'! MAlES
STANDARD ERROR
663.88
1'-STADSTICS
032273
1A1(AMI7I IN
CONSSANI
TQ
lOX!
TQX2
lOX)
TQTQ
Xi
XIX!
X2X2
X3X73
XIX2
XIX3
X2X3
-0051155
24597
24.946
11.49!
-44394
0.19119
-0.30834
-12.632
-0.98006
.1.0305
1.1516
13033
200.11
34.8
9.1357
21,309
0,4863
0,4008-4
10.685
3.8767
055804
-0.429-41
-0,037524
1.2202
0.71685
1.2516
-1.6262
039315
.0.74428
-1.1811
-0.20513
-1,8466
-0,49451
lOX)
1010
Xi
XI
>13
XIXI
X2X2
X3X3
X1X2
30703
X2X3
29.202
1.4706
2.0972
.028643
-1.73-13
-17530
-55,434
7.7613
19354
0,45668
-0,040004
8.6636
-1.2055
-0.012216
52.051
6,2754
2341
0,68761
19323
21836
48.453
12328
21.405
032332
0.19847
8.4219
1.6817
0,4926
056103
0,23434
0.85742
-0,4714
-0,80754
-0.804
-1.1451
061951
030417
13506
-0.50156
19287
.0.74055
-0.0248
X2
Xi
10
TOXI
TQX2
10>3
'1010
Xl
XIX!
X2X2
X3X3
XIX)
XIX)
X2X3
-537.2!
30751
-53852
-03306!
0.10072
-0.70188
175.03
63723
0.87027
-14906
056545
-0,029599
-0.94328
-0.42513
-0,3943
624 (38
47071
5,9231
03722
0.19454
1,1928
2103
25762
5234-I
20.821
035786
0.17328
6.6659
1.1137
036074
-S'TAIlS'TICS
-0,8688
0,714075
-0.65796
-057778
055889
-058842
0834!
0.24735
0,16770
.0.72626
1.4236
-0,16087
-0.14131
-030-174
-1.093
I' 'IRAQ El 1:11
CONSTAN'I
X2
>3
IQ
TQXI
'I'0X2
50>13
TOIl)
XI
XIX!
>1212
X3X3
XIX2
XIX3
X2X3
-93307
23037
-2 2057
-0,9(733
038805
-08870
303.82
91,120
-23.252
-43.245
-0.99042
05786
-30.804
5,042
0.11247
86113
47.407
78302
1,4417
0,65753
1,5079
3273
40,835
10713
32.52
037238
0,18383
89187
1535
0.32795
I -S IA'! IS'T!CS
-(0635
035262
-020(80
.0 03627
038772
4268250
1.175
2.2316
-2,1704
.13298
-2,6597
3.1507
-23326
3,2847
0,34297
1.759501171 1711
CONS'1A7-71'
S FANDARD ERROR
1.1.7,5
IQ
1Q73
30795
SI! MATES
1-1511
11501
I'.SI!MAlES
STANDARD ERROR
I-SI MATES
STANDARD ERROR
(1111K
XIX!
Xl
0 0732oo
-STATISTICS
IlL!-!'
'72
0.11065
'IA'! 1.5
'1 7,7,I)AI7l) I'l(I!(l(
111(1 --5 I)
10
-40864
-o 11053
7,!
I
(.15157,1
24
7 I(.\.\IIl :17
10
1Q73
10>12
70>13
15310
XI
>12
>3
XLI
X2X2
>13703
73>12
XIX)
>12.703
I-S IISIATI'.S
-33235
81 660
0,07000)
.0503
-0.68,303
-2.99 74
111980
-79.783
3.399?
12917
-038374
038801
4,0562
12123
0.73895
SI .\515949) I:RROIO
35277
35,742
31!
0.79594
0.43987
3394
12(34
2057!
64827
11978
02399
0,14168
4.4700
0,74288
024570
SIA1 5341 S
-09421
229(1
0022733
1327!
-13087
-2 2.37')
-04022?
-1.444
031968
10701
-13996
2,739
0,90802
1.0322
3.0053
-717,51171 1:17
('ONS'IANl
I
30
16)701
'10592
I-SI SlATES
-552.03
29407
-22402
-010314
SIANDARD ERROR
378,49
44,153
38976
038701
I STATISTIC'S
-1,4507
0.88603
-057083
-0,23747
l'AIIASIEl ER
CONSTANT
1.511 MATES
S l'ANI)ARI) ERROR
I 71511511(5
10
TQXI
TQX2
10>13
TQ'!Q
Xl
X2
>13
XIXI
X2702
XJX3
XIX2
XIX3
>12>13
.083220
(6236
28.706
-4.0027
-18.676
-0.1115
-0,037086
-6.8686
0.67(63
.0.22943
0,1652
1.2881
79332
17341
33799
65894
0,13658
0,070481
29772
05705!
0.13225
1.1986
-0.04711
2.2452
1.6365
.13618
-2.8343
-0.81634
-2.3032
1.1865
-(.73-19
019801
70>13
TOIl)
XI
X2
>13
XIX!
>12>12
-052618
X3X3
X!X2
XIX3
X2X3
-1354.7
11817
-9,1688
13240
0.81978
-3.1472
3-4-17!
7,0282
.13.08
-29,2-42
.0,11348
-0,1503!
-5966
1,1692
-0 78557
7183
91.416
93368
1.4475
046235
2.4019!
(95,77
4248?
93514
14.059
028857
0.14313
7.8318
1.1888
0.31598
.072
12051
-09803
0915(19
1.7016
-303,?
207)4
010541
-13(65
.208
-039325
-1,037
-0.76177
0.98151
24891
K-SQUARE
0.9156
K-SQUARE
0.9796
K-SQUARE
0,9414
K-SQUARE
0.9516
R-SQUARE
0.9824
R-SQUARE
0.9838
K-SQUARE
0.9275
K-SQUARE
0.9960
K-SQUARE
09963
Table 6-3d:
j(I
BREAD
IAI(AN1I1tI!
Estimation Results for Part I - Japan Post-war Period Sample
('(ONS'IANF
1(3
IQXI
1'QX2
'IQX3
'IQlQ
XI
X2
X3
XIXI
X2X2
XIX2
X3X3
XIX3
K-SQUARE
X2X3
ESI'IMA'll'.S
308.61
-95.545
9,7707
-3.4955
-1.4653
432-47
17312
63.492
33,744
-13188
-0.37368
-0.44739
-1.0882
-14186
-0.16304
SI'ANI)AKI) ERROR
270.88
26343
4.455
039559
0.94509
0.82526
24.463
6.0746
41362
1.001
0.076153
0.28421
049478
0.84366
0.48358
T-STA11SI1CS
1.1007
-3.2549
-67453
-88266
-43504
5.4825
71477
10.457
49435
-1.2176
.49068
-2.4551
-21128
-1.7525
-0.88797
PARAMETER
CONSTANT
TO)
TQXI
TQX2
TQX3
13743)
XI
X2
2(3
XIXI
X2X2
XOX2
2(32(3
XIX3
K-SQUARE
X2X3
ES11MATES
990.84
-143.27
4357
036327
-29462
49464
-45355
64621
38.355
-142
-015496
13504
-19613
-034288
-19234
STANDARD ERROR
396.47
93.652
5618.5
2.7467
3237
4.12.8
72.076
28988
39344
3.4212
055942
082624
23031
23483
1.4445
T-STA11S11CS
14611
-15288
090254
0.43226
-091633
1.1982
-092624
023006
097486
-041506
-0.43257
19343
-0.13555
-090452
BARLEY PARAME1ER
CONSTANT
XI
20
2(3
BEES
TQXI
TQX2
TQX3
TQTQ
XIXI
2(22(2
XIX2
X3X3
XIX3
-52715
73459
-082249
-0.69692
-025992
-0921831
45190
58441
68156
-42.321
-2347
-5.7504
9.2212
-72244
-014403
STANDARD ERROR
65935
21987
2.7568
1.4193
1.2679
0.5328
228
59442
10184
19.154
45486
48248
10905
16951
39973
T-SIA11S11CS
-0.76486
833637
-015835
-042253
-0.595
-0.040974
0.66267
0.98653
04153
-042)83
-4.8254
-1.1918
-09171
-0.4262
-0.061048
PARAMETER
CONSTANT
1(1132
Xl
5(2
TO)
TQXI
TQX2
1Q20
20
XIXI
5(22(2
XIX2
2(32(3
X1X3
81.112
-14919
-4.924
.4.7452
-081555
442.39
74.734
12343
-V.128
-15459
-2.3506
-40.819
-1482
-0.87408
1133.1
72.558
93952
1883
3.9358
1863
3183$
68082
10782
23498
1.2407
45756
1094
16453
35607
T-STAT1SE1CS
-13486
1.127
-4.2686
-19217
-42537
-0.45984
13444
4.4309
1.1465
-4.4543
-4146
-035744
-19814
-090053
-014464
PARAMETER
CONSTANT
153
lOX!
TQX2
1522(3
TEllS)
Xl
20
2(3
XIX!
X2X2
XIX2
X3X3
XIX3
940.17
-4883
48051
8.8761
8.146
-2.9244
-.84
-44.701
-111.11
10.404
-5.1552
1.1626
-14927
73628
-5.0134
STANDARD ERROR
394.64
26.625
5.2505
2.449
2.4497
1.030!
48733
38668
814fl
15307
4.7442
3.7988
79026
13.488
3.4787
T-STATISTICS
43444
-4.6483
3.438
3.6243
3343
-2,8637
-1.42-44
-0.36619
-13472
0.65457
-2.9557
030613
-14153
05601
-1.5118
(IIICKEN PARAMETER
CONSTANT
20
>3
HSII
TOld
TQTQ
XI
XIX!
X2X2
XIX2
X3X3
XIX3
127,4
-34.264
-16.004
-15144
3.8849
2.4728
-142.19
-18.738
41.648
14.194
65051
43.836
28206
21309
STANDARD ERROR
247.44
22.374
43657
4.6381
2.6458
0.62683
85.74
33.972
39.179
8.1185
4.7399
2.4044
7934
7.262
3.8281
TSTAS1SOCS
-1.718.2
54348
-68478
-5704
-5.8276
61682
0.02884
.4.1836
-047025
2.4738
7.9302
3.0911
5.7423
2.7824
53649
PARAMEfER
CONSTANT
Xl
2(2
5(3
TO
TQXI
1732(2
102(3
1(3153
Xix!
X2X2
XIX2
X3X3
XIX3
-9.6539
4142!
15992
09546
-0.49533
77.419
99578
85.108
-10932
-0.44535
-13439
-2.674
-16396
0.2
42413
85.066
93583
19798
33422
43096
21534
55643
10336
16.656
0.90472
35
73465
13214
23248
T-Sl'AlSSll('S
-043704
011351
045654
046775
027470
027099
-04448
08318
065653
-049223
.2(1446
-036647
-1.2408
0.070244
PARAMETER
CONSTANT
Xl
2(2
15)
1(3>3
TOX2
102(3
157132
5(3
XIXI
X2X2
2(32(3
XIX2
XIX3
2(22(3
ESTIMATES
.484.9
7.5073
-17141
-11.221
-1.6668
3.7315
16481
41.811
4739
3.40196
7(682
-43451
16141
-5.6794
-0.19088
STANDARD ERROR
271.14
35,099
8.1493
6.0953
2.8424
3.0073
85908
33.787
37.187
7.4869
3.4875
1.4267
8.4486
5.4994
2.742
T.ETATIS'TICS
.4.7773
0.24339
.2.1248
.4.8409
.03864
1.2408
4.9267
1.1683
4.2878
0.44876
2.1987
-0.73293
19247
-1.0327
-0069644
PARAMETER
CONSTANT
1(3
lOX!
'1702(2
132>3
1Q10
Xl
X2
2(3
XIX!
2(22(2
2(3>3
XIX2
XIX3
0998.7
R-SQUARE
X2X3
-172.01
-032844
09906
K-SQUARE
X2X3
.425.04
STANDARD ERROR
MILK
TQX2
ES11MATES
ESTIMATES
EGGS
lOX!
09743
K-SQUARE
2(22(3
ES11MATES
1(3
0.9862
K-SQUARE
5(22(3
-1514.6
STANDARD ERROR
09934
K-SQUARE
2(22(3
ESTIMATES
ESI1MATES
PORK
TQ
-0.78355
0.9988
2(27(3
EStIMATES
82913
-82.452
43574
-3,9516
4,2525
23503
-427.65
38,6
-75,114
73413
23756
48662
2.0709
4.4771
-2,6549
STANDARD ERROR
231.84
21.924
2,3921
19477
13931
1.0365
36908
38.354
21.457
37434
1,0741
0.73648
2.7688
25564
1.1552
1-STATISTIC'S
33767
-2.9520
46753
-20288
30524
2.4663
-3.488
48875
-3304
2,2826
2.2117
2354
0.74703
1.7516
-2.2982
0.8888
R-SQUARE
09958
K-SQUARE
0.9994
Table 6-3e:
RIlE
115,25
TO
63781
16596
T-STA'USTJCS
026442
PARAMETER
CONSTANT
PARAMETER
ESTiMATES
STANDARD ERROR
WHEAT
Estimation Results for Part I - Korea Sample
CONS'l'ANT
10X2
5,7025
-13541
TQX3
43075
28381
3,9904
1.2074
13957
TQTQ
-1390?
13452
0.23178
1.4291
.1.2871
3.2296
-0.90016
TO
TQXI
TQXI
1337(2
TOX3
TQTQ
-2.3151
XI
X2
-107.11
253.19
30328
6.7974
2.0661
14324
1.261
10336
49436
1-STATISTICS
-2.5367
23145
091998
2.8432
-1.4763
-19297
-13047
BARLEY PARAMETER
CONSTANT
Xl
X3
150.91
14.633
43934
1.157
1.1755
057407
6239
-0.28277
-098127
-032029
-084354
038497
-13366
.0.060966
PARAMETER
CONSTANT
TO
TOXI
TQX2
-09022
TQX3
10553
-64963
Xl
44994
25964
41957
X2
-0,018647
0.221
X3
0.61885
T'STATTSiTCS
X2
-45513
X3X3
19309
16.452
2.109
1.7142
0.19472
-16881
1.0883
X!X1
33535
XIXI
-30325
2(27(2
7(22(2
-0.21222
X3X3
-032519
XIX2
-14005
99244
-14552
XIX2
83848
2.4342
-066488
SQ
XIXI
X2X2
2(32(1
-03005
XIX2
75.709
49.077
36.276
21348
3.0544
2.1297
058760
-0.13287
0.63834
030297
-0.11077
.0.16367
099438
PARAMETER
CONSTANT
Xl
7(2
7(3
TQX2
036123
TQX3
TOIlS
0.47654
0.11874
-25944
25.157
19,775
3.1792
23285
-1.0444
1.0452
0.64429
26.632
65077
3.0253
1.714
0.75199
78255
57.368
0.45872
0.18551
0178)3
023760
-033161
095366
42382
-13178
CHICKEN PARAMETER
CONSTANT
37465
X2
53
282.82
T-FTATISTICS
0.13318
-13045
PARAMEIIJO
CONSTANT
STANDARD ERROR
5.4578
43659
13941
13571
Xl
.10241
71356
-1.1826
.1.2847
-0.46232
13642
-0.14373
.6.4545
TQX2
-5.6086
TOX3
TQT73
-0.6445
2,4356
X3X3
33231
0.10846
TQXI
X2X2
-28.654
26436
TO
-53270
40943
XIX!
-052161
-64.635
0.86064
ESTTMATI-3
10.838
54.700
T-STAITST1CS
STANDARD ERROR
2(22(3
-091858
034172
2.9852
X133
63296
091302
TQXI
1.7351
-1.0272
41253
60.884
28367
-0.016872
2.0315
23
XIX!
X2X2
1.6291
X3X3
-0.62115
.2.16)9
8.9364
3288
095628
-13313
-0.18882
-2.2629
-11397
-22660
10.198
-0.
XIX2
XOX3
43112
8312
099486
XIX3
-12321
-1.6184
X2X3
2.6454
X2X3
-73447
7.6571
4.7164
-14352
-13997
XIX3
7(22(3
-024
4.7901
7.7827
4.0021
2.2855
-037005
-0.060725
2.9977
-2.88
X!X2
XIX3
2(27(3
Xl
>3
6362!
7.0324
0.6875
1.6033
-0.18871
-60.927
-31533
9 194
10.48
-1.763
-0.49682
-3.472
6.0566
1.791
1757')
1642!
3.4588
1.4336
1.2228
0.68698
63.273
36.819
19.623
93007
2.0265
1.2573
6.6788
4.3534
2,3479
T-OIA1TST'I( 3
06332!
042935
20332
0,42376
13112
.0.26306
-0.96291
0.83644
046443
1.116!
-0.86497
0.39515
-031985
13912
0.76279
PARAMETER
CONSTANT
XI
>3
2(3
OTAN1)ARI) ERROR
103
lOX!
lOX!
103X2
107(3
1Q23
1Q11)
ESTiMATES
467,59
-46.255
-12232
-5326
0.4377!
TQTQ
03302!
ST.ANI)ARD ERROR
11596
2210!
4.1245
1,6315
1996!
1 .S"IATISTICS
40324
-20929
-2965'?
.3,1673
0.21928
TO
TQXS
XIX!
X2X2
X2X2
7(37(3
X3X3
XIX2
XIX3
0.6344
K-SQUARE
0.8636
K-SQUARE
09736
K-SQUARE
09425
49335
03967
-146.47
ESTiMATES
XIX!
X1X2
-6.8637
K-SQUARE
43519
033036
-1.4442
1.7519
2.8885
X2X3
16711
1.3429
-039597
105
XIX3
13347
73979
-0.24066
2.1375
23899
-0.16457
19565
-0.42208
-6.4816
-0.75133
-0.11431
6.0654
0.27784
1.4800
13.717
-0.62782
-0.60360
29173
-2.2136
17.252
-33979
-0.24502
25.446
.0.4545
-7.8411
7(27(3
-2.1618
-01756
-8.8525
.1055
-21362
XIX3
-6.4615
199.07
ESTIMATES
EGOS
X3X3
1-STATiSTiCS
STANDARD ERROR
IISII
X2X2
43952
99016
ESTiMATES
PORK
TQTQ
-099135
X3
-2.1701
I9904
32447
STANDARD ERROR
BEEF
TQ)C3
15042
13585
-135S3
-037058
-0.90772
0.028575
-13928
TQX2
10085
15.86
-2.4102
-22107
1.1212
22.677
2.8743
lOX!
0.74482
43.791
6.2535
-4.1433
1.1181
XIXI
6942
70.195
TO
6.1027
XIX2
53618
39976
-36,719
-642.28
-2.814
-0.67608
30.507
X2
EStiMATES
STANDARD ERROR
ESTiMATES
1.1276
1.2515
Xl
7(22(3
25036
97.185
89457
28.020
4.9219
-037385
23.799
2.4523
12196
1.6339
4635!
21,962
13.012
53556
1.1997
0.84903
4385!
29605
1.0362
033059
4.9609
4.4252
0.67978
50452
4.4352
.044033
5.1904
0.8258
1.177
K-SQUARE
09499
K-SQUARE
09009
R-SQUARE
0.977
R-SQUARE
0.9978
Table 6-3f:
RIO
Estimation Results for Part I - Taiwan Sample
49.773
'[OX!
43626
494.41
69365
T-STATIS11CS
-0.13267
0.71756
lAILAMIIIlI
(ONS1AO4I
EFflMATLS
STANDARD ERROR
WIII'AI
10
-26.44
-11976
XIXI
3579!
42.152
15.633
39.813
23338
-034902
-092649
-2.612
-14913
-0,1011
15336
7(2
Xi
3.4314
10978
1.2114
14377
T(JXI
'10X2
10X3
'IQIQ
'I(/IQ
XI
XI
X2
Xi
XIXI
X2X2
Xix)
X1X2
XIX)
X2X3
0.3652
3.8498
0.6062
0.13907
050708
052237
0.92467
16543
0.43608
-12769
097073
039495
23272
13392
-0.17758
X2XS
Xix)
XIX2
XIX)
XZX3
-0.18)64
45225
14149
86.038
-6.1258
-13414
-040666
23402
-99161
-15735
-36493
327.5I
16313
2.7074
0.4078
15181
0.70481
*5.678
152*3
47.110
52748
024878
1.4398
2.3381
4936
0.63648
TSTAflSTICS
0.74305
-6.3809
-0.11093
-5.0452
-0.11699
6.4163
1.7626
5.631
-0.13984
-2.6433
-1.6105
15814
-4.1983
-021149
-5.7335
PARAMEFER
CONSTANT
316.27
TQ
13381
TQXI
-12667
TQX2
-053063
lOX)
-0.41585
172173
-0.23971
Xi
-260.78
-13234
51219
21501
19795
1I039
04626
14.711
0.65533
5.6869
19687
2.868
-1,1982
632.70
17.397
2.300!
041301
1965
053625
18894
37.149
091587
0.78!
-03481
-13268
-023039
-0.64701
-138)2
-33670
PARAMETER
CONSTANT
TO
X2X2
X3X3
-6*164
XIX2
30971
64444
32541
XIX)
XIX)
-097433
1528
-0263759
X2X3
84782
23873
35514
2.3986
0.10767
95.876
1769
13.124
-15739
-0.016738
-3.1849
-16283
XIX)
-3368)
83185
0.658)3
33774
23417
17.367
39505
9,7823
0.4071
17336
2.6639
7774
1.0146
.0.16384
-23696
047047
0.042361
I95.fl
048)74
12301
0.16501
.0,77424
-0.041115
-I93fl
-0.61123
-043335
12103
1(9194
XI
112XI
-13639
1157.6
107.24
T-STAflST1CS
.0.16641
-0(88445
(IIi( KIN IARAMI1II1
CONSTANT
31)
Xi
XIX!
-1.4955
-6.482
STANDARD ERROR
10)0
Xi
XI
T-STA11STICS
-193.63
[OX!
TQX2
303X2
TL3X)
109102
Xl
Xi
>3
Xi
XIXI
XIX!
X2X2
X2X2
20703
XJX3
XIX)
XIX)
302)3
1228
Xix)
-39515
44.442
-3.8264
0.81994
-1.199
-1.1991
74.1642
-43.731
58.81
.5.8863
4.62162
-3.4303
33633
-19611
5.00473
233.71
15367
20338
0.42239
096219
032513
61393
11.163
27,457
43904
0.26248
1.0149
1.8481
3,6324
0.6755
T.FrATicrIcs
-1.04308
2.0549
-1.8814
19394
.13439
-330
1306
-4097!
1.4135
.1.1669
-2.3682
-33701
19198
-033062
7,4912
PARAMIOLR
ES'I]I4ATES
CUNF1ANT
EFI1MATES
STANDARD ERROR
85663
10385
5.2478
1.1163
1.6433
-1.8544
-75506
-1508
45412
XIX!
23149
93937
106,07
6.2733
0.82(09
3.1537
2.8516
105.76
15,154
62.811
T.STA'IlSTICS
0089063
0.099733
0,83627
13603
0.46056
-036244
-07177
.009512
0072399
PARAMETER
CONSTANT
TO
3336!
STANDARD ERROR
EX)1MATES
-648.22
TO
lOX!
lOX)
-13149
ToX.2
TQX2
0.0885!
TQX3
173X3
1.8873
TOIl)
109102
Xl
X2
Xl
X2
-1.9821
182.139
10.768
>3
>3
41924
X2X2
Xix)
XIX)
XIX)
X2X3
0.035611
-1.4542
035534
-1.708)
.0,12767
4.7078
0.21999
14944
1.7039
52393
0.68721
0.49171
0.16215
.050638
0,198)8
432678
-0.18577
XIXI
X2X2
-15377
-05733
X3X3
-3.0463
XIX)
XIX)
-4.0239
-74256
lOX]
-0.2113
733.04
76345
6.8788
15333
2.783
23084
149,26
25.904
53.409
06672
0.44546
1.7191
3.0518
63738
099705
TSTATIS'TICS
-0.88439
0.43583
.0.19115
05)947
087837
-0.8.0865
1224
0.41566
0.78408
-1,7742
-1.287
.17123
-13185
-1,165
-0.21176
PARAMETER
CONSTANT
>3
>3
STANDARD ERROR
MILK
-112.63
1.9576
-23406
23263
-0.68326
-2.0574
E1,11MATES
EGOS
lOX)
15784
-0322
STANDARD ERROR
11011
10>3
-10453
ES11MAThS
PORK
CONS'IANT
264.01
ES1IMAII06
STANDARD ERROR
BEEF
'10
-66.087
PAL ANIEI ER
TO
TOXI
lOX)
lOX)
171TQ
XI
XIX!
X2X2
>3>13
XIX)
XIX)
X2X3
ES'IlMATES
STAO'OI)ARD ERROR
-26840
-56.234
7.7667
0.6173
4.2653
0.12583
26086
85,763
8.6277
-23316
4.94944
096094
-13304
-10541
-6.1252
8)039
40305
6,1066
0.0778
1,9443
049541
265.46
33.838
153.46
21.917
030819
6.057
3.7753
24,852
3.5048
rs-rA'ns'TIcs
.033)3
-1.4057
1,2594
0.9)103
2.1953
0.25398
0.75665
2.4754
0036773
-1.0638
-1.8683
1.14709
-23036
-0.42416
-2.0385
k-SQUARE
09953
k-SQIJAHIL
09508
R-SQUARE
09578
k-SQUARE
0.9563
R-SQIJARIO
0.9981
RSQUARE
09506
k-SQUARE
0992
k-SQUARE
0.9735
166
6.3. Estimation Method and Procedure for Part XI
Additive random disturbances for each equation were
assumed for estimation purposes.
The model has the
following structure:
Part II (La/S/H/AIDS):
Wi = fi{
XF
D
}
+ ei
where
Wi =
/ XF.
XF =
Q*j
= exp{ Predicted Value of log Qi
from Part I
)
i = 1,...,n
(6-7)
For Part II, two different estimation techniques were
applied:
one is called the "system estimation" approach
using the iterative SUR technique with an adding-up
condition imposed.
The other is called "non-system
estimation" which uses the equation by equation estimation
while considering homogeneity restrictions and the first
order autocorrelation of error terms.
Therefore, two
different kinds of error structure were assumed for ei
depending upon which approach was taken.
In the following
section, the system and non-system estimation procedures
will be explained consecutively.
167
6.3.1. System Estimation Approach
It is reasonable to apply SUR for Part II since the
data generating process in the demand system may require
that the optimum quantities for every good in the system be
jointly determined at once by an individual at the given
level of prices and budget for that system (with an
appropriate separability assumption).
Assuming the independent variables are nonstochastic
and bounded, the following error structure, similar to that
of Part I, was assumed for ei:
eit is normally distributed with mean
E(ei) = 0, i = l,...,n
variance of the errors in the i-th equation is
E(ei) = c2ii, i = l,...,n
the errors are contemporaneously correlated among
equations, i.e.
E(eit ej) =
a2ij, i,j
l,...,n
(6-10)
there is no autocorrelation, i.e.
E(eit...a ej_) = 0 for a * b,
i,j
= 1,...,n
(6-11)
A complete demand system is constructed such that
"expenditures automatically add-up to total expenditure
without error" (DEATON, 1986, p. 1781); or in the share
equation case, each share must always add-up to unity
without any leakage.
To satisfy this condition, the sum of
168
error terms across equations in the system f or the given
time period must be zero.
This means the
variance-covariance matrix will be singular and not
invertible.
Therefore, procedures such as the SUR requiring
the inverse of the variance-covariance matrix will be
undefined.
To avoid this consequence, the most common
approach taken in practice is dropping any one equation
arbitrarily; the coefficients in the omitted equation will
be recovered through adding-up conditions.
It has been
proven that "the estimates are invariant to the particular
equation which is selected f or omission" (DEATON, 1986, p.
1781).
Note this "singularity" condition was not applied for
the LA/S/H/AIDS or the LA/H/AIDS model since there were some
regressors (the habit variables) which prevent the model
from satisfying the adding-up condition.
Therefore, for
these two specifications, no equations had to be dropped.
On the other hand, for the LA/AIDS and the LA/S/AIDS
specifications, any one equation must be dropped to avoid
the "singularity" problem.
6.3.2. The Model Specification Test
Before proceeding further, the four alternative
specifications of LA/AIDS, LA/S/AIDS, LA/H/AIDS, and
LA/S/H/AIDS were compared by the Likelihood Ratio (LR) test.
The test results are reported in Figure 6-1.
The test is
169
basically examining the statistical significance of the
differences made by additional features in the model
(additional variables in this case).
Nesting one model to
the other, the values of (the log of) the likelihood
function from the model without additional features
(restricted model) and from the one with additional features
(unrestricted model) are compared.
The test statistic has
an asymptotic property of chi-square distribution with the
degrees of freedom equaling the number of restrictions
tested.
Since every equation in the system must have a common
specification in a complete demand system, the evaluation of
the whole system is more meaningful.
Therefore, the system
estimation approach was taken to calculate the values of the
log-likelihood function.
The iterative SUR method was
applied while dropping one particular equation in every
specification for the same data set.
The estimation was done by HALL's (1988) computer
program TSP (Time Series Processor) Version 4.lB on the IBM
Model 4381.
In the TSP, several commands for both the
non-iterative and the iterative SUB methods are available:
the "SUB" command is used for the non-iterative-SUB method,
and the "LSQ" command (non-linear least square -
multivariate regression technique) and the "FIML" command
(full information maximum likelihood technique) are used for
the iterative-SUR method.
Given multivariate normal error
170
distribution, LSQ and FIML should reach the same result.
First, the Full Information Maximum Likelihood (FIML)
method was attempted, which resulted in numerical failure in
calculation.
As a best alternative, the Non-linear
Multivariate Regression method (LSQ command with default
algorithm) was applied and was successful.Th
was estimated with zero starting values.
First LA/AIDS
Utilizing the
value of estimated coefficients in the LA/AIDS model for
starting values, LA/S/AIDS and LA/H/AIDS were estimated
independently.
Finally, using the value of estimated
coefficients in the LA/S/AIDS as starting values and adding
habit variables, LA/S/H/AIDS was estimated.
It turned out that the LA/S/H/AIDS model outperformed
the other three; the statistical evidence was very strong
and supported at the 0.0005 level of statistical
significance for all of the six different data samples from
Japan, Korea, and Taiwan.
Given the results of the model specification test, only
the LA/S/H/AIDS specification will be considered hereafter.
For the rest of this study, the iterative SUR method
in TSP program will refer to the Non-linear Multivariate
For
Regression method (LSQ command with default algorithm).
details, see TSP User's Guide Version 4.1, pp. 51-2.
171
Figure 6-la: Model Specification Tests for Part II - Japan
All-period Sample
Likelihood Ratio (LR) Tests
For The ModeLs Estimated By Iterative-SUR Method
With Dropping One Equation.
Country:
JAPAN
Period:
ALL-PERl
Dropped Equation:
MILK
1911-87
Nuter of Equation
Estimated:
8
HO: No difference between restricted and unrestricted models.
Hi: HO is false.
Specification:
LA/AIDS
1915.19
LnL
\
/
LR TEST:
LR TEST:
chi-square critical, value =
Chi-square critical, value
27.9
65.0
at 0.0005 leveL of significance
at 0.0005 Level, of significance
for degrees of freedom =
for degrees of freedom =
Conclusion:
evidence
8
LR
53.72
LR
Conclusion:
The data provides very strong
and in favor of the alternative (Hi).
and in favor of the alternative (Hi).
Specification:
LA/S/AIDS
2031.76
Specification:
LA/H/AIDS
=
ml.
The data provides very strong
against the null hypothesis (HO)
evidence
against the null hypothesis (HO)
32
233.14
LnL
1942.05
LR TEST:
LR TEST:
Chi-square critical value =
Chi-square critical value
65.0
27.9
at 0.0005 leveL of significance
at 0.0005 Level of significance
for degrees of freedom
32
for degrees of freedom =
8
241.54
LR r
62.12
LR
Conclusion:
evidence
Conclusion:
The data provides very strong
evidence
against the null, hypothesis (HO)
and in favor of the aLternative (HI).
and in favor of the aLternative (HI).
\
/
LA/S/H/AIDS
Specifi:ation:
lnL
Lnl.
:
=
2062.82
Value of The Log of Likelihood Function
Fornsjla: LR = -2 * (lnLr -
where
The data provides very strong
against tre nuLL hypothesis (HO)
inLu)
InLu
lnL for Te Unrestricted Model (The One Below)
LnLr
lnL for Te Restricted Model (The One Above)
Chi-square critical values are taken from OSTLE and MALONE (1988, p.SEO).
172
Model Specification Tests for Part II - Japan
Figure 6-ib:
Pre-war Period Sample
Likelihood Ratio (LR) Tests
For The Models Estimated By Iterative-SUR Method
With Dropping One Equation.
CoI.stry:
JAPAN
Period:
PRE-WAR PERIOD 1911-37
Dropped Equation:
MILK
Nuer of Equation
8
Estimated:
HO: No difference between restricted and unrestricted mooeis.
HI: HO is false.
Specification:
(nL
LA/AIDS
1091.29
\
/
LR TEST:
LR TEST:
65.0
Chi-square critical vaue
at 0.0005 level of significance
27.9
Chf -square critical value
at 0.0005 leveL of significance
for degrees of freedom
8
for degrees of freecm
32
LR
43.34
LR
197.2
Conclusion:
Conclusion:
The data provides very strong
1112.96
LR TEST:
LR TEST:
27.9
Chi-square critical '.aue
65.0
Chi-square critical value =
at 0.0005 level of s:gnificance
at 0.0005 leveL of significance
for degrees of freedom
32
LR
305.4
evidence
LA/S/AIDS
1189.89
Specificaticr.:
LA/H/AIDS
lnL
Conclusion:
rovides very strong
and in favor of the a.ternative (Hi).
and in favor of the alternative (Hi).
Specification:
The data
against tre null hypothesis (HO)
evidence
against the null hypothesis (HO)
evidence
8
for degrees of freeooni
=
Conclusion:
The data provides very strong
evidence
against the null hypothesis (HO)
151.54
The data crovides very strong
against tre null hypothesis (HO)
and in favor of the a.:ernative (Hi).
and in favor of the alternative (Hi).
/
\
Specification:
InL
mt.
:
LA/S/H/AIDS
1265.66
Value of The Log of Likelihood Function
lnLu)
Formula: LR = -2 * (lnLr
InLu = lnL for The Unrestricted Model (The One Seow)
-
where
lnLr = tnt. for The Restricted Model (The One Abc'. e)
Chi-square critical values are taken from OSTLE and MALC'S
1988, p.SSO).
173
Figure 6-ic: Model Specification Tests for Part II - Japan
Mid-period Sample
Likelihood Ratio (LR) Tests
For The Models Estimated By Iterative-SUR Method
With Dropping One Equation.
Country:
JAPAN
Period:
MID-PERI
Dropped Equation:
MILK
1925-70
Nusber of Equation
8
Estimated:
HO: No difference between restricted and unrestricted rnoaels.
Hi: HO is false.
Specification:
lnL
LA/AIDS
1042.85
\
I
LR TEST:
LR TEST:
at 0.0005 level of significance
at 0.0005 LeveL of significance
for degrees of freedom =
8
LR =
40.5
Conclusion:
for degrees of freecom =
Conclusion:
The data provides very strong
and in favor of the alternative (Hi).
LA/S/AIDS
Specification:
LA/H/AIDS
=
LnL
The data provides very strong
against the null hypothesis (HO)
evidence
and in favor of the alternative (Hi).
Specification:
32
224.4
LR
against the nuLl hypothesis (HO)
evidence
65.0
Chi-square critical value
27.9
CM-square criticaL value =
nL
1063.1
=
1155.05
LR TEST:
LR TEST:
CM-square critical value
65.0
Chi-square critical value =
27.9
at 0.0005 level of significance
at 0.0005 level of significance
for degrees of freedom
32
for degrees of freeom =
LR
331.04
ConcLusion:
evidence
LR
Conclusion:
The data provides very strong
evidence
against the null, hypothesis (HO)
The data orovides very strong
against
and in favor of
and in favor of the alternative (Hi).
S
147.14
he a
null hypothesis (HO)
ernative (Hi).
\
Specification:
lnL
lnL
:
LA/S/H/AIDS
1223.62
Value of The Log of Likelihood Function
-2 * (tnLr - IriLu)
Formula: LR
lnLu = lnL for The Unrestricted Model (The One Below)
where
lnLr = LnL for The Restricted Model (The One Above)
Chi-square critical values are taken from OSTLE and MALONE (1988, p.58O).
174
Figure 6-id: Model Specification Tests for Part II - Japan
Post-war Period Sample
Likelihood Ratio (LR) Tests
For The Models Estimated By lterative-SUR Method
With Dropping One Equation.
Country:
JAPAN
Period:
POST-WAR PERIOD 1955-87
MILK
Dropped Equation:
of
N.snber
Equat,on
8
Estimated:
HO: No difference between restricted and unrestricted models.
Hi: HO is false.
Specification:
lnL
LA/AIDS
1238.34
\
I
LR TEST:
LR TEST:
at 0.0005 leveL
Chi-square critical value
27.9
Chi-square critical vaLue
of significance
for degrees of freedom =
B
LR =
29.02
The data provides very strong
LR
Conclusion:
Conclusion:
and in favor of the alternative (Hi).
and in favor of the aLternative (Hi).
Specification:
LR TEST:
Chi-square critical value =
Chi-square critical value
65.0
for degrees of freedom =
32
for degrees of freedom
LR =
27.9
at 0.0005 level of significance
at 0.0005 level of significance
evidence
1310.79
lnL
1252.85
LR TEST:
Conclusion:
LA/S/AIDS
Specification:
LA/H/AIDS
lnL
144.9
The data provides very strong
against the null hypothesis (HO)
evidence
against the null hypothesis (HO)
evidence
65.0
at 0.0005 level of significance
32
for degrees of freedom =
LR
145.14
Conclusion:
The data provides very strong
evidence
against the nuLl hypothesis (HO)
5
29.26
The data provides very strong
against the null hypothesis (HO)
and in favor of the alternative (Hi).
and in favor of the alternative (HI).
I
Specification:
lnL
lnL :
Formula: LR
where
LA/S/H/AIDS
1325.42
Value of The Log of Likelihood Function
-2 *
(tnLr -
InLu)
lnLu = tnL for The Unrestricted Model (The One Below)
lnLr
lnL for The Restricted Model (The One Above)
Chi-square critical values are taken from OSILE and MALONE (1988, p.580).
175
Figure 6-le:
Sample
Model Specification Tests for Part II - Korea
Likelihood Ratio (LR) Tests
For The Models Estimated By Iterative-SUR Method
With Dropping One Equation.
Country:
KOREA
Period:
1962- 87
Dropped Equation:
EGGS
NuiDer of Equation
7
Estimated:
HO: No difference between restricted and unrestricted mooels.
Hi: HO is false.
LA/AIDS
Specification:
tnt.
=
680.768
\
/
LR TEST:
LR TEST:
at 0.0005 level of significance
at 0.0005 level of significance
for degrees of freedom =
LR =
Conclusion:
7
for degrees of freedom
28
62.976
LR
200.1.6
Conclusion:
The data provides very strong
and in favor of the alternative (Hi).
and in favor of the alternative (Hi).
Specificaton:
LA/H/AIDS
Specification:
The data provides very strong
against the null hypothesis (HO)
evidence
against the nuLL hypothesis (HO)
evidence
Chi-square critical value =
59.3
Chi.square critical value =
for degrees of freedom
28
for degrees of freedom =
26.0
at 0.0005 level of significance
at 0.0005 level of significance
LR =
LR = 246.914
evidence
LA/S/AIDS
780.998
LR TEST:
LR TEST:
Conclusion:
=
nL
712.256
mt.
59.3
Chi-square critical value
26.0
Chi-square critical value =
Conclusion:
The data provides very strong
evidence
against the null hypothesis (HO)
7
109./.3
The data provides very strong
against
ne null hypothesis (HO)
and in favor of the alternative (Hi).
and in favor of the alternative (Hi).
\
LA/S/H/AIDS
Specification:
tnt.
mt.
835.713
Value of The Log of Likelihood Function
2 * (tnLr -
Formula: L
where
:
=
InLu)
nLu = tnL for The Unrestricted Model (The One Telow)
LnLr
Int. for The Restricted Model (The One Above)
Chi-square critical values are taken from OSTLE and MALCE (1988, p.SBO).
176
Figure 6-if:
Sample
Model Specification Tests for Part II - Taiwan
LikeLihood Ratio (LR) Tests
For The Models Estimated By Iterative-SUR Method
With Dropping One Equation.
TAIWAN
CoLrltry:
Period:
1963-87
Drocoed Equation:
MILK
Nl.er of Equation
7
Est I mated:
HO: No difference between restricted and unrestricted mocets.
Hi: HO is faLse.
Specification:
lnL
LA/AIDS
=
825.93
\
/
LR TEST:
LR TEST:
at 0.0005 level of significance
at 0.0005 LeveL of significance
for degrees of freedom
7
LR
4.302
ConcLusion:
21
for degrees of freeoom
LR = 130.804
Conclusion:
The data provides very strong
and in favor of the alternative (Hi).
and in favor of the alternative (HI).
irt.
81.9.081
lnL
LA/S/AIDS
Specificaticn:
LAIN/AIDS
Specification:
The data orovides very strong
against the null hypothesis (HO)
evidence
against the nuL' hypothesis (HO>
evidence
49.0
Chi-square critical va'ue
26.0
Chi-square critical value
891.332
=
LR TEST:
LR TEST:
26.0
Chi.square critical vaue
49.0
Chi-square critical value
at 0.0005 Level of significance
at 0.0005 level of significance
for degrees of freedom
21
for degrees of freeoom =
7
LR = 155.788
=
71.286
Conclusion:
evidence
Conclusion:
The data provides very strong
against the null
evidence
vpothesis (HO)
and in favor of the a:ernative (Hi).
and in favor of the alternative (Hi).
/
\
LA/S/H/AIDS
Specification:
LnL
ln&
FormuLa: LR
where
The data rovides very strong
against :e null hypothesis (HO)
:
=
926.975
Value of The Log of Likelihood Function
-2 * (lnLr - InLu)
InLu
LnL for The Unrestricted Model (The One Seow)
lnLr
lnL for The Restricted Model (The One Above)
Chi-square c-iticaL values are taken from OSTLE and MALONE (1988, p.580).
177
6.3.3.
System Estimation of LA/S/H/AIDS
As mentioned above, without dropping any one equation,
the LA/S/H/AIDS model was estimated by the iterative SUR
method with adding-up conditions imposed.
However, direct
application of the method with zero starting values was
usually unsuccessful.
Thus, the following procedure was
applied generally:
Run OLS equation by equation and store the
estimated coefficients.
Run the non-iterative SUR using the values of
estimated coefficients obtained in 1 as starting
values.
Run the iterative-StIR using the values of
estimated coefficients obtained in 2 as starting
values.
The estimated coefficients are reported in Table 6-7.
Tables 6-4 through 6-6 summarize notations commonly used in
Tables 6-7 and 6-9 for the system and non-system results,
respectively.
Sometimes it was better to skip the second step and
use estimated coefficients from the OLS estimation as the
starting values to attain convergence without numerical
.
errors.
178
Attempts to impose homogeneity conditions were
unsuccessful.
Any one parameter had to satisfy the
adding-up and homogeneity conditions simultaneously at the
same parameter value,
difficult.
which seemed to make the computation
Therefore, the homogeneity
conditions
were
neither tested nor imposed in the context of the system
estimation.
Table 6-4: Notation Used in the Tables of Estimated
Coefficients for Part II
corrresponding
Variable
Parameter Symbol Used
in La/S/H/AIDS
Name in
(see Eg. 4-6J
Tables
C/ft
cr*it
B#
&i
Constant
1
Log of Per Capita Real
Group Expenditure
(Deflated by STONE'S
Index)
G$#
Tt
logPj
Log of Nominal Retail
=
Price of Commodity
$
A!#
KS
Oki
1ogDk
Q*j1
Log of k-th Demographic
Variable, k =
The Habit Variable of
Good i = $
Note: "#" and "5" are commodity codes and "!" is a code for
demographic variables shown in Tables 6-5 and 6-6.
179
Table 6-5:
Commodities Included in Part II By Country
Commodity
Initials ($)
Commodity
Names
Commodity Codes (#) for
Each Country
Taiwan
Korea
Japan
R
BR
W
Rice
Bread
Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
1
1
1
2
2
2
B
3
3
BF
P
4
5
4
3
4
C
F
E
6
5
6
7
8
M
9
7
8
5
6
7
8
Note: "---" indicates that the commodity is not
included for the country.
Table 6-6:
Country
Demographic Variables Included in Part II By
Demographic
Variable
Child Population Share
Code (!)
Japan
Country
Korea
C
*
*
*
W
*
*
*
0
*
*
*
H
*
*
Variable
(Age 0 - 14)
Working Population Share
(Age 15 - 64)
Old Population Share
(Age 65 and over)
Household Size
Taiwan
Note: "*" denotes the variable is included; "---" denotes
the variable is not included.
39776
82
-033326
002)283
1036
83
0.026304
028536
(3
094944
023482
4.0433
C4
0.02637
CS
063105
022)55
014016
('6
'018(6l0I5
SARAMEII,R
ESSSMAThS
SFANDARD ERROR
T-STATISI1CS
PARAM17IU4
ESTIMATES
STANDARD ERROR
T-OTAS1STSCS
l'ARAMEI'I,R
ESTIMATE.'.
STANDARD ERROR
T'S3'A11STICS
PAR9MLTER
033164.4753
23956
-13824
02863473
2.0446
04079219
032445
445
07
Cl
-4.5734
SARAMI,1'ER
l'ShlElAl3,5
I31l6L4'lt'.S
)'AIIANIIII 6
SON) Ill'
0A97485
99-07
.3671
.43,07
42973
206230-Il
.2,7033
05346612
33606
1601
391,06
42033
4)3308
4.6)967
5309-4
2607
00730352
00075614
08639394
.23394
271!3
08)00064
04033935
0(059103
0013572
-I 0214
6)9
0034396
600-')
0(3
0)2336
'00I3195
43010333
T.STA11STICS
011119
659
S'I'ANDARD 0-4144040
GRO
-1,3570
.20220
'370-67
'471,54
34,06
134-40
0.006946
4,9493!
04065949
04035718
-I.I684
0,444307
.0.0762277
AC')
7)4005
12340055
-66.00
.6.96713.65
19739
-3.4)63
0.23304
0.0090643 (716240-05
0.014801
0.39702
33038
KM
-5.6802
0.072234
.4119
-6.2609
2.002)033 3505940-05
AOl
RE
4.0401030
0.4068257 028061862
-0.01I252
AHO
0,44926
-0.050365
AW9
-6.5368
236)0
-19577
6649
0.010254
0.082300
00370)7
4337)0
-0.10620
08)69964
A08
0)9407
428.9497
AWl
ACO
.0072446
DM0
.6414!732
649
6.9803
0.8)65267
04000)35
0.5004068
0403
0058631
60-0
-0450386
.0.036.2
4072443!
06036332
-0033392
89
0.033635
(5
'00626
P(IIAOIEI6.R
1,flMA1E'.S
2.146
00768304
0.0663574
002849)
0.!23l
I5593
4.061)4
.55340
.13400
-36392
6449
0,5033504
0.0073007
05030333
0(6157061
0012235
0)430
0.0274!!
T-STATIS77CS
OaFS
606
STANDARD ERROR
08403
-06023170
ORB
4.033065
4,012734
Cl
6C8
-032639
-3266I
-49455
34.4460
059
0.084976 454981106
0,1)076
0,56)44
0235)4
!.4533
5408
436220
.2)722
0024134
0.02330!
0.054637
4.39774
0647730
KS
.I3OIE-06
0.020993
I.7664
0,027394
0056558
05674711
-33040
!4954
-0.24226
04
0.03483
0016.945
-020034
0057367
.9.007
-27105
-1.1078
0.3206!
AC7
4)031
0853031
6467
6C7
.407
436)53
6.9474
2.3062
23727
AWl
080003637
0,0I2834
4,005702
0014604
00333)2
4046034
ESI1MATES
1.0964
KC
0078)56633
13749
.4016
.0.0430368
0024847
0038362
657
0347
4.42476
-064686
6407
03571)60
045056
A06
-2.630
3.023911-05
0.035039
AWE
K?
-0.I936
0)2049
ACE
1.0309
0.015768
081784
AHS
'6010706
3)414
0,3043619
-059623
08)07631
0350
'0403I502
-02832032
4,04363
0.056167
3.I958
032027
.403
.086114I7
0,40844
AW)
0064314
-13354
5!430
00I6II3
0400
-28076
-hIll
39759
60-0
0.059336
0.06343
00362400
0.0030530
03)2!
0.I057!
0045
-0,033002
GE)
40772448
48653497
0(39
00-3
0.025274
ACS
09071
30672
-49408
.33371
2.4538
022699
0013900 39045045
0023126
0.12719
0037108
0010564
4.80038
0283302
-16124
0.4053072
6657
00033846
02877458
0034
48071974
0041503
C16E6
PARAMETER
6003540
333006
'4544!
T.S1ATISTIC
-0.!2307
687
-6.433
0.303647!
0068093
0)1117
-067254
03054663
066
.007946347
0R7
-23703
I7033
0 64.45
00681246
0,0105
-065373
0039308
6162
-0000323!
80565
.23653
.1,3600
-0)0043
00406
.653250
-CR76
02895403
0.0602700
00607990
0.03014!
44036710
0.003304
0506686I
0.07654)
.00664 .6.0544234
6440-5
005
.0022303
4.0106)2
0(3
.2.2303
021I5
053
0815428
08061552
0077066
.031400
.0.I9057
.4494
02830739 7.7058845
.404
AW4
AC4
0044
0.026631
KBF
-55617
2-282
6.6828
33134
0.0467
0014237 43904846
6023944
0.13138
0.648462
84061
-08049417
0164
KB
-24550-62
0,016818
A013
0816348
A03
0.03784
.456'S
0.22101
AC3
0.19783
0M3
2-9531
-0.0l3064
054
DCI
4027734
08074575
DPI
0.13602
0411(5
(38(5
-05610396
T'STAllSiSCS
00
0.023456
19621
00060322
.2,4138
0lI594
-1,1737
0,4086024
0014048
032743
0.007067
(10854
0.06917
(384
02825703
084114
01(4
.0,0I5573
064
08050260
-59343
-2.4710
.49436
3,7363
-13700
0.45402
0.0003030
02861708
02886156
00079752
0.0540745
0,0090459
GEl
0,011557
6463
-0.016149 0,006804673
23939
6.02732
0002453
0C3
083
653
6114(3
-010025
88040726 028001184
19905
83)33
-82364
-2.1014
.2.0062
08008432
0.044683
0,030255
02837444
02816837
.0,79087
.18049
0.4018068
0.0542052
4.0152)6
05040
13440
0,040721
0(811
-0.0054862 347350-03
-0008626
AW2
0042
AC2
DES
0034602
AIlS
-1818
-08046350
A02
39956
41802
2-0712
3.0245
0.44351
038904
KR
18303006
0053033
AHI
-0823956 3386411-06
830(Z3
0282)68
1052
AWl
AOl
018354
ACI
-0804074
00-2
-0.00I4I73
0C2
.08001067
61103
0.4021171
0283957
0P2
058710222
OBF2
08045261
-0042068
61(3
08041142
StANDARD 6.1(46011 0(0654705
STANDARD ERROR
0.040203
0.08)955
.7.183
08
4.85)
09462)3
4.86441
0.023391
-6.1974
0.11262
0831864
004!
-003768
61(1
-0.l4313
088)88
091
Dci
0.353195
(SF!
.0.2169)
oosi
0,039790
1.0724
0.4027367
0.4030776
9,01
02839347
682
4.1500
0018714
0W2
-21417
0.024594
0.12286
.0046833
0.022847
014!
64401
-02895421
.3.0127
0.4030906
-0011814
61(2
2.8490
0034124
0.09495
011%
-004688
-1,3071
0019360
-0,023807
02866568
0.068207
-02825449
1-STATISTICS
0.0470
STANDARD ERROR
ESS1MAT06
CS
.2,268
0.00793
STANDARD ERROR
T.65A73351c9
PARAMLThR
0.072683
46492
L'SI3MASLS
.0.165
C!
PARAMLTII%
Ill
Table 6-la: Estimated Coefficients for Part II from System Estimation: LA/S/H/AIDS
Specification - Japan All-Period Sample
C3
J'ARAMCIER
IiI11MATES
-2,0789
05606
4.4932
CS
03455
0.95496
2,2247
STANOAIOD 19900
T.S'IA'lISi'l( 2.
I'ARAME1E3O
ES'IlMATES
STANDARD 90909
T.S"I'ATIS71CS
0093
64
(SI (91.91325
PAIOASICI( (1
SCSI (IF
I 22404?
.4 fl1.(9
.92001
-2353-4
30.67
039(0273
.23642
T.S'I'A'0(S'7ICS
05013933
STANDARD ERROR
-23194
(.0909
00679407
.0,0343(94
050244.9105
'042061
4(9
(.9(9
'2231,06
4.0193
'2.42006
00634417
'06(023
0014010
61(4-9
.0.606159
068(4052
(.604
5.0722
23153
.2.4352
.6519
003-1314
(.9
4'AO9M('lI:R
06861917
06825704
05031316
06824433
-40524
9,640
00847531
'050431094
0.12235
T.STATI55'ICS
.03030513
016672
STP.90A440 9946096
9,160
0.023976
(((((8
670.01
.0,66354
(.4'S
'0.066694
068(0184
2,1393
038366(2
06816812
(.6.6
0(7)
491'67
-0,08276
0.362172
'00929293
305636
A189
KM
.1299(56
.20.00
670.46
240.03
.6244.96
171,14
-031800
-60-01 009)19(64
0,42274
5.0020667 435730.45
00010550
00120(0
20636
2.2916
'2,1446
0036703
06802405
06874373
.073522
06811004
01939(3918
-44625
.909
-6.0624373
AWO
33(0.5
0.088352
ACS
-1.6993
06874043 633(40.07
4.083083
DMA
-3.5643
0.01484
2.7050
0.074827
0.0546326
1(6.
AIlS
'0.0538873
AOl
.4.052484
-3021,8
-0,27767
.094403
-00092750
0056401 438470-07
-0,093664
0.22744
431471
011752
AWl
-5.0304
9.2349
-6.5379
000162063
036(5619
O1
4067657
'0.8346123
((39
'0.74872
-6.30006
0,078135
00040641
(5042
'3.8386
.2,6242
00095376
0509627
0.0536934
.9(8
'0.021)21
(.430
0,35366
-19173
.06830124
0023928
1,46
'60670139
(046
.0050799)
(19.3
0.04793
41193
0.97056
.1.2993
1.181
0.69046
.13533
0.0539512
(9(10
.3.0711
0.041213
'0.0225(8 .40676996
68
'0022029
-(42O6
18
5036397
E311'IMATES
0.74998
3353
0.7(0(5
'9.3271
T-STATISTICS
lARAMI '9(11
0.030495
0.94324
0076425
24973
STANDARD ERROR
AC?
412279
0.063012
DM7
0022671
0025330
0167
0.02562
0.029772
0359W
0.027658
(907
(3(77
0.010602
017
'0.033936
30'
9304,2
AM)
AOl
-3.0007
-5.72
AWl
0.0632756
4,1642
6,0774
0.034894
03829632
0681217
'7.944
0,45902
KC
0.8542366
0,017364
A046
06824286 323870.06
.906
7132.9
.00074017
AWO
1.0713
06835504 146450-06
0014112
16818
0.92511
0.076623
KY
0014138
3017624
Am
00037499
AOl
3701.6
0070801
0,48616
-0.030177
0.3012815
2.4061
0.016212
00079178 4.3110E-06
AWS
3.7388
0.632575
0.17565
KBF
.9344
0.5038493
442.77
42904
A04
1.12370-06
00053311
0.1215
33093
1(8
17326
8022636 005006749
A043
434367
3.6141
0.61986
AW4
KBR
0010113
8.0643813 39984E-07
-03814744
/.312
0.045798 493960-08
4631626
.15172
KR
.03810039
0.023339
0.13115
AGO
Alit
-0.5014484
0,16258
00075961
06851394
-0,11772
9.5935
0262476
2.0523
005367
AC6
0.073279
06839730
DM6
0.31903
/3125
06839936
0433
4.0664
06873339
'0,016309
066
-3.0296
.0.26403
006
0.061506
05018474
0.097895
-0.12637
0057452
0.061174
2.1212
06839487
OCO
2.0904
0.0637503
003
-0.954786
-6.KR6086
(395
-30723
43934
.403728
OCS
00017531
8.366086
00035039
0.0641776
0688856
2.5522
038121
0.031201
6.19994
AC4
04.44
-001963
0.6321
4,6065
0164
0.13485
012203
0F4
0.48349
05064363
0.8279
AW3
0.19627
.6040528
DCI
GM)
0.0087442
-0.0670396
-4.7819
0.0624634
-6.01179
0163
0097
007
6697
007
67
0055063
'3.396
C7
ESTIMATES
PARA\ICII:R
-0.047662
0.0612191
00034122
47(20
4.2842
79576
06816115
05037244
0.3014023
'020166
0.507068 .0I626
066
06813606
066
0695
.06865909
08630323
-0.010619
0006
3.1512
.19436
2.7439
0446
0.5017103
0.9960464
06625173
1,26
050(9063
.53661
.0.012903
(205
0.41104
0.3031333
.0011811
0095
-4.8662
005
0.0670(7
0693
03039459
2.9494
60038767
0.011556
014
0.008)08
-33306
06829187
00650815
0393
093
0.0027014
0(13
-6010313
0I3
06810816 0,526I6
0.5026031
.0.013657
0843
-0.47067
33599
2.6943
.67849
.36534
03
0,013100
0.6858135
00071990
00062664
0010939
0094
-0.0665076
0.019533
0.019156
0,24845
85056
0604
0.5090111
03840861
0813
AC3
407046
090603
-0.2065
-02379
-2.0214
0,62938
.1.4716
0685732
9,061964
0.0044464
1.1147
6.016095
0080711
410166
1.1185
40028444
AWl
AC2
1.0616
0432
GF2
062
06813892 -02633 .46812516
06899024
004
.0,010112
Gd
-00066037
00622668
03823384
004
092
00025351
0.050316
0.63476
063
0.19734
4,1648
-13814
-0.96948
-3.1517
0.50316
06821137
4.041112
.1.6391
050-49053
'030827(5
1.156
0.18388
099948
095348
0051801
0038777
0.024172
024851
0014237
0,022461
A02
0.036209
AOl
AWl
41643
ACI
-1-3142
0431
0.086583
GEl
-0010697
OFt
-0276103
OCt
0011302 0.664
614
03071947
0013
86800471
062
00635605
00030836
1.104
0.009254
0033426
1.4674
(0695
0087812
2.049049
089
-0,059037
T-STA17S71CS
STANDARD ERROR
CO
0684066
1.6)68
OSlIMATES
013674
84
Cl
PARAMETU99
ES11914ATE5
.0.20632
4.0358
T'0I'A139111.5
9.92.996393319
05042224
05075715
020116
Si'ANI)AKl) 01444(044
.3.4733
-0.014666
.05015167
02.3
-19951
1.054
443
1.1222
'060319
T'STAS1S51CS
-0.92684
05038018
0.0660055
017103
STANDARD ERROR
00033053
OWl
62
01.2
0.5066268 46856627 -0.9533236
CS
-0.11064
.0.0848
0.14067
6311144.916.3
3.2525
035834
PARAMIDIIR
0.035115
0.063741
10138
-003625
0443972
0.7079
0609
002
09
0034327
1-STATISTICS
1A27
CI
STAI4I)ARD ERROR
ESTIMASTS
4'ARAMCIER
Table 6-7b: Estimated Coefficients for Part II from System Estimation: LA/S/H/AIDS
Specification - Japan Pre-war Period Sample
C2
102
-1.7796
8.083301
0,0670418
.23154
0,012449
0005912
0,18161
0.07364
T.STATIISTICS
1511 MATES
PARAME1 CR
31)6461
.0,017713
.0.006973
CO
0547059
ESISMATIS
STANDARD ERROR
1.1662259
42101
89
21.06
(.409
-3100
045115
00169
0321045
0(093113
1,9179
.39145
0,21032
PARANIFI'ER
05448327
0011232
00908
-3.0657
0013194
111118
021043
STANDARD ERROR
T.STA'[ISlS(S
1.118
004)967
11)1
0902633
1.110
0,901987
0.906396
13106
.51.46
1,0130
GOES
090102%
4.IEOO
1.2614
04642613
00653153
2,0496-Il
-1.3475
0464933
.0.015537
(90
.1,1971
099555
GPO
0014157
0,3067419
0,01(915
51003
1,1 8
'0.01848
1,19
.34E50
-1E48
.0,49348
.1352
32606
7E.06
-1.6371
0,032042
04674627
03272562
0.003390
2.132
AC9
511
.10.04
1.14005
-9E'08 0,00)62532
0.01903 2.329219-06
039039
-03682
0,17653
0.03102
2.402
0.006375
KM
-03237052 13731806
/2549
0.016084 5.15788-06
-84988
-19968
.0904363
0.005476
ADO
-3.0246
0.039923
A418
0.031846
-9.6493
030747
AV,9
-2.5752
.0,08690
0,11061
-33054
.013075
.0.30)82
0,049493
81.58
AW)
14755
0,05111 7.1412007
-2.644
0.16539
.2037
0.45431
0.083635 .63060-06
-043528
51°
39.079
AOl
All?
-13051
0.013811 Il0-05
092949
-1.0434
0015912
089401(61
SC
AW7
0,011187
.0,25,48
819
0649
000
4.2415
0011420
1,459
.0019367
.00035023
009
0.8460
0.460
08252506
004541
049746
.74617
1.1.8
0,24657
0,030535
.254%?
14,719
0,019087
005097%
6.104
0,0.45444
0.15615
05041%
AC?
GMJ
0107
3075465
(90533
4,4454
2.0646
035199
026)54
.6465619%
1.98
0.019577
0,071739
-2.4154
0.030173
25355
00046114
OF?
0.11432
0,469752
027739
0577%
/25)6
-0.050644 050047251
ADO
0.040503
0.039599
AW6
AC6
4092290
0.10407
64.441
0022438 939070-06
0.14881
ADS
0,071936
0.7903
ICE
AIlS
05022352 030061903
026171
00)0706
0,085579
0456
0106
03011462
0 19082
0.12215
3.258%
044778
030436
AWS
AdS
.1.7049
13107
0019846 1.1392005
KBF
0013331 0l00I4
0.14004
/I841
A04
0963114
030643
0,17405
AW4
0335659
GC7
0062302
1.1110
089
-1.1409
0,010207
001173?
.7,8343
2.021683
9032
0,031002
4.9261
50203
-11303
003392
0021939
0,035746
19
3092%
-3,164
0*004
0553257
0,95960
STANDARD 1100014
T.S1ASISI5CS
.0.14996
0,0065,15
08407
0107
-1.0426
032163
16307
01167
091654
(.101
l.5,'IlMAILS
0,1959
'91)36
ESI1MATES
IA IIAP.IICI III
III
C?
PARAMEII:R
0.012121
0,0457588
03218257
04605566
0.0453346
001280
1.0721
GPO
00430706
GCO
GPO
09019)4
0,012024
099106
.030465
0.016863
0.24465
021366
2.0533
0,17781
0.016222
0,0400042
092294
0,4695167
0,013934
0105
0,019942
-0,018405
0.4630326
GC5
0645
-14457
433.44
'124
.03205108
0.10734
0105
-0.15035
001488
AC4
0.064526
-14.176
23046
095413
0644
0022714 53419007
0072887
0,26)42
0.77113
0.92376
004
0040526 -10070-OS
1.784
AIlS
0.14812
KB
-40.622
A03
0545020
0.0050302 1.76711406
0.10232
-3.6382
0019288
-7.1781405
8115
-14698
AW3
0014341
03228844
-0,2901
03276495
MOO
-13426
0370174 000055001
AOl
KR
-4.1340-03
0067130 223472-07
AHI
.6300091
0.11833
3.7758
0250
03411
AWl
l.249
0.20406
035737
AOl
AdS
.4017705
0,0035645
-14668
00105
033975
.035401
-2,1127
.33264
0,73176
1.5982
-14746
085
0.060413
0.012304
0.016019
6116
1.2382
0.014871
002376
0017034
0104
-0025373
OC4
0.013363
4.7834
084
0)11
0545650%
0,0094947
0017504
STANDARD ERROR 3.2400E-03
T'STATISTIC,2
0115
0,018711
GOES
.6.010143
0017079
-13179
935553
.1.1834
0.0680022
003019
0.3084351
0016434
0.081736
-3,7849
0043
083
084721
.43473
0018402
0352730
03245067
03256749
0.027736
AdS
15742
057423
033106
0.75534
030011
AWl
0.26447
Ad
83243224
085
0.014107
-1,3866
0942
-0.019292
0032822
19718
2.309910-30
33709
0.049704
016785
064%
GC3
03273195
19056
0018130
00053264
0102
63245731
UCO
0142
15076
03775
0323995
0062765
004*371
GEl
-0.24377
0101
0.026008
0175
03234237
.601956
08103
0,10605
.0,01236
118.03
GORO
110
0106
ESmMATES
0032277
CO
PARAMEtER
-2,4008
0,012406
-0,79526
07102
0306)659
0,015496
0 077787
0.4254
0,65039
1-STATISTICS
0,0696246
0003
-0373144
0145
0.0)2403
95
0,848%
0013138
03003108
10889
0033068
084
3.3795
0013057
005
083
0.21435
1.1764
03226391
90037233
.14727
GPO
09499
0882
0.462907
002
-0.0066403 0.03079743
0317779
0854
0,019734
ESmMATES
STANDARD ERROR
CS
.53041
.24005
0290)8
0,013464
0.023300
0404
.0.044542
14006
04
0.061057
03320
037504
C4
PAIOAME70)R
1 SSASISS3O 0
STANDARD ERROR
PARAMEThR
ESTIMATES
.10492
081811
-4,6405
0,0027443
0015469
003013
I SF8115511
00103
I 0444
T1ANI)A14I) 13414(19
0103
0.4311
0.77373
ESflMATES
9.633
0048143
CS
PAIOAMEIER
-2.6529
0322268
0024737
03241067
GW2
.2.9350
52083
.3.0224
0142
0.059546
0.020411
0041312
53774
-0.17439
0044103
Gd
.23506
0.3494
0.028756
.6.011107
-0.67313
0.044994
GPI
.332496
0357264
-0081971
011101
(18%
(.640%
0014961
0.10899
(.40%
-0.026258
93
0.073614
03274067
0.1142
-081409
T-STASSS1SCS
STANDARD ERROR
0%
.01406%
-0342660 0.08074020
PARAMEtER
ESTIMATES
1.2609
2.0178
SrANDARI) ERROR
1-STA57S1SCS
3395%
PAKAMUIEO
E%rnMAmS
Cl
Estimated Coefficients for Part II from System Estimation: LA/B/If/AIDS
Table 6-7c:
Specification - Japan Mid-period Sample
183
E
çO (°9
H
oo
4
p4
'
$.4
144
co
0
co
o:
Estimated Coefficients for Part II from System Estimation: LA/S/H/AIDS
Table 6-7e:
Specification - Korea Sample
Cl
PAKAMI(1114
Ill
035
.0308309
0031822
0031549
-0092776
.007208!
0043963
0033406
0039253
0033207
0030333
003510
0.04.4032
0043069
3.323
*4206
03493
54920
0091651
.2.2440
*3424
01*573
.0.6312
.30339
0073375
052074
-2.9340
0.72304
213990
02252!
0.25191
STAPIIIAI4I) 1.3104014
36.067
0004334
I SIAIISll(
3.7423
260)!
(4W!
0W2
FARAMEThR
CS
ESTIMAThS
.32.936
0.31840
-0064654
037153
02
AOl
009!
.0.0032452
(11(1
ESI1MATES
OR.!
001
002
GOES
0.030594 0060*6630
OCI
0C2
OF!
092
GE!
0003097
AC!
049209
Ad
AWl
4.1116
AW2
040411
AIlI
KR
4.25634 OI3939
0.35782 900978.01
'049336
3900*
KW
AO5
A142
0.036359
049312
4033044
0.3066
0.63053
38359
.0.20933
4.20351 000034009
CI'S
GE2
8.6235
0.033612
0.035907
0.0*3659
03*3534
0013121
0.035061
0033721
0017105
0.016072
04220
052306
033009
0.10031 24008800
TSTA2.I10SICS
-2.1409
.33102
4.0962
3.6659
*9932
0012072
-2.425
8.7393
.14063
-6.3103
*492
34256
-13771
23Z187
PARAMIOS1(R
(23
ESTIMATES
.11434
STANDARD ERROR
0W3
0*03
0063
.0034016
0006856
.0.013195
0.939766
-005004!
4050909
0074234
0053178
303
0313
03'S
005
093
0303
31(23
AW3
401106
0.28432
13499
A03
0.30057 009211017
0.10846 14050806
11.773
0.06523
0.0333*4
0.023081
0.020619
0,024348
0020335
0025703
0.032366
003362
012265
30406
0.40196
09712
-030805
.19949
-0.30963
2.7034
-2.0305
-1.0111
22098
0090.595
034977
034357
*4063
411074
PARAME1ER
Cl
OWl
0094
Gd
AWl
3(30
0044331
TS1ATIIGC5
STANDARD ERROR
*313
3203
A04
2.0193
50.420
094
084
4043313
00196
.0063657
0030080
0052363
30070
43745
33633
037055 00634012
5.7
0021092
0034763
0.011331
0.012839
001002
0.012415
0.011334
0014177
0313915
0.33433
0.44051
0.37459
0003144 284928.03
TSTA11SmCS
.10434
4.0200
.022367
.35253
13508
2.6144
039559
.54405
2.1193
3.7765
10.716
9.0843
89453
PAI1AMI:n.R
63
GIOFS
665
E701MATES
-1697!
0054073
05437344
'0036333
-0015644
0.024795
0,018322
'00052519
'0543084
0330176
3.3645
0.67886
1.1031
4194
0053620
0012372
0.0693035
0.010708
00002197
0010503
00090624
0535063
0011643
0.29436
037203
4.0463
2.3007
03(544
.1.4028
2.6083
1.7314
'024354
032191
0.540
4.6354
68200
-5236!
STANDARD ERROR
STANDARD 0041400
I 10(311157165
Co
1319316161114
-0035.553
l'.S'IIMATI23
STANDARD ERROR 0.0.061177
1.106Al25]'ICS
.39 11$
*04
CR4
4*3403 00533315
*13
(.4*03
(;W5
-3.1934
0*04
(1143
064
0021207 00549135
696
((Cl
013
63-6
603
600
31C4
ACS
ACO
AW5
AWO
AOl
0370300
3365431.05
5.126
.1.1338
15684
AO6
6046
61106
0011002
0016363
0.050272
.00019607
'0.010532
0024627
005513116
00018653
0.011405
00063392
00543068
0005475
00047002
06054454
00649671
0055907-4
00554684
0.084674
0053005
0.052292
'1.5471
'1.2453
.3,6372
23107
2.36
.33751
54920
.13287
.1192
038259
0.08.1816
(.030646
AC]
AWl
AOl
(1094
K?
0.14682
0013746
GWO
31445
.84407
0002433
.00070824
(.318
.4367
3(89
4059096
'00360,8 .6)0.316206
66
*344
A136
4040004
Rd
000*3264
0.034030 19674803
.1.1953
All?
-67316
931
(017
OWl
(.6'!
61117
(197
((Cl
((('7
(13(7
1.1,1150 'III .8
45324
0 070,'.,
0(106(53
002242!
'0.032/1 I
0062481
0063016
0.0254(4
0079(63
00(4760,
'4 II)!
.23(0!
.39055
0.56103
-0.0610299
SI \NIISI1I) 1.1430(10
93)1/!
0055.4.08
8 /54502
5.022354
0.025944
002070.!
0024928
0022778
0020349
0027633
067105
08307
033953
0.16513
7.653E.86
031880
-(3294
(796
-5.8305
33975
.3334
Cl
19113181! 'Il-lI
(.SlAJl8Il1S
401.',3
Il'
I' (II 811(11(1
1811514113
SI 931)3(31)
1-S'IA'I 19111 S
14(3(114
67
(4(031
(10
-20909
4,318
1.1819
(.666
-(2908
(.338
'2.8792
41336
24873
Iil'6
.31167
(,('6
27923
1.36
(l0
31(8
31666
AGO
2!,
005(411
.0,09.3(79
.0046(532
04671421
-00033.34
.00.4)0031
'003(420
046277(5
0034(81
'(3032
.1.4316
4,65306
28473
6011241
706510546
010)45176
03050!
00.42803
05049186
0.10444405
0.2456245
09635224
014312
0.38(07
0070644
10142
.40,50,5
.10029
.0033907
.1 4456
.0.0(4743
'0192
.2.5447
045301
o1903
'9.1343
.0.017
.12.119
0065.447
21.00,
51.0?
.760.07
3(07
360.97
31818-01
(0.07
318.07
3(36
.3.0(1.03
206133
31118
0.0049!
RE
0.601881
0.032907 667180.65
0.45300
28(93
.5131.66 -13883117
0.06021933
SCSI ((I'
PARASICI 00
(0(3'! St
91 00.
Table 6-7t:
Estimated Coefficients for Part II from System Estimation: LA/S/H/AIDS
Specification - Taiwan Sample
Cl
6W!
0119!
GM!
Ad
ES11EIATES
-4,1524
0.10080
0.10206
0.080253
03021921
-0.0026333
0.011576
.10081082
-0.014039
8.038024
-090646
-1.1239
.1066484
(.23(90-06
STANDARD ERROR
0.98209
0.052586
0.026239
0,025073
0,0092622
0010046
09137
0.839183
0012547
00301457
030660
037009
0.10853
l.I627E-06
T-STA11ST1CS
.42286
2.0730
39096
3.8084
0.2339
.0,24077
12826
-2.0826
-1,1827
-19150
.32166
.3.8408
.5,9343
(9766
PAKAE4EIEK
Cl
ESflMATES
.72754
.2622
-00517
29271
PAIIAMEELIO
STANDARD ERROR
T'SSAiISi 95
I'AIOAMI(I ((0
SI
Ill
OR!
GPl
DCI
Gd
093
0.16246
.0,06437
0,848317
03097728
0068195
0922636
0.069709
-0063405
.2.0041
-2.4911
.030265 1991311-06
0.055302
0.027148
0023686
08694456
0011330
0.014426
0.831704
0.013014
0038997
030285
044208
013877 2.270211-06
-2.371
20339
(.0304
0.12276
13680
21931
-33306
1.0575
-5.7770
-5.6248
-6.1350
III
(683
0,024040 -8.8600245 027?273 -020102394
STANDARD ERROR
0.1022)
030194035
0.3040085
0.0(510954
0.8616061
8.8628557
1-STATISTICS
.17574
-0.40374
-2.2306
3.0719
.4.7636
037645
6111-5
1393
6(2
6(3
092
DM2
0.034
Ad
AWl
A02
KW
13176
6M3
Ad
AWl
A03
0.0100
-00032378
.0.011984
-0.12436
-095006
.0061399
00624724
00652824
0,0822531
03030200
0,050623
0.070015
0.038008 2.078710-00
-33326
2.423!
.1 437!
-33017
.2.1555
-0.41073
1(4
0464
OWl
0894
094
OC4
I-'S'OIMA135
0 (54!
0011
'0062130
0010505
-0016370
0065949
093330!
S1'ANI)ARIO ERROR
14318
0391746
0646805
0.341324
0010074
0019037
T-S'SATISTIC.'S
59449
I 7778
'1.0159
030750
-3.34!
3.3499
Cl
KR
092
6(03
Cl
AOl
0892
-0.010551
PARAMI7II(R
AWl
0W2
-0,3037966
('3
GE!
0(02
-03(475
ESTIMATES
GE!
GE)
AWl
-3.0687
83W
0.080(8499
80894
094
0444
-0.5033933
0053441
-005008!
3277!
3.0568
1.1107
(.3659805
0,024(3
0.3492
0.022124
0.030016
04793!
037863
0.16224
2383411-06
2.395!
-0.091107
23032
-13340
6.8371
5.0437
7.2133
3.7307
264
AC-I
AOl
IC?
05
(.1(7
OWl
4011
.013333
-0.0(5349
0.040352
.00616413
-0.028434
0,080033
'0.064300
-01956343
00644557
13236
1.4159
045687
-5.423003
STANDARD ERROR
114006
0057142
002000
0023041
0.013428
0.012773
0015096
0031911
0013391
0022(93
033207
0,42532
0.11755
6.542806
I SIASIS'Il(S
43187
-2,3305
'053557
101(4
-073271
-2 226!
5,9977
-2.6410
-0.4(999
0.2.0(77
3.7511
434
3.8849
-0.2419
(16
0406
PARAOIEII(R
EO2OIMATES
PAIOASII(I,l(
I'SIIMA'1330
Co
(,Wn
08(5
((01-3
095
690
0(2
0(5
60.0
61-6
095
4300
0035
6446
AC-S
ALO
AWl
.4W6
AOl
AO4
KC
KR
-053)3479
-030477
00031.20
-0,1.01
0036051
-0030611
-0(7991
00(44002
-00006645
00355(4
-12421
-1.2230
-0,11008
STANDARD ERROR 053709396
0(3073
0030570
0062151
0.025884
0.028439
0,034110
0065598
0,033112
0055104
043639
045085
0.300373 228790-06
T.STATISTICS
-26185
-23313
.3599
-31100
2.3036
-1.3027
-5.1824
(.4452
.002(5)7
024465
-3.0755
.2.6750
IAI&A61I1 I H
Cl
(.1(7
63.7
E30ISMA'IES
045392
.03030344
'00(6001
0011307
-0.0831.044
'0.0(0251
006(9226 0003340461
00(7623
0.0079(65
0.17(41
031083
0000969 736720-65
S1ANDARD ERROl!
032.473
0014100
00072264
00062794
0302749!
0.4031014
05038703
0.0003934
00603873
00653471
010102
0.1232
0.035(96 0.08061774
14140
-024845
-00025
(.0(33
-2.0562
-32415
04957!
0347033
50542
1422
.7406
2.5(65
T-S'I'A'I'ISS'ICS
1A4(A51I:'( I-lI
I6
Ill
19
ESF(NIASE5
-034(57
0,01(401
STANDARI) 11(16(110
0(931!
00003261
-3,19!
3(070
(0003702(4
II: 27
T'SlA'FISTI('S
2
(.1(0
(.3.0
681-7
01(1 8
6(7
(.18
((Cl
((6
0,000300
0,08573.3
0305(11
0.504(2!!
00636110
03612401
00817257
00021740
-040502
03(56
39805
2963-4
0.73304
-0.41. (0.
390-06
.2!. 27
231: (7
390 (0.
-0.50(672-4
(.1-'?
6(0
43)7
(((.0
(.037
AC!
AWl
.12633
Aol
0.75471
-9625907
-0.41087
830
440!
A(6
AWO
ADO
-0.9(30634 0I1)52
-0.2309
'030206
-0.027727
-4024E-05
040043019
030(9154
0032133
0.063422
0.018575
(.13638.05
'I 623
-(5524
00435(9
-4402!
4,7754
-1.4926
-4238!
4.90-02
-(.24006
-1.6007
30-05
11(05
3711-05
0(6417151
0,0010627 -0,5076(46
64(0
0283
KM
1
I'Al0.66Il.'(
'11
[II MAlES
186
6.3.4. Non-system Estimation of LA/S/H/AIDS
The estimation results from the system estimation
procedure were satisfactory.
However, tests of homogeneity
conditions could not be implemented.
autocorrelation was not considered.
Also, the problem of
BLANCIFORTI, GREEN, and
KING (1986) recommended single equation estimation of the
dynamic LA/AIDS model with a homogeneity condition and/or
autoregressive error specification as options (p. 47).
They argued that the first-order autoregressive error
specification for the whole demand system developed by
BERNDT and SAVIN (1975) is less flexible since the
autocorrelation coefficient is specified to be common for
all equations in the system.
If any one equation has no
autocorrelation problem (i.e., the autocorrelation
coefficient is zero) or any two equations have different
autocorrelation coefficients (for example one is positive
and the other is negative), the BERNDT-SAVIN approach will
result in misspecification of at least one equation in the
system.
The problem of autocorrelation is very common in
time series data sets, and if severe autocorrelation exists,
the conventional t-tests for parameter estimates will not be
reliable since the estimated variance of the disturbances
and of the coefficients are both biased (GUJARATI, 1988,
pp.361-4).
Considering these points, I took the suggestion of
BLANCIFORTI et al.
(1986) and estimated the model equation
187
by equation, which is referred to as non-system estimation.
The basic assumption of the error structure for eit is
as follows, assuming the independent variables are
nonstochastic and bounded:
ei is normally distributed with mean
E(ei) = 0, i = l,...,n
(6-12)
variance of the errors in the i-th equation is
E(ei) =
a2ii, i = 1,...,n
(6-13)
the errors are not contemporaneously correlated
among equations, i.e.
E(ei ej) = 0, i,j = l,...,n
(6-14)
The LA/S/H/AIDS specification was applied for each
equation75 and each equation was separately specified
with/without the homogeneity condition and/or with/without
the first-order autoregressive error structure.
The
specification search for the homogeneity condition and
autocorrelation error structure was conducted as shown in
Figure 6-2.
The rationale behind this exercise was to
provide additional theoretical and statistical restrictions
for the individual demand equations based on the nature of
the combination of the data and the functional form:
All the data was mean centered, which affects the
estimates of constant terms but affects neither any other
parameter values nor associated statistics.
188
Figure 6-2:
Estimation
Model Specification Search for Non-System
Testing Homogeneity
F-Test
(5% level)
not rejected
rej ected
Testing First Order Autocorrelation
Likelihood Ratio (LR) Test
rej ected
not rejected
I
I
I.
I
Method
I
Method
(5% level)
rej ected
Method
II
not rejected
Method
IV
III
First the homogeneity condition was tested using the F-test;
based on the results, the first-order autocorrelation was
tested using the likelihood ratio (LR) test.
Then,
depending on the results, four different estimation methods
were applied using TSP Version 4.1B:
Method I used OLS.
Method II used GLS with the first-order autoregressive error
structure.
The default method of AR1 command was applied.
189
Method III utilized restricted OLS with the homogeneity
condition imposed.
Method IV used nonlinear least square
estimation imposing the first-order autoregressive error
structure and homogeneity condition.76
Note that the first
observation had to be excluded with Method IV.
The
specification tests were done using the six different data
samples, i.e., four different periods for Japan and one each
for Korea and Taiwan, and the results are summarized in
Table 6-7.
The estimated coefficients are reported in Table
6-8 (for notation, see Table 6-4) .'
Note that the adding-up condition was generally not
well satisfied (see the last rows in Tables 6-8).
An
interesting finding was that there seemed to be an inverse
relationship between frequency of rejection of homogeneity
conditions and frequency of rejection of the autocorrelation
error hypothesis:
the Japan all-period data rejected
homogeneity most frequently, while it had the most severe
autocorrelation problems; in other cases homogeneity
conditions were frequently accepted and fewer
76
The formulation introduced in the TSP User's Guide,
p. 51, was applied for this procedure.
"RHO$" presents the autocorrelation coefficient for
good $ when first order autoregressive error structure is
imposed; when autoregressive error structure is rejected by
the likelihood ratio test at 5% level of significance, 'tNo
Adjusted R2 and DURBIN-WATSON
Autocorrelation" is printed.
statistics (after autocorrelation is corrected when needed)
are presented also.
190
autocorrelation problems arose.78
This phenomenon can be
taken as supportive evidence for the findings of BLANCIFORTI
et al.
(1986) that in some cases "the imposition of
homogeneity actually improves the autocorrelation problems"
with their dynamic AIDS model (p. 47)
78
The homogeneity condition was not rejected for 36
equations. It was rejected for 16 equations. Of the 36
"homogeneous" equations, autocorrelation was rejected for
21, or 58.33%.
For the 16 non-homogeneous equations,
autocorrelatjon was rejected for 5, or 31.25%.
Although I did not confirm this assertion, the
improvement made by the imposition of homogeneity
restriction may not be so great as to sufficiently correct
severe autocorrelatjon problems. The equations which
rejected autocorrelatjon after imposition of the homogeneity
condition may have been able to reject autocorrelation
without the homogeneity condition; i.e., autocorrelation may
not be so severe in these cases.
Dynamic specification is
more likely to have contributed to eliminating
autocorrelation.
191
Summary of Estimation Method for Non-System
Table 6-8:
Estimation
METHOD:
I
= OLS
II = AR1 (TSP DEFAULT METHOD)
III = RESTRICTED OLS (HOMOGENEITY IMPOSED)
IV = RESTRICTED NON-LINEAR LEAST SOUARE (HOMOGENEITY IMPOSED)
* - Not rejected at 5% Leve' of statstica1 significance.
F-test was used for testing homogeneity condition and
Ukelihood ratio test was used for testing 1st order
autocorrelation of disturbances.
JAPAN:
ALL-PERIOD
1911-87
DROP 1ST
ST ORDER
1
HOMOGENEITY
AUTOCORRELATION
*
METHOD
RICE
E02
BREAD
E03
BARLEY
E04
BEEF
EQ5
PORK
EO6
CHICKEN
E07
FISH
E08
EGGS
*
II
E09
MILK
*
II
JAPAN:
*
*
IV
*
II
Yes
I
*
II
III
1911-37
PRE-WAR PERIOD
1
DROP 1ST
ST ORDER
AUTOCORRELATION
METHOD
PERIOD
*
EQi
RICE
E02
BREAD
*
EQ3
BARLEY
*
EQ4
BEEF
EQ5
PORK
E06
CHICKEN
*
EQ7
FISH
*
EQ8
EGGS
*
EQ9
MILK
*
MID-PERIOD
*
*
HOMOGENEITY
JAPAN:
PERIOD
II
E01
III
*
IV
Yes
III
III
*
IV
Yes
III
III
*
IV
Yes
1925-70
1
HOMOGENEITY
*
DROP 1ST
ST ORDER
METHOD
PERIOD
*
IV
Yes
*
II
AUTOCORRELATION
EQ1
RICE
EQ2
BREAD
EQ3
BARLEY
*
III
EQ4
BEEF
*
III
EQ5
PORK
*
III
E06
CHICKEN
*
E07
FISH
*
EQ8
EGGS
EQ9
MILK
*
III
*
IV
*
II
*
IV
Yes
Yes
192
Table 6-8:
Summary of Estimation Method for Non-System
Estimation (Cont.)
MET HcC:
= OLS
AR1 (TSP DEFAULT METHOD)
ILl = RESTRICTED OLS (HOMOGENEITY IMPOSED)
IV
RESTRICTED NON-LINEAR LEAST SQUARE (HOMOGENEITY IMPOSED)
- Not rejected at 5% Level of statistical significance.
F-test was used for testing homogeneity condition and
Likelihood ratio test was used for testing 1st order
autocorrelation of disturbances.
JAPAN:
POST-WAR PERIOD
1955-87
1
HOMOGENEITY
DROP 1ST
ST ORDER
AUTOCORRELATION
METHOD
EQ 1
RICE
*
EQ2
BREAD
*
EQ3
BARLEY
*
EQ4
BEEF
*
EQ5
PORK
*
E06
CHICKEN
EQ7
FISH
*
EQ8
EGGS
*
III
EQ9
MILK
*
111
KOREA:
PERIOD
III
*
IV
Yes
III
*
IV
Yes
III
*
IV
Yes
1962-87
1
HOMOGENEITY
*
EQ1
RICE
EQ2
WHEAT
E03
BARLEY
*
EQ4
BEEF
*
EQS
PORK
EQ6
CHICKEN
*
EQ7
FISH
*
E08
EGGS
*
TAIWM:
1963-87
ST ORDER
AUTOCORRELATION
*
DROP 1ST
METHOD
PERIOD
IV
Yes
I
III
III
*
III
*
IV
Yes
III
1
HOMOGENEITY
II
ST ORDER
AUTOCORRELATION
DROP 1ST
METHOD
PERIOD
EQ1
RICE
EQ2
WHEAT
EQ3
BEEF
*
*
IV
Yes
E04
PORK
*
*
IV
Yes
EQS
CHICKEN
*
EQ6
FISH
*
EQ7
EGGS
E08
MILK
I
I
III
*
IV
*
III
Table 6-9a:
Estimated Coefficients for Part II from Non-System Estimation:
LA/S/H/AIDS Specification
Japan All-period Sample
4/' /5/I II,
40
1151811/,
5) 95415141) 14014/Ill
I 1
0. II',
I' I
05/0 054/
I
54
I0.)ISlAIt-2,
SIASI)A600) 1:1110/210
I SISIISIIls
51/0.514 / 1/4
34./94
07:,],)
00/4405
/7 7,
42
1./I /l,l(I(/(,IIllIf4UI'/
4.2
1.11
4,44
0.04/
90
AWl
0544
AIlS
XIS
11110410
U lI2
/03/42,
04/2/5
024404
002/0,22
4052/01
-6/14
'00510/,]
04421142
40230
481.03
037098
'0,041.142
00/4356
00040024
-03040,45
086720
0023303
0034(53
001211$
0010,242
005-4505
002/35.5
00232184
00.0022
025/tI)
0230.34
0 10303
0 141944
/11/14
/1w,
I'l/) I
0055007 000060512
/2/41
0, ((/00
0 (#5,/1
0461,5
SI'S
I 246,
1 -251
4 2,2.1
14//ll
I 3130
.1/ SI 162
-224322
441
1,142
2
4,44(02
4,112
4,1/I 2
4.12
3,12
0,1-2
0,5:2
1,042
090 2
091,01
AOl
AlSO
388
KIIK
101100K
003-1/00
-0/0.363'47I
-00/547/
0046637
008567/2
00620/12
7368/54
-0005/151
003/0152
04)64(9
'0,01474
-0,005046
0/8042141
0058/933
0.25210
0.547402
00/04/4,23
044236534
0083/0849
00652497
000294
0.0056244
0.0028258
00625606
00052844
00240987
04)60374
0.013259
00075312
0.0(0008281
0127700
10461
.0040450
-30340
5)502
1.54,32
0.53-104
2.7723
'0.73002
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0964543
16183
YES
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0964433
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ADO. K-SQ
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093058
1.9615
NO
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D-W
IIOMOGENEI1Y
0253050
4.53501
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OW
IIOMOGENEITY
0938473
2.227
YES
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0.902007
4.720
YES
KM
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0.025340
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104(00,1
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2,115-I COKKEIATION
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III
.55
(1(1
00)4403
1,11/,
540
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1.5
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5/55(25/0/)
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(,10104
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60
1511.515/40.
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0021584
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164
13133
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0900008
404955
1811045
51A5/)AI0I) 1:1/10011
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AIlS 513-03
00.40.1.493 0.02220537
400502
Table 6-9b:
Estimated Coefficients for Part II from Non-System Estimation:
LA/S/H/AIDS Specification - Japan Pre-war Period Sample
rAIlAMcrLlo
CI
6101
GORI
681
614(1
GIl
I7S7I1MATI7S
039103
00100,48
0.017322
-0030233
0.060625
0.037770
0.022456
-020154522
-0.082906
-0.022251
0.111)57
-0.28114
5.154
0.0026481
0006512
0533321
0.054646
0.039161
0.096668
0027329
0.063018
0.03275
0.022885
0.063932
13152
1.4137
223.19
076103
033943
-035323
1.5513
0.5971
0816145
-0046171
'23315
-096808
13253
-0,2)31
36433
0(9(41)9)60 110140)0
3 7.19110111 S
IA (AM)
I
B!
02
02
0
I1SIIMATLS
0,1)1
0.054755!
0.0033176
0.0075001
08024475
00059539
.03940036
13012
0.16855
0.40551
1.6652
.0.1639
030123!
0,46365
T-STAI1S'TI(S
GEL
61-2
-0.06097)9 0(0)51141
GE)
6122
6631
6632
AC!
AOL
0.040117 O718l2
0305719
089403
-0203)615
-330421
AU)
All)
0.0062892
0306516
0.13168
0.0228-42
0.063)08
0.19081
-2.1320
.036635
-03(36!
0.05353)
4.82003
-0.1307
AC)
AWS
(III-)
0029105
08071960
642940.60
00111)2
.0012662
-0.013651
0.06617663
00(5045
0.0000315
0.010301
0.0662239
0014418
0.0047076
0.013181
0.005723
0.3944804
6632-I
-1,6253
.037712
-1,2923
4,7327
045914
0,0013654
0.44278
-23019
-3106
6)44
9(54
C.C3
0.24336
03022198
61)3
(910
GE)
-04)015 -03007808
0.031312
0.0612864
-0.63954
0.01113
0.21143
0.22867
0242643
0.010701
O.1X4Y9304
02.0535
.03272)
-027108
1.7562
1.2237
2.9043
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-2.4277
0144
68144
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60(4
AOl
9304
03039364
0.012524
0.030433
0.01)0,5)
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-0,018163
.0,903155!
0,0060819
0.11538
0.16331
0.033735
0.033026
00(1425
0012147
0.0(085
0.019843
00087453
0010233
0.0050032
039(9412
0.015619
0,20(33
0.31495
0051080
0.011243
-2.6260
.2.904!
0.73325
1.2461
1534
1.2041
.0.10907
-2,3147
-032709
038865
04413
031055
0.61403
13817
T.S1ATISTICS
59.708
6(4
6(5
6C4
6F4
6)04
9W4
6103
08)43
AdS
AW5
001(408
-03600070
.00(3203
0.EN2730
030)0.557-1
-00681051
00604062
039402(6
.06Z31
.03059525
03027046
0.17476
0052508
-06625676
03032187
YOANDA100 0.10)600 03003236
0.3074812
0.0642.03
0.0049293
025234504
0303365
0.0062862
03029522
0.5823114
0.0063343
0(040
0.13542
003032
0,5853305
-13206
.3(628
1.2727
l6
00679703
1.0232
2.1307
0,63945
.021113
-1.6623
0.43306
1.6708
038032
.0.084)28
0.61200
PAIOAMC!ER
CS
LSIIMA1I'2,
TOTATISTICS
11)
35.106
)'AI0AN)EIEIO
67TAN1)ARI) 1.1)16)0
'17.19117.1)07.
GOES
OCS
6(5
6(6
6146
60146
686
0)1(6
0.011739
-0,0(9238
-0.014020
011540171
0.0123(4
.0,012930
00039116
.00613667
.0.01246
09.017234
0,1004134
014)24049
09036546
0302.554
00652397
006107)2
0857992!
68109
-2,566!
.59102
14602
3,4087
'2450!
2335
'030907
80
CO
0-SI'IMASTS
61)5
GPO
6<78
6105
6)45
.031516
00650739
008215019
04664000.1
0074436
0002375
0.0(739.7
04.4731103
0.3971809
832012
-60023
4.5001
33090
30258
33925
13017
.37137
601
60(7
6107
6)47
0664070
007503
.0.19197
0.070532
'0.332558
0060920
0.13710
0.017045
.0.10166
-13149
00665539
0.1550)
0190786
0.1009
0.077
0.15682
0,034800
0,11941
0.070686
0.047233
0 12501
2.0193
-1.4532
.0739(0
41.786
(8
10307
08
0.10134
0008
0,74937
60168
-2.0663
608
0.4.4975
60(9
'09)095
6(8
031026
GC8
6(7
2.3476
6)8
034088
6108
6)48
ACI
AC8
AWl
A07
9141
.0.43954
0.023262
23102
0.44841
0.10841
'2.2023
-090622
021458
-5.7204
AWO
A08
9118
0022140
.0,022407
-0023709
-0.0063120
03086503
0,010748
0,0030734
'0307342
-0.0005196
0.021661
03019002
0.065473
0.20725
.0.02459!
-0267)580
STANDARD 0.16)6014 0.00034125
0,0075753
0664(423
006495!)
0.0034903
0.069(064
00627056
0,0060634
0.0429635
03024300
00009313
0.1073
0.12001
0,020663
0.005834
049
-3,117
.57259
-! 9273
23066
11802
(.433!
.1,1133
.22101
8,9(22
021502
063370
1.637
-0.45703
-1.391
010679304
00(9365
'00633621
.0363079
0.0623020
03624434
00(4)60.123
-03015331
-0067239!
.0003(407
00(3598
0.10432
0.2(0(9
074)014429
010043906
036(0638
0903150!
0001620,1
00045313
0.00(3638
00033536
006(601!
005(4611
00033106
6063052
0.036758
-030)03
-4456!
-2,0712
39(39
1.4030
26027
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.00027076
-00059597
-005.00)27
-(205467
00729210
L2SISMASTS
1"S']AlISilC'S
Cl
PA8IASII7LR
5)97.1)9)61)
!'SIA!I7.111IS
10)1(110
119
0109
(.8169
609
60(9
06.825
.2.2753
200000
-(.1631
(.4(0
033652
I (0.0:724
008:0125
-0057(192
00050437
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00(51033
0(9
040(6!
GC9
00.9
010)
6619
AC9
AW9
A09
9119
0066704 03626665
KC
1(10
IISI (0397)3
IQIOF
03014518 NO
A1>J. K-SQ.
OW
0.914721
24111
IIOMOGENIOLlY
YES
ADJ.K-SQ.
D-W
IIOMOGENIOIJY
0.164507
2.1238
YES
9350K-SQ.
136W
IIOMO(0ENEIIY
0.913036
2.4356
YES
AOL. K-SQ.
D-W
IIOMOGE74167IY
0929491
2.5304
YES
ADJ. K-SQ.
OW
1IOMOGENEIIY
0451623
2.0866
YES
AOl. K-SQ.
OW
IIOMOGENIOITY
0966704
2.39)8
YES
901K-SQ.
OW
HOMOGENEITY
0.985793
2.8970
YES
-0.47264 COKKEL'.TION
1(00
1611010
03073)59 NO
03026391 AU135-
2.774 CORREI,ATION
1(0,4
16100)4
01152(305
0.20410
0.01824
5.4620
-23257
.00663306 0(02049419
0,0321017
2.138!
YES
0.0630717 AUTO-
.0,66171
0.6624034
IIOMOOI(NIirIY
2.0442
-1.4009
1,801
SOS) 1)'
lAI0901I'I 1:10
RHOC
0.011630
0022119
OW
0.015392
l.1737C0RR111.A1SON
0.014051
6201
AOl. K-SQ.
03087516A1f1'O-
-0.01158!
0,15978
(9)62963(70.14
KIlO)'
0,0226)3
07
1'35ASSS'TICS
KF
020(0272(40
03284
AIlO
NO
-042968 CORREL&]10N
0.22534
Cl
STANDARD 126)60)0
KIIOBE
0.01321 NO
9(36
9W6
HOMOOENOJ1Y
2.336
0.030743 AIJTO-
0.013012
027435
GC7
KIll'
141100
6)46
6106
ESIIMATES
6)'?
A06
MIS
1(1)
0.0567546
PARAMIO'0.R
08147
905
D-W
09893)8
20373 CORRELATION
All)
-0.03306)
ADO K-SQ.
030)8795 Al.710-
0.082)86
00(0077
84
10110014
AU)
.0.049581
C4
1(1114
0305829! NO
045427
003507
'AIOAOIEI'I:R
-3.115
4,0573(3
6)43
-0.0060257
STANDARD 0.10140)6 0C4)090333
12S1153203175
1611016
-0.721091
03027386
-0.013313
KR
-0.0022360
0.0014230
(11)0.3
GPJ
MI)
0.218 -0.00)3336
-0,03)915
-60622.257
(.10.3
AWL
AOL
-0.0654446
-00)5.428
63
AWL
42004734
0032162
13
STANDARD 0.1010010
6C2
-1.2.434
YrANoARo 110)0(116 000031972
19109011 'I III
GIl
008401)3
00039430
1.7.11639112,
0,114.2
40633323
00634973
37.023
1,1)2
03073476 039)55916
0011637
T-Yl'ATIS'llCS
(.1)102
Gd
Table 6-9c:
Estimated Coefficients for Part II from Non-System Estimation:
LA/S/H/AIDS Specification - Japan Mid-period Sample
PARAMEFER
ESS1MAThS
SFANDAR1) ERROR
T.STATIS11CO
002
Cl
8!
Gd
OF!
GEl
054332
.083233
-6232367
.0,11239
0,29356
-0038357
0,16051
.0.19100
-055365
6038288
0.10638
0.23641
0.012241
0,13605
0.039713
0.032,718
0216341$
0343535
0047813
0.11136
0.040943
0.060568
0074443
05270
46.069
097445
-15502
-34280
4.8562
0.07762
33451
.1.7139
-5,4689
0.63807
25051
002
OBRI
(394
lIBEl
098
6841
ACt
044791
AWl
AOl
057875
090443
4043191
AHI
0026872
0.27177
0.63973
-0.85349
PARAMETER
C2
FSflMATIS
S'PANDARI) 844000
0049!
0013533
.001404
0,020334
-0005439
0.0556862
00061001 40044639
05034811
00048789
0019123
0045696
0.89338
0030704
03928339
001040$
08010880
09056068
0.00536.52
00044394
09041503
03988328
00050461
07050675
00063358
0049886
0018951
0028493
20395
.1.237
-2.1226
71620
-10568
1.2751
2.3316
-0.52040
0.8104
-2.0771
0.87416
23983
.8.7795
83
0022118 0391181!
1-STATIS11CI
PARAMETER
ES11MATES
0TANF)ARD 12306)14
1-STATISTICS
PARAMETER
ESTIMATES
STANDARD ERROR
1-STATISTICS
PARAMETER
ESTIM1TES
STANDARD ERROR
T-STATIS11CS
PARAME1I734
ESTIMATES
82
C3
003
000.2
(002
083
0065
(192
((BR
057897
-14268
AU3
KB
0012293
0.12334
0011281 009068608 NO
0.01515
0024302
0018244
0.016313
0039422
0083423
0.027883
0028235
0.28965
030963
0.11467
003363* 002408AIJIO.
34146
0.027395
-012787
-8.8333
032777
2.5049
039500
0049782
(339
C4
04
004
-2.3
0004
29394
-(4402
039872
0.08234
094
0994
694
0(0
094
684
0843
6844
AdS
AW3
A03
A04
0033323
0.363595
0.08644
0038354
0084957
0010082
0022827
-0023003
00(3422
0979
AC4
-038499
AW4
0.0237
03942938
0559070
0.019359
0.011941
0018141
0014455
0.0(2207
0.024043
00003092
0.05042
0028477
0.13044
036623
000
011388
0080797
35393
-1.4216
-22125
13767
1.7586
-80347
016334
-616089
-2.7747
0.75521
2.7926
-13661
-1.4062
CS
B7
005
0803
085
GOES
0040000 0.RD49398
022!!!
008550!
0642087
0962(82
.43155)9
0073034
0.02207!
09037725
0540624
0.022637
3.083537
0.023306
73139
0,2293
.04714
.13392
048133
CI
160
606
0606
606
19236
GBF6
097
0030931
GPO
0083957
00096034
0215144
0,966272
-3.3362
0.98897
2.0549
090963
1397
Cl
87
607
6607
007
GEE?
097
I.
040210
00013152
03813296
056
0(486325
0.023708
0020396
090436
032332
0.12,335
0233178
.0.057339
002529
.0037234
03379!
11662
030744
0.1624
0037899
0.64788
lIES
0845
AdS
AWS
042879
ATE
AILS
0027923 03879
KP
OMO
AC6
-000702
0033354
0,080832
0.23440
0.8287
4013439
00042111 NO
0019723
0283679
0.13313
0.19061
0039483
0219623
0.0632344 AUTO-
031309
0080039
89688
055257
13331
8369!
406346
OR?
-8.156
0.961819
032637
AW6
A06
AHO
((C
6847
035.346
0.11614
0.023375
0.13424
0,14392
.0.349714
0.23487
-0.83992
-8.3426
A07
415099
3.15375
0,056065
3.035476
033119
(024309
038
AC?
-4.027315
3.34373
3.339282
3070833
0.027375
0237246
0571524
031992
038947
0.27576
026370
000138!
00030420
20374
033(7!
43946
39401
40758
055433
-2.773!
2.1106
73190
-33634
-000399
-11664
055118
0.61073
0.49639
1,05
6111(8
GIll
6(358
698
0(4
GE?
0.80074
AW8
AOl
0040707
XE
004996
00636433
-000(0745
.0224682
.00089588
0028879
0080720
0328249
0349032
Alto
0045764
00084959
0.637939
0.011981
0.010371
0011643
0082462
0232533
0.035336
0.014618
00078423
0.087305
0.0660900
0.011788
0013030
0.09273
0.1496
0.37202!
0.021148
00014905
0.195375
0.41324
.4 1637
036348
094047
2.2256
0.24560
-1 4102
0279(2
2.3905
0.773!
039715
0.84068
0.68432
.2.1639
.0.88430
326315
CO
89
099
088(9
(309
()8F9
OP9
0(0
099
0849
-096596 -(0614336
(0020337
-0.0331407
0.0606483
-00003326
038235524
0.0659852
09054420
.0269066
AdS
0,87492
0835
A9,9
ATE
AH9
ICE
(0,4
(81(08
019084
0.34144!
0078888 -7.7090-05 084I43
03311/
0,09612
0.084497 0.9694632
'83030 -0.961746
091887
0.39624
0062348
0.08602
0085562
03832380
0.011224
0.5060735
0.3062307
0.005212
0.062436
0.010652
0013273
0.15225
0.2513
13.339
.0,051331
-012110
054(9
-027998
1.2010
0,0332!
0.87106
-1.7215
038004
066328
-1.1480
(27235
1038837
-02838008
.00223850
.0.0(33443
00004513
0.0.49639 0.08030030
.0.1588775
-0,020039 0.2028284
027(3044
(085074
-0237766
5.17.108.'
0,89692
ADS. 0-SQ.
DW
UOM000N801Y
0926240
1.7900
YES
D-W
HOMOG83IEITY
11081
YES
ADS. 0-SQ.
D-W
HOMOGENEITY
0.984056
2.2007
YES
ADS. 0-SQ.
D.W
HOMOGENEflY
0.973994
2.1249
YES
ADS. R-SQ.
0163948
.02639827 006660339
D.W
IIOM000NIOIY
2.7149
YES
ADS. K-SQ.
D-W
IIOMOGI8NIITIY
0923706
8337
NO
ADS. R-SQ.
099519
D-W
2.6670
HOMOGENETIY
YES
-3.3508
00084443
0,03(335
NO
021325
0.0744)5
8206
HOMOG654EITY
24923
((((OF
ADS. R-SQ.
471447
0906325
00046811
GPO
0848
ATE
RLIOC
ACI
00216!
(18
6810
AWl
D-W
0996254
016844 CORRELATION
0.2206
(SC?
ADS. 0-SQ.
0010022 AUTO.
0.21670 CORRELATION
30111(1
(6
01409
00021720 NO
006
GPO
YES
-0.28387 CORRELATION
4022003 00018467
GC6
HOM060NEnY
2.3351
013174 CORRELATION
KEF
014089
0086847 00037494 NO
0026528
001326892700-
0(4
0224101 -0.523
GC5
01408
AH4
09.3306
D-W
097300
.337039
0.82415
003
ADJ.R-SQ.
0.193673
0070727
0223003
ESTIMATES
0322793
-0024618 Ol39l
-0059019
PARAMEU.R
008080
0028111
090957(3
00577686
STANDARD ERROR
I S1'AT1SFI('5
AH2
03863732 00903992
00082243 000036743
093
0020641 09048733
OBF3
0.0687574
PARAMETER
ESTIMATES
A02
0071918
0.028335
I'SIIMA'I323
AW2
-8.4977
08895
4037871
-03808043
STANDARD ERROR
T.STATISfl(,'$
AC2
2.8211
0023489
0239567
PARAMITIR
093
0M2
0.28240
-0089388
.05083(24
T-STATISTICS
1.1428
002
0.075180
-056205
037527
9043393
0.084392
STANDARD ERROR
0(0
(SF2
01400
03864774
STANDARD ERROR 03834564
1-STATISTICS
4.16!!
PARAMETER
ESTIMATES
GC2
ICR
0903236
03811471
-1.3272
Table 6-9d:
Estimated Coefficients for Part II from Non-system Estimation:
LA/S/H/AIDS Specification - Japan Post-war Period Sample
Gal
PARAMETER
Cl
0(11
Ad
AWl
AOl
ESI1MATES
0.4000
-O.75
000(05
0001373
-0030146
-0037250
-0.0096533
-0030399
-01601
-0011094
4993146
4999197
-034137
-034875
001344)
013000
0042484
0041199
0043986
0031302
0034044
0970304
0038393
002)795
0031145
014344
031436
0.34403
45944
.14349
68.3!
22179
.967627
(3390
-0017124
'03)113
4.1)44
.0.50103
1.63
901505
-1,102$
-1,7346
STANDARD 1-34)4(00
1 0141111(7
PARAMETER
ESTIMATES
Cl
0.043
Bi
02
002
GERI
GIIR2
GUI
G02
GEFI
081-2
Opt
012
OCI
0C2
OF!
((PS
GEl
003
AVG
A02
0.16376 0.440S3
0442
1320
-00033562
-0017427
0049706
049 90035299
00096467
8.024428
0056767
0024633
-044098
-19243
1.7513
292
0.017501
AN)
1634.
0340K
027 00028)83 NO
1333$
AH2
0.118.
81408K
-05063267
005(3067
-00096123
-0003108
-80070349
00049057
0.9917674
0019132
00006336
00072463
0.0050842
00049913
00058)55
0.0091834
T.TAI1STICl
25976
-18109
4.93375
69093
-1.6144
-10)94
-1.2127
034397
PAKAME1ER
ESTIMATES
0143
03103
(7163
001-3
0643
AC)
AW3
A03
.4.113
0.0375564
00040936
.0014111
-0.047743
0034112
-0018063
40092342
-0013008
.00075035
00032430
0071946
0044736
-0.030548
0070276
0010167
STANDARD ERROR 0.5033056
0.044022
00(3652
0.0(636
0.014501
00096503
0.01166
0021343
0013496
0.0013308
0050361
005085
0.11030
0.050196
0.063642 000567224 AUTO-
T.S1A1WDC'S
0.091314
-30336
-1.9399
23393
-1.8738
0.79(93
.0.61313
.0.53403
6.44249
33537
0.00011
'034979
15634
STANDARD ERROR
PARAMEI'ER
ES1SMATES
STANDARD ERROR
T-STATIYI1CS
PAKAMIIIIR
ESISMAS3.S
STANDARD 1-1(3400
1-STATISTICS
PARAMIII1IR
1-341164411-3.
311A341)ARI) 1.14360(1
T-STAS1STICS
(13
1,9056
03
GP4
0(14
01-4
0114
0644
AC4
AW4
A04
AH4
REF
0030137
-0075317
0017052
0023038
-0.044057
-015660
'033979
416613
-0005662
0017364
-0.59200
0.9934981
0074441
0037421
0021374
0.022421
001419
0.022831
0052135
0019306
0012273
0.033326
0004991
0.17504
0.1013)
0.11301
0.0972351
0.22483
-1.1627
2.3039
1.7306
-23502
091038
1.899
-1332
-3.0843
-3.1376
-14337
-0.73737
0.3499
-2.6368
14317
3.1364
III
4,3(5
(.3(363
-3.0644
(.9)5
(.135
0(3
(il-i
(31.3
0643
AC5
AWl
403
HOMOGENEITY
2.4021
YES
AOl. R-SQ.
D-W
HOMOGENEITY
0985416
1.7685
YES
All)
K?
RHOHE
AOl. K-SQ.
D-W
HOMOGENEITY
0988636
23707
YES
Kilo?
ADJ.R-SQ.
15W
IIOMOOIONIOTIY
0984633
2-0554
YES
ADJ,R-SO.
15W
HOMOGENEITY
0.903320
2.1138
NO
AD). R-SQ.
D-W
HOMOOR14EITY
0970524
2.6115
YES
ADS. K-SQ.
D-W
HOMOGENEITY
0970472
1.7832
YES
ADJ. R-SQ.
15W
IIOM(xilONl.IIY
0973165
11374
YES
-8.2(340
0034243
4.557723
-0.02303
0053064
-402254
0913074
90377436
-000866(5
0019470
017099
0.72673
0,14536
0004)47
00273)9
0028300
0.026071
0023498
0.023967
0.031304
0027213
0.015123
0.0)944
0.11053
0.22260
0.11601
0.12049
0.004488A1JTO-
(0.963
.2.2628
1.2134
.1.0248
.0.05706
1.2337
-0.90647
015303
-018439
057275
0.49349
21634
3.2727
1.283
407138
-0.19804 CORRELATION
AH6
KC
4,06
6686
0116
(.01-6
0961(48
67044401
.00.4342
90(0656
0.098943
0027020 010606564
0503(603
0,54134
0.01)960
0013753
001301
17502
-1071
-29223
-13556
CO
86
4.0470
087
0074715
0.10534
0.055546
0.3570(0
0040364
11-2(8
4(023
-3.4096
14402
(6177
CO
0 062346
0.0353030
11.983
.5
00
GPO
0(00(1061
0.011823
3.038
0056249
(5646
4(16
-4020(54
00(7349
9.02(6(67
0032886
0037336
0(0543
014444
0.034861
0.012809
00001851
0030592
0604(07
0.12148
0074394
0.059621
13344
-2.7033
13019
033038
151
-19418
-20317
OCO
.1.1799
OR
0(17
0078776
935(17
003634!
0044234
0,086458
43(32
1.7028
-1.61)4
GOFO
0.15(364
'0.11063 -050096(4 NO
01-6
01-7
01.6
0877
GM7
AC?
AW6
AW7
AO6
407
AH7
935624
05"05
0013271
-0.46145
0.044084
0.030514
0975966
012709
030271
0.39545
033062
00818496
0.18751
2.8452
-3.0304
-19428
0.72402
034221
-1311
0.6883
-2.461
2
083(8
0163
061-8
GPO
01-8
0(10
0.030982
0.035442
0050248
0.024467
9067623
-00086072
0027162
9015537
005(961
0.19306
.05088
'011534
00.43643
0.01852
0.016701
00(2025
00(3437
0.034783
09(9506
0.9985306
0.022645
034(909
0,72470
0.060768
0.074217
(.6952
-2.4516
-30472
0.43127
9.446(3
.0038)9
0349
AC9
1.6100
1224
0(49
3.9001
1.7099
RHOF
0.27257
00(8623
63(349
0.14
0.1646
03(6
0349
0.11404 CORRELkTION
'0070116
0046(47
189
00021009 AlfIO-
0032449
0.13360
0.062463
(SC)
RIIOC
'016023 03I8I0N0
01-8
(183-9
63(0
0(9
61.9
3,1867
0(24
ACO
AWO
13877
AW)
AGO
-3.436
4(59
A080
-1.9015
AltO
0.076187
0034(66
.0.023461
349935574
9.03(6362
0 0(6374
0011100
902006
0024725
9.0091734
0.0367042
0(0452
0.40636
'0.137
0.3976349
0054333
00(0201
0.017571
0010019
0.015056
0.015381
0,03(302
0021653
09991307
0,024903
0345367
0.74653
0.061936
0.077620
9.72.11
0.073731
-1.2272
-050245
-01019
0810
072278
-0.67907
1.1314
-(3341
0,26833
767))
3(628
-(0665
-638188
09(1)94
0108079
00/0(2)7
0936034
005(41(5
0290010 04432(905
-0 (0)0303
099(999
-0.0310040
9110(400
02123
09(04052
08039173
0247305
0.11
143408
49615556 340
0.3013971AUT00.75328 CORRELATION
KM
34(34)64
9.037401 05S4(3 NO
00610365 AUTO-
032147 CORRELATION
0(34343'
I'Sll0(Al'I S
D-W
0930024
0(0.544
0.077753
('24(73403(44
AOL R-SQ.
00996007
00827(6
T.STATIS7JCS
-1.0095
4,1)3
YES
2.7ThCORRIILATION
-0.0228.39
003(7
STANDARI) ERROR
16340$
0083
-6.78647
ESTiMATES
KU
00018670 NO
.0.00072
(3
HOMOOE74ETIY
23395
.2.0879
0114
(.3(7
PARAOIL (((4
0.15973
2.9703
0.097037
07
T.STATISTICS
t.ml
(30R4
0.68406
STANDARD 1-1(0014
033516
0063367
0.23344
ESTIMATES
GE)
002509 008680844
0R4
114
Cl
PARAMI.TEI(
01-3
.0491
-0096534
CO
ESTiMATES
T'STATISTI(3
(SC)
00029462
0.05031
0.078883
PAR&3.ICrER
STANDARD 1-3(3(00
0)35
-1.4393
D-W
0897643
01462 (:(3KRLIIAfl0N
.4034445
9.0979027
AIM. IESQ.
0.18502 07302I AIJIO-
0,03398(6
Table 6-9e:
Estimated Coefficients for Part II from Non-System Estimation:
LA/S/H/AIDS Specification - Korea Sample
PAR674IEThR
ESTIMATES
OTANDARI) 17474(711
ci
05639
0036166
I OIAIISIII '.
cojj
6891
0.7735!
0024115
0.14013
0.064003
0.001719
-003641368
-0049633
-0.019446
-0,082,577
3.7399
3.0000
1.2674
012252
0 11361
0084864
0051232
004305
0050172
0055977
0099146
0.0592)9
20554
23571
7.013
-4462
024244
I 6515
-1,2495
1114
-007674
-4100(73
-0 11839
-13929
.0275
73672
2401
81
0145
PARAMEIS60
ESrIMATES
001471!
-0.1(963
STANDARD ERROR
0.077334
T-STA1isTICs
PARAMETER
ESTIMATES
STANDARD ERROR
T-TFA73STR-S
PAI4AMEFER
ESTIMATES
S7rA?-71,AIol) 7-1414(411
Cl
0.85215
C)
c;w1
61!
dci
OF!
0737
0W2
(382
GIIF2
092
0C2
0078949
0075066
09057097
.007053
0040404
-0022014
GE2
-000(940
0.063943
.0064247
0040939
0033050
0.030300
0022338
0.020723
0037327
0.033524
.23756
-13060
23317
032313
0.22534
.0.63005
13020
-064052
82
6163
AC2
AWL
AW2
AOl
AOl
AU!
KR
1651016
-0,60643 -0406
030ES 0.07955
079905
-78(93
-1.2191
.7.7739
-030694
0.47095
-1.0761
AIlS
.011393
0038762
097943
10673
055367
0.19111
-2.243
-043185
044874
-1045$
-1.1142
KW
.0.2420
77.070W
-0101 NO
733
0403
083
08F3
093
005
AC)
AWl
A03
-0.07221
03610416
0.097770
.0027557
-0.032496
-0.0016621
0.022259
-0.33982
.0.48752
0.088992
0,78202
00611061 NO
0.557735
0037340
0.044597
0.031000
0.043173
0.940416
0.949639
0073236
0049399
.0.64095
0021911
0.66548
-13099
0.20752
-7.1806
0.027742
00670895 AUTO-
2.7076
0.06507
-0.74571
0.947067
0,44824
0.26794
1334
0.49646
7.4330
2.7325
033374
-41770245
0.63504
014
0C4
84
0164
OW4
084
0894
0(23
094
0734
074722
0(43345
-0.7777!
.0.09(44
-0.056074
0.073343
0074789
-0060043!
-0047564
0.059370
8555537
0546230
0024753
0079487
0079624
0044303
0.025504
007024
0.023559
0.022264
40.05
.2.7(97
003274)
-28744
0.67900
0.98780
039227
-2.0076
1.2439
2.494
T-33A1SS1I(3
AC4
4.1503
0.15027
5.9788
AW4
A04
AH3
AH4
41563
7.7156
0754-46
044406
-037376
015434
6437
42797
-2422
KB
160708
KBF
1434089
-0403051$ 740
6103
(.W3
0.l3.
6444-3
615
(.C5
0034.465
00397474
003477
-0032.877
0O34
0.033559
0.020537
-4.037)33
-0076303
0.0514174
77304
00079324
0.70033
41662
-0.20332
0022332
0046673
00040349
0073334
0072009
5015241
0011411
0070643
00131(9
(3375
0339
0.20990
73734
0(93703
00030907
1.619
-3.0113
-2.4755
2.007!
1.9439
-2.3709
0,80775
30464
3.44949
079738
096840
.0711131
0165775
53322
-2.4984
2,592
-3.0(94
I'ARAMISIR
l-SISMAIIS
STANDARD 1-016016
T-SSATSS33CS
PARAMETIR
ESOMASEZ,
STANDARD ERROR
T-41TAS'35T(Cs
CS
4.5
STANDARD ERROR
T-STA77ST1CS
1,746
OWl
080
(.7-3
AC5
AW5
AO3
AILS
RI'
(195
(166
ACS
AWl
A06
0022464
-03675033
43657357
03673424
09944344
0011364
5048223
077724749
-09064074
.078575
046564(9
4.26243
0048886
0070507
0.8870936
03662373
00400405
0.0(05511
0.0077856
0,0597620
09393344
0348.3
035074
2.9926
-73345
4,71795
000007
.010146 -09070036 0ES40905 740
014511
0.05349
077039413 AUTO-
0.94635
13946
-13636
23400
403736
0,72452
0,09379
074946
87
1,167
(,W7
0777
0777
AC?
AWl
67
(387-5
(367
1,4177
07'S
007
076
07-3,
.4.2494
AOl
AIM
-8 13116
AU?
ftC
77315 MATES
I4JI0C
RE
771109
0,942625
0,11335
0.0(3414
-0.0276,54
-0.03403
-6.030475
0072737
0.014724
002(940
0,023477
-3,287
001519
-37047
083704
044703
0.573543
0245799
00039772
09435412
036812
0.031133
0,024349
0,032637
0025324
0347435
0.02)26
070473
1,3041
052053
6,0584
6544
0.25257
04027972
0.26146
-01999
-030087
-4.0224
-43738
2.2206
437342
034175
0,79211
-420(4
-37656
-13033
2.3159
2.4353
-2,0964
49
6748
(,W3
(i159
(7447-9
(.79
0(8
(74-9
(1770
AC9
ADO
AIlO
0.012615
-6.054702
-0.7787(49)3
-087472494
-0.18549672
0070079
-0(8775471
04637496
-4,7337-03
003337!
-10421
-0.77264
00040342
065355
0.026307
0,015947
0,4094959
04680438
0.3073233
0.5900713
04087247
0011019
0.010573
033617
03(965
045389
2.6113
-10241
4.63735
00277774 0(6054413 NO
0030260 04053325 AUTO.
0.99303
-0310505
.4.4986
-0.71(93
0.42492
-00643753
3.1562
-3.4719
-23920
-4,2553
4031146
01391541
-0(04)969
0.0733403
0(4.476977
066967)6 -0.0743776 -0003541S 0,02433355
0997(6100
377306
4,06062
097651
AWO
0.49771
D-W
HOMOG806ErIY
0.997907
1.0511
NO
ADS. ft-SQ
D-W
0404357
2.2554
HOMOGENEITY
YES
ADJ.R-SQ.
D-W
23677
HOM008NErry
ADS K-SQ
12.96
IIOMOGENEITy
0909215
14244
NO
ADS ft-SQ
D-W
hOMOGENEITY
070011$
22775
YES
ADS ft-SQ.
D-W
09416254
2.7080
HOMOGENEITY
YES
ADJ ft-SQ
D-W
I7OMOGENEITY
0850000
23966
YES
0967857
YES
07345 CORRELATION
KR
RUbE
0008207 CORRELATION
SL)I 41)7
PARAMETER
167701
0044449
(9
EOIIMA
746
(.7-5
ADS 16-SQ.
.004171 CORR87LA'flON
(3
(.745
YES
03669924 AUTO-
LODMA 417,
OIAS17AIII) 4.04(7(0
T-S1A1lS'T1(7
HOMOGES-lErrY
24621
73659 CORRElATiON
I'AKAMIII 11
773
D-W
0.892566
.0.72439 CORRElATiON
-0.77000
093
ADLR-SQ.
03674540 ALfl53-
0.059072
C4
Owl
092
AC!
07030379 001144309
Table 6-9f:
Estimated Coefficients for Part II from Non-System Estimation:
LA/S/H/AIDS Specification - Taiwan Sample
PAAOIU( (4
(.1
STANDARD ERROR
(,WI
(.1(11
DCI
(il-I
0,029(56
0.14546
007454!
00124)32
-0.020193
0.02627!
-0.10745
-00254)44
-0.016324
0013202
-1.4646
.1.8431
0.08594
0.044654
0034827
KR
RILOR ADO H-SQ
-0.64367 006299 NO
0.994398
0.015309
0.02142
00(20919
0.617255
0.01993
003017
0.03775
0.84411
13.437
03039
018543 000668826A1JTO.
31(13
2.1432
-89-4272
I.2258
-2.2738
-1.2366
-0.54107
-22365
-2 1833
.34713
T-SIAT(S'IlCS
}9I(961(.l( II
L0(IIMAl44
OTANDARI) 1J4ROR
TS1AI1S11('I,
PAR.AOIE(li(
(RI
0 1774
l-201(M9ThI.
42
14!
442
(.412
041471
((P1
(,W2
(01140
(.435
(.02
(0102
(.101
((MI
(0402
DM2
AU
AU
AWL
AW2
AOL
ADO
018253
-0.082357
0.05648!
0.8434859
0.004444
0010253
-00440777
0.040695
0.019512
-13688
-19.411
0,044012
0540478
0039025
0.010227
OW
44340W ADO H-SQ
-086077 084054443 NO
0.660117
0,017204
00(23237
0.055238
0.020058
0.032164
074439
096304
.6846
19415
-1.7935
023392 088276A(JJ55.
4.4473
0,19125
0.20731
0.78400
1405
-2.0338
42746
-2.1075
-2.4172
CS
413
0(93
GSQ
08103
013
((CS
OF)
044.3
DM3
AU
AW3
-2.8357
AO3
44.340841 ADO. H-SQ
lSflM11)5
01866759
-0.027325
04073467
00(9099
4.9964469
0.9919775 0.0(9l
00014161
-0.991306
-00(22404
-0.35843
09923471
4.185(5
001723
00(0395
-0.084913
00036326
0.9903249
-044625
0.8531648
09927664
00033556
0,01139
00045530
0.6072569
077873
009057
0056599
070233
03023828
026376
2.2942
-2 0036
0 60631
0.19287
0 12561
4.28670
-3.0840
-3,0203
-28443
-3 1924
1.2601
-24501
2.8799
-1304
PARkMIOIS,.R
04
(.R4
GW4
0414
004
(3414
004
ESTIMATES
DM4
036433
0.24072
-01.8)58
0.04
00(102(3
4.06486
00(61191
00(64758
0.013468
00(58445
.60(22863
4.12.17
3417
14691
0.11396
0050763
49915542
0.047787
4.73269
0,013870
0010883
0026086
0261715
0,030583
0.063
04(993
0,73839
13214
2.2078
035498
08410003
0.34164
-22233
0.58(43
-4.0846
32482
2.4025
026498
1911
2.027
41385
466344
-29391
STANDARD ERROR
T-STATITflCS
PARAMETER
l',SIlMASS-.S
S7ANI)AI((
CS
0.067601
(14(40(9
T.T(Al($7((7.
PAI00441001(
LEDELATES
STANI)5I4() (:1400(4
101005(00(14
l_TF(MAI LI.
S"IAliDAI4I) ((9(443(9
1.SIA (1010 S
(A(401N1I.I( (0
844
83
'0(02(7
(3(93
GBF4
OWS
GB45
(315
003
0105
((ES
-83620
((MS
AC4
56
AU
AWl
AWS
AOl
AO5
OC
0037293
08554294
046481
0073400
-6(9005
'00116(2
'00(0034
12908
17793
035223
0037902
08546260 NO
0035504
0015.1(4
0020799
00(9625
0.038)44
0020593
003356
52247
031(6
037436
025(3!
015344
099(8534 AuTO-
-210435
104016
033463
023(26
39634
-2403!
-037361
'029098
2.0343
(.100
DM6
(5440
(iWO
GUI-a
016
(006
016
AC6
3098
AWl.
2.266
AGo
RE
ADS R-SQ
1611010
-00071(9
'0.04792
0023201
-0003677
4,10160
032320
-8.029(99
0.025134
-2.6473
-22084
-40263
0.0454997
0100-45
011570
445499
0(004002
003762
05999(1
0052953
0090.432
0057658
01(596
(3379
10542
(2477
032359
003(224
04629862
0404)0
I 0200
044122
00(0,72
.1(949
'1.9201
33513
'067983
021501
23597
.23234
.31521
84(1
67
(.447
(iWO
0(14-7
(.l'7
CC)
((Fl
0101
DM7
AOl
AWl
AOl
RE
0020023
0.020(5
'0020302
0.02001
'0002941
0012205
08672721
0011<417
00(2906
-0.405742.2
-00496(2
'8.031908
0014509
.6(2555
08655808
4040345
0 (43701
03050(21
059(9405
0,44(4.4232
0,85334(7
0,8574034
06638740
04037957
0072399
0090635
005(627 085082179
0.245571
4152
.13(031
2.7790
2(7<2
(6(56
4.4991
-044.977
'6696(2
'0.54077
(MO
.8
(9401
418
10374
((<8
(000
(.11(8
(.18
(000
(.18
(.L8
.39(98
AC8
AW9
A06
0303024<
'006202(4
046175(5
006(3027
046237o5
'00289237
-8(8335207
S1ANGA(OD (:0(9044
-01497
.019.405
059(06(5
'024367
.0022523
0.0(0291
00450040
00(9(7406
04020235
0852(305
0.4020588
030(48571
04030860
0239(2406
(2253
0068573
'020743
O0.56
00(9153
076526
'035205
23197
043122
((043
'(.0373
.0(7(27
.0056397
.2.9380
3 (.459
-(.2423
0 6$997<:.00205<24
.00(759
02,00059
'00473(57
'0011(879
.0 743592
(544320
67652
031<91
U 0013.13591
D-W
2.1315
D-W
2953
OW
23733
HOMOGENEITY
YES
IIOMOGENEflY
YES
HOMOGENEITY
YES
0964523
ADO ft-SQ
0909595
ADO (9-SQ
0953037
0(43084011 AUTO-
1.6907 CO40REL'SSlON
.0745290 0.0(0)0030(2
D-W
2.3109
IIOM000NEIIY
YES
OW
23074
IIOMOGI0NETIY
NO
-2.5668
KM
RIIOM
006(449-400
54,511<4:
I 0(5700
NO
.13042
04(00
059(7934
II <005
HOMOGEI.18flY
2.4217
2.4973 CORRELATION
00(00(4
'0301(340
ES 7 I. 0 5' (.5
0486317
040(502
Cl
D-W
09.48532
ADO H-SQ
0030271
05920779
1Aft\65(l 1:11
IGIOC
00687073
1(0
0920539
RHO? ADS H-SQ.
0.06070!
1011(6191(5
I'll. (IS0(5
K?
00(2954
(.0
NO
061674 CORRELATION
REF
STANDARD ERROR
T.STA 115.11(5
HOMOGENEITY
22741
1.25.34 CORO(ELAI]ON
0.0325.68
008
D-W
OW
22244
hOMOGENEITY
YES
199
6.4. Estimation Method and Procedure for Part III
Additive random disturbances for each equation were
assumed for estimation purposes.
In this section, the model
structure is
Part III: XF = h{ XT, PIF, PIT
where e
}
(6-15)
+ e
is the disturbance term at time t and the translog
form is applied for h.
Assuming the independent variables
are nonstochastic and bounded, the following error structure
was assumed for e:
et is normally distributed with mean
E(e) =
0
variance of the error is homoscedastic, i.e.,
E(e) =
a2
there is no autocorrelation, i.e.
E(eta eb) =
(6-18)
0 for a
The equation was estimated by OLS using SHAZAM Version 5.13.
The estimation results are shown in Table 6_9.80
Similar
to Part I estimation results, many t-statistics were not
significant but the adjusted R2 were high; this is a typical
characteristic of multicollinearity.
80
Along the same lines
PIF and PIT are further abbreviated to PF and PT,
tf1nI stands for natural logarithm.
respectively.
200
of reasoning as Part I, it was not attempted to apply
alternative methods or specifications to correct
multicollinearity; the functional form was mathematically
reasonable.
Also, all of the variables are theoretically
justified for the model, thus arbitrarily dropping
statistically insignificant variables would make the model
theoretically misspecified.
Note that the OLS estimator is
BLUE (i.e., the best linear unbiased estimator) in the
This situation is different
presence of multicollinearity.
from Part I, however, because the accuracy of parameter
estimates are essential in this part (i.e., Part III) to
obtain better estimates of the allocation factors.
Therefore, the statistical weakness of the results should
not be overlooked.
However, the translog specification in
this part is believed to be appropriate with reference to
the trends in the variables.
According to Figure 5-13, per
capita nominal group expenditures plotted against per capita
nominal total expenditures in log form for each country show
clear log-linear relationships.
Again, the translog form
gives linear approximation to any unknown function locally - if linearity is observed globally in the objective
relationships, the form may be able to have a global
property in approximation also.
Therefore, the point
estimates of coefficients are expected to be good
approximation of the true parameters.
201
Table 6-lOa: Estimated Coefficients for Part III - Japan
All-period Sample
FOR
DEFINITION OF
MEAN OF
VARIABLES
DATA
AXT
ALL-PERIOD
JAPAN
1911-87
VARIABLE ESTIMATE STANDARD T-STAT.
OF COEF. ERROR
NAME
50 DF
InXT
9.3242
CONSTANT
0.70256
5.3534
0.13124
APT =
nPT
0.81469
AXT
-3.7582
2.0136
-1.8664
APF =
tnPF
5.0168
APT
-3.9531
2.9946
-1.3201
GXT =
Ij,XT*InXT
103.04
APF
9.4313
4.6162
2.0431
OPT =
InPT*I.nPT
11.632
GXT
0.25145
0.20947
1.2004
GPF =
InPF*tnPF
36.933
GPT
-0.7275
0.40869
-1.7801
GXTPT =
nXT*nPT
20.848
GPF
-0.9402
0.48178
-1.9515
GXTPF =
1nXT*1rPF
60.526
GXTPT
-0.55371
0.26185
-2.1146
GPTPF =
LnPT*trPF
15.432
GXTPF
-0.08609
0.49316 -0.17456
GPTPF
2.0678
R-SQUARE =
0.9999
R-SOUARE ADJUSTED
VARIANCE OF THE ESTIMATE =
0.9999
0.34223E-01
LOG OF THE LIKELIHOOD FUNCTION =
122.825
RESIDUAL SUM =
VON NEUMAN RATIO = 1.8105
0.29651E-12
CORRELATION
2.1318
O.11712E-02
STANDARD ERROR OF THE ESTIMATE r
DURBIN-WATSON = 1.7804
0.96997
MATRIX
RESIDUAL VARIANCE =
OF
RHO =
0.05909
0.11712E-02
VARIABLES
AXT
1
APT
0.99736
APF
0.99915
0.99873
1
CXI
0.99616
0.98884
0.99393
OPT
0.78502
0.75318
0.7749
0.83537
1
GPF
0.9935
0.98591
0.99167
0.9994
0.8488
1
GXTPT
0.99463
0.9893
0.99347
0.9989
0.84059
0.99933
GXTPF
0.99552
0.9886
0.99384
0.99983
0.83864
0.99978
0.99937
GPTPF
0.98662
0.97774
0.98396
0.99642
0.87391
0.99832
0.99777
0.9971
AXI
APT
APF
GXT
OPT
GPF
GXTPT
GXTPF
1
1
1
GPTPF
202
Table 6-lOb: Estimated Coefficients for Part III - Japan
Pre-war Period Sample
FOR
DEFINITION OF
MEAN OF
VARIABLES
DATA
PRE-WAR
JAPAN
PERIOD
1911-37
VARIABLE ESTIMATE STANDARD T-STAT.
OF COEF. ERROR
NAME
17 DF
AXT
LnXT
4.9894
APT =
IrQT
-2.8067
AXT
-21.219
23.862 -0.88922
44.656 -0.51487
CONSTANT -0.29035
121.9 -0.00238
APF =
InPF
1.2938
APT
-22.992
GXT
tnXT*LnXT
25.067
APF
38.743
25.826
1.5002
GPT =
LnPT*InPT
7.9572
CXI
1.5759
1.2546
1.2562
GPF =
InPF*LnPF
1.7873
OPT
-4.3645
GXTPI =
InXT*tnPT -13.891
GPF
-2.8331
1.4033
GXTPF =
lnXT*(nPF
6.5798
GXTPT
-2.5133
3.8354 -0.65529
GPTPF =
LnPT*(OPF -3.5406
GXTPF
-1.1851
GPTPF
8.8797
R-SQUARE =
0.9868
R-SQUARE ADJUSTED
VARIANCE OF THE ESTIMATE =
0.50649E-01
48.4704
VON NEUMAN RATIO = 2.0115
0.96993E-13
CORRELATION
MATRIX
AXT
1
APT
0.95993
1
APF
0.88889
0.95578
CXI
0.9997
0.96198
OPT
-0.96747 -0.99921
RESIDUAL VARIANCE =
OF
RHO
0.25654E-02
VARIABLES
1
0.89534
1
-0.9498 -0.96878
GPF
0.85824
0.94085
0.99479
0.8662 -0.93122
1
0.17592
0.44217
0.52943
0.18688 -0.41223
0.58292
GXTPF
0.9199
0.97008
0.99673
092587 -0.96543
0.98971
-0.85786 -0.91478
AXI
APT
1.6621
-0.00431
GXTPT
GPTPF
1.9846 -0.59718
5.3424
0.9797
LOG OF THE LIKELIHOOD FUNCTION =
RESIDUAL SUM
-2.0189
0.25654E-O2
STANDARD ERROR OF THE ESTIMATE =
DURBIN-WATSON = 1.9370
4.5953 -0.94979
-0.9889 -0.86439
APF
CXI
1
0.48249
0.91084 -0.97724 -0.48275
-0.9799
GXTPT
GXTPF
OPT
GPF
CPTPF
203
Table 6-lOc: Estimated Coefficients for Part III - Japan
Mid-period Sample
FOR
DEFINITION OF
MEAN OF
VARIABLES
DATA
JAPAN
MID-PERIOD
1925-70
VARIABLE ESTIMATE STANDARD 1-STAT.
OF COEF. ERROR
NAME
19 OF
AXI =
EnXT
8.8867
2.8698
10.043
0.28574
APT =
LnPT
0.55577
AXT
-5.778.4
3.4986
-1.6516
APF =
LnPF
4.6265
APT
-4.378
4.2108
-1.0397
GXT =
LnXT*LnXT
90.163
APF
12.216
6.1463
1.9876
GPT =
InPT*LnPT
9.057
CXI
0.61445
0.37925
1.6202
GPF =
LnPF*LnPF
30.25
GPT
-0.76919
0.50352
-1.5276
GXTPT =
LnXTLnPT
14.808
GPF
-0.35549
GXTPF =
InXT*LnPF
51.054
GXTPT
-0.4764
0.40586
-1.1738
GPTPF =
LnPT*LnPF
11.358
GXTPF
-1.0041
0.86488
-1.1609
GPTPF
1.964
1.1187
1.7555
R-SOLJARE =
1.0000
CONSTANT
R-SQUARE ADJUSTED =
VARIANCE OF THE ESTIMATE =
024397E-01
LOG OF THE LIKELIHOOD FUNCTION =
72.6672
RESIDUAL SUM =
AXT
VON NEUMAN RATIO = 1.8607
0.51020E-13
CORRELATION
0.9999
059524E-03
STANDARD ERROR OF THE ESTIMATE =
DURBIN-WATSON = 1.7966
0.6233 -0.57033
MATRIX
RESIDUAL VARIANCE =
OF
RHO =
0.07785
O.59524E-03
VARIABLES
1
APT
0.99748
APF
0.99908
0.99886
GXT
0.99882
0.99306
0.99622
1
OPT
0.85607
0.82166
0.83559
0.87863
GPF
0.99928
0.99471
0.99736
0.99976
0.8725
GXTPT
0.99973
0.99743
0.99851
0.99876
0.85941
0.99937
1
GXTPF
0.99937
0.99465
0.99751
0.99983
0.87139
0.99995
0.99928
GPTPF
0.9984
0.99416
0.9956
0.99901
0.87785
0.99926
0.99917
0.99896
GXT
OPT
GPF
CXTPT
GXTPF
AXT
1
APT
1
APF
1
1
GPTPF
204
Table 6-lOd:
Estimated Coefficients for Part III - Japan
Post-war Period Sample
FOR
DEFINITION OF
MEAN OF
VARIABLES
DATA
JAPAN
POST-WAR
1955-87
PERIOD
VARIABLE ESTIMATE STANDARD T-STAT.
OF COEF. ERROR
NAME
23 OF
AXT =
InXT
12.871
CONSTANT
-8.2261
7.4319
-1.1069
APT =
UPT
3.7777
AXT
-1.8334
2.4441
-0.7501
APF =
LnPF
8.0629
APT
-11.632
5.7436
-2.0252
GXT
LnXT*LnXT
166.83
APF
12.045
4.5077
2.6721
GPT =
nPT*1nPT
14.639
GXT
0.3005
0.46594
0.64494
1.082
-1.4833
GPF =
LnPF*LnPF
65.688
GPT
-1.605
GXTPT =
LnXT*LnPT
49.271
GPF
-0.63565
GXTPF =
1nXT*nPF
104.66
GXTPT
0.15976
GPTPF =
LnPT*InPF
30.955
GXTPF
-0.81558
GPTPF
2.6474
R-SQUARE =
R-SOUARE ADJUSTED =
0.9998
VARIANCE OF THE ESTIMATE =
O.13998E-01
100.004
AXT
VON NEUMAN RATIO = 1.5023
O.17899E-11
CORRELATION
0.15529
2.2071
1.1995
0.9997
LOG OF THE LIKELIHOOD FUNCTION =
RESIDUAL SUM =
1.0288
1.5258 -0.53453
O.19594E-03
STANDARD ERROR OF THE ESTIMATE =
DURBIN-WATSON = 1.4567
1.5764 -0.40323
RESIDUAL VARIANCE =
MATRIX
OF
0.20553
RHO =
0.19594E-03
VARIABLES
1
APT
0.98897
APF
0.99767
0.99348
1
GXT
0.9995
0.99279
0.9983
GPT
0.98079
0.99865
0.98654
0.98617
1
GPF
0.9959
0.99645
0.99942
0.99759
0.99124
1
GXTPT
0.99098
0.99977
0.99434
0.99454
0.99807
0.99701
1
GXTPF
0.99813
0.99536
0.99952
0.99928
0.98951
0.99951
0.99647
GPTPF
0.9891
0.9998,8
0.99398
0.99296
0.9985
0.99699
0.99974
0.99574
APT
APF
GXT
GPT
GPF
GXTPT
GXTPF
AXT
1
1
GPTPF
205
Estimated Coefficients for Part III - Korea
Table 6-be:
Sample
FOR
DEFINITION OF
MEAN OF
VARIABLES
DATA
KOREA
1962-87
VARIABLE ESTIMATE STANDARD T-STAT.
OF COEF. ERROR
NAME
16 DF
74.081
63.391
1.1686
-18.564
17.414
-1.066
APT
43.061
34.573
1.2455
APF
-9.7151
AXT =
InXT
12.021
CONSTANT
APT =
LnPT
3.3565
AXT
APF =
tnPF
5.6059
GXT =
LnXT*tflXT
146.67
OPT =
InPT*LnPT
12.219
GXT
1.249
1.2071
1.0346
GPF
LnPF*LnPF
33.218
OPT
9.8321
5.0282
1.9554
GXTPT =
InXT*tnPT
41.783
GPF
2.3023
1.7341
1.3277
GXTPF =
InXT*LnPF
69.354
GXTPT
-5.1391
4.7946
-1.0719
GPTPF =
InPT*LnPF
20.12
GXTPF
1.0669
1.9074
0.55934
GPTPF
-8.4114
4.0503
-2.0768
R-SOUARE =
0.9991
R-SQUARE ADJUSTED =
0.9995
O.15374E-O2
VARIANCE OF THE ESTIMATE =
STANDARD ERROR OF THE ESTIMATE =
O.3921OE-01
LOG OF THE LIKELIHOOD FUNCTION =
53.6284
RESIDUAL SUN = -O.21232E-11
CORRELATION
RHO = -0.23217
VON NEUMAN RATIO = 2.5398
DURBIN-WATSON = 2.6422
11.578 -0.83909
MATRIX
RESIDUAL VARIANCE =
OF
O.15374E-O2
VARIABLES
AXT
1
APT
0.99849
APF
0.99745
0.99807
GXT
0.99888
0.99788
0.99858
1
OPT
0.99024
0.99297
0.99511
0.9952
GPF
0.98999
0.99157
0.99611
0.99521
0.99896
1
GXTPT
0.9964
0.99775
0.9985
0.99887
0.99844
0.99767
1
GXTPF
0.99524
0.99581
0.99876
0.99848
0.99825
0.99901
0.99935
GPTPF
0.9908
0.99298
0.99605
0.99573
0.9998
0.99966
0.99853
0.99896
APT
APF
GXT
GXTPT
GXTPF
AXT
1
1
1
OPT
GPF
GPTPF
206
Estimated Coefficients for Part III - Taiwan
Table 6-lOf:
Sample
FOR
DEFINITION OF
MEAN OF
VARIABLES
DATA
TAIWAN
1963-87
VARIABLE ESTIMATE STANDARD T-STAT.
OF COEF. ERROR
NAME
15 DF
AXT =
LnXT
9.8665
CONSTANT
-18.81
27.475 -0.68463
APT =
LnPT
6.1651
AXT
5.0531
10.453
0.48341
APF =
InPF
3.9269
APT
5.7789
5.7775
1.0003
GXT =
LnXT*LnXT
98.229
APF
-9.1555
13.527 -0.67706
OPT =
1nPTtnPT
38.31
GXT
-0.17109
1.2026 -0.14227
GPF =
InPF*InPF
15.968
OPT
0.28762
0.854
0.33679
GXTPT =
LnXT*InPT
61.34
GPF
-1.7428
1.7181
-1.0144
GXTPF =
nXT*LnPF
39.437
GXTPT
-1.3237
1.7126 -0.77292
GPTPF =
nPT*1nPF
24.613
GXTPF
1.7301
2.7218
0.63564
GPTPF
1.0072
1.5763
0.63894
R-SQUARE =
R-SOUARE ADJUSTED =
0.9996
VARIANCE OF THE ESTIMATE
0.9994
0.31981E-03
STANDARD ERROR OF THE ESTIMATE =
O.17883E-01
LOG OF THE LIKELIHOOD FUNCTiON =
71.5091
DURBIN-WATSON = 2.3528
RESIDUAL SUM =
0.27524E-12
CORRELATION
AXT
VON NEUMAN RATIO = 2.4508
MATRIX
RESIDUAL VARIANCE =
OF
RHO
-0.20266
0.31981E-03
VARIABLES
1
APT
0.9942
1
APP
0.99651
0.99214
1
GXT
0.99942
0.99408
0.99501
OPT
0.99343
0.99963
0.9909
0.99421
GPF
0.99579
0.99309
0.99808
0.99637
0.99348
1
GXTPT
0.99797
0.99823
0.99448
0.99864
0.99846
0.99641
GXTPF
0.99819
0.99422
0.99833
0.99847
0.99427
0.99948
0.99788
GPTPF
0.99697 099671
0.99794
0.99719
0.99678
0.99931
0.99845
0.99937
APF
GXT
OPT
GPF
GXTPT
GXTPF
AXT
APT
1
1
1
GPTPF
207
CHAPTER
7
ANALYSIS OF ESTIMATION RESULTS
7.1. Introduction
This chapter has two main objectives:
one is to
identify important factor(s) responsible for changes in food
consumption patterns accompanying rapid economic growth; the
other is to test the hypothesis that as per capita real
income grows, income elasticity for a good declines from
(high) positive values to be negative values at high income
level.
The model employed in this study is designed to
extract the effect of income (total expenditure) on food
consumption patterns.
Various elasticities are calculated from the estimated
coefficients of Part I through III.
estimate is analyzed individually.
system elasticities are compared.
Each elasticity
The system and nonElasticities that do not
differ much (various criteria are specified below), are
assumed to be more reliable.
208
The data samples defined by the combination of study period
and country are:
JA - Japan all-period 1911-37:1955-87
JR - Japan pre-war period 1911-37
JM - Japan mid-period 1925-37:1955-70
JT - Japan post-war period 1955-87
K - Korea 1962-87
T - Taiwan 1963-87
As a first step, the elasticities evaluated at the mean
are summarized commodity by commodity.
Table 7-1 and Table
7-2 contain all results from Part I, II, and III; Table 7-1
contains the system estimation results and Table 7-2
contains the non-system estimation results.
The last two
columns show the average predicted values of the dependent
variables in Part I and Part II, which are used to calculate
the elasticities at the mean.
"estimated mean lagged
common to both tables.
Q*
"Custom effect" and
hat" are obtained in Part I and
Other elasticities depend on the
estimation methods applied for Part II.
209
Table 7-la: Summary Sheet of Calculated Elasticities From
System Estimation - Rice
COMMODITY:
RICE
METHOD:
SYSTEM ESTIMATION (ITERATIVE SUR>
ALL-PERIOD 1911-87
JAPAN:
ITEM
ESTIMATEDESTIMATED
ELAST I CITIES
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
cUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.666241 O.O3196 -0.68901 1.949031 3.746191 0.585049 -0.1092 0.00077? -0.03461 0.494369 112.7321
S.E.
0.145808
(.) 0.422963 0.937579 0.187245 0.107274 0.000372
T-STAT
4.5693
4.608255 3.995599 3.124514 -1.01797 2.077164
PRE-WAR PERIOD 1911-37
JAPAN:
ITEM
ELASTICITIES
EST IMATEDEST IMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.058976 0.029006 .0. 95687 -2.60149 7.154526 0.062312 -0.00249 -0.24306 -0.91971 0.582051 133.9826
S.E.
0.109511
1.638842 1.717856 0.315917 0.078684 1.6E-05
C-)
I - STAT
9. 670041
-1.58739 4.1648 0.197243 -0.03163 -15171.8
MID-PERIOD 1925-70
JAPAN:
ITEM
ESTIMATE
S.E.
1-STAT
ELASTICITIES
ITEM
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
0.7335 0.170367 -0.92749 0.476193 1.058925 0.463409 -0.16221 -0.00903 -0.37574 0.555384 121.2606
0.149988
(-) 0.632103 1.033934 0.371023 0.120826 4.88E-O5
4.890383
0.753347 1.024171 1.249005 -1.34254 -184.987
POST-WAR PERIOD 1955-87
JAPAN:
EST IMATEDEST IMATED
MEAN
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0411466 -0.02896 -0.11374 -0.19797 -0.73521 -0.34934 0.453451 -0.00301 -0.08265 0.422626 96.01344
S.E.
0.226091
(+)
0.369334 0.653911 0.340112 0.334551 0.001609
1-STAT
1.81991
-0.53603 -1.12433 -1.02713
1.3554 -1.86987
1962-87
KOREA:
ITEM
ELASTICITIES
EST I MATEDEST IMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
C* HAT
ESTIMATE 1.430948 0.280199 -0.76841 -1.14045 -7.98252 0.773281 -0.49013 0.046023 0. 27166 0.522592 133.9227
S.E.
0.165548
2.148906 2.718375 1.051108 0.493406 0.000232
1-STAT
8.643706
-0.53071 -293651 0.735682 -0.99337 198.011
TAIWAN:
1963-87
ELASTICITIES
ITEM
ESTIMATEDESTIMATED
-
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.501657 0.486313 -0.70024 -4.54463 -5.19624 -2.96709
S.E.
0.241895
1.412876 1.708876 0.499998
C-)
1-STAT
6.207877
-3.21658 -3.04074 -5.93421
*
MEAN
0. 000693 -0.34843 0.217061 120.2483
0.000644
1.076632
Sign of Hicksian own price elasticity is presented below each Marshall ian own price elasticity estimate.
210
Table 7-ib: Summary Sheet of Calculated Elasticities From
System Estimation - Bread and Wheat
CI4MODITY:
BREAD (JAPAN) AND WHEAT FLOUR (KOREA AND TAIWAN)
METHOD:
SYSTEM ESTIMATION (ITERATIVE SUR)
ITEM
GROUP
TOTAL
EXP.
EXP.
ESTIMATEDESTIMATED
ELASTICITIES
ALL-PERIOD 1911-87
JAPAN:
OWN
PRICE *
MEAN
MEAN
CHILD
WORKING
OLD
FAMILY
HABIT
cUST4
BUDGET
LAGGED
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
7.39085
ESTIMATE 0.922193 0.044232 -0.37867 1.141397 2.951899 -0.61281 -0.01601 0.008446 0.355345 0.030394
S.E.
0.219015
1-STAT
4.210647
JAPAN:
(-)
1.715472 1.998552 -2.10618 -0.10025 2.933699
PRE-WAR PERIOD 1911-37
ITEM
0.665354 1.477019 0.290959 0.159655 0.002879
GROUP
TOTAL
EXP.
EXP.
ESTIMATEDEST IMATED
ELASTICITIES
OWN
PRICE *
MEAN
MEAN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTC*I
BUDGET
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
LAGGED
Q
HAT
1.69584
ESTIMATE 1.565427 0.042878 -1.35892 -8.40339 7.181806 -0.23579 -0.12215 1.476628 -0.22213 0.012074
S.E.
0.503864
T-STAT
3.106847
(-)
-1.23786 0.966333 -0.17688 -0.34366 17526.34
ESTIMATEDEST IMATED
ELASTICITIES
MID-PERIOD 1925-70
JAPAN:
6.788638 7.432019 1.333062 0.355442 8.43E-05
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
C1JST4
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
ESTIMATE 1.024953 0.238062 -0.17824 0.924905 6.706031 -2.34007 0.008567 -0.0151 0.086617 0.029988
S.E.
0.247089
1-STAT
4.148105
(-)
ITEM
GROUP
TOTAL
EXP.
EXP -
OWN
PRICE *
ESTIMATE 0.142501 -0. 01003
0.06226
S.E.
0.328665
(+)
1-STAT
0.433577
ITEM
1.091739 1.776014 0.643192 0.199587 0.000372
ESTIHATEDESI IMATED
ELASTICITIES
MEAN
MEAN
CHILD
WORKING
OLD
FAMILY
HABIT
CUST4
BUDGET
LAGGED
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
-0.052
2.23678 -1.43441 0.876271 0.093873 -0.81969 0.045387 12.00638
0.564241 0.990489 0.514006 0.486729 0.016003
-0.09216 2.258258 -2.79065 1.800326 5.865842
ESTIMATEOESTIMATED
ELASTICITIES
1962-87
KOREA:
HAT
6.31003
0.847185 3.775888 -3.63822 0.042924 -40.6214
POST-WAR PERIOD 1955-87
JAPAN:
MEAN
GROUP
GROUP
TOTAL
EXP -
EXP -
OWN
PRICE *
MEAN
MEAN
CHILD
WORKING
OLD
FAMILY
HABIT
CUST
BUDGET
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
LAGGED
Q* HAT
ESTIMATE -0.82648 -0.16184 -0.03184 9.721553 28.26067 -4.46074 -3.10699 0.184786 0.669744 0.064857 34.37049
6.515881 8.249852 3.239284 1.546789 0.001536
S.E.
0.519174
1-STAT
-1.59192
1.491978 3.425597 -1.37708 -2.00867 120.3043
TAIWAN:
1963-87
ELASTICITIES
MEAN
MEAN
ITEM
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
c1JST
BUDGET
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
LAGGED
Q* HAT
C-)
ESTIMATEDESTIMATED
ESTIMATE 4.140756 1.340988 1.431723 -40.4842 -48.1592 -15.5172
S.E.
1.072992
7.00706 8.561972 2.528109
(+)
1-STAT
-
3.859075
Sign of Hicksian own
-5.77762 -5.62477 -6.13788
0.00133 1 0.040109 0.051726 23.01887
0.00101
1.317637
price elasticity is presented below each MarsholLiari own
rice elasticity estimate.
211
Table 7-ic:
Summary Sheet of Calculated Elasticities From
System Estimation - Barley
JAPAN:
ALL-PERI
C3QIITY:
BARLEY
METH:
SYSTEM ESTIMATION (ITERATIVE SUR)
1911-87
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -0.09709 -0.00466
S.E.
0.90137
1-STAT
-0.10772
JAPAN:
PRE-WAR PERI
0.21665
(+)
1911-37
9.07589 10.21734 6.312541 0.753046 -0.0078 0.796114 0.021709
2.739027 6.061034 1.195071 0.656727 0.001402
3.313545 1.685743 5.282146 1.146665 -5.56269
ELASTICITIES
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
MID-PERIOD 1925-70
ELASTICITIES
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
POST-WAR PERIOD 1955-87
ELASTICITIES
ITEM
8.02046
ESTIMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -2.39531 0.168609 3.271525 -5.26605 -24.6804 -0.4783 4.709823 0.012783
5.8.
2.558665
4.385148 7.700807 3.994056 3.788071 0.004926
(+)
1-STAT
-0.93616
-1.20088 -3.20491 -0.11975 1.24333 2.595009
KOREA:
MEAN
GROUP
ESTIMATE 0.014215 0.003302 2.275799 5.068564
-6.653 7.201276 1.735896 -0.00346 1.658258 0.023346
S.E.
1.204924
5.301154 8.627653 3.122052 0.97302 0.00019
(+)
T-STAT
0.011797
0.956125 -0.77113 2.306584 1.784029 -18.1761
JAPAN:
9.60758
ESTIMATEDESTIMATED
MEAN
ITEM
MEAN
GROUP
ESTIMATE 0.952719 0.026095 0.080578 6.118425 25.80855 4.088406 0.706267 0.241845 -0.76368 0.032079
S.E.
0.23603
(.) 3.822807 3.891389 0.728182 0.17233 0.000546
1-STAT
4.036429
1.600506 6.63222 5.614538 4.098334 442.7757
JAPAN:
6.97933
ESTIMATEDESTIMATED
MEAN
ITEM
MEAN
GROUP
1962-87
GROUP
0.19814 0.013219
ELASTICITIES
TOTAL
O%JN
CHILD
PRICE *
pOP
WORKING
4.6833
ESTIMATEDESTIMATED
OLD
FAMILY
1A8IT
CUSTOM
MEAN
MEAN
BUDGET
LAGGED
Q* HAT
EFFECT
SHARE
pop.
SIZE
EFFECT
pop.
ESTIMATE 0.565432 0.110719 -0.04938 3.366489 18.3645 -0.5276 4.509308 0.046549 0.153287 0.084397 35.46625
S.E.
0.749201
9.747438 12.32989 4.762755 2.23303 0.000797
T-STAT
0.754714
0.345372 1.489429 -0.11073 2.019368 58.42916
EXP.
EXP.
()
*
-
Sign of Hicksian own price elasticity is presented below each MarshaL lian own once elasticity estimate.
212
Table 7-id: Summary Sheet of Calculated Elasticities From
System Estimation - Beef
COMMODITY:
BEEF
METHOD:
SYSTEM ESTIMATION (ITERATIVE SUR)
ALL-PERIOD 1911-87
JAPAN:
ITEM
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.198191 0.009506 -0.42603 -3.3232 -8.96464 1.343893 0.053638 0.002184
S.C.
0.332163
0.995861 2.217966 0.438152 0.242705 0.001099
C-)
1-STAT
0.596668
-3.33701 -4.04183 3.067181 0.221002 1.987687
JAPAN:
PRE-WAR PERIOD 1911-37
ITEM
0.3954 0.057345
ELASTICITIES
ESTIMATEDESIIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0
ESTIMATE -0.10385 -0.00284 -1.32821 19.23987 17.25384 3.381959 0.107145 0.276634 0.489679 0.035926
S.E.
0.304488
4.731402 4.889227 0.906727 0.220392 7.35E-05
C-)
1-STAT
-0.34108
4.06642 3.52895 3.729854 0.486158 3761 638
MID-PERIOD 1925-70
JAPAN:
ITCH
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
S.C.
0.610751
T-STAT
-0.84588
C-)
POST-WAR PERIOD 1955-87
ITEM
HAT
0.61189
ESTIMATEDESIIMATED
ELASTICITIES
ESTIMATE -0.51662 -0.11999 -1.18264
JAPAN:
1.6099
-3.7346 -7.41282 3.627546 0.336102 0.002473 0.001171 0.040259
2.666261 4.342672 1.569204 0.492963 0.000281
-L40069 -1.70697 2.311711
0.6818 8.790379
ELASTICITIES
0.99416
ESTIMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
0.05943 -0.94841 -0.53468 -4.22395 3.449472 0.933965 0.003382 0.534559 0.074864 2.42601
0.948004 1.67037 0.864959 0.827892 0.002603
C-)
-1.50395
-0.56401 -2.52875 3.988016 1.128124 1.299044
ESTIMATE -0.84429
0.561381
S.E.
1-STAT
1962-87
KOREA:
ITEM
ESTIMATE
ESTIMATEDESTIMATED
ELASTICITIES
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
-0.7617 -0.14915 -0.34079 49.90931 53.55547 20.55074 -4.97305 -0.07647 0.342486 0.07608
2.41692
S.E.
0.365431
1-STAT
-2.08438
4.657334 5.895242 2.29745 1.088906 0.000905
10.71628 9.084524 8.94502 -456702 -84.4869
TAIWAN:
1963-87
ELASTICITIES
MEAN
MEAN
ITEM
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.687817
0.773162
S.E.
1-STAT
-
0.889616
C-)
ESTIMATEDESTIMATED
0.22275 -1.65553 -10.3894 -4.77373 -5.04826
C-)
4.820019 5.830675 1.6.4507
-2.15547 -0.81873 -3.06872
0.013831 -0.06492 0.012162
0.90931
0. 0015 54
8.899312
Sign of Hicksian own price elasticity is presented below each MarsnalLian own price elasticity estimate.
213
Table 7-le: Summary Sheet of Calculated Elasticities From
System Estimation - Pork
JAPAN:
ITEM
ALL-PERI
ITEM
SYSTEM ESTIMATION (ITERATIVE SUR)
ELASTICITIES
ESTIMATEDEST IMATED
MEAN
MEAN
GROJP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTC*I
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
-1.1718 1.797952 6.972401 -0.01942 0.303429 -0.00515 0.308845 0.058795
(-) 0.992199 2.18166 0.445057 0.268545 0.001951
1.812089 3.195915 -0.04363 1.129901 -2.63804
PRE-WAR PERIOD 1911-37
ELASTICITIES
GRJP
TOTAL
OWN
EXP.
EXP.
PRICE *
ESTIMATE 0.092672 0.002538
S.E.
0.436616
T-STAT
0.212251
JAPAN:
PORK
METHOD:
1911-87
ESTIMATE 1.399458 0.067124
S.E.
0.361955
T-STAT
3.866391
JAPAN:
COMMODITY:
CHILD
-0.5029
(-)
MID-PERIcO 1925-70
WORKING
ESTIMATEDESTIMATED
MEAN
MEAN
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
SIZE
EFFECT
E F FE CT
SHARE
Q* HAT
18.7576 6.361793 1.268572 0.33647 0.585255
6.575165 6.875395 1.266239 0.314083 8.18E-05
2.852795 0.925299 1.001842 1.071278 7153.175
0.48496 0.011145
ELASTICITIES
GROUP
TOTAL
OWN
EXP.
EXP.
PRICE *
CHILD
POST-WAR PERIOD 1955-87
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ELASTICITIES
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
pQp
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
0.08692 -0.96219 0.891882 4.695249 -0.14564 -0.87141 -0.01436 0.078327
(-) 0.987086 1.736455 0.905577 0.879262 0.002276
0.903551 2.703928 -0.16083 -0.99107 -6.31138
1962-87
ITEM
ELASTICITIES
ITEM
ESTIMATE
S.E.
T-STAT
-
0.09778
5.45499
ESTIMATEDEST IMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
ESTIMATE 1.866556 0.365497 -0.86821 21.66667 10.7795 18.94503 -1.26723 0.153008
S.E.
0.375185
(-) 4.674093 5.920105 2.33133 1.117679 0.000976
T-STAT
4.975026
4.63548 1.82083 8.126277 -1.1338 156.8377
TAIWAN:
MEAN
GROUP
ESTIMATE -1.23482
S.E.
0.589212
T-STAT
-2.09571
KOREA:
1.58183
EST!MATEOEST IMATEO
MEAN
ITEM
MEAN
WORKING
ESTIMATE 1.453228 0.337536 -1.01472 0.825842 14.87746 0.245883 0.053632 0.022453 0.029548 0.043541
S.E.
0.63818
2.8054 4.566267 1.652143 0.51533 0.000348
(-)
1-STAT
2.277145
0.294376 3.258123 0.148827 0.104073 64.44055
JAPAN:
0.36999
ESTIMATEDESTIMATED
MEAN
ITEM
3.16475
1963-87
-0.0585 0.062977
ELASTICITIES
3.92983
ESTIMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q* HAT
1.74568
0.419429
4.162034
0.56534
-1.0029 14.98171 13.94761 5.352016
2.191232 2.610085 0.741931
6.83712 5.343737 7.21363
0.001369 -0.13857
0.2 1874 21.92129
0. 000239
5.730889
Sign of Hicksian own price elasticity is presented beow each Marshatlian own price elasticity estimate.
214
Table 7-if: Summary Sheet of Calculated Elasticities From
System Estimation - Chicken
COMMODITY:
CHICKEN
METHOD:
SYSTEM ESTIMATION (ITERATIVE SUR)
ALL-PERIOD 1911-87
JAPAN:
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.814514 0.087032 -1.08619 1.11761 3.665952 1.015505 -0.03005 0.003961
S.E.
0.478205
(-) 0.812862 1.545096 0.425574 0.371957 0.002034
1-STAT
3.794426
1.374906 2.372637 2.386203 -0.08079 1.947368
JAPAN:
PRE-WAR PERIOD 1911-37
0.64787 0.034504
ELASTICITIES
2.41449
ESTIMATEDESTIMATED
MEAN
ITEM
MEAN
GROUP
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -0.39842 -0.01091 -0.62985 0.667592 14.26982 -2.65193 -0.65749 0.12314 -0.27121 0.011379 0.33073
S.E.
0.326418
(-) 1.520661 3.426761 0.463615 0.213423 9.41E-05
1-STAT
-122058
0.439014 4.16423 -5.72011 -3.08066 1308.179
MID-PERIOD 1925-70
JAPAN:
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.567809 0.364149 -1.64709 -4.09179 1.755516 1.795593 -0.73787 0.017609
S.E.
0J75993
(-) 1.692298 3.180358 0.867894 0.612274 0.000451
1-STAT
2.020389
-2.41789 0.551987 2.068907 -1.20513 39.07948
JAPAN:
POST-WAR PERIOD 1955-87
-0.248 0.022557
ELASTICITIES
GROUP
TOTAL
OWN
EXP.
EXP.
PRICE *
CHILD
WORKING
OLD
FAMILY
pep.
POP.
SIZE
1962-87
CUSTOM
BUDGET
LAGGED
EFFECT
EFFECT
SHARE
Q* HAT
ELASTICITIES
4.14215
ESTIMATEDESTIMATED
MEAN
ITEM
MEAN
ABIT
ESTIMATE -0.13286 0.009352 -1.63044 0.001783 2073894 -3.11926 -2.77224 -0.01773 0.483677 0.053426
S.E.
0.468797
(-) 0.606539 1.100306 0.611481 0.740537 0.004428
1-STAT
-0.2834
0.00294 1.884835 -5.10117 -3.74355 -.00396
KOREA:
0.84065
ESTIMATEDESTIMATED
MEAN
ITEM
MEAN
GROUP
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.065107 0.012749 0.306187 1.369454 0.271534 0.087623 -2.14118 -0.13908 0.113727 0.019005 1.99577
S.E.
0.604302
2.350597 3.359837 2.751431 1.791019 0.002066
(+)
T-STAT
0.107739
0.582598 0.080817 0.031846 -1.19551 -67.3173
TAIWAN:
1963-87
ITEM
GROUP
TOTAL
OWN
CHILD
JORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q* HAT
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ESTIMATE -1.21663 -0.39401 0.114559 21.95564 30.61946 7.575159
S.E.
0.947861
5.853344 7.055131 1.949898
(+)
38849
1-STAT
-1.28355
3750956 4.340026
-
MEAN
-0.00824 -0.13141 0.060285 9.15316
0.000993
-8.29196
Sign of Hicksian own price ea5ticity is presented beLow each MarshaLLian own price eLasticity estimate.
215
Summary Sheet of Calculated Elasticities From
Table 7-ig:
System Estimation - Fish
COMMODITY:
FISH
METHOD:
SYSTEM ESTIMATION (ITERATIVE SUR)
ITEM
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP,
POP,
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.753692 0.084115
S.E.
0.3829
4.580024
1-STAT
JAPAN:
-0.67802 -5.13674 -12.7342 -1.65794 0.12555 -0.00011 0.152343 0.218193 15.59087
(-) 1.155126 2.574972 0.507625 0.279459 0.000329
-4.44691 -4.94536 -3.26607 0.449259 -0.32638
ESTIMATE
S.E.
T-STAT
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
1.19593 0.032757 -0.64007 2.176073 -21.9483 -0.75546 -0.05511 -0.07751
0.275906
(-) 4.155389 4.345038 0.800255 0.198484 2.57E-05
4.334557
0,523675 -5.05134 -0.94403 -0.27768 -3021.86
MID-PERIOD 1925-70
JAPAN:
ESTIMATEDESTIMATED
ELASTICITIES
PRE-WAR PERIOD 1911-37
ITEM
ESTIJ4ATEDESTIMATED
ELASTICITIES
ALL-PERIOD 1911-87
JAPAN:
-0.4424 0.284209 17.33771
ESTIMATEDESTIMATED
ELASTICITIES
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
ESTIMATE 2.005244
S.E.
0.325096
6.168166
T-STAT
JAPAN:
ESTIMATE
T-STAT
MEAN
MEAN
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
IABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
TOP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
-2.0729 2634098 0.562631 0.01264 -0.44017 0.164219 14.48153
1.577402 2.785055 1.43564 1.364577 0.001277
0.257555 -0.74429 1.33479 0.412312 9.901907
5.04666 -0.35524 -1.44768 0.406267
<-
1962-87
KOREA:
ESTIMATEDESTIMATEO
ELASTICITIES
GROUP
0.932837
5.41001
S.E
ESTIMATEDESTIMATED
ELASTICITIES
MEAN
ITEM
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.595904
S.E.
HAT
0.46575 0.114743 0.716776 -4.77691 -2.24731 0.440113 -0.00057 0.098057 0.19458 16.15561
1,441929 2.345106 0,849986 0.262669 5.93E-05
(+)
0.497095 -2.03697 -2.64393 1.675543 -9.64936
POST-WAR PERIOD 1955-87
ITEM
MEAN
GROUP
0.3125 -0.57405 -31.1968 -19.0992 -14.9837 4.255198 -0.10956 -0.12899 0.131843 14.14849
5.089748 6.452349 2.575251
1.25247 0.000821
1-STAT
0.420559
3.794717
TAIWAN:
1963-87
ITEM
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q* HAT
C-)
-6.12935 -2.96003 -5.81834 3.397444 -133.399
ESTIMATEDESTIMATED
ELASTICITIES
MEAN
ESTIMATE 0.257101 0.083263 -0.04374 -3.27146 -2.99066 -0.25783
S.E.
0.318664
1.063732 1.11848 0.227847
(+)
1-STAT
0.806811
-3.07546 -2.67386 -1.26325
*
-
MEAN
-7.3E-05 0.063776 0.410244 31.15222
0.000175
-041887
Sign of Hicksian own price eLasticity is presented below each Mershallian own price elasticity esticate.
216
Table 7-lh: Summary Sheet of Calculated Elasticities From
System Estimation - Eggs
JAPAN:
ALL-PERI
ITEM
ESTIMATE
S.E.
1-SlAT
JAPAN:
C4MOOITY:
EGGS
METH:
SYSTEM ESTIMATION (ITERATIVE SUR)
1911-87
ELASTICITIES
CHILD
GROUP
TOTAL
OWN
EXP.
EXP.
PRICE *
ESTIMATEDESTIMATED
MEAN
MEAN
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POp.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
0.70369 0.033752 -0.40942 -1.68627 4.534462 -2.47305 -0.26182 -0.01616 0.199503 0.042975
0.284698
(-) 0.861355 1.919915 0.378217 0.207647 0.00284
2.471707
-1.9577 2.361803 -6.5387 -1.26091 -5.68851
PRE-WAR PERI
1911-37
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
()
MID-PERIcO 1925-70
0.09132 0.021617
ELASTICITIES
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
2OP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
ESTIMATE 1.083797 0.251729 -0.04772 -5.04751 -5.91274 -2.37339 -0.62595 -0.03957 0.175578 0.050877
0.394681
S.E.
1.365914 2.29595 0.784703 0.315057 0.000466
(+)
1-STAT
2.746009
-3.69534 -2.57529 -3.02457 -1.98677 -84.9877
JAPAN:
ITEM
HAT
1.48162
ESTIMATEDEST1ATED
MEAN
ITEM
MEAN
GROUP
ESTIMATE -0.05558 -0.00152 0.136174 -1.10638 10.06226 -2.44682 -0.27234 0.31751
S.E.
0.218486
3.614448 3.652473 0.686484 0.160264 S.71E-05
1-STAT
-0.25441
-0.3061 2.754917 -3.56429 -1.69932 5560.41
JAPAN:
MAT
6.55572
POST-WAR PERIcO 1955-87
ELASTICITIES
4.61067
ESTIMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
pop.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
ESTIMATE 2.295295 -0.16157 -0.75114 -0.60946 3.143739 -317997 -3.13348 -0.04936 0.744607 0.06045 lo.6c672
S.E.
0.566565
0.967142 1.699743 0.881352 0.837763 0.005418
C-)
T-STAT
4.051251
-9.11
-0.63017 1.849538 -3.60806 -3.7403
1962-87
KOREA:
ITEM
ELASTICITIES
GROUP
TOTAL
OWN
CHILD
WORKING
EXP.
EXP.
PRICE *
POP
POP.
ESTIMATE -0.34421
S.E.
0.288671
-0.0674
-1.1924
1-STAT
TAIWAN:
ESTIMATEDESTIMATED
OLD
OP.
MEAN
MEAN
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
SIZE
EFFECT
EFFECT
SHARE
0
0.33985 -36.4257 -37.9526 -22.3036 0.389827 0.231622 -0.03589 0.038248
3.741925 4.734142 1.831326 0.860366 0.008215
(+)
-9.73449 -8.01679 -12.1789 0.453095 28.19329
1963-87
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
-
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q
ESTIMATE 0.836654 0.270951 -0.18377 7.916667 13.80119 1.18247
S.E.
0.657452
(-) 4.496973 5.48433 1.566774
T-STAT
1.272371
1.760443 2.516477 0.754716
*
HAT
4.70973
0.019923 -0.24632 0.022464
0.004527
4.400902
HAT
5.73244
Sign of Hicksian own price elasticity is presented below each Marshal [Ian own price elasticity estimate.
217
Table 7-li: Summary Sheet of Calculated Elasticities From
System Estimation - Milk
JAPAN:
ITEM
MILK
SYSTEM ESTIMATION (ITERATIVE SUR)
ALL-PERIOD 1911-87
ELASTICITIES
ESTIMATEDEST IMATED
MEAN
MEAN
GROUP
TOTAL
GUM
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.811556
S.E.
0.291781
T-STAT
6.208607
JAPAN:
COMMODITY:
METHOD:
0.08689
-1.0891 1.062106 9.536351 -1.21212 0.050419 0.010802 0.616609 0.041716 12.85315
(-) 0.789814 1.732038 0.354802 0.21729 0.005473
1.344755 5.505854 -3.41632 0.232037 1.973821
PRE-WAR PERIOD 1911-37
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
GROUP
TOTAL
GUll
EXP.
EXP.
PRICE *
ESTIMATE 0.286244
S.E.
1-STAT
0.307736
0.930162
MID-PERI
JAPAN:
ITEM
1925-70
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ELASTICITIES
EST IP1ATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
-0.5997
C-)
POST-WAR PERIOD 1955-87
ITEM
MEAN
WORKING
0.00784 -0.41181 -8.68602 9.245781 -0.25506 0.177942 0.888031 0.324428 0.009556 1.13794
(-) 4.050157 4.480466 0.800038 0.216274 0.005189
-2.14461 2.063576 -0.31881 0.822761 171.1371
ESTIMATE 0.972814 0.225952
5.6.
0.316423
T-STAT
3.07441
JAPAN:
CHILD
-2.1918 5.256489 0.138741 -0.09388 0.000277
1.338812 2.188409 0.785927 0.254958 0.000469
-1.63713 2.401968 0.176531 -0.3682 0.590374
-0.0635 0.039469
ELASTICITIES
7. 95394
E ST IMA T EDE ST 1MM ED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
HAT
ESTIMATE 1.100237 -0.07745 -0.97955 1.153784 6.35226 -1.44987 -0.48878 0.048122 0.088238 0.068031 22.40957
5.6.
0.560525
C-)
0.961916 1.688497 0.876131 0.830152 0.008087
1-STAT
1.96287
1.199465 3.762079 -1.65486 -0.58878 5.950228
TAIWAN:
ITEM
ESTIMATE
5.6.
T-SIAT
-
1963-87
ELASTICITIES
EST IM.ATEDEST IMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
0* HAT
2.5591 0.828767 -0.94653 -31.3665 -41.3947 -3.78971
1.138087
2.248598
C-)
7.125499 8.668471 2.538817
-4.40201 -4.77531 -1.49271
-0.01369 1.335365 0.00736
2.0757
0.003229
-4.23825
Sign of Hicksian own price elasticity is presented below each Marshallian own price elasticity estimate.
218
Summary Sheet of Calculated Elasticities From
Table 7-2a:
Non-System Estimation - Rice
JAPAN:
COMMODITY:
RICE
METHOO:
NONSYSTEM ESTIMATION
ALL-PERIOD 1911-87
ITEM
GRJP
TOTAL
EXP.
EXP.
OWN
PRICE *
MEAN
MEAN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
LAGGED
Q* HAT
ESTIMATE 0.468717 0.022482 -0.19814 2.070753
S.E.
0.176568
1-STAT
2.654592
(+)
ITEM
3.77959 0.750456 -0.09389 0.246782 -0.03461 0.494313 112.7321
0.485381 1.109298 0.210049 0.115488 0.138025
4.266244 3.407193 3.572763
PRE-WAR PERIOD 1911-37
JAPAN:
ESTIMATEDESTIMATED
ELASTICITIES
-0.813 1.787945
ESTIMATEDESTIMATEO
ELASTICITIES
MEAN
MEAN
LAGGED
Q* HAT
GRC*JP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
ESTIMATE 1.104221 0.030245 -1.03114 -0.48319 8.863778 0.374623 -0.00264 -0.51503 -0. 91971 0.581919 133.9826
S.E.
0.148667
T-SIAT
7.427489
C-)
-0.21379 3.643427 0.894212 -0.03167 -3.11494
MID-PERIOD 1925-70
JAPAN:
ITEM
ESTIMATEDEST IMATED
ELASTICITIES
MEAN
MEAN
GRJP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0
ESTIMATE 0.761255 0.176814
S.E.
0.245013
1-STAT
3.107004
JAPAN:
2.260109 2.432813 0.418942 0.083202 0.165342
C-)
0.950656 1.629425
0.447916
POST-WAR PERIOD 1955-87
0.50031 0.133085 0.250539'
0.63975 -0.15549 -0.28248 2.821027
ESTIMATEDEST IMATED
ELASTICITIES
MEAN
ITEM
ESTIMATE
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
C
HAT
0.52665 -0.03707 -0.13563 -0.23472 -1.28144 -0.58858 0.663873 0.638044 -0.08265 0.422626 96.01344
0.286021
T-STAT
1.841295
(+)
0.344134 0.743826 0.341271 0.432818 0.165891
-0.68205 -1.72277 -L72468 1.53384 3.846154
E ST IMAT EDE ST IMAT ED
ELASTICITIES
1962-87
MEAN
ITEM
MEAN
GROWP
S.E.
KOREA:
HAT
-0.9111 0.425814 1.042425 -0.07779 -0.03759 0.706776 -0.37574 0.555196 121.2606
GRJP
TOTAL
OWN
EXP.
EXP.
PRICE *
CHILD
ESTIMATE 1.332457 0.260914 -1.04639
MEAN
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
7.16591 6.132957 2.428427 -1.31529 -0.24135
HAT
0.27166 0.521902 133.9227
3.899969 4.505447 1 .944811 0.739775 0.225697
S.E.
0.234757
TSTAT
5.675903
1.837428 1.361232
TAIWAN:
1963-87
ELASTIC 111 ES
MEAN
MEAN
ITEM
GR.JP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
0
C-)
0.441998
1-STAT
2.566354
*
-
(-)
EST IMATEDEST IMATED
-8.4912
-2.9654
0.477919 -0.34843
2.938128 3.888833
0.85428
0.381287
-2.29651 -2.18348 -3.47123
1.253436
ESTIMATE 1.134322 0.367351 -0.28975 -6.74744
S.E.
1.24867 -1.77795 -1.06935
HAT
0.21706 1202483
Sign of Hicksian own price eLasticity is presented below each Marshattian own price elasticity estimate.
219
Table 7-2b: Summary Sheet of Calculated Elasticities From
Non-System Estimation - Bread and Wheat
COMMODITY:
BREAD (JAPAN) AND WHEAT FLOUR (KOREA AND TAIWAN)
METHOD:
NON-SYSTEM ESTIMATION
ALL-PERIcO 1911-87
JAPAN:
ITEM
ELASTICITIES
EST IMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.986926 0.047337 -0.38342 0.992816 2.772128 -0.48534 -0.16516 0.061521 0.355345
S.E.
0.269781
(-) 0.954456 1.987938 0.43658 0.24798 0.166168
T-STAT
3.658245
1.04019 1.394474 -1.1117 -0.66603 0.370235
PRE-WAR PERIOD 1911-37
JAPAN:
ELASTICITIES
0.03037 7.39085
ESTIMATEDESTIMATED
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.289707 0.035325 -0.41745 -2.93203 0.614968 -1.5532 -0.06376 0.537908 -0.22213 0.012072
S.E.
0.598997
(-) 7.997613 11.48788 1.89217 0.423133 0.26603
T-STAT
2.153112
-0.36661 0.053532 -0.82086 -0.1507 2.037297
MID-PERIOD 1925-70
JAPAN:
ITEM
ELASTICITIES
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
-1.69 -0.21243 -0.17019 0.086617 0.030002 6.31003
0.94969 0.274121 0.119345
0.874151 2.391323 -1.77952 -0.77496 -1.42606
1.632656
JAPAN:
POST-WAR PERIOD 1955-87
ITEM
1.69584
EST THAT EDE ST THAT ED
ESTIMATE 0.568935 0.132144 0.048485 1.433085 6.452148
S.E.
0.348472
1.639401 2.69815
(+)
T-STAT
MEAN
GROUP
ELASTICITIES
ESTIMATE0EST IMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.240252 -0.01691 0.551489 1.091783 3.635038 0.009661 1.103274 0.696729 -0.81969 0.045601 12.00638
S.E.
0.419556
0.623413 1.244873 0.539949 0.622579 0.233921
(+)
7-STAT
0.572634
1.751302 2.920006 0.017892 1.772103 2.978479
1962-87
KOREA:
ITEM
ELASTICITIES
ESTIMATEDEST IMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -1.61883 -0.31699 1.515148 -8.9572 7.384673 -16.5918 -3.28305 -0.09589 0.669744 0.064857 34.37049
S.E.
1.016771
14.1762 16.45613 8.53674 2.946622 0.770957
(+)
1-STAT
-1.59213
-0.63185 0.448749 -1.94358 -1.11418 -0.12438
TAIWAN:
1963-87
MEAN
MEAN
ITEM
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q* HAT
ELASTICITIES
ESTIMATEDESTIMATED
ESTIMATE 4.528759 1.466643 -0.79097 -30.3288 -37.5263 -12.7743
S.E.
1.817486
(-) 14.39091 15.52676 4.504856
1-STAT
2.491771
-2.1075 -2.41719 -2.83568
*
-
0.242278 0.041271 0.051726 23.01887
0.392839
0.616736
Sign of Hicksian own price elasticity is presented below each Marshattian own price elasticity estimate.
220
Table 7-2c:
Summary Sheet of Calculated Elasticities Prom
Non-System Estimation - Barley
JAPAN:
ITEM
COMMODITY:
BARLEY
METHOD:
NON-SYSTEM ESTIMATION
ALL-PERIOD 1911-37
ELASTICITIES
EST IMATEDEST IMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.542579 0.073989 0.354513 4.390064 3.169202 2.689776 0.945038 0.477889 0.796114 0.021282
S.E.
0.678752
3.051214 5.052699 1.594281 0.719586 0.14926
1-STAT
2.272668
1.438793 0.62723 1.687141 1.313308 3.201723
6 97933
()
JAPAN:
PRE-WAR PERIOD 1911-37
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
GROUP
TOTAL
OWN
EXP.
EXP.
PRICE *
CHILD
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
ESTIMATE 0.193097 0.005289 -0.12132 -1.81877 14.41529 1.656011 0.993619 0.393107 -0.76368 0.031513
G.E.
0.496461
C-)
6.709278 8.208338 1.353248 0.342112 0.280608
1-STAT
0.388947
-0.27108 1.736176 1.223731 2.904369 1.400913
JAPAN:
MID-PERIOD 1925-TO
ELASTICITIES
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
POST-WAR PERIOD 1955-87
ELASTICITIES
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
OUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
1962-87
0.19814
ELASTICITIES
0.01322
-
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -0.40857
S.E.
1.184772
F-STAT
-0.34485
*
4.6833
ESTIMATEDEST IMATED
MEAN
ITEM
lEAN
GROUP
ESTIMATE 1.309599 -0.09218 1.534394 3.383964 -2.2351 5.943949 0.769062 0.661684
G.E.
3.390469
3.844932 8.947806 3.796974 4.81407 0.238253
(+)
1-STAT
0.386259
0.88011 -0.24979 1.565443 0.159753 2.777233
KOREA:
8.02046
ESTIMATEDEST IMATED
MEAN
ITEM
MEAN
GROUP
ESTIMATE 0.949409 0.220516 2.086027 5.34348 0.659293 6.576657 0.736779 0.235699 1.658258 0.023346
(4) 8.980045 13.26254 4.911719 1.527357 0.283376
S.E.
1.833403
1-STAT
0.51784
0.595039 0.049711 1.338973 0.482388 0.831753
JAPAN:
HAT
9.60758
EST IMATEDEST IMATED
MEAN
ITEM
MEAN
WORKING
Sign o
-0.08 0.061933 -7.68919 -4.02641 -5.70537 2.166173 0.716951 3.153287 0.084398 35.46625
()
15.80612 17.22559 8.122023 3.406728 0.457838
-0.48647 -0.23375 -0.70246 0.635851 1.565948
Hicksian own price elasticity is presented below each Marshattian own price elasticity estimate.
221
Table 7-2d:
Summary Sheet of Calculated Elasticities From
Non-System Estimation - Beef
JAPAN:
ITEM
ALL-PERI
T-STAT
JAPAN:
BEEF
METH:
NON-SYSTEM ESTIMATION
1911-87
ELASTICITIES
ESTIMATEDESIIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.298559
S.E.
COMM!TY:
0.348114
0.857647
0.01432 -0.34983 -2.87567 -7.72884 0.816206 0.108137 0.154606
C-)
1.2169 2.689133 0.578935 0.294366 0.115361
-2.36311 -2.8741 1409841 0.367356 1.360194
PRE-WAR PERI
1911-37
0.3954 0.057361
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
GROUP
TOTAL
OWN
EXP.
EXP.
PRICE *
CHILD
1.6099
MEAN
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -0.37997 -0.01041 -0.26728 3.210946 4.544805 0.938822 0.663061 -0.22495 0.489679 0.035933
S.E.
0.525334
C-)
7.272635 8.764842 1.526967 0.479861 0.523541
T-STAT
-0J233
0.441511 0.518527 0.614828 1.381778 -0.42966
JAPAN:
ITEM
M!D-PERI
GROUP
EXP.
1925-70
OTAL
EXP.
ELASTICITIES
0.61189
ESTIMATEDESTIMATED
MEAN
MEAN
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -0.37913 -0.08806 -67972 -5.08181 -9.1544 2.828662 0.418462 -0.09259 0.001171 0.040259
S.E.
0.970161
C-)
3.890777 6.509799 2.205626 0.658928 0.326157
1-STAT
-0.3903
-1.30612 -1.40625 1.282476 0.635065 -0.28387
JAPAN:
ITEM
POST-WAR PERI
GROUP
EXP.
OTAL
EXP.
1955-87
OWN
ELASTICITIES
CHILD
PRICE *
0.99416
ESTIMATEDESIIMATED
MEAN
MEAN
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
ESTIMATE -0.13571 0.009553 -0.51104 -3.39948 -708278 -2.15012 -1.12404 0.552741 0.534559 0.076212 2.42601
S.E.
0.976768
(-) 11O2208 22574 1.332869 1.484158 0.230312
1-STAT
-0.13893
-3.08425 -3.13738 -1.63566 -0.75736 2.399967
1962-87
KOREA:
ELASTICITIES
ESTIMATEDESTIMATED
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0
ESTIMATE -0.67863 -0.13288 1.685116 54.55179 63.83412 23.33859 -4.12408 -0.12236 0.342486 0.07608
S.E.
0.608031
+)
9.216746 9.916667 5.453339 1.702681 0.190685
1-STAT
-1.11611
5.918769 6.437054 4279689 -2.42211 -0.64171
TAIWAN:
1963-87
ITEM
GROUP
EXP.
ESTIMATE
MEAN
GROUP
ELASTICITIES
CTAL
EXP.
EST IMATEDEST (MATED
MEAN
MEAN
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
0* HAT
-1.238 -0.40093 -1.66007 -19.5281 -15.1643 -6.95462
S.E.
1.412825
F-STAT
-0.87626
HAT
2.41692
C-)
6448213 7.417944 2.178535
-3.02845 -2.04428 -3.19234
0.270538 -0.06447
0.01221
0.90931
0.214697
1. 260094
Sign of Nicks:an own price elasticity is presented below each Marshallian own price elasticity est1nate.
222
Table 7-2e: Summary Sheet of
Non-System Estimation - Pork
CO4O0 I TY:
PORK
METHOD:
NON-SYSTEM ESTIMATION
ALL-PERIOD 1911-87
JAPAN:
ITEM
GRWP
TOTAL
Calculated
Elasticities From
ESTIMATEDESTIMATED
ELASTICITiES
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
MEAN
MEAN
BUDGET
LAGGED
0' HAT
ESTIMATE 1.263578 0.060607 -1.18017 2.199349 8.474748 -0.35672 0.285468 0.196523 0.308865 0.058795 3.16475
0.426348
S.E.
1.28282 2.89499 0.643511 0.30877 0.135526
(-)
1-STAT
1.714464 2.927384 -0.55433 0.924535 1.450075
2.963724
EXP.
EXP.
PRICE *
PRE-WAR PERIOD 1911-37
JAPAN:
GRJP
ITEM
TOTAL
OWN
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
ESTIMATEDESTIMATED
EI.ASTICIT!ES
CHILD
WORKING
SHARE
OLD
FAMILY
HABIT
CUSTOM
MEAN
MEAN
BUDGET
LAGGED
0' HAT
SHARE
PRICE * POP.
EFFECT
EFFECT
POP.
POP.
SIZE
ESTIMATE 0.112907 0.003093 -0.42727 15.67875 4.71798 -0.23035 0.293434 0.340969 0.48496 0.011146 0.36999
S.E.
0.671721
(-) 9.354283 12.14933 2.758129 0.478948 0.2905
1.670746 0.388333 -0.08413 0.612663 1.173728
1-STAT
0.168086
EXP.
EXP.
MID-PERIOD 1925-70
JAPAN:
ITEM
851 IMATEOEST IMAIED
ELASTICITIES
GRJP
TOTAL
OWN
EXP.
EXP.
PRICE *
CHILD
MEAN
MEAN
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0' NAT
1.58183
ESTIMATE 1.211319 0.281348 -1.05962 1.585973 14.28033 0.870958 0.123741 0.078936 0.029548 0.043542
0.93299
4.693429 7.531167 2.832915 0.761982 0.364126
S.E.
C-)
1-STAT
0.337913 1.896164 0.307442 0.162394 0.216781
1.298319
POST-WAR PERIOD 1955-87
JAPAN:
ITEM
ESTIMATEDEST IMATED
ELASTICITIES
MEAN
MEAN
GRJP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOII
BUDGET
LAGGED
EXP.
EX?.
PRICE *
POP.
POP,
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
ESTIMATE -1.18335 0.083297 -0.34066 2.829693 7.453193 L686589 -1.13161 -0.04978 0.078327 0.097781
5.8.
0.964882
(-) 1.130384 2.277336 1.186428 1.29872 0.250376 0.078327
1-STAT
2.503302 3.272768 1.252995 -0.87133 -0.19884
-1.22642
GRJP
ITEM
ESIJMATEDEST IMATED
ELASTICITIES
1962-87
KOREA:
TOTAL
OWN
CHILD
WORK1NG
OLD
FAMILY
HABIT
PRICE * PoP.
EFFECT
POP.
SIZE
POP.
ESTIMATE 1.864778 0.365149 -0.76654 27.45989 12.51258 18.5415 -4.17938 0.500979
S.E.
0.571403
(-) 8.372366 12.6559 3.477206 1.488193 0.193277
3.26351
3.094991 0.988675 5.332299 -280836 2.592022
1-STAT
EXP.
HAT
5-45499
EXP.
MEAN
MEAN
CUSTOM
BUDGET
LAGGED
EFFECT
SHARE
0' HAT
3.92983
-0.0585 0.063005
ESTIMATEDEST IMATED
ELASTICITIES
TAIWAN:
1963-87
MEAN
MEAN
iTEM
GRCt)P
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
a' HAT
ESTIMATE 2.194532 0.710701 -0.79744 18.89341 17.48822 6.730925
5.8.
0.522127
(-) 3.711378 3.478813 1.6264
5.090673 5.027065 4.138543
1-STAT
4.203065
*
-
-0. 12396 -0,13902 0.218261 21.92129
0.186841
-0. 66344
Sign of Hicksian own price elasticity is presented below each Marshallian own price elasticity estimate.
223
Summary Sheet of Calculated Elasticities From
Table 7-2f:
Non-System Estimation - Chicken
CV4cIfl:
CHICKEN
METH:
NON-SYSTEM ESTIMATION
ITEM
ESTIMATEDESTIMATED
ELASTICITIES
ALL-PERIOD 1911-87
jAPAN:
MEAN
MEAN
GRCUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.787018 0.037749 -0.87086 -0.09114 4.143746 -0.02407 -0.87602 0.261639
S.E.
0.38061.8
c-) 1.637495 3.185439 1.400622 0.40039 0.164832
-0.05566 1.30084 -0.01718 -2.18793 1.587308
1-STAT
2.067576
ITEM
ESTIMATEDESTIMATED
ELASTICITIES
PRE-WAR PERIOD 1911-37
JAPAN:
0.64787 0.034394 2.41449
MEAN
MEAN
GRt*JP
TOTAl.
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
ESTIMATE -0.67938 -0.01861 -L16736 19.67546 28.70266 1.975752 -1.01097 0.422991 -0.27121 0.011455
0.646892
S.E.
(-) 6.502441. 8.063893 1.517802 0.27223 0.224874
3.025857 3.559405 1.30172 -3.71364 1.88101
I-STAT
-1.05023
ITEM
GRWP
TOTAL
ESTIMATEOESTIMATED
ELASTICITIES
MiD-PERIOD 1925-70
JAPAN:
CHILD
OWN
WORKING
OLD
FAMILY
HABIT
EFFECT
SIZE
pop.
POP.
PRICE *
ESTIMATE 0.939609 0.218239 -2.04827 3.97311 11.28031 5.704874 -0.57798 0.15692
4.660089 8.449154 3.567563 0.871157 0.195797
1.023198
S.E.
C-)
0.852583 1.335082 1.599095 -0.66346 O.8013
T-STAT
0.918306
EXP.
EXP.
POST-WAR PERIOD 1955-87
JAPAN:
MEAN
MEAN
CUSTOM
BUDGET
LAGGED
EFFECT
SHARE
Q* HAT
-0.248
0.02256
TOTAL
OWM
CHILD
WORKIHG
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
OPIYJP
TOTAL
OWN
CHILD
WORKING
EXP.
EXP.
PRICE *
POP.
POP.
ESTIMATE -0.32378
0.994164
S.E.
1-STAT
-0.32568
TAIWAN:
%3-87
ITEM
OLD
OP.
MEAN
MEAN
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
SIZE
EFFECT
EFFECT
SHARE
0* HAT
-0.0634 -2.38861 -15.1655 -13.8065 -9.55757 -0.36912 0.042951 0.113727 0.019007
1-)
4.14215
ESTIMATEDESTIMATED
ELASTICITIES
1962-87
ITEM
MEAN
OBJP
ESTIMATE -0.29303 0.020627 -3.33557 0.696406 3.433363 -2.70394 -3.14886 0.024666 0.483677 0.053426
1.293515 2.27381 1.392474 1.11596 0.216304
0.773784
S.E.
C-)
0.538383 1.50996 -1.94182 -2.82166 0.114036
1-STAT
-0.37869
KOREA:
0.84065
ESTIMATEDESTIMATED
ELASTICITIES
MEAN
ITEM
0.33073
1.99577
16.74653 18.42171 7.529362 2.814238 0.319343
-0.90559 -0.74947 -1.26937 -0.13116 0.134498
ESTIMATFOESTIMATED
ELASTICITIES
MEAN
MEAN
ORJP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
0* HAT
ESTIMATE -1.68994 -0.54729
S.E.
1.139554
-1.48298
1-STAT
0.67792 21 .51023 29.51353 5.842495
8.48599 9.527001 2.578308
c.)
2.534793 3.097883 2.266019
0.702782 -0.13138 0.060288
9.15316
0.281392
2.497513
Sign of HicKsian own price elasticity is presented below each Marshallian own price elasticity estimate.
224
Table 7-2g: Summary Sheet of Calculated Elasticities From
Non-System Estimation - Fish
COMP4OD I TY:
F I SN
METHOD:
NON-SYSTEM ESTIMATION
ITEM
ESTIMATEDESTIMATED
ELASTICITIES
ALL-PERIOD 1911-87
JAPAN:
GRQJP
TOTAL
OWN
EXP.
EXP.
PRICE *
CHILD
MEAN
MEAN
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
pop.
pop.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.298731 0.062293 -0.58121 -4.23194 -10.6704 -1.43566 0.464268 0.383346 0.152343 0.218193 15.59087
1.316038 2.78286 0.519357 0.308942 0.181216
0.478293
S.E.
C-)
-3.21567 -3.83432 -2.7643 1.502767 2.115413
1-STAT
2.715349
PRE-WAR PERIOD 1911-37
JAPAN:
ITEM
ESTIMATEDEST IMATED
ELASTICITIES
MEAN
MEAN
GRJP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.562287 0.042792 -0.76623 -5.33114 -20.1608 -15468 0.081862 -0.08858
S.E.
0.545501
(-) 7.212467 8.833733 1.578015 0.381509 0.187416
-0.73916 -2.28225 -0.98022 0.214574 -0.47264
1-STAT
2.863949
ITEM
ESTIMATEDESTIMATED
ELASTICITIES
MID-PERIOD 1925-70
JAPAN:
-0.4424 0284161 1733771
MEAN
MEAN
GRJP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 0.856362 0.198904 0.104252 -0.7338 -8.62035 -0.79172 0.213591 0128086 0.098057 0.190584 16.15561
2.728037 4772018 1.436428 0.345202 0.257935
S.E.
0.528638
(+)
-0.26898 -180644 -0.55118 0.618741 0.496582
1-STAT
1.61994
POST-WAR PERIOD 1955-87
JAPAN:
ESTIMATEDESTIMATED
ELASTICITIES
MEAN
ITEM
ESTIMATE
S.E.
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTI
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
5.i3839
0.994208
5.168324
1-STAT
-0.3617 -0.90018 0.988563 1.637014 -1.53894 3J44362 1.075954 -0.44017 0166504 14.48153
3.0192 1.173843 1.505186 0.160867
1.363868
C-)
0.724823 0.542201 -1.31103 2.089019 6.688473
ITEM
ESTIMATEDESTIMATED
ELASTICITIES
1962-87
KOREA:
MEAN
GR.JP
MEAN
MEAN
GBJP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTHI
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP
POP
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE 1.342177 0.360724 -L62083 -24.379 -28.0436 -6.21423 3.469797 0.627226 -0.12899 0.134829 14.16849
74472 3.919997 1.498266 0293738
0.545453
5.682226
SE
C-)
-4.29039 -3J6566 -L58526 2.315875 2.135324
1-STAT
3377333
TAIWAN:
1ST IMATEDEST IMATED
ELASTICITIES
1963-87
MEAN
ITEM
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTI
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q* HAl
ESTIMATE 1.025646 0.332157 -023728
0481846
S.E.
(+)
T-STAT
*
-
MEAN
GRQJP
2.128578
-6.3965 -7.00321 -247978
2.49887 3.014253 0.786702
-2.55976 -2.32337 -3.15212
0.414367 0.062996 0.413867 31.18222
0Z24991
1.841705
Sign of Hicksian own price easticity is presented betow each Marshallian own price e1astcity estimate.
225
Table 7-2h: Summary Sheet of Calculated Elasticities From
Non-system Estimation - Eggs
COMMODITY:
EGGS
METHOD:
NON-SYSTEM ESTIMATION
ITEM
ESTIMATE
S.E.
1-STAT
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BIJDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
0.73207 0.035113
0.28794
2.542438
-0.4085 -2.11347 3.550914 -2.50339 -038453 -0.04462 0.199503 0.042978
C-)
ITEM
1.006918 2.056161
-2.09895 1.726963
-5.6947 -1.51726 -0.40414
ESTIMATEDESTIMATED
MEAN
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
ESTIMATE -0.09202 -0.00252 0.098913 3.028665 9.587017 -1.13754 -0.34966 0.501753
4.7781.75 5.856754 1.325899 0.251367 0.180876
0.350332
S.E
(+)
0.633814 1.636917 -0.85794 -1.39102 2.774014
1-STAT
-0.26266
ITEM
0.09132 0.021618
MEAN
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q* HAT
POST-WAR PERIOD 1955-87
ITEM
MEAN
GROUP
ESTIMATE 1.165899 0.270799 -0.44312 -0.54243 0.417463 0.979011 -0.89909 -0.1355 0.175578
1.821795 2.809215 1.430658 0.415479 0.15322
S.E.
0.399526
C-)
-0.29775 0.148605 0.684308 -2.16399 -0.88436
T-STAT
2918206
JAPAN:
1.48162
ESTIMATEDESTIMATED
ELASTICITIES
MID-PERIOD 1925-70
JAPAN:
6.55572
0.4396 0.253434 0.110411
ELASTICITIES
PRE-WAR PERIOD 1911-37
JAPAN:
ETIMATEDES1IMATE0
ELASTICITIES
ALL-PERIOD 1911-87
JAPAN:
CHILD
TOTAL
OWN
ExP.
EXP.
PRICE * pp
4.61067
ESTIMATEDESTEMATED
ELASTICITIES
GROUP
0.0509
MEAN
MEAN
WORKING
OLD
FAMILY
HABIT
CUSTOM
EUDGET
LAGGED
pop.
POP.
SIZE
EFFECT
EFFECT
;HARE
Q* HAT
ESTIMATE 2.364192 -0.16642 -0.46572 -0.85957 3.276443 -3.45411 -3.5623 -0.18679 0.744607 0.06045 10.69672
1.02563 2.064361 1.005266 1.227748 0.247326
S.E.
0.804719
C-)
-0.83809 1.587146 -3.43602 -2.90149 -0.75524
1-STAT
2.937911
ESTIMATEDESTIMATED
ELASTICITIES
1962-87
KOREA:
MEAN
ITEM
MEAN
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
DUDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
0* HAT
4.70973
ESTIMATE -0.02236 -0.00438 0.894582 -27.2466 -22.816 -17.1399 0.757551 0.036219 -0.03589 0.038247
1.5219 0.41034
7.848213 7.860763 4.073883
S.E.
0.530788
(+)
-3.4717 -2.90251 -4.20521 0.497767 0.088266
1-STAT
-0.04212
I1STIMATEDEST IMATED
ELASTICITIES
TAIWAN:
1963-87
MEAN
MEAN
ITEM
GROUP
TOTAL
OWN
CHILD
WORKING
OLD
HABIT
CUSTOM
3UDGET
LAGGED
EXP.
EXP.
PRICE *
POP.
POP.
POP.
EFFECT
EFFECT
SHARE
Q
ESTIMATE 2.252068 0.729335
S.E.
0.645338
T-STAT
3.489751
-
-0.3688 -2.21556 -1.41922 -558427
(-)
3229O91 4.164739 1.40672
-0.68613 -0.34077 -3.96971
1.424416 -0.24578 3.022483
HAT
5.73244
021O552
6.765158
Sign of Hicksian own price elasticity is presented below each Marshailian own price elasticity estunate.
226
Table 7-2i: Summary Sheet of Calculated Elasticities From
Non-System Estimation - Milk
MILK
NON-SYSTEM ESTIMATION
COMMODITY:
METHOD:
ITEM
GROUP
TOTAL
EXP.
EXP.
OWN
PRICE *
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
LAGGED
Q* HAT
0.91962 0.044109 -0.76568 -1.12112 5.029614 -1.97346 -0.14065 0.448225 0.616609 0.041703 12.85315
ESTIMATE
S.E.
0.249478
1-STAT
3.686169
(-)
GROUP
ITEM
1.055056 2.013788 0.574731 0.262235 0.164968
-1.06262 2.497589
TOTAL
-3.4337 -0.53635 2.717048
ESTIMATEDESTIMATED
ELASTICITIES
PRE-WAR PERIOD 1911-37
JAPAN:
ESTIMATEDESTIMATED
MEAN
MEAN
ELASTICITIES
ALL-PERIOD 1911-87
JAPAN:
OWN
PRICE *
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
MEAN
MEAN
BUDGET
LAGGED
Q
HAT
ShARE
EFFECT
EFFECT
POP.
SIZE
POP.
POP.
EXP
ESTIMATE -0.02773 -0.00076 0.294289 10.71597 21 .59014 6.221263 0.027391 1.360385 0.324428 0.009735 1.13794
EXP.
S.E.
0.451649
1-STAT
-0.06141
(+)
1.60364
ITEM
ESTIMATE
GROUP
TOTAL
EXP.
EXP.
EST IMATEDEST IMATED
OWN
PRICE *
MEAN
MEAN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BIJDGET
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SNARE
LAGGED
0* HAT
0.98548 0.228894 -1.21931 -430754 1.685413 -1.94292
-0.0019 0.164813
3.749274 6.188458 1.490155
0.36685 0.185358
S.E.
0.463457
1-STAT
2.126367
C-)
-1.1489 0.272348 -1.30384 -0.00517
POST-WAR PERIOD 1955-87
JAPAN:
2.60259 2738098 0.108245 5.462567
ELASTICITIES
MID-PERIOD 1925-70
JAPAN:
6.68228 8.195634 2.272111 0.253046 0.249038
-0.0635 0040608 795394
0.88916
ESTIMATEDEST IMATED
ELASTICITIES
MEAN
GROUP
ITEM
EXP.
TOTAL
EXP.
OWN
PRICE *
MEAN
CHILD
WORKING
OLD
FAMILY
HABIT
CUSTOM
BUDGET
LAGGED
POP.
POP.
POP.
SIZE
EFFECT
EFFECT
SHARE
Q
HAT
ESTIMATE 1.058885 -0.07454 -0.88132 1.609856 6.855119 -0.97099 -0.54985 0.182532 0.088238 0068031 22.40957
1.14107 0.346038
0.960843 2.153874 0.91041
S.E.
0.798652
C-)
T-STAT
1.325841
1.675463 3.182693 -L06654 -0.48188 0.527492
TAIWAN:
1963-87
ELASTICITIES
ITEM
GROUP
TOTAL
EXP.
EXP.
ESTIMATE
1.406489
T-STAT
0.503566
-
MEAN
MEAN
CHILD
WORKING
OLD
HABIT
CUSTOM
BUDGET
POP.
POP.
PQ
EFFECT
EFFECT
SHARE
LAGGED
Q* HAT
0.70826 0.229371 -1.04365 -26.6032 -33.5762 -3.48855
9.371993 11.0234 2699677
C-)
SE.
*
OWN
PRICE *
ESTIMATE0ESTIMATED
-2.83858
-3.0459 -1.29221
3.416853 1.334801 0.007317
2.0757
3. 246557
1.690695
Sign of Hicksian own price etasticity is presented below each Marshatlian own price Elasticity estimate.
227
7.2. Group Expenditure Elasticity Estimates
Group expenditure elasticity estimates are summarized
in Tables 7-3.
In an attempt to sort out statistically and
theoretically reasonable estimates for group expenditure
elasticities (denoted as Xi), the robustness of the
estimates with respect to the different estimation methods
and the statistical significance of the original
coefficients, 13i, are examined.81
Higher priority is
given to robustness in selecting "reliable" estimates.
Table 7-4 summarizes the estimates showing differences less
than 0.3 between the two estimates (the estimates showing
differences less than 0.7 in
[
]
parentheses).
The group
expenditure elasticities based on statistically significant
original coefficients at 5% are typed in bold face.
81 It was found that t-tests for Xi are not always
appropriate. The t-statistics for Bi are given by 13i I
SE[13iJ, whereas the t-statistics for Xi are given by Xi /
SE(Xi) = (Wi + 13±) / SE[13i] from (4-14) and (4-15), where Wi
is the predicted budget share for good i and should always
be positive. When 13i is positive, the t-values for Xi
always become larger than the t-values for 131; the
statistical significance of Xi is more likely to be
However, when Bi is negative, the
supported by the t-tests.
t-value for Xi may or may not be larger than the t-value for
may or may not
WI + 13i
13i; given 13i < 0 and Wi > 0,
depending on the values of Wi and
become greater than Bi
13i.
Therefore, the t-tests for Xi are not symmetric for
An example is the case
positive and negative cases of 61.
of beef in Japan, where the original coefficients 13i's are
shown to be statistically significant, whereas the
corresponding Xi's are found to be largely ins:Lgnificant.
Considering this point, the statistical signif:Lcance for Xi
will be evaluated based only on the t-tests for the
corresponding 13i, not for Xi, in the following analysis.
I
I
I
I
228
Table 7-3a: Group Expenditure Elasticity Estimates
Summary - System Estimation
FROM
SYSTEM
ESTIMATION
JAPAN ALL-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
ESTIMATE 0.666241 0.922193 -0.09709 0.198191 1.399458 1.814514 1.753692
S.E.
I-STAT.
0.145808 0.219015
0.90137 0.332163 0.361955 0.478205
EGGS
MILK
0.70369 1.811556
0.3829 0.284698 0.291781
4.5693 4.210647 -0.10772 0.596668 3.866391 3.794424 4.580024 2.471707 6.208607
JAPAN PRE-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
ESTIMATE 1.058976 1.565427 0.952719 -0.10385 0.092672 -0.39842
FISH
EGGS
MILK
1.19593 -0.05558 0.286244
0.23603 0.304488 0.436616 0.326418 0.275906 0.218486 0.307736
S.E.
0.109511 0.503864
1-STAT.
9.670041 3.106847 4.036429 -0.34108 0.212251 -1.22058 4.334557 -0.25441 0.930162
JAPAN MID-PERIOD:
RICE
ESTIMATE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
0.7335 1.024953 0.014215 -0.51662 1.453228 1.567809 2.005244 1.083797 0.972814
0.63818 0.775993 0.325096 0.394681 0.316423
S.E.
0.149988 0.247089 1.204924 0.610751
1-STAT.
4.890383 4.148105 0.011797 -0.84588 2.277145 2.020389 6.168166 2.746009
3.07441
JAPAN POST-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
ESTIMATE 0.411466 0.142501 -2.39531 -0.84429 -1.23482 -0.13286
S.E.
1-STAT.
FISH
EGGS
MILK
5.04666 2.295295 1.100237
0.226091 0.328665 2.558665 0.561381 0.589212 0.468797 0932837 0.566565 0.560525
1.96287
-0.2834 5.41001 4.051251
1.81991 0.433577 -0.93616 -1.50395 -2.09571
KOREA:
RICE
WHEAT
BARLEY
ESTIMATE 1.430948 -0.82648 0.565432
BEEF
PORK
CHICKEN
FISH
EGGS
-0.7617 1.866556 0.065107 1.595904 -0.34421
S.E.
0.165548 0.519174 0.749201 0.365431 0.375185 0.604302 0.420559 0.288671
1-STAT.
8.643706 -1.59192 0J54714 -2.08438 4.975026 0.107739 3.794717
-1.1924
TAIWAN:
RICE
WHEAT
ESTIMATE 1.501657 4.140756
BEEF
0.687817
PORK
CHICKEN
FISH
EGGS
1.14568 -1.21663 0.257101 0.836654
MILK
2.5591
S.E.
0.241895 1.072992
0.173162 0.419429 0.947861 0.318664 0.657452 1.138087
T-STAT.
6.207877 3.859075
0.889616 4.162034 -1.28355 0.806811 1.272571 2.248598
229
Table 7-3b: Group Expenditure Elasticity Estimates
Summary - Non-System Estimation
FROM
NON-SYSTEM
ESTIMATION
JAPAN ALL-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
ESTIMATE 0.468717 0.986926 1.542579 0.298559 1.263578 0.787018 1.298731
EGGS
0.73207
MILK
0.91962
0.28794 0.249478
S.E.
0.176568 0.269781 0.678752 0.348114 0.426348 0.380648 0.478293
T-STAT.
2.654592 3.658245 2.272668 0.8576.47 2.963724 2.067576 2.715349 2.542438 3.686169
JAPAN PRE-WAR PERIOD:
MILK
EGGS
CHICKEN FISH
PORK
BEEF
BARLEY
BREAD
RICE
ESTIMATE 1.104221 1.289707 0.193097 -0.37997 0.112907 -0.67938 1.562287 -0.09202 -0.02773
S.E.
0.148667 0.598997 0.496461 0.525334 0.671721 0.646892 0.545501 0.350332 0.451649
T-STAT.
7.427489 2.153112 0.388947
-0.7233 0.168086 -1.05023 2.863949 -0.26266 -0.06141
JAPAN MID-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
ESTIMATE 0.761255 0.568935 0.949409 -0.37913 1.211319 0.939609 0.856362 1.165899
S.E.
0.245013 0.348472 1.833403 0.970161
1-STAT.
3.107004 1.632656
0.51784
MILK
0.98548
0.93299 1.023198 0.528638 0.399526 0.463457
-0.3908 1.298319 0.918306
1.61994 2.918206 2.126367
JAPAN POST-WAR PERIOD:
RICE
ESTIMATE
BREAD
BARLEY
BEEF
PORK
CHICKEN
0.52665 0.240252 1.309599 -0.13571 -1.18335 -0.29303
FISH
EGGS
MILK
5.13839 2.364192 1.058885
S.E.
0.286021 0.419556 3.390469 0.976768 0.964882 0.773784 0.994208 0.804719 0.798652
T-STAT.
1.841295 0.572634 0.386259 -0.13893 -1.22642 -0.37869 5.168324 2.937911 1.325841
KOREA:
EGGS
PORK
CHICKEN FISH
BEEF
BARLEY
WHEAT
RICE
ESTIMATE 1.332457 -1.61883 -0.40857 -0.678.63 1.864778 -0.32378 1.842177 -0.02236
S.E.
0.234757 1.016771 1.184772 0.608031 0.571403 0.994164 0.545453 0.530788
T-STAT.
5.675903 -1.59213 -0.34485 -1.11611
3.26351 -0.32568 3.377333 -0.04212
TAIWAN:
RiCE
WHEAT
ESTIMATE 1.134322 4.528759
BEEF
PORK
CHICKEN
FISH
EGGS
-1.238 2.194532 -1.68994 1.025646 2.252068
MILK
0.70826
S.E.
0.441998 1.817486
L412825 0.522127 1.139554 0.481846 0.645338 1.406489
T- STAT
2.566354 2.491771
-0.87626 4.203065 -1.48298 2.128578 3.489751 0.503566
230
Table 7-4:
Reliable Group Expenditure Elasticity Estimates
Sample (System Result:Non-System Result)
Commodity
Rice
JA(O.67:O.47); JR(106:1.l0); JM(0.73:0.76);
[T(1.50:l.13)]
JT(O.41:0.53); K(1.43:l.33)
Bread/Wheat
JA(0.92:0.99); JR(1.57:1.29) ;
JT(O.14:O.24) ;
[JM(l.02:0.57)];
[T(4.i.4:4.53)]
Barley
Beef
JA(O.20:O.30); JR(-O.i.O:-O.38);
JM(-O.52:-0.38); K(-O.76:-O.68)
Pork
JA(l.40:1.26); JR(0.09:0.1l) ; 314(1.45:1.21)
JT(-l.23:-i..18); K(l..87:1.86); [T(l.75:2.19)J
Chicken
JR(-O.40:-O.68) ; [314(1.57:0.94)];
JT(-O.13:-0.29); [T(-i..22:-1.69)]
Fish
[JA(l.75:1.30)]; [JR(1.20:1.56)];
JT(5.05:5.14); K(1.60:1.84)
Eggs
JA(0.70:0.73); JR(-O.06:-O.09); 314(1.08:1.17);
JT(2.30:2.36); [K(-O.34:-0.02)]
Milk
JM(0.97:0.99) ; JT(l.10:l.06)
Note:
Group expenditure elasticity estimates are in
parenthesis. Figures are rounded of f at two
(
The results based on the
decimal places.
original coefficient 13i being statistically
significant at 5% level are typed in bold face.
The results having difference more than 0.3 and
less than 0.7 between the two estimates are in
parenthesis.
[
)
]
231
The results for wheat in Taiwan and fish in post-war
Japan show very high values, and the results f or eggs in
post-war Japan have slightly high values.
Unexpectedly,
many group expenditure elasticities for animal origin foods,
such as beef, pork, chicken, and eggs are found to be
negative.
Comparing the results from the three Japanese samples,
JR (Japan pre-war), JM (Japan mid-period), and JT (Japan
post-war), it is observed that Xi is not constant throughout
the various phases of economic development.
7.3. Price Elasticities
Marshallian (uncompensated) and Hicksian (compensated)
price elasticity matrices are reported in Table 7-5 for the
system estimation and in Table 7-6 for the non-system
estimation.
232
Table 7-5a: Price Elasticity Matrix From System
Estimation - Japan All-period Sample
MARSI4ALLIAN
(UNCOMPENSATED)
FOR
FROM
JAPAN
SYSTEM
ELASTICITIES
PRICE
ALL-PERIOD
ESTIMATION
RICE
-0.68901 -0.07555 0.201079 -0.32308 0.056605 0.081758 -0.17756 -0.05542 0.203029
BREAD
-0.36277 -0.37867 0.085528 0.175475 0.260696 -0.17578 -0.02039 -0.12925 -0.15843
BARLEY
0.554016 -0.36243
BEEF
-0.00515 0.264092 -0.06915 -0.42603 0.164575 -0.54513 0.030152 -0.02952 0.4048Th
PORK
-0.15141
CHICKEN
-0.82617 -0.16383 -0.13115 0.881255 -0.24374 -1.08619 0.190836 -0.1439 -0.03083
FISH
-0.29571 0.127649 -0.45233 0.036204 -0.08167 -0.03738 -0.67802 0.115359 -0.41932
EGGS
-0.64412 -0.03319 0.14305 0.739929 0.211869
MILK
-0.59538 0.064117 -0.20107 0.415253 -0.09343 0.038631 0.069944 -0.14862 -1.0891
RICE
0.21665 -1.58461
-0.2085 -0.32302 1.164154
BREAD
HICKSIAN
(COMPENSATED)
FOR
JAPAN
FROM
0.72729
-1.1718 -0.04425 0.260419 -0.14726 -0.53332
PRICE
-0.3999 -0.00754 -0.40942 -0.16161
CHICKEN
PORK
BEEF
BARLEY
-0.6557 1.174882 -0.37192 0.105728
FISH
EGGS
MILK
ELASTICITIES
ALL-PERIOD
SYSTEM
ESTIMATION
-0.0553 0.215542 -0.28488 0.095776 0.104746 -0.03219 -0.02679 0.230822
RICE
-0.35964
BREAD
0.093135 -0.35064 0.105548 0.228358 0.314917 -0.14396 0.180822 -0.08962 -0.11996
BARLEY
0.506017 -0.36538 0.214543 -1.59018
BEEF
0.092832 0.270116 -0.06485 -0.41467 0.176228 -0.53829 0.073396 -0.02101 0.413146
PORK
0.540438 -0.16596 -0.29264 1.244406 -1.08952 0.004042 0.56577 -0.08712 -0.47494
CHICKEN
0.070866 -0.10868 -0.09176 0.985308 -0.13705 -1.02358 0.586751 -0.06592 0.044865
FISH
0.571264 0.180951 -0.41426
EGGS
-0.29624 -0.01181 0.158326 0.780282 0.253242 -0.37562 0.146001 -0.37918 -0.13225
MILK
0.300199 0.119177 -0.16174 0.519137
RICE
BREAD
BARLEY
-0.6614 1.171532 -0.39311 0.101556
0.13677 0.021438
BEEF
0.72324
0.02313 -0.29537 0.190724 -0.34616
0.01308 0.101137 0.465213 -0.07077 -L01353
ORK
CHICKEN
FISH
EGGS
MILK
233
Table 7-5b: Price Elasticity Matrix From System
Estimation - Japan Pre-war period Sample
MARSHALLIAN
PRICE
(UNCOMPENSATED)
JAPAN
FOR
FROM
PRE-WAR
ELASTICITIES
PERI0
ESTIMATION
SYSTEII
RICE
-0.95687 -0.06931 0.087999
BREAD
-0.77464 -1.35892 0.331025 0.741625 0.185996 -0.56011 -0.12247 -0.46618 -0.04987
BARLEY
-0.43126 -0.25215 0.080578
BEEF
-0.53743 0.664774 0.473879 -1.32821 0.336155 -0.17772 -0.03945 -0.23549 0.459752
PORK
-0.67315 0.462352 0.572235 -1.18024
-0.5029 0.710728 0.336012 -0.37284 0.200494
CHICKEN
-0.16468 0.114786 0.580167 -0.19989
0.13842 -0.62985 -0.30193 0.732389 0.340983
FISH
0.128999 0.108425 -0.43295 -0.38816 -0.12902 0.034277 -0.64007 0.063949 -0.41345
EGGS
-0.46145 -0.22669 0.208525
MILK
BREAD
HICKSIAN
BARLEY
BEEF
JAPAN
FROM
BARLEY
0.96882 0.180389
-0.3405 -0.05652
PORK
PRICE
(COMPENSATED)
FOR
BREAD
0.06724 0.033725
0.01718 -0.30155 -0.36456 0.174561
-0.4322 0.117907 0.136174 -0.24082
0.00828 -0.36492 -0.14141 1.371447 -0.06392 -0.00704 -0.21068 -0.02949 -0,41181
RICE
RICE
0.15871 0.018646 0.000213 -0.15559 -0.03583 0.153703
PRE-WAR
SYSTEM
CHICKEN
EGGS
MILK
ELASTICITIES
PERI
ESTIMATION
0.12197 0.196755 0.030448 0.012263 0.145385 -0.01294 0.163822
0.136521 -1.34002 0.381242 0.797864 0.203442
0.12327 -0.24065
FISH
-0.5423
0.32244 -0.43234 -0.03491
0.11114 0.101467 0.044344 0.028021 -0.03077 -0.34396 0.183665
BEEF
-0.59788
PORK
-0.61921 0.463471 0.575208 -1.17691 -0.50186 0.711783 0.362351 -0.37083 0.201379
CHICKEN
-0.39658 0.109976 0.567386 -0.21421
FISH
0.825091 0.122865 -0.39458
EGGS
-0.49381 -0.22736 0.206742 0.966823 0.179769 -0.43283 0.102111 0.134973 -0.24135
MILK
0.174889 -0.36147 -0.13223 1.381731 -0.06073 -0.00379 -0.12933
RICE
0.66352 0.470548 -1.33194 0.334998
BREAD
ARLEY
-0.1789 -0.06897 -0.23773 0.458759
0.13398 -0.63439 -0.41516 0.723777 0.337176
-0.3452 -0.11569 0.047885 -0.30018 0.089802 -0.40202
BEEF
PORK
CHICKEN
FISH
-0.0233 -0.40907
EGGS
MILK
234
Table 7-Sc: Price Elasticity Matrix Prom System
Estimation - Japan Mid-period Sample
MARSHALLIAN
FOR
FROM
RICE
BREAD
-0.92749
ELASTICITIES
PRICE
(UNCOMPENSATED)
MI0-PERI
JAPAN
ESTIMATION
SYSTEM
-0.1141 0.375465 -0.17774 0.249976 -0.27919 -0.33724 0.11432 0.274236
-0.3822 -0.17824 -0.21652 0.022171 0.101387 0.334726 0.142977 0.000668 -0.65064
BARLEY
-0.26472 -1.93816 2.275799 -0.66371 0.076381 -1.00774 -1.03017 -0.78331 3.397565
BEEF
-0.39382 0.632228 0.573249 -1.18264 0.223691 -0.43662 -0.05022
PORK
-0.49955 -0.58887 0.518728 0.832864 -1.01472 -0.48355 -0. 12321 0.064012 -0.14785
CHICKEN
-1.69779 -0.61931 0.821649 0.468205 0.173713 -1.64709 -0.08015 0.066377 0.97194
FISH
0.598468 0.429065 -1.75564 0.133465 -0.80129 1.290878 0.114743
-0.4342 1.443495
-(L3929 -1.45734
EGGS
-0.9036 0.247032
-0.2143 0.818226 -0.10448 -0.34438 0.071239 -0.04772 -0.35266
MILK
-0.43597 0.052686
0.24842 0.164028 0.134247 -0.30142 -0.10848 -0.09057 -0.5997
RICE
BREAD
HICKSIAN
BARLEY
BEEF
(COMPENSATED)
FOR
FROM
JAPAN
PORK
PRICE
CHICKEN
FISH
EGGS
MILK
ELASTICITIES
MID-PERIOD
SYSTEM
ESTIMATION
RICE
-0.52011
-0.0921 0.392589 -0.14821 0.281913 -0.26264 -0.19451 0.151638 0.303187
BREAD
0.187043
-01475
BARLEY
-0.25683 -1.93773 2.276131 -0.66314
BEEF
-0.68074 0616736 0.561188 -1.20344 0.201197 -0.44827 -0.15075 -0.46048 1.423105
PORK
0.307553 -0.54529 0.552655 0.891369 -0.95145 -0.45077 0.159558 0.137948 -0.09049
CHICKEN
-0.82705 -0.57229 0.858252 V.531323 0.241977 -1.61172 0.224911 0126143
FISH
1.712148 0.489198 -1.70883 0.214194 -0.71398 1.33611 0.504924 -0.29088 -1.37819
EGGS
-0.30168 0.279533
MILK
0.104315 0.081859 0.271132 0.203193 0.176605 -0.27947 0.080807 -0.04108
RICE
BREAD
-0.1926 0.063434 0.146015 0.357846 0.342412 0.052814 -061019
0.077 -1.00742
-1.0274 -0.78258 3.398126
1.03382
-0.189 0.861859 -0.05729 -0.31994 0.282124 0.007419 -0.30988
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
-0.5613
MILK
235
Table 7-5d: Price Elasticity Matrix From System
Estimation - Japan Post-var Period Sample
MARSHALLIAN
(UNCOMPENSATED)
FOR
JAPAN
FROM
ELASTICITIES
PRICE
POST-WAR
PERI
ESTIMATION
SYSTEM
RICE
-0.11374 0.148761 -0.20574 -0.23014 -0.02742 0.473634 -0.22569 -0.01633 -0.21595
BREAD
0.063008
0.06226 -0.04479 0.249632 -0.04614 -0.0427 0.250527 -0.01123 -0.38858
BARLEY
0.326376
-5.1279 3.271525 -0.82078 -1.12005 -0.09612 0.284314 0.339101 6.647374
BEEF
0.141691 -0.03529 -0.43711 -0.94841 0.847201 -0.50308 0.738973 0.104274 0.536958
PORK
0.9594Th
CHICKEN
-0.53356 -0.28609 1.098882 0.792541
-0.4915 -0.23842 0.988525 -0.96219 0.068217 0.691256 0.077525 0.366308
0.20368 -1.63044
0.78338 -0.35328 0.665547
FISH
-2.2563 0.000598 0.008655
EGGS
-1.00693 0.451632 0.395474
MILK
-0.38587 -0.06924 0.066191 0.279096 0.141424 -0.29542 0.418235 -0.16814 -0.97955
RICE
BREAD
HICKSIAN
BARLEY
-0.5954 -0.41865 -0.34446 -1.44768 0.017117 -0.39352
0.54092
BEEF
(COMPENSATED)
FOR
JAPAN
FROM
0.15898
PORK
PRICE
POST-WAR
SYSTEM
-0.9203 -0.65255 -0.73114
CHICKEN
FISH
EGGS
-0.4519
MILK
ELASTICITIES
PERI
ESTIMATION
RICE
0.060153 0.167436
BREAD
0.123233 0.068727 -0.04291
BARLEY
-0.68594 -5.23662 3.239861
BEEF
-0.21513 -0.07361 -0.44827 -1.01162 0.764646 -0.54815 0.600325 0.053237 0.47952
PORK
0.437611 -0.54755 -0.25474 0.896082 -1.08203 0.002245 0.488475 0.00288 0.282302
CHICKEN
-0.58971 -0.29212 1.097126 0.782595 0.190689 -1.63754 0.761562 -0.36131 0.656508
FISH
-0.12345 0.229651 0.075367 -0.21759 007481 -0.07484 -0.61893 0.322187 -0.05019
EGGS
-003688 0.555808 0.425816 0.712755 0.383414 -0.79767 -0.27562 -0.61239 -0.29575
MILK
0.07912 -0.01931 0.080735 0.361464 0.249005 -0.23664 0.598915 -0.10163 -0.90469
RICE
BREAD
-0.2003 -0.19933 0.012818 0.495617 -0.15812 0.008546 -0.18796
BARLEY
0.2603
-0.0322 -0.03509 0.273928 -0.00261 -0.37888
-1.0001 -1.35426 -0.22409 -0.10904 0.194305 6.484419
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
236
Table 7-5e: Price Elasticity Matrix Prom System Estimation
- Korea Sample
IIARSHALLIAN
FOR
FROM
ELASTICITIES
PRICE
(UNCOMPENSATED)
KOREA
ESTIMAT1ON
SYSTEM
RICE
-0.76841 0.047397 -0.17402 0.028859 -0.01879 -0.06967 -0.26566 -0.14894
WHEAT
-0.23497 -0.03184 0.494663
BARLEY
-0.53961 -0.21048 -0.04938 -0.52201 -0.52434
0.30053
-0.2279 0.972431 0.056219 -1.03753
0.77435
0.16489 0.013172
BEEF
0.98015 -0.78845 0.279758 -0.34079 0.259055 -1.25142 0.921967 1.275483
PORK
-0.44468 -0.46938 -0.25923 0.252346 -0.86821 0.133539 -0.31535 0.191607
CHICKEN
0.194587 -0.52096 0.735031 0.736174 -0.79484 0.306187 -0.14558 -0.24485
FISH
-1.01677 0.274537 -0.25559 -0.57119 0.359495 -0.04357 -0.57405 -0.08863
EGGS
0.61546 -0.17115
RICE
WHEAT
HICKSIAN
-0.1724 0.124372 0.223361 -0.63528 0.460294
BARLEY
BEEF
PORK
PRICE
(COMPENSATED)
FOR
FROM
CHICKEN
EGGS
ELASTICITIES
KOREA
SYSTEM
ESTIMATION
RICE
-0.02061 0.140204 -0.05325 0.137725
WHEAT
-0.66689 -0.08544
BARLEY
-0.24412
BEEF
0.582092 -0.83785 0.215472 -0.39874 0.211085
PORK
FISH
0.33985
0.07133 -0.04247
-0.077
-0.0942
0.42491 0.237651 -0.27995 0.956723 -0.05275 -1.06914
-0.1738 -0.00165 -0.47899 -0.48873 0.785096 0.239439 0.034799
0.53077 -0.34832
-1.2659 0.821542 1.246349
-0.1017 0.394354 -0.75066 0.169013 -0.06925 0.262999
CHICKEN
0.228612 -0.51674 0.740526 0J41127 -0J9074 0.307425
FISH
-0.18276 0.378042
EGGS
0.435578 -0.19347 -0.20145 0.098185 0.201683 -0.64182 0.414912 0.326685
RICE
WHEAT
-0.1209 -0.44977
BARLEY
BEEF
-0.137 -024236
0.46 -0.01324 -0.36364 -0.02759
PORK
CHICKEN
FISH
EOGS
237
Table 7-5f: Price Elasticity Matrix From System
Estimation - Taiwan Sample
MARSHALLIAN
ELASTICITIES
(UNC4PENSENTED) PRICE
FOR
FROM
TAIWAN
SYSTEM
ESTIMATION
RiCE
-0.70024 0.650404 0.002513 -0.21664 0.162976 -0.86654 -0.16551 -0.08487
WHEAT
-2.32878 1.431723 0.140674 -1.12174 0.951021 -1.71819 -1.45131 0.741996
BEEF
-0.75959 1.828321 -1.65553 0.190912 -0.72859 1.357453
PORK
-0.64667 0.43102 -0.26902
CHICKEN
0.510657 -0.38761 -0.09321 0.431903 0.114559
FISH
0.483727 -0.91732 0.156578 0.208689 -0.55998 -0.04374 0.142253 0.070355
EGGS
-0.78094 0.428999 -0.23168
MILK
-0.76657 0.899294 0.764286 0.063086 0.473981 -2.56217 -0.72156 -0.94653
RICE
WHEAT
HICKSIAN
BEEF
-1.0029 0.375074 -0.74613 0.099912 -0.21253
0.76523 0.335529 0.025104
-0.3897 0.058898 0.177504 -0.18377 0.348833
FISH
CHICKEN
PORK
(COMPENSATED)
PRICE
EGGS
MILK
ELASTICITIES
TAIWAN
FOR
FROM
-0.2059 -0.99192
SYSTEM
ESTIMATION
RICE
-0.37429 0.728079 0.020776 0.111836 0.253503 -0.25049 -0.13178 -0.07388
WHEAT
-1.42998 1.645908 0.191034
BEEF
-0.61029 1.863899 -1.64716 0.341365 -0.68713 1.639626 -0.19045 -0.98688
PORK
-0.26775 0.521317 -0.24779 -0.62105 0.480313 -0.02998 0.139127 -0.19976
CHICKEN
0.246574 -0.45054 -0.10801 0.165777 0.041215 0.266115 0.308199 0.016203
FISH
0.539533 -0.90402 0.159705 0.264928 -0.54448 0.061736 0.148029 0.072236
EGGS
-0.59933 0.472275 -0.22151 -0.20669 0.109336 0.520736 -0.16498 0.354954
MILK
-0.21109 1.031666
RICE
WHEAT
-0.216 1.200646 -0.01947 -1.35829 0.772289
0.79541 0.622863 0.628256 -1.51232 -0.66408 -0.92781
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
238
Table 7-6a:
Price Elasticity Matrix From Non-System
Estimation - Japan All-period Sample
MARSHAILIAN
(UNCOMPENSATED)
FOR
FROM
ELASTICITIES
PRICE
ALL-PERI)
JAPAN
NON-SYSTEM
ESTIMATION
RICE
-0.19814 0.014139 0.19994 -0.23115 0.139879 0.268795 -0.07541
BREAD
-0.48561 -0.38342 0.185077 0.09678 0.270823
-0.1323 -0.08369 -0.12781 -0.10492
BARLEY
-0.76669 -0.71541 0.354513 -1.11789 -0.47098
0.16219 -0.41638
BEEF
0.534669 0.329184 -0.12735 -0.34983 0.248858 -0.11405 0.168746 -0.08053 -0.02286
PORK
-0.31641 -0.24848 -0.28561 0.992585 -1.18017 -0.03467
0.21786 -0.11081 -0.44501
CHICKEN
-0.32168 0.159539 0.226955 0.192052 0.149889 -0.87086
0.03245 -0.14453 0.050689
FISH
-0.24407 0.14286 -0.56473 0.087562 -0.14814
0.05645 -0.58121 0.109537 -0.50423
EGGS
-0.50519 0.021393 0.152614 0.678271
-0.2542 0.061833
MILK
0.2023
-0.1102 0.075575
-0.0245
0.81777
-0.4085 -0.18519
-0.2797 0.058758 0.117359 0.079351 0. 127016 -0.04359 0.013286 -0.05837 -0.76568
RICE
BREAD
HICKSIAN
BARLEY
BEEF
(COMPENSATED)
FOR
FROM
APAN
CHICKEN
PORK
PRICE
FISH
EGGS
ELASTICITIES
ALL-PERIOD
NON-SYSTEM
ESTIMATION
RICE
0.033549 0.028374 0.209915 -0.20427 0.167437 0.284917 0.026859 -0.09006
BREAD
0.002245 -0.35345 0.206081 0.153391
BARLEY
-0.00417 -0.66856 0.387342 -1.02941 -0.38029 0.215245 -0.0798 0.041797
0.68225 0.338251
-0.121
MILK
0.32885 -0.09836 0.131647
0.09512
-0.0854 -0.06376
0.8821
-0.3327 0.266412 -0.10378 0.233889
-0.0677 -0.01041
PORK
0.310198 -0.21011 -0.25871 1.065065 -1.10588 0.008788 0.493564
-0.0565 -0.39232
CHICKEN
0.067351
-01107
FISH
0.397908 0.182303 -0.53709 0.161858 -0.07178 0.099119 -0.29784 0165354 -0.45007
EGGS
-0.14331 0.043626 0.168194 0.720264 0.245342 -0.22902 0.221565 -0.37704 -0.15466
MILK
0174878 0.086687
BEEF
RICE
0.18344 0.143704 0.237196 0.196162 -0.84379 0204172
BREAD
0.08351
0.13693 0.132101 0.181085 -0.01196 0.21394 -0.01884 -0.72733
BARLEY
GlEE
PORK
CHICKEN
FISH
EGGS
MILK
239
Table 7-6b: Price Elasticity Matrix From Non-System
Estimation - Japan Pre-war Period Sample
MARSHALLIAN
(IJNcOMPENSATED)
FOR
JAPAN
FROM
PRE-WAR
NON-SYSTEM
ELASTICITIES
PRICE
PERI
ESTIMATION
RICE
-1.03114 -0.06135 0.126187 0.107925 0.038826 -0.00656 -0.18902 -0.04747 0.183217
BREAD
0.157427 -0.41745
BARLEY
0.400805 -0.34957 -0.12132 0.161514 -0.00162 0.330927 -0.03948 -0.36799 -0.27181
BEEF
-0.13611 0373186 0.239181 -0.26728 0.30453 -0.39749 0.093919 0.026011 0.031653
PORK
-0.67384
CHICKEN
-0.24327 0.502288 0.942245 -1.26882 0.337706 -1.16736 -0.33769 0.738895 1.038288
FISH
-0.10309
EGGS
-0.45902 -034277 0.327835 0406679 0.176867 -0.36889 0.184717 0.098913 -0.02073
MILK
0.200263 -0.28423
RICE
0.06926 0.316582 0.338297 -0.07339 -0.08405 -0.41749 -0.42257
0.64294 0.518062 -0.80563 -0.42727 0.336998 0.340611 -0.27575 0.159597
0.21613 -0.63101 0.294805 -0.18369 0.229527 -0.76623 0.010154 -0.58322
BREAD
HICKSIAN
0.15509 0.165746 0.065878 -0.13447 -0.28145 -0.23553 0.294289
BARLEY
BEEF
PORK
(COMPENSATED)
FOR
FROM
JAPAN
PRICE
PRE-WAR
NOW-SYSTEM
CHICKEN
FISH
EGGS
MILK
ELASTICITIES
PERIOD
ESTIMATION
RICE
-0.38857 -0.04802 0.160985 0.147603 0.051133 0.006084 0.124759
BREAD
0.907932 -0.40188 0.109902 0.362925 0352672 -0.05862 0282438 -0.38961 -041002
BARLEY
0.513172 -0.34723 -0.11523 0168452 0.000528 0.333139 0.015393 -0.36381 -0.26993
BEEF
-0.35723 0.368599 0.227207 -0.28093 0.300295 -0.40184 -0.01405 0.017797 0.027954
PORK
-0.60813 0.644303
CHICKEN
-0.63862 0.494087 0.920836 -1.29323 0.330133 -1.17514 -0.53074 0.724208 1.031674
FISH
3.806039
EGGS
-051256 -0.34388 0.324935 0.403372 0.175842 -0.36994 0.158569 0.096924 -0.02163
MILK
0.184126 -0.28456 0.154217 0164749 0.065569 -0.13479 -0.28933 -0.23613 0.294019
RICE
-0.0236 0.193966
0.52162 -0.80157 -0.42602 0.338291 0.372695 -0.27331 0.160697
0.23499 -0.58178 0.350942 -0.16628 0.247423 -0.32229 0.043927 -0.56801
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
240
Table 7-6c: Price Elasticity Matrix From Non-system
Estimation - Japan Mid-period sample
MARSHALLIAN
(LJNCOMPENSATED)
FOR
FROM
RICE
JAPAN
PRICE
ELASTICITIES
MID-PERIIDO
NON-SYSTEM
ESTIMATION
-0.9111 -0.14514 0.491245 0.050954 0.38471 -0.49321 -0.31496 0.064135 0.40258
BREAD
-0.03517 0.048485 -0.15215 0.405736 0.466698 -0.42437 0.274575 0.153901 -0.53721
BARLEY
-0.77643 -1.61042 2.084027 -1.11574 -0.19179 -0.89294 -1.03598
BEEF
0.408559 0.739274 0.879256 -0.67972 0.721458 -1.41062
PORK
-0.57335 -0.59914 0.510633 0.826557 -1.05962 -0.42157 -0.09037 0.034363 -0.09378
CHICKEN
-1.68944 -0.37361 1.172303 0.418409 0.43565 -2.04827 -0.05983 -0.31236 1.592771
F I SN
-0.3925 3.043595
-0.0652 -0.41018 1.874064
1.48479 0.643863 -1.72732 0.194724 -0.50967 0.686979 0.104252 -0.26135 -1.29728
EGGS
-1.15813 0.074986 0.217709 0.555927
MILK
-0.02015 0.073276 -0.07632 0.227468 -0.00329 0.057247 -014044 0.133752 -1.21931
RICE
BREAD
HICKSIAN
BARLEY
(COMPENSATED)
FOR
FROM
RICE
BREAD
BEEF
JAPAN
-0.0921
PORK
PRICE
-0.3815 -0.09934 -0.44312 0.164506
CHICKEN
FISH
EGGS
MILK
OLASTICITIES
MID-PERIOO
NON-SYSTEM
ESTII4ATION
-0.48845 -0. 12231 0.509017 0.081601 0.417057 -0.67604 -0.16988 0.102883 0.433693
0.2807 0.065554 -0.13887 0.428641 0.491671 -0.41153 0.383005
0.18286 -0.51411
BARLEY
-0.24932 -1.58194 2.106192 -1.07752 -0.15045 -0.87152 -0.85503 -0.34417 3.082149
BEEF
0.198067 0.727899 0.870405 -0.69499
PORK
0.097167
CHICKEN
-1.16777 -0.34742 1.194239 0.456237 0.476563 -2.02707 0.119249 -0.26453 1.630927
FISH
1.960239 0.669556 -1.70782 0.229201 -0.47238 0.706299 0.267461 -0.21776 -L26251
EGGS
-0.51088 0.109966 0.244928 0.602845 -0.04134
MILK
0.70695 -1.41917 -0.13745 -0.42948 1.858668
-0.5628 0.538912 0.875323 -1.00687 -0.39424 0.140487 0.096019 -0.0646
-0.3552 0.122861 -0.38377 0.21 1851
0.526984 0.102842 -0.05331 0.267142 0.039615 0.079479 0.047378 0.183912 -1.17929
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
24].
Table 7-6d: Price Elasticity Matrix From Non-System
Estimation - Japan Post-war Period Sample
MARSHALLIAN
(UNCOMPENSATED)
FOR
FROM
JAPAN
PRICE
POST-WAR
NON-SYSTEM
ELASTICITIES
PERIO
ESTIMATION
RICE
-0.13563 0.498744 -0.00016 0.193161 0.191383 -0.72748 -0.29841
BREAD
0.143234 0.551489
BARLEY
-1.18224 -3.79675 1.534394 -1.58112 -0.82437 -0.56139 -0.58815 0.264589
5.54953
BEEF
1.279236 1.557475 -0.73319 -0.51104 0.963257 -2.5637 0.424546 0.230537
-0.9654
-0.0556 -0.38183
-0.098 0.413866 0.155306 -0.94299 -0.04416 -0.08134 -0.64135
PORK
1.16063 0.712932 0.088436 2.058296 -0.34066 -2.89108
CHICKEN
-0.2841 0.456211 1.313911 1.418797 0.53943 -3.33557 054439 -0.49386 0.176478
0.29176 -0.22399 -0.54535
FISH
-2.67405 -1.97702 -0.14706 -1.91334 -1.21391 4.342976 -0.90018 0.061796 0.936084
EGGS
-1.26981
MILK
-0.36964 -0.08748 -0.03578 0.199792 0.139279 -0.22258 0.353434 -0.13114 -0.88132
RICE
-0.3183 0.135748 -0.11613 -0.15796 0.539081 -0.37419 -0.46572 0.208139
BREAD
HICKSIAN
BARLEY
(COMPENSATED)
FOR
FROM
RICE
BREAD
BEEF
JAPAN
PORK
PRICE
POST-WAR
NON-SYSTEM
CHICKEN
FISH
EGGS
MILK
ELASTICITIES
PEJUOD
ESTIMATION
0.086947 0.522759 0.006804 0.233298 0.242879 -0.69935 -0.21072 -0.02376
-0.346
0.24477 0.562444 -0.09432 0.432176 0.178798 -0.93016 -0.00415 -0.06682 -0.62501
BARLEY
-0.62877 -3.73704 1.551707 -1.48131 -0.69632 -0.49142
BEEF
1.221881 1.551286 -0.78498 -0.52138 0.949987 -2.57095 0.401949 0.222333 -0.97463
PORK
0.660516
CHICKEN
-0.40794 0.442849 1.310037 1.396465 0.510777 -3.35123
FISH
-0.50243
-1.7427 -0.07914 -1.52173 -071147
4.6175 -0.04462 0.372412 1.285654
EGGS
-0.17364 -0.21049 0.167002 0.064053 0.075211
0.66539 0.019462 -0.32281 0.368978
MILK
0.077872 -0.03919 -0.02179 0.280491 0.242815
-0.166 0.529742 -0.06713 -0.80928
RICE
0.65897 0.072792 1.968111 -0.45637
BREAD
BARLEY
BEEF
PORK
-0.3701 0.343755 5.638623
-2.9543 0.094727 -0.29552 -0.62585
CHICKEN
0.4956 -0.51157 0.156543
FISH
EGGS
MILK
242
Table 7-6e: Price Elasticity Matrix From Non-System
Estimation - Korea Sample
MARSHALLIAN
(UNCOMPENSATED)
FOR
FROM
PRICE
ELASTICITIES
KOREA
ESTIMATION
NON-SYSYTEM
-0.166 0.493976 -0.34391
-0.4905
RICE
-1.04639 0.103736 -0.10153 -0.33828
WHEAT
-0.26184 1.515148 0.078244 3.986146 0.430448
BARLEY
-0.46398 0.710416 0.061933 1.770081 0.002171 2.51581 1.636001 1.565071
BEEF
0.456396 0.095712
PORK
0.338108 -1.23994 -0.45613 -0.75302 -0.76654 1.009447 -1.13294
CHICKEN
-0.03063 0.35403 0.312752
FISH
-0.33128 -0.61733 -0.18257 -1.52414 0.310065 1.384536 -1.62083 -0.67009
EGGS
0.106947 0.317085 -0.19568 1.234602 0094149 -1.71334
RICE
WHEAT
HICKSIAN
-4.0111 2.199648 1.277598
0.06873 1.685116 0.351584 -3.59944 2.021433 2.408094
2.41418 -0.25287 -2.38661 1.162812 0.965131
(COMPENSATED)
FOR
FROM
CHICKEN
PORK
BEEF
BARLEY
-0.0397
PRICE
0.9947 0.894582
FISH
EGGS
ELASTICITIES
KOREA
NON-SYSYTEM
ESTIMATION
RICE
-0.35098 0.190156 0.010932 -0.23691 -0.08205 0.519302 -0.16426 -0.43953
WHEAT
-1.10671 1.410156 -0.05838 3.862985 0.328453 -4.34187 1.981383 L215683
BARLEY
-0.67721 0.683918 0.027451 1.738997 -0.02357 -2.52358 1.580914 1.549444
BEEF
0.102717 0.051698 0.011455 1.633486 0.308827 -3.61234 1.929934 2382138
PORK
1.311339 -1.11899 -0.29874 -0.61115 -0.64905 1.044891 -0.88151 0.031618
CHICKEN
-0.19962
FISH
0.630152 -0.49785 -0.02709 -1.38398 0.426131
EGGS
0.095277 0.315635 -0.19757 1.232901 0.092741 -1.71376 0.991686 0.893727
RICE
0.33303 0.285425 2.389547 -0.27327 -2.39276 1.119157 0.952748
WHEAT
BARLEY
8EEF
PORK
1.41955 -1.37245 -0.59963
CHICKEN
FISH
EGGS
243
Price Elasticity Matrix From Non-System
Table 7-6f:
Estimation - Taiwan Sample
MARSHALLIAN
(UNCOMPENSENTED) PRICE
FOR
ELASTICITIES
TAIWAN
FRI NON-SYSYTEM
ESTIMATION
RICE
-0.28975 0.310265 0.066929 -0.10176 0.099698 -0.59114 -0.10923 -0.07081
WHEAT
-0.56013 -0.79097 0.272621 -0.13423 -0.20755 -0.93866 -0.62727 0.907454
BEEF
-0.06898 2.124238 -1.66007 0.283116 0.411342 1.717819 -0.20949 -1.90745
PORK
-0.19654 -0.25208
CHICKEN
-0.65607 1.291133 -0.06639 0.254963 0.67792 0.103076 -0.31871 -0.25396
FISH
-0.22702 -0.12704 0.057551 -0.20386 -0.24976 -0.23728 -0.09355 0.061558
EGGS
-0.88567 0.612324 -0.05795 0.465318 0.124596
MILK
0.436702 -0.28524 0.632927 0.205236 0.371092 -1.01077 -0.08573 -1.04365
RICE
WHEAT
NICKSIAN
-0.2277 -0.79744 0.106987 -0.79201 0.322315 -0.06584
BEEF
CHICKEN
PORK
(C0IPENSATED)
FOR
FROM
PRICE
-0.3352
FISH
-0.3688 -0.21453
EGGS
MILK
ELASTICITIES
TAIWAN
NON-SYSYTEM
ESTIMATION
RICE
-0.04353 0.368939 0.080779 0.145815 0.168084 -0.12168 -0.08372 -0.06257
WHEAT
0.422878 -0.55672 0.327917 0.854223 0.065475 0.935641 -0.52545 0.940591
BEEF
-0.3377 2.060201 -1.67519 0.012909 0.336705 1.205452 -0.23732 -1.91651
-0.2009 -0.31846 0.239291
0.11623 0.371655 -0.04978
PORK
0.279809 -0.13856
CHICKEN
-1.02289 1.203719 -0.08702 -0.11389 0.576037 -0.59633
-0.3567 -0.26633
FISH
-0.0044 -0.07398 0.070074 0.019998 -0.18792 0.187202 -0.07049 0.069062
EGGS
-0.39683 0.728815 -0.03045 0.956857 0.260369 0.596855 -0.31817 -0.19805
MILK
0590437
RICE
-0.2486 0.641574 0.359822 0.413792 -0.71765
WHEAT
BEEF
PORK
CHICKEN
FISH
-0.0698 -1.03847
EGGS
MILK
244
Table 7-la: Marshallian Own Price Elasticity Estimates
Summary - System Estimation
FROM
ESTIMATION
SYSTEM
JAPAN ALL-PERIOD:
RICE
BREAD
BARLEY
BEEF
-0.68901 -0.37867 0.21665 -0.42603
(-)
C-)
(+)
(-)
PORK
CHICKEN
FISH
EGGS
-1.1718 -1.08619 -0.67802 -0.40942
(-)
(-)
C-)
C-)
MILK
-1.0891
(-)
JAPAN PRE-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
-0.95687 -1.35892 0.080578 -1.32821
(-)
(-)
(+)
(-)
PORK
CHICKEN
FISH
EGGS
MILK
-0.5029 -0.62985 -0.64007 0.136174 -0.41181
(-)
(-)
(-)
(+)
(-)
JAPAN MID-PERIOD:
BREAD
RICE
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
-0.92749 -0.17824 2.275799 -1.18264 -1.01472 -1.64709 0.114743 -0.04772
(-)
C-)
(+)
(-)
C-)
C-)
(+)
(+)
MILK
-0.5997
(-)
JAPAN POST-WAR PERIOD:
BREAD
RICE
-0.11374
(+)
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
0.06226 3.271525 -0.94841 -0.96219 -1.63044 -1.44768 -0.75114 -0.97955
(+)
(4.)
(-)
(-)
(-)
(-)
(-)
(-)
KOREA:
RICE
WHEAT
BARLEY
BEEF
PORK
CHICKEN
FiSH
-0.76841 -0.03184 -0.04938 -0.34079 -0.86821 0.306187 -0.57405
(-)
(-)
(-)
(-)
(-)
(4.)
(-)
EGGS
0.33985
(+)
TAIWAN:
RICE
WHEAT
-0.70024 1.431723
C-)
Note:
(+)
BEEF
-1.65553
(-)
PORK
CHICKEN
FISH
EGGS
MILK
-1.0029 0.114559 -0.04374 -0.18377 -0.94653
(-)
(+)
(+)
(-)
Signs of Hicksian Own Price E'asticities are in parentheses.
(-)
245
Table 7-7b: Marshallian Own Price Elasticity Estimates
Summary - Non-System Estimation
FROM
NON-SYSTEM
ESTIMATION
JAPAN ALL-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
-0.19814 -0.38342 0.354513 -0.34983 -1.18017 -0.87086 -0.58121
(+)
C-)
(+)
(-)
(-)
C-)
C-)
EGGS
MILK
-0.4085 -0.76568
C-)
C-)
JAPAN PRE-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
-1.03114 -0.41745 -0.12132 -0.26728 -0.42727 -1.16736 -0.76623 0.098913 0.294289
(-)
C-)
C-)
C-)
C-)
C-)
C-)
()
(+)
JAPAN MID-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
-0.9111 0.048485 2.084027 -0.67972 -1.05962 -2.04827 0.104252 -0.44312 -1.21931
(-)
(+)
(+)
(-)
(-)
(-)
(+)
(-)
(-)
JAPAN POST-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
-0.13563 0.551489 1.534394 -0.51104 -0.34066 -333557 -0.90018 -0.46572 -0.88132
(+)
(+)
(+)
(-)
(-)
(-)
(-)
(-)
(-)
KOREA:
RICE
WHEAT
BARLEY
BEEF
PORK
CHICKEN
FiSH
EGGS
-1.C-.639 1.515148 0.061933 1.685116 -0.76654 -2.38661 -1.62083 0.894582
(-)
(+)
(+)
(+)
(-)
(-)
(-)
(4)
TAIWAN:
RICE
WHEAT
-0.28975 -0J9097
(-)
Note:
(-)
BEEF
PORK
-1.66007 -0.79744
(-)
(-)
CHICKEN
FISH
0.67792 -023728
(+)
(+)
EGGS
MILK
-0.3688 -1.04365
(-)
Signs of Hicksian Own Price ELasticities are in parentheses.
(-)
246
7.3.1. Own Price Elasticities
Marshallian own price elasticity estimates are
summarized in Tables 7-7.
Based on theoretical a priori
expectations, unreasonable Marshallian own price elasticity
estimates are listed in Table 7-8:
Table 7-8:
Estimates
Unreasonable Marshallian Own Price Elasticity
System Estimation
< -2.0
E*ii > 0
Eu
JT
Commodity
Rice
Bread/Wheat JT, T
Barley
JA,
, JM
Beef
Pork
Chicken
K,T
JM, T
Fish
Eggs
JR, JN ,K
Milk
Frequency of
unreasonable
14
results
Total
Non-System Estimation
E*ii > 0 Eu < -2.0
JA,JT
JM, JT, K
JA,JM, JT,K
,JT
K
T
JM ,JT, K
JM, T
JR, K
JR
15
0
3
18
14
Unreasonable results are where:
"E*ii > 0", Hicksian
own price elasticity is positive, which is theoretically
unacceptable; "Eu
< -2.0", Marshallian own price elasticity
is theoretically consistent but unusually large and more
than 2.0 in absolute value.
The unacceptable elasticities
for either method are typed in bold face.
From this table,
it is concluded that beef, pork, and milk yield fairly
reasonable own price elasticity estimates.
On the other
hand, grains (especially barley) for the three countries,
247
chicken for Korea and Taiwan, eggs for Japan and Korea, and
fish for Japan and Taiwan yield theoretically unacceptable
results.
Overall performance of the system estimation
method is superior in the estimation of own price elasticity
according to the frequency of unreasonable results.
About
two thirds of the unacceptable results are common between
the system and non-system estimates.
This implies that
these unacceptable results are largely due to data problems
and not to the statistical method.82
One possible cause of positive Hicksian elasticity
estimates, for grains in the Japan post-war period, is
higher than average governmental market intervention.
The
Japanese government controlled prices and quantities of
several staple grains (such as rice, wheat, and barley) from
the early 1940's to the early 1960's.83
diminished since the 1950's.
Restrictions have
Governmental control of the
rice market still continues, but to a lesser degree.
Government intervention of grain supplies has stabilized
domestic grain markets.
Demand for grains has shifted
substantially in the post-war period:
Plots of per capita
consumption data (for direct food use, Figure 5-2) imply
82
According to the correlation matrices of variables
for the Part II shown in Chapter 7, the price variables are
highly correlated with each other and the correlation
coefficients are more than 0.9 in most of the samples,
except the Japan pre-war sample where they are slightly
lower.
83
The period of the full government control varies
among commodities.
248
that demand for wheat expanded, demand for rice first
expanded then declined, and demand for barley84 continue
to decline.
Estimates for grain demand in Japan's post-war
period may be the controlled price-supply relationship and
not the price-demand relationship.
If this is the case, the
positive Hicksian own price elasticities are understandable.
For the other unreasonable own price elasticity estimates of
the commodities such as chicken and fish in Taiwan and eggs
in Korea, no specific explanation could be found.85
The reliable Marshallian own price elasticity results
in terms of theoretical consistency and statistical
robustness are sorted out (see Table 7-9).
The own price estimates for pork turn out to be the
most reliable, followed by those for rice, eggs, and milk.
The least reliable case is barley, followed by and chicken
and bread/wheat.
84
85
Except the period just after the war.
One assumption for demand analysis is that an
individual consumer is a price taker, i.e., she faces a
perfectly flat supply curve. However, in practice when
using per capita consumption data derived from aggergete
market information, this assumption may be violated, i.e., a
supply curve having a positive slope. In this case,
identification of a demand curve may be statistically
difficult, and the locus of equilibria (intersection of
demand and supply curves) may be estimated as a positively
sloped curve.
This will be the case when demand curve
shifts are greater than supply curve shifts. SHEI (1983)
reported the substantial expantion of the poultry industry
in Taiwan during the study period, i.e., the supply curve of
chicken largely shifted out in Taiwan. These things suggest
that the demand expantion for chicken in Taiwan during the
period was so rapid as to exceed the supply expansion.
249
Table 7-9:
Estimates
Reliable Marshallian Own Price Elasticity
Commodity
Results Not Appeared in Table 7-8 and The
Difference Between The Two Estimates is
Less Than 0.3
Rice
JR(0.96:l.03); JM(0.93:0.91) ; K(0.77:l.05)
Bread/Wheat
JA (0. 38 : 0 . 38)
Barley
Beef
JA(0.43:O.35); T(l.66:l.66)
Pork
JA(l.17:l.18); JR(0.50:O.43) ; JN(l.0l:l.06);
K(0.87:0.77) ; T(1.00:0.80)
Chicken
JA( 1. 09: 0. 87)
Fish
JA(0.68:0.58); JR(0.64:0.77)
Eggs
JA(0.41:0.41); JT(0.75:0.47); T(0.18:0.37)
Milk
JT(0.98:0.88) ; T(0.95:l.04)
Note:
Marshallian own price elasticity estimates in
absolute terms are in parenthesis, where
Non-system results).
(System Results
Figures are rounded off at two decimal places.
:
250
7.3.2. Cross Price Elasticities
In this section, "net" substitution and complementary
effects shown by Hicksian (compensated) cross price
elasticities are inspected.
Comparing the price elasticity
matrices in Table 7-5 and Table 7-6, robust and/or
numerically significant results are summarized.86
kinds of tables are provided:
Two
one reports the most robust
and numerically significant results (Table 7-10 and 7-12)
and the other reports the results being less robust but
consistently showing numerical significance (Table 7-11 and
7-13).
The criteria are noted below each table.
All
figures are rounded at two decimal places in the tables.
Plant origin foods refer to rice, bread/wheat, and barley,
and animal origin foods are beef, pork, chicken, fish, eggs,
and milk.
The strong substitutionary or complementary
relationships between the plant origin foods and the animal
origin foods are indicated in Tables 7-10 and 7-11 for 20 of
the 45 net substitutionary cases, and in Tables 7-12 and 713 for 25 out of the 40 net complementary cases.
These
results not only support a common belief that animal origin
foods substitutes for plant origin foods under economic
development, but also bring our attention to the existence
86
The non-system estimation results for the Japan
post-war period and Korea contain many unreasonably large
cross-price elasticities; also, the Korea results show the
least consistency between the system and non-system
estimates.
251
of broader complementary relationships between the two
groups of foods.87
87
This phenomenon may largely be influenced by food
eating styles in the three countries. Usually, staple
grains (rice, wheat, or barley) and main and side dishes
(meats and fish for main dishes and vegetables for side
dishes), generally are served at one time, but the
proportion of them in one meal varies largely. This is not
the case for hamburgers, in which bun and patty are always
in relatively fixed proportions.
In Japan, Korea, and
Taiwan, staple grains are consumed in equal amounts
However, main and
(depending on person) three times a day.
side dish quantities vary largely from day to day, and from
family to family depending on various economic and noneconomic reasons. The relationships between staple foods
and foods for main or side dishes may change for various
income levels, which are described as follows:
At low
income level in which people fulfill their nutrition
requirements first, the proportion of staples is higher than
that of other foods; other foods do not substitute for
staples so much during this stage. As people become
wealthier, they want to eat more of other foods. At the
same time, staple food consumption also increases and total
caloric intake increases.
Staples and other foods
At higher income
complement each other during this stage.
levels, due to an upper limit of physiological requirement,
caloric intake stops increasing, and the proportion of other
foods increases.
Staples and other foods substitutes for
each other during this stage.
Therefore, not only
substitution but complementary relationships need to be
considered for the same combination of food items as rapid
economic development occurs.
252
Table 7-10: Hicksian Cross Price Elasticities
Consistent and Significant Results of Substitutionary
Relationships
NET SUBSTITUTES
Commodity
Combination
Sainle
System Estimates: Non-System Estimates
Row! Coluinn*
Between Plant Origin Foods and Animal Origin Foods
Pork/Rice
JT
0.44
Fish/Rice
Fish/Rice
JR
3M
0.83
1.71
Beef/Bread
Beef/Bread
Beef/Wheat
JR
JN
:
T
0.66
0.62
1.86
:
0.37
0.73
2.06
Pork/Bread
JR
0.46
:
0.64
Fish/Bread
3M
0.49
:
0.67
Eggs/Wheat**
T
0.47
:
0.73
Wheat/Milk
T
0.77
:
0.94
Pork/Barley
Pork/Barley
JR
3M
0.58
0.55
Chicken/Barley
JT
Barley/Milk
JA
Note:
:
:
:
:
0.66
0.81
1.96
:
0.52
0.54
1.10
:
1.31
0.72
:
0.88
:
The table contains the pairs satisfying:
the difference between the two estimates is less
than 0.3, and
either one of the results is greater than 0.5 in
absolute value.
* - Rows and columns are those of the original price
elasticity matrices in Table 7-5 and 7-6.
** - Violating symmetry condition.
253
Table 7-10: Ricksian Cross Price Elasticities
Consistent and Significant Results of Substitutionary
Relationships (Cont..)
NET SUBSTITUTES
Commodity
Combination
Sample
System Estimates: Non-System Estimates
Row! Column*
Between Plant Origin Foods and Plant Origin Foods
Rice/Barley
JM
0.39
:
0.51
Between Animal Origin Foods and Animal Origin Foods
Pork/Beef
Pork/Beef
Beef/Pork
JA
JM
JT
1.24
0.89
0.76
Chicken/Beef
JM
Beef/Fish
:
1.07
0.88
0.95
0.53
:
0.46
JT
0.60
:
0.40
Pork/Fish
JA
0.57
:
0.49
Chicken/Fish
JT
0.76
:
0.50
Eggs/Beef
Eggs/Beef
JA
JN
0.78
0.86
Chicken/Eggs
JR
Eggs/Fish
:
:
:
0.72
0.60
0.72
:
0.72
T
0.52
:
0.60
Milk/Beef**
T
0.80
:
0.64
Milk/Pork
T
0.62
:
0.36
Milk/Chicken
T
0.63
:
0.41
Milk/Fish
JT
0.60
:
0.53
Note:
:
The table contains the pairs satisfying:
the difference between the two estimates is less
than 0.3, and
either one of the results is greater than 0.5 in
absolute value.
* - Rows and columns are those of the original price
elasticity matrices in Table 7-5 and 7-6.
** - Violating symmetry condition.
254
Table 7-11: Hicksian Cross Price Elasticities Other
Significant Results of Bubstitutionary Relationships
NET SUBSTITUTES
Commodity
Combination
Sample
System Estimates: Non-System Estimates
Row! Column*
Between Plant Oriqin Foods and Animal Origin Foods
Pork/Rice
K
0.53
Beef /Barley**
JM
0.56
Chicken/Barley
JR
Chicken/Barley** JM
0.57
0.86
Barley/Milk
Barley/Milk
3.40
6.48
JM
JT
:
1.31
0.87
:
:
0.92
1.19
3.08
5.64
Between Animal Origin Foods and Animal Origin Foods
Pork/Beef
JT
0.90
1.97
Chicken/Beef **
JT
K
0.78
0.74
1.40
2.39
Beef/Fish
Beef/Fish
K
T
0.82
1.64
1.93
1.21
Fish/Chicken
JM
1.34
0.71
Beef/Eggs
K
1.25
2.38
Beef/Milk
JM
1.42
1.86
Chicken/Milk
JM
1.03
1.63
Chicken/Beef**
Note:
The table contains the pairs not shown in Table 7-10
but that both of the estimates are greater than 0.5
in absolute value.
* - Rows and columns are those of the original price
elasticity matrices in Table 7-5 and 7-6.
** - Violating symmetry condition.
255
Table 7-12: Hicksian Cross Price Elasticities Consistent
and Significant Results of Complementary Relationships
NET COMPLEMENTS
Commodity
Combination
Sample
System Estimates: Non-System Estimates
Row/ Column*
Between Plant Origin Foods and Animal Oriclin Foods
Beef/Rice
Beef/Rice
JR
T
-0.60
-0.61
Pork/Rice
JR
-0.62
Chicken/Rice
Chicken/Rice
JR
JT
-0. 59
-0.64
-0.41
Eggs/Rice
Eggs/Rice
Eggs/Rice
JR
JM
T
-0.49
-0.30
-0.60
-0.51
-0.51
-0.40
Pork/Bread
JM
-0. 55
-0.56
Chicken/Bread
JM
-0.57
-0.35
Bread/Milk
Bread/Milk
JN
JT
-0.61
-0.38
-0.51
-0.63
Beef/Barley
JT
-0.45
-0.73
Barley/Pork
JA
-0. 66
-0.38
Barley/Chicken** JM
-1.00
-0.87
Fish/Barley
Fish/Barley
Barley/Fish
Fish/Barley
-0.41
-0.39
-1.03
-1.71
-0. 54
-0. 58
Note:
JA
JR
JM
JN
-0.40
-0.36
-0.34
:
-0.61
-0.86
-1.71
The table contains the pairs satisfying:
the difference between the two estimates is less
than 0.3, and
either one of the results is greater than 0.5 in
absolute value.
* - Rows and columns are those of the original price
elasticity matrices in Table 7-5 and 7-6.
** - Violating symmetry condition.
256
Hicksian Cross Price Elasticities Consistent
Table 7-12:
and Significant Results of Complementary Relationships
(Cont.)
NET COMPLENENTS
Conunodity
Combination
Sample
System Estimates: Non-System Estimates
Row/ Column*
Between Plant Origin Foods and Plant Origin Foods
Barley/Rice
JT
-0.69
-0.63
Between Animal Origin Foods and Animal Oriqin Foods
Chicken/Fish
JR
-0.42
0.53
Chicken/Eggs
JT
-0.36
-0.51
Fish/Pork
JN
-0.71
-0.47
Fish/Milk
Fish/Milk
JR
JM
-0.40
-1.38
-0.57
-1.26
Note:
The table contains the pairs satisfying:
the difference between the two estimates is less
than 0.3, and
either one of the results is greater than 0.5 in
absolute value.
* - Rows and columns are those of the original price
elasticity matrices in Table 7-5 and 7-6.
257
Table 7-13: Hicksian Cross Price Elasticities Other
Significant Results of Complementary Relationships
NET COMPLEMENTS
Commodity
Combination
Sample
System Estimates: Non-System Estimates
Row I Co lumn*
Between Plant Oriclin Foods and Animal Origin Foods
Chicken/Rice
JM
-0.83
Wheat/Eggs**
T
-1.36
Barley/Beef
Barley/Beef
JA
JM
JT
-1.59
-0.66
-1.00
-1.03
-1.08
-1.48
Barley/Pork
JT
-1.35
-0.70
Barley/Beef **
-1.17
:
-0.53
Between Plant Oriqin Foods and Plant Origin Foods
Wheat/Rice
K
-0.67
Barley/Bread
Barley/Bread
314
-1.94
-5.24
JT
:
-1.11
-1.58
-3.74
Between Animal Origin Foods and Animal Origin Foods
Pork/Beef
JR
-1.18
Beef/Chicken**
Beef/Chicken**
JT
K
-0.55
-1.27
Beef/Milk**
T
-0.99
Eggs/Chicken
K
-0.64
Milk/Fish
T
-1.51
Note:
:
:
:
-0.80
-2.57
-3.61
The table contains the pairs not shown in Table 7-12
but that both of the estimates are greater than
0.5 in absolute value.
* - Rows and columns are those of the original price
elasticity matrices in Table 7-5 and 7-6.
** - Violating symmetry condition.
258
These results also indicate an implication for the
separability assumption.
Many previous studies included
only commodities considered to be "close" to each other;
that is, it is not common to see the price of pork in the
rice demand equation.
The substitutional or complementary
relationships are thought to have been overly restricted in
these previous studies.
There seems a need for
reconsidering the separability assumptions in food demand
studies.
As pointed out at the end of Chapter 5, fish
consumption data trends for Japan are not as expected.
Fish
demand has two peaks, and the pre-war level is higher than
the post-war level.
According to the significant Hicksian
elasticities, only rice is a net substitute for fish in the
pre-war period.
In contrast, beef, chicken, and milk are
net substitutes for fish in the post-war period.
Lower fish
demand in the post-war period is due to the increased supply
(and corresponding lower prices) of other animal origin
foods. 88,89
88
For instance, strong substitution relationships
between fish and chicken, and between fish and milk are
observed in post-war Japan.
Real prices of chicken and milk
declined in the post-war period.
259
SHEI (1983a, pp. 14-5) mentioned that the possibility
of a strong substitutionary relationships between chicken
(poultry) and other meats, and between fish and meats in
Taiwan.
According to this result, strong net substitution
relationship is found between beef and fish in Taiwan.
However, no significant results are found for the other
cases.
89
HAYES, WAHL, and WILLIANS (1990) applied the LA/AIDS
model for post-war Japan to investigate the market condition
for meat and fish demands and concluded that chicken and
dairy beef, and chicken and pork are net complements with
each other, and fish demand is separable from meat demand in
Japan (p. 556). This result does not support the net
compleinentarity between chicken and beef (symmetry condition
is violated for this case) or between chicken and pork.
Whether fish is separable from meat or not is also
questionable since this result indicates that beef and
chicken are net substitutes of fish in the post-war Japan.
260
7.4. Age-Population Composition
As shown in Tables 7-1 and 7-2, some age-population
composition elasticities are unexpectedly large.
At the
same time, there are substantial variation among the
results.
To comprehend a general trend of age-population
effects, Tables 7-14 and 7-15 are constructed.
Both tables
show that the impacts of age composition changes on food
demands become smaller over time in Japan.
The patterns are
similar among the Japan pre-war period, Korea, and Taiwan in
terms of magnitudes.
This may suggest a general trend that
in the earlier phase of economic development, the age
composition change effects on food demands are pronounced,
becoming less significant as income levels rise.
The interpretation of each elasticity requires some
considerations.
A straightforward interpretation for the
extreme case of beef in Korea from the system estimation
results is that a 1% increase (decrease) in child, working,
and old age population share is expected to result in 49.9%,
53.6%, and 20.6% increases (decreases) in average per capita
demand for beef in Korea, respectively.
261
Table 7-14:
Summary Table of Population Share Elasticities
Prom System Estimation Results
Japan
All-Period
1911-87
Child WorkIOld
+
+
Commodity
Rice
Bread! Wheat
+
+
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
++
+++
++
+
--
+
+
++
+
++
+
+
19 11-3 7
Childj Work Old
++
-++
++ ++++ +
+++
+++ +
+
++
+++
+++
+
Child WorkO1d
+
++
++
+
++
+
+
+++
+
Commodity
Rice
Bread/Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Japan
Mid-Period
1925-70
Japan
Pre-War
++
++
Japan
Post-war
1955-87
Child Work Old
Korea
1962-87
Child Work Old
++++
+++
++++ ++++ ++++
+++ +++
Taiwan
1963 -87
Child Work Laid
++
+
+
+
+
+
+
na
na
+++
+++
++++ ++++
na
++
++
+
++
+
+
++
na
na
+++
+
na
Note:
I
= Absolute Value of Population Share Elasticity
< 1.0
= PSE
No Mark
<
5.0
= 1.0 < PSE
+ or < 10.0
++ or -= 5.0 < PSE
< 20.0
+++ or --- = 10.0 < PSE
++++ or ---- = 20.0 < PSE
Bold Face = Statistically Significant at 5% Level
na = not available
PSE
I
I
I
I
I
I
I
I
262
Table 7-15: Summary Table of Population Share Elasticities
From Non-System Estimation Results
Commodity
Rice
Bread/Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Commodity
Rice
Bread/Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Japan
All-Period
1911-87
Child'Work Old
+
+
+
+
+
Japan
Pre-War
1911-37
Child WorkJ Old
++
++
+
+++
++
+
+
Japan
Post-war
1955-87
Child WorkO1d
+
+
++
+
+
+
++
--
+
+
+
+
+
++
+
Taiwan
1963-87
Child WorkIOld
na
++++ ++++ ++++
++++ +++ +++
na
++
++
++
++
++
+
+++
+++
++
Korea
1962-87
ChildJ Work Old
++
+
++
+
-
++
+++
++
+
+
++++
+
+
+
+
++
+
+
+
Japan
Mid-Period
1925-70
Child Work Old
na
na
+++
++++ ++++
na
++
++
na
Note:
I
PSE
I
= Absolute Value of Population Share Elasticity
=
No Mark
+or-
++or-+++or---
=
=
=
++++ or ---- =
Bold Face =
na =
IPSEI <1.0
1.0IPSEI < 5.0
5.0 <IPSEI <10.0
< 20.0
10.0
PSE
PSE
20.0
Statistically Significant at 5% Level
not available
I
I
I
263
Although some population share elasticities are
gigantic, the total effect of the age-population share
changes may be not so large, since the effect of changes in
the child population share and the effects of changes in the
working and old population shares tend to wash each other
out (see section 5.4.5.).
To consider this point, "net
population effects" are calculated, which approximately
measure the impacts of the simultaneous changes of the three
population shares on the average per capita consumption of
good i at the margin.
The formula follows:
Net Population Effect for good i at time t
=
(Elasticity of Child Population Share for good i at t)
x
(% Change in Child Population Share from t-1 to t)
+
(Elasticity of Working Population Share for good i at t)
x (% Change in Working Population Share from t-1 to t)
+
(Elasticity of Old Population Share for good i at t)
x
(% Change in Old Population Share from t-1 to t)
264
The population (share) average growth rates for the six
data samples are reported in Table 7-16.
The net population
effects evaluated at the mean are derived using the
population share elasticities at the mean and the average
population share growth rates for each sample.
The results
for the two estimation methods are reported in Table 7-17.
Table 7-16:
Population Share Average Growth Rates
Child
Population
Share
Average
Growth
Rate
Working
Population
Share
Average
Growth
Rate
(%)
(%)
(%)
-0. 74102
0.216795
-1.17276
-1.52468
0.150955
-0.12475
0.427443
0.37653
1.001794
-0.51817
0.759797
2.245401
KOREA:
-1.56818
0.858403
1.140975
TAIWAN:
-1.90882
1.002182
3.248209
Old
Population
Share
Average
Growth
Rate
JAPAN:
ALL-PERIOD
PRE-WAR
MID-PERIOD
POST-WAR
Note:
Growth rate at time t = (At/At-i - 1) * 100
where At stands for some variable A at time t.
265
Table 7-17: Net Population Effect at the Mean from System
and Non-system Estimation
I//I
FROM SYSTEM ESTIMATION
JAPAN
JAPAN
JAPAN
JAPAN
AU.-
PRE-WAR
MID-
POST-
PERIOD
WAR
KOREA
TAIWAN
1925-70
1955-87
1962- 87
1963-87
PERIOD
1911-87
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
1911-37
-0.29266 -1.48878 0.246267 -0.75939
RICE
-6.17043
WHEAT
-21.3903
-8.84655
BEEF
-1.35044
-3.10818
PORK
2.765062
CHICKEN
-1.81449
CHICKEN
13.38253
RICE
-4.1815
-1.0141 -2.59554 0.003773 -2.29932
WHEAT
3.924327
1.140846 -4.01159 -3.31649 -2.33786
BARLEY
9.883131
6.97022
BEEF
-0.29924 2.615605 5.577579 0.031038
PORK
2.45559 0.266313 3.967437
0.742555 -0.26124
6.91336 -6.22584
FISH
0.22321 3.601203 -4.58996 4.514671
FISH
15.43137
FISH
2.31254
EGGS
-0.54343 -0.22722 1.588868 -5.02736
EGGS
-0.90448
EGGS
2.560695
MILK
-0.56177 -2.90431 4.922731 -2.62288
MILK
6.0784
I//I
FROM NON-SYSTEM ESTIMATION
JAPAN
JAPAN
JAPAN
ALL-
PRE-WAR
MID-
POST-
PERIOD
WAR
KOREA
TA I WAN
1925-70
1955-87
1962-87
1963-87
PERIOD
1911-87
1911-37
JAPAN
RICE
-021211
BREAD
-0.80344 0.092457 -0.20679 -0.27423
BARLEY
-1.4046 -0.11291 -1.44624
-0.0801 -3.05066 -098789 7.345506
RICE
WHEAT
-3.20209
1.45465
EARLEY
2.092038
RICE
-5.26229
WHEAT
-21.2095
BEEF
1.781881
-0.3573 4.195967 -2.37899
BEEF
-4.12269
BEEF
-0.51176
PORK
O.7O78 2.929885 4.905815 1.829961
0.668965
-0.3388 4.496733 -5.84046
PORK
-111657
PORK
3.325633
CHICKEN
1.025691
CHICKEN
7.496344
0.086957 2.160739 -3.42569 -4.34639
FISH
7.067547
FISH
-2.86356
EGGS
-0.40574 0.050085 1.558436 -5.21161
EGGS
3.586005
EGGS
-15.3321
MILK
-0.38698 -3.59381 6.295908 -2.05361
MILK
5.799794
CHICKEN
FISH
266
The net population effect at the mean for wheat in
Taiwan is the largest and about -21%, which implies that the
marginal age population composition changes in Taiwan
reduced the average per capita wheat consumption by 21% on
average during the study period, with holding everything
else (other than the three population shares) constant.
On
the other hand, the net population effect for beef in Korea,
the example mentioned above, is moderate of -4.1% to
_8..8%.90
It is clear that the population share elasticities are
reasonable and the effects of changes in age-population
composition play significant roles in shaping food demands
in many cases, although no systematic relationships seem to
be found.
90 Notice that the average per capita consumption
amounts of wheat in Taiwan and beef in Korea were increasing
during the study periods. Therefore, the other factors in
Part II such as prices and real group expenditures must have
contributed to the demands in the opposite direction more
than offsetting the negative age-population composition
effects.
267
7.5. Household Size91
Household size elasticities are statistically not
significant in many cases.
Note again that household size
elasticity and the coefficient for economy of household size
are different.
The coefficient for economy of household
size at means are reported in Table 7-18, where positive
values imply economies of household size and negative values
imply diseconomies.
These figures were calculated using
mean values of group expenditure, household size, and prices
of each sample.
T-statistics for the resulting coefficients
are common to those of household size elasticities;
statistically significant results at 10% level of
significance are typed in bold face in Table 7-18.
Existence of economies of household size is statistically
supported for chicken in Japan and Korea, for eggs in Japan,
also for beef in Korea.
On the other hand, existence of
diseconomies is statistically supported for bread in Japan,
also barley and fish in Japan and Korea.
The economies of size is one way to consider the
relationships between household size and per capita quantity
demanded for good i; various other interpretation may be
possible for the observed relationships.
As noted earlier,
the phenomenon of shrinking family size is very likely to be
associated with rapid economic development.
Family size and household size are equivalent in
this study and used interchangeably.
Household size was not
considered for Taiwan.
268
Table 7-18:
at the Mean
Coefficients of Economies of Household Size
FROM SYSTEM ESTIMATION
RICE
BREAD/WHEAT
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
1911-37
JAPAN
MIDPERIOD
1925-70
JAPAN
POSTWAR
1955-87
0.01285
0.008304
-0.26474
-0.00259
-0.0048
0.008545
0.036552
0.015688
-0.00863
0.852242
-0.00274
-0.66271
-0.01976
-0.00416
0.031412
-0.40289
0.143215
0.039513
-2.83778
-0.67836
-1.63991
-0.14271
0.307469
0.685224
-0.45697
2.489168
0.743418
JAPAN
ALLPERIOD
1911-87
JAPAN
PRE-WAR
0.64856
0.006728
-0.34924
-0.00509
-0.0522
0.003887
-0.1099
0.119778
-0.0381
KOREA
1962-87
2.734641
6.672602
-8.14603
0.486596
0.215638
0.206295
-2.41026
-0.10293
FROM NON-SYSTEM ESTIMATION
RICE
BREAD/WHEAT
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
Note:
1911-37
JAPAN
MIDPERIOD
1925-70
JAPAN
POSTWAR
1955-87
0.013606
0.004334
-0.36589
-0.01603
-0.00419
0.013227
-0.05428
0.020142
-0.00135
0.197445
0.067978
-0.28128
-0.0246
-0.0096
0.024609
-0.19151
0.205806
0.000822
-4.15464
-0.85812
-0.26779
0.174849
0.399284
0.778308
-2.58938
2.829783
0.836311
JAPAN
ALLPERIOD
1911-87
JAPAN
0.55757
0.069367
-0.42965
-0.01026
-0.04911
0.112965
-0.40638
0.175921
0.106254
PRE-WAP.
KOREA
1962-87
7.328781
7.050753
-3.91323
0.403527
0.711499
0.035567
-2.0099
-0.20002
Statistically significant results at 10% level
of significance are typed in bold face.
269
In the earlier stage of development, smaller households are
considered to have the following two major attributes:
1)
living in urban areas, and 2) having young household heads.
(In the later phase of development, small households
consisting of elderly people may increase.)
The effect of
increases in household size of the former type on food
demands may be called as the "urban family effect" and the
latter as the "young family effect".
Younger households may
be seen mainly in urban areas; thus those two effects may be
correlated with each other.
Urban/young households are
exposed to larger amounts of information and goods related
to the new styles of living.
Thus, for traditional
(non-traditional) food items, increase in urban/young
households may have negative (positive) effects on
consumption.
effect".
This may be called the "urban-information
Also, the urban-rural income gap may be large in
the early phase of development.
Then, the increase in urban
household income may have a positive effect on consumption
of relatively expensive items.
"urban-income effect".
This may be called the
On the other hand, younger
households may have fewer assets; therefore, for luxury
items, increases in younger/smaller households may have
negative
effects on consumption.
the "young-less-income effect".
This case may be called
The "urban-income effect"
and the "young-less-income effect" may be washing out each
other.
270
The various information contained in the family size
variable measured at the national average level may prevent
us from extracting the pure economies of size effect.
The
results supporting economies or diseconoinies of family size
may be some combination of these effects, and happening to
show the same patterns recognized as econoinies/diseconomies
of size:
Economies of Size Effect
(+ for economy ; - for diseconomy)
/
Household Size Effect
-
Urbanization Effect (+ or -)
\
Young Family Effect (+ or -)
For instance, when economies of size are sufficiently small
(positive sign), and urbanization and/or young family
effects is (are) negative and sufficiently large (i.e.,
smaller urban/young households consume more of the good;
this may be the case of non-traditional items), then the
total effect explained by the household size may become
negative, implying diseconomies 92
The influences of household size on the demands may be
not quite systematic due to these various types of
simultaneous effects; as a consequence, the resulting
estimates are not statistically significant in general.
92
Bread in Japan may be the case.
271
7.6. Habit and Custom Effects
The elasticity of TQit_2 for Qit.
in Part I (the TL
equation) is referred to as the "custom effect"; also, the
elasticity of Q*it_1 for
in Part II (the LA/S/H/AIDS
model) is referred to as the "habit effect".
They are
reported in Table 7-1 and 7-2.
The most notable differences between system and
non-system estimates are found in the numerical and
statistical magnitudes of the habit effects.
In general,
the habit effects are numerically smaller and statistically
more significant in the system estimates than in the
non-system estimates.94
The inconsistent results being
different in signs between the two results are summarized in
Table 7-19.
According to the table, the robustness of the
habit effects are not high.
Both of the custom and habit effects are not the
coefficient for partial adjustment procedures in habit
formation; therefore, they do not have to take values
between zero and one in absolute value.
Some estimated standard errors for the coefficients
of the habit variables in the system estimation were very
small which yielded abnormally high t-statistics for the
habit effects.
Dr. Alan LOVE of the Department of
Agricultural and Resource Economics, Oregon State
University, provided the following suggestions: This
phenomenon reflected the very small curvature on the surface
of objective function.
Under this situation, numerical
accuracy of the estimated variance-covarjance matrix, by
which the observations had been weighted following a
generalized least square method, may be questionable,
although theoretically it is consistently estimated. More
rigorous convergence criteria may yield better results.
Also, non-iterative SUR method using OLS parameter estimates
may be more robust in a small sample case.
272
It is difficult to conclude something out of these
results, but several things are noted:
The habit effects from the system estimation are
consistently most significant in the Japan pre-war case,
then in the Korea case.
However, the non-system results do
not show any systematic trends across the samples.
Negative habit effects are observed in some cases.
BLANCIFORTI et al.
(1986) also obtained a negative
coefficient of the Qt
variable for "miscellaneous foods"
in their dynamic AIDS model.
They noted that this is
"reflecting either an "inventory effect" or perhaps a
negative time trend" (p. 43).
273
Table 7-19: Consistency and Statistical Significance Check
for Habit Effects Between System and Non-System Estimation
Results
Commodity
JA
JR
Samples
JT
JN
K
T
Rice
Bread/Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Note: System and non-system estimates are:
- Different signs and both results are
respectively significant at 5% level.
(*) - Different signs and one of
the two estimates is statistically not
significant at 5% level.
+
- Both results show positive signs.
- Both results show negative signs.
Bold - Both results indicate statistically
significant at 5% level.
na - Not available.
*
274
The inventory effect happens when a consumer tries to
maintain a certain level of consumption of a good; if the
current period consumption is lower than the planned level,
she will increase her consumption of the good in the next
period; on the other hand if the current level is higher
than she plans, she will reduce consumption of the good in
the next period.
This interpretation may be applicable for
the goods already consumed almost at the satiation level,
such as grains, but not intuitively so for the animal origin
foods in the developing economies.
According to Table 7-19,
the negative habit effects (derived from the negative
coefficients of the habit variables) are more consistently
and frequently observed among the animal origin items.
Although the interpretation of the habit effect is
still questionable, it has been confirmed already by the
model specification tests that inclusion of the habit effect
makes the model a more reasonable one statistically.
It may
be better to consider the habit variable as the adjustment
factor for the systematic fluctuation in food demand due to
the inertia in the individual consumption behavior or the
effects of socioeconomic trends.
The primal purpose of the
inclusion of the habit variable is to exclude those
fluctuations out of the model to increase precision of the
measurement of the other variables and not to quantify the
amount of habit precisely.
As seen, the habit variables are
statistically very significant in many cases.
Therefore, it
275
is said that our original aim has been achieved
successfully.
3. Also, negative custom effects are observed in some
cases.
Note that the variable TQ's are increasing in almost
all cases except the following cases:
For rice, after 1963 in Japan and after 1974 in
Korea.
For barley, after 1953 in Japan and after 1974 in
Korea.
Except these cases, negative custom effects imply that per
capita consumption levels are on the declining trends,
holding everything else constant.
In many cases, per capita
consumption increases despite the negative custom effect;
this may imply that the contributions of the other
socioeconomic factors on the per capita consumption levels
are greater than those of the total domestic consumption
levels.
276
7.7. Total Expenditure Elasticities and Allocation Factors
Total expenditure elasticities (Yi's) are derived by
multiplying group expenditure elasticities (Xi's) and
allocation factors (AF's) in each period.
The allocation
factors are estimated independently in Part III and common
to the system and non-system results.95
The differences
in the total expenditure elasticity estimates between the
two estimates depend on the differences in the group
expenditure elasticities.
Total expenditure elasticities at the mean are
summarized in Table 7-2O.
It is observed that the total
expenditure elasticities measured at the mean for the Japan
all-period, the Japan pre-war period, and the Japan post-war
period are very inelastic and one digit smaller than the
case of the Japan mid-period, Korea, and Taiwan.
This is
because the mean values of the AF's are close to zero in the
former cases.
The numerical results of the allocation factors and
the resulting total expenditure elasticities from system
estimation are reported in Appendix S.
96 Total expenditure elasticity for good i at the mean,
Yj, is calculated as
Ii = (Et Yit)/T
{Et(Xit* AFt)}/T
t =
and not as
= {(Et Xit)/T} * {(
AFt)/T}
t =
277
Table 7-20:
Summary of Total Expenditure Elasticities
FROM
SYSTEM
ESTIMATION
JAPAN ALL-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
ESTIMATE 0.031956 0.044232 -0.00466 0.009506 0.067124 0.087032 0.084115 0.033752
MILK
0.08689
JAPAN PRE-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
ESTIMATE 0.029006 0.042878 0.026095 -0.00284 0.002538 -0.01091 0.032757 -0.00152
MILK
0.00784
JAPAN MID-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
ESTIMATE 0.170367 0.238062 0.003302 -0.11999 0.337536 0.364149
FISH
EGGS
MILK
0.46575 0.251729 0.225952
JAPAN POST-WAR PERIOD:
RICE
BREAD
BARLEY
ESTIMATE -002896 -0.01003 0.168609
BEEF
0.05943
PORK
CHICKEN
FISH
EGGS
MILK
0.08692 0.009352 -0.35524 -0.16157 -0.07745
KOREA:
RICE
WHEAT
BARLEY
BEEF
PORK
CHICKEN
ESTIMATE 0.280199 -0.16184 0.110719 -0.14915 0.365497 0.012749
FISH
0.3125
EGGS
-0.0674
TAIWAN:
RICE
WHEAT
ESTIMATE 0.486313 1.340988
BEEF
0.22275
PORK
CHICKEN
FISH
EGGS
MILK
0.56534 -0.39401 0.083263 0.270951 0.828767
278
Table 7-20:
Summary of Total Expenditure Elasticities
(Cont.)
FROM
NON-SYSTEM
ESTIMATION
JAPAN ALL-PERIOD:
RICE
BREAD
BARLEY
ESTIMATE 0.022482 0.047337 0.073989
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
0.01432 0.060607 0.037749 0.062293 0.035113 0.044109
JAPAN PRE-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
ESTIMATE 0.030245 0.035325 0.005289 -0.01041 0.003093 -0.01861 0.042792 -0.00252 -0.00076
JAPAN MID-PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
ESTIMATE 0.176814 0.132144 0.220516 -0.08806 0.281348 0.218239 0.198904 0.270799 0.228894
JAPAN POST-WAR PERIOD:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
ESTIMATE -0.03707 -0.01691 -0.09218 0.009553 0.083297 0.020627
FISH
EGGS
MILK
-0.3617 -0.16642 -0.07454
KOREA:
RICE
WHEAT
ESTIMATE 0.260914 -0.31699
BARLEY
BEEF
PORK
-0.08 -0.13288 0.365149
CHICKEN
FISH
EGGS
-0.0634 0.360724 -0.00438
TAIWAN:
RICE
WHEAT
ESTIMATE 0.367351 1.466643
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
-0.40093 0.710701 -0.54729 0.332157 0.729335 0.229371
279
Allocation Factors - the Three Countries
Figure 7-la:
Estimated by Translog Specification
0.8
L
2
0
0
0.6
0.4
U-
0
U
-0.2
2 -0.4
-0.6
-0.8
11
1921
1931
1941
1951
Year
-- All-period -4-- Pre-wcir
-
Figure 7-ib:
Post-war
-p4-- Korea
196 1
1971
1981
-- Mid-period
-b-- Taiwot,
Allocation Factors - Japan
1911 37:1955-87
Transfog Specification
16 19211926 19311936 1957 1962167 1972 1977 1982 1987
Year
- All-period -p- Pre-wor
-4- Mid-period -a- Post-war
280
Figure 7-ic: Allocation Factors - Korea and Taiwan
Estimated by Translog Specification
0.8
0.7
0.6
0.5
0.4
0.3
0.2
L___law
A
I,
0.1
0
0.1
U
0.2
962
1967
1972
1977
Year
Korea
-*-- Taiwan
1982
1987
281
The reason for the insignificant allocation factors at
the mean is not because they are globally small but they are
distributed symmetrically around zero (see Figures 7-1 and
7-2).
The allocation factor is negative at the mean for the
post-war Japan, therefore the total expenditure elasticities
for the all items in the food group have the opposite signs
to the group expenditure elasticities.
Then, rice, bread,
(barley)97, fish, eggs, and milk are shown as inferior
goods for the post-war Japan, while (barley), beef, pork,
and chicken are shown as necessity goods.
The sign of the
group expenditure elasticities are directly transmitted to
the total expenditure elasticities in other samples.
Since the allocation factors are critical to determine
the level of the total expenditure elasticities, the
validity of the allocation factors are considered in the
following.
In Figure 7-lb, the ending year of the pre-war period,
1937, and the beginning year of the post-war period, 1955,
are directly connected.
The existence of the break in trend
of the AF'S between the pre- and post-war periods is
consistently indicated by every sample for Japan.
Taking
the pre-war 1911-37 period and the post-war 1955-87 period
as the two separate developing period, it is commonly
observed that the AF's increase from negative value to the
Sign of the group expenditure elasticity for barley
is different between the system and the non-system
estimation results.
282
positive values as economic development proceeds.
With respect to Korea and Taiwan (see Figure 7-ic), it is
also observed that as economic development progresses the
AF's increase from negative to positive.
Sharp declines in
the AF's are common to the both countries during the 1973-74
period and the 1979-81 period, which are corresponding to
the periods of the two oil shocks.
According to Figure 7-la, the movements of AF's for
Korea and Taiwan show similar patterns with the latter half
of the Japan pre-war period.
The drastic changes in
allocation factors are common to the pre-war Japan, Korea,
and Taiwan, which imply that the level of food consumption
expenditures under the developing economies is quite
responsive to the level of income.
To see the point more
clearly, the AF's are plotted against real total
expenditures (XT*; roughly corresponding to the real
income).
The results are shown in Figures 7_2.98
it is
clear from Figure 7-2b and 7-2c that Korea and Taiwan have
AF's that are responsive to real total expenditure; when
98
Figures 7-2 have the equivalent time dimension of 71, real expenditure values corresponds to time as
represented in Figure 5-12. The first point in Figures 72a, b, and c are for 1911, 1962, and 1963, respectively.
The points connected by the line in Figures 7-2a, b, c,
represent succeeding years ending in 1987.
283
real total expenditures stops growing, AF's decline.99
The phenomena that allocation factors for food
expenditures are low or negative in the early phase of
development and become higher in the later phase imply the
tendency among the people to give higher priority to the
consumption of non-food items during the low income phases.
Further, the large fluctuation in the allocation factors
observed in the early phase of economic development may
reflect that the people tend to absorb the shock (short-run
reduction) in the real income level by adjusting the food
expenditures; the food expenditures are considered as a
buffer for the shock, motivated by the enthusiastic
attitudes toward the non-food items.
The clear contrast of the trends in the AF's between
the Japan pre-war and post-war periods can be understood by
the difference in the consumption environment:
the AF's
were relatively responsive to the real income growth in the
pre-war period simply because there were not so many
substitutes to attract people to spend the additional
income; therefore, the increase in the real income affected
the level of the AF's
rather quickly and persistently.
In
For instance, in the center of Figure 7-2b for
Korea, the allocation factor increases from 0.1 to 0.5 as
log of real per capita total expenditure increases from 13.1
to 13.22 won, the allocation factor falls from 0.5 to 0.05
as log of real per capita total expenditure "stops growing"
or slightly declines from approximately 13.22 to 13.18 won.
The same phenomenon is observed for four other times. This
also happens three times for Taiwan (Figure 7-2c).
284
the post-war period, the situation was substantially
different under which the consumers were surrounded by the
variety of attractive and affordable non-food substitutes;
then, to spend additional income on those non-food items was
more attractive than to spend on the food items for almost
every level of income.
Why the Japanese consumers tend to spend less on the
food groups as per capita real income grows in the post-war
period is also explained by the declining trend in the real
price levels.
Particularly after the 1970's, the real price
levels of many commodities declined (see Figure 5-5).
As a summary, the resulting AF's are said to be
properly reflecting the decision making process in the top
stage of the two stage budgeting procedure under the
changing socioeconomic conditions.
285
Figure 7-2a: Allocation Factors vs. Per Capita Real Total
Expenditure - Japan
Japan
1 9 11 87 : Translog Specification
0.8
0.6
L
o
U
0.4
0.2
0
0.6
0.8
12
12.5
13
13.5
14
Log(Reot Total Expenditure in 1985 Yen)
--- Asperiod -'--- Prewar
-*-- Midperiod
14.5
Postwor
Figure 7-2b: Allocation Factors vs. Per Capita Real Total
Expenditure - Korea
Korea
1962-87
Translog Specification
0.8
0.7
0.6
0.5
I
U
a
0
()
0
o.i
0
0.1
-0.2
I
12.4
1 2.6
13
1 2.8
13.2
13.6
13.4
Log(Real Total Expenditure in 1985 Won)
13.8
14
286
Figure 7-2c: Allocation Factors vs. Per Capita Real Total
Expenditure - Taiwan
Taiwan 1963-87: Translog Specification
0.7
0.6
0.5
L
0
0.4
0.3
0.2
0.1
0
0.1
0.2
98
1
10
11
10.8
10.2
10.4
10.6
Log(Reol Total Expenditure in 1985 NT$)
11.2
11.4
287
7.8. Comparison of Factors: Is Expenditure So Important?
To identify which factor(s) is (are) most contributing
to food demands, the factors having been examined are
compared with each other.
Note that it is not legitimate to
compare each elasticity directly; the "real" marginal impact
of each variable on per capita food demands is measured by
the multiplication of each elasticity and actual percent
change in each variable, as has been done in the calculation
of net population effects.10°
In the following, among the important factors, group
expenditure, own price, and age-population composition are
compared.
These factors are estimated in the same equations
in Part II; therefore the relative significance of these
factors is determined on statistical grounds.
The focus of
the following exercise is to examine the relative importance
of the effect of (group) expenditure on food demands.
Cross
price effects are important but excluded to simplify the
analysis.
Household size effects and habit effects are not
significant in magnitudes, and therefore not considered
here.
As a first step, average growth rates of each factor
(except age-population composition, which has already been
calculated) are calculated.
100
Growth rates are calculated by
Even when the measured elasticity is small, if the
actual change of the variable is large, the outcome may be
large, and vice versa.
288
Growth Rate at t = (Xt/Xt_i - 1)
* 100
where Xt is some variables such as nominal group expenditure
at t.
Then, the arithmetic mean of the growth rates for the
each sample period is calculated (see Table 7-21 and 7-22).
Table 7-21: Average Growth Rates of Nominal Group
Expenditure For Each Sample (%)
Sample
JA
JR
JM
JT
K
T
Nominal Group Expenditure
Average Annual Growth Rates
6.07894
4.62344
4.044557
7.261534
18.26016
9.550312
Table 7-22:
Average Growth Rates of Nominal Retail Prices
For Each Sample (%)
Sample
JA
JR
JM
JT
K
T
Sample
JA
JR
JN
JT
K
T
Rice
5.119557
4.189356
2.619017
5.875345
17.09821
7.68189
Chicken
2.523714
2.360821
0.492334
2. 656065
14.02492
4.170418
Bread
/ Wheat
4.350793
2.291842
3.420736
6.023691
10.79205
6.31538
Fish
6.660911
3.717071
5.828701
9.052781
18.00047
9.594323
Barley
6.070786
6.686013
2.233613
5.570914
15.71207
Eggs
1. 537997
2.639438
-1.70929
0.643076
11.38119
1.676817
Beef
5.491816
3.425222
4.397118
7.170924
17.12894
7.529271
Milk
3.679821
3.790897
2.451475
3.589573
6.600882
Pork
4.660885
5.301691
1.735483
4.140231
16.46718
5.639639
289
Next, each average growth rate is multiplied by the
elasticity evaluated at the mean for each sample and each
commodity.
Group expenditure elasticities (from Table 7-3)
and Marshallian own price elasticities (from Table 7-7) from
the system and non-system estimation results are applied
separately.
The impacts of the group expenditures on food
demands are reported in Table 7-23 and the impacts of the
own price levels on food demands are reported in Table 7-24.
Next, net population effects shown in Table 7-17,
impacts of group expenditure shown in Table 7-23, and
impacts of own price level shown in Table 7-24 are compared
cell by cell.
The results are summarized in Table 7-25
using the following notation:
when the impact of own price
is greater than impact of group expenditure in absolute
value, "P" is marked on the table.
Also, when impact of net
population effect is greater than impact of group
expenditure in absolute value,
ttDII
is marked on the table.
This procedure is done for the both of system and non-system
estimation results separately.
The results appearing in the
both tables are typed in bold face.
Blank cells in the both
tables indicate the impacts of group expenditures are
greater than own price and age-population effects in
absolute value.
290
Table 7-23:
Impacts of Group Expenditures on Food Demand
AVERAGE IMPACTS OF CHANGE IN GROUP EXPENDITURE ON FOOD DEMANDS
/1/I
FROM SYSTEM ESTIMATION
JAPAN
JAPAN
JAPAN
ALL-
PRE-WAR
MID-
POST-
PERIOD
WAR
KOREA
TAIWAN
1925-70
1955-87
1962-87
1963-87
PERIOD
1911-87
RICE
1911-37
JAPAN
4.05004 4.896112 2.966681 2.987871
RICE
26.12934
RICE
14.34129
WHEAT
-15.0917
WHEAT
39.54551
BARLEY
10.32488
BEEF
-13.9087
BEEF
6.568863
8.507222 0.428465 5.877663 -8.96669
PORK
34.0836
PORK
16.67179
CHICKEN
11.03032 -1.84208 6.341092 -0.96473
CHICKEN
1.188865
CHICKEN
-11.6192
FISH
10.66059 5.529309 8.110324 36.64649
FISH
29.14145
FISH
2.455398
EGGS
-6.18536
EGGS
7.990309
MILK
24.44021
BREAD
5.605955 7.237659
BARLEY
-0.59023
BEEF
1.204792 -0.48017
-2.0895 -6.13082
PORK
4.14548 1.034779
4.40484 0.057493 -17.3937
EGGS
4.277693 -0.25699 4.383479 16.66737
MILK
11.01234 1.323432 3.934603 7.989407
f//I
FROM WON-SYSTEM ESTIMATION
JAPAN
JAPAN
JAPAN
JAPAN
ALL-
PRE-WAR
MID-
POST-
PERIOD
WAR
KOREA
TAIWAN
1925-70
1955-87
1962-87
1963-87
PERIOD
1911-87
1911-37
RICE
2.849303 5.105299 3078941 3.824285
RICE
BREAD
5.999462 5.962883 2.301089 1.744596
WHEAT
BARLEY
9.377246 0.892772 3.839941 9.509699
BARLEY
BEEF
1.814923 -1.75678 -1.53343 -0.98544
BEEF
PORK
7.681217 0.522019
PORK
CHICKEN
4.784236
CHICKEN
-5.91229
CHICKEN
-16.1395
FISH
7.894909 7.223143 3.463607 37.31259
FISH
33.63843
FISH
9.795239
EGGS
4.450211 -0.42544 4.715544 17.16766
EGGS
-0.40824
EGGS
21.50796
MILK
5.590313 -0.12823
MILK
6.764108
4.89925 -8.59294
-3.1411 LB003O3 -2.12783
3.98583
7.68913
2433088
RICE
10.83313
WHEAT
43.25106
-12.3918
BEEF
-11.8232
34.05114
PORK
20.95846
-29.56
-7.46054
291
Table 7-24:
Impacts of Own Price Levels on Food Demand
AVERAGE IMPACTS OF CHANGE IN OWN PRICE LEVEL ON FOOD DEMANDS
f//I
FROM SYSTEM ESTIMATION
JAPAN
JAPAN
JAPAN
JAPAN
ALL-
PRE-WAR
MID-
POST-
PERIOD
WAR
KOREA
TAIWAN
1925-70
1955-87
1962-87
1963-87
PERIOD
1911-87
1911-37
RICE
-3.52743 -4.00869
RICE
-13.1385
RICE
-5.37916
BREAD
-h64752 -3.11444 -0.60971 0.375033
WHEAT
-0.34363
WHEAT
9.041875
BARLEY
1.315237 0.538746 5.083255 18.22538
BARLEY
-0.77579
BEEF
-2.33969 -4.54941 -5.20021 -6.80099
BEEF
-5.83741
BEEF
-12.4649
PORK
-5.46164
-2.6662 -1.76104 -3.98369
PORK
-14.2969
PORK
-5.65599
CHICKEN
-2.74123 -1.48697 -0.81092 -4.33055
CHICKEN
4.294251
CHICKEN
FISH
-10.3332
FISH
-0.41963
EGGS
3.867902
EGGS
-0.30816
MILK
-6.24793
FISH
-2.4291 -0.66828
-4.5162 -2.37919 0.668804 -13.1056
EGGS
-0.62968 0.359423
MILK
-4.00769 -1.56113 -1.47014 -3.51615
1//I
FROM NON-SYSTEM ESTIMATION
0.08157 -0.48304
0.47776
JAPAN
JAPAN
JAPAN
JAPAN
ALL-
PRE-WAR
MID-
POST-
PERIOD
WAR
KOREA
TAIWAN
1925-70
1955-87
1962-87
1963-87
PERIOD
1911-87
1911-37
RICE
-1.01441
BREAD
-1.66818 -0.95673 0.165854 3.321997
BARLEY
2.152171 -0.81113
BEEF
-1.92118
PORK
CHICKEN
-2.19781 -2.75593 -1.00843
-4.3198 -2.38.618 -0.79687
RICE
-17.8914
RICE
WHEAT
16.35155
WHEAT
BARLEY
0.973096
-0.9155 -2.98883 -3.66462
BEEF
28.86426
BEEF
-5.50065 -2.26528 -1.83895 -1.41041
PORK
-12.6227
PORK
-6.49725
CHICKEN
-33.4719
CHICKEN
2.827209
4.65491 8.547977
-8.8595
-2.22581
-4.9953.
-12.4991
FISH
-3.87142 -2.84812 0.607655 -8.14917
FISH
-29.1757
FISH
-2.27653
EGGS
-0.62827 0.261076 0.757416
EGGS
10.1814
EGGS
-0.61841
MILK
-2.81758 1.115621
MILK
-6.88899
-0.2995
-2.9891 -3.16355
292
Comparison of Impacts on Food Demand Among
Table 7-25:
Group Expenditure, Own Price, and Age-Population Effects
From
Commodity
Rice
Japan
AllPeriod
System Estimation Results
Japan
Pre-war
Period
Japan
MidPeriod
Korea Taiwan
P
Bread! Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Japan
Post-war
Period
PD
PD
P
PD
PD
PD
P
D
PD
D
D
P
PD
P
PD
PD
D
From Non-System Estimation Results
Commodity
Rice
Bread/Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Japan
AllPeriod
Japan
Pre-war
Period
Japan
MidPeriod
Japan
Post-war
Period
Korea Taiwan
P
D
P
P
PD
PD
D
D
P
P
PD
D
P
PD
PD
P
P
P
D
P
Note: P - Own price effects are more dominant than the
impacts of group expenditure.
D - Age-population effects (demographic effects)
are more dominant than the impacts of group
expenditure.
293
According to Table 7-25, interestingly, price and
demographic effects have relatively larger impacts than
group expenditure on the demands of animal origin foods such
as beef, pork, chicken, and milk, more frequently than on
the demands of plant origin foods.
This result is somewhat
opposite to the intuition that animal origin foods at the
developing stages are mainly driven by the rise in
expenditure level.
Although not examined here, the impacts of cross price
effects may also have some substantial effects on food
demands.
According to Table 7-21 and 7-22, the average
growth rates of prices are more or less the same level as
those of group expenditures.
As seen in the prior section,
cross price elasticities are significant even between animal
origin foods and plant origin foods.101
The
multiplication of those are expected to show substantial
impacts of cross price changes in various directions.
The conclusion of this section is as follows:
the
effect of (group) expenditure on food demands is not always
significant even under the substantial economic growth.
Particularly for the demands of animal origin foods, own and
cross price changes and demographic changes play significant
roles.
101 In the prior section, only Hicksian
elasticities
were presented to support this point. However, this
phenomenon was also observed for Marshallian elasticities,
which can be easily checked by inspecting Table 7-3-1 and 73-2.
294
7.9. Movements of Expenditure Elasticities
In this section, the hypothesis about the change in
income elasticity is examined using total expenditure
elasticity estimates.
The null hypothesis is that total
expenditure elasticity of a good decline from (high)
positive value to zero or negative value as per capita real
total expenditure grows.
In the following, Ii denotes the
total expenditure elasticity for good i.
Yi is considered
to be approximately compatible with "income elasticity".
Also, the real per capita total expenditure is denoted by
XT*, which is approximately equal to real income.
Yi for each time period t is calculated by the formula:
= Xi
x AF
The pairs of (XT*t, Ylt) are plotted on the XT*-Yi plane,
which are presented in Figures 7-3 (the time subscript t is
now ignored).
Only the system estimation results are
plotted to conserve space.
295
Figure 7-3a: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Rice: Japan 1 9 11 87 (System Estimation)
0.8
0.6
0.4
.
UI
a
iu
0.2
0.2
aLi
0.6
0
0.8
12
14
13
13.5
12.5
Log(Reaf Total Expenditure in 1985 Yen)
-- Allperiod ---- Prewar
-*- Midperiod
1 4.5
Postwar
Figure 7-3b: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Bread: Japan 1 9 11 87(System Estimation)
1.5
>-
J
0.5
L
a.
0
T5
1.5
12
14
13.5
13
12.5
Log(Reol Total Expendilure in 1985 Yen)
--- Altperiod -4--- Prewar
--- Midperiod
Pastwar
1 4.5
296
Figure 7-3c: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Badey:Japan 19 11 87(System Estimation)
3
Cr
4
14
12.5
13
13.5
Log(Real Total Expenditure in 1 985 Yen)
12
- Atperiod -'- Prewor
1 4.5
-- Midperiod s--- Postwar
Figure 7-3d: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Beef: Japan 1 9 11 87 (System Estimation)
..
0.8
0.6
0.4
0.2
0.4
0.8
12
14
12.5
13
13.5
Log(Real Total Expenditure in 1 985 Yen)
-- Allperiod -4- Prewar
-- Midperiod
Postwar
1 4.5
297
Figure 7-3e: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Pork: Japan 1 9 11 87 (System Estimation)
2.5
U
U
-o
c
0.5
a
0.5
12
-*
14
12.5
13
13.5
Log(Real Total Expenditure in 1 985 Yen)
Allperiod -4- Prewar
Figure 7-3f:
-- Midperiod -
1 4.5
Postwar
Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Chicken: Japan 1 9 11 87 (System Est.)
3
U
Ui
L
-o
c
a.
I
x
Ui
2
a
o3
4
12
13
12.5
13.5
14
Log(Real Total Expenditure in 1 985 Yen)
-- AlTperiod -4-- Prewor
Midperiod -E--- Postwar
14.5
298
Figure 7-3g: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Fish: Japan 1 9 11 87 (System Estimation)
1.5
0.5
1
C
x_
I-j
-
0
2
2.5
3
12
14
13.5
12.5
13
Log(Real Total Expenditure in 1 985 Yen)
-*-- Allperiod -'-- Prewor
-*-- Midperiod
14.5
- Postwar
Figure 7-3h: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Eggs: Japan 1 9 11 87 (System Estimation)
0.8
0.6
w
0)
0.4
0.2
0)
-
a
0.4
0.6
0.8
12
14
13
13.5
12.5
Log(ReaI Total Expenditure in 1 985 Yen)
-- Allperiod -4-- P ewar
Midperiod -E- Postwar
1 4.5
299
Figure 7-3i: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Milk: Japan 1 9 11 7 (System Estimation)
3
C
0.5
0
12
12.5
13.5
13
14
14.5
Log(Real Total Expenditure in 1985 Yen)
--- Allperiod -4-- Prewar
--- Midperiod
- Postwar
Figure 7-3j: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
1 962-87(System Estimation)
Grains:Korea
1.5
I
a
2 1.5
2
12.4
12.6
1
2.8
13
13.2
13.4
13.6
Log(ReaI Totai Expenditure in 1985 Won)
-- Rice
-4-- Wheat -- barley
13.8
14
300
Figure 7-3k: Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Meats:Korea 1962-87 (System Estimation)
1.5
0.5
L
a
0
1.5
12.4
I
12.6
I
I
12.8
13
13.4
13.6
13.2
Log(ReaI Totof Expenditure in 1 985 Won)
-- Beef
-i-- Pork
13.8
14
-- Chicken
Total Expenditure Elasticity vs. Real Per
Figure 7-31:
Capita Total Expenditure
Fish & Eggs: Korea (System Estimation)
1.4
1.2
:
1
('I
0.8
2
w
a
0
/
0.6
0.2
0.4
0.6
12.4
12.6
13.4
13.6
13
12.8
13.2
Log(ReoI Total Expenditure in 1985 Won)
-- Fish
Eggs
13.8
14
301
Figure 7-3m:
Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Grains: Taiwan 1963-87 (System Est.)
3.5
.-
3
.-
2.5
a
ui
2
.1.5
C
-
w
0.5
0
.
0
-. 0.5
9.8
10
10.2
10.4
11
10.6
10.8
Log(ReoI Total Expenditure in 1 985 NT$)
Rice
11.2
11.4
e-- Wheat
Figure 7-3n:
Total Expenditure Elasticity vs. Real Per
Capita Total Expenditure
Meats:Taiwan 1 963-87 (System Estimation)
1.5
I
.
a
w
,
L
0.5
0
C
w
a.
x
w
:
0
0.5
9.8
10
10.2
10.4
10.8
10.6
11
Log(Real Total Expenditure in 1 985 NT$)
--- Beef
4-- Pork
s-- Chicken
11.2
11.4
302
Total Expenditure Elasticity vs. Real Per
Figure 7-30:
Capita Total Expenditure
Fish,
Eggs & Milk: Taiwan (System Est.)
2
9.8
10
11
10.2
10.6
10.8
10.4
Log(ReaI Tota' Expenditure in 1 985 NT$)
Fish -- Eggs
Milk
11.2
11.4
303
To summarize the resulting trends in Ii, the trends are
abstracted and classified based on the plots (see Figure 74).
Conutton notations follow:
the case in
[
3
parenthesis
is the result supported only by the system estimation.
"Range" indicates the observed range of values Xi or Yi
takes.
"-" indicates almost constant, h1/h1 indicates
increasing, and "\" indicates decreasing.
Each of the five
sample period except the Japan all-period will be labelled
as "short-run".
For Japan, it may be better to treat the pre-war and
the post-war results as two separate results belonging to
the different demand cycles.
Even though per capita real
total expenditure level is consistently higher in the postwar period than in the pre-war period, the process of
economic development seems to be divided due to the war.
A
whole new cycle seems to have started after the war period,
and there are differences in trends.
Then, the patterns of
the movements of Yi are classified based on the "medium-run"
(the pre-war or the post-war) trends for Japan.
To avoid
confusion with previous notation, the first phase of higher
XT* (corresponding to the pre-war period) is referred to
"Ji" period, and the second phase of higher XT*
(corresponding to the post-war period) is referred to "J2"
period.
The trend in Ji depends on the JR and JM results,
and the trend in J2 depends on the JM and JT results.
304
In some cases, JR, JM, and JT (the Japan pre-war, mid-,
and post-war periods, respectively) results do not coincide
in their overlapping periods.
In such cases, the mean of
each short-run samples (JR, JM, or JT) are chosen as
representatives of the trends.
For example, the case of
chicken in Japan shows inconsistency between JR and JM
estimates, also between JN and JT estimates.
interpreted in the following way:
This case is
first, Ii declines in JR
period; then increases in the end of the JR period according
to the JM estimates; then Yi increases from the negative in
the JT period according to the JN estimates and reaches to
zero according to the JT estimates.
These inconsistencies among the short-run results imply
the limitation of the model employed in this study.
The
results for the observations being close to the sample edge
may be inaccurate, and only the estimates around the mean of
the samples may be reliable.
This is due primarily to the
functional forms of the models in Part I through III; all of
these are capable to approximate unknown underlying
relationships only locally.
Therefore, higher priorities
are placed on the estimates closer to the mean values of
samples.
305
Figure 7-4: kbstraction of the Trend of Total Expenditure
Elasticity (Yi) against Real Per Capita Total Expenditure
(XT *)
I. Constant Trend
1. Short-run
Range
i) Zero
Commodity
Beef
Chicken
Country
Korea
Korea
Range
i) Negative (A)
to Positive (B)
Commodity
Rice
Pork
Country
Taiwan
Taiwan
yi
XT *
II. Increasing Trend
1. Short-run,
Monotonically
Increasing
yi
B
/
/
/
A
XT *
2. Short-run,
Constant to
to Increasing
Range
i)
to Positive
(B)
Yi
Zero (A)
Commodity
Rice
Pork
Fish
[Beef
/
B
----A----!
XT*
Fish
Eggs
Country
Korea
Korea
Korea
Taiwanj
Taiwan
Taiwan
306
Figure 7-4:
Abstraction of the Trend of Total Expenditure
Elasticity (Yi) against Real Per Capita Total Expenditure
(Cont.)
(XT*)
2. Medium-run,
Monotonically
Increase, and
Constant
Yl
Range
i) Negative (A)
tq Positive (B)
and Negative (C)
to Zero (D)
Rice
Bread
Country
Japan
Japan
Japan
Commodity
CountrY
Taiwan
Conunoditv
Fish
B
A
/
/
/
/--D-C
XT*
III. Decreasing Trend
1. Short-run
Yi
\
Range
i) Positive (A)
to Negative (B)
Chicken
A
\
\
XT*
2. Short-run
Range
i) Zero (A)
to Negative
Yi
---A--- \
(B)
B
\
XT*
Commodity
[Barley
Wheat
[Barley
Eggs
Country
Japan]
Korea
Korea]
Korea
307
Figure 7-4: Abstraction of the Trend of Total Expenditure
Elasticity (Yi) against Real Per Capita Total Expenditure
(XT*) (Cont.)
3. Medium-run,
Monotonical ly
Decrease and
Constant
Yi
Range
i) Positive (A)
to Negative (B)
and Positive (C)
to Zero (D)
Commodity
Beef
Country
Japan
Commodity
Wheat
CountrY
Taiwan
Taiwan]
\
A
\
C
\
\--D--
\
B
XT *
IV. Increasing-Decreasing Trend
Range
i) Negative (A)
to Positive (B)
to Negative (C)
1. Short-run
Yi
/-B-\
[Milk
C
/
A
XT *
V. Decreasing-Increasing Trend
1. Long-run, with
DecreasingIncreasing in
Middle-run
Yi
\A
I--C--
\--/
B
/
XT*
Range
i) Positive to
Zero (A) and
Negative (B)
to Zero (C)
Commodity
Pork
Chicken
Eggs
Milk
Country
Japan
Japan
Japan
Japan
308
Followings are generally observed in the movements of
Yi:
At the highest level of XT* in Japan, Yi converges
to zero in every case.
This is primarily due to the AF's
which are almost zero at that level of XT*, but for bread,
beef, and chicken, group expenditure elasticities (Xi) are
also almost zero at high level of XT*.
A priori expected trend in Yi is decreasing as XT*
increases.
25 cases.
This is suggested by only 6 cases out of total
11 cases suggest increasing trend; also, 8 cases
suggest up-and-down trend (see Table 7-26):
Table 7-26: Short- and Medium-run Trends in Total
Expenditure Elasticities
Commodity
Rice
Bread/Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Decreasing
Increasing
J, K, T
Up-and-Down
K
J
T
[T]
K
3
3, K
[J],
3
[K]
K, T
T
J, K, T
K
J
T
3,
[T]
Note: 3 - Japan, K - Korea, and T - Taiwan.
The results only supported by the system
] parenthesis.
estimation are in
[
309
3.
Many commodities share the same trends among the three
countries (see Table 7-27):
Table 7-27: Changes in Direction of Total Expenditure
Elasticities
Commodity
Rice
Bread / Wheat
Barley
Beef
Pork
Chicken
Fish
Eggs
Milk
Note:
Changes in Direction
Rising
Declining
to - + to 0 0 to - - to +[- to 0 0 to +
J2
K
Jl,T
J2
Jl,T
K
[K,J2)
Jl,K*
J2
K*, T
Ji
Ji
[TJ
T
Ji
Ji
Ji
K
[T]
J2
J2
J2
J2
J2
Jl,K
Ji
K,T
Jl,T
Jl
Ji - Japan lower total expenditure phase,
J2 - Japan higher total expenditure phase,
K - Korea, and T - Taiwan.
Appearing on both sides, meaning that
oscillating around zero.
The result only supported by the system
] parenthesis.
estimation is in
Bold - Unique cases; not sharing trend with any
other countries.
*-
[
According to Table 7-27, the trends of Yi for rice,
pork, fish and milk are consistent for the each commodity
among the countries.
Also not definitely but Ii for barley
show the same type of trend between Japan and Korea.
For chicken, the Korea case is said to be equivalent to
the Japan higher total expenditure phase, where Yi
fluctuates around zero.
The Taiwan case is classified as a
unique case since no other case shows similar trends in the
table.
However, the Taiwan case shares the same trend with
310
the Japan pre-war case, which is the part of the Ji case.
Therefore, it is not totally unique but close to J]. case.
Also, the Korea beef case may be taken as an equivalent
case with the higher total expenditure phase of the J2 case,
where Ii fluctuates around zero.
Taiwan's increasing trend
of Yi for beef is a unique case.
For eggs, the Taiwan case seems to be equivalent to the
latter half of the Ji case; however, the Korea case is
unique.
For wheat, the Korea case deviates from the others.
Conclusion of this section follows:
the null
hypothesis of the trends in total expenditure elasticity has
been rejected for many cases.
Furthermore, for some animal
origin foods, total expenditure elasticities are found to be
negative even at low levels of per capita real total
expenditures.
Although this result differs from a priori
expectations, there is a consistency in the trends of Yi;
this phenomena is observed across countries and across time
periods.
311
7.10. Possible Explanations of the Negative Expenditure
Elasticities for Foods of Animal Origin
Negative total expenditure elasticities are a result of
the combination of positive (negative) group expenditure
elasticities and negative (positive) allocation factors.
Negative allocation factors appearing in the lower income
stages were most likely caused by enthusiastic attitudes
among people toward non-food items, which may be
justifiable.
The negative group expenditures frequently observed for
animal origin foods such as beef, pork, chicken, and eggs is
harder to justify since such empirical results have never
been reported in any publications.
It may indicate the
limitation of the use of per capita consumption data for
demand analysis in developing nations.
In the earlier stage
of development, more nutritious/higher priced/nontraditional food items, such as meats and dairy products,
may be consumed constantly by only a limited portion of the
total population; i.e., for some people the consumption of
these goods is almost nil.
In such a situation, the
representative level of consumption for the goods defined by
a per capita calculation may be misleading -- almost nobody
consumes the per capita amounts.
When using this type of
consumption data, especially when the effects of other
variables, such as prices and demographic factors, are
substantial, large variations in the changes in levels of
312
consumption are likely to be overwhelmed by these other
variables.
Thus the "pure" group expenditure effects
explained by the residual isolated variation may be somewhat
artificial.
The consequence could be estimated group
expenditure elasticities that are negative.
If individual
consumption data instead of per capita consumption data are
used, this phenomenon may not be observed.
Therefore, it is
desirable to apply other methods or data sets to mitigate
this problem (e.g., cross-sectional analysis based on
individual household expenditure survey data).
This is
another possible explanation within the framework of
conventional way of thinking.
A recent paper by GILLEY and KARELS (1991) presents a
new approach to explain Giffen goods.
They demonstrate that
such goods may result from considering the utility
maximization problem as being defined by multiple
constraint(s) in addition to the budget constraint.
This
argument can be expanded to explain the inferiority of
animal origin foods at low income levels.
For example if
two goods, beef and rice are being examined, a nutrition
requirement constraint would be added to the utility
maximization construction.
Suppose, a unit of beef provides
Cb units of nutrition (nutrition can be protein, vitamin,
etc.), and a unit of rice provides Cr units of nutrition.
The total nutrition level N has to be satisfied.
The
quantity of beef demanded will be represented as B and the
313
quantity of rice demanded as R.
Pb and Pr per unit, respectively.
Prices of beef and rice are
The total budget is X.
The new utility optimization problem is then written as:
max U = U (B, R)
subject to:
<Budget Constraint>
Pb * B + Pr * R
<Nutrition Constraint>
Cb * B + Cr * R
It is now possible to illustrate one feasible situation
where beef is inferior to rice during a budget expansion
(see Figure 7-5).
Consider the case where Pb > Pr and Cb >
Cr, Pr/Pb > Cr/Cb (the budget line XX is steeper than the
nutrition line NN), and XX and NN intersect each other.
Suppose an individual A's indifference curves UO to U4 are
tangent to budget lines at EU to E4, respectively.
The
locus of the tangents (the income-consumption curve) is the
expected path A chooses as her income increases from XOXO to
X4X4.
Figure 7-5:
A Feasible Case of Negative Expenditure Elasticity
- Two Goods and Two Constraints Case
B
(B%?)
315
However, due to the nutrition constraint, she cannot
choose all points on the income-consumption curve such as EO
and El.
Instead she must choose DO and Dl for income levels
XOXO and X1X1, respectively.
solutions.
Note that DO and Dl are corner
Only after income level X2X2 is attained (i.e.,
after the point E2), will her consumption behavior be
illustrated by ordinary theory using only the budget
constraint.
Feasible solutions to her optimization problem
are indicated by arrows (>), Ni to E2 to E4.
Notice that
beef consumption declines up to E2 then increases as income
increases.
expands.
At the same time, rice consumption continuously
The consideration of multiple constraints allows
new conceptualization in understanding consumption behavior.
This is also one explanation of the empirical observations
found in this study.
316
CHAPTER
a
SUMMARY AND CONCLUSION
8.1. Study Motivation
The influence of the NIC's pattern of development on
other LDCS for the world economy can be expected to be
substantial.
As rapid technological progress is attained by
other LDCs, the pattern of economic development is also
expected to change rapidly, or at least to proceed much
faster than before.
If other LDCs are also to repeat the
rapid growth of Japan, Korea, or Taiwan, the growth in real
per capita income is expected to change food demands both in
volume and composition, similarly to the Asian NICs.
The
global food market is therefore expected to be substantially
influenced by the demands of the future NICs.
Many food demand studies have been focused on the role
of (real) per capita income growth in developing nations.
These studies present one hypothesis about the trend in
income elasticities for each food commodity demand.
However, these studies were neither theoretically nor
statistically satisfactory.
The role of income may be
emphasized too much; omission of other factors may create
substantial bias in projected income elasticities; as a
consequence, the hypothesis about the income elasticity is
not appropriately tested.
317
8.2. Main Objectives
This study has two main objectives:
I.(i) To find key factors shaping food demand in NICs
where drastic socioeconomic changes occur,
drawing on the experiences of Japan, Korea, and
Taiwan.
(ii) To reconsider the role of income on food demands
under economic development.
II.
To examine the changing income elasticity
hypothesis rigorously by isolating the effect of
income on food demands.
8.3. study Characteristics
Japan, Korea, and Taiwan are culturally and
geographically close to each other.
Development patterns
are similar among the three countries.
Japan, Korea, and
Taiwan share a similar demand structure and Korea and Taiwan
have followed Japan's food consumption pattern changes.
The nine commodities of rice, wheat (bread), barley,
beef, pork, chicken, fish, eggs, and milk were chosen as
basic and important food commodities.
Note the first three
are of plant origin and the rest are foods of animal origin.
Study periods are from the early 1910's to the end of
1980's for Japan (the end of 1930's to the early 1950's were
excluded as the war period), and from the early 1960's to
the end of the 1980's for Korea and Taiwan.
For Japan,
318
three sub-periods were considered separately, besides the
entire period.
Therefore, in total, six data samples (four
for Japan, and one each for Korea and Taiwan) were
estimated.
Data were largely compiled by the author from a variety
of secondary sources.
8.4. Model and Estimation Procedures
The model employed in this study is designed to
determine the effect of income (total expenditure), from
that of other factors, on food conunodity demand.
Assuming
weak separability, the second (bottom) stage of a two-stage
budgeting procedure was modelled by a complete demand system
approach.
A simpler version of the AIDS model (by DEATON
and MUELLBAUER), the LA/AIDS specification using STONE's
index, was applied as a core model.
Demographic variables
of age-population composition and household size were
included in the demand system using a BARTEN type scaling
method.
Also, assuming that the inertia in individual
consumption behavior is conditioned by exogenous
socioeconomic changes, a modified habit formation procedure
was incorporated into the system following BLANCIFORTI,
GREEN, and KING.
The resulting model was developed using
all of these features (the LA/S/H/AIDS model).
319
For the price and quantity data sets, nonparametric
tests of the stability of preferences were conducted.
It
was confirmed that neither WARP nor SARP were violated in
the six samples; i.e., the data sets are consistent with the
assumption that no drastic changes in tastes occurred in
Japan, Korea, and Taiwan during the study periods.
Furthermore, the tests imply that 1) the weak separability
assumption is reasonable for the food group, and 2) the
problem of aggregation over individuals is not severe in the
data sets.
As a consequence, it is possible to say that an
ordinary neoclassical demand model with quantities, prices,
and expenditures only is applicable for the six samples.
On the other hand, the model specification tests for
the demand system strongly indicated the misspecification of
an ordinary Marshallian demand model having prices and
expenditure only as independent variables (the LA/AIDS
specification).
It was demonstrated that the inclusion of
other demand shifters such as habit and demographic effects
statistically significantly improve the model performance.
(The LA/S/H/AIDS specification was the best.)
Thus, even when the given data sets are shown to be
satisfactory by the nonparametric tests, the potential
danger of misspecification bias, in the estimation of the
ordinary parametric model, is still large.
320
The potential danger from niisspecification of
parametric models due to a limited flexibility in functional
form102 is largely avoided by the particular model
specification utilized in this study.
In this way, it is
justifiable to apply the model, even though it is derived
from a specific underlying utility function, to the given
data sets.
Two different estimation procedures were applied to the
LA/S/H/AIDS model.
One is called system estimation, which
imposes adding-up restrictions using the iterative SUR
method, and which yields maximum likelihood estimates.
The
other is called non-system estimation, which imposes
homogeneity conditions and a first order autoregressive
error structure, equation by equation.
As a final step, group expenditure elasticities
calculated by the LA/S/H/AIDS model in the second stage of a
two-stage budgeting procedure were converted into total
expenditure elasticities, which are roughly comparable with
income elasticities.
The conversion rates are referred to
as allocation factors, which are defined as the percentage
change in the nominal per capita expenditure on a given food
group due to the percentage change in the nominal per capita
total expenditure.
They were calculated using a translog
model.
102 A different implied utility function (different
from that underlying the AIDS) may be appropriate.
321
8.5. Empirical Findings
Two major Conclusions are drawn from this study:
1. What has previously been believed to be the effects
of real per capita income growth on food demands may not be
the "pure" income effects but a combination of income,
prices (own and cross), and demographic effects.
The "pure"
effects of income growth are not always significant.
The
effects of price and/or demographic changes accompanying
rapid economic development play more dominant roles in
shaping food demands, particularly for foods of animal
origin.
The common belief among researchers that real per
capita income growth is a key factor in shaping food demands
is probably due to the fact that many other factors
contributing to increase food demands are, to some extent,
simultaneously changing with real per capita income growth.
Thus while the change in real per capita income could
be a good proxy for these changes, there may be a
substantial loss of accuracy in the measurement, since those
effects are not always moving in systematic ways.
While demographic changes play a substantial role in
the commonly observed phenomenon of significant increases in
food demands at the "taking-off" stage in developing
nations, age-population composition effects are less
significant as economic development proceeds.
Total
expenditure elasticities also become less significant as
322
real per capita total expenditure increases, implying that
the "pure" effect of income on food demand becomes less
significant as economic development proceeds.
Substantial substitution and complementary
relationships, even between the plant origin foods and the
animal origin foods, were observed in the three countries.
Therefore, the own and cross price effects on food demands
are expected to be more important at higher levels of
economic development.
2. Expenditure elasticities change during the stages of
development or real per capita income growth.
patterns of change are not singular.
However, the
It was expected that
total expenditure elasticities would decline from (high)
positive magnitudes to zero or negative.
different trends were observed.
In fact, several
The "pure" expenditure
elasticities for animal origin foods were sometimes
negative, even for low income levels, and then increased for
higher levels of income.
There are several possible explanations.
The
previously observed trends in income elasticities may be the
results of biased estimation.
Note that if Engel curve
analysis is applied to the same data sets, these results are
not observed and "reasonable" figures for expenditure
elasticities could be obtained.
(This is apparent from the
consumption - time plots, given per capita total
323
expenditures both in real and nominal terms monotonically
increased over time in the three countries.)
However, there
exists no appropriate interpretations for "income
elasticities" derived from the Engel analysis.
Thus earlier
findings may be the consequence of misspecification error,
in which important demographic and other socioeconomic
variables were omitted from the analysis.
The finding in this study of negative expenditure
elasticities at early stages of development may be
associated with constraints on food demand caused by the
need to satisfy nutritional requirements at low income
levels.
In any event, breaking down the "real income
effects" is important for understanding the changes in food
commodity demands for future NICs.
324
8.6. Implication for Future Study
The effects of demographic changes on food demand need
to be studied more comprehensively.
It is desirable that:
1) the effects are more carefully studied based on narrower
age categories and/or sex specific classifications in order
to identify the age/sex class(es) having dominant effects on
each commodities demand; and 2) the effects are reconsidered
in the context of other countries' economic development
processes to find general patterns of the effects on food
commodity demands.
The effects of household size were, in general, not
statistically significant in this study.
But this may not
reject the significance of household size effects on food
demands.
As noted earlier, household size effects may be
correlated with other effects such as urban/young household
effects.
To identify the role of household size on food
demands, more appropriate methods and data should be
employed (e.g., cross-sectional analysis).
A limitation of the present study is the choice of
functional form in estimating food demand parameters.
Examination of trends in expenditure elasticities indicate
that the functional form of the model is not flexible enough
to capture the wide range of changes in food demands.
A
more flexible functional form such as a FOURIER form may
provide better results.
325
Negative expenditure elasticities for animal origin
foods were observed frequently in this study, suggesting
that something systematic is at work.
The problem of using
market level data as if they were generated by a single
consumer has been discussed by many researchers.
This
difficulty may lie behind some of the results of the present
study.
On the other hand, this observation is consistent
with the optimization behavior under multi-constraints.
Further inspection by refining data sets and applying
different approaches should be conducted to explore these
theoretically interesting findings.
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APPENDICES
336
APPENDIX
A
LIST OF SIGNS AND ABBREVIATIONS
A.l. Signs for Xathmatical Operations
* = multiplication sign.
/ = division sign.
= "approximately equals to".
exp{ } = exponential function.
log = natural logarithm.
Units
cc = cubic centimeters.
ha = hectare.
kg = kilograms.
nit = metric tons.
Names of Public and Private Organizations
BK = The Bank of Korea.
EPB = Economic Planning Board, Republic of Korea.
FAQ = Food and Agricultural Organization of The
United Nations, Rome.
MAFFJ = Ministry of Agriculture, Fishery, and Forestry
(Ministry of Agriculture and Forestry), Japan.
MAFK
= Ministry of Agriculture and Fishery, Republic
of Korea.
MCA = Management and Coordination Agency (Soinucho),
Japan.
MF = Ministry of Finance, Japan.
OECD = Organization for Economic Cooperation and
Development, Paris.
OPM = Office of The Prime Minister (Prime Minister's
Office; Sorifu), Japan.
TKS = Toyo Keizai Shinposha, Tokyo, Japan.
(A Publishing Company.)
337
USDA
= United States Department of Agriculture.
Names of Economic Models and Functional Forms
AIDS = Almost Ideal Demand System.
LA/AIDS = Linear Approximate AIDS.
LA/S/AIDS = Linear Approximate-Scaling AIDS.
LA/H/AIDS = Linear Approximate-Habit AIDS.
LA/S/H/AIDS = Linear Approximate-Scaling-Habit AIDS.
QES = Quadratic Expenditure System.
TL
= Translog (Transcendental Logarithmic).
TTL
= Third-order Translog.
Names of Statistical Estimation Methods
GLS = Generalized Least Squares.
OLS = Ordinary Least Squares.
SUR = Seemingly Unrelated Regressions.
Others
CPI = Consumer (Retail) Price Index.
FBS = Food Balance Sheet.
HES = Household Expenditure Survey.
IRP = Implied Retail Price.
= Expenditure for good i divided by Quantity
Purchased for good i.
LTES = Estimates of Long-Term Economic Statistics of
Japan Since 1868 (Choki Keizai Tokei).
N.A. or n.a. = Not Available.
RPS = Retail Price Survey.
SARP = Strong Axiom of Revealed Preference.
SNA = System of National Account of The United
Nations.
v.1. = Various Issues.
WARP = Weak Axiom of Revealed Preference.
WPI = Wholesale Price Index.
338
APPENDIX
B
CHOICE OF THE DEMM4D SYSTEM
B.1. Basic Requirements
The most desired property of the demand system for the
Part II is capable to deal with inferior goods, and
hopefully to portray the change in expenditure elasticities
from positive to negative, and vice versa.
Paying attention to the functional forms of Engel curve
in various theoretical plausible demand systems already
developed, only the three has been found to pass the above
criteria103; which are the Almost Ideal Demand System
(AIDS) of DEATON and MUELLBAUER (1980); the Quadratic
Expenditure System (QES) developed by HOWE (1974), POLLAK
and WALES (1978), or HOWE, POLLAX and WALES (1979); and the
Third-order Trans].og (TTL) mainly developed by NICOL (1988,
1989).
The next desirable property is flexibility for
extensions with some non-economic variables. The first two
models listed above, particularly the AIDS model, have been
widely applied and, in some cases, modified for particular
study objectives. Followings are just a part of these
applications:
103
One more possibility was presented by THOMAS,
STPAtJSS, and BARBOSA (1989).
They introduced a quadratic
term of log of real expenditure to the AIDS model. Although
their approach is very interesting, it is thought to be
theoretically less rigorous in the sense that the connection
to the corresponding cost or utility functions is not clear.
We deeply appreciate Professor James A. CHALFANT at
University of California, Berkeley for providing their paper
and other valuable information to us.
339
Table B-i: List of Applications of the Three Demand Systems
with Additional Features
Additional Features
Dynamic Aspect
Demographic Asiect
Model
AIDS
QES
BGK 1983,1986
RAY 1984
RAY 1980,1982,1986
TSB 1989
HPW 1979
PW 1978,1980
BG 1984
STRAUSS 1986
Others
CHALFANT 1987
NICOL 1988,1989
TSB 1989
TTL
Abbreviation:
BG
BGK
HPW
PW
TSB
-
BARNES and GILLINGHAM.
BLANCIFORTI, GREEN, and KING.
HOWE, POLLAK, and WALES.
POLLAK and WALES.
THOMAS, STRAUSS, and BARBOSA.
These previous efforts for the model extension are quite
helpful and providing a good guideline.
Interestingly,
there has been little applications to include both dynamic
and demographic aspect into complete demand systems.
Next thing to consider is the cost of calculation; this
is less important in terms of theoretical development but
sometimes critical in practice. The smaller number of
parameters and the less nonhinearities in parameters are two
desirable properties.
In the following, first, we briefly summarize property
of the AIDS model, then, compare the AIDS with the QES, and
with the TTL consecutively to determine which model to use.
B.2. The AIDS Model
It is convenient to review how the original AIDS model
was constructed to understand why it deserves to be called
"almost ideal" named by the creators, DEATON and MUELLBAUER.
They started with specifying preference based on the
theorems of MUELLBAUER (1975, 1976) on a general form of
340
cost functions satisfying exact nonlinear market aggregation
condition, called Generalized Linearity (GL) form and its
subclass of Price Independent Generalized Linearity in
logarithmic form (PIGLOG) of NUELLBAUER (DEATON AND
MUELLBAUER, 1980a, pp. 154-6; and l980b, p. 313). The model
satisfying this property can be "the representation of
market demands as if they were the outcome of decisions by a
rational representative consumer" (1980b, p. 313).
Next,
DEATON AND MUELLBAUER specified some estimable functional
forms for unknown functions of prices in the PIGLOG class
cost function using the second-order TAYLOR expansion.104
Invoking SHEPHARD's lemma, the original AIDS demand
functions were derived. The AIDS model provides local
first-order approximation to any true demand system, whether
derived from utility-maximizing behavior or not (1980b, p.
315).
It is optional to make the model satisfy the
utility-maximizing assumption by imposing homogeneity
conditions and/or symmetry conditions. Alternatively, it is
possible to test these conditions statistically; then impose
what has been not rejected.
The AIDS demand equation for i-th good is written as
Wi = ai + E nj logPj + i3i log(X/Po)
where
(B-i)
Po = ao + E
104
aj logPj + 1/2 EjEk rjk logPj logPk
This approximation is only reliable locally at the
point of expansion which is usually around mean.
WOHLGENANT, who employed the FOURIER approximation technique
in his demand study, stated that "as emphasized by
WHITE(1980) and by GALLANT (1981), no known statistical
More over, even if
properties flow from TAYLOR's theorem.
the approximation is valid for some points in the sample,
there is no assurance it will hold at other data points"
(WOHLGENANT, 1986, p. 35). CHALFANT (1987) featured the
FOURIER series approximation technique for this part, and
his modified AIDS model attained globally valid
approximation with respect to prices.
341
Po is considered to be a price index. There are several
First, the dependent variable Wi, the
things to be noted.
i-th budget share, itself is not endogenous. Wi = PiQi/X,
and Pi and X are assumed to be exogenous. Therefore, the
truly endogenous variable is only Qi. Sometimes it is
convenient to rewrite the equation such as
Qi = X/Pi
cxi +
E nj (X/Pi logPj)
+ 131 [X/Pi log(X/Po)
to calculate various elasticities and so forth.
]
(B-2)
The AIDS
model (D-1) requires, with adding-up restrictions only,
(2+n) (n-i) parameters to be estimated in the system as a
whole, and 2+(n-l)n parameters in each equation. In case of
9 commodities the number of the parameters to be estimated
is 74 in each equation; this raises the degree of freedom
problem particularly with annual time series data. Besides,
the price index Po has to be estimated simultaneously,
non-linear estimation is required. Then, DEATON and
MUELLBAUER introduced the simpler version of the AIDS model
by replacing Po with predetermined price index proposed by
"In
DEATON and MUELLBAUER pointed out:
STONE (1953).
situation where prices are closely collinear, it may well be
adequate to approximate P0 as proportional to some known
index P, say", which is "widely occurring (circumstances)
Thus,
in time-series estimation" (1980b, pp. 316-7).
it is reasonable to assume in time-series as
P0
*
0 P
Applying the STONE'S Index for P
logP*
=
E
Wj logPj
342
This modification makes the AIDS estimable with linear
regression technique with substantially smaller numbers of
parameters. The resulting model is
Wi = ai + E nj logPj + 8i log(X/P*)
(B-5)
where P is the STONE's index defined as (B-4). It is
sometimes more convenient to write this more explicitly as
nij logPj
Wi = (ai - 13i 1og) +
+ Ji [logX - E
Wj logPj)
(B-6)
Or a little more compactly as (note this is the form used in
actual estimation procedure; ai is not estimable as long as
0 is not known)
Wi = a*i + Enij logPj + 13i logX*
where
a*i = ai - 13i log
logX* = logX - E Wj logPj
(B-7)
This version is called the linear approximate version of
AIDS or LA/AIDS, following BLANCIFORTI, GREEN, and KING
(1983, 1986).
comparing the two systems of the AIDS and
LA/AIDS, DEATON and MUELLBAUER concluded that "the (LA/AIDS)
is an excellent approximation of the (AIDS)" (1980b, p.
317).
The Engel curve derived from AIDS model is called
semi-log in budget share, the one used by WORKING (1943) and
LESER (1963):
WI = ai + bi logX
(B-B)
343
Or, solving for Qi,
Qi = a*i X + b*i X logX
(B-9)
where
a*i
= ai/X
b*i = bi/Pi
This form permits luxury goods in one case (bi > 0), goods
shifting from necessity to inferior in other case (bi < 0 ),
and unit income elasticity case as a third possibility (bi =
0)
(DEATON and MUELLBAUER, 1980a, pp. 19-20):
Figure Bi:
The AIDS Enge]. Curve
/3>0
q £
U nit
elasticity
fJi=0
Luxury
f3<0
Necessity
Note:
Inferior
This figure shows the relationship presented in
(B-9).
/3i in the figure is equivalent to bi in
(B-9)
Source:
Copied from DEATON and MtJELLBAUER, 1980a,
P.20.
344
B.3. AIDS vs. QES: A Curvature Problem
SINGH, SQUIRE, and STRAUSS (1986) pointed out that
(The AIDS model) only permits a limited amount of
nonlinearity in the Engel curves. It also restricts
Engel curves to zero intercepts. Although this might
be intuitively acceptable, real world incomes ... are
sufficiently far from zero ... If the functional form
used to fit the Engel curve has sufficient curvature,
this will be less of a problem. ... however, the AIDS
does not have much curvature. The consequences of
this are twofold: Engel curve slopes may be badly
estimated even at the sample mean, and changes in the
slopes as income changes may be missed. These
consequences will be most damaging when the real Engel
curves are very nonlinear, as might be expected when
commodities are more highly disaggregated.
This
problem can be solved by using Engel curves with more
curvature or by introducing nonzero intercepts, or both
(p.
61)
On the other hand, BRAVERNAN and HAMMER (1986) justified
their use of the AIDS model under the situation with "rapid"
changes in quantity demanded for "sufficiently
disaggregated" food items (p. 239).
If some other variables are introduced to the model to
vary intercept more flexibly, the problem will be surely
reduced.
On the other hand, if some other specification of
Engel curve is applied, as is done by the QES, the
estimation procedure may become more complex and expensive;
nonlinear and a larger number of parameters will be required
to be estimated.105 Further note that the fundamental
conditions of the demand theory other than adding-up, i.e.,
homogeneity and symmetry conditions are already imposed on
the QES; if the true market behavior does not follow the
utility maximizing principle, the result will be biased
105
For the description of the QES, see POLLAK and
WALES 1978, 1980; HOWE, POLLAK, and WALES 1979; BARNES and
GILLINGHAN 1984; and STRAUSS 1986.
345
since the model is misspecified.
Although the QES allows
more flexibility in Engel curves, it is more restrictive in
other aspect than the AIDS model.
In addition, the QES is
less advantageous in calculation since it is nonlinear in
parameters and having large numbers of parameters as well as
the original AIDS is, but there is no simplified version
exists such as the LA/AIDS for the AIDS. Again, the
curvature restriction in the AIDS is not definitely a
shortcoming; it still can deal with an inferior good, and
how badly it approximates the reality or how well the QES
improves over it is never known since those two cannot be
nested on each other.
Considering the cost and benefit the AIDS was chosen
over the QES.
B.4. AIDS vs. TTL
In a recent paper, Christopher J. NICOL suggests the
"reinterpretation" of the AIDS model from the point of more
general framework of the third-order tianslog model (TTL)
(NICOL, 1988, pp. 117-29). Note that the TTL is flexible
than AIDS. The author reveals the implicit restrictions
embedded in the AIDS; then the original AIDS model is called
"restricted" model.
Comparing the original "restricted"
AIDS by nesting it both on the "unrestricted" version of the
AIDS and on the TTL, the author concluded that the original
AIDS model is reasonable enough.
Most interestingly, the TTL demand function with
adding-up restriction has quadratic total expenditure term.
However, before the homogeneity restriction is imposed, this
term has to have been vanished; since that requires the
Marshallian demand function to be homogeneous of degree zero
346
Ifl P and X (see, for example, PHLIPS, 1983, pp. 34_8).106
This suggests that whenever the AIDS model is applied and
find that homogeneity restriction is rejected, it is
reasonable to choose the TTL and test the possibility of the
existence the quadratic expenditure term.
While the TTL is interesting and easier to estimate
than the original AIDS which is nonlinear in parameters, the
AIDS has the simpler LA/AIDS using the STONE's index. Also,
AIDS has an advantage of having many applications done so
far, which are valuable source of information on
modification of AIDS model. As we will see in the later
section, there are totally 13 to 15 variables in our full
model: 1 expenditure term, 8 to 9 price terms, 3 to 4
demographic terms, and 1 habit term in each demand
equations. The straightforward application of TTL demand
function requires (n2 + n) / 2 for each equation; in this
case, 91 to 120 parameters have to be estimated. It is too
many to be estimated. As an alternative, one may wish to
set some of the parameters to be zero arbitrarily to reduce
the size. This type of modification, however, may destroy
the meaning of the original specification severely; the
resulting model may have little theoretical meanings.
Although the TTL is less restrictive than the AIDS in
theoretical point of view, it is not possible to apply it
adequately for our case.
106
In the case of the AIDS model, X is divided by some
price index P which is homogeneous of degree one. Thus, X/P
is always homogeneous of degree zero in X and prices.
347
Considering all of the above, the LA/AIDS model was
chosen as a basic demand system for our study.
348
APPENDIX
C
EXTENSION OF THE LA/AIDS MODEL
This section explains 1) how habit formation has been
introduced into the LA/AIDS model, and 2) how demographic
variables has been introduced into the LA/AIDS model, with
considering some alternative methods for each process. Note
first that some compromises had to be made in both of the
modification procedures due to insufficient number of
observations in the data sets.
I believe, however, that
those compromised points do not harm the outcomes at all as
long as the limits are well understood.
C.l. LA/AIDS with Habit Formation
BLANCIFORTI, GREEN, and KING (1986) introduced two ways
to "reflect persistence in consumption patterns" in the AIDS
model; one is their own method, and the other is due to RAY
(1984)
(pp. 8-9).
First RAY's specification is reviewed.
His strategy
was replacing the constant term in the AIDS cost function,
O, with
ao = a*o + E
cZ
The resulting cost function is
logC =
+ E
1j Qi-1 +
cr*j logPj
+ 1/2 EjEk T*jk logPj logPk + U Bo
H
Pj'
(C-1-2)
Invoking
Wi = 3logC/3logPi
(C-1-3)
349
The corresponding AIDS demand function is
Wi = a*i + E nj logPj
+ 13i [logX- a*o - E flj Qit-i. + E
- 1/2 EjEk r*jk logPj logPk]
a*j logPj
(C-1-4)
This demand system adds-up globally, however, "the only
way in which habits shift the ordinary demand function is
through an income effect" (p. 9). This is because
(j = l,...,n) are "deflating" the expenditure X together
with price terms.
BLANCIFORTI, GREEN, and KING proposed a functional form
for the AIDS demand function where
term is working
through two channels: one is through 13's; note that these
contain not only the effect of expenditure but also the
cross prices. The other one is through the constant term to
measure the effect independent of price and income.
Following the scheme taken by POLLkK and WALES (1969) and
MANSER (1976), BLANCIFORTI et al. (1983, 1986) modified the
constant terms in the original AIDS model, ai (i =
as
ai = a*i + fli Qit-1
(C-1-5)
The resulted modified AIDS model is
Wi
a*i +
+ E
+ 13i [logX - ao
'nj logPj
-
E
(*j
+
- 1/2 EjEk T*jk logPj logPk]
which is called "Dynamic AIDS" or D/AIDS.
j Qi-) logPj
(C-1-6)
350
It has a corresponding cost function as
logC =
ao + E
(a*j
+ flj Qi-i) logPj
+ 1/2 EjEk rjk logPj logPk
+ U 130 ll
BJ
(C-1-7)
This improvement over RAY'S cannot be taken without
costs; that is, D/AIDS proposed by BLANCIFORTI et al. adds
up only locally, thus violates the property of demand
function in global sense.
Simpler version of D/AIDS, may be called LA/D/AIDS/BGK
(BGK is abbreviation for BLANCIFORTI, GREEN, and KING) with
replacing P term with the STONE's index must be
wj
(*j
- 131 log) + czi
+ 131 [logX - E
Qit_i + E nj logPj
Wj logPj]
(C-l-8)
In other words,
Wi = LA/AIDSi + fli
(C-l-9)
The corresponding adding-up conditions are
EL ai =
where
= 0, E
ai =
a*i - 13i logq
Ti =
0, E
13i = 0
(C-i-b)
Notice that by the use of STONE's index, the system has
become much simpler; however, since the Qi1 term only
shows up independently as the intercept shifter, the
original advantages over RAY's model mentioned by
BLANCIFORTI et al. has vanished.
351
As a counterpart, applying the RAY's dynamic scheme for
the LA/AIDS, the resulting model called LAID/AIDS/RAY will
be
Wi
(a*i
- 13i log) + E nj logPj
+ 13i [logX - E
logpj
Wj
- E
1ij Qi)
(C-i-il)
That is,
Wi = LA/AIDSi - E
K*ij
Qi-1
where iij = 13i flij, i,j = 1,...,n
(C-l-12)
with the adding-up conditions
E1- ai = 1, E
where
ai =
ni = 0, E
13i = 0, E Kij = 0
a*i - 13i logØ
Note that this requires only linear estimation.
(C-1-13)
flij is
always determined by
flij = K*ij/13i
(C-l-14)
Notice that the LA/D/AIDS/BGK (C-1-9) can be considered as a
special case of the LAID/AIDS/RAY (C-1-12), operationally.
That is, setting fZij = 0 for i is not j, the LA/D/AIDS/RAY
reduces to the form that is practically equivalent to the
LA/D/AIDS/BGK, although the 1ii in the former has the
different meaning from the fli in the latter. For n
commodities case, the LA/D/AIDS/RAY requires n additional
parameters to be estimated for each equation, whereas the
LA/D/AIDS/BGK requires only one additional parameter for
each equation. Then, the LA/D/AIDS/BGK has a practical
advantage over the LA/D/AIDS/RAY when n is fairly large but
not so many observations are available, such as the case of
this study. Again, the demerit of the LA/D/AIDS/BGK
352
specification is the loss of the adding-up property for the
habit coefficients, 12i, I = 1,. ..,n.
The LA/D/AIDS/BGK specification was adopted in this
study considering the degree of freedom problem in the
estimation procedure.
Then, the Part I and the Part II are connected by
replacing Qit-1 terms in the LA/D/AIDS/BGK with Q*it_l terms
estimated in the Part I.
The resulting model will be
referred as LA/H/AIDS where "H" stands for "Habit":
Wi = (ai - Bi log) + Erij logPj
+ 131 [iogX - E
+
fl
1 Q it-i
Wj logPj]
(C-l-15)
353
C.2. LA/AIDS with Demographic Variables
C.2.l. The Methodological Alternatives
To incorporate demographic variables into any ordinary
(Marshallian) demand function, many studies have been done
to search for any theoretically reasonable possibilities.
POLLA}Z and WALES (1981) is a good summary of these
possibilities; DEATON and MUELLBAUER (1980a) has a chapter
concerning the point (chapter 8); also, DEATON (1981a)
describes the treatment of demographic variables through an
empirical application, which is highly recommended for those
who look for intuitive understandings for the nature of the
problem.
Followings are largely owing to these three
articles.
When people speak of demographic variables in demand
study, many times it is related to the measurement of
welfare level of an individual or a household. One strategy
frequently taken is to design a deflator for each individual
(household) utility level, by which each individual's
(household's) utility is "normalized" for the purpose of
comparison.
Any observable differences in demographic
characteristics of individuals (households)
are commonly
used for the material of the deflator.
The deflator is
sometimes called "(adult) equivalence scale", which is a
relative weight against some standard (or representative)
class of individual (household) designed in each study.
Because of the nature of the problem for which
demographic variables are usually adopted, the ways many
authors have dealt with them are quite different from the
procedures applied in the habit formation scheme.
POLLAX and WALES (1981) introduced five different
specification for the problem which are "demographic
scaling" developed by BARTEN (1964); "demographic
translating" by POLLAK and WALES; "GORMAN specification" by
GORMAN which is the combination of the first two, doing
first scaling then translating; "reverse GORMAN
354
specification" by first translating then scaling; and
"modified PRAIS-HOUTHAKKER procedure".
It was decided to adopt the demographic scaling method
by BARTEN since some previous studies indicate it obtains
superior results than other method. For example, DEATON and
MtJELLBAUER (1980a) compared the BARTEN scaling method and
the PRAIS-HAUTHAKKER model and concluded that "(the
PRAIS-HAUTHAKKER model's) embarrassing consequences
seems sufficient to suggest that it is not a suitable model
of household composition effects. The BARTEN model, on the
other hand, seems more plausible" (p. 204; emphasis is in
original). Another example is POLLAK and WALES (1980)
compared the scaling and the translating with the QES model
and the GTL model (the Generalized Translog model proposed
by themselves). They concluded as follows:
Although demographic translating and demographic
scaling are not nested specifications, some comparison
of these two procedures is possible. For both the QES
and the GTL, demographic scaling always results in a
higher likelihood value than demographic translating
(p. 609)
They admitted similar results with other type of demand
system called "generalized CES" model. They concluded:
"translating made the weakest showing while the two GORMAN
procedures were dominated by scaling and the modified
PRAIS-HOUTHAXKER was best of all" (POLLAK and WALES, 1981,
p. 1547)
On the other hand, in a recent paper RAY (1986)
compared his new method called Generalized Cost Scaling
(GCS) with the BARTEN's scaling with nesting the latter on
the former by taking advantage of the fact that the latter
can be the special case of the former. The demand system
used was the AIDS model, and the author claimed that "as the
empirical results show, (the scaling method) is easily
355
rejected on nested likelihood criterion" (RAY, 1986, p.
266)
At any rate, there is no panacea to be the best for
every case, analogous to the case of choice of functional
form for demand functions.
However, as mentioned, scaling
is relatively easier than other method such as GCS and
empirically supported frequently among the methods with same
level of complication.
C.2.2. The BARTEN's Scaling Method
According to DEATON and MUELLBAUER (1980a, p.197),
starting from the utility function specified for n goods
case as
U = v(Q1/M1, Q2/M2,
...
,
Qn/Mn) = v(Q/M)
(C-2-1)
where Mi is so called "scaling factor" (POLLAK and WALES,
1981, p. 598), is function of population (or household)
characteristics consists of k pieces of attributes
(demographic variables) Dl, ..., Dk, which can be written as
Mi = Mi(Dl,
..., Dk) = Mi(D)
(C-2-2)
According to DEATON (1981a, p.48), "Mi are commodity
specific measures of needs depending on the household
composition D." The examples of D's are such as number of
family members, number of children, average age of family
members, and so forth.
On the specification of functional form for Mi, POLLAK
and WALES (1981) proposed "linear demographic scaling" as
Mi(D) = 1 + Sil Dl + 8i2 D2 + ... + Si]c Dk
= 1 + E Sij Dj
(C-2-3)
356
Alternatively, using COBB-DOUGLAS type specification
Mi(D) = Dl1 D2-2
=
ll
...
Dlnäk
(C-2-4)
Dj6'-J
which was used by BARNES and GILLINGHAM (1984) with the QES
model.
Following DEATON and MUELLBAUER (1980a, p. 197), in the
above utility function (C-2--1), let
Qj*
pj*
= Qi / Mi(D)
(C-2--5)
= Pi
(C-2-6)
X
Mi(D)
The budget constraint can be rewritten using vector notation
as
X = P'Q
p*IQ*
(C-2-7)
By this modification, the BARTEN problem reduces to:
max U = v(Q*)
s.t. X
(C-2-8)
The dual problem for this is (p. 38)
mm
C
p*tQ*
s.t. U = v(Q*)
(C-2-9)
Solving the dual problem to obtain the Hicksian demands as
Qj* = Hi(U,P*)
(C-2-1O)
357
Qj*t
Substituting
X at the optimum
into the budget constraint, and since C =
p*H(UP*) = C(U,P*)
(c-2-ll)
which is the resulted cost function.
Invoking SHEPHARD's lemma and ROY's identity, via the
indirect utility function, the general form for the
Marshallian demand is obtained as
Qi = Qi(M1P1, M2P2,
MnPn, X)
(C-2--12)
Next, consider a Marshallian demand in share form.
Define Wi* as
Wi* = (Pi*Qi*)/x
(C-2-13)
By definition,
Wi* = (MiPi)(Qi/Mi)/X = (PiQi)/X = Wi
(C-2-14)
Therefore, for the share form demand function
Wi = Wi(Pl, P2, ..., Pn, X)
(C-2-15)
the corresponding "scaled" form is
Wi* = Wi(M1P1, M2P2, ..., MnPn, X)
(C-2-16)
Given this result, consider modifying the LA/AIDS with
the BARTEN scaling method. Straightforward application of
the result yields
Wi* = LA/AIDSi(M1P1, M2P2, ..., MnPn, X)
(C-2-17)
358
or compactly
Wj* = LA/AIDS1(P*, X)
(C-2-18)
That is,
Wi* = (ai - 81 log) + Bi [logX - E
Wj log{Pj Mj(D)})
+ E nj log{Pj Mj(D)}
(C-2-19)
By the way,
log{Pj Mj(D)} = logPj + logMj(D)
(C-2-20)
then, Mj(D) must be multiplicative for the estimation
purpose; i.e., the linear demographic scaling specification
cannot be applied. Applying the COBB-DOUGLAS specification
(C-2-4) for k demographic variables case,
log{Pj Mj(D)} = logPj + Sjl logDl + 6j2 logD2
+ ... + Sjk logDk
(C-2-21)
After some calculation, (C-2-19) is rewritten as
Wi* = (ai - 61 logØ) + 13i [logX - EjEk Wj (Sijk logDk)]
+ Enij [logPj + Ek Sijk logDk]
(C-2-22)
Thus,
Wi* = LA/AIDSi - Bi EjEk 6ijk (Wj logDk)
+ EjEk nj Sijk logDk
(C-2-23)
This system has a problem: It does not add-up nicely
with some ordinary linear restrictions due to the last term
above.
To see this, considering the case with 2 goods and 2
demographic variables.
359
The last term is written as
ru
(Si].11ogDl + 6il2logD2) + ri2 (6i2llogDl + 6i22logD2)
= (rilSil]. + ri2Si2l) logD]. + (rilSil2 + ri26i22) logD2
(C-2-24)
For the parameter such as (rilSill + ri2Si2l) there is no
such a restriction exists that guarantees the adding-up with
respect to i.
Giving up imposing the right restriction implied by the
true relationship for the coefficients of logAk, restate
them as
01k = (ru
Silk + ri2 Si2k + ... + tin Sink)
= E nj Sijk
(C-2-25)
If cross-equation restrictions as
E
Oik = 0 for all k = l,...,k (1 = l,...,n)
(C-2-26)
are given to the system, then the system will add up nicely.
Note that the parameters Sijk are never recovered.
For n goods and k demographic characteristic case, the
additional (nk)+k = k(n+l) parameters have to be estimated
in each equation of the LA/S/AIDS.
The resulting model is the following:
Wi* = (cr1 - 13i logØ) + 13i [logX
- E
+ E nj logPj + Ek Oik logDk
- 13i EjEk Sijk (Wj logDk)
Wj logPj]
(C-2-27)
i,j = 1,...,n; k =
This specification will be referred as LA/S/AIDS, where "Sn
stands for "Scaling".
360
C.2.3. Further Restrictions
Consider the situation that this "scaling" type method
is applied for the LA/H/AIDS, which will be referred to
LA/S/H/AIDs. Total number of parameters to be estimated in
the each share demand equation will be (n+2)+l+k(n+l) =
n+k+kn+3 for n goods and k attributes (demographic
variables) for the LA/S/H/AIDS specification (with adding-up
restrictions only); (n-I-2) required for LA/AIDS, 1 additional
for "habit formation" by the BGK formula, and k(n+l)
additional for the "scaling" type method.
In this study,
(n,k) = (9,4) at maximum, therefore totally 52 parameters to
be estimated in each equation at maximum. The degree of
freedom problem has to be considered again; i.e., some
parameters have to be excluded somehow.
Searching for alternatives, although this is totally ad
hoc, the model (C-2-27) is modified with dropping all of the
(Wj logDk) terms (superscript for Wi is dropped):
Wi = (i - Bi log) + Bi [logX - E Wj logPj]
+ E nj logPj + Ek Oik logDk
(C-2-28)
= l,...,n; k= 1,...,k
This is the final specification for the LA/S/AIDS, which
adds up nicely. By this modification, n times k degrees of
freedom have been saved.
For example, with (n,k) = (9,4),
36 degrees of freedom has been saved.
How have other researchers been collaborating
regard? There are a few studies on application of
model with demographic variables, and as long as I
which are all done by RAY (1980, 1981, and 1986).
on this
the AIDS
know,
In every
case, the author introduced only one demographic variable
into the model.
For the first two studies the author used
the same specification, namely the AIDS model with BARTEN
type method.
They ended up with the same form as the one I
361
arrived with compromises.
On the other hand, the third
study in 1986 utilized a different approach, which is
further complicated and very expensive method.
If one really needs to calculate the adult equivalence
scale to measure an individual's or a household's welfare
level at micro level precisely as possible, then we should
have employed a method like the one by RAY (1986). However,
the real concern in this study is not considering welfare
level of each different type of individual nor household
based on the micro relationships, but considering
demographic aspect in changes in food consumption pattern of
the average or representative individual based on the market
relationships. Demographic variables are regarded as demand
shifters; they are introduced for eliminating the demand
fluctuation explained by non-economic factors, so that the
theoretical relationship predicted by economic theory is
able to be captured in the more pure sense.
DEATON and MUELLBAUER (1980a) noted
The basic point is that, in general, "micro" and
"macro" functional forms can tell us relatively little
about one another. In particular, it is extremely
dangerous to deduce microeconomjc behavior on the basis
of macroeconomic observations, particularly if such
deductions are then used to make judgements about
economic welfare (p.163).
Even though my specification has some obvious deficiency, as
long as these additional information on demographic effect
can give our model more sense than without having them at
all, it will be satisfactory since measuring neither
"equivalence scale" nor welfare level will be attempted.
362
APPENDIX
D
A DATA COMPILING METHOD: THE RATIO METHOD
Usually it is not possible to connect any two different
data sets directly. For example, it is practically observed
that even though the name of the product and sampling area
are identical, different inspecting agents report different
results because each agent has a different channel of
information. The "ratio method" is applied to overcome this
kind of problems. The basic idea is to utilize a trend
contained by any existing data sets.
The method can be described as follows.
Suppose there
are two kinds of data sets Dl and D2 measured in the same
unit, which have some overlapping period such as between ti
and t2 in the Figure D-l:
Figure D-l:
Illustration of the Ratio Method
Data Sets
Time Series
Dl
D2
Time
tl
t2
It is possible to extend Dl after t2 period, or D2 before tl
period.
Notice that the result will be different in figures
between the two cases. Suppose Dl is a desirable data set
and D2 is a supplement for Dl. In this case, Dl should be
used as long as possible. A strategy is using Dl fully up
to t2, then connect D2 after t2. The connection can be done
by using an average difference between the two sets. The
difference in each period is calculated by taking the ratio
of the two, i.e., calculating (Dl/D2) or (D2/D1).
In the
present case, (Dl/D2) is calculated for every period between
ti and t2, then the average ratio, say Ri is calculated.
By
363
multiplying D2 with Ri, an estimated data after t2 period in
terms of Dl is obtained.
This estimated data may be called,
"Di equivalent data". Also, the average ratio Ri. will be
called a "conversion factor (rate)" to make D2 into Di.
equivalent.
If D2 has a priority, then D2 should be used
fully, and Dl can be connected to D2 by calculating (D2/Dl),
taking the average ratio, and multiplying Di by the number,
say, R2. Note that these two procedures should give
different data sets particularly when the conversion factor
(rate) is calculated based on data for more than two
periods. As in the former case, if Dl has a priority and D2
is used as a supplement, it is said that "D2 is adjusted to
Dl" by the ratio method. In the latter case, it is said
that Dl is adjusted to D2 by the method.
A conversion rate is not always calculated based on the
entire overlapping period. If the ratio is changing with
the trend, i.e., if it changes consistently as time goes by,
using the conversion factor (rate) based on the whole
overlapping period may be misleading. For example, if the
ratio (D1/D2) at ti is 0.4 and is consistently increasing to
0.8 at t2, then the choice of Ri should be made with
caution.
If the average ratio, say 0.6, was used, the
entire data Dl after t2 will be multiplied by 0.6; whereas
if the other rate 0.8 at t2 is used the data after t2 will
be multiplied by it and the result will be larger than the
former result.
The choice of 0.8 in stead of 0.6 may be
justified if the unobservable relationship between data Dl
and data D2 after the time period t2 is expected to have a
closer trend observed around t2 than around the mid-point
between ti and t2. The choice of a conversion factor (rate)
is very important since a large portion of data may be
affected.
364
APPENDIX
E
FARMER'S HOME CONSUMPTION IN JAPAN
There were some evidences to support the importance of
the issue of farmer's home consumption (see Table E-1). In
the early twenties century in Japan, the farmer's home
consumption rate was close to lOO6 for the crop like barley
and naked barley, meaning that almost every production
amount was not going into the markets in the city places.
Clearly, according to the table, as the economy grew the
home consumption rates became lower, but the rate of change
was different among the goods.
It may be said that the home
consumption rate tends to decline at a faster rate for the
good with the higher popularity or markettability. The case
of rice may be one good example of a popular cash crop.
Interestingly, it is observed that barley and naked barley
seemed to become a cash crop rather suddenly after the
1930's; which probably started after the mid-1930's due to
the shortage in the food supply under the massive
militarization.
365
Table E-l: Farmer's Home Consumption Rates of Farm Products
in Japan(%)
Rice
Paddy Rice
Field Rice
Wheat
Barley *
1900
1910
1920
1930
1938
62.96
56.56
48.73
38.01
34.13
34.00
42.00
31.00
95.12
Two-row Barley
Six-row Barley
Naked Barley *
Soybeans
Other Beans **
Red Beans
Peanuts
Sweet Potatoes **
White Potatoes **
Cow Milk
Hen Eggs
Source:
Notes:
95.13
95.13
95.12
57.57
86.11
86.11
86.11
86.11
57.18
51.43
46.16
39.65
30.98
29.42
51.43
51.43
Percentage of
Labor Force in
69.96
Agriculture, Forestry,
and Fishery.
(taken from AGPOPJ)
Barley
Naked Barley
1964
1931
84.10
99.93
18.00
55.00
59.00
55.00
46.16
46.16
39.65
39.65
30.98
30.98
29.42
29.42
33.00
21.00
29.00
45.00
6.00
14.00
63.01
54.48
49.70
46.04
24.68
1932
99.81
80.12
1933
99.75
75.85
1934
99.24
93.01
1935
92.76
81.66
Average
95.12
86.11
1900-1940: SINOHARA's estimates; J8.
1964: Official estimates by MAFFJ; J4, 1965, p.118.
* - SHINOHARA assumed the constant rate for the
period before 1931 for barley and naked barley.
The rates were the five years average of the
author's own estimates for 1931-35, which are
reproduced at the bottom of the table (J8,
p. 58)
** - The rates for sweet and white potatoes and
other beans in SHINOHARA's estimates were
common, which were assumed to be identical with
those for vegetables (not cited here). The rate
of change in the home consumption rates before
1930's were assumed to be identical with the
case for rice in the author's estimation
procedure (J8, p. 59).
366
APPENDIX
F
THE CHOICE OP RETAIL PRICE DATA:
RPS VS. HES
In this section, two types of price data from the
Retail Price Survey (RPS) and the Household Expenditure
Survey (HES) are contrasted to consider which has more
desirable properties in the food demand analysis with the
Food Balance Sheet (FBS) quantity data.
As a background,
the difference between a product and a commodity is
considered first. We define a product by the finest
classification of the final goods consumed directly by
consumers.
A commodity is considered to be a group of
similar products. More specifically, following the notion
of EDWARDS (1969, p. 9), we define a commodity as a
composite of products with very high substitutability and
with the same categorization code, such as beef or pork.
In
this way, beef is considered to be a commodity as it
consists of various kind of beef products, for example,
sirloin beef, beef rib, beef filet, and so on, and beef and
pork are always separated. Note that we retain the term
"good" as a neutral term between product and commodity.
Let's take the case of beef.
Note first that in our
analysis, only the raw meat of beef is considered and other
types of processed beef products such as canned beef are
excluded from the commodity category. Beef has a various
kinds and grades, and so the price range is naturally wide.
A RPS price covers very narrowly defined category of beef
products, such as sirloin or rib, to make the data set
consistent overtime.
Thus it is said to have product
specific nature.
It is desirable to have the price
representing the most dominant or popular portion of the
marketed beef.
Problems arise when the representative
portion is changing; which is very likely under a rapid
income growth. RPS data is considered to have an advantage
367
in data consistency and accuracy, however as the other side
of the coin, it is less flexible and may fail to represent
the whole commodity under the rapid socioeconomic changes.
HES implied price data has a different property - that
it is commodity specific rather than product specific.
Usually an individual consumes beef in some mix of cheap and
expensive parts, thus HES implied price data is considered
to represent an automatically weighted average price per
unit of beef commodity consumed by the individual.
Now, consider the consequences of the application of
the two different types of price data for good i - HES price
data (PHESi) and RPS price data (PRPSi) with per capita
consumption quantity data (Qi).
Suppose a sudden rise in a
true commodity price P1 occurs caused by rise in price of
every product in the commodity by the same percentage.
In
this case, PRPSi seems a good proxy for the Pi since both
changes by the same percentage; however, in the context of
demand analysis with the FBS quantity data, this may not be
true.
Due to the existence of inertia in consumption, under
the given income and ceteris puribus situation, an
individual reaction is expected to be the following: 1.
he/she reduces his/her consumption of higher price portion
of the commodity and shifts to the lower price substitutes,
and/or 2. if this is not enough, then he/she cuts back the
total consumption amount of the commodity.
These processes
can be shown in the following diagram, where the length of
arrows represents the degrees of rise in prices and
quantity:
368
Figure F-i: A Hypothesized Situation of Change in Own Price
and Corresponding Change in Quantity Demanded for A
Commodity Consists of Several Products
Initial Change
t
Sequence 1
pi
Sequence 2
PHESiT
Qi
t
PRPSi
* The equal sign holds only if the price of the product
reported by RPS and the true commodity price change
by the same percentages.
In sequence 1, the individual virtually reduces the price of
the commodity by changing the consumption mix within the
commodity classification; this may be referred as a
"portfolio effect". Notice that now there is a gap between
two observed prices PHESi and PRPSi.
In sequence 2, Qi may
be reduced, but not by very much, since the individual has
already adjusted his/her consumption mix for the commodity,
in an attempt to make the reduction in Qi as small as
possible.
Observing the relationship between the change in PRPSi
and Qi one may conclude that Qi is relatively irresponsive
to the change in the own price.
On the other hand, observing the relationship between
the change in PHESI and Qi one may conclude that Qi is
relatively responsive to the change in the own price, since
a relatively small change in the own price results in
relatively small changes in quantity; for a large change in
the own price, a large change in the quantity results.
369
This example shows that the movement of price and
quantity may or may not show a clear inverse relationship in
some cases, which must depend both on the nature of the
commodity and the combination of the price and quantity
data.
When demand for a largely compounded commodity is
considered, attention should be paid to the change in the
composition within the commodity. The use of price data
from RPS does not take into account this step, and therefore
may be misleading. The use of price data from HES may not
be the perfect solution but it may be the better proxy of
the price changes.
370
APPENDIX
G
WARP AND SARP TESTING PROCEDURES
The basic ideas and procedures of the tests for WARP
and SARP are summarized in this section.
Given time series data with n commodities and t period,
so called "expenditure matrix" X is constructed first.
typical element X(i,j) is defined as
X(i,j) = Pi x Qj
Its
i,j =
where Pi is the i-th period price vector (Pli,P2i,...,Pni)
and Qj is the j-th period quantity vector (Qlj,Q2j,...,Qnj).
Note that the diagonal elements of X matrix, X(i,i), are
actual choice in each period i (CHALFANT and ALSTON, 1988,
p. 398).
This means we construct a feasible set of "choice" with
every observed sets of prices and quantities hypothesizing
the consumer(s) can rationally judge any combination of
prices and quantities across time periods. For example,
with the data for the 1915-85 period, the consumer(s) is
(are) hypothesized to be informed total expenditures given
by the price vector of 1985 and the quantity vector of 1915
as a feasible choice in 1985, and the price vector of 1915
and the quantity vector of 1985 as a feasible choice in
1915, for example. Thus, virtually the realistic meaning of
time is not applied here: the consumer(s) is (are)
hypothesized to be able to go back and forth in time to make
a decision.
In other words, the null hypothesis is that a
representative consumer in each period has the identical
property as an economic entity thus it is possible to
replace a person in 1985 with a person in 1915, say, to
obtain the equivalent outcomes of the consumption quantities
given the same price. Therefore, any inconsistencies found
371
in the consumption behavior shown in X matrix based on the
criteria explained below directly lead one to conclude that
the null hypothesis is incorrect, i.e., people from
different time periods have different preferences; in other
words, there is a shift in the preference.
Define "i R j" as i-th quantity set (bundle) is
revealed preferred to j-th quantity set (bundle).
WARP is violated if and only if the following two
results are established simultaneously:
at time 1,
at time
j,
X(i,i) > X(i,j)
implies i R j
X(j,i)
implies j R i
< X(j,j)
i,j = 1,...,t
Note that X's are compared with each other in the different
column within the same row; i.e., different quantity
(bundle) is used to calculate X's for the given prices at
each period. One result shows bundle i is preferred to j,
while the other result shows bundle
to 1.
This is a
j
contradiction (CHALFANT and ALSTON,
1988, p. 398;
VARIAN,
1987, pp. 120-4).
SARP is essentially WARP plus transitivity condition
(CAl, 1990). Transitivity is defined among the three
feasible sets of expenditures calculated from the price and
quantity vectors of the any three time period, a, b, and c.
A typical situation when transitivity condition is violated
is the following:
X(a,b) < X(a,a)
implies a R b
X(b,c) < X(b,b)
implies b R c
X(c,a) < X(c,c)
implies c R a
a,b,c = 1,. ..,t
Verbally, bundle a is revealed preferred to bundle b, and
bundle b is revealed preferred to bundle c, but bundle c is
revealed preferred to a. This is a contradiction (CHALFANT
372
and ALSTON, 1988, P. 399; VARIAN, 1987, pp. 124-6).
To test the transitivity condition for any choices of
bundle a, b, and c, it is necessary and sufficient to check
the following six cases of violation (CAl, 1990):
a R b R c R a
a R c R b R a
bRaRcRb
a,b,c = 1,... ,t
b R c R a R b
c R a R b R C
c R b R a R c
The case in the above paragraph corresponds to the first
case.
Detection of any one of the six cases leads to the
rejection of the transitivity condition in the data sample.
If and only if both conditions of WARP and the transitivity
are established, SARP is established for the data sample.
373
APPENDIX
H
DATA SOURCES
The parenthesis at the beginning contains a code number
attached by the author. Library call numbers and/or
locations are presented in parenthesis [ ] at the end of
each reference as much as possible. Library call numbers
without location names are those of Kerr Library at Oregon
State University, Corvallis, Oregon.
Subtitles and agent
names in < > parenthesis are the author's translation.
[El] Economic Statistics 1900-1983: United Kingdom, United
States of JUnerica, France, Germany, Italy, Japan.
by Thelma LIESNER.
Facts on File Publications. New
York, New York. Oxford, England.
1985.
[REF HC1O6
L68 1985]
[Fl] FAO Trade Yearbook.
Food and Agricultural Organization of the United
Nations, Rome.
[HD9000.4 F62]
fF2] World Crop and Livestock Statistics 1948-1985.
FAO Processed Statistics Series. Vol. 1.
Food and Agricultural Organization of the United
Nations, Rome.
[REF SB91 W671]
FAO Production Yearbook.
Food and Agricultural Organization of the United
Nations, Rome.
[HD1421 F62]
Food Balance Sheets 1964-66 Average.
Food and Agricultural Organization of the United
Nations, Rome.
1971.
[HD9000.4 F6l3 1964-66]
Food Balance Sheets 1975-77 Average and Per Caput
Food Supplies 1961-65 Average, 1967 to 1977.
Food and Agricultural Organization of the United
Nations, Rome.
1980.
[HD9000..4 F614 1975-77]
Food Balance Sheets 1979-81 Average.
Food and Agricultural Organization of the United
Nations, Rome.
1984.
[HD9000.4 F6l4 1979-81]
374
[F7] Yearbook of Fishery Statistics - Fishery Commodities.
(FAO Yearbook: Fishery Statistics - Commodities.)
Food and Agricultural Organization of the United
Nations, Rome.
[SH1 F6]
{Jl] Meiji-Taisho Kokusei Soran <A Comprehensive
Statistical Survey of Japan in Meiji-Taisho Era>.
Toyo Keizai Shinposha. Tokyo, Japan.
1926.
<Japanese>
(Harvard-Yenching Library, Harvard University)
[J2) Showa Kokusej Soran (A Comprehensive Statistical
Survey of Japan Since 1920).
Toyo Keizai Shinposha. Tokyo, Japan.
1980.
<Japanese>
[J3] Keizaj Tokej Nenkan <Economic Statistics Yearbook>.
Toyo Keizai Shinposha.
Tokyo, Japan.
1989.
<Japanese>
[J4) Japan Statistical Yearbook (Nihon Tokei Nenka.n).
Statistics Bureau, Prime Minister's Office (Office of
the Prime Minister; Sorifu), Japan.
[HA1832 J3]
Honpo Shuyo Keizai Tokei (Hundred-year Statistics of
The Japanese Economy).
Statistics Department, The Bank of Japan.
1966.
[Harvard-Yenching Library, Harvard University]
Monthly Statistics of Japan (Nihon Tokei Geppo).
Statistics Bureau, Management and Coordination Agency
(Somucho), Japan.
[HA1831 J28]
Abstract of Statistics on Agriculture, Forestry and
Fisheries Japan.
Ministry of Agriculture, Forestry and Fishery
(Ministry of Agriculture and Forestry), Japan.
[HD2091 A4]
Chokj Keizaj Tokei (Estimates of Long-Term Economic
Statistics of Japan Since 1868).
Kazushi OHKAWA, Miyohei SHINOHARA, and Mataji UNEMUPA
ed.
Vol.6.
"Personal Consumption Expenditures."
by SHINOHARA, Miyohei. Toyo Keizai Shinposha. Tokyo,
Japan.
1967.
375
[J9]
Choki Keizai Tokei (Estimates of Long-Term Economic
Statistics of Japan Since 1868).
Kazushi OHKAWA, Miyohei SHINOHARA, and Mataji
ed.
Vol.9.
"Agriculture and Forestry."
by Mataji tJNEMURA, Saburo YAMADA, Yujiro
Nobukiyo TAKAMATSU, and Minoru KUMAZAXI.
Toyo Keizai Shinposha.
Tokyo, Japan.
UMEMURA
HAYANI,
1967.
Chokj Keizai Tokei (Estimates of Long-Term Economic
Statistics of Japan Since 1868).
Kazushi OHKAWA, Miyohei SHINOHARA, and Mataji UNEMURA
ed. Vol.8. "Prices." by Kazushi OHXAWA, et al.
Toyo Keizai Shinposha. Tokyo, Japan. 1967.
Cholci Keizai Tokei (Estimates of Long-Term Economic
Statistics of Japan Since 1868).
Kazushi OHKAWA, Miyohei SHINOHARA, and Mataji UMEMURA
ed. Vol.1. "National Income." by Kazushi OHKAWA,
Nobukiyo TAKAMATSU, Yuzo YAMANOTO.
Shinposha.
Tokyo, Japan.
1974.
Toyo Keizai
Annual Report on The Family Income and Expenditure
Survey (Kakei Chosa Nenpo).
Statistics Bureau, Management and Coordination Agency
(Soinucho), Japan.
[313) Japan Statistical Handbook.
Statistics Bureau, Management and Coordination Agency
(Soinucho), Japan. [HA1832 S7]
[314] Statistical Survey of Japan's Economy.
Economic and Foreign Affairs Research Association.
Supervised by Economic Affairs Bureau, Ministry of
Foreign Affairs, Japan.
[HC461 A4]
[Ki] Korea Statistical Yearbook.
National Bureau of Statistics, Economic Planning Board,
Republic of Korea. [HC466 A4 A3]
Agricultural Cooperative Yearbook.
National Agricultural Cooperative Federation, Seoul,
Republic of Korea.
[HD2095 K6 K6]
Price Statistics Summary (MulGa Chong Lam).
Research Department, The Bank of Korea. [4562.49 4682:
Annex, Periodical Room, Doe Library, University of
California, Berkeley]
376
{K4] Annual Report on The Price Survey (MulGa Yeon BO).
National Bureau of Statistics, Economic Planning Board,
Republic of Korea.
[4545.49 4623, 4562.49 4618: Annex,
Periodical Room, Doe Library, University of California,
Berkeley]
Monthly Statistics of Korea.
National Bureau of Statistics, Economic Planning Board,
Republic of Korea.
[HC466 A4 A35]
Korea Statistical Handbook.
National Bureau of Statistics, Economic Planning Board,
Republic of Korea.
[HA1851 S7]
[K7) SigPum SuGeubpoe (Food Balance Sheet), 1978.
Hanguic Nongchon Geungje Yeunguwon <Korean Rural
Economics Institute>, by JU, Yong-Je., Gi-Seong Kim,
and Yeong-Gi Lee. Dec., 1979.
[HD9016 K6 C45 1978
EAST: University of California, Berkeley]
[KB] Susan YeonGam <Fishery Yearbook> 1989.
HanGug SusanHoe <Korea Fishery Agency>.
Food Consumption Statistics 1954-1966.
Organization for Economic Coorperation and
Development (OECD), Paris. 1968.
[HD9000.4 qO7l
1954-66]
Food Consumption Statistics 1970-1975.
OECD, Paris.
1978.
[HD9000.4 071 1970-75]
[Ti] Taiwan Food Balance Sheet, 1935-1980.
Council for Agricultural Planning and Development,
Executive Yuan, Republic of China. July, 1981.
Taiwan Food Balance Sheet.
Council for Agricultural Planning and Development,
Executive Yuan, Republic of China.
Commodity-price Statistics Monthly, Taiwan Area,
Republic of China. Directorate-General of Budget,
Accounting and Statistics, Executive Yuan, Republic of
China.
[Stanford University]
Commodity-price Statistics Monthly, Taipei City.
Bureau of Budget, Accounting and Statistics, Taipei
City Government.
[Stanford University]
Taiwan Statistical Databook.
Council for Economic Planning and Development,
Executive Yuan, Republic of China.
[HAl7lO.5 T35
DOCUMENTS; University of Oregon]
377
Statistical Yearbook of The Republic of China.
Directorate-General of Budget, Accounting and
Statistics, Executive Yuan, Republic of China.
[HA1848 F7 S7)
Industry of Free China.
Council for Economic Planning and Development,
Executive Yuan, Republic of China.
[HC430 5 15]
[Ui] (Production, Supply & Distribution) - Country Report
United States Department of Agriculture, Economic
Research Service, Commodity Economics Division.
Unpublished data.
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by Karl GUDMUNDS and Alan WEBB. Version 1.01.
(A computer program of USDA production, supply and
distribution commodity data.) United States Department
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EUN1] Statistical Yearbook for Asia and The Pacific.
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Pacific, Bangkok, Thailand. United Nations.
[HA1665 S7]
378
APPENDIX
I
DATA FOR JAPAN, KOREA, AND TAIWAN
379
Table I-i:
Japan Retail Price Data Series
TitLe:
RICE
BREAD
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
Item:
Non-
White
Oshisugi Beef
Pork
Chicken
Fresh
Hen Eggs Milk
Glutinous Bread
(Pressed
Rice
Barley)
MILK
CPI
Consuner
Fish
Price
Index
TotaL,
ALL Japan
Unit:
yen/kg
yen/kg
yen/kg
yen/kg
yen/kg
yen/kg
yen/kg
yen/kg
yen/L iter19851O0
Source:
J8,J2,
J2,J12
J2,J8,
J10,J2,
J1O,J1,
J1,J2,
J8,J12,
J10J2
J10,J2,
J10,J2,
J12
J12
J2,J12
J12
J2
J12
.112
.16
.18
J8
J12
Ref.:
J8
J8,J1
J2
Variable
Name:
PRJ
PBRJ *
P9.1
PBFJ
PPJ
PCJ
PFJ
PEJ
PMJ
CPIJ
Year
1900 0.103945 (0.72027)0.082556 1.240971 0.631915
0.80315 0.735429
0.4183 0.247446 0. 029587
1901 0.106513 (0.38716)0.06904,6 1.376786 0.778253 0.670672
0.6505 0.366013 0.219952
1902
0.6505 0.339869 0.219952 0. 030064
0.10921 (0.60339)0.081055 1.282914 0.625263 0.695511
0.02894
1903 0.124747 (0.38458)0.106572 1.128459 0.665173 0.716211 0.735429 0.366013 0.219952 0.03 1565
1904 0.117556 0.344639 0.132089 1.52525 0.764949 0.757611 0.735429 0.366013 0.219952 0. 032298
1905 0.113319
0.3083 0.124584 1.867449 0.964501 0.943909 0.848357 0.4444.44 0.219952 0. 033543
1906 0.131167 0.344639 0.081055 1.579842 0.95785 0.997728 0.848357 0.444444 0.219952 0.034202
1907
0.14343 0.289452 0.099067
1.72764 1.077581 1.001868 0.933286 0.4444.41. 0.219952 0.037779
1908 0.132901 0.300486 0.114077 1.809265 1.104188 1.014287 0.93328.6
0.4183 0.219952 0.036479
1909 0.112677 0.289452 0.108073 1.837272 0.818163 0.906649 0.933286 0.444444 0.218303 0.035063
1910 0.110751 0.290921 0.096564 1.695369 0.738342 0.993588 0.712245 0.47058.8 0.211704 0.035155
1911 0.144457
0.3055 0.105071 1.637487 0.731691 1.105968 0.638564 0.496731 0.208955 0.037773
1912 0.163718 0.282544 0.156105 1.773789 0.784905 1.155589 0.589444 0.496731 0.208955 0.039867
1913 0.176238 0.282544
0.1486 1.775657 0.937235 1.207436 0.540323 0.522875 0.199607 0.041076
1914 0.133864 0.283968 0.094564 1.682299 0.716496 1.106706 0.515763 0.496731 0.194658
0.03784
1915 0.104972 0.281417 0.084057 1.603879 0.581701 0.946333 0.466643 0.47058.8 0.186959 0.035417
1916 3.110751
0.28047 0.090061 1.603879 0.96528 1.175627 0.466643 0.496731 0.186959 0.038262
1917 0.15794
0.31952 0.154604 2.094775 1.185114 1.26973 0.663124 0.601306 0.218853 0.046936
1918 0.247503
0.42816 0.25219 2.717319 1.487273 1.932427 1.129767
0.8366 0.279339 0.063179
1919 0.353439 0.424475 0.307707 3.444181 2.314138 2.589825 1.301688 0.993462 0.361822 0.084056
1920 0.359217 0.42816 0.295699 4.09701 2.438077 2.801887 1.277123 0.993462 0.531735 0.087926
1921 3.256171 0.389055 0.192129 3.933803 2.29695 2.331373 1.449049 0.758169 0.549881 0.080577
1922 0.291804 0.362257 0.169614 3.883326 3.166335 2.327395 1.301688 0.862744 0.549831 0.079356
1923 3.262912 0.347871 0.165111
3.58888 2.86689 2.302898 1.277128 1.411763 0.439905 0.078641
1924 3.282173 0.419988 0.234155 3.589574 2.396492 2.302898 1.203448 1.210456 0.439905 0.079343
1925 3.308175 0.419988 0.262677 3.589574
1.97971 2.271461 1.129767 1.126795 0.439905 0.080302
1926 0.288914 0.406114
0.19363 3.589574 2.124767 2.195746 1.031527 1.048364 0.549881 0.076658
1927 0.269653 0.372061 0.151602 3.421345 1.97971
1.9686 0.884166 0.949018 0.527886 0.075486
1928 3.243651 0.336747 0.180121 3.253117 1.526154
1.9686 0.982406 0.896731 3.494893 0.072617
1929 0.231132 0.336747 0.165111
3.19634 1.579274 1.930742 0.933286 0.797385 0.494893 0.070956
1930 0.218612
0.31909 0.138093
3.19634 1.542499 1.805055 0.589444 0.653594 0.483895 0.063747
1931 3.148309 0.293865 0.096065 3.084888 1.270774 1.590023 0.491203 0.533333 0.436406 0.056397
1932 0.165644 0.331702 0.111075 2.91666 0.790658 1.237189 0.442083 0.44183 0.384917 0.057026
1933 3.169496 0.305216 0.111075 2.91666 0.962274 1.299276 0.442083 0.546404 0.423408 0.058766
1934 0.20224 0.29891 0.124584 2.954511 1.152277 1.362877 0.614004 0.546404 3.412411 0.059596
ote: * -It was found that real price of bread in the earLy 1900's were extremely high.
It is not recomenoed to use those data in parentheses.
380
Table I-i:
TitLe:
RICE
Japan Retail Price Data Series (Cont.)
BARLEY
BREAD
BEEF
CHICKEN
PORK
MILK
EGGS
FISH
CPI
VariabLe
PRJ
PBRJ *
PBJ
PBFJ
PPJ
PCJ
PFJ
PEJ
PMJ
CPIJ
Year
1935 0.229205 0.349359 0.151602 3.139563 1.199267 1.374991 0.810485 0.546404 0.423408 0.0610Th
1936 0.238836 0.393502 0.180121 3.177414
1.22787 1.602137 0.736805 0.632679 0.439905 0.062483
1937 0.255208 0.476743 0.220649 3.459197 1.452605 1.552165 0.957846 0.656208 0.439905 0.067361
1938 0.276395 0.460347 0.360242 2.144912 1.430131 1.947778 0.859605 0.807862 0.439905 0.073825
1939 0.284099 0.393502 0.420283 3.229985 2.165628 2.436142 1.203448 0.888888 0.439905
N.A.
1.59641 1.158168 0.511389
N.A.
1940 0.319732 0.399808 0.405273 3.326717
1950 50.21185
46.9706
1951 61 .30659 48.84593
2.05326 2.265404
40.8523 262.4273 288.8507
M.A. 72.05813 239.1155 60.40711 14.06976
41.822 352.9737 390.5418 389.2309 83.41104 224.6732 66.76637
16.1849
1952 68.87331 56.00566 47.47699 367.4692 340.1431 388.7458 86.50729 218.0928 69.48721 16.95709
1953
78.4137 59.90671 52.85356 372.1164 349.0596 398.8283 95.60838 240.0677 72.75703 18.15048
1954 84.96857 64.09712 54.55294 396.0316 429.6904 423.5269 98.89228 236.2829 77.62051 19.22178
1955 81 .87494 65.47305 54.80257 372.8712 414.6447 409.1498 102.7391 221.0697 71.77433 19.24131
1956 78.85326 66.58407
1957 85.03745
54.1689 361.9532 384.2955 408.7463 113.4353 244.3766 72.77855 19.38415
67.0441 54.25531 394.8838 394.9055 435.8961 117.4698 240.1292 75.10998 19.92865
1958 86.56447 69.30352 54.28411 399.1976 387.7117 435.306 121.2228 222.2085 75.60023 20.03426
1959 85.80711 69.94674 54.06329 412.1527 408.5432
447.374 126.3832 231.3006 74.01835 20.31139
1960 85.26247 79.41046 54.37052 496.0545 516.8269 502.3301 136.0906 229.3827 75.76922
20.9322
558.986 521.8833 529.3981 155.4144 217.9953
82.8785 21.97725
1962 88.35934 99.35921 54.94658 615.9322 490.2878 551.8375 168.3274 225.6924
91.5808 23.42091
1961 86.11424 97.67735 54.52414
196.1 239.3244 94.18966 25.13317
1963 97.58955
101.2
57.03 654.2553 607.5081 570.7763
1964 101 .0478
103.3
60.82 672.9186 641.8747 581.9152
1965 116.6097
107.4
62.61 740.5813 656.1328 586.2289
252.2 209.1927 106.5525 28.22315
1966 125.3635
109.4
67.52 892.1397 633.0372 605.5079
265.6 226.1016 106.7174 29.68188
1967 130.0842
110
70.76 1016.391 647.8754 603.7156
299.6 215.3463 112.5395 30.82349
1968 146.5785
113.8
74.62 1119.637 737.6496
333.2 223.6715 115.5949 32.47248
631.333
224 220.4264
101.822
26.1831
34.1849
157.939
124.1
79.02 1163.333 836.9048 643.0305
378.2 216.7793 128.3911
1970 161.9369
135.1
83.95 1215.128 820.8241 646.4063
454.4 221.4626 132.7098 36.78523
1971 165.2508
148.2
86.02 1290.625 845.3458 678.2589
534.1 219.9666 146.5405 39.00503
1972 173.4689
156.9
88.94 1408.537 904.6777 686.7042
1973 190.0467
171.3
92.64 1836.761 1016.589 780.0039
1974 213.4798
246.3
126.69 2122.188 1133.944 939.5011
810.5 325.8051 206.4553
56.7
275.89
254
139.58 2385.149 1385.211 1024.548
926.4 353.6734 220.7281
63.3
1976 320.1616
286.6
157.66 2672.716 1505.335 1110.688
1048.9
326.298 238.6488
69.3
1977 350.7307
308.3
130.92 2727.414 1463.702 1108.651
1178.5
351.684 236.1922
74.9
1978 374.6715
312.9
181.24 2754.804 1441.491 1018.176
1216.8
297.383 239.6134
78.1
1979 384.3325
314.2
1375.54 974.1056
1275.5
289.678 238.5975
81
1980 398.0049
343
212.4 3093.303 1372.023 991.0376
1321.7 345.9763 231.9335
87.3
1981 415.4326
361.5
245.7
3100
1471.62 1041 .249
1388.7 378.4424 229.2728
91.5
1982 435.9761
372.9
246.3 3143.698
1501.5 1031.186
223.328
94.1
1983 448.1891
380.8
277.1
3140.91 1534.686 1022.829
1425.4 293.4858 221.2382
95.8
1984 461.6854
390.1
288.2 3120.396 1524.513 1015.095
1415.3 299.7327 220.6008
98
1985 477.4189
391.4
285.7 3190.466 1481.787 981.936
1476.1
306.364 218.6123
100
391.4 290.9328 3210.447 1421.276 947.4444
1494.7
1969
1975
1986
479.478
183.5 2844.881
1987 482.6858
389.7 288.4762 3183.135
1371.14 903.3398
1988 478.4081
385.6 285.5281
3157.67
1351.63 873.7387
583 229.6641
153.582 40.78087
657.4 256.6966 169.1385 45.60101
1477.5 319.6216
319.595 215.7404
100.6
1564.6 228.0084 212.7725
100.7
210.468
101.4
1548 217.6866
381
Japan Consumption Data Series
Table 1-2:
Titte:
WHEAT
RICE
FLOUR
Item:
"Bread
BREAD
Use Rate"
(Wheat
Wheat
Non-
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
MILK
Crude
Beef &
Veal
Pork,
PouLtry
Cow's
Meat
FiSh,
Fresh,
Eggs
pig
in the
Milk,
Flour)* BarLey
Glutinous Flour
for
Rice,
Polished
Meat
(Bread
Use Rate)Food
* i.zs
Unit:
percent
kg,
kg,
capita/ capita/
Source:
Ref.:
year
48,44,
year
J8,44,
47
37,01
J7,J8
47,48
kg!
kg,
kg/
capita/
capita/ capita/
year
48,44,
year
48,44,
year
45,42,
47,01
37
F1,u1
Frozen
Milk
kg!
as FLuid
kg/
capita/ capita/
capital capital capital
year
45,42,
year
J8,42,
F1,U1
year
49,47,
F2,F3,
34,F7
year
48,49,
42,46,
year
48,42,
46,44
Fl
Fl
47,01
47,46,01 37,01
F4,F5,F6 47,48
F4,F5,F6
Marketed
kg/
kg!
kg!
ChilLed, ShelL
or
F7,J12
J7,48,F4,F4,F5,F6,
47
F5
VariabLe
Name:
QRJ
QBRJ
QWFJ
OBJ
QBFJ
QPJ
QCJ
QFJ
QEJ
QMJ
Year
8.158 0.750232 13.06796 0.518257 0.069332 0.191142
106.312 7.357014
1901 107.8544 6.993958 8.51812 0.744692 13.40779 0.446967 0.081156 0.194053
1902 125.7562 6.045681 8.87824 0.670938 11.96353 0.449226 0.096077 0.196491
1903 111.2272 7.806284 9.23836 0.901466 10.81832 0.521736 0.098362 0.198964
1904 137.7015 7.401084 9.59848 0.887989 12.78136 0.610057 0.084968 0.202601
9.9586 0.913374 12.08857 0.457164 0.12012 0.204805
1905 152.9584 7.337366
1906 103.5857 6.976147 10.31872 0.899811 13.28354 0.377312 0.117352 0.209022
1907 123.9568 7.906993 10.67884 1.055469 14.132 0.382128 0.160283 0.224165
1900
M.A. 0.511437 0.401031
H.A. 0.551636 0.419486
N.A. 0.570212 0.496686
N.A. 0.57577 0.516906
M.A. 0.584957 0.566836
N.A. 0.6.4408 0.479043
N.A. 0.642863 0.50051
N.A. 0.728362 0.551523
N.A. 0.794955 0.558782
1908 129.3321 6.562056 11.03896 0.905478 13.05696 0.340603 0.180131 0.228729
1909 116.3912 5.941323 11.39908 0.84657 12.62263 0.411418 0.146641 0.227149 12.04265 0.794517 0.593422
1910 132.9451 6.580189 11.7592 0.967222 12.47971 0.585821 0.154522 0.236581 8.990159 0.813334 0.632929
1911 119.1372 5.158008 12.11932 0.781394 12.43061 0.613717 0.181337 0.236119 11.18216 0.793047 0.651147
1912 130.2682 7.386025 12.47944 1.152168 12.82456 0.632817 0.167665 0.228286 11.99609 0.777784 0.653479
1913 129.2118 8.973882 12.83956 1.440259 13.74864 0.62528 0.160608 0.216977 15.6928 0.773005 0.641594
1914 118.6013 6.690271 13.19968 1.103868 12.13405 0.552259 0.169104 0.209785 15.28873 0.742943 0.650608
1915 138.9912 6.163539 13.5598 1.044704 12.74056 0.630649 0.216864 0.218778 17.49156 0.79796 0.675425
11.772 0.727381 0.225811 0.24342 17.89285 0.889898 0.675378
1916 134.4834 6.970802 13.91992 1.212912
1917 137.1206 6.880685 14.25004 1.228206 11.24436 0.629641 0.212805 0.274393 16.13249 0.985111 0.724665
1918 138.5264 7.983942 14.64016 1.461077 10.28473 0.499242 0.284989 0.261276 12.0228 0.996036 0.70067
1919 136.7754 11.65057 15.00028 2.184523 11.99448 0.551887 0.279105 0.29019 13.64223 1.046318 0.688078
1920 131.5182 5.967389 15.3604 1.721784 9.811268 0.578597 0.323893 0.284224 17.26421 1.171703 0.717206
1921 142.4449 10.35963 15.72052 2.041631 10.54182 0.642396 0.440088 0.251826 11.94861 1.579801 0.913017
17.2 2.371822 10.13631 0.661248 0.325562 0.248702 15.67153 1.605611 1.087629
1922 130.7365 11.03173
16.1 2.256341 8.694231 0.674323 0.304823 0.35078 16.37526 1.690411 1.163647
1923 138.3215 11.21163
1924 134.7857 11.52473
1925 136.7756 11.58659
1926 137.3431 11.55626
1927 134.0618 11.44912
1928 138.1073 11.94408
1929 136.0369 11.28931
1930 134.9393 11.59665
1931 142.4087 11.42176
1932
124.725 9.899524
1933 134.3931
10.2375
1934 140.7797 10.13562
1935 127.0776 10.25173
15 2.160587 9.070878 0.666333 0.432961 0.375977 17.88379 1.639598 1.194358
14.35 2.078344 9.727757 0.641579 0.563252 0.387699 20.41599 1.517284 1.297052
13.7 1.97901 9.056354 0.625393 0.411666 0.376797 19.45972 1.537808 1.433891
13.42 1.920589 7.986571 0.589808 0.347151 0.368381 23.10832 1.655622 1.394882
13.14 1.961816 8.008243 0.623564 0.434635 0.395623 17.14504 1.763607 1.372234
12.56 1.814757 7.411119 0.600763 0.513953 0.435638 17.66018 1.855559 1.462788
12.58 1.823573 7.253801 0.515997 0.439255 0.463988 17.30518 1.895966 1.483026
12.3 1.756095 7.388728 0.583467 0.419359 0.382373 21.6934 2.151076 1.59633
7.37833 0.654108 0.594801 0.419032 23.82441 2.361306 1.628142
6.8349 0.627106 0.560357 0.507563 20.6611 2.228808 1.689569
0.5689 0.566909 0.411966 26.32328 2.275454 1.75886
14.2 1.799605 6.201555
16.4 2.101604 6.299781 0.583952 0.603878 0.451757 22.40236 2.300719 1.969013
12.15
1.50349
12 1.535625
382
Japan Consumption Data series (Cont.)
Table 1-2:
Title:
RICE
BREAD
'Bread
Use Rate"
WHEAT
FLOUR
BARLEY
CHICKEN
PORK
BEEF
FISH
MILK
EGGS
Variable
QRJ
Name:
QBRJ
QWFJ
OBJ
QBFJ
QPJ
QCJ
QFJ
QEJ
QNJ
Year
1936 129.7319 9.71428
1937 140.456 9.093855
1938 161.6056 8.555025
18.6 2.25857 5.717745 0.608731 0.704838 0.434293 25.06052 2.226203 1.949682
20.8 2.364402 6.663712 0.712955 0.715999 0.413734 18.54818 2.279612 1.924947
23 2.45957 5.894892 0.744047 0.688916 0.38845 14.61183 2.122963 1.963359
1939 140.3955 8.724166 22.20268 2.421248 7.054287 0.775707 0.768941 0.399117 16.49895 2.143696 2.262356
1940 140.737 9.716153 22.5628 2.740295 6.876218 0.835847 0.683136 0.358459 11.86886 2.153407 2.803122
N.A. 13.35435 0.70357 0.551671 0.096166 9.371266 0.809736 1.625481
N.A. 12.92498 0.607327 0.444577 0.157391 10.41223 1.38722 2.293361
14.A. 19.32803 0.630116 0.73133 0.201088 12.85744 2.465306 3.01557
N.A. 23.94824
1952
1953 100.2403 24.59158 24.51122 7.534621 22.06229 0.70274 0.857325 0.239742 12.19773 2.679068 3.62501
1950
M.A.
1951
N.A.
N.A.
P1.A. 22.80779
N.A. 23.38011
99.2985 26.59822 25.06808 8.334578 18.29123 0.743855 0.668435 0.269427 11.53877 3.129229 4.661465
1955 110.6344 25.14674 25.61783 8.052561 15.60576 1.162765 0.737511 0.28348 12.11782 3.401127 5.312962
1956 111.6311 23.89877 26.1595 7.81473 15.67005 1.164619 0.95615 0.297676 11.53225 3.314898 5.89052
1.2058 0.320495 12.45436 3.649261 6.660655
1957 115.9709 24.86583 26.69211 8.296517 15.53977 1.159819
1958 113.0254 24.82374 27.21467 8.444622 15.04898 1.070701 1.404775 0.34264 12.09006 3.889939 7.902623
1959 113.3515 25.86328 27.72621 8.963633 12.21921 1.205956 1.586393 0.339612 12.1163 3.928369 9.065748
1954
10.354
1960 114.3343 25.75493 28.22576 9.086908 7.15058 1.200323 1.335917 0.360805 12.19494 4.567925
1961 116.4636 25.84662 28.71238 9.276475 5.186293 1.181393 1.764824 0.789313 13.38905 6.106738 11.48515
1962 117.2293 25.97157 29.18513 9.474795 4.675303 1.193705 2.723233 0.977128 12.29919 6.893592 12.23185
1963 116.2902 26.9042 29.64308 9.96904 4.170307 1.490515 2.404426 1.160125 11.16807 7.185615 14.23791
1964 114.7435 28.10191 30.08533 10.56819 5.289045 1.780433 2.502521 1.47517 10.47905 8.327087 16.57838
1965 110.4554 28.99008 30.51101 11.05646 3.785296 1.75307 3.313152 1.647591 11.55516 8.572913 17.2117
1966 104.5175 31.29165 30.91927 12.09394 2.948423 1.297205 4.563997 2.028101 11.54668 9.084888 19.90953
1967 103.4073 31 .55815 31 .30928 12.35079 3.103916 1.319264 4.814563 2.381532 13.23951 11.21308 21.84059
1968 100.1372 31.30335 31 .68027 12.39623 2.940857 1.429049 4.768531 2.664959 14.45023 11.81203 23.24975
1969 97.03909 31 .29633 32.03148 12.53084 2.204104 1.904736 5.055785 3.313158 15.16542 13.25312 24.50573
1970 95.06363 30.77516 32.36219 12.44939 1.465484 2.226658 5.019996 3.807029 15.48653 14.13559 26.14404
1971 93.05245 30.92872 32.67172 12.63118 1.68339 2.514299 5754453 4.358914 18.82762 14.48826 26.41419
34.78 13.2613 1.617176 2.760723 6.260997 4.75142 15.60269 14.10304 26.85106
1972 90.73842 30.50328
34.8 13.42031 2.218067 2.91312 7.230716 5.135999 16.16402 13.95731 27.64738
1973 90.75744 30.8513
6.4395 5.42476 17.32144 13.75735 27.02305
35.26 13.70804 2.025811 2.558771
1974 89.71449 31.10163
1975 88.04717 31 .50795
1976 86.23297 31 .74491
1977 83.37859 31 .80791
1978 81.58959 31 .69986
1979 79.77922 31 .89447
1980 78.92534 32.23988
1981 77.79682 31 .81093
1982 76.43248 31.83844
1983 75.71788 31.76184
1984 75.26926 31.78775
1985 74.56485 31.70617
1986 73.4187 31 .55204
1987 71 .94268 31 .50559
1988
?LA.
35.29 13.89894 1.965339 2.726541 7.176219 5.304485 17.41515 13.50907 28.33893
35.56 14.11061 1.821574 2.863002 7.515797 5.929038 17.61452 13.90674 28.71827
36.15 14.3732 1.752019 3.10951 8.250942 6.642833 14.63099 13.9512 30.4592
35.98 14.25701 1.198187 3.488174 8.493549 7.197527 12.0483 14.43393 31.32964
35.81 14.27676 0.964411 3.683174 9.35672 7.68729 15.78883 14.S0085 32.54768
36.45 14.68929 0.990945 3.742816 9.821767 7.784487 15.75846 14.46602 33.38664
36.46 14.49783 0.746497 3.937133 9.473788 8.055758 16.34095 14.35068 34.25893
36.47 14.51435 0.89306 4.097992 9.559485 8.417421 16.9445 14.65922 34.80155
36.48 14.4834 0.602596 4.263569 9.530728 8.805964 16.31707 14.76758 35.03896
35.97 14.29257 0.598827 4.508654 9.699339 9.280592 17.9097 14.9849 35.21105
36.09 14.30345 0.611323 4.785071 9.9171.71 9.278011 18.21184 15.03787 34.86902
35.94 14.17475 0.591755 5.065734 10.48688 9.316161 19.35077 15.51007 34.8276
36.65 16.4335 0.507099 5.344893 11.12062 10.25779 19.32119 16.43961 36.22178
N.A. 38.00114
N.A.
N.A.
N.A. 5.745806 11.39795
LA.
N.A. 34.27266
383
Table 1-3:
TitLe:
Japan Socioeconomic Data Series
Nuther
Private
tage of
of
of
Final
65 Years
Labor
Motor
TetephoneConsumpt ion
15 Years Years
Old and
Force in Vehicles Lines
OLd
Over
Primary Oned
Total
Popu-
Popu-
Popu-
HousehoLd Percen- Nutter
Popu-
(at ion
(at ion
Lation
Size
Lation
Under
15-64
OLd
Expenditure
Industry
Unit:
thousand thousand thousand thousand persons
persons
persons
persons
persons
percent
units
thousand biLLion
Lines
yen
J1,J4,
J1,J4,
J11,i2,
J13
J6
J6
per
household
Source:
J4,J6
E1,J4
E1J4
E1,J4
J1,J12
E1,J6
Variable
Name:
TPOPJ POPLT15J POP1S64J POPGT65J
HSJ
AGPOPJ
AUTOJ
PHONEJ
PFCJ
Year
1900
43847
14576
27260
2268 5.457602 69.96367
N.A.
18.668
1.914
1901
44359
14804
Z7'480
2302 5.457602 69.27083
N.A.
24.887
1.898
1902
44964
15032
27700
2336 5.457602 68.63057
N.A.
29.941
1.984
1903
45546
15260
27920
2370 5.457602 67.94466
N.A.
35.013
2.103
1904
46135
15488
28140
2404 5.457602 67.25629
N.A.
35.528
2.259
1905
46620
15716
28360
2438 5.457602
66.5625
N.A.
36.694
2.278
1906
47038
15944
28580
2472 5.457602 65.83754
N.A.
43.3
2.312
1907
47416
16172
28800
2506 5.457602 65.15855
16
58.6
2.787
1908
47965
16400
29020
2340 5.457602 64.45899
17
78.5
2.884
1909
48554
16702
29322
2602 5.457602
63.7409
19
102.6
2.88
1910
49184
17004
29624
2664 5.457602 63.01108
121
128.5
2.967
1911
49852
17306
29926
2726 5.457602
62.262
235
157.2
3.295
1912
50577
17608
30228
2788 5.457602 61 .51803
512
181.9
3.657
1913
51305
17910
30530
2850 5.231502 60.78728
892
200.3
3.92
1914
52039
18170
30906
2904 5.153536 59.99244
1066
211.5
3.595
1915
52752
18430
31282
2955 5.059977 59.25368
1182
221.4
3.616
1916
53496
18690
31658
3012 4.983571 58.43373
1723
231.7
4.147
1917
54134
18950
32034
3066 4.907164 57.65325
3690
251
5.416
1918
54739
19210
32410
3120 4.830758 56.83696
5100
270.1
7.756
1919
55033
19815
32505
3030 4.754351 56.01052
7320
277.1
11.302
1920
55963
20420
32600
2940 4677945 54.47542
10430
321.7
11.326
1921
56666
20720
33040
2956 4.989808 53.96364
12719
371.6
11.171
1922
57390
21020
33480
2972 5.301671 53.44392
15939
415.1
11.59
1923
58119
21320
33920
2988 4.911842 52.94959
19427
430.9
11.796
1924
58876
21620
34360
3004 5.613534 52.46367
27139
442.9
12.149
1925
59737
21920
34800
3020 5.847431 52.00422
34863
494.8
12.74
1926
60741
22252
35402
3028 5.769465
51.4993
47196
553
12.359
1927
61659
22584
36004
3036 5.379637
51.055
61026
609
12.141
1928
62595
22916
36606
3044 5.301671 50.60034
79798
656
12.21
1929
63461
23248
37208
3052 4.911842 50.13615
94328
690
11.782
1930
64450
23580
37810
3060 4.522013 49.69615
116940
715
10.85
1931
65457
23974
38342
3094 4.911842 51.05209
128209
730
9.754
1932
66434
24368
38874
3128 4.911842 50.82248
132118
761
9.804
1933
67432
24762
39406
3162 5.145739
49.6642
135243
797
10.85
1934
68309
25156
39938
3196 5.067774
48.0026
156573
830
12.097
1935
69254
25550
40470
3230 4.911842 47.03822
175904
870
12.668
384
Japan Socioeconomic Data Series (Cont.)
Table 1-3:
TitLe:
Totat
Popu-
Popu-
Popu-
Popu-
Lation
Lation
Lation
under
15-64
Lation Size
65 years
15
years years
Name:
oLd and
over
oLd
oLd
Nixrber ofTeeplonePrivate
Nuter ofFiriat
tage of Motor
Consuition
VehicLes Lines
'abor
Expenditure
force in Owned
primary
industry
HouseholdPercen-
TPOPJ POPLT15J POP1564J POPGT65J
HSJ
AGPOPJ
AUTOJ
PHONEd
PFCJ
Year
1936
70114
25714
40798
3274 4.833876 47.21322
194574
914
13.328
1937
70630
25878
41126
3318 5.145739 46.6303
215190
982
15.121
1938
71013
26042
41454
3362 5.067774 46.04385
222000
1006
16.012
1939
71380
26206
41782
3406 4.755911 45.46885
218000
1034
17.912
1940
71933
26370
42110
3450 4.911842 44.33498
217000
1054
20.29
1950
83200
29430
49660
4110 4.857391 50.79496
414000
1215 2586.389
1951
84541
29504
50674
4238 4.867531 46.76102
532000
1369 3256.455
1952
85808
29578
51688
4366 4.958797 45.76677
760000
1550 4092.688
1953
86981
29652
52702
4494 4.968938 42.40346
1095000
1769 4945.107
1954
88239
29726
53716
4622 4.938516 40.58661
1338000
1966 5477.059
1955
89276
29800
54730
4750 4.867531 40.15538
1502000
2175 5864.424
1956
90172
29454
55784
4870 4.674858 38.47987
1775000
2397
1957
90928
29108
56838
4990 4.634295 34.26769
2069000
2638 6987.674
1958
91767
28762
57892
5110 4.644436 32.75942
2404000
2903 7480.782
1959
92641
28416
58946
5230 4.634295 31.09573
2898000
3216 8177.823
1960
93419
28070
60000
5350
4.55317 30.20739 3404000
3633 9331 .286
1961
94287
27490
61386
5516
4.4112 28.96843
4135000
4153 10677.89
1962
95181
26910
62772
5682 4.350356 27.80948
4922000
4781 12424.81
1963
96156
26330
64158
5848
4.3 25.98477
5937000
5477
1964
97182
25750
65544
6014
4.3 24.68314
6985000
6301 16932.92
1965
98275
25170
66930
6180
4.29 23.53066
8123096
7303
18611
1966
99036
25100
68858
6410
4.26 22.20841
9906926
8646
21470
1967
100196
25030
68786
6640
4.19 21.05691 11690755
9989
24743
1968
101331
24960
69714
6870
4.15
19.7521 14021970
11362
25627
1969
102536
24590
70642
7100
4.07 18.76984 16528521
13005
33212
1970
103720
24820
71570
7330
3.99 17.39301 18919020
15173
38647
1971
105145
25300
72418
7638
3.98 15.91486 21222715
17818
43559
1972
107595
25780
73266
7946
3.96 14.72583 23869198
20985
50267
1973
109104
26260
76114
8254
3.93 13.40559 25962870
24166
60489.2
1974
110573
26740
74962
8562
3.91 12.88906 27870475
27444
73628.5
1975
111940
27220
75810
8870
3.9 12.65556 29143445
30343
85538.7
1976
113089
27490
76400
9200
3.89 12.19882 31048135
32427
96885.6
1977
114154
27650
76950
9550
3.84 11.56821 32965084
33945 107836.1
1978
115174
27710
77540
9920
3.82 11.70488 35179501
35494 117923.1
1979
116133
27660
78160
10310
3.83 11.18817 37333000
37046 130077.9
1980
117060
27510
75840
10650
3.83 10.42269 38992000
38490 141324.2
1981
117884
27600
79270
11010
3.82
9.98029 40834000
39831 149384.7
1982
118693
27250
80090
11350
3.79 9.710759 42687000
41734 159606.1
1983
119483
26910
80900
11670
3.78 9.262166 44559000
42455 167809.3
1984
120235
26500
81780
11960
3.76 8.879639 46363000
43542 175984.4
1985
121049
26030
82510
12470
3.72 8.765283 48241000
44861 184764.1
1986
121672
25430
83370
12870
3.71 8.457201 50223000
46325 191495.7
1987
122264
24750
84190
13320
3.69 8.272712 52646000
47976 199291.4
1988
122780
N.A.
N.A.
N.A.
N.A.
49904 209367.8
3.67 7.885543
6370.48
14568.8
385
Korea Retail Price Data Series
Table 1-4:
TitLe:
RICE
Item:
PoLished Wheat
WHEAT
BARLEY
PORK
BEEF
Potished Fresh
CHICKEN
FISH
EGGS
CPI
Fersh
Fersh
5 Items
White
Retail
Meat
Meat
Weighted High
Average
Flour High
QuaLity
SemiHard QuaLity
Average
Average
77% Extr.
QuaLity
Price
Meat
Price
Quality
Index
istOrade
A(lCitiesAtLCities*
Area:
AttCitjesALtCitiesAtlCitjesALtCitiesA(LcitiesAttCjtjes5eou[
Source:
K1,K3,K4,K1.K3,K4,K1,K3,K4,K1,K3,K4,K1,K3K4,K3 & K4
K1,K3,K4,K1,K3,K4,K1,K3,K4,
&K5
&K5
Ref.:
J5&F5
Unit:
won/kg
&K5
&K5
J5&F5
&K5
won/kg
won/kg
won/kg
&KS
&K5
won/kg
won/kg
won/kg
PPK
PCK
PFK
&UN1
won/kg 19851OO
Variable
Name:
PRK
PWK
PBK
PBFK
PEK
CPIK
Year
1954
5.375 10.18182
1955
12.75 15.13636 7.581699 63.49649 40.72668
1956
2.48366 46.03496 35.83948
N.A. 43.29311 1.595943
N.A.
48.9714 24.79607 50.79725 2.687673
19.875 20.86364 12.54902 69.84614 47.24295 48.51373 28.16834 58.68621 3.305881
1957
22.75 21.36364
1958
18.75 19.04545 12.54902 95.24474 65.16269 55.37888 28.51548 71.77035 3.928474
1959
16.75 17.36364 10.19608
1960
19.25
1961
1962
1965
1966
54.9212 27.87079 66.57518 4.073161
100.007 65.16269 59.04029 39.84729
69.6538 4.055624
12.4183 123.8182 78.19523 64.53241 40.04566 67.34483 4.384458
23.125 25.36364 16.73203 149.2168 84.7115 70.93989 38.20356 73.11725 4.749829
23.75 27.13636
1963
1964
18.5
15.1634 92.06992 63.53363
17.9085 158.7412 97.74404 77.80504 49.61657 82.73794 5.115201
38 27.86364 28.49673 174.6154 112.4056 84.67019 55.30305 98.13104 6.089525
46.5
46.5 38.56209 204.7762
144.987 101.1465 76.16684 148.1586 7.794592
43.75 37.36364 32.15686 243.3333 188.9718 176.2055 95.01225
46.5 38.63636 31 .50327
280 195.4881 200.0047 109.9292
194 8.829811
202
9.8547
1967
50.125 36.59091 36.20915 351.6667 246.9468 241.6939 143.3859
220
10.8819
1968
58.875 37.40909 38.16993
540 347.1572 293.3178 158.5935
220
12.0696
1969
70.25 38.40909 41 .83007 638.3333 361.4729 288.0381 179.4496
230
13.5783
1970
78.75
278
15.7611
270
17.8797
1971
38.5 48.62745 683.3333 387.1312 303.2906 221.1618
97 42.68182 66.27451 868.3333 533.3333 321.4764 255.9221
1972
134.875 48.81818 76.47059
1040 521.6667 285.6916 247.6665
276
19.9662
1973
133.625 58.04545 78.30065 1118.333 593.3333 344.9418 274.1712
336
20.6082
1974
187.875 88.09091 91 .63399
1320
700
507 349.3471
432
25.6158
1975
248.125 111.0455 139.2157
1480 996.6667
654 433.3673
544
32.1
303.5 118.4545 154.2484 2141.667 1341.667
810 616.1481
578
37
2795 1381.667
904
803.351
610
40.7
1978
337.875 113.1818 194.3791 3373.333 1913.333
976
994.976
690
46.6
1979
432.125 119.2727 196.6013
3820 1846.667
902 1220.504
670
55.1
229.281 4788.333 2028.333
1238 1474.039
794
70.9
1467
1839.6
940
86.2
92.3
1976
1977
1980
1981
319 115.6818 178.8235
639.5 177.8636
804.375 244.6364 360.1307 6436.667 3418.333
1982
824 248.4545 314.7712 7473.333
3460
1575' 1966.367
1078
1983
829.25 231.6818 401.6993 8278.333
3220
1504 2110.823
1178
95.4
8370 2788.333
1698 2049.065
1192
97.6
1984
1985
1986
852.125 229.7727 439.7386
866.75 246.6364 -56.0784 7638.809 3395.734 1666.741 2205.667 1131.411
934.375 254.1818 483.0065 6714.513 3714.933
1580.07 2576.219 1066.921
1987 969.125 244.1818 499.4771 6852.012 3086.722 1533.401
1988
1061.75 239.1818 499.4771
NOTE:
*
100
102.8
2779.14 1031.847
105.9
8792.27 3025.599 1691.742 3136.458 996.7732
113.4
Seou' retail price index is used for 1954-64.
386
Table 1-5:
Korea Consumption Data Series
Title:
RICE
WHEAT
BARLEY
BEEF
PORK
CHICKEN
FISH
EGGS
Item:
MilLed
Wheat
BarLey
Beef
Pork
Chicken
Fish
Eggs
Rice
Flour
& Naked
kg,
kg,
kg/
kg,
kg!
kg,
kg,
kg!
capita/
capita/
capita!
capita/
capita/
capita/
capita/
capita/
year
year
year
year
year
year
year
year
Barley
Unit:
Source:
U2,F1,F2 U2,F1,F2 U2,F1,F2 U1,F1,F2 U1,F1,F2 F1F2,F3 F1.F2,F3 F1,F2,F3
Ref.:
K1,K2,
K1,K2,
K1,K2,
F5,F6
F5,F6
F5,F6
F5,F6
F5,F6
F5,F6
F5,F6
F5,F6
VariabLe
Name:
ORK
Q%K
08K
QBFK
QPK
QCK
FK
QEK
Year
1955 136.6598 10.82359 44.85497 0.789743 1.997584 0.789743 4.703713 0.975564
1956 123.1165
1957
18.8245 47.69464 0.868558 2.219649 0.820305 6.555931 0.820305
136.711 17.95707 45.05713 0.844238 2.345106 0.844238 6.924531 1.172553
1958 139.6964 11.10211 37.57069 0.867184 2.510269 0.775901 7.036672 1.004108
1959 131.8553 14.57786 41.40584 0.870549 2.611648 0.957604 6.131532 1.392879
1960 120.9848 15.36929 45.23497 0.919559 2.318887 0.719655 5.043555 1.359348
2.32865 0.659784 5.003762 1.513623
1961 129.6456 16.74768 43.99647
0.93146
1962 114.3375 30.27549 39.37555
1.13152 1.433259
1963 133.2149 26.46645 34.22588 1.393882
1964 135.7949
0.56576 5.795738 1.659563
2.01746 0.623579 4.659875 1.870736
18.9035 34.73896 2.036878 2.251286 0.643225 5.755426 2.036878
1.95088 0.661906 6.895936 2.264414
1965 116.3389 20.14593 41.67215
1.70702
1966 135.9193 21.24442 45.33051
1.52874 3.261313 0.645468 7.114164
1967 122.7628 30.16132 46.19926
1.46029 2.389566 0.796522 5.910302 2.323189
2.20818
1968 121.7751 33.64609 47.76963 1.199818 2.010507 1.134963 7.836479 2.529347
1969 131.5686 33.80234 38.48624 1.204666 2.377631 1.363175 9.169776
3.99442
39.1277 40.88425 1.705902 2.574362 1.426755 9.169:47
3.9701
1970 149.2265
1971 117.3564 47.79348
49.6296 1.490132 2.463279 1.550953 11.1c119 4.227108
1972 124.6244 43.82526 50.59072 1.283391 2.686166 1.641546 14.23688 4.566483
1973 132.3608 33.52627 48.63056 1.378178 2.580418 1.554115 18.00'.28
4.39844
1.58538 17.3S53
4.75614
1974 131.9596 36.77187 50.43834 1.412429 2.623083
1975 128.0507 39.58463
1976 138.4352
45.5658 2.012415 2.550948
42.9507 30.75098 2.901057
1977 154.6921 35.59676 38.21811 3.048446
1978
178.34
1.6156 18.7C695 4.931833
2.51053 1.757371 17.15529 5.104745
3.87235 2.059761 20.73-.92
5.84972
35.303 30.62298 3.841056 4.814845 2.272174 19.6103 5.842733
1979 150.1178 38.39573 36.41986 4.582512 6.101135 3.277029 18.25023 6.447488
1980 138.6949 41.73453 26.90817 3.619767 6.321477 3.252544 16.83521 6.898542
1981 135.8475 39.38274 27.06043 3.279705 5.423134 3.150582 20.33242 6.766005
1982 131.2742 38.75923 15.60365 3.763414 6.051976 2.542847 20.261 6.916544
1983 135.0795 38.45761 20.49924 4.007113 7.363069 4.407824 19.84222 7.538381
1984
132.09 38.40072 19.33468 3.677832 8.392368 4.146817 21.94601 7.528448
1985 137.7384 38.33496 12.15338 4.091972 8.451871 4.627825 21.93461 7.672447
1986 133.5167 38.26522 9.519834
4.93156 7.722101 4.233924 25.41599 8.684356
1987 129.5485 41 .52676 10.72771 4.990494 8.864068 4.348859 22.85199
8.98289
387
Table 1-6: Korea Socioeconomic Data Series
Ti tie:
Unit:
Total
Popu-
Popu-
Popu-
HousehoidFarm
Ni.mler ofNuter ofPrivate
Popu-
Lation
Lation
tation
Size
Popu-
Motor
Lation
under
15-64
65 years
Lation
Vehicles Sub-
15 years years
old and
old
old
over
persons
persons
thousand persons
persons
Telephonefinal
Consution
scribers Expenditure
persons
thousand nuter
per
persons
nuiler
billion
won
household
Source:
K1,UN1
Ki
Ki
K1,K6
Ki
K1,K6
K6
K1,K6
K1,UM1
Variable
Name:
TPOPK POPLT15K P0P1564K POPGT65K
HSK
AGP0PK
AUT0K
PHONEK
PFCK
N.A.
N.A.
Year
1954
N.A.
N.A.
13170
15950
1955
21526
8865007 11947493
713500 5.753774
13300
18356
32423 89.54171
1956
20724
9238233 10750266 735500.6 5.881418
13445
25328
38753 158.5216
1957
21321
9611460 10952039 757501.2 5.911494
13592
28086
49417 218.198
1958
21910
9984686 11145812 779501.8 5.954824
13750
28933
59548 253.8916
1959
22974 10357913 11814585 801502.4 5.977053
14126
30392
72552
1960
25012 10731139 13457358
823503 5.921183
14559
31339
86604 288.7164
1961
25766 11056586 13862942 846472.3 5.946646
14509
29234
97016 326.1476
1962
26513 11382033 14261526 869441.7 5.835859
15097
30814
127686 375.7991
1963
27262 11707480 14662109
892411 6.024323
15266
34228
157327 441.6269
1964
27984 72032926 15035693 915380.3 6.049588
15553
37815
191012 556.1786
1965
28705 12358373 15408277 938349.7 6.007197
15812
41511
220635
1966
29636 12683820 15790861
961319 5.907835
15781
50160
277756 922.2986
1967
30131 12823223 16326943 980833.8 5.908018
16078
60697
339280 1110.371
1968
30838 12962627 16875025
1000349 5.852093
15908
80951
384514 1349.464
1969
31544 13102030 17422107
1019863 5.795032
15589
108669
442452 1652.558
1970
32241 13241433 17960189
1039378
5.46841
14422
126506
481207 2052.159
1971
32883 13234824 18575354
1072822 5.580671
14712
140706
563129 2603.079
1972
33505 13228215 19170519
1106266 5.629585
14677
145637
644888 3269.998
1973
34103 13221606 19741683
1139711 5.616442
14645
165307
763214 3969.713
1974
34692 13214997 20303848
1173155
5.28636
13459
177505
876702 4729.931
1975
35281 13208388 20866013
1206599 5.198877
13244
193927
1058075 6828.444
1976
35849 13097865 21496633
1254502 5.100214
12785
218978
1270837
9322.92
1977
36412 12987343 22122252
1302405 4.968013
12309
275312
1537135
11273
1978
36969 12876820 22741872
1350308
4.80534
11527
384536
1879263
14865
1979
37534 12766293 23369491
1398211
4.65533
10883
494378
2292686
19419
1930
38124 12655775 24022111
1446114 4.641097
10827
527729
2704498
24786
1981
38723 12556302 24662298
1504400 4.538762
9999
571754
3263322
30498
1982
39326 12456829 25306485
1562686 4.466403
9688
646996
4079600
34001
1983
39929 12357356 25950671
1620973 4.354926
9475
785316
4809900
37282
1984
40513 12257883 26575858
1679259 4.190931
9015
948319
5595000
40778
1985
41056 12158410 27160045
1737545 4.052986
8521
1113430
6517400
44126
1986
41569 12058937 27714232
1795831 3.926726
8180
1309434
7521000
47449
1987
42080 11959464 28266.479
1854117 3.794847
7771
1611375
8626000
52103
1912404
N.A.
N.A.
N.A.
H.A.
1988
N.A.
M.A. 11859991
N.A.
N.A.
M.A.
N.A.
268.607
808.542
388
Taiwan Retail Price Data Series
Table 1-7:
Title:
RICE
WHEAT
BEEF
PORK
CHICKEN
FISH
EGGS
Item:
Pongtai
Wheat
Beef,
Pork
Chicken
Fresh
Hen Eggs Fresh
White
Products Inçort & Hind-Leg
Fish,
Milk,
Average
Wei-chuanindex
2ndGrade
Domestic
MILK
CPI
Consl.suer
Price
Brand
Unit:
HIS/kg
HIS/kg
Source:
13,14
13,14,15 13,14,17 13,14,17 13,14,17 13,14,17 13,14
MIS/kg
NTS/kg
17
HIS/kg
Dr.Lin
HIS/kg
MIS/kg
MIS/I iterl985=100
13,14
15
Dr.Lin
Variable
Name:
PRT
PUT
PBFI
PPI
PCI
PFT
PET
1952 2.016171 4.786806 11.22348 15.14223
N.A.
M.A.
N.A.
N.A. 10.51138
1953 3.190992 5.327166 16.23984 15.37286
M.A.
N.A.
N.A.
N.A. 12.48647
15.947 25.30691
N.A.
N.A. 12.69459
1955 2.994592 5.379903 18.82403 17.99168 17.37803 23.72721
N.A.
N.A.
1956 3.249912 5.847654 19.00137 22.22931 19.69937 24.94998
N.A.
N.A. 15.41914
1957 3.452041 6.469029 21.80091 23.59924 21.59252 26.64398
H.A.
N.A.
1958 3.572438 7.065947 23.66304 22.33891 22.92785 27.22429
N.A.
M.A. 16.79088
1959 3.807812 7.936484 31.36494 30.48539 26.21997 34.38608
N.A.
N.A.
1960 5.412463 8.302533 36.95134 35.12487 30.27199 38.20921
N.A.
M.A. 21.98875
1961 5.944892
N.A.
N.A. 23.71052
PMT
CPu
Year
1954 2.785201 4.855593 18.22865 17.38891
8.0817 42.93044 35.78243 33.06137 32.40133
13.9528
16.5796
18.5652
1962 5.680665 9.079338 39.61153 33.34031 29.46246 28.51604 30.27376 12.96296 24.26552
1963 5.778104 9.099041
38.6488 36.12939
28.2024 30.57097 28.51389 12.96296 24.80896
1964 5.885286 9.138447 41.52434 37.46458 30.69718 31 .62932 28.40199 12.96296 24.75955
1965 5.943749
9.36006 53.83723 39.56133 34.29372 34.42699 24.41432 12.96296 24.73958
1966 6.031444 9.324356 59.34166 40.38223 35.96534 36.45104
1967
6.40171 9.359306
1968 6.927878
52.0131
42.7658 40.66363
24.16 12.96296 25.23151
36.2826 25.08571 12.96296 26.07663
9.42518 49.32757 45.46586 42.44924 39.21101 23.14274 14.25926 28.14211
1969 6.966854 9.113558 53.27985 46.30654 48.94579 41.75361 21 .60667 15.55556
1970
7.67 9.122208 55.14199 46.53402 48.78116 46.80833
1971
7.53 9.635907 72.37453
1972 7.935269 9.232369
49.7286 46.56904 51 .50573
29.5601
21.12 15.55556 30.61859
23.2 15.55556 31 .48473
97.6731 54.78256 52.10704 61.48885 22.69514 16.26742 32.43286
1973 9.032491 10.10434 112.0326 58.79803 52.10704 75.24417 25.69607 16.59277 35.08278
1974
17.58 16.50824
143.268
79.87
54.96 87.92108
37.16
26.5 51.73701
1975
18.06
19.161 109.6888
97.12
53.36 89.35801
34.53
26.5 54.44789
1976
17.85
19.6611 118.3748
93.25
52.11 106.6842
32.01 26.33333 55.80911
36.21 23.72222 59.73827
1977
15.87 19.82822 137.5128
97.21
57.72 132.6909
1978
16.35 19.82822 135.0074
106.25
59.52 144.1298
34 31.11111 63.18495
1979
19.51 21 .61542 149.4227
97.78
58.38 168.1899
33.26 35.27778 69.34567
1980
23.99 26.53266
173.171
107.45
68.16 197.3893
44.47 41.11111 82.52799
1981
25.21
30.7598 179.9651
127.81
73.58 221.1715
47.87 51.33333 96.01303
1982
28.09 32.00782 182.1731
131.07
73.35
243.89
39.89 52.16667 98.85531
1983
26.04 32.55723 186.0322
127.16
75.62 267.2463
39.58
51.75 100.1903
1984
27.8 33.12698 188.4233
116.62
72.9 257.5074
44.49
51.75 100.1703
1935
26.24 33.86969 182.9534 91.95768
69.89 258.5168
39.66
51.75
1986
25.51 33.91378 180.8773
114.74
77.64 259.2947
38.47 47.68962 100.7001
1987
25.42 33.85952 181.3048
117.38
71.3 259.8651
1988
26.36 33.70012
117.62
75.37 273.0892
N.A.
34.66
100
51.75 101.2235
36.87 51.71019 102.5196
389
Taiwan Consumption Data series
Table 1-8:
Titte:
RICE
wHEAT'
BEEF
PORK
CHIcXEN
FISH**
EGGS
MILK
FISH
FISH
Item:
Rice
Wheat &
Beef
Pork
PouLtry
Fresh
Eggs
MILk
Fish,
Fish,
Wheat
Fish,
Fresh,
Fresh,
FLour
TotaL
Fatty
LOW Fat
kg/
kg!
kg!
kg!
kg,
kg,
kgl
kg!
capital capital capital capital capital capital capital capital capita/
kg/
Unit:
Source:
VariabLe
year
year
year
year
Ti & 12 Ti & 12 Ti & 12 ii & T2
year
year
Ti & 12 Ti & 12
year
year
year
11 & 12 11 & 12 Ti & T2
kg!
capital
year
Ti & 12
ORT
OWl'
QBFT
OPT
OCT
OFT"
GET
aNT
FISHFF
F!SHFLF
1945
86.73
0.07
116.81
2.18
7.49
8.56
0.14
0.15
0.08
0.15
0.08
0.78
2.68
3.07
1.4
4.81
122.35
128.2
135.44
133.56
4.19
6.29
7.75
8.58
8.91
1.21
1.38
1947
0.15
2.42
1.33
0.36
0.36
0.37
0.39
0.34
1.41
1946
0.41
10.13
0.68
0.34
0.34
0.28
0.3
0.29
0.28
0.25
0.26
0.29
0.31
0.52
0.69
0.68
0.39
0.41
0.5
0.65
0.63
0.64
0.53
0.37
0.44
Name:
Year
1948
1949
1950
1951
1952
1953
1954
1955
131.37
126.06
141.19
124.85
134.18
1958
132.59
133.91
131.74
1959
135.31
1960
137.74
136.78
1956
1957
1961
1962
1963
1964
1965
1966
1967
1968
1969
1970
1971
1972
1973
1974
1975
1976
1977
1978
1979
132.1
134.36
129.87
132.85
137.42
141.47
139.93
138.74
134.45
134.28
133.52
129.84
134.15
130.39
123.12
125.06
113.99
107.58
1980
106.49
1981
1983
99.44
95.77
90.32
1984
.37.17
1985
35.97
1986
85
1987
78.18
1982
NOTE:
*
3.68
7.88
8.83
11.33
15.15
15.88
14.29
16.55
21.92
20.16
19.09
20.02
24.57
22.04
18.47
17.17
22.33
16.58
13.43
20.37
24.88
25.42
25.51
27.1
26.96
28.72
24.31
20.78
22.58
23.92
23.81
23.62
23.38
23.29
24.21
23.75
25.5
28.03
28.09
1.41
16.7
1.65
1.4
1.37
1.42
1.44
1.4
1.4
1.73
17.33
1.63
17.98
1.6
1.66
1.68
1.62
1.63
1.63
13.46
15.07
15.72
15.48
14.56
1.37
11.01
1.36
1.69
1.44
12.49
15.31
6.91
14.5
13.84
13.73
15.45
15.96
16.77
18.3
20.57
20.7
1.71
1.75
1.99
4.13
5.12
5.66
18.93
5.52
5.6
19.11
6.7
19.67
21.67
20.17
7.22
17.09
17.51
21.36
23.69
23.37
27.2
26.18
25.38
24.28
23.38
31.12
34.23
34.69
34.83
6.51
6.82
8.36
3.97
10.31
11.53
11.65
12.28
13.26
16.05
18.94
18.56
18.02
19.78
20.43
9.34
9.37
19.32
20.01
20.38
23.82
24.55
25.64
26.42
26.01
26.97
25.02
25.59
25.58
29.17
29.65
34.44
35.57
32.14
33.45
33.57
33.2
34.46
36.19
37.02
33.84
33.33
31.36
33.35
37.11
34.69
42.33
1.91
2.06
2.35
2.63
2.63
3.47
3.86
4.11
4.12
4.61
4.75
4.49
5.22
5.86
6.27
7.55
7.83
8.02
8.56
8
10.76
11.17
11.16
10.9
11.45
0.07
0.09
0.09
0.19
0.18
0.21
0.29
0.28
api
1947-68: Wheat
FISHFF+FISNFLF
heat FLour;
5.49
3.91
7
3.35
3.36
3.94
5.99
6.01
7.07
8.01
8.31
9.38
4.1.8
4.63
5.24
5.99
10.71
6.21
11.12
0.32
0.34
0.43
0.52
0.44
0.68
0.93
1.1
6.44
6.92
7.17
7.3
8.54
3.8
9.19
9.59
91,5
11.54
12.4
12.84
13.08
15.28
15.75
1.03
9.7
1.01
6.64
7.39
1.06
1.05
0.95
0.94
1.52
2.31
2.45
2.66
2.55
2.53
2.42
2.35
2.55
2.69
2.88
2.96
3.44
4.26
5.5
7.13
7.81
7.34
7.99
6.97
5.37
5.75
3.91
..2
..32
3.4
3.58
.i2
2.59
3.09
2.67
2.5
16.45
16.83
16.56
17.27
18.38
18.2
17.77
21.33
21.66
27.47
29.7
26.39
29.54
29.37
28.38
31.06
32.61
32.9
31.25
30.24
28.69
30.85
6.31
30.8
5.88
3.26
28.81
The titLe has been changed as folLows in the originaL data sheet:
1945-46: Wheat;
**
12.94
14.62
10.91
14.91
1.77,
1.52
1.52
1.54
1.59
1.59
1.45
1.69
1.3
1.3
1.32
17.18
0.38
0.94
1.22
1.14
1.08
1.17
0.93
1.24
1.4
1.6
1.62
1.66
1.89
1.39
1.51
1.61
1969-87: Wheat Flour
34.07
390
Table 1-9:
Titte:
Taiwan Socioeconomic Data Series
Popu-
Popu-
tation
tation
Lation
under
15-64
Lation ment in Nuiiber ofleLepEtoneFinaL
Service Consuntion
65 years primary Motor
oLd and industry VehicLes Nuiter ofExpenditure
Subover
Regisscribers
tered
oLd
oLd
Source:
Private
Popu-
Popu-
15 years years
Unit:
ErLoy-
TotaL
TotaL
LocaL
thousand thousand thousand thousand percent nuiter
persons persons persons persons
15
15
15
15
15
mitt ion
ni.siter
MIS
15
15
VariabLe
Name:
16
1989,88
IPOPT POPLT15T P0P15641 POPGT65T
AGPOPT
AUTOT
PHONET
PFCT
Year
18133
29969
16102
33531
16753
37194
18736
40322
N.A.
N.A.
N.A.
N.A.
N.A.
N.A.
51.1
23941
46101
M.A.
50.3
50.2
49.8
49.7
35533
51824
N.A.
49.4
49.5
46.5
68631
1952
8128
3442
4483
203
56.1
10710
24609
1953
8438
3605
4624
209
14519
27072
1954
8749
3769
4765
215
1955
9078
3941
4915
222
1956
9390
4123
5038
229
1957
9690
(.289
5165
236
55.6
54.8
53.6
53.2
52.3
1958
10039
4472
5319
248
1959
10431
4674
5501
256
1960
10792
4904
5620
268
1961
11149
5112
5759
278
1962
11512
5293
5932
287
1963
11884
5446
6135
303
1964
12257
5572
6368
317
1965
12628
5667
6626
335
1966
2993
5712
6929
352
45
152636
1967
13297
5755
7175
367
211750
1968
13650
5794
7474
382
1969
14335
5806
8125
404
42.5
40.8
39.3
14676
5821
8426
429
36.7
819104
203333
249420
113861
1970
1971
14995
5805
8737
453
35.1
957295
307473
142531
1972
15289
5796
9013
480
33
1114737
382608
164580
1973
15565
5769
9292
504
1365123
486829
206919
1974
15852
5733
9586
533
1678942
598504
299346
1975
16150
5705
9881
564
30.5
30.9
30.4
1988659
774233
336846
1976
16508
5723
10186
599
29
2341298
986012
368690
1977
16813
5705
10465
643
2786562
1196055
426802
1978
17136
5699
10755
682
3167559
1502831.
497669
1979
17479
5713
11042
724
26.7
24.9
21.5
3911439
1860602
604473
1980
17805
5716
11329
762
19.5
4665433
767742
1981
18136
5731
11606
799
18.8
5413407
2322816
2818027
1982
18458
5763
11857
838
18.9
6045268
3229618
1002305
1983
18733
5768
12090
875
3617528
1085429
19012
5737
12353
922
18.6
17.6
6674135
1984
7342801
1189459
1985
19258
5696
12589
973
17.5
7949993
3947087
4227992
1986
9455
56.3
12788
1027
17
8696045
4540359
1366466
1987
9673
5583
13001
1089
15.3
7702150
4909036
1537782
1988
19904
5562
13200
1142
13.7
8930878
5332285
1765247
48745
56774
61813
81098
105590
567302
731719
N.A.
65314 47709.94
72736 52305.09
79880 56112.21
88209 64352.24
98520 71464.64
113414 77062.46
135035 87289.69
165984 101678.7
56168
127636
922154
1261580
391
APPENDIX
J
JAPAN RETAIL PRICE DATA SERIES
J.1.1. An Overview
Japan's retail price data were obtained from various
publications: For both the pre-war and post-war periods,
the Showa Kokusej Soran (A Comprehensive Statistical Survey
of Japan Since 1920), Toyo Keizai Shinposha (TKS), 1980 (J2)
was used largely. For pre-war period, the Choki Keizai
Tokei (Estimates of Long-Term Economic Statistics of Japan
Since 1868; will be referred to LTES), Vol. 6, "Personal
Consumption Expenditures" by SHINOHARA, TKS, 1967 (J8)
was a
main source of retail price data and the Choki Keizai Tokei
(LTES), Vol. 8, "Prices" by OHKAWA et al., TKS, 1967 (J10)
or the Neiji-Taisho Kokusei Soran <A Comprehensive
Statistical Survey of Japan in Meiji-Taisho Era>, TKS, 1926
(Ji) were used as supplements. For post-war period, the
Annual Report on The Family Income and Expenditure Survey
(Kakei Chosa Nenpo), Statistics Bureau, Management and
Coordination Agency (MCA) / Office of The Prime Minister
(OPM) (J12) were used mainly; the Monthly Statistics of
Japan (Nihon Tokei Geppo), Statistics Bureau, MCA / OPM (J6)
was used as a supplement of J2.
The original data sources (except some data in the
early pre-war period) in these publications could be
summarized into the following three:
Table J-1:
Japan Price Data Original Sources
# Period
Kind
1 1924-66(67) RPS
2 1950-88
RPS
3 1947-88
HES
Agent
The Bank of Japan (BJ)
Management and Coordination Agency (MCA)
/ Office of The Prime Minister (OPM)
Management and Coordination Agency (MCA)
/ Office of The Prime Minister (OPM)
392
where RPS stands for Retail Price Survey and HES stands for
Household Expenditure Survey. Hereafter, "#" refers to
"data set number".
Following is a summary of source
publications (# corresponds to these in Table J-1):
Table J-2:
Japan Price Data Publications
# Publication
1 J2
2 J2
Period Covered
Table 13-7
Table 13-19
2 J6, v.i.*
3 J12, vJ.*
3 J2
1924-66 (67)
1950-78
1974-1989
194 7-88
Table 15-12
1947-78
* v.i. stands for various issues.
It was fortunate to have BJ-RPs data which was a bridge to
Connect pre-war series to post-war series.
J.1.2. Correction on HES data
The HES data had received a major revision in 1962.
This was due to the change in the size of the survey.
In
the pre-1962 period, the survey was limited to the larger
cities; after then it was expanded to all cities in Japan.
For example, the survey was originally done in 28 cities
until 1962, which expanded to 120 cities in 1978 (J2, p.
408)
To use this data fully, an adjustment was made against
the pre-revisjon period data. HES data of post-revision
period contained data for all cities, cities with more than
50000 population, and so on. Officially, the pre-revision
data were assumed to be equivalent to the post-revision data
for cities with more than 50000 population (J5,
p. 573).
Calculating the ratios of (post-revision HES data for all
Cities) / (post-revision HES data for cities with more than
50000 population) for some years close to 1962, then the
average of the resulting ratios was calculated.
The
393
resulting average ratio was used as a conversion factor and
the pre-revjsjon HES data was multiplied by the average
ratio.
Then, all the pre-revision HES data were made
equivalent to the post-revision HES data and the resulting
series covered the 1947-88 period.
J.2. Data Construction Procedures by Commodity
J.2.1. Rice
Rice retail price for Japan was compiled from the
following data sets:
Table J-3:
# Period
1900-40
1924-40
3 1947-66
4 1957-66
5 1947-62
6 1947-62
7 1947-88
1
2
List of Japan Rice Price Data
Name
Rice
Rice
Rice
Rice
Rice
Rice
Rice
Kind
RPS
RPS
(Rationed)
RPS
(Non-rationed) RPS
(Rationed)
HES
(Non-rationed) HES
(All item)
HES
Source
J8
J2
J2
J2
Agent
?
BJ
BJ
BJ
Area
Tokyo
Tokyo
Tokyo
Tokyo
J12,1962 MCA/OPM Japan
J12,1962 MCA/OPM Japan
J12,v.i. NCA/OPM Japan
Data #1 was a only source providing a consistent series for
the 1900-40 period. Although original source was not clear,
SHINOHARA mentioned it was Tokyo retail price data.
In
fact, comparing it with #2, the differences were very small.
Then it was decided to use #1 for 1900-40 period and assumed
that #1 and #2 are equivalent for pre-war period.
After April, 1941, rationing started (J2, p. 267).
There were dual prices of "rationed price" and "non-rationed
price" for rice until the mid-1960's. Before 1941, rice
price was formed in the market, generally.
In this context,
the HES data set #7 in post-war period had an advantage
since it was a weighted average price of different products
under the same commodity classification; in this case, these
products were rationed and non-rationed rice. Also, the use
of HES data may reduce a difficulty to deal with the recent
394
phenomenon of diversification in rice prices.
Within rice
category, there are various kinds from
the lowest grade of
the governjnent standard rice to the
super high grade
Presently. Prior to the 1980's in post-war period,
the
government standard rice was dominant in the market.
However, the market share of the high
grade rice have become
larger and larger in Japan recently.
Under this situation,
application of the government standard rice price
will lead
to substantial underestimate of
average expenditure for
rice. HES price may
reflect the price actually paid by
consumers on average.
Considering these points, HES price
data was used as post-war retail
average rice price in
Japan.
HES data had a revision in 1962, which
was adjusted
by the method mentioned above.
To connect post-war HES data
for all Japan with pre-war BJ data for Tokyo
area, the
following way was taken. First, a weighted
average price of
rationed and non-rationed rice was
calculated using #3 and
#4 for 1957-62 period, based
on the quantity data in #5 and
#6.
Next, the ratio between HES data and
the weighted
average BJ data from #3 and #4 was calculated
for each year.
Then, the average ratio was
calculated, which was about
0.96.
Using 0.96 as a conversion factor,
all #1 data was
multiplied.
Then, average retail prices for rice in all
Japan for 1900-88 were obtained.
395
J.2.2. Bread
The following data sets were used:
Table J-4:
#
1
2
3
Period
1924-67
1947-88
1955-88
List of Japan Bread Price Data
Name
White bread
White bread
White bread
standard quality
Kind
RPS
HES
RPS
Source
J2
J2,Jl2
J2,J6
4 1900-26 Wheat flour
wp*
Ji
5 1924-70 Wheat flour
WP
J2
6 1874
Wheat flour
RP**
J8
-1940
*
WP stands for wholesale prices.
** RP stands for retail prices.
Agent
BJ
NCA/OPM
MCA/OPM
Area
Tokyo
Japan
Tokyo
BJ
BJ
Japan
Japan
Tokyo
?
Retail price for bread was not found for the 1900-23
period.
To fill the blank, a regression analysis was
conducted for the 1924-40 period with "white bread retail
price" of #1 as a dependent variable and "wheat flour
wholesale price" of #4 as an independent variable. After
several trials, the following result was obtained as the
statistically best fit specification:
ln(RPBR) = 4.714587 - 16.11362 * ln(WPWF)
(T-Ratio)
(1.9854)
(-2.7432)
+ 13.38393 * [ln(WPWF)]2 - 3.485350 * {ln(WPWF)]3
(2.8375)
(-2.8280)
R-Square Adjusted=O. 7562
D-W=2.0104 Rho=-0.0584j.
where
RPBR = Retail Price of Bread
WPWF = Wholesale Price of Wheat Flour
The estimated equation was used to backcast 1900-23
bread prices. The predicted retail white bread prices were
calculated for 1900-23 by inserting wholesale wheat flour
396
price into the above estimated equation. Then retail bread
price was obtained for the entire pre-war period of 1900-40.
Note that the predicted values for the early 1900's looked
not reliable, which were unreasonably high. This was due to
the shape of cubic function.
(This caused no problem in our
analysis since only the data after 1909 period were Used.)
To have a consistent series for both pre- and post-war
period, the "BJ equivalent" pre-war series obtained so far
had to be connected to any reasonable post-war data.
It was
decided to take HES data as the base data to which all prior
series was adjusted. HES data had a major break in 1962-63
due to a change in the size of survey. The data of 1950-62
were adjusted for this change. To connect BJ data and HES
data, the ratios were calculated only for 1950-56 period;
since BJ data had a break and the post-war data was
consistent with the pre-war data only up to 1956 period.
The result was fairly stable, then the average ratio for
1950-56 was calculated.
The all 1900-40 data obtained
already were multiplied by the average ratio.
Finally Japan bread retail price series for 1900-88
period (except 1941-49) was obtained in yen/kg unit.
Together with the bread consumption data, the resulting
data set of Japan bread price should be used with caution
since the procedure used for the earliest period of 1900-23
may have caused some measurement errors.
1.2.3. Barley
Retail price under the name of "barley" itself was
available only for the pre-war period at hand; instead of
that, a retail price for a commodity called oshimugi was
available for both pre- and post-war period. Oshimugi could
be translated as pressed or rolled mugi and again mugi was a
Japanese name for a family of wheat or barley type grains.
It was still in question what type of mugi used for this
product, but there were some clues.
Speaking the conclusion
397
first, oshiinugj. was made from naked barley or barley.
According to Tanaka's Cyclopedia of Edible Plants of the
World (1976), "(naked barley) is often pressed to make it
boiled easily" (TANAKA, p. 367), however the author did not
mention about "pressed barley". On the other hand, notes
for oshiinugi retail price data in J2 specified "oshimugi of
barley made", which implied oshimugi could be made from
barley or other mugi grains. According to the study of the
Institute of Agricultural Economic Research of University of
Tokyo, "barley and oats grains are pressed flat and mixed in
rice", (UNIVERSITY OF TOKYO, 1964, p. 72) and the term
"pressed barley" was explicitly used in the article.
According to the article, pressed barley was a major but an
inferior substitute for rice (p. 79). One of data set in
Jl2 was titled "rolled barley".
Considering these points, it was assumed that barley
and oshimugi were very similar conunodities and almost
perfect substitutes with each other.
As a basic strategy of data construction, then, it was
considered to make pre-war barley retail prices into
post-war oshiinugj retail price equivalent using several data
sources:
Table J-5:
#
1
2
3
4
List of Japan Barley Price Data
Period
1900-40
l924-37;48-67
1947-63
1947-85
5 1980-88
Notes:
Name
Barley
Oshimugi
Oshimugi
Kind
Source
RPS
J8
RPS
J2
HES
J12
Mugi,
HES
J12
miscellaneous cereals
Other
HES
J12
cereals
Agent
BJ
MCA/OPM
MCA/OPM
Area
Tokyo
Tokyo
Japan*
Japan**
MCA/OPM
Japan
?
* 1947-62 data were originally for larger cities
only, which was converted into all Japan prices
using overlapping data in 1963.
** Similar to the #3 data, data for larger cities
were converted into data for all Japan using the
overlapping period of 1963-67.
398
In #2 BJ data, frequent changes in product definition were
observed which are summarized below; dotted line indicates
changes in definition:
Table J-6:
The Bank of Japan Barley Price Data
2-2
Period
1924-37
1948-51
Unit
yen/sho
yen/kg
Name of Observed Item
oshimugi made from barley
oshimugi made from barley
2-3
2-4
1952-53
1954-59
yen/sho
yen/kg
high grade
high grade
2-5
1960-67
yen/kg
oshimugi made from barley
2-1
For pre-war period, only one consistent and long time
series was found in J8 compiled by SHINOHARA. According to
the author, the data before 1922 were totally lacking, thus
retail price before 1922 were estimated by wholesale price
for barley times retail/wholesale average price ratio of
1.36 in 1923-27 (J8, p. 84). The original source of the
data was not clarified.
For post-war period, HES oshimugi (rolled barley) price
were available for the 1947-63 period in J12. The data
after 1964 were lacking, which were partially filled using
HES data #4 with the title of "mugi, miscellaneous cereals"
(item code 160 in J12). Comparing #3 and #4 for the
overlapping period of 1947-63, it was found that #3 and #4
were almost identical for the 1951-63 period.
Inspecting
the consumption quantity data attached to the both series,
it is said that the portion of rolled barley in "mugi,
miscellaneous cereals" were dominant. Considering this, #3
and #4 were directly connected; #3 was used until 1963, and
#4 after that.
There was another problem in the HES data
set that the classification for the item code 160 was
changed in the 1980's.
Fortunately, there were the
overlapping period of 1980-85 for the new and old series.
399
Comparing the two by taking ratios as usual, it was found
that the ratio was changing with some trend even for this
short term.
It was not known which product(s) in the "other
cereals" contributed to this change. Then the revised HES
prices after 1986 with new title were multiplied by the
average ratio between the old and new series for 1983-85 and
the new HES prices were converted into the old HES prices.
For pre-war to post-war period, BJ survey reports Tokyo
retail price of "oshimugi made from barley" for 1924-67 with
the war break of 1938-47. This was the key statistics to
connect pre-war series with post-war series. In this case,
pre-war J8 data were first converted into this BJ
equivalent, then the resulting series were converted into
the post-war HES equivalent. Therefore two steps were
required with two different conversion rates: one was
calculated between J8 and BJ series, and other was between
BJ and HES series. The procedure was the following:
Figure J-l:
J8
Japan Barley Price Data Converting Procedure
(BJ)
HES
BJ(sho) /J8(kg)
= "ratiol"
HES(kg) /BJ(kg)
= "ratio2"
where the relevant formula is J8*ratjol*ratio2
HES.
A problem was that the pre-war period BJ data was
measured in yen/sho while post-war in yen/kg, where sho was
a Japanese old measurement unit for volume, and 1 sho was
about 1.8039 liter; however, the weight of 1 liter of
oshimugi was unknown. To calculate this, data #2-3 (BJ) in
yen/sho, #2-4 (BJ) in yen/kg, and #3 (HES) in yen/kg were
used.
Comparing #3 and #2-3 for 1954-59, the average ratio
of the two series was calculated. Using this, unobservable
#2-4 data in 1952-53 were predicted. Then, the ratio
between the predicted data in yen/kg and #2-3 data in
yen/sho was calculated, and average of them was obtained.
400
The result indicated that 1 sho = 0.992085 kg. This rate
was applied to #2-1 data to convert it into yen/kg unit.
The official rate of 1 sho of crude barley is 1.08750kg
according to J5, p. 483, and oshimugi and crude barley might
not have much difference in physical structure (since
oshimugi is just a pressed barley) but oshimugi might be
slightly lighter than crude barley due to some processing
loss.
Therefore the weight of 1 sho of oshimugi was
expected to be slightly less than 1 kg. The result met this
expectation.
To calculate "ratiol", 1924-37 period data of #1 (J8)
and modified #2-1 were taken and the ratio of #2-l/#1 were
calculated.
The result was not very stable, but since there
was no clear indication of skewness in the distribution, the
mean value of 1.56267 was taken as a "ratio 1".
To calculate "ratio2", another problem raised from
definition changes in BJ series as summarized in the second
table above. As a solution, 1947-51 and 1960-67 BJ data
under the same title of "domestic oshimugi made from barley"
were used and 1952-59 data were skipped since the title was
different called "high quality". However, the resulting
ratios for 1947-51 were quite unstable, probably reflecting
the war time confusion in the late 1940's.
On the other
hand, the ratios for 1952-59 showed some trend thus the
simple arithmetic mean could not be used.
Then it was
decided to take the ratio in 1951,
the conversion factor "ratjo2".
0.960542, as the value of
This meant that the
oshimugi price observed in Tokyo area by BJ was about 4%
higher than the price paid by all urban household. Note
similar result was obtained in the case of rice.
Finally, pre-war #1 (J8) Tokyo retail barley price for
the 1900-40 period were converted into post-war #3 HES all
Japan retail oshimugi price by the conversion factor 1.50101
obtained as the multiplication of "ratio 1" and "ratio 2".
401
J.2.4. Beef
Beef retail price data were compiled from the following
data sets:
Table J-7: List
of Japan Beef Price
Data
# Period Name
Kind Source
1 1892-25 Female beef
RPS
J1 0, p. 140
medium grade
Table 4
2 1924-59 Sirloin beef
RPS
J2, p. 225
3 1947-88 Beef
}IES
J2,J12 v.i.
Mote: MF - Ministry of Finance
Agent
MF
Area
Tokyo
BJ
Tokyo
Japan
MCA/OPM
MF series #1 and BJ series #2 had an overlapping period only
for the two years of 1924-25, but the prices were unchanged
for those years in both cases, i.e., they had the same
trend.
The ratio of (BJ data / MF data) were calculated for
1924 and 2.15861488 was yielded, by which MF data were
multiplied and connected to BJ series.
So far Tokyo retail price data for the 1900-59 period
were obtained. Next step was connecting this with some
reasonable post-war data.
Mainly two data were available;
one was RPS data by MCA or OPM starting at 1950, and other
was household expenditure survey (HES) data by the same
agent since 1947
It was decided to take HES data for
post-war series, then BJ pre- to post-war data were
connected to it. According to notes on J2, there was a
definition break in the HES data in 1957-58 period, which
seemed negligible and therefore ignored.
The break in the
HES data in 1962-63 was corrected using the method mentioned
above.
Taking the ratio of BJ series and HES series for
1947-59, it was found that the ratios were very unstable for
1947-50 while fairly stable for 1951-59 period. This
phenomenon was considered to reflect the war time confusion
in the late 1940's. It was decided to calculate the average
ratio using only 1951-59 data.
The resulting average ratio
402
was 0.8411420; which was reasonable meaning that for an
average household consumed variety of beef items the
weighted average retail price of beef was about 84% of
sirloin beef retail price in 1951-59 period. Then, the
average ratio was used as a conversion factor between "BJ
sirloin beef" series and "HES beef mix" series, and all
pre-war data was multiplied by 0.8411420. In this
procedure, it was assumed that consumption mix for beef of
an average household had virtually unchanged between 1900-40
period and 1951-59 period.
All units were adjusted to be yen/kg for convenience.
J.2.5. Pork
Following data sets were used:
Table J-8:
#
1
2
3
Period
1900-40
1913-26
1924-56
4 1947-88
Note:
List of Japan Pork Price Data
Name
Pork
Pork
Top of medium
grade pork
Pork
Kind
J2
Agent
LTES
TKS
BJ
Area
Japan
Tokyo
Tokyo
J12, J2
MCA/OPM
Japan
FP
wi
Source
J10
Ji
RPS
HES
FP - Farm Price
WPI - Wholesale Price Index
The data construction procedure was basically the same
as the case of beef for 1924-40 and 1950-88 periods. Based
on the overlapping periods, the ratios of (HES data / BJ-RPS
data) were calculated. The top of medium grade pork price
were converted into weighted average pork retail price for
an average household in the 1950's in Japan.
Data for 1900-1923 period were required to complete the
data set. They were found in Jl and JlO, but could not be
readily taken. Ji contained the Tokyo wholesale price index
for the 1913-1926 period (surveyed by Toyo Keizai
Shinpo-sha; will be referred to TKS). For the earlier
403
period, LTES data in JlO was the only source; it was the
producer (or farm) prices for pork and the figures were
largely the authors' estimates. According to the notes in
J10, p. 82, "average relative price ratio of beef and pork
at some base years" times "wholesale beef price" yielded
"pork producers price" for the 1902-1912 period.
To connect the TKS data for 1913-23 with the BJ data
for 1924-40, the ratios of (BJ data / TKS data) were taken
for the overlapping period of 1924-26, and the average ratio
was obtained.
The TKS series were multiplied by the average
ratio.
Then, The farm price data from Jl0 for 1900-12 period
were connected to the resulting data set by the similar
method.
That is, using overlapping years of 1913-17 between
LTES data (J10) and the adjusted TKS data, taking the
ratios, calculating the average, then the J10 data were
multiplied by the average ratio.
The resulting data series
was BJ data or its equivalent for 1900-40.
Next, the resulting data set was converted into HES
post-war series equivalent, using similar method with the
overlapping years of 1951-59.
The overlapping period of
1947-50 were excluded according to the findings in the beef
price data. HES data had been already adjusted for the
survey break problem. The obtained average ratio between
HES and BJ data was 0.8172179, which implied that an average
household in the 1950's in Japan consumed less expensive
pork with 20% lower price than "top of medium grade pork" on
average. Again, an assumption was made that an average
household in the 1950's and that in the pre-war period had
the same consumption mix for the commodity of "pork".
All data was converted into yen/kg for convenience.
404
J.2.6. Chicken
Following sets of data were available:
Table J-9:
List of Japan Chicken Price Data
Year
1890-1910
Title
Chicken and Duck
Kind Source
WP(I) BJ
Ref.
31
1913-1926
Duck, High Grade
WP(I) TKS
31
368
1920-1925
Chicken*
WPI
CCT
31
381
1921-1926
Chicken, Male
WP
CCT
31
379
1874-1916
1917-1940
1949-1963
Hen Slaughtered
Hen Slaughtered
Hen Slaughtered
FP
FP
FP
est.
310
310
310
1923-1967
Chicken
RP
BJ
32
225
1950-1961
1962-1970
Chicken, Medium Grade
Chicken, High Grade
RP
RP
MCA/OPM J2
MCA/OPM J2
238
238
1971-1979
1980-1988
Broiler, Thigh in Bone RP
Broiler, Thigh Boneless RP
MCA/OPM J2
MCA/OPM 32
238
238
1950-1978
1963-1988
?? -1988
Chicken
Chicken
Chicken
MCA/OPM 32
MCA/OPM 312
MCA/OPM J6
396
IRP
IRP
IRP
?
?
Page
354
181-3
183
183
Notes: CCT -- The Chamber of Commerce of Tokyo
WP -- Wholesale Price
WPI -- Wholesale Price Index
WP(I) -- Wholesale Price Index with Base Period Price
FP -- Farm Price
RP -- Retail Price
IRP -- Implied Retail Price = Expenditure / Quantity
est. -- Estimated by LTES (310)
?
-- Original data source was unknown
??
-- The starting year was unknown for J6
*
-- Price of 1920 equalled 100/110 of 1921 value
Japan chicken retail price data had some problems; for
pre-war period there was no retail price data available at
hand for 1900-22 thus several different sources of data had
to be used.
Even worse, 1911-12 data was totally lacking.
For post-war period RPS data had several definition changes
405
causing some obvious breaks in the data set.
For pre-war period, wholesale price index data of duck
and chicken by B1 from Ji for 1900-10, wholesale price index
data of duck by TKS for 1913-26, and retail price data of
chicken by BJ were used to construct a data set.
CCT
wholesale chicken price was not used since the inclusion of
the data required an additional conversion rate which might
bring more measurement errors in the resulting data set.
The use of duck wholesale price data was ad-hoc, however, it
might be safe to assume chicken price and duck price have a
similar trend at wholesale level, following the treatment of
BJ for its own data. Then the additional ad-hoc assumption
was made that the retail price trend and the wholesale price
trend was close to each other. LTES data of farm prices
were not used since the figures were not reasonable for some
years; for example, the LTES farm price were constant over
the 1909-1928 period, whereas other wholesale and retail
price data showed fluctuations for that period.
To fill the blank period of 1911-12, backcasting was
done based on TKS wholesale index series (TKSWPI). After
several trials, it was decided to use the following
specification:
TKSWPI
(T-Ratio)
= 0.95706 * LAG(TKSWPI)
(20.146)
R-Square Adjusted = 0.7140
DURBIN-WATSON = 1.5886 Rho = 0.47890
DURBIN'S H Statistic = 0.47890
For the purpose of comparison, backcasting was done up to
1900. The backcasted data were compared to the actual
observation by calculating the ratios between them.
It was
observed that the ratios were not very stable but fairly
normally scattered around the average. Then the estimated
data by backcasting was taken for the
1911-12
period.
406
For the 1900-10 period, the ratios between the
estimated data by backcasting and BJ data were calculated
and the average ratio was obtained.
Using it as a
conversion factor, BJ data for 1900-10 were adjusted.
The resulting data for 1900-26 were connected to BJ
retail price data for 1923-40. Taking the ratios of the two
series for 1923-26, calculating the average ratio, 1900-22
data were adjusted. The resulting data was the BJ retail
price equivalent series for 1900-40.
For post-war period, there were two choices, i.e., RPS
data or HES data. As mentioned above, RPS data had some
obvious break caused by definition changes, thus HES data
were superior in terms of consistency. HES data for chicken
started from 1951. Note again that HES data was not the
observed prices but the implied prices, which was calculated
by household expenditure on the commodity divided by the
quantity consumed for that commodity per household. To see
if this property of HES price data causes any problems, the
ratios between HES data and BJ data for 1951-67 were
calculated by dividing the former by the latter.
Note that
HES price data was average of all cities in Japan and BJ
data was Tokyo retail price. The result was interesting:
the ratio had the increasing trend starting from about 0.57
in 1950's to 0.82 in 1967. The chicken retail prices
sampled by BJ were much higher than HES price in the 1950's
but the gap became narrower as time went by. This implied
that the chicken consumption mix for an average household
shifted toward the one containing more of higher grade
chicken. And finally almost no more improvement in
consumption mix became possible which was shown by the fact
that HES data and BJ data converged in the recent period.
Since the ratio was changing with some obvious trend,
something had to be done. It was assumed that consumption
attitude toward chicken in the 1950's and that for the
1900-40 period were same in Japan. Then the ratios between
407
HES data and BJ data for 1951-59 were calculated and the
average ratio of 0.5678653 was obtained. Note that the
ratios in the 1950's were fairly stable. Using the average
ratio as a conversion factor, BJ pre-war series were
multiplied by 0.5678653. Then Japan chicken retail price
series for 1900-88 was obtained.
Units were yen/kg.
J.2.7. Fish
The following sets of data were used to compile retail
fresh fish price in Japan:
Table J-1O:
1
2
3-1
List of Japan Fish Price Data
Period Name
Kind
1900-09 Fish, shellfish &
RPS
other aquatic foods
1909-40 Fresh and frozen
RPS
fish
Source Agent
MAFFJ*
J8
J8
MAFFJ*
MCA/OPM Large
Cities
MCA/OPM All
Japan
MCA/OPM Large
Cities
MCA/OPM All
Japan
HES
J12
HES
J12
4-1
1947-67 Fresh fish and
shellfish
1963-88 Fresh fish and
shellfish
1960-67 Fresh fish
HES
J12
4-2
1963-88 Fresh fish
HES
Jl2
5-1
5-2
6-1
1926-40 Tuna
1947-64 Tuna
1947-63 Tunny fillet
RPS
RPS
HES
J2
J2
Jl2
6-2
1947-63 Tunny fillet
HES
J12
7-1
7-2
8-1
1924-40 Mackerel
1949-67 Mackerel
1949-67 Mackerel
RPS
HES
J2
J2
J12
8-2
1949-67 Mackerel
lIES
J12
3-2
Area
All(?)
Japan
All(?)
Japan
BJ
Tokyo
BJ
Tokyo
MCA/OPM Large
Cities
MCA/OPM All
Japan
BJ
BJ
Tokyo
Tokyo
MCA/OPM Large
Cities
MCA/OPM All
Japan
Note: HES - Household Expenditure Survey Data
RPS - Retail Price Survey Data
*
Modified by SHINOHARA.
408
First, pre-war data for 1900-40 was compiled from #1
and #2 data by the ratio method based on the overlapping
period of 1909. All price was adjusted to "fresh and frozen
fish".
Note the data themselves were SHINOHARA's estimates;
for the original data construction procedure, see J8, pp.
66-67.
Next, the post-war data sets were compiled into the one
data set covered 1950-88. It was decided to exclude
shellfish, i.e., include fish prices only. Using #4-1 and
#4-2, with the overlapping period of 1963-67, "fresh fish"
HES implied price in all Japan for 1960-88 was obtained.
In the same manner, "fresh fish and shell fish" liES implied
price in all Japan for 1947-88 was obtained from #3-1 and
#3-2 data sets. Taking the ratios of the resulting "fresh
fish" and "fresh fish and shellfish" price data sets for
1960-65 period, and the average ratio was calculated. Using
this rate, "fresh fish and shell fish" price in all Japan
was converted into "fresh fish" price in all Japan for the
1947-59 period.
So far, the pre-war "fresh and frozen fish" retail
price and the post-war "fresh fish" HES implied price in all
Japan were obtained. To connect them into one consistent
data set, the following approach was taken using data
through #5-1 to #8-2:
409
Figure J-2:
Japan Fish Price Data Converting Procedure
J8
Fresh
& frozen
fish
BJ
Tuna
(Mackerel)
Rituna
(Rimack..)
HES
Tuna
(Mackerel)
R2tuna
(R2mack..)
Ratios based on Tuna Price
Rltuna = Bjtuna / J8
R2tuna = HEStuna/Bjtuna
R3tuna
HESfish/HEStuna
HES
Fresh
fish
R3tuna
(R3mack.)
Ratios based on Mackerel
Price
Rimack. = BJmack. / J8
R2mack. = HESmack./BJmack.
R3mack. = HESfish/HESmack.
Implied CF to convert J8 into HES fresh fish
= {
(Rituna * R2tuna * R3tuna)
+ (Rlinack. * R2mack. * R3mack.)
}
/ 2
The problem was that there was no data set represented
average fish price for the war period; only the price data
for each fish species available. To convert J8-RPS data of
"fresh and frozen fish" into HES data of "fresh fish", it
was decided to apply two sets of single fish commodity
prices as medium; one data set was taken from RPS side, and
the other from HES side.
It was desirable to have any
single fish commodity price that could represent a closer
trend with total fresh fish price. However, only two
different commodities of "tuna" and "mackerel" were
available in BJ-RPS data found in J2. Then, the implied
conversion factor to convert J8 into HES fresh fish was
calculated for the two cases using tuna or mackerel prices
separately.
First, using overlapping periods, the partial
conversion factors Ri, R2, and R3 were calculated for each
case.
For Ri, 1924-38 average ratio was used; for R2 and
R3, 1951-63 average ratio was used. The resulting ratio for
"tuna" case was 2.265287, whereas that for "mackerel" case
was 2.646744. Then, the average of the two results was
calculated since which one was closer to the case of true
410
average fish price was unknown. The implied conversion
factor was obtained as 2.456016. By this number, all
pre-war series from J8 was multiplied and a consistent data
set over 1900-88 period based on post-war HES implied "fresh
fish" price was obtained.
Units were yen/kg.
J.2.8. Eggs
The following data sets were used:
Table J-ll:
List of Japan Eggs Price Data
# Period Title
1 1900-40 Eggs
2 1900-23 Hen eggs,
Shanghai egg
3 1924-67 Hen eggs,
domestic egg,
medium size
4 1950-88 Hen Eggs
Kind Source
Agent
RPS J8
MF (?)
RPS Jl0, Table 5, MF
pp. 153-4
RPS J2
BJ
Area
HES
Japan
J2 and
J12, v.1.
MCA/OPM
Tokyo(?)
Tokyo
Tokyo
First, the pre-war data series was taken from #1, #2,
and #3.
For the 1900-23 period, #1 was compared to #2 and
it was concluded that they were identical but #2 was better
with having less rounded-off errors.
This was reasonable
since both data were taken from different volumes of the
LTES studies done by the same body of researchers. Also, it
was found that #2 and #3 were directly connected both in J8
and J10. We followed their approach.
Next, post-war series was constructed. HES data was
taken from J2 and Jl2, however which had some problems in
the measuring unit; 1950-64 data were measured in piece but
after that units changed to grains. For an appropriate
weight for an average piece of eggs, the official rule of
53.5 grams per egg set by BJ was applied (see Japan egg
consumption data document), and all the data was converted
into grams for 1950-88.
411
Similarly to the other price data, an adjustment was
made for the break in HES data due to change in the regional
coverage in the survey.
Lastly, the ratios of the pre-war and the post-war
series were calculated for the overlapping period of
1950-67.
The overall performance of the result was fairly
stable, and average for this period is 0.9781106. However,
similarly to the other results, 1950 data was a little away
from the average. Considering the assumption being made
that an average household consumption attitude toward eggs
consumption in 1950-67 period persists in 1900-40 period,
the overlapping period might be a little too long to state
the assumption, even though the ratios are stable over the
years in this case.
Following to the other examples, here
1951-59 data were used in the calculation of the ratios and
the average was 0.9803908.
Then, the data in pre-war series were multiplied by
0.9803908 make them into post-war series equivalent.
Units were yen/kg.
J.2.9. Milk
The following data sets were used:
Table J-12:
# Period
1 1900-40
2
1900-22
3 1923-64
4 1950-88
List of Japan Milk Price Data
Title
Milk
Milk
Kind Source
RPS
RPS
Processed milk RPS
in 18 0cc bottle
Milk
HES
J8
J10,
Agent
?
Table 5, KKK(?)
Area
?
Tokyo
pp. 153-4,
J2, J10
BJ
Tokyo
J12, v.i.
MCA/OPM
Japan
KKK - Kinyu-Kenkyu-Kaj <Institute of Finance>
For the earliest period of 1900-22, #1 (J8) and #2
(J10) were available.
By a brief inspection, it was
412
concluded that they were almost identical and differences
between them are due to rounded-off errors.
#1 were more
rough than #2, thus #2 was taken.
For 1923-40, BJ series was available in J2 and J10,
which were compiled into one. It seemed that rounded-off
errors are bigger in J10 for BJ data, thus J2 were taken as
much as possible.
Following the practice done in the LTES study in J10
(also probably in 38), 1900-22 data and 1923-40 data were
directly connected.
For 1950-88, HES implied price data series from J2 and
312 was chosen.
Note that HES data was measured in
yen/liter while other data were done in yen/l8Occ.
Then all
of the others were converted into yen/liter for convenience.
Lastly, for connecting pre-war and post-war series, the
ratios between BJ data and HES data were calculated for
1950-64 period.
The resulting ratios were very close to
unity and fairly stable, which implied indifference between
two data sets.
This reflected the fact that milk was a
commodity of very narrow category in these days in Japan,
thus less price variations were observed in the data sets
from agent to agent. The average ratio was 0.9897852 which
was used as a conversion factor by which the whole pre-war
series were multiplied. Then, Japan milk retail prices for
the 1900-88 period were obtained.
Notice again that the units were yen/liter, which were
exception in our data set. We used this unit as an
approximate measure of yen/kg.
413
APPENDIX
K
JAPAN TOTAL DOMESTIC CONSUMPTION DATA SERIES
K.1. An Overview
Total domestic consumption data of various food
commodities in Japan were taken mainly from two sources:
one was an elaborate work done by Miyohei SHINOHARA (J8),
which was a valuable and rich data source for the 1874-1940
period. Detailed food balance sheets for various
commodities were available in the volume.
The other source was the food balance sheet of MAFFJ
(Ministry of Agriculture, Forestry, and Fishery, Japan; used
be Ministry of Agriculture and Forestry until July 1978),
which was originally prepared by request of FAO (Food and
Agriculture Organization of The United Nations, Rome). This
was the main source of Japan's post-war data in this study.
Duplications of the data was found in J4, J7, 01, and 02.
Among several duplications, the one in J7 provided most
information on utilization and also disaggregated some
groups of commodities such as meats into sub-commodities
such as beef and pork. We took J7 as a principal source.
Whenever some volumes were not available or very probably
misprints were found in J7, the other data sources such as
J4, 01, or 02 were used occasionally.
For example, data for
the year 1966 was totally lacking in J7 and therefore taken
from J4.
The SHINOHARA data and the MAFFJ data were consistent
with each other in terms of basic concepts of data
construction. Both of them used the food balance sheet
approach, under which one starts from the physical amount of
total domestic production, then accounts net trade volume
(if any), change in stock (if any), non-food use (if any)
and waste, and applies some extraction rate (if possible),
and then finally reaches to net domestic food consumption
414
amount.
Note that "net food supply" of SHINOHARA was
synonymous with "net food" of MAFFJ, which was referred to
as "total domestic consumption (for direct food use)" in
this study.
For Japan, per capita consumption amounts were derived
by dividing total domestic consumption amounts by total
population for that year.
Followings are the details of construction procedure
for total domestic consumption data for each commodity.
K.2. Data Construction Procedure by Commodity
K.2.i. Rice
Consumption data for pre-war period were taken from
SHINOHARA series (J8), which were largely the author's
estimates, particularly before the 1930's. Formula used
there was:
Production - Change in Stock
Import - Export
= Domestic Supply
(Domestic Supply - Seeds - Manufacturing Use)
* Extraction Rate of 92% = Net Food Supply
Note that "net food supply" was interpreted as "total
domestic consumption" in our study.
For the post-war period, "net food" data were taken
from the food balance sheet of MAFFJ in J4, J7, 01, and 02.
The formula utilized was the same as above, thus SHINOHABA's
"net food supply" equals to MAFFJ's "net food". There might
be a slight difference in extraction rate, which would be
only 1% or so. Note that the resulting amount was in
polished units since extraction meant transformation of
crude "brown" rice into polished or husked "white" rice.
Also note that the Japanese old unit of "koku" was used in
the SHIN0HARA series for 1900-1908, which was converted into
415
metric tons with reference to an official rate of "1 koku of
crude rice = 150.000kg of crude rice", according to J2, p.
208 and J5, p. 483.
K.2.2. Bread
K.2.2.i. Consideration On The Use of Wheat in Japan
Generally, there were two main staple food groups in
Japan in its recent histor; one was rice origin foods and
the other was niugi origin foods.
Mugi is the name for the
family of grains such as wheat, barley, naked barley, buck
wheat and so on. Viewed from a different aspect, except for
the war time, the three most common ways to consume staple
grains in Japan were directly boiled (after polished) or in
the form of noodles or bread:
Table K-i:
Japanese Way of Eating Staple Grains
Just Boiled
Rice
Wheat
Barley
Rice
.7'
Mixed w/Rice
Naked Barley Mixed w/Rice
7,
Buck Wheat
Noodles
.7.7
Udon, etc.
'.7
.7'
Soba
Bread
.7'
Bread
.7
.7
'.7
Note: "tJdon" and "soba" are names for different types
of Japanese traditional noodles.
This table was based on my personal knowledge.
The double
question mark means "very unlikely" and the single question
mark means "unlikely".
There was "barley bread" in Taiwan
(found in Taiwan price data publication), thus there was a
possibility of consumption of such commodities in Japan also
though this may be rare. "Just boiled" and "noodles" foods
in the table were very popular staples for perhaps these two
hundred years (this may be still an underestimated figure, I
think), on the other hand, bread just became popular after
World War II.
In this sense, it could be said that "just
416
boiled" and "noodles" categories were traditional foods, and
"bread" was a non-traditional food. One exceptional case
was that of Chinese noodles iiade by wheat flour which also
became very popular after the war period.
Every staple mentioned above substitutes each other.
Interestingly, wheat is the only grain used for both
traditional and non-traditional foods.
In terms of
"characteristic" arguments (LANCASTER, 1971), wheat has at
least two dominant characteristics which are competing each
other.
If this point is ignored and wheat is taken as a
single characteristic commodity, the analysis will be
difficult since it is expected that the traditional portion
is staying at the same level or declining while
non-traditional portion is growing overtime, and these two
wash out each other's influences.
There is another possibility of wheat use in Japan as
"wheat flour" itself. But Japanese wheat flour consumption
at home is not very high and here too the traditional and
non-traditional usages are mixed. Basically, wheat flour is
used as a complement of some non-traditional type dishes;
for example, it is used for a coating of deep-fried dishes
with meats, fishes, and vegetables.
Bread and noodles are
mainly purchased at shops and not commonly made at home.
Thus wheat flour itself is not a good representative of
either one of traditional wheat origin foods or
non-traditional ones.
K.2.22. On the Data Specification
It may be interesting to distinguish traditional
portion and non-traditional portion of wheat usage and
compare them with rice and barley consumption. The simplest
way was including both "noodles" and "bread" in the current
study; however, it was impossible due to data constraints.
Data of the domestic supply of wheat and wheat flour
were available, but noodles or bread supplies were not
417
directly available.
For the latter it was necessary to
estimate using information on utilization, which were
limited to some years but not all years.
Price data for bread were available back in 1924 as an
official series.
78 reports pre-war noodles price, but
information on the commodity was unknown.
J12 reported
prices for different kind of noodles, which started at 1947.
Data for wheat flour were available for 1900-88 period in
J8, J2, J3, etc.
As mentioned above, bread or noodle data was preferred
to wheat flour data, however the former was expected to
contain more measurement errors than the latter.
Choice between noodles and bread was made based on our
interest. Noodles was traditional food but the importance
of the item in daily consumption had become smaller and
smaller.
On the contrary, bread consumption was increasing.
To obtain some implication toward the future, bread was more
attractive than noodles.
Considering these, it was decided to construct wheat
flour consumption data first and then construct bread
consumption data.
K.2.2.3. Wheat Flour Data Construction
Wheat flour consumption data for the pre-war period was
directly available from J8. Note that "domestic supply"
rather than "net food supply" was taken as a base quantity
in this case. The author tried to estimate "wheat flour
consumption at home" as a "net food supply" excluding the
portion used in manufacturing bread, noodles, and
confectionery, and other industrial uses.
This was,
however, irrelevant for our study based on the reasons
mentioned above. On the other hand, "domestic supply" in J8
was defined as total production plus net trade, which itself
represented every type of consumption of wheat flour.
418
Post-war period wheat flour consumption was a direct
copy of the quantity of "net food" of wheat in Japan in
MAFFJ's food balance sheet data in 34, 37, and 01. As
usual, food balance sheet calculation was formulated as
Production+ (Import-Export) -Feed-Seed-Manufacture-Waste
= Gross Food
Gross Food * Extraction Rate = Net Food
In the above formulation, the interpretations for
"manufacture use" and "extraction rate" were important to
understand wheat flour consumption. MAFFJ gave no
explanations for them, but FAO explained them for their own
publications. MAFFJ's balance sheet should follow some
instructions given by FAO. Then, an interpretation was made
for these terms based on the assumption notes of FAO food
balance sheet in F5, comparing these assumptions with the
actual food balance sheet tables in F5 and F6.
According to
the tables in F5 and F6, "manufacture use" meant exclusively
"non-food use".
In fact, according to SI-IINOHARA (38), there
were non-food uses such as paste for cast metal, for textile
manufacturing, and for ingredients of tooth paste or
cosmetic goods (p. 65). Thus it was assumed that
manufacture use in MAFFJ'S food balance sheet meant those
non-food industrial uses.
"Extraction rate" to convert "gross food" into "net
food" could be interpreted as "extraction rate for wheat
flour".
This was backed up by the following two points:.
First, wheat could not be eaten by people without making it
into flour initially. Second, FAQ official "conversion rate
of wheat into wheat flour" for Japan was very close to
MAFFJ's "extraction rate"; the former was set at 78% in F5
(in the balance sheet of F4 this is set at 76.7%) and the
latter was set at 76% or 77%.
419
Assuming the whole assessment above was reasonable, it
was concluded that MAFFJ's definition of "net food" of wheat
could be defined as "total wheat flour used for food".
Then, the figures of "net food" collected from J4, J7 and 01
were taken as net wheat flour consumption in the post-war
period in Japan and were directly connected to SHINOHARA's
pre-war "domestic supply" in J8.
K.2.2.4. Bread Data Construction
So far total wheat flour consumption data for 1900-88
was obtained.
The next step was estimating wheat flour use
for bread.
For the pre-war period, SHINOHARA (J8, p. 65) noted
that there were only 6 years of information on "utilization
of wheat flour", part of which were reproduced in the table
below.
Note that the author cited data from two different
sources labeled (a) and (b) (for details, see J8, pp.
64-65)
Table K-2:
Wheat Flour Utilization Rates in Japan (%)
Source
Year
Noodles
J8
J8(a)
J8(b)
J8(a)
J8(a)
J8(b)
J8(b)
1909
1922
1924
1926
1931
1933
1938
(51.3)
50.3
J7
J7
J7
J7
1972
1977
1983
1987
38.4
54.0
49.9
49.6
43.0
32.0
35.].
33.7
35.4
Bread
Confectionery
(35.4)
17.2
15.0
13.7
12.3
12.0
23.0
20.7
19.0
25.8
32.6(incl."others")
43.0
30.0
34.8
36.1
36.5
36.7
12.8
13.7
13.8
12.6
Based on this limited information the author filled the
blank periods by interpolation and for outside of the range
(i.e. for 1909-1921 and 1939-40) by extrapolation.
For
example, for the year of 1909 the author's estimates yielded
420
51.3% for noodles and 354% for sum of bread and
confectionery.
Consumption data for noodles was already calculated by
SHINOHARA, but not for bread; thus it was necessary to be
estimated. Here, following SHINOHARA, the same kind of
method was used. At first, the rate between the years was
calculated by interpolation.
Secondly, extrapolation was
used for the 1939-40 period, but it was not used for the
1900-21 period and simple regression was employed to obtain
data for that period. Note that the utilization rate for
bread use was steadily declining up to 1933, but increased
in 1938; what happened between 1933 and 1938 was unknown.
Because of interpolation, the plot of the bread use rate
against time had a v-shape with 1933 at the bottom.
The
simple regression with one independent variable of "year"
and dependent variable of "wheat flour use rate for bread"
was run over the 1922-33 period:
Bread Rate = 844.71 - 0.43112 * Year
(T-Ratio)
(9.3595) (-9.2073)
R-square adjusted = 0.8839
D-W = 0.4028
RHO = 0.62318
Although there was a strong positive autocorrelation, this
was not adjusted. Using SHAZAN, the COCHRANE-ORCUTT type
GLS estimates were calculated and the resulting wheat use
rate for bread using OLS estimates and GLS estimates were
compared.
The GLS result was not taken because the
resulting rate of change was more rapid than that of the OLS
result, which led to the unreasonable results for the
earlier periods.
For the post-war period the same procedure was taken
based on MAFFJ data on wheat flour utilization reported in
J7.
For reference, data for some years were cited in the
above table.
The data were only available after 1972 in J7,
421
and 1978, 81, 82 and 88 data were lacking at hand. For the
1972-87 period, the interpolation method was employed to
fill those blank years and based on the resulting figures, a
simple regression was run for estimating the 1950-71 period
assuming the marginal rate of change in utilization rate for
bread was constant for the post-war period:
Bread Rate = -162.75 + 0.10035 * Year
(T-Ratio)
(-4.1233) (5.0323)
R-Square Adjusted = 0.6186
D-W = 0.8867
RHO = 0.52410
The years 1950-71 and 1988 were plugged into the estimated
equation and wheat flour utilization rate for bread in
post-war period were obtained.
The total wheat consumption data were multiplied by the
bread use rate year by year, and consumption of wheat flour
for bread was calculated.
According to FAQ (F5), an official conversion rate of
Using this
information, the figures obtained above were multiplied by
wheat flour into bread in Japan was 125%.
1.25 to have bread consumption amount, finally.
K.2.3. Barley
Japan's barley consumption data was constructed by the
procedure explained below. Note that the entire calculation
was based on the quantity of crude barley, not polished
barley.
For the 1900-40 period, the SHINOHARA series was
directly applied from J8. The formula used in the article
was the following:
{ Production + (Import - Export)
}
* 0.606
= Net Food Supply
422
According to SHINOHARA, the percentage of net direct food
use was assumed to be constant at 60.6% throughout the
period 1900-1940. This number was calculated based on the
survey of grain use held in 1922 and 1926, where the
percentage of direct food use were reported to be 64.9% and
56.3%, respectively. The author took an average of these
figures (see table below). Note that production data was
based on mugi year of July to June (mugi is a Japanese name
for the family of grains such as wheat, barley, naked
barley, and rye) and figures for change in stock were not
available and therefore ignored (SHINOHARA (J8), 1967, pp.
57-58).
Table K-3:
Change in the Use of Barley in Japan
(Percentages are in terms of total supply.)
1922
1926
1957
1972
1987
feed use
22.0%
28.8%
14.5%
56.3%
n.a.
seed use
2.9%
2.5%
1.3%
0.2%
n.a.
manufacturing
flour
soy-source
beer
confectionery
miso paste
8.8%
0.2%
1.2%
3.8%
1.0%
3.3%
11.9%
0.0%
1.0%
4.7%
1.5%
4.7%
7.4%
33.0%
39.7%
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
n.a.
64.9%
56.3%
74.5%
10.1%
(2.8%)
extraction rate
n.a.
n.a.
59.0%
50.0%
(50.0%)
net food use
n.a.
n.a.
43.9%
5.1%
1.4%
others
0.7%
0.5%
n.a.
n.a.
n.a.
waste
0.0%
0.0%
2.3%
0.3%
n.a.
gross food use
Note: n.a. - not available.
423
In the above table, the first two columns were copied from
SHINOHARA (J8), p. 57, and the last three were calculated
from the food balance sheet by MAFFJ (J7 and J4). Only for
the first two periods the details of manufacturing use were
available.
According to this table, there were substantial changes
in barley use over the years in Japan. While direct food
use had been declining, feed use and manufacturing use had
been increasing and the shifts were more drastic in the
post-war period, particularly between 1957 and 1972.
Data for the post-war period was constructed mostly
based on the food balance sheet by MAFFJ (J7 and J4). For
1950-52, ready made data were not available.
For barley,
OECD data for 1954-1965 could not be used directly since it
included naked barley under the name of barley, which was
found by the comparison with MAFFJ data.
To maintain consistency between SHINOHARA pre-war
series and post-war MAFFJ data, it was necessary to use
the gross food consumption figures and not the net food
consumption figures. The relationships among net food,
gross food and extraction rate were:
Net Food = Gross Food * Extraction Rate
SHINOHAflA did not mention the extraction rate for food
consumption nor which crude or polished unit was used in his
estimates.
Compared with official production data for the
1878-1964 period in J5, it was found that SHINOHARA
production data was identical to those.
It used crude unit;
as a consequence, it was concluded that the SHINOHARA series
was based on crude unit. This was an important point since
some post-war data reported in both crude and polished unit
(after extraction rate accounted) showed a large difference
between them which was about 50% in physical amount (see
above table).
424
In the MAFFJ food balance sheet, extraction rates to
convert gross available food supply into net food use were
available for the 1957-72 periods. After 1973, neither the
official extraction rates nor details of utilization were
available; only trade, change in stock, and resulting net
food use figures were reported in J7. J4 was not helpful
since it contained the same information only.
To convert
the net figure back into a gross figure the extraction rate
had to be known. Inspecting the existing food balance
sheets (by calculating the ratio of (net food/gross food)
for some cases), it was found that the extraction rate was
revised several times and became smaller in the post-war
period:
59% in 1955, 56% in 1959, 54% in 1960, about 52% in
1964, and about 50% in 1970. Considering the rapid cycle of
revision in extraction rates, the following method was taken
to fill the blank: for 1953, 1954, and 1956, the 1955 rate
of 59% were adopted; for 1958, the average between 1957 and
1959, 57.5% was applied; for 1966, the 1964 rate of 52% was
applied; after 1973, the extraction rate of 50% during the
1970-1972 period was assumed.
For the 1950-52 period, production and trade data were
available in J4 and total available supply was calculated
directly.
Changes in stock were not available and ignored.
The unit used was "koku" in this data; then "1 koku of crude
barley = 108.750 kg of crude barley" was applied to convert
them into metrics measures (this is an official rate for
Japan, according to J2, p. 208 and J5, p. 483). To obtain
gross food use from total supply it was required to subtract
non-food use, waste and so on. However, data for each
component of use was not available.
Considering that the
structure of utilization changed very rapidly, the average
ratio of (gross food/total supply) in the closest years of
for each blank period was calculated as a conversion factor.
For the blank period of 1950-52, 1957 and 1959 data were
used, yielding the result of 0.7337.
Then total available
425
supply figures of 1950-52 were multiplied by 0.7337 to be
converted into gross food use figures.
K.2.4. Beef
Availability of data from the food balance sheet by
MAFFJ (J7) was limited for the years 1954-72. Afterwards,
beef and other meats were integrated into one aggregate
FAO food balance sheets in terms of
category of "meats".
three years average was available for some limited period
(F4, F5, and F6). On the other hand, fairly long time-series
data was available for beef production in J2 and J5, and FAO
provided reliable trade figures for the post-war period.
By a brief inspection of MAFFJ's food balance sheet
data, two things were discovered: one was obvious that
MAFFJ set the extraction rate of food use to be 100% for the
period 1954-65, but changed it to 77% for 1967-69, and then
to 71.9% for 1970-72. Another point was less obvious but
MAFFJ seemed to set waste rate at 25% for the 1954-65 period
and changed it to 2% for 1967-72. In terms of the following
formulation:
Production + (Import - Export) = Total Available Supply
(Total Available Supply) * (Waste Rate)
* (Extraction Rate) = Net Food Consumption
If one puts
(Waste Rate) *(Extractjon Rate)=Conversion Factor [C.F.]
then C.F. of 75.00%, 75.46%, 74.26% were obtained for
The result
1954-65, 1967-69, and 1970-72, respectively.
showed that it was very likely that MAFFJ changed the
definition of waste rate and extraction rate simultaneously,
and as a consequence the overall conversion rate were almost
unaffected.
Mean of the C.F. over the 1954-72 period was
426
74.26%.
For the pre-war period no information was available
to assess these conversion factors, and it was therefore
assumed that this C.F. of 74.26% was in effect for every
year after 1900 to present. This was just rescaling the
entire consumption figures, thus the fundamental trend in
the data should not be affected.
J5
J5
J2
and J5 were almost identical but
used 100 metric tons as a unit while J2 used metric tons.
covered 1894-1963 while J2 covered 1920-78. J5
Production data in
J2
separated them.
Then, J2
was used as much as possible to obtain more precision and J5
was used for the early years.
Due to the fact that only the
aggregated beef and veal while
sum of veal and beef consumption was reported in NAFFJ food
balance sheet (J7) and due to lack of a disaggregated data
before
1920, aggregated quantity of (beef+veal) was used
as
beef consumption.
Trade data was taken from FAO (Fl) for the 1955-1987
period, and data from food balance sheet were used only for
1954 as a supplement. Data prior to 1953 were not
available, and therefore ignored. According to the data of
the five year 1934-38 and 1948-52 periods found in the 1958
issue of Fl, there were no exports in those two periods and
some negligible amounts of imports, which were 12200 metric
tons (average of 2440 metric tons annually) and 500 metric
tons (average of 100 metric tons annually), respectively.
For the latter period the magnitude of annual imports were
negligible; also for the former period the magnitude might
be fairly small provided the fact that the production was
52331 metric tons in 1934 and was increasing thereafter.
Then, the formula applied here was the following:
{Production + (Import - Export)} *
0.7426 =
Consumption
And again, trade figures were assumed to be zero for the
period prior to 1954.
427
As a test, the constructed data was compared with the
available food balance sheet value of net consumption
between 1954 and 1972 and discrepancies were calculated.
For the total of 18 samples, 10 were within a
+1- 3% range,
15 were within a +/- 6% range, and the largest three
differences observed were +10.6% in 1965, +8.59% in 1972 and
-7.19% in 1963.
Note that by closer inspection of the result, it was
concluded that it was not the differences in trade data but
those of domestic production data mainly which contributed
to the statistical discrepancies. The source of
discrepancies was coming out of the same agency, MAFFJ, and
no statement was found to explain this point. To maintain
consistency in the data set, the constructed data set was
taken through the entire period which might be more
appropriate than mixing it with MAFFJ's original food
balance sheet data.
Another source of data was provided by USDA for the
period 1960-1990 (Ui). Comparing the USDA data with our
result and MAFFJ food balance sheet, it turned out that
there were clear gaps between the USDA data and the latter
two.
Production data were almost identical between our
result (taken from J2, J6, and J7) and USDA, whereas there
were two distinct differences between them; one was in
import figures, and other was in changes in stock.
USDA import figures were consistently larger than our
result (using Fl) which implied that USDA might take into
account live cattle imports for beef production. According
to a livestock specialist of the Department of Agricultural
and Resource Economics, Oregon State University, at least
after the 1970's, some live cattle imports for the purpose
of beef production had been conducted by the Japanese beef
producers. Due to an import quota for beef, imports of beef
itself was limited; to fulfill a growing domestic demand for
beef, some people imported live cattle and slaughtered them
428
domestically.
This was very likely which meant our figures
underestimate the reality.
Another point of difference was that USDA data
contained "stock" columns, which were non-zero and growing
after 1974. Reasons behind this was unclear, however, it
was possible that some technological improvement on large
scale storage facilities could have occurred thus carry
overs had become not a negligible amount.
Considering these points, it was concluded that USDA
data was the most appropriate after the 1960 period, thus
1960-88 figures were replaced by them.
Consistency with the
data prior to that period was almost not affected, since the
only thing which made a difference between our data and USDA
data in the 1960's was the trade volume which was small in
those days.
After the replacement, the conversion factors assessed
above were also applied to the USDA data to calculate net
food consumption amounts.
K.2.5. Pork
Production data were available from J5 for 1894-1963,
from J2 for 1920-78, from J6 at hand for 1973-88, and also
from F2 and F3 for 1951-87. Note that J2, J5 and F2 were
identical but J2 contained most digits, thus J2 was used as
much as possible. Also, J2 and F2 did not coincide in one
year of 1964, when F2 provided the figure about 15% higher
than J2.
J2 data was taken in such a case.
The first task here was to put those together to
construct a 1900-1988 production series being consistent
with MAFFJ's food balance sheet data in J7. Production data
for the pre-war period were taken from J2 and J5. There
were some problems in the post-war period; J2 and J7 were
fairly consistent until 1965, however J2 provided
consistently higher values by about 10% than J7 after 1967.
MAFFJ data itself had some problems in 1967 and 1968, where
429
suddenly "change in stock" appeared, which accounted for
4.2% and 2.2% of production volume in each period
respectively.
Taking into account this "stock adjustment",
J7 became closer to J2.
According to notes in J2, there was
a slight change in the official estimate method in 1965.
This might be a possible origin of this confusion in MAFFJ
data.
(Note also J2 and J7 were both MAFFJ data.) After
all, although there was some inconsistency between J7 and
J2, to maintain overall consistency in our data set, J2 was
taken and stock adjustments were ignored.
The next thing to be considered was the mechanism in
the calculation of the food balance sheet.
Ignoring those
exceptional years of 1967 and 1968 which contained changes
in stock, MAFFJ seemed to apply the following formula in J7:
{ Production + (Import - Export)
}
* (Waste Rate)
* (Extraction Rate) = Net Consumption
Also, as in the case of beef, MAFFJ seemed to change its
policy in determining waste rate and extraction rate around
1965-67; that is, waste rate was changed from about 20% to
2% after 1965 period, and extraction rate from 100% to about
82% after 1965 and then to about 70% after 1970.
(There was
one exceptional year of 1960 when waste rate was about 30%,
which was just unreasonably high; thus this discrepancy was
simply ignored.)
Calculating a conversion factor defined as
a product of waste rate and extraction rate, it was found
that up to 1969 about 80% was used for a conversion factor
(except 1960) and thereafter 68.6% was applied. These
differences might be a part of an official adjustment to
mitigate a change in measurement method in 1964-65,
therefore these conversion rates were directly used here,
i.e.:
430
{ Production + (Import - Export) }
* (0.80 for 1900-1969; 0.686 for 1970-1987)
= Net Consumption
Trade data was taken from FAO (Fl) which was fairly
consistent with J7. They could not be identical since J7
used Japanese the fiscal year starting April 1st, while Fl
used the calender year. Live pig trade was ignored since it
was not very significant even in the 1980's.
Similar to the case of beef, another data provided by
USDA (Ui) let us reconsider these conclusions.
USDA data
covered 1960-1990, which was coincident in production
figures but not in import figures, besides which it
contained information about changes in stocks.
Import figures of USDA data were consistently larger
than ours taken from Fl, which was probably due to USDA's
consideration about live pig imports. Change in stocks
were added after 1974, probably reflecting the fact of some
technological improvement and expansion in the scale of
storage facilities.
At any rate, USDA data seemed to contain some
additional information, therefore it was adopted and with
which our data for 1960-88 was replaced. Data consistency
was virtually not affected, since the difference between our
data and USDA data was very small in the 1960's.
After the replacement, the conversion factor assessed
above was applied to the USDA data to calculate net food
use.
K.2.6. Chicken
Production data for poultry meat were available from J9
for 1874-1963 which contained partly non-official estimates;
from food balance sheet of MAFFJ (J7) for 1954-1972 or (02)
before 1975; from J6 "shipments of broiler" figures
available; and finally from FAQ in F2 and F3 from 1951.
431
Totally, there were four different series at hand;
unfortunately, none of them was consistent with each other.
As usual, the following basic strategies were applied:
Assuming the food balance sheet data of MAFFJ
reflects the most realistic nature of the utilization
process from initial production to final consumption,
information on the utilization contained in the MAFFJ data
was used as much as possible as basic assumptions of the
food balance sheet.
For the periods that the MAFFJ's food balance sheet
data were not available, if several sources of data are
available, the data being most consistent with MAFFJ's food
balance sheet data during the overlapping period was taken.
With respect to these points, the available data sets
were examined. Production data of J9 and J7 were consistent
only for the 1954-55 period and after that J9 gave
substantially smaller figures than J7.
The estimates for
1954-63 in J9 were non-official, thus there must be some
differences in basic assumptions between J7 and J9.
According to notes in J2, p. 211, the growth rate of the
poultry industry was unexpectedly high in these post-war
period; even MAFFJ revised production figures several times
to correct their underestimation. J9 was published in 1967;
thus, it was likely that the figures in J9 were not fully
adjusted for the actual trend for post-war period. Even so,
the figures in J9 for the early years of 1900-1953 would not
be so misleading. Then, production data before 1953 were
taken from J9. Note that 39 had two items of "hens
slaughtered" and "ducks slaughtered"; only the "hens"
figures were taken. Ducks production was only about 2% in
1900 out of total hens and ducks production, 1.3% in 1951
and further reduced to 0.16% in 1963, and therefore it was
considered to be negligible.
432
For the post-war period there were two alternatives
other than the food balance sheet (J7) which were J6 from
MAFFJ's "broiler marketing statistics" and
and
F3.
FAQ data
J6
in F2
figures were readily available only after 1973.
J6 figures were about 10% bigger than J7 in the 1970's
period, but the reason was unknown. F2 and F3 figures were
very consistent with J7 after around 1967, however these
reported much higher figures than J7 before 1967. Based on
these observations, J7 figures were used directly for
1954-1975 (1973-75
were taken from 02) and FAQ data used
after 1976 to keep overall consistency in the data set.
Next, the food balance sheet was examined to
investigate MAFFJ's data construction procedures.
It turned
out that MAFFJ used the following formula to obtain net
consumption:
{ Production + (Import - Export) - Waste }
*(Extractjon Rate) = Net Consumption
Two things should be noted:
first, the extraction rate was
revised from 100% to about 77% after 1967; and second,
wastes in terms of percentage of available supply suddenly
became smaller from about 22% to 23% to about 2% after 1967.
These imply that MAFFJ changed its definition of wastes and
extraction rates jointly around 1966. The product of waste
and extraction rates were fairly stable for over 1954-72
period and yielded 76.69% on average. This percentage was
used as a conversion factor to obtain a net consumption
figure out of total available supply for the entire period.
Thus the formula used here was the following:
{ Production + (Import - Export)
* (0.7669)
}
= Net Consumption
433
Trade data were taken from FAO (Fl), which was fairly
consistent with J7 for the 1954-72 range.
exports of live poultry were ignored.
Imports and
K.2.7. Fish
Pre-war fish food balance sheet was available in J8 for
the 1909-40 period. The data was titled "fresh and frozen
fish", excluding shellfish, crustaceans, mollusks, and other
aquatic animals. The formulation was as follows:
Net Food Supply = Production + ( Imports - Exports )
- Used for Manufacture
To maintain consistency through pre- and post-war period
data sets, it was desirable to apply the same approach.
Note that the above "used for manufacture" amount was
derived from the very detailed research procedures, which
could not be reproduced here.
Then, we took a different
approach to approximate SHINOHARA's procedure for post-war
period.
Post-war production data was taken from J2 and J4. J2
had a data of total fish production for 1920-78, which was
the sum of marine fisheries, culture in marine water, inland
water fisheries, and inland water culture. Comparing it
with SHINOHARA's (J8) data, it was found that both series
were very close in numbers. The same kind of data was found
in J5, which was smaller in numbers than these two data
sets.
It was decided to assume J8 production data and J2
production data were consistent each other, and to fill the
period after 1978, data from J4 was utilized. J4 had
figures for each type of production for 1970, 75, and 79-87:
434
Table K-4:
Fish Data in Japan Statistical Yearbook (J4)
Types of Fisheries
Marine Fisheries
Marine Culture
Inland Water Fisheries
Inland Water Culture
Species of Fish Covered in J4
Fishes Total
Horse Mackerel (after 1983 Jack
Mackerel, Yellow Tails, Sea Bream,
and Globefish
Fishes Total
Trout, Ayu (Sweet Fish), Carp,
and Eel
The sum of all of the production figures were found to be
very close to J2 data:
for the overlapping period of 1970
and 1975, the calculated J4 figures were smaller than J2
only by 0.1%. Therefore, the calculated J4 data was
connected directly to J2 after 1978.
So far, a consistent Japan fish production data for
1909-87 period was obtained. Looking for trade data,
various statistics by different forms of conuuodities were
available in the FAO Yearbook of Fishery Statistics (Fishery) Commodities (F7) for post-war period. The items
in the trade data in J8 were unknown. Comparing trade
figures of "fish, fresh, chilled or frozen" in F7 and that
in J8 for only one overlapping year of 1938, the following
was observed:
Table K-5:
Discrepancies in Japan Fish Trade Data in 1938
between SHINOHARA (J8) and FAO (F7)
Imports of 1938 (MT)
SHINOHARA (38)
FAQ
(F7)
Exports of 1938 (NT)
54813
19817
0
16405
For that year, FAO reported no imports for each type of
fishery commodity for Japan except "miscellaneous inedible
aquatic animal products" with 3735 metric tons of imports.
Therefore, it was considered that the discrepancy between J8
and F7 was made not by the choice of conunodities included
435
but some fundamental difference in the source of citation.
Note that this kind of discrepancy was not unusual in trade
statistics.
Still inconclusive and knowing these facts, it
was assumed that SHINOHARA used trade data of "fresh,
chilled or frozen" fishes, and the data for that category in
F7 was taken for post-war period.
The last thing to be considered was "used for
manufacture" in SHINOHARA's data set.
Instead of the
SHINOHARA's formulation, the following was considered:
Fresh and Frozen Fish Consumption
= Fresh and Frozen Fish Production
+ Fresh and Frozen Fish Imports
- Fresh and Frozen Fish Exports
02 provided separate food balance sheets for fish items such
as "fish fresh or frozen", "fish salted, cured or dried",
"canned fish", "fish meal", "sea weeds", and "total fish".
On the other hand, some recent volumes of J7 reported
"supply and demand for fishery products".
It was found that
both of them use the common data. However, according to J7,
"fish" represented "fishes and sheilfishes" in all the data
shown there. This data in J7 was the one from which MAFFJ
derived domestic fresh and frozen fish and shellfish
consumption for their food balance sheet.
Note that these
figures were not consistent with the FAQ fishery commodity
production data of "fish, fresh, chilled or frozen" in F7.
It was decided to use MAFFJ production data of "fresh and
frozen fish and shellfish". Note that the data was readily
available for the limited periods of 1970-78,
1983-87.
1980, and
Next, to exclude shellfish from the MAFFJ data, the
ratios of {fish / (fish + shellfish)} were calculated using
annual purchased quantity figures in HES data (J12) for the
1950-88 period.
The MAFFJ "fresh and frozen fish and
436
shellfish" production data were multiplied by the ratios
year by year, and the approximated "fresh and frozen fish"
production data for 1970-78, 1980, and 1983-87 periods were
obtained.
To fill the blank periods, this revised MAFFJ "fresh
and frozen fish" production data and the "total fish"
production data already obtained above was compared and the
ratio of the two were calculated. Then, running a simple
regression with the ratio as dependent variable and year as
an independent variable, the ratio of the blank periods was
estimated by inserting years into the estimated equation:
Ratio = 12.063 - 0.0060038 * Year
(T-Ratio) (6.4394) (-6.3394)
R-square Adjusted = 0.7368
D-W = 1.9520 Rho = 0.00867
"Total" fish production data was multiplied by the ratio
year by year, "fresh and frozen fish" production was
estimated for 1950-69, 1979, 1981-82 and 1988. The ratio
can be interpreted as a conversion rate to obtain "fresh and
frozen fish production amount for food" out of "total fresh
fish production".
To check the consistency with our treatment here and
the one done by SHINOHAHA, the estimated ratios were
extended and applied for pre-war total production data in
J8.
The result was fairly close to the SHINOHARA's
estimates, i.e., it may be said that
SHINOHARA's Net Food Supply (as defined above)
- Imports + Exports
= Our Total Fresh and Frozen Fish Production
*
Estimated Conversion Rate
437
Finally, applying trade data taken from F7 titled
"fish, fresh, chilled or frozen" (Table 4 or 1-A or B2-1),
total domestic consumption of fresh and frozen fish was
obtained. The resulting data was directly connected to the
SHINOHARA's data.
K.2.9. Eggs
Consumption data for the period 1900-1940 was directly
taken from SHINOHARA series (J8).
Production data was available in J5 for the period
1906-1963 and in J2 for 1920-78, however the unit in these
data was not consistent; numbers of eggs in stead of weight
was used for some periods. Comparing these with the
SHINOHARA series reported in metric tons for the 1909-1940
period, it was found that the conversion rate for eggs was
about 53.576 grams per egg. This result was consistent with
the official conversion rate defined by the statistics
department of the Bank of Japan as 53.5 grams a piece (J5,
p. 483). Also, it was very close to the FAQ rule of 50
grams per egg (Fl, 1970, Section of "Commodity Notes").
Then, all production data were converted into metric tons by
using the official rate of 53.5 gram per egg.
The figure
for 1950 was not reported in J2 and J5, and therefore an
estimated figure available in J9 for that year was used.
J2
was consistent with J6; thus the data after 1979 were taken
from J6.
Trade data were taken from Fl, however this was
available only from the 1954 period annually. According to
the average trade figures during 1948-52 in Fl, there were
only negligible amount of trade in the early 1950's. Thus,
trade amounts for the 1950-53 period were assumed to be
zero.
In construction of the food balance sheet for eggs,
MAFFJ, SHINOHARA, and FAQ had, in essence, taken the same
path in using the following formulation:
438
Total Available Supply - Seed - Waste
Consumption
(Seed means those eggs used for hatching)
where
Total Available Supply = Production + (Import-Export)
Seed = Total Available Supply * Seed Rate
Waste = Total Available Supply * Waste Rate
In other words
{ Production + (Import - Export) } * CF = Consumption
where CF stands for conversion factor and
CF = (100 - Seed Rate - Waste Rate)
I
100
Although the formula was common, the conversion factor
used in each case varied among them. The following table is
a summary of those, and taking into account the extraction
rate, the implied conversion factor is calculated by
Implied C.F. =
{
(100 - Seed Rate - Waste Rate)
Table K-6:
Basic
Assumptions in
* Extr. Rate
}
/ 10000
Food Balance Sheet for Eggs
in Different Sources
Source
(Reference #)
Year
Observed
SHIN0HARA (J8)
1930-34
4.72
13.00
100.0
0.8228
MAFFJ
MAFFJ
(J7)
(J7)
1954-65
19 67-72
2.71
3.36
12.72
1.94
100.0
89.0
0.8457
0.8428
FAO
FAO
(F4)
(F5)
19 64-66
197 5-77
3.00
3.00
2.00
2.00
100.0
100.0
0.9500
0.9500
Seed
Waste
Extraction Implied
Rate(%) Rate(%)
Rate(%)
CF
439
Note that for the MAFFJ data, seed and waste rates were
directly calculated from the food balance sheet data and the
average over the period was reported in the table.
There was a substantial difference in the assumed waste
rate between Japanese source and FAQ, or even within
Japanese source before 1965 and after 1967. Note that the
definition of "waste" was common to SHINOHARA and FAO.
The frequent revision of basic statistical assumptions
was considered to reflect confusion by MAFFJ in construction
of a food balance sheet. Note that MAFFJ's food balance
sheet was initially prepared at the request of FAO, thus it
might be influenced by any changes in policy of FAQ. Then,
it was likely that MAFFJ revised waste rate to the new
standard following FAO during 1965-67 and changed extraction
rate at the same time to adjust their data after 1967 to be
consistent with the prior period.
In fact, the ratios of
consumption (after adjusted by extraction rates) and
available supply were fairly stable all during the period of
1957-81, yielding 0.8423 on average. The same procedure was
applied for the SHINOHARA's data; the average ratio was
0.8228.
This implied that the formulation used in these
two Japanese sources were consistent with each other.
Concerning the MAFFJ's revision in 1965-67, there was
another interpretation that it might be a reflection of
their statistical concern with a growing diversification of
According to the FAQ Trade
Yearbook (Fl), eggs were supplied in several ways and
classified as follows:
supply and consumption of eggs.
Eggs in the shell
Eggs - /
\ Eggs not in the shell -
I
Liquid or Frozen
\ Dried
440
In the case of Japan, there was little external trade of
"eggs not in the shell" before 1963; after then, imports of
dried eggs had increased substantially.
Therefore, it was
likely that MAFFJ considered only "eggs in the shell" in its
food balance sheet before the 1965 period and corresponding
waste and seed rates were derived for that particular item.
By the way, the available price data were only those
for "eggs in the shell".
Thus, for analytic purposes,
consumption of shell eggs excluding all other egg items was
considered to be the most suitable.
Above all, the following formulation was used to
construct post-war eggs consumption data:
Net Domestic Total Consumption of "Eggs in the Shell"
= { Total Domestic Production of Eggs
+
( Import - Export)
}
* 0.8457
Trade data were those exclusively for "eggs in the shell"
taken from Fl. The conversion factor of 0.8457 was taken
from the MAFFJ's assumption prior to 1965, based on the
assumption that most likely it had been specified
exclusively for the calculation of "eggs in the shell" data.
K.2.9. Milk
MAFFJ's food balance sheet did not disaggregate dairy
products.
Fortunately, SHINOHARA series (J8) contained
"consumption of milk for drinking" for the 1909-1940 period,
and therefore was taken as a primal source for the pre-war
period. The series included some estimates made by the
author using the following formula:
(Production of Fresh Milk - Industrial Use)
= Production of Milk for Drinking
441
(Production of Milk for Drinking) * (Waste rate 5% = 0.95)
= Consumption of Milk for Drinking
The author assumed a waste rate of 5%, however, he did not
mention seed rate. According to the author, official
statistics were available for "production for industrial
use" and "for drinking" separately after the 1926 period,
but not prior to that period.
The author estimated
industrial use and calculated drinking use for 1909-1925.
For the post-war period, production data for drinking
was available in addition to total production, and were
found in J6 and J4. While J4 was discontinuous, J6 was
continuous for the 1955-88 period. There were, as usual,
some problems.
In J6, title changed three times during
1955-88, from "production for drinking" (1955-70) to
"consumption for drinking" (1965-75) and to "cow's fresh
milk marketed as fluid milk" (1976-88).
The discrepancies
between the first two sets were at most about 5%, according
to the overlapping period for the first two of 1965-70.
Note that "consumption for drinking" is greater than
"production for drinking" consistently except for 1965.
Then it was decided to treat them as if they were identical,
and take "production" series for 1955-64, and "consumption"
series thereafter until 1975. Although there was no
overlapping period for the last two series of "consumption
for drinking" and "marketed as fluid milk", no obvious
discontinuity was observed between 1975 and 1976.
Therefore, "marketed as fluid milk" data were taken for
1976-88 and directly connected to "consumption for
drinking".
There was a new category of "fresh milk" (1976-88).
This is only available after the 1976 period and was not
coincident with the other figures, and therefore not used.
There were two blank periods in the data set, namely
1900-08 and 1950-54. For the 1900-08 period, the following
442
procedures were taken:
based on SHINOHARA data, the, ratios
of "total production" and "consumption for drinking" were
calculated.
The ratios looked like they were inonotonically
changing, then "consumption for drinking" was regressed on
"total production". The result was fairly good;
particularly, the first 12 years of 1909-20 showed very
stable trend according to the residual plot.
Then the
regression was performed only for the 12 samples of 1909-20.
The result was
Consumption = 7341.1 + 0.50116 * Production
(T-Ratio)
(8.8520)
(33.551)
R-Square = 0.9902
The data for "consumption for drinking" for 1900-08 were
calculated by inserting "total production" figures into the
estimated equation.
For the 1950-54 period, the ratios of "total
production" and "for drinking" were calculated for the year
of 1950 (J4) and 1955-64 (J6). Note that J4 and J6 figures
were close during the overlapping periods.
The ratios were
not very stable but looked like having some trend. Then the
average ratio of 5 samples for 1950 and 1955-58 were
calculated, and the ratio ("for drinking" / "total
production") of 0.452 was obtained. Total production for
1951-54 was multiplied by 0.452 to obtain an estimated
"production for drinking" figure.
So far consumption data for the pre-war period and
"production for drinking" data for the post-war period were
obtained.
The next thing to be considered were trade
figures and conversion factors for post-war period.
According to FAQ (Fl), trade of "fresh milk" had been nil
prior to 1980. After 1981, very little exports of two-digit
metric tons had been conducted and there had been no
imports.
Thus, trade of "fresh milk" was ignored for the
entire period.
In short, for Japan milk trade had mainly
443
been carried out in the form of "dried; powdered milk" or
"condensed; evaporated milk".
To determine the conversion factor, the FAO food
balance sheet (F4, F5, and F6) was inspected. The results
are summarized in the following table:
Table K-7:
Basic Assumptions in Food Balance Sheet for Milk
in Different Sources
Source
Year
SHINOHARA (J8)
FAQ
(F4)
FAO
(F5)
FAO
(F6)
Note:
1885-1940
1964-1966
1975-1977
1979-1981
Feed
Waste
Rate(%) Rate(%)
---5.00
1.59
1.00
1.72
Extraction
C.F.
Rate(%)
100.00
100.00
100.00
100.00
0.9500
0.9650
0.9700
0.9774
FAQ Average C.F. = 0.9708
1.90
2.00
0.55
Feed and waste rates of F4 and F6 were calculated
from food balance sheet tables. Those of F5 were
direct copy of FAO assumption sheet available in that
volume.
In the case of milk, two factors had to be considered
in order to calculate net consumption from total production
of raw milk; namely, one was feed rate and other was waste
rate.
Now, "milk for human drinking use" was the point of
interest.
It was not clear if the official statistics under
"milk for drinking" included feed use or not; however,
common sense says feed use should be excluded from that
category (drinking implied an action of human, not of
animals). Taking it for granted, only the issue on waste
rate was considered.
F4 and F5 provided nearly identical
results of 1.9% and 2.0%, but F6 showed a different value of
0.55%.
Note that the values of 1.9 and 0.55 were derived
from figures on the table where original numbers had already
been rounded off; thus the possible errors were expected in
those figures.
It was hard to imagine that within only two
to three years such a substantial improvement on waste
444
control had been achieved in the late 1970's in Japan.
Here, it was decided to take FAO'S assumption value of 2% on
waste of milk, and each "production for drinking" data of
post-war period was multiplied by 0.98.
445
APPENDIX
L
JAPAN SOCIOECONOMIC DATA SERIES
Variable names are in parenthesis followed by titles.
L.1.. Total Population, Persons (TPOPJ)
Data was taken from J4 and J6.
Population By Age, Persons (POPLT15J, POP1564J, and
POPGT65J)
Data sources were El and J4.
Population by age were
not annually surveyed until recent period.
For the 1903-70
period, the data was reported almost once in every 5 years;
whereas, it was available annually after 1975. Then, the
blank periods before 1974 were filled by interpolation and
extrapolation methods.
Household Size, Persons per Household (HSJ)
Following data were used:
Table L-l:
List of Japan Household Size Data
Period
la 1908-64
lb 1922-78
2
1950-88
3
1963-88
#
Source
Ji, p. 358-60
J2, p. 179
J12, v.i.
J12, v.i.
I tern
Farm Household Size
Farm Household Size
Urban Household Size
All Japan Household Size
First, farm household size data series for 1900-78
period was constructed from #la and #lb. Note that #la data
was incomplete; then, the blank years of 1900-07, 1909-10,
1916-19, and 1921 were filled by interpolation and
extrapolation methods.
Second, from #2 and #3, all Japan average household
size data series for 1950-88 was constructed by the ratio
method.
Both data were taken from the same source, The
446
Family Income and Expenditure Survey (J12), which had major
revision in 1962 (see Japan price data document also).
Before 1962 the survey area was limited for urban areas;
after 1963, it was expanded to all Japan. The pre-revision
urban households data was considered to be equivalent to the
post-revision data of households in the cities with more
than 50,000 population, officially. Then, household size
data found in J12 pre-revision series were directly
connected to those in J12 post-revision series for the
cities with more than 50,000 population.
Data #2 was the
resulting series; #3 was taken from the post-revision series
for all Japan households.
Next, the (All Japan / Urban)
household size ratios were calculated for 1950-62 period;
the ratios were fairly stable and the average for the period
was calculated. The average ratio was 1.014069. By this
number, the #2 data was multiplied and connected to #3 data.
The resulting data was the all Japan average household size
series for 1950-88.
Third, these two resulting data sets were connected by
the ratio method. For the overlapping period of 1951-60,
the ratios of (All Japan / Farm) household size were
calculated.
The ratios were fairly stable and an average of
0.779657 was yielded. The pre-war farm household size data
for 1900-40 were multiplied by the number and connected to
the post-war all Japan average household data.
L.4. General Consumer Price Index (CPIJ)
Following data were used:
Table L-2:
#
1
2
3
List of Japan Consumer Price Index Data
Period
1900-38; 1950-65
1950-79
1974-88
Source
J1O, Table 2 & 3
J2
J6, Mar. 1989
Notes
1934-36 = 100
1975 = 100
1985 = 100
447
Connecting #1 through #3 by the ratio method using one
overlapping period each, 1900-88 CPI series with 1985 = 100
was obtained.
L.5. Private Final Consumption Expenditure, Yen Per
Capita (PFCJ)
Following data were used:
Table L-3: List of Japan Private Final Consumption
Expenditure Data
1
Period
1900-40
Ji].
2
1950-76
J2
3
1965-88
J2, J6
#
Source
v.i.
Notes
Estimated by LTES (revised from results
reported in J8)
Old SNA (including non-profit
organization expenditure)
New SNA (including non-profit
organization expenditure)
The data set #3 was revised several times (which is not
unconmion practice in GNP accounts data). The most updated
figures were adopted by inspecting sources backward from the
most recent volumes to the earlier volumes.
The data sets
#2 and #3 were connected by the ratio method using the
overlapping period of 1965-76; #2 was adjusted to #3. Then,
the data set #1 and the resulting series were directly
connected. The figures were divided by total population and
converted into per capita amounts.
L.6. Percentage of Labor Porce in Primary Industry (AGPOPJ)
Data sources were El and J6.
El reported numbers of
total employed labor force and numbers of labor force in
agriculture, forestry, and fishery for 1900-83 period; the
same series was found in J6 for the recent period.
Connecting these two, the two data sets covering 1900-88
period were obtained. The AGPOPJ was calculated as the
percentage of labor force in agriculture, forestry, and
fishery in total employed labor force.
448
L.7. Numbers of Motor Vehicles (AUTO))
The following data were used:
Table L-4:
List of Japan Automobiles Data
2
Period
1907-14
1913-37
3
1933-87
#
1
Source
Item
Jl, p. 118
Total Motor Vehicles
Ji, p. 118
ordinary Motor Vehicles
(Excluding Small & Special Cars from Total)
J1,J4,J13
Total Motor Vehicles
By the ratio method, data set #2 was connected to #3, then
#1 was connected to the resulting series. All data was
adjusted to the #3 data.
L.8. Telephone Subscribers, Numbers of Lines (PHONEJ)
Data sources were Ji, J4, and J6. Until 1965, "total
telephone subscribers" was used; for the period after 1965
"general telephone subscribers" was used. Note that
difference between the two series were very small during the
overlapping period, thus they were directly connected.
449
APPENDIX
M
KOREA RETAIL PRICE DATA SERIES
M.l. An Overview
Mainly two sources of retail price survey (RPS) data
were available for Korea: one from the Research Department,
The Bank of Korea (BK), published in their Price Statistics
Summary (K3); and the other from the Bureau of Statistics,
Economic Planning Board, Korea (EPB), found in their Annual
Report on the Price Survey (K4).
By connecting these two
sets of data appropriately, a consistent time series data on
retail prices in Korea from the mid-1950's to the present
(1987 for our study) were constructed. Data were mainly
taken from K3 and K4, but occasionally also taken from the
Korea Statistical Yearbook (K].) and the Monthly Statistics
of Korea (K5).
The basic data used were, with some exceptions, the
following:
Table H-i:
Basic Source of Korea Retail Price Data
1954-66: Seoul Retail Prices, BK.
1965-84: Approximated All Cities Retail Prices, EPB.
1981-88: All Cities Retail Price Index, EPB.
The BK data covered the periods since the 1950's, however
they represented the Seoul retail prices. Note that BK
reported only the wholesale prices in the recent period. On
the other hand, the EPB data covered all cities in Korea and
the most recent periods after the mid-1960's.
The second set of the data consisted of retail prices
in the following nine regions: Seoul, Busan, Daegu, Incheon,
Daejeon, Gwangju, Jeonju, Chuncheon, and Cheongju.
Since
the consumption amount of each region was not available, it
450
was impossible to weight them; as called "approximated", the
data were calculated as the simple arithmetic mean of the
retail prices of the nine regions.
When only the monthly data were available, the annual
average figure was calculated by simple arithmetic mean of
the retail prices of the twelve months.
To connect the different sets of data, the ratio method
was applied.
In cases when no overlaps between any two
retail price data sets was found, wholesale price data were
adopted assuming that the price trend shown by the latter
approximates the former favorably.
The second data set "1965-84: Approximated All Cities
Retail Prices, EPB", was chosen as the primary data because
it had the largest geographic and temporal coverage and
reported the name of each surveyed commodity clearly.
Accordingly, all other data sets were adjusted by the ratio
method.
For fish retail price data, a different approach was
taken to overcome the data limitation. First, the retail
price index of all fresh fish items for the entire period
was constructed. Then, for one particular period a weighted
average price for all fresh fish items was calculated, and
the resu1ting index was multiplied by the base price.
Price for fresh milk was not found, therefore the item
was excluded from the study.
Details of data construction procedure for each
commodity follows.
451
M.2. Data Construction Procedure by Commodity
M.2.l. Rice
The following data were readily available:
Table M-2:
Data List of Korea Rice Retail Price
# Period
1 1954-64
Name
Kyonggi Origin,
1st Grade.
2 Jan.,1965 Kyonggi Origin,
- 1967 1st Grade.
3 1965-84
Polished,
Average Quality.
4 1981-88
Free Market
Unit
Area Agent Source
won/2Oliter Seoul BK
K3,1964
won/2Oliter All
EPB
Cities
won/101iter All
EPB
Cities
Index
EPB
All
Price.
Cities
* "v.1." stands for "various issues".
1(5,
v.i.*
K4,
v.i.
K4,1988
Hereafter, "#" stands for "data set number" with refer
to the table provided in each section.
First, connection of #1 and #2 was done by calculating
a conversion rate based on the overlapping period from
April, 1964 to December, 1964.
The BK price #1 was adjusted
to be the EPB price #2.
Next, the resulting data set from #1 and #2 was
connected to #3.
The data of 1967 in #2 consisted of dual
prices of government controlled selling price and free
market price. Since there was no additional information on
the marketing quantity of these two items, 1967 data was of
no use.
By using the two years of 1965-66, a conversion
rate was calculated. Then the EPB Seoul price was adjusted
to the EPB all cities price.
Lastly, the resulting data set was extended to 1988.
There were dual prices in the data set, but again no
information on marketing quantities was available. Some
Korean researchers suggested to me that the free market
price should have a more realistic trend than the government
price.
Taking this suggestion, the free market price was
452
chosen and a conversion rate was calculated based on the
1981-84 overlapping period.
All data set was adjusted to #3 and a 1954-88 EPB based
all cities retail price series was constructed. It was
known that 10 liters of rice equalled 8 kilograms (for
example, by comparing the tables in Ki, 1977, p. 416 and Ki,
1979, p. 416), then all of the figures were divided by 8 to
change the units into won/kg.
14.2.2. Wheat Flour
For the wheat items, "wheat" and "wheat flour" were
available, but only "wheat flour" was used in this study.
Wheat is seldom taken as it is but is usually processed into
noodles, bread, or flour, for which wheat flour is used as a
raw material. Then, it was expected that the retail price
of wheat flour should have the closer trend with prices of
other retailed wheat products than the price of wheat
itself.
The following data were available at hand:
Table 14-3:
Data List of Korea Wheat Flour Retail Price
# Period Name
Unit
Area
1 1954-64 Semihard,
won/22kg Seoul
70% Extraction
Agent
BK
1(3, 1964
2 1964
77% Extraction
3 1965-67 Semihard,
77% Extraction
2nd Grade
4 1965-71 Semihard,
77% Extraction
2nd Grade
5 1972-84 Semihard,
77% Extraction
1st Grade
6 1981-88
EPB
EPB
Ki, 1965
K5,v. i.
won/22kg All
EPB
Cities
K4,v.i.
won/22kg All
EPB
Cities
K4,v. i.
Index
K4, 1988
won/22kg Seoul
won/22kg Seoul
All
EPB
Cities
Source
453
The overlapping periods existed in the data sets were:
Table 14-4:
Data List of Korea Wheat Flour Retail Price:
Data Combination and Overlapping Period
Data Combination
1&2
2 & 3
3 & 4
4&5
5 & 6
Overlapping Period
None
Jun.-Dec., 1964
1965-67
None
1981-84
There were two cases having no overlapping periods.
To make
the Connection complete, all cities' wholesale prices of
wheat flour for the period of 1963 to 1964 and that of 1971
to 1972 found in K3 arid Ki, 1972 were employed as trend
indicators.
Assuming the changes in wheat flour retail
price in 1963-64 and 1971-72 were well approximated by
wholesale prices, first the retail prices of 1963 and 1971
predicted by the wholesale price trend were calculated, then
the ratio method was applied.
The resulting figures were converted into won/kg units.
14.2.3. Barley
The following data were available at hand:
Table 14-5:
Data List of Korea Barley Retail Price
# Period
1 1954-64
Name
1st Grade.
2 Jun.,1964 1st Grade.
- 1967
3 1965-84
Polished,
High Quality.
4 1981-88
Unit
Area Agent Source
won/2Oliter Seoul BK
K3,1964
won/2Oliter All
EPB
Cities
won/101iter All
EPB
Cities
Index
All
EPB
Cities
Kl, 1965
K5,
v.1.
K4,
v.i.
K4,1988
454
There were three overlaps in the data set:
Jun.-Dec.,1964: Seoul BK & Seoul EPB.
1965-67: Seoul EPB 1st Grade
& All Cities EPB Polished High Quality.
1981-84: All Cities EPB Polished High Quality
& All Cities EPB Index.
The ratio method was applied using these overlaps and the
data set for 1954-88 was constructed. One thing to note is
that in the third overlapping combination, the ratio for
1981 was substantially different from the ratios for 1982-4.
The reason behind this was unknown. Then simply the ratio
of was excluded in the calculation of the average ratio.
It was known that 10 liters of barley equalled 7.65
kilograms, according to the original tables (for example,
see Ki, 1982, p. 432), then the resulting figures were
divided by 7.65 to convert the units into won/kg.
14.2.4. Beef
The available data sets were the following:
Table M-6:
#
1
2
3
4
Data List of Korea Beef Retail Price
Period
1954-Dec.1964
Jun.1964-1967
1965-1984
1981-1988
Name
Unit
Area
Fresh Meat won/600g Seoul
Fresh Meat won/600g Seoul
Fresh Meat won/600g All Cities
Index
All Cities
Agent
BK
EPB
EPB
EPB
Source
K3,1964
KS
K4
K4,1988
The first two data sets from BK and EPB were observed
to be identical during the overlapping period of June, 1964
to December, 1964. Therefore, they were directly connected.
Otherwise, the procedures by the ratio method were carried
out to construct 1954-88 retail price series. All the data
were adjusted to the third data set.
Units were changed to
455
won/kg.
14.2.5. Pork
The available data sets for pork were following:
Table 14-7:
#
1
2
3
Data List of Korea Pork Retail Price
Period
1954-Jul.l964
Jun.1964-1966
1961-1978
4 1970-1976
5 1971-1984
6 1981-1988
Name
Unit
Area
Agent
Fresh Meat won/600g Seoul
BK
Fresh Meat won/600g Seoul
EPB
Wholesale Price (Butcher's Price)
Boneless Lean, Good Quality
won/ 600g All Cities EPB
Fresh Meat, Average Quality
won/600g All Cities EPB
Fresh Meat, Average Quality
won/600g All Cities EPB
Index
All Cities EPB
Source
K3,1964
K5
Ki
Kl
K4
K4
The first two data sets were directly connected since
they were identical during the overlapping period of June
-July, 1964.
Since retail price data for 1966-70 was not
found, all cities' wholesale price was applied assuming the
wholesale price has a similar trend of change to the retail
price.
The overlapping periods were fully utilized to
calculate the conversion rates in each case of connection.
All the data sets were finally adjusted to the fifth set.
Units was changed to won/kg.
456
14.2.6. Chicken
The available data sets were following:
Table 14-8:
Data List of Korea Chicken Retail Price
# Period
1 1955-May 1960
2 Jun.1960-1966
3 1965-1976
4 1974-1984
5 1981-1988
Name
Leghorn,
Approx. 2.4kg
Native,
Approx. 1.8kg
Wholesale
Price
Fresh Meat
Unit
Area
won/head Seoul
Agent Source
BK
K3,1964
won/head Seoul
BK
won/kg
won/kg
Index
All
BK
Cities
All
EPB
Cities
All
EPB
Cities
K3,1964
K3,
v.1.
K4,l984
K4,l988
The first two sets were connected without any
adjustment since no other data was available. Note that
there was no obvious break in trend between these two sets,
thus this treatment would not be so risky. There was no
retail price data from 1967 to 1973, so wholesale price data
were applied as the best alternative. In every case the
overlapping period was fully utilized to calculate a
conversion rate for connection. Every data set was finally
adjusted to #4.
457
14.2.7. Fish
The available retail fish price data for Korea were
very limited in terms of years and types, however, there
were some wholesale and retail (consumer) price indices for
aggregated fish items. The available price indices were the
following:
Table 14-9:
#
1
2
3
4
Period
1955-1962
1960-1964
1955-1962
1960-1971
Data List of Korea Fish Retail Price
Name
Fish
Fresh Fish
E.M.P.Avg.**
E.M.P.Avg.
Kind*
WPI
CPI
WPI
WPI
Area
All Cities
Seoul
All Cities
All Cities
5 1965-1983
Fish
CPI
All Cities EPB
6 1975-1988
Fresh Fishes CPI
& Shells
All Cities EPB
Agent Source
BK
Kl,1963
BK
K3,1964
BK
K1,1963
BK
K3 & K5
v.i.
Notes: *
and
**
Kl,1967
& K4,v.i.
K4,1988
WPI and CPI stand for Wholesale Price Index
Consumer (Retail) Price Index, respectively.
E.M.P.Avg. stands for Edible Marine Products
Average, which includes all marine products.
Using this information, first a retail (consumer) price
index series covering all of the period was constructed
using several available indices, including some wholesale
price indices. Second, a weighted average retail price of
all fresh fish for a particular one year was calculated,
using the information of the official (EPB) weights for each
fish items in consumer price index calculation:
458
Table N-b:
Weights for Fish Items in Consumer Price Index
I tern
Specification
Hair-tail
Approx. 80cm lengthy,
Weight for*
All Korea
Weight for*
Seoul
6.6
4.2
7.5
5.6
3.6
4.0
3.8
2.9
high quality
Corvenja
Approx. 30cm lengthy,
high quality
Pollack
Approx. 45cm lengthy,
high quality
Horse
Approx. 30cm lengthy,
Mackerel high quality
Mackerel
Approx. 30cm lengthy,
high quality
Flatfish
Approx. 30cm lengthy,
15 cm in width,
high quality
Saury
Approx. 25cm lengthy,
Pike
high quality
Cuttlefish Approx. 45cm lengthy,
high quality
1.2
1.6
3.6
3.7
2.1
2.0
Source: K5, Mar.,1967, pp.41-42.
Notes: *
These weights are measured against total goods
which is given the weight of 1000.
For the first part, two alternatives were considered:
Alternative 1: CPI(5 & 6) + WPI(3 & 4)
Alternative 2: CPI(5 & 6) + WPI(l) + CPI(2) + WPI(3)
The number in parenthesis represents the corresponding data
set "#" in the Table M-9. The "+" sign means connection of
indices of different kinds/specifications, and the "&" sign
means connection of indices of the same kind/specification.
The first alternative was considered to provide more
consistency in the data set since the fewer numbers of
different data sets were combined. The second alternative
was considered to provide more sense of "fish" price trend
since the first alternative partially used "Edible Marine
Products Average't which includes non-fresh and/or non-fish
marine items, whereas the second alternative included "fish"
459
or "fish & shells" prices only.
The resulting price trends
were found to be very different from each other; the rate of
growth was much bigger in the first case than the second.
Then, the second alternative was selected based on the
judgement that the trend shown by the first alternative was
not realistic.
The second part was to calculate a weighted average
retail price of all fresh fish for a particular period.
Only the data for the year of 1963 were available in terms
of won/kg for various kind of fishes, which was the Seoul
retail prices surveyed by BK. The weights for "All Korea"
were applied for these data (weights for Seoul in the above
table is just for reference). The following five items were
the common fish items between the available retail price
data and the weights: Hair-tail, Corvenia, Alaska Pollack,
Horse Mackerel, and Saury Pike. Using these items only, the
weighted average price was calculated as a proxy of entire
fresh fish average price in 1963. The resulting figure was
55.30305445 won/kg.
Finally, the results from the first part (consumer
price index for fresh fish) and from the second part (a
weighted average price of fresh fish for 1963) were
combined. The final product may be referred to the all
Korea weighted average fresh fish retail price for
1955-1988. The resulting data was derived from price trend
and commodity mix representing all Korea; but base prices
were Seoul prices, thus the absolute level of the resulting
price may be consistently higher than the true all Korea
weighted average fresh fish retail price for the entire
period of 1955-1988.
460
M.2.8. Eggs
The following four data sets were used to construct
Korea retail price data for eggs:
Table H-li:
Data List of Korea Eggs Retail Price
# Period
Name
1 1954-Jul.,1964 Avg. Quality
Light Yellow
2 JUn.,1964-l967 Avg. Quality
White
3 1965-1984
High Quality
White
4 1981-1988
Unit
Area Agent Source
10 Eggs* Seoul BK
1964
& K1,1965
10 Eggs* Seoul EPB
v.i.
10 Eggs* All
EPB 1(4, 1977
City
Index
All
1988
EPB
City
* According to the notes of the data, 10 eggs was
approximately 500g.
In the following calculation,
10 eggs was set to be equal to exactly 500g.
This data was connected by the ratio method using the
overlapping period. All data was adjusted to the all city
EPB price #3.
Since each unit was won/500g, the resulting
figures were multiplied by 2 to change the units into
won/kg.
461
APPENDIX
N
KOREA DOMESTIC TOTAL CONSUMPTION DATA SERIES
N.l. An Overview
Food consumption data for Korea could be obtained from
the Korean food balance sheet compiled by the Ministry of
Agriculture and Forestry, Korea (MAFK) or by that of the
Korean Rural Economics Institute; however neither of them
were fully available at hand.
Utilizing all available data
and information, Korean food consumption data was
constructed by food balance sheet method.
The raw data were
obtained from three sources:
Korean domestic official
statistics found in statistical periodicals such as the
Korea Statistical Yearbook (Ki) and the Agricultural
Cooperative Yearbook (K2); FAO production and trade
statistics in the FAO Trade Yearbook (Fl) and the World Crop
and Livestock Statistics 1948-1985 (F2); USDA production and
utilization statistics in PS & D View (U2; a computer
package). Unfortunately, the figures from the three sources
were not always consistent with each other.
For example,
many inconsistencies were found in the statistics issued by
MAFK.
It was hard to keep track of the source of
differences between the inconsistent statistics under the
same title by the same agent in the same periodical, without
any additional and sufficient information.
The basic strategies taken were the following: first,
the highest priority was given to the USDA data since it
contained the widest variety of data from production to
utilization, and was considered the most reliable, and
covers the longest time period of 1960-88 generally.
Second, since the price data for Korea started at 1955, it
was desiable to have the consumption data cover the same
period. The additional data were compiled by using either
MAFK or FAO, whichever seemed to be better in each case.
462
For grains, change in stock is an important factor in total
domestic utilization.
Unfortunately, the data was not
available for the period of 1955-59 and therefore ignored.
Third, the assumptions on utilization were necessary to
construct food balance sheets.
The only source of this
information were FAO food balance sheets such as F4 (1964-66
average) and F5 (1975-77 average). Since F4 and F5 reported
different assumptions on utilization, F5 was taken as a
reference since it was closer to the mean of the data
period. The following reports detail the data construction
process for each commodity.
N.2. Data Construction Records by Commodity
N.2.1. Rice
Production, trade, and stock data were taken from U2
for 1960-88. The production data was found to be identical
to the MAFK data, which was the milled (polished rice)
production.
Also, the trade data was found to be very
consistent with the FAQ data.
For 1955-59, production data
was taken from Ki and trade data was taken from Fl; stock
data was ignored since it was not available.
It should be
noted that the USDA seemed to use the following formula for
rice "consumption", which should be referred to total
domestic supply:
(Total domestic supply at time t) = (Production at t) +
(Trade at t+l) -'-I- (Change in stock at t (?))
In this formula, the time period for the production data
corresponds to that of the MAFK data, and for the trade data
to the FAQ data; there was no information regarding to the
stock data.
This formulation was applied to our study.
The assumptions on utilization were taken from F5,
according to which waste was set to be 2.0 % of total supply
and seed was set to be 29 kg/ha. According to the
463
calculated food balance sheet in F5, there was neither
non-food use nor feed use in utilization.
Therefore, the
formula used here was (subscript t's are dropped and the
unit is 1000 metric tons and 1000 hectares):
(Total domestic consumption) = {(Total domestic supply) (Total Area harvested * 0.029)} * 0.98
N.2.2. Wheat
Trade and stock data were taken from U2, however there
were problems with production data.
It was found that USDA
(U2) and FAO (F2) provided partially the same figures but
not for 1960 and 1967-78; the latter case (F2) seemed to be
caused by a sort of misprinting. To find out which data set
was more reasonable, they were compared to MAFK data found
in Ki and K2.
It was concluded that F2 and MAFK data had
the same trend, whereas U2 had different trend for 1967-78;
F2 was then taken for 1961-85, and U2 used for 1986-88.
For the period before 1961, there was another problem
in F2 where there seemed to be a break in the series before
and after 1960.
USDA seemed to consider this and applied a
different figure for 1960, thus U2 and F2 did not coincide
at 1960.
This point was common to the case in barley. The
break was mainly caused by the large difference in the data
of area harvested in F2. It was found that F2 data had a
right trend for 1955-60 backed up by a MAFK data in Kl and
K2.
The reason why the gap was made in 1960-61 was unknown.
It was possible to smooth out this gap by the ratio method.
There was data (Ki, 1968) being identical to F2 data for the
period before 1960, which was available for 1956-67. This
Ki data and U2 data were compared for 1960-67 by taking
ratios of (K1/U2), which was very stable and yielded the
average of about 1.63.
F2 data of 1955-59 were multiplied
by 1.63 and connected to the U2, 1960 data.
Trade data for 1955-59 were taken from Fl, and stock
464
was ignored; actually stock was reported to be zero in U2
for 1960-66.
Combining total domestic production and net trade
amounts, domestic total supply was obtained.
Next the utilization in the domestic total supply was
considered.
The amount of feed use was reported in U2 for
1960-88, which was directly applied. To calculate feed use
amounts for 1955-59, the information contained in this U2
data was utilized. The feed rate in terms of percentage
against total domestic supply was calculated for 1960-65,
which was stable and yielded the average rate of about 3 %.
Using the average rate, feed use for 1955-59 were calculated
based on total domestic supply.
Seed rate was obtained from F5, which was 55 kg/ha.
calculate seed use amounts, area harvested was required.
To
Following the same procedure used in the production data,
the data on area harvested was compiled for 1955-88 using F2
and U2.
Waste rate was obtained from F5, which was 1 % of total
domestic supply.
There was evidence of other non-food use found in F5.
However the amount was small and there were insufficient
data to take it fully into account and it was ignored.
In F5, wheat used for food was converted to wheat flour
by an extraction rate of 78 %. Wheat consumption data
obtained so far was converted to be wheat flour equivalent
applying this extraction rate.
In summary, the following formula was used:
(Total domestic consumption of wheat flour)
= {(Total domestic supply) - (Area harvested * 0.055)}
* (1 - 0.01) * 0.78
where unit used was 1000 mt and 1000 ha.
465
N.2.3. Barley
After some comparison among the data sets, it was found
that FAO and USDA data were most likely representing the
combined sum of barley and naked barley under the title of
barley. MAFK also mixed the two kinds of barley categories
in their food balance sheet reported in Ki. This finding
was based on the production data of barley and naked barley
found in Kl and K2. There was a consistency in the
production data among the three sources, FAO, USDA, and
MAFK; however, there was inconsistency in MAFK data from
publication to publication. The trade data were very
inconsistent among the three sources.
It was decided to
take USDA data of production, trade, and stock although it
included naked barley because it was better to include stock
and trade in some consistent ways. To fill the blank period
of 1955-59,
the trade figures were taken from Fl directly
and the production figures were taken from F2 and slightly
modified. Comparing U2 and F2 for 1960-85, only the
production data of 1960 were different from each other,
whereas they were identical for the rest of 1961-85. The
other data found in K1 and K2 suggested that the trend in F2
for 1960-61 was very likely misleading; U2 was correct.
F2
data was then adjusted by the ratio method as follows: the
ratio of 1960 data of F2 and U2 were calculated and the F2
data of 1955-60 were multiplied by the resulting ratio to
make F2 equivalent to
U2. The resulting data trend was
checked by the other data in Ki and K2. The stock data for
1955-59 was not available and therefore ignored. The
assumptions on utilization were taken from F5: feed and
waste were 4 % and 6 % of total supply respectively, and
seed rate was 63 kg/ha. Combining these information, the
resulting formula was (unit were 1000 mt and 1000 ha):
(Total domestic consumption) = {Total domestic supply (Total area harvested * 0.063) } * (1 - 0.04 - 0.06)
466
where total domestic supply followed the definition used in
rice consumption.
N.2.4. Beef and Pork
The data provided by USDA (Ui) was fully utilized for
these items. The data covered 1960-90 and consisted of
production, trade, and stock. Data for 1955-59 had to be
calculated using other sources. The only set of data
available for production and trade was from FAO, such as F2
and Fl.
In the 1950's, there was zero trade in unit of 1000
nit according to Fl, and stock could be almost negligible
according to the data in Ui for the 1960's; therefore, these
were set to be zero. Note that there were some
discrepancies in production data between Ui and F2 which was
thought to be negligible, and Ui and F2 were directly
connected.
Other information on utilization obtained from
food balance sheet tables in F5 and F6 suggested that there
were some manufacturing food use for both items of
negligible amounts; nothing else was considered by FAO. The
production data was then assumed to be equal to domestic
total consumption for the 1955-59 period.
For the 1960-88
period, domestic total supply in tJi was taken as domestic
total consumption.
N.2.5. Chicken and Eggs
The available data was limited for these items. Only
FAO provided production and trade figures in Fl, F2, and F3.
Information on stock was not available, and therefore
ignored.
Other information on utilization was not indicated
for these items in F5 and F6. Domestic total supply was
calculated by taking total production plus imports minus
exports for each item, which was taken as domestic total
consumption in our data set.
467
N.2.6.
Fish
To compile fish consumption data, the following data
sets were used:
Table N-i:
#
1
2
3
Korea
Period
1971-78
1950-88
1958;1961-87
4 1958;1961-87
5 1950-85
6 1985-88
Fish
Production and Trade Data Sources
Title
Food Supply: Fish, Total
Total Catches: Fishes
Imports: Fish, fresh,
chilled, or frozen
Exports: Fish, fresh,
chilled, or frozen
Exports: Live and
fresh fish
Exports: Live and
fresh fish
Source
1979
K1,v.i. & K8,1989
F7,v. i.
F7,v. i.
K2,v. i.
1989
The official food balance sheet data such as #1 was only
available for the limited periods at hand. As an
alternative, using information obtained from other sources,
total gross domestic supply data was constructed first and
then converted (rescaled) into #1 data.
Production data #2
represented all fish production out of adjacent waters
fisheries, marine culture, distant waters fisheries, inland
waters fisheries, and inland water culture. For trade data,
the import data was only available from FAO, whereas export
data were available from several sources.
Note that #4 and
#5 were not consistent each other.
Particularly after the
mid-1960's, there was a large gap between the two; for
example, in 1985 #4 was more than three times larger than
#5.
"Frozen" category existed in #5 and #6.
Even including
this amount, #5 was still about 50% of #4 in 1985. However,
there was not a such large difference between #4 and #6,
which indicated that FAO data was not irrelevant. For
example, #4 was about 40% higher than #6 in 1985, but when
including "frozen", #4 was about 20% lower than #6 in that
year.
Note that "frozen" may include shrimp in these data
468
sets.
Since #4 included only fish products, it was
understandable that the figures of #4 fell between #6
without "frozen" and #6 with "frozen".
Since import data
was taken from FAQ, to maintain consistency between import
and export data FAO export data was chosen.
Import data for
1950-60 was set to zero since the existing #3 data until
1966 indicated nil. Export data for 1950-60 was taken from
#5 since before the mid-1960's there were small differences
between #4 and #5. Applying the following formula:
Gross Total Domestic Supply
= Total Production + Imports - Exports
Next, this result was compared with the existing official
food balance sheet data #1 for 1971-78 by taking the ratios
for each period.
The resulting ratios showed fairly large
variations with a minimum of 0.412445 in 1976 and a maximum
of 0.619994 in 1977, but no trend seemed to exist. Then,
the simple average ratio for the period 1971-78 was
calculated and the result was 0.534036. This number was
used as a conversion factor to make gross supply figures
into net food supply figures, and all of the gross supply
figures were multiplied by the number. The original K7 data
were retained for the 1971-78 period to keep the best
information as much as possible in the data set, and the
estimated data was only adopted for the 1950-70 and 1979-87
periods.
469
APPENDIX
0
KOREA SOCIOECONOMIC DATA SERIES
Variable names are in parenthesis followed by titles.
0.1. Total Population, Persons (TPOPK)
The data for total population in Korea were taken from
Ki, and tJN1 was used for 1987 period due to lack in volume
for Ki.
0.2. Household Size, Persons Per Household (HSK)
For Korea, national average household size (HSK) was
not available for every year. Rather, farm population
(FPOP) and number of farm households (FHN) were obtained
from the Korea Statistical Handbook (K6) annually since the
early 1950's. Based on this data, national average farm
household size (FHS) can be defined as
FHS
FPOP/FHN
(0-1)
Here we made the assumption that national average non-farm
household size (NFHS) has a constant relationship with FHS
over time, i.e.,
NFHS = a * FHS
(0-2)
where a is a scalar.
Also,
NFPOP = TPOP - FPOP
(0-3)
where NFPOP and TPOP stand for non-farm population and total
population, respectively. TPOP data were available. Then
HS may be approximated by the weighted average of NFHS and
FHS using population ratio as
470
FISK = FHS*(FPOP/TPOP) + NFHS*(NFPOP/TPOP)
(0-4)
Using (0-2) and (0-3),
HSK = FHS * {(1-a)*(FPOP/TPOP) + a}
(0-5)
To determine 'a', we made a further assumption that
NFFIS
Average household size in all cities in Korea
= UHS (Urban Household Size)
(0-6)
Note UHS and actual HS were
only available for the census years of 7 periods in 1955-88
from Ki. The following table summarizes the results:
and then compare tiltS and FItS.
Table 0-1: Household Size in Korea - Comparison of
Different Data
Year
1955
1960
1966
1970
1975
1980
1985
(Number of person per household)
Estimated
tIES
FHS
HSK
a=UHB/FHS
5.60
6. 00
0.9333
5.75
5.55
6.20
0.8952
5.92
5.26
6.21
0.8470
5.91
5.12
5.47
5.81
0.8812
4.90
5.57
0.8797
5.20
4.49
502
4.64
0.8944
4.10
4.05
4.42
0.9276
Actual
118K
5.66
5.71
5.62
5.37
5.13
4.62
4 . 16
'a' was determined by the mean of the ratios which was
0.894121.
The last two columns show the performance of the
estimated HS comparing to the actual HS derived by total
The
population divided by total number of households.
result was fairly good thus used in the model.
471
0.3. Population by Age, Persons (POPLT15K, POPI.564K, and
POPGT65K)
These data were surveyed as a part of the national
population census, which was done only seven times during
the 1955-88 period.
The blank years were filled by
interpolation and extrapolation methods.
0.4. General Consumer Price Index (CPIK)
Consumer Price Index data were taken from K3 and K4,
and Ki and UN]. were used as supplements.
For the period
before 1965, there was only the Seoul consumer price index,
and the all city consumer price index appeared only after
that period.
These two CPI's were simply connected. Data
for 1964-65 were not available and therefore data in tJN1
were used.
0.5. Private Final Consumption Expenditure, Won Per Capita
(PFCK)
Data were taken from K]. and UN]..
K]. was not available
for the recent period of 1986-87; data in UN1, 1988 volume
was therefore used for 1977-87 period. Note that the
figures in K]. were revised many times (sometimes almost
every year).
Then, gathering the data from Ki for 1967-82,
1963-76, and 1954-64 separately, the ratios were calculated
for the latest five years of the overlapping periods. The
average ratios were calculated in each case, and the three
series were connected by the ratio method.
All the data was
adjusted to the most recent data set and the resulting data
were connected with the data from UN]., also by the ratio
method using the two years of overlapping periods.
0.6. Farm Population Percentage (AGPOPX)
Data of farm population were taken from K6. The data
were converted into percentage using total population
figures.
472
0.7. Number of Automobiles (AUTOK)
Data were taken from Ki and K6.
Data after 1970
separated motorcycles, whereas data before mixed them with
cars, trucks, buses, and so forth. Data for motorcycles
after 1985 were not available, so we decided to exclude
motorcycles.
The data series including motorcycles was
connected directly to the series excluding them at 1970.
This may cause no problem since the difference between the
two series was only about 2.2 % in 1970, and the portion of
motorcycle was smaller in the earlier period.
0.8. Number of Telephone Subscribers (PHONER)
Data were taken from Ki and K6.
473
APPENDIX
P
TAIWAN RETAIL PRICE DATA SERIES
An Overview
The two main source of retail price data for Taiwan
were:
Commodity-Price Statistics Monthly, Taiwan Area (T3)
and Commodity-Price Statistics Monthly, Taipei City (T4).
T3 was readily available after 1970 and T4 covered after
1962 only. To have the longer time series, occasionally
wholesale price data from Taiwan Statistical Databook (T5)
or Industry of Free China (T7) were used. As an additional
source of information, there was a data set provided by Dr.
B.H. LIN of the University of Idaho.
The original source of
the data set was, however, not known. Only when there was a
need and/or this data was found to have superiority to other
alternatives, was it used.
T3 was considered to be the
principal data source since it represented the national
average retail prices; T4 was considered to be the second
best since it represented retail prices.
The quality of the
other data was basically determined by checking the trend
with reference to T3 and/or T4. All data was finally
adjusted to the T3 Taiwan area retail prices.
Price data
for barley could not be found and was therefore excluded
from the study.
Also, price data for milk was found only
for the periods after 1962. Unit used was NT$/kg generally,
where NT$ stands for New Taiwan Dollar.
Details on data
construction for each commodity are summarized below.
Data Construction Procedure by Commodity
P.2.1. Rice
T3 and T4 reported prices of two different varieties of
rice: "ponglai" and techajiajil
The price differences
between these two varieties were very small. T5 reports
wholesale price of "ponglai" rice. To maintain consistency
474
in the data set as much as possible and considering the
connection of T3, T4, and T5, "ponglai" rice was used.
First T3 and T4 were connected by the ratio method.
T3
covered the periods 1970-71 and 1974-88; T4 for 1962-84. T3
was used as it was, and T4 was applied to 1962-69 and
1972-73 with modification. Observing a ratio between P3 and
T4, it was found that the ratio was changing consistently;
T3 had a lower price than T4 in the early 1970's, then the
price gap between them disappeared after 1981. Considering
this point, the conversion rate applied for T4 for the
1962-69 period was calculated by the average ratio of T3 and
T4 for the 1970-71 period, whereas the conversion rate used
for the 1972-73 period was the average ratio of the two
years of 1971 and 1974. So far we obtained Taiwan rice
retail price for the 1962-88 period.
Next, the resulting data called modified T3 was
extended by being connected with T5 wholesale price data.
The ratios between the two were calculated and it was found
that the ratio was changing consistently; similar to the
case above, there was a gap between the two in the early
1960's and then it had almost vanished in the late 1970's.
Considering this situation, the conversion rate applied to
T5 data was calculated based on the ratio of 1962-66 period.
T5 was multiplied by the 5 years average ratio.
As a result, "ponglal" rice retail price in Taiwan
area for 1952-88 period was obtained.
475
P.2.2. Wheat Products
Followings is a summary of the data used:
Table P-i:
#
1
2
3
4
5
List of Taiwan Wheat Products Price Data
Period
1962-88
1962-88
1962-88
1971;1974-88
1952-77
Name
Wheat flour
Noodle dried
White bread
Wheat products
Wheat flour
Kind
RP
RP
RP
RPI
WP
Area
Taiwan
Taiwan
Taiwan
Taiwan
Taiwan
Area
Area
Area
Urban Area
Source
T3, T4
T3, T4
T3, T4
T7
T5
where RP, RPI, WP stands for retail price, retail price
index, and wholesale price, respectively.
The first three
data sets were constructed by the ratio method using the
data from T3 and T4.
T3 and T4 included various retail
prices for products made from wheat such as flour, noodles
(dried and wet), white bread, and steam bread.
It was found
that the data for three items of wheat flour, noodle dried,
and white bread were available for the 1962-88 period. Then
we had a problem of which one to choose. Note that our
consumption data taken from Taiwan Food Balance Sheet (Ti
and T2) had only one classification, which could be
understood as a sum of wheat and wheat flour consumption
amounts in wheat flour equivalent.
For this kind of
aggregated product data, data set #4 may be the most
preferable since it represents a weighted average price of
various wheat products retail prices. However, it was
available only for limited periods and therefore some
alternative had to be considered.
Data plots and simple
regression analyses revealed that a simple arithmetic mean
of the three prices had a very close trend with the RPI.
Then, the blank period was filled with the average of the
three products' prices wherever possible, which provided
average wheat products price for the 1962-88 period. To
extend the data into the 1950's, wheat flour wholesale price
data set #5 was used. The ratio method was applied using
476
the overlapping period of 1962-66. The wholesale wheat
price did not have a very close trend with the three
products average price; still, it was the best alternative
and therefore used. All data was adjusted to the three
products average prices.
Unit was NT$/kg.
P.2.3. Beef
The following data sets were used to obtain Taiwan beef
retail price series:
Table P-2:
#
1
2
3
4
5
6
7
8
List of Taiwan Beef Price Data
Domestic
Period /Imported Kind
1952-81 Domestic Wholesale Price
1971-82 Domestic Retail Price
1974-82 Domestic Retail Price
1981-88 Domestic Retail Price
1971-82 Imported Retail Price
1974-82 Imported Retail Price
1981-88 Imported Retail Price
1981-88 Domestic Wholesale Price
9 1971-87
Area
Taiwan
Taipei
Taiwan
Taiwan
Taipei
Taiwan
Taiwan
Taiwan
Source
District
City
Area
Area
City
Area
Area
T5
T4
T3
T3
T4
T3
T3
T7
Imports and Domestic Production Quantities T1,T2
Note first that #7 data does not report the price in
1985. Using #8, the ratio between #7 and #8 for 1984 and
1986 were calculated. Then taking the average, multiplying
#7 by that, the estimated figure of #7 for 1985 period was
obtained.
T3 and T4 report two prices for beef: domestic and
foreign (imported).
SHEI (1983b) pointed out that after the
mid-1970's the import quantity of beef had been drastically
increased while domestic beef production had decreased. In
fact, data from Taiwan Food Balance Sheet (Ti and T2) showed
substantial shifts in the beef supply structure from
domestic source oriented to foreign source oriented:
477
Table P-3:
Structural Change in Taiwan Beef Supply
Domestic Imports Change
Net
Gross
Ratio Ratio
Production
in
Imports Domestic
Ri
R2
Stock
Supply
].000MT
1000NT 1000MT 1000MT 1000MT
(A)/(C)(B)/(C)
(A)
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
7.6
4.4
5.6
4.8
4.3
10.6
15.8
9.7
8.5
5.5
5.2
5.7
6.6
6.5
4.4
3.9
4.2
0.3
1.2
1.2
1.2
25.5
------14.8
9.3
3.2
8.6
(B)
(C)
0.3
1.2
1.2
1.2
10.7
9.3
3.2
7.9
5.6
6.8
8.6
12
12
10.9
17.3
19.8
23.1
24.1
27.4
32.7
32.8
10.9
17.3
19.8
23.1
24.1
27.4
32.7
32.8
----------
-------
6
15
19.9
19
18.3
20.5
16.4
22.5
25.5
29.7
30.6
31.8
36.6
37
0.038
0.214
0.176
0.2
0.713
0.467
0.168
0.47
0.59
0.665
0.769
0.776
0.778
0.788
0.862
0.893
0.886
0.962
0.786
0.824
0.8
0.287
0.533
0.832
0.53
0.41
0.335
0.231
0.224
0.222
0.212
0.138
0.107
0.114
Note:
"----" stands for not reported.
Source: Ti and T2
Considering the situation after the 1970's, the weighted
average price of domestic beef and imported beef was
calculated by the following formula for the period 1971-87:
Taiwan Retail Price of Beef
= (Retail Price of Domestic Beef) * Ri
+
(Retail Price of Imported Beef) * R2
Domestic and imported retail prices were compiled separately
by using #2-#4 for the former and #5-#7 for the latter
series.
Note that #3-#4 and #6-#7 were connected directly
in each case.
However, the latter period data of #4 and #7
showed no price fluctuation for the overlapping periods of
478
1981-82, although the other sets of #3 and #6 showed
variation.
Then, the #4 and #7 were used after 1983 and #3
and #6 were taken as they were for 1974-81. The connection
between the data obtained so far and #2 or #5 were done by
the ratio method using 5 years average ratio of 1974-78
overlapping periods. Using the resulting two data sets and
the above formula, Taiwan average retail price for beef was
obtained for 1971-87.
Since there was no retail series for 1952-70 period,
the #1 data of wholesale domestic beef price was applied as
a price trend indicator. The ratio between the obtained
data above and #1 was calculated but was not very stable
globally; therefore the average ratio of 1971-75 period was
used as the conversion rate.
As a result, Taiwan Area beef retail prices for 1952-88
were obtained.
P.2.4. Pork
The following four sets of data were used:
Table P-4:
List of Taiwan Pork Price Data
#
1
2
3
4
Kind
Wholesale Price
Retail Price
Retail Price
Wholesale Price
was skipped thus
Period
1952-77
1962-84
1974-88*
1974-88
* 1985
Area
Taiwan District
Taipei City
Taiwan Area
Taiwan
not available.
Source
T5
T4
T3
T7
As a basic strategy, all the data were adjusted to the
#3 data. #3 was used as it was, however, 1985 data was not
available in the series.
To fill the blank, #4 data was
utilized. As usual, the ratios between #3 and #4 were
calculated.
It was found that the ratio was not very stable
for the entire overlapping period. To calculate the 1985
value, the 4 years average ratio from 1983-84 and 1986-87
479
was used as a conversion rate, and #4 data in 1985 was
multiplied by the resulting rate.
For 1962-73 period, #2 data was connected to #3 data.
Here, the same phenomenon was observed as in the case of
beef:
the two prices converged in the 1980's.
Then,
1974-78 was chosen as the closest period for the point of
connection and the conversion rate was calculated by taking
the average ratio of #2 and #3 for the 1974-78 period.
So far, 1962-88 data was obtained. Comparing this and
#1 data, the data for the 1952-61 period was calculated.
Again, the ratio between them was not very stable over the
whole overlapping period; the 5 years average ratio of
1962-66 was used as a conversion rate.
As a result, Taiwan Area pork retail prices for the
1952-88 period were obtained.
P.2.5. Chicken
The following four sets of data were used:
Table P-5:
List of Taiwan Chicken Price Data
#
1
2
3
4
Kind
Unknown
Retail Price
Retail Price
Retail Price
Period
1954-85
1962-74
1971-84
1974-88
Name
Unknown
Hen
Chicken
Chicken
Area
Unknown
Taipei City
Taipei City
Taiwan Area
Source
Dr. B.H.Lin
T4
T4
T3
The priorities among the data sets were in the order:
#4, #3, #2, and #1, with every data set adjusted to #4. To
connect sets #4 and #3, the ratios between the two sets were
calculated and was found not to be very stable for the whole
overlapping period. Here the same kind of phenomenon was
observed as has been seen in the case of beef and pork, that
the price gap between Taiwan Area data and Taipei City data
vanished in the early 1980's.
Considering this point,
similarly to the other cases, the 5 year average ratio from
480
1974-78 was calculated as a conversion rate which was
applied to the data for 1971-73 in set #3.
The ratio between the data obtained so far and set #2
were calculated for the 1971-74 period. The ratios were not
stable but they showed no obvious trend; the mean value of
the ratios was then calculated as a conversion rate and the
data was extended for the 1962-88 period.
Finally, to extend the data set further into the
1950's, data set #1 provided by Dr. Lin was compared with
the resulting data set for the 1962-85 period. It was very
unfortunate that these two data sets showed completely
different trend.
Since the source of set #1 was unknown, it
was not possible to put too much credibility in the data
set.
As a compromise, the overlapping period of 1962-66
was taken as the closest period to the connecting point, and
the average ratio of this 5 year peroid was calculated as a
conversion rate.
From this data, the additional 8
observations of the 1954-61 period was obtained, which was
about one fourth of total length of the resulting data set.
The last treatment may cause some problem later and
therefore the data should be used with caution.
481
P.2.6. Fish
The following were all obtainable information to
construct a Taiwan fish retail price series for the 1950's
to present:
Table P-6:
List of Taiwan Fish Price Data
# Period
1 1954-85
2 1971;
1974-88
Name
Fish
Fresh Fishes
3 1962-88
Spear Fish
RP
Taiwan
(Marlin and Sailfish)
Milk Fish
RP
Taiwan
Red Seabream
RP
Taiwan
Hair-tail
RP
Taiwan
Pomfrets Fish RP
Taiwan
4
5
6
7
1962-88
1962-88
1962-88
1962-88
Kind
?
RPI
Area
Source
Taiwan
Dr. Lin
Urban Area in Taiwan T7
Area
T3, T4
Area
Area
Area
Area
T3,
T3,
T3,
P3,
T4
T4
T4
T4
The data through sets #3 to #7 were compiled from P3 and T4
by the ratio method. All data was adjusted to each T3
Taiwan area retail price data of most recent definition.
There were many other fish items in T3 and T4 which were
found to be insufficient to cover the entire period thus not
used.
The trend in each data set was checked by a graphical
method.
Then all the data was deflated by the total
consumer price index taken from T5 and then made into an
index form with the base period of 1975 and each result was
plotted against years.
It was observed that data sets #1
and #2 behaved quite differently; set #1 showed Taiwan fish
real price increased after 1975 from 100 to about 160 in the
mid-80's, whereas set #2 indicated that the price decreased
by 10 points from 100 to 90 in the end of 1970's and had
remained at the same level since then. Compared with other
fish items, set #1 was superior than set #2 since set #1
went through the average level of sets #3 to #7, whereas set
#2 did not reflect any trend of these individual items.
It
482
was decided to use set #1 as a trend indicator for 1954-85,
and connect set #2 to it for 1986-88 based on the 1985
overlapping data. The data set #1 could not be applied
directly since the figure reported was much smaller than
price of any single items of sets #3 to #7. As an
alternative, a simple arithmetic mean of the prices of the
five items was calculated for each year.
(The resulting
trend was checked by the same graphic method, and it was
confirmed that the trend was much more similar to #1 data
set than #2 data set.)
The following ad hoc approach was
taken:
first, the five year average ratio between #1 data
set and the data of simple average of five product prices
was calculated for the 1968-72 period, which was about the
center of the entire period. Then the data compiled from
sets #1 and #2 was multiplied by the number and an
approximate measure for the average fish retail price in
Taiwan area for 1954-88 period was obtained. The unit is
NT$/kg.
P.2.7. Eggs
The followings were the available data sets for eggs:
Table P-7:
List of Taiwan Eggs Price Data
# Period
1 1962-84
2 1970-71; 1974-84
Name
Hen Eggs
Hen Eggs
Area
Taipei City
Taiwan Area
Source
T3
T4
Only the data after the 1962 period was available for eggs.
As usual, the ratios between sets #1 and #2 were calculated,
which were fairly stable over the whole overlapping period.
Similar to the case of other animal products, the price
difference in these two data sets vanished in the early
1980's but there was no such obvious trend in the change of
the ratios as in the case of beef or pork. The conversion
483
rate was calculated as the 5 year average of the 1970-71 and
1974-76 periods, following the other cases. Note that the
result was not very different if the average ratio from the
whole overlapping period was used.
P.2.8. Milk
The following four sets of retail price data under the
title of "fresh milk" were used:
Table P-8:
#
1
2
3
List of Taiwan Milk Price Data
Period
1962-82
1970-71; 1974-82
1981-88
Name
Wei-chuan Brand
Wei-chuan Brand
Other Brand
Area
Taipei City
Taiwan Area
Taiwan Area
Source
T4
T3
T3
As was the case of eggs price data, data before 1962 was not
found.
T5 reported a wholesale price of powdered milk for
1952-81.
It was compared with other data sets but was found
to have a completely different trend than other sets, thus
was not used.
In the above table, sets #1 and #2 had a
common characteristic which was the same product, but
observed in the different area. On the other hand, sets #2
and #3 had a common characteristic which was observed in the
same area, but different products. Then, it was decided to
take data set #2 as the primary data to which the other two
data sets were adjusted. Using set #1, data for the 1962-69
and 1972-72 periods were obtained. The ratios calculated
between sets #1 and #2 were not very stable for the entire
overlapping period; the 4 years average ratio of 1970-71 and
1974-75 was used as a conversion rate. For the connection
between sets #2 and #3, the ratio of the 1981-82 period was
calculated and the average was used as a conversion rate.
Note that unit was converted to NT$/liter, which was taken
as being approximately equivalent to NT$/kg.
484
APPENDIX
Q
TAIWAN PER CAPITA CONSUMPTION DATA SERIES
All of Taiwan consumption data was taken from Taiwan
Food Balance Sheet, 1935-1980 (Ti) for the periods up to
1980 and from each annual issue of Taiwan Food Balance Sheet
(T2) for 1981-87 period. All the data was used as original
except for fish.
Several notes follow.
Wheat Flour (QWT)
Two different categories of "wheat" and "wheat flour"
were provided for wheat in the both publications. However,
several changes in categorization were observed.
Although
the method is unknown, fortunately these changes seemed to
have already been adjusted for per capita annual consumption
amounts.
It is common practice to update old data into a
new standard. Thus, it may be reasonable to assume that the
old data for wheat items has been updated to the new
category of wheat flour in the compiling process of Ti to
maintain consistency. Therefore, assuming the figures were
adjusted consistently with the best knowledge available, we
adopted them as they were as wheat flour per capita annual
consumption amounts. The figures used appeared under the
following categories in the original tables:
"wheat" for
1945-46; "wheat plus wheat flour" for 1947-68; and "wheat
flour" for 1969-87.
Fish (QFT)
Fish was divided into two categories in the original
table according to their fat contents:
"fish, fresh, fatty"
and "fish, fresh, low fat". No further information was
available and we simply summed up these two into one data
set called "fish, fresh".
485
APPENDIX
R
TAIWAN SOCIOECONOMIC DATA SERIES
Variable names are in parenthesis followed by titles.
Total Population, Persons (TPOPT)
Population by Age, Persons (POPLT15T, P0P1564T,
POPGT65T)
R,3. General Consumer Price Index (CPIT)
All of these three were taken from T5.
The consumer
price index was originally reported in 1952 = 100, which was
converted into 1985 = 100 for convenience.
R14. Private Final Consumption Expenditure, New Taiwan
Dollar Per Capita (PFCT)
Data was taken from T6, 1989 and 1986 volumes.
Data
for 1969-88 and 1961-85 were obtained from each volume,
however, they are not identical.
To connect them, the ratio
method was employed by overlapping the period of 1969-73.
Employment in Primary Industry, Percentage (AGPOPT)
Total Number of Motor Vehicles Registered, Number
(AUTOT)
Local Telephone Service, Number of Subscribers (PHONET)
These three were also taken from T5.
486
APPENDIX
S
ALLOCATION FACTORS AND TOTAL EXPENDITURE ELASTICITIES
487
Allocation Factors Year by Year
Table S-i:
ALLOCATION
FACTORS
YEAR
BY
YEAR
Japan
Japan
Japan
Japan
Japan
Japan
ALL-
Pre-war
Mid-
ALL-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
Taiwan
(Cont.)
1911 0.088282 -0.81179
1955 -0.40622 -0.42712 -0.27585
1912 0.102212 -0.68264
1956 -0.37375 -0.34193 -0.23155
1913 0.109611 -0.63601
1957 -0.35233 -0.31617 -0.22888
1914 0.114885 -0.60299
1958 -0.32681 -0.26045 -0.20416
1915 0.159339 -0.29999
1959 -0.29665
1916 0.173011 -0.18049
1960 -0.25623 -0.11722 -0.14456
1917 0.163624 -0.22843
1961 -0.22828 -0.08186 -0.13906
1918 0.141021 -0.33321
1962 -0.19879 -0.01637 -0.10754 0.489949
1919 0.146761 -019546
1963 -0.17278 0.019019 -0.09933 0.303236
1920 0.106964 -0.45813
1964 -0.13048
0.10838 -0.05995
1921 0.154907 -0.14458
1965 -0.14071
0.05191 -0.09786 0.105735
1922 0.168349 -0.12879
1966 -0.10803 0.107647 -0.07873 -0.12346 0.077971
-0.1959 -0.17585
0.18248 -0.04904
-0.1659
-0.1538 -0.09135
-0.0228
-0.1133 0.076552
1923 0.177592 -0.06656
1967 -0.06919
1924 0.180676 -0.04091
1968 -0.04045 0.205382 -0.05541 -0.02474 0.031718
1925 0.190384 0.029663 0.325862
1969 -0.01123 0.219784
1926 0.199676 0.097421 0.374201
1970 0.012064 0.280349 -0.03628 -0.13383 0.069806
1927 0.197922 0.118367 0.413804
1971 0.021842
-0.06804 -0.02743
1928 0.219141 0.248182 0.473381
1972 0.051158
-0.05008 0.106699 0.407327
1929 0.209651 0.185337 0.452989
1973 0.060332
-0.07234
1930 0.240784 0.435685 0.629107
1974 0.018905
-0.04992 0.042906 0.110732
-0.08341 0.066624 0.167666
0.58853
0.73202
1975 0.013024
1932 0.251513 0.510565
0.69813
1976
1931 0.263921
0.00972
-0.0699 0.009624 -0.03578
-0.10123
0.15994
0.48123 0.534553
0.40206 0.338396
1933 0.279553 0.724174 0.804203
1977 0.011416
-0.07208 0.541893 0.517966
1934 0.301583 0.737718 0.700726
1978
0.028
-0.02408 0.722194 0.595808
1935 0.287966 0.552958 0.537869
1979 0.048004
-0.01135 0.727148 0.620646
1936 0.295101 0.622804 0.579357
1980 0.060304
0.008541 0.239263 0.571513
1937 0.305225 0.698115 0.596134
1981 0.034051
0.04261 0.592093
1982 0.044495
1983 0.056551
0.034746 0.123496 0.649147
1984 0.062763
0.047104 0.179177 0.576908
1985 0.070284
0.050406 0.272303 0.572392
0.0808
0.054651 0.372752 0.559278
1987 0.096409
0.061907 0.429681 0.660306
1986
Japan
Japan
Japan
Japan
ALL-
Pre-war
Mid-
Post-war Korea
period
Period
Period
Period
MEAN 0.047964
0.00143 0.009157 0.521383
0.004793
Taiwan
0.02739 0.232266 -007039 0.195814 0.323851
488
Table S-2a:
Rice
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
YEAR
BY
YEAR
ESTIMATION
RICE
COMMODITY:
Japan
Japan
Japan
Japan
Japan
Japan
ALL-
Pre-war
Mid-
ALL-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
Taiwan
(Cont.)
-0.8568
1955
-0.3063 -0.33602 -0.17443
1912 0.075856 -0.71835
1956
-0.2798 -0.26698 -0.14196
1913 0.081542 -0.67084
1957
-0.2623 -0.24412
1914 0.082947 -0.63851
1958 -0.24317 -019982 -0.12533
1915 0.111464 -0.31837
1959 -0.21898
1916 0.124291 -0.19173
1960 -0.18521 -0.08859 -0.08583
1917 0.117852 -0.24151
1961 -0.16088 -0.05995 -0.07894
1918 0.102799 -0.35148
1962 -0.13824
1919 0.108509 -0.20595
1963 -0.11858 0.013711 -0.05284 0.413292 -0.22285
0.07874 -0.48397
1964 -0.08881 0.077553 -0.03006 -0.20974 -0.12379
1911
1920
0.06419
1921 0.108281
-0.1497
-0.107
-0.0118 -0.05888 0.693797
1965 -0.09371 0.036152
-0.1533
-0.1402
-0.0497 0.149531
-0.0312
1966 -0.07233 0.074446 -0.03856 -0.17137 0.108083
1922 0.119439 -0.13652
1923 0.122607 -0.07078
1967 -0.04308 0.121948 -0.02133 -0.16233 0.106152
1924 0.128299 -0.04349
1968 -0.02467 0.136045 -0.02403 -0.03526 0.043945
1925 0.135967 0.031473 0.241927
1969 -0.00683 0.146587 -0.02829 0.013624 -0.04981
1926 0.142168 0.103235 0.278195
1970 0.006967
0.17895 -0.01256 -0.18865 0.098661
1927 0.140343 0.125376 0.306411
1971 0.012344
-0.01919 -0.03929 0.232377
1928 0.156279 0.263073 0.347142
1972 0.026769
-0.01313
1929 0.150429 0.196363 0.333344
1973 0.027915
-0.01539 6.683556 0.823726
1930 0.176292 0.460035 0.479574
1974 0.008858
-0.0077 0.060181 0.152376
1931 0.184749 0.623659
0.53798
1975 0.006376
-0.01897 0.094243 0.231627
1932 0.179287 0.541802 0.512698
1976 0.004787
-0.02538 0.574875 0.483879
1933 0.199823 0.766894 0.606479
1977 0.005607
-0.01755 0.788522 0.812198
1936 0.212104 0.783199 0.511735
1978 0.013939
-0.00573 1.060841 0.982121
1935 0.202867 0.587787 0.388602
1979 0.022514
-0.00214 1.054545
1.03026
1936 0.210345 0.660888 0.628103
1980 0.017441.
0.001304 0.342278
0.92273
1937 0.211903 0.741248 0.436192
1981 0.015581
0.000221 0.013125 0.863742
1982 0.019616
0.000821 0.062114 0.992173
1983 0.026764
0.006443
1984 0.029416
0.008831 0.268449 1.026293
1985 0.030193
0.008201 0.409579
1986 0.031841
0.006785 0.560949 1.095887
1987 0.035991
0.0065 0.648957 1.397977
0.14735 0.616979
0.18273 1.178468
1.0699
489
Table 8-2b: Total Expenditure Elasticities Year by Year Bread and Wheat
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
COMMODITY:
YEAR
BY
YEAR
ESTIMATION
BREAD/WHEAT
Japan
Japan
Japan
Japan
Japan
Japan
Alt-
Pre-war
Mid-
ALL-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
(Cont.)
1911 0.070064 -1.42415
1955 -0.38261 -0.43581 -0.00357
1912 0.077266 -1.15481
1956 -0.35159 -0.34869
1913 0.082181 -1.01654
1957 -0.33102
1914 0.087958 -0.98274
1958 -0.30725 -0.26548 0.001368
1915 0.126685 -0.47122
1959 -0.27908 -0.19954 -0.00415
1916 0.142636 -0.28953
1960 -0.24253 -0.11914 -0.01901
-0.3711
1961 -0.21697 -0.08307 -0.03407
1917 0.135151
Taiwan
0.00077
-0.3224 0.016716
1918 0.115563 -0.56629
1962 -0.18869 -0.01662 -0.02476 0.129859
1919 0.115493 -0.32279
1963 -0.16401 0.019304 -0.01933 -0.03415 -0.54059
-0.0101 0.046214 -0.33376
1920 0.082164
-0.7354
196.4 -0.12384 0.110033
0.12597
-0.2331
1965 -0.13339 0.052718 -0.01775 -0.02681
1921
-0.0788
1922 0.134437 -0.19373
1966 -0.10249 0.109272 -0.01668 0.054614 0.297164
1923 0.137053 -0.10187
1967 -0.06551 0.185575 -0.00582 0.042782 0.354341
1924 0.148813 -0.06407
1968 -0.03827 0.209001 -0.00455 0.006558 0.127388
1925 0.151027 0.045098 0.345338
1969 -001064 0.223813 -0.00397
1926 0.157019
0.15576 0.400742
-0.0041 -0.11856
1970 0.011428 0.285473 -0.00324 0.107511 0.261759
1927 0.153514 0.189501 0.442662
1971 0.020709
-0.00586 0.018004 0.663211
1928 0.161236 0.394874 0.507705
1972 0.048478
-0.00438 -0.17337 1.716933
1929 0.163314 0.294978 0.480281
1973 0.057216
-0.00619
-0.5333
1930 0.192073 0.675551 0.670113
1974 0.018012
-0.01228
-0.0266 0.405207
1931 0.219967 0.862968 0.767177
1975 0.012387
-0.01348 -0.04822 0.680738
1932
0.21052 0.766073 0J34509
1976 0.009252
-0.01839 -0.30998 1.403199
1933
0.22679 1.222039 0.857036
1977 0.010858
-0.01431 -0.50393 2.378429
1934 0.238355 1.200609 0.746045
1978 0.026618
-0.00433 -1.39803
1935 0.238294 0.847414 0.567013
1979 0.045579
-0.00162 -1.63086 3.236895
0.24543 0.890885 0.609235
1980 0.038285
0.001646 -0.41592 2.823466
1937 0.266625 0.946468 0.620523
1981 0.032349
0.00027 -0.01956 2.306126
1982 0.042282
0000979 -0.09071 2643956
1983 0.053756
0.007255 -0.47755 2.895374
1984 0.059621
0.009336 -0.32535 2.747964
1985 0.066633
0.008218 -0.48313 2.570047
1986 0.076484
0.008028
1987 0.091248
0.008954 -0.85632 2.799357
1936
2.37368
3.09374
-0.8849 2450232
490
Table S-2c:
Barley
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
BY
YEAR
YEAR
ESTIMATION
BARLEY
COMMODITY:
Japan
Japan
Japan
Japan
Japan
Japan
ALL-
Pre-war
Mid-
ALL-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
(Cont.)
1911 0.042203 -0.78641
1955 -0.22052 -0.27992 -0.07914
1912 0.056988 -0.66548
1956 -0.20224 -0.20885 -0.05808
0.05809 -0.61842
1957 -0.18177 -0.18001 -0.04069
1914 0.052839 -0.58159
1958 -0.16622 -0.14313 -0.02633
1915 0.062824 -0.28898
1959 -0.14188 -0.09383 -0.00389
1916 0.070991 -0.17395
1960 -0.08657
1917 0.069968 -0.22153
1961
1918 0.077216 -0.32423
1962 -0.02466 0.007134 0.184994 0.361164
1919 0.074794 -018966
1963 0.026788 -0.03147 0.362677 0.227103
1920 0.046852 -0.43962
1964
1921 0.020941 -0.13744
1965 0.145722 -0.14266 0.314558 0.079841
1922 -0.00463 -0.12147
1966 0.081993 -0.72931
0.72992 -0.08955
1923 -0.01742 -0.06081
1967 027994
0.19206
1924 0.056127 -0.03893
1968 0.755722 -067811
1925 0.073759 0.028326 0.081264
1969 -0.44973
1926 0.046217 0.091455 0.081081
1970 0.103278 -075031 0.483935 -0.08353
1913
-0.03
0.08152
-0.0408 0.024999 0.332722
0.04055 -009844 0.146555 -0.11345
-0.3594
-00848
0.39923 -001741
-0.2953 0467337 0.006508
1971 0.401561
1.046457 -0.01823
1928 0.054599 0.232465
0.07994
1972 0.156625
0.349856 0.065454
1929 0.029843 0173189
-0.031
1973 0.191082
2136448 0.318321
1930 -0.00513 0.404143 -0.02959
1974 0.143629
0.538182 0.025286
1931 -0.17018 0.539992 -0.84244
1975 0121478
1.253626 0.042032
1932 -0.03857 0473389 -017317
1976
0.17049
1.694612 0.126492
1933 -0.03917 0.668193 0.089335
1977 -0.09822
0.807082 0.311624
1934 -0.15245 0.674252 -0.25351
1978 -0.25699
0.257402 0.330256
1935 -0.14069 0.505061 -0.22155
1979 -0.62454
0139838 0118964
1936 -0.02093 0564746 -0.04457
1980 -0.38823
-016572 0.026634
1937 -0.11644 0.646615 -0.05009
1981 -0.06632
-0.01232 -0.00285
1927
-0.0031 0.108327 -0.15509
1982 -0.14924
0.15737 -0.04519
1983 -0.09988
-0.30488 -0.04079
1984 -0.11065
-0.89674 0.019053
1985 -0.18373
-378421 -0.08022
1986 -0.29143
-2670&4 -0.33376
1987
10.35316 -0.40018
-0.9379
491
Table S-2d:
Beef
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
YEAR
BY
YEAR
ESTIMATION
BEEF
COMMODITY:
Japan
Japan
Japan
Japan
Japan
Japan
ALL-
Pre-war
Mid-
AU-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
(Cont.)
1911 -0.03041 0.075073
1955
0.15485 0.391416 0.843041
1912 -0.05635 0.109155
1956
0.15945 0.310074 0.724497
1913 -0.06766 0.216956
1957 0.202087 0.399864 0.995963
1914 -0.01747 0.056309
1958 0.167164 0.259236
1915
-0.0137 -0.00588
1916 -0.01753 -0.02118
1917 -0.02484
0.00577
Taiwan
0.7891
1959 0.144204 0.192193 0.569165
1960 0.074709
0.09126 0.350352
1961 0.039619 0.042656 0.363831
1918 -0.07789 0.109921
1962 0.021726 0.006556 0.294518 -1.23588
1919 -0.21333 0.095509
1963 0.014987 -0.00786 0.204292 -1.17307 -0.11117
1920 -0.04104 0.163426
1964 0.003676 -0.02204
1921 -0.01104 -0.00367
1965 -0.00445 -0.01111 0.181587 -0.18225 -0.01319
1922 -0.01469
0.00551
1966 0.010152 -0.04446 0.182015 0.301931 0.043706
1923 -0.07759 0.019927
1967 -0.01024 -0.03942 0.098515 0.207274 0.041106
1924 -0.07829 0.002124
1968 -000764 -0.04744 0.093339 0.055535 0.017243
1925 -0.12181 -0.00853 -0.38957
1969 -000305 -0.00477 0.087723 -0.02125 -0.02106
0.11354 0.441816 -0.05841
1926
-0.0543 -0.02234 -0.23458
1970 0003699 0.008517 0.041766 0.188787 0.040712
1927
-0.0523 -0.03483 -0.36098
1971 0.007415
0.061034 0.032469 0.099394
-0.0689 -0.39432
1972 0.022071
0.030301 -0.17245 0.174408
1929 -0.08687 -0.02341 -0.33815
1973 0.028038
0.038515 -0.88641 0.298568
1930 -0.06387 -0.01485 -0.37041
1974 0.009282
0.029726 -0.08178 0.037992
1931 -0.01485 0.099748 -0.24813
1975 0.006244
0.045668 -0.11211 0.115356
0.00409 0.090754 -0.17249
1976 0.004869
0.05046 -0.33877 0.251757
1933 -0.00301 0.102021 -0.22025
1977 0.005843
0.033704 -0.29515 0.386152
1934 -0.11421 -0.11746 -0J7408
1978 0.015277
0007742 -0.19426 0.461284
-0.0697 -0.04783 -0.43982
1979 0.027015
0.003132 -0.02643
1936 -0.07341 -0.16302 -0.37695
1980 0.023294
-0.00251 -0.07678 0.409695
1928 -0.09762
1932
1935
1937 -0.05574 -0.10669 -0.30347
0.45184
1981 0.019922
-0.00035 -0.00215 0.381972
1982 0.026615
-000084 -000278 0.444287
1983 0.034734
-0.00536 0.001699 0.505764
1984 0.038989
-0.00753 -0.01145 0.438007
1985 0.044944
-0.00431 -0.03514 0.447201
1986 0.052691
-0.00298 -0.05244 0.438965
0.06463
0.000639 -0.05249 0.514119
1987
492
Table S-2e:
Pork
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
YEAR
BY
YEAR
ESTIMATION
PORK
COMMODITY:
Japan
Japan
Japan
Japan
Japan
Japan
AU-
Pre-war
Mid-
ALL-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
(Cant.)
1911 0.390527 0.905532
1955 -0.81359 -0.86429 2.280534
0.68771 1.009243
1956 -0.74444 -0.62652 1.613092
1912
1913 2.120811 0.936436
1957 -0.60717 -0.51317
1.39865
1914 0.594136 0.656811
1958
-0.5432 -0.40236
1.03856
1915 0.608048 0.362344
1959 -0.47304 -0.28836 0.780534
1916 -4.14969 0.029534
1960 -0.37397 -0.16479 0.494524
1917 0.823912 0.083715
1961
1918 0.614846 0.104378
1962 -0.26795 -0.02137 0.247599 1.327342
1919
1963 -0.22916 0.024236 0.207943
0.39635 0.015254
Taiwan
-0.3174 -0.11115 0.446902
0.8041 -0.28245
0.13557 0.126316 -0.43651 -0.15383
1920 0.410733 -0.04925
1964 -0.17184
1921 0.326194 -0.04692
1965 -0.18114 0.063938
1922 0.381149 -0.04351
1966 -0.13299 0.127756 0.081028 -0.23693 0.128934
1923 0.420088 -0.01289
1967 -0.08464 0.217446 0.055319 -0.22889 0. 122163
1924 0.531656 -0.01371
1968
1925 0.562067 0.006411 0.735713
1969 -0.01369 0.261091 0.070171 0.021368 -0.06178
1926 0.698039 0.021875 1.137849
1970 0.014637 0.332385 0.036026
0.16012 0.226629 -0.03787
-0.0491 0.243614 0.055803 -0.04996 0.050753
-0.2895 0.119618
1927 0.603543 0.007025 1.326208
1971 0.026387
0.068273 -0.06383 0.276889
1928 0.663976 0.058777 1.105004
1972 0.061933
0.046275 0.242938 0.707699
1929 0.658908 0.048439 1.093799
1973 0.071586
0.049898 1.120057 0.945019
1930 0.970466 0.097829 1.530035
1974 0.022593
0.041465 0.100865 0.201657
1931 0.603749 0.199816
1.76533
1975 0.015472
0.0596 0.160356 0.293006
1932 0.608648 0.090481 1.628373
1976 0.011501
0.066579 0826875 0.601559
1933 0.822611 0.192377 1.858698
1977 0.013531
1.49317
1978 0.033153
0.014487 1.202597 1.028267
1935 0.637846 0.146007 1.214802
1979 0.056611
0.007315 1.222747 1.118158
1936 1.127123 0.184544 1.591131
1980 0.047449
-0.00583
1937 0.820205 0.283834 1.393161
1981 0.040322
-0.00107 0.015075 0.942596
1982 0.052503
-0.00337 0.068441
1.10637
0.06703
-0.02512 0.192211
1.21512
1934 0.671065
0.15764
1983
1984 0.074567
0.04668 0.997699
-0.03708
0.89069
0.4173 1.025092
0.27693 1.068084
1985 0.083369
-0.0379 0.417084 1.097316
1986 0.095823
-0.04196 0.569751 0.993802
1987 0.113993
-0.04491 0.658809 1.192829
493
Table S-2f:
Chicken
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
Japan
Japan
Japan
Pre-war
Mid-
Period
Period
Year period
YEAR
Japan
Japan
Japan
ALL-
Mid-
Post-war Korea
Period
Period
Year period
(Cont.)
1911 0.460817
BY
CHICKEN
COMMODITY:
ALL-
YEAR
ESTIMATION
(Cont.)
0.59847
1955 -3.39483 -3.32156 2.157935
1912 0.553162 0.586388
1956 -2.14806 -1.02408 0.984977
1913 0.970092 0.692177
1957 -1 .31835 -0.78377 1.393192
1914 0.526187 0.524552
1958 -1.11383
1915
0.45069 0.312026
Taiwan
-0.5728 1.130262
1959 -0.91285 -0.38217 1.050344
1916 0.783854 0.110505
1960 -0.62741 -0.20441
1917 0.589805 0.180454
1961
1918 0.543384 0.271397
1962 -0.37387 -0.02367 0.125247 -0.31092
1919 0.603018 0.159202
1963 -0.31943 0.026558 0.100866 -0.25859 1.482332
1920 0.422734 0.199239
1964 -0.23339 0.144333 0.038009 0.520961 0.345396
1921 0.369682 0.118355
1965 -0.24379 0.068262 0.052848 -0.02198 0.057459
1922 0.513697 0.105972
1966 -0.17232 0.138734 0.029224 0.018432 -0.14019
1923 0.572454 0.014852
1967 -0.10622 0.229573 0.010181 -0.00582 -0.08051
1924 0.733202 0.009989
1968 -0.06141 0.257154 0011675 -0.00438 -0.03619
1925 0.801874 -0.01286 0.775124
1969 -0.01703 0.271933 0.005311 0.001117 0.027894
1926 0.775235 -0.03296 0.789158
1970 0.017909 0.342677
1927 0.721197 -0.03782 0.899153
1971 0.031697
-0.00454
1928 0.735381 -0.03883 0.878125
1972 0.074037
-0.00522 -003373 -0.34698
0.42029
-0.4701 -0.12877 0.228317
-0.0015 -0.00748 -0.05711
-0.001 -0.13514
-0.0122 0.822066
1973 0.084184
-0.00985 -0.15592 -0.61777
1930 0.919518 0.030953 1.137793
1974 0.026304
-0.01168 -0.00202
1931 0.659087 0.039569
1.21806
1975 0.018133
-0.01487
1932 0.706958 -0.04063 1.344203
1976 0.013436
-0.02162 0.041989 -0.59224
1933 0.858517 0.003487 1.458602
1977 0.M15789
-0.01686 0.149059 -0.62364
0.83734 -0.28755 1.362593
1978 0.038591
-0.00591 0.130588 -0.60181
1935 0.862345 -0.25803 1.136394
1979 0.065379
1936 1.152756 -0.13086 1.207142
1980 0.054587
0.001793 0.043083 -0.65672
1937 0.984943 -032955 1.196865
1981 0.046415
0.000297 0000877 -0.46969
1982 0.060379
0.001041 0.006678 -0.55426
1929
1934
0.70603
-00024
-0.268
-0.0009 -0.27928
0.07823 -0J7161
1983 0M77165
0.00893 0.035053 -0.49676
1984 0.085757
0.010828 0.077561 -0.54492
1985 0.095453
0.009484 0.093834 -0.44286
1986 0.109285
0.008493 0.074416 -0.38544
1987 0.129448
0.013404 0.142343 -0.55742
494
Table S-2g:
Fish
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
YEAR
BY
YEAR
ESTIMATION
COMMODITY:
FISH
Japan
Japan
Japan
Japan
Japan
Japan
AU-
Pre-war
Mid-
Alt-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
(Cont.)
1911 0.142013 -0.99289
1955 -1.13022 -1.64089 -2J7505
1912 0.171827 -0.85901
1956
1913 0.181198 -0.78071
1957 -0.93906 -0.95097 -1.66578
1914 0.181639 -0.71626
1958 -0.90731 -0.76346 -1.59899
1915 0.244205 -0.35333
1959 -0.80324 -0.58614 -1.35295
1916 0.270814 -0.21412
1960
-0.6765 -0.34406
1917 0.261046
1961
-0.5718 -0.20221 -0.95127
-0.2789
Taiwan
-0.9882 -1.25974 -1.88915
-1.1716
1918 0.229429 -0.41533
1962 -0.50778 -0.04135 -0.72735 1.251137
1919 0.243949 -0.24287
1963 -0.42021 0.050478 -0.65093
1920 0.176643 -0.55238
1964 -0.31467 0.315829 -0.34907 -0.39314 -0.00637
1921 0.240963 -0.17167
1965 -0.31717 0.129113 -0.64691 0.261446 -0.00098
1922 0.262711 -0.15283
1966 -0.31966 0.285843 -0.57274 -0.31244 0.005509
1923 0.266291 -0.07732
1967 -0.14996 0.442313 -0.26877 -0.21994
-0.0013
1924 0.281662 -0.04835
1968
0.48067 -0.31004 -0.04603
-0.001
1925 0.296778 0.034981 0.538501
1969 -0.02326 0.561135 -0.38143 0.016835 -0.00126
0.11563 0.625717
1970 0.023376 0.642687 -0.17607 -0.22804 0.007194
1926 0.310033
-0.0846
1.05148 -0.01079
-0.29156 -0.04401 0029731
1927 0.304821 0.139946 0.672589
1971 0.042128
1928 0.342897 0.294906 0.776757
1972 0.091042
-0.2203 0.175768 0.105633
1929 0.332149 0.221501 0.747289
1973
0.10529
-0.31685 0.727571 0.166157
1930 0.39688,6 0.536417 1.152203
1974 0.033948
-0.20859 0.068953 0.021391
1931 0.415116 0.713467 1.239258
1975 0.023727
-0.3713
0.10456
0.0229
0.61123 1.173896
1976 0.017887
-0.47406 0.625805 0.077254
1933 0.448538 0.871634 1.478625
1977 0.020837
-0.33591
1932 0.405175
0.83626 0.150592
1934 0.463924 0.868508 1.121834
1978
0.05143
-0.11466 1.061284 0.198789
1935 0.445222
1979 0.085853
-0.04783 1.087607 0.235246
0.64824 0.846322
1936 0.458011 0.729804 0.959544
1980
0.07085
0.034375 0.354672 0.199286
1937 0.463337 0.821373 0.986153
1981 0.060954
0.005674 0.013288 0.179717
1982 0.078303
0.019465 0.061196 0.223304
1983 0.103088
0.147154 0.176354 0.259044
1984 0.113437
0.192756 0.261452 0.229196
1985 0.122355
0197885 0.378941 0.237167
1986 0.137551
0.20447 0.502288 0.214716
1987 0.162067
0.233566 0.585458 0.275861
495
Table 8-2h:
Eggs
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
YEAR
BY
YEAR
ESTIMATION
EGGS
COMMODITY:
Japan
Japan
Japan
Japan
Japan
Japan
ALL-
Pre-war
Mid-
ALL-
Mid-
Post-war Korea
Period
Period
Period
Period
Year period
Year period
(Cont.)
(Cant.)
1911 0.006819 0.477298
1955 -0.31329 -0.46166 -067816
1912 -0.00818 0.636345
1956 -0.29365 -0.36574 -0.53489
1913 -0.03166 0.828475
1957 -0.27641 -0.33874
1914
Taiwan
-0.5249
1958 -0.25554 -0.27996 -0.48389
0.00321 0.373563
-0.2094 -0.41176
1915 0.039136 0.144809
1959 -0.13608
1916 0.047549 0.079358
1960 -0.20909 -0.12448 -0.31037
1917 0.054536 0.073921
1961
1918 0.019026 0.181936
1962 -0.16485 -0.01726 -0.11604 -0.50099
1919 -0.00335 0130581
1963 -0.14455 0.020031 -0.19677 -0.41854 -0.13993
1920 0.018517 0.199359
1964 -0.10905 0.114406 -0.12058 0.096593 -0.07711
1921 0.044891 0.041498
1965 -0.11726 0.054854 -0.20206 -0.02143 -001875
1922 0.056811 0.009836
1966 -0.09135 0.113701 -0.15774 0.049544
1923 0.112251 -0.02292
1967 -0.05808 0.192032 -0.09726 0.018281 0064194
1924 0.099579 -0.00833
1968 -0.03387 0.116309 -0.11144 0.003351 0.026362
1925 0.084146 0.001175 0.389327
1969 -0.00933 0.232058 -0.14129
1926 0.088836 0.002743 0.445644
1970 0.010003
-0.1875 -0.08659 -0.17865
0.06462
-0.0002 -0.03009
0.29534 -0.07287 -0.01299
0.05811
1927 0.093604 0.006847 0.482476
1971 0.017852
-0.14358 -0.00069 0.135009
1928 0112875 0.035277 0549366
1972 0041618
-0.10848 -0.01597 0.335261
0.02922 0.527268
1973 0049398
-0.15621
1930 0.130728 0.070845 0.740766
1974 0.015492
-0.10461 -0.00696 0.090527
1931 0161365 0.158092
1975
0.01045
-0.18666 -0.00973 0136843
1932 0.126988 0.080654 0806361
1976 0.007537
-0.24555 -0.12259 0.275108
1933 0.159935 0.192484 0.914058
1977
0.0087
-0.17979 -0.34228 0440199
1934 0.142693 0.049211
0.83101
1978 0.020235
-0.06538 -0.47247 0.500587
0.13428 0.033914 0.647534
1979 0.033689
-0.0322 -0.40607 0.515114
1936 0.141344 0.037235 0675241
1980 0.029469
0.022198 -0.12728 0486884
1937 0.157497 0.019653 0.691608
1981
1929
1935
0.11243
0.82078
-0.0324 0.441548
0.02466
0.00384 -0.00667 0.450023
1982 0.030605
0.013983 -0.01586 0.492248
1983 0.038105
0. 102406 -0.04564 0.547526
1984 0.042338
0.136063 -0.08365 0.490504
1985 0.046676
0.152471 -0.15518 0.490699
1986 0.054818
0.164149 -0.23699 0.467002
1987 0.057261
0.223325 -0.25426 0.538121
496
Table S-2i:
Milk
Total Expenditure Elasticities Year by Year -
TOTAL
EXPENDITURE
FROM
ELASTICITIES
SYSTEM
YEAR
BY
YEAR
ESTIMATION
COMMODITY:
MILK
Japan
Japan
Japan
Japan
Japan
Japan
ALL-
Pre-war
Mid-
ALL-
Mid-
Post-war Taiwan
Period
Period
Period
Period
Year peNod
Year period
(Cont.)
(Cont.)
1911 0.545285 0.311311
1955 -0.90146 -0.41096 -0.33891
1912 1.197267 0.402681
1956 -0.78902 -0.33038 -0.27887
1913 6.736212 0.702864
1957 -0.68256
-0.3064 -0.27282
1914 1.534384 0.222864
1958 -0.61196
-0.2532 -0.24115
1915 0.810053 0.131678
1959 -0.53767 -0.19088 -0.20577
1916 2.145165 0.109168
1960 -0.43355 -0.11456 -0.16531
1.93343 0.123268
1961 -0.36136 -0.08024 -0.15622
1918 2.528163 0.426893
1962 -0.30466 -0.01608 -0.11996
1919 1.162831 0.311449
1963 -0.26047 0.018705 -0.10978 -0.59893
1920 1.224644 0.110001
1964 -0.19335 0.106752 -0.06573 -0.3056
1921 0.551306 -0.01483
1965 -0.20522
1922 0.738293 -0.03279
1966 -0.15174 0.106208 -0.08534 0.257651
1923 0.822006
1967
1917
-0.0088
0.05117 -0.10691 -0.07123
-0.0978 0.180043 -0.05323 0.287477
-0.0601 0.123779
1924 1.059523 -0.00495
1968 -0.05709 0.202668
1925 1.172919 0.003624 0.281207
1969
1926 0.926562 0.034853 0.338028
1970 0.016971 0.276853 -0.03914 0.285598
1927 0.767223 0.051424 0.374684
1971 0.030586
-0.07326
1928 0.869932 0.110336 0.431194
1972 0.072791
-0.05409 1.226795
1929 0.746544
-0.0159 0.216985 -0.07565
-0.1307
0.55916
0.08742 0.417742
1973 0.084608
-0.07805 1.711706
1930 0.850031 0.244337 0.588121
1974 0.026593
-0.05392 0.269216
0.69062 0.370962 0.690571
0.01842
-0.09027 0.407636
1932 0.765081 0.310659 0.655832
1976 0.013782
-0.10963 0.864293
1933 0.907573 0.447618 0.755616
1977 0.016391
-0.07827 1.397198
0.84612 0.402942 0.647288
1978 0.040532
-0.02617 1.541062
1935 0.879538 0.290684 0.495743
1979 0.069482
-0.01235 1.612203
1936 1.124889 0.340552 0.533172
1980 0.058597
0.009327 1.407314
1937 0.98505'. 0.365138 0.553969
1981 0.050433
0.001569 1.224388
1982 0.065703
0.005252 1.349267
1983 0.084533
0.038168 1.484905
0.09407
0.051909 1.189644
1985 0.105964
0.055805 1.138187
1986 0.121979
0.060704 0.989466
0.1449
0.068533 1.097204
1931
1934
1975
1984
1987