AN ABSTRACT OF THE THESIS OF
for the DOCTOR OF PHILOSOPHY
PAL YONG MOON
(Name)
(Degree)
in AGRICULTURAL ECONOMICS presented on
(Major)
/972
a
(Date)
Title: AN ANALYSIS OF FOODGRAIN MARKET IN KOREA
Abstract approved
Redacted for privacy
/
John A Edwards
In order to effectively use foodgrain prices as tools of government policy, it is necessary to know the possible effects of price
incentives on different economic variables. The present study
attempts to measure the effects of foodgrain prices, upon various
economic variables, including production, consumption, both at the
farm and urban levels, and farm sales of two major foodgrains, and
to explore possible measures to improve the current foodgrain price
policy.
The results of the empirical study of farm producers' response
indicate that Korean farmers respond significantly in their grain production to price changes and that they are more responsive in barley
than in rice production in terms of both input use and planted acreage.
This suggests that policy designed to raise barley price to farm producers would contribute relatively more to increasing overall foodgrain production.
In studying the consumption behavior of farm and urban con-
sumers, and sales d.ecisions of farmers, a simultaneous equation
model was used. The system comprises eight equations: six behav-
ioral and twomarket identity equations. In specifying the model,
special attention was given to a pecuflar feature emerging from the
dual role of farmers in semi-monetized agriculture, that is, as consumers on the one hand and as sellers of products on the other.
Two types of analyses were carried out on the basis of the estimated behavioral parameters of the model. First, an analysis was
made of the partial response to price changes by treating each behavioral equation as an independent single equation under the usual
cetéris paribus assumption. Secondly, the total behavioral responses
were analyzed by taking account of simultaneous changes in all
endogenous variables in the system.
The partial response analysis indicates that both farm and
urban consumers have a upotentialu tendency to respond negatively to
price changes in their consunption of rice and barley and also a
"potential" tendency to substitute one grain for another in the face of
changing relative prices. It also shows a positive response in the
foodgrain marketings of farm producers. But the total response
analysis showsthat the responses measured in the partial analysis are
substantially offset by the interdependence of the prices of rice and
barley on the open market, resulting in positive changes in the
quantity demanded or no substitution at all. The empirical results
also provide counter-evidence concerning the validity in the Korean
economy of the so-called "target cash requirements hypothesis, "
advanced by a number of economists.
One important policy implication that can be drawn from the
study is that if the government's objective is to reduce foreign
exchange spending on rice imports by restructuring foodgrain consumption (in addition to increased domestic production), it can be
done through the use of price incentives by inducing the consumers to
reveal their "potential" responses on the market. This is equivalent
to forcing the ceteris paribus assumption made in the partial response
analysis to operate in the real world through an appropriate governmental operation.
An Analysis of Foodgrain Market in Korea
by
Pal Yong Moon
A THESIS
submitted to
Oregon State University
in partial fulfillment of
the requirements for the
degree of
Doctor of Philosophy
June
1973
APPROVED:
Redacted for privacy
Professor of Agricultural Economics
in charge of major
Redacted for privacy
Head of Department of Agricultural Economics
Redacted for privacy
Dean of Graduate School
Date thesis is presented
Typed by Clover Redfern for
/Q'72
Pal Yong Moon
ACKNOWLEDGMENT
The author owes a special debt of gratitude to Dr. John A.
Edwards, major professor, for his ideas and valuable suggestions at
all stages of this study and guidance throughout the author's graduate
program at Oregon State University.
He would like to thank Drs. Richard S. Johnston, Bruce R..Rettig,
Joe B. Stevens and David R. Thomas, members of his graduate cornmittee, and Dr. William G. Brown for their helpful comments on the
dis sertation.
The author is under a deep obligation to the Agricultural Development Council, Inc., New York and its staff for providing financial
assistance and encouragement throughout the entire period of his
graduate study.
Special thanks are also due to Professor Herman M. Southworth,
Associate with the Agricultural Development Council, Inc., for his
valuable comments at the initial stage of the study and Mr. Hae Sung
Yoo and other members of theEconomic Research and Statistics
Bureau, Ministry of Agriculture and Forestry, Korea,for collecting
and arranging the data which made this research possible.
Indebtedness to my wife, Haeduck, for her encouragement
throughout this study, should be particularly mentioned.
TABLE OF CONTENTS
INTRODUCTION
1
Problem
Objective of the Study
PRODUCER RESPONSE TO FOODGRAIN PRICES
Some Conceptual Definitions
Actual vs. Intended Response
Normative vs. Positive Approach
Methodological Basis
Estimation Procedures
Yield Response (E)
Derivation of Yield Elasticity
Acreage Response (EAP)
Empirical Results
Production Function
Input Demand Function
Deviation of Price Elasticity of Yield
Response to Planted Acreage
Aggregate Supply Response
Conclusion
MARKET ANALYSIS
Model Construction
Farm Demand
Farm Sales
Urban Demand
Market Identities
Complete Model
Identification of the Model
Order Condition
Rank Condition
Empirical Results
Data
1
4
6
7
8
8
10
15
15
19
20
22
22
24
33
34
37
37
40
44
55
59
62
64
67
67
68
69
69
Estimation Procedure
Possible Source of Errors
Predictive Tests
Interpretation of the Estimated Results
Analysis of Partial Effects
Farm Level Demand
Farm Sales
74
74
75
Urban Demand
96
80
89
89
93
Page
Analysis of Total Response
Summary of Findings
103
115
POLICY IMPLICATIONS
119
BIBLIOGRAPHY
127
APPENDICES
130
130
134
137
139
Appendix A: Estimation of Foodgrain Supply and Demand
Appendix B: Estimated Seasonal Response Model
Estimated Nonlinearized Model
Appendix C: Basic Data
LIST OF FIGURES
Figure
3-1. Effect of price changes on the consumption of the
seller-consumer.
49
3-2. Monthly per capitafarm: demand for rice, 1963-7l.
81
3-3. Monthly per capita farm demand for barley, 1963-71.
82
3-4. Monthly per capita farm sales of rice, 1963-71.
83
3-5. Monthly per capita farm sales of barley, 1963-71.
84
3-6. Monthly per capita urban demand for rice, 1963-7 1.
85
3-7. Monthly per capita urban demand for barley, 1963-71.
86
3-8. Monthly wholesale price of rice per kilo (polished),
1963-71.
87
3-9. Monthly wholesale price of barley per kilo (polished),
1963-71.
88
LIST OF TABLES
Page
Table
1-i. Supply and demand for major foodgrains for 1967-71
and projection for 1972-76.
2
2-1. Per farm planted acreage for rice, common barley
and naked barley, by year, 1963-70.
13
Total availability and actual consumption of government
supplied fertilizer by year, 1963-1970.
26
2-3. Total supply and consumption of agricultural chemicals,
by year, 1963-1970.
27
2-4. Acreage of paddy land by irrigation status, by year,
1961-1970.
36
3-1. The estimated structural coefficients.
76
2-2.
3-2. The coefficients of responses to price changes; partial
versus total.
114
Appendix
A- 1. Estimation of rice demand and supply.
13 1
A-2. Estimation of barley demand and supply.
132
A-3. Estimation of wheat demand and supply.
133
C-i. Relative prices of rice and input factors used for 1/10 Ha,
by size of farm, 1963-70.
C-2. Relative prices of common barley and input factors used
per 1/10 Ha, by size of farm, 1963-70.
C-3. Relative prices of naked barley and input factors used
per 1/10 Ha, by size of farm, 1963 -70.
C-4. Data used for estimating simultaneous equation model,
1963-71.
142
144
146
149
AN ANALYSIS OF FOODGRAIN MARKET
IN KOREA
INTRODUCTION
Problem
The Korean Government has been making various efforts for a
past decade to increase foodgrain production. And yet the gap between
supply and demand has been widening in recent years. This gap has
been filled by the importation of foreign grain; partly by commercial
imports and partly by imports under the U.S. FL 480 program.
As shown in Table 1-1, the foodgrain shortage is estimated as
more than 2,000,000 metric tons for the 1972 consumption period.
The shortage of rice alone is estimated to exceed 600, 000 metric
tons. If the current consumption and production trends continue, the
overall foodgrain deficit, it is estimated, will reach nearly 3,000,000
metric tons by 1976. As is projected in Table 1-1, there will be a
deficit of over 800, 000 metric tons of rice and a deficit of over
2, 000, 000 metric tons of wheat in 1976.
This increasing imbalance between domestic production and
demand is ascribable to numerous factors which govern grain pro-
duction and consumption. Historically, government policy has been
directed toward maintaining foodgrain prices at low levels in favor of
the growing industrial sector. Such a negative price policy has
Table i-i. Supply and demand for major foodgrains for 1967-71 and projection for 1972-76.
Unit: 1000 M/T (Polished).
consumptionb
Wheat
Barley
Ricea
Year
Domestic Production
Barley Wheat
Rice
1967
3919
2002
310
3780
1999
1032
113
0
922
1035
1968
3603
1940
324
4032
1984
1460
216
105
1127
1343
1969
3195
2037
356
3899
2042
1529
760
64
1205
2029
1970
4090
2045
360
4320
2105
1562
540
0
1254
1794
1971
2929
1917
352
4474
2166
1684
535
0
1333
1868
1972
3998
2375
361
4736
2226
1825
629
0
1464
2093
1973
4190
2565
380
4786
2287
1979
596
0
1609
2205
1974
4254
2565
380
4950
2349
2149
666
0
1769
2435
1975
4378
2660
389
5119
2413
2335
741
0
1946
2687
1976
4475
2755
398
5296
2478
2540
824
0
2142
2966
Rice
Imports
Rice Barley Wheat
Total
a.Rice-year begins on November 1 in the previous year and ends on October 31 in the current year
bconsumption includes staple food, non-staple food, seeds, and feedsuse.
Source: Table A-i, 2 and 3 in Appendix.
N)
3
naturally created disincentives for increased production of grain in the
farm area on the one hand and stimulated the consumption of rice in
the urban area on the other.
Under the present policy, the foodgrain gap will continue to
widen, possibly forcing the country to spend nearly 300 million dollars
of foreign exchange every year in order to meet the foodgrain shortage
alone in the near future. Since a large portion of the foodgrain short-
age has been met in the past by local currency purchases under the
U. S. PL 480 the food gap itself did not impose a serious problem on
Korea1s foreign exchange position. But in anticipation of a change in
U.S. policy to cash, or credit, salesinUS. currency, the foodgrain
situation will be directly related to the countryT s balance of payments
position.
It is an accepted notion that foreign exchange plays an essential
role in industrialization efforts of the less developed countries by
making it possible to import the capital items not produced domestically as well as technical know-how from the developed countries.
Considering the foreign exchange earning capacity and the magnitude
of the foreign debt servicing obligation of the country, the spending
of 300 million dollars of foreign exchange on foodgrain imports will
bring about a continuous deterioration in an already weak balance of
payments position. A shortage of foreign exchange will eventually
impose a serious restraint on industrial growth.
ru
Thus, a policy designed to achieve the goal of industrialization
may have a negative effect upon industrialization itself. The solution
to this paradox is a complex one. A positive price policy that favors
a general rise in the prices of all foodgrains relative to nonagricultural commodities may foster some increase in aggregate grain
production. it is, however, likely to have a conflicting impact upon
other target variables in the overall development plan.
In this context food grain price policy should be integrated into
overall development policy and it should be used as a tool of govern-
ment to induce the relevant economic variables to move in such a
direction that Korea reduces to a minimum the foreign exchange
spending on foodgrain imports, with minimum discouraging effect on
other economic goals.
In order to effectively use grain prices as tools of government
policy, it is necessary to know the possible effects of price incentives
on different economic variables.. The present study is undertaken to
provide an empirical foundation that will contribute to improving the
current foodgrain price policy.
Objective of the Study
The primary objectives of this study are to measure the effects
of foodgrain prices on farm production decisions, consumption
behavior, both at farm and urban level, and farm sales of foodgrains
5
and to explore possible measures to alleviate the overall grain shortage in Korea.
Specifically, the study intends:
1.
to analyze the effect of price changes on farm producers'
decision-making in the production of major foodgrains, rice,
common barley and naked barley;
2. to analyze consumer response to price changes with particular emphasis on the substitution effect between different
foodgrains for both farm and urban populations;
3. to examine the farm producers' response to price changes
in market sales of major foodgrains, and
4. to draw appropriate policy implications on the basis of the
empirical results.
6
PRODUCER RESPONSE TO FOODGRAIN PRICES
From the standpoint of public policy, it is important to know the
degree of
producers7
response to price changes; the degree of
response measured in terms of price elasticities. The price elasticity
of supply measures the willingness as well as the ability of farm producers to adjust to changing price condition, one of the most important
aspects of a dynamic economy.
A change in the relative level of grain prices induces farm
producers to change the level of input use as well as its allocation
among different crops, resulting in changes in aggregate production.
The effect of grain prices can b& analyzed from two different aspects;
the effect on the aggregate output through increased or decreased use
of input resources and the effect on the short-run decisions of the
producers with respect to market sales from already produced output.
The present chapter deals with the former effect, that is, the effect on
the aggregate production of major foodgrains and the latter effect upon
market sales will be discussed in the next chapter.
It is widely questioned whether or not the farmers in the
traditional-oriented agriculture, where a large portion of their own
products goes to family consumption, respond to changes in market
pricesin their production decision making [29,32]. It is generally
said that the extent to which farmers are sensitive to external
7
economic condition varies depending on their stage of agricultural
development and by different regions. For example, farmers in relatively more commercialized agriculture or those living close to urban
areas respond more sensitively to changing market condition than
those in subsistence farming or those living in remote areas. How-
ever, where the rural structure is such that the commercial and subsistence farmers co-exist side by side in terms of both residence and
cultivated land, there is every reason to believe that the behavior of
subsistence farmers cannot but be influenced by that of commercial
farmers. This simple fact has been seldom recognized by the development economists in the past.
The main hypothesis to be tested here is that Korean farmers
do respond to price changesin their production plan for major foodgrains. The secondary hypothesis is that Korean farmers are more
responsive to price condition in barley production than in rice production. The practical implication in terms of price policy of this
hypothesis is that the potential for increasing barley output is greater
than that for rice.
Some Conceptual Definitions
We need to distinguish between two conceptually different supply
responses; the Hactualhi or hlrealizedU and the HintendedTl or planned"
responses, and also between two different approaches; "normative"
and "positive."
Actual vs. Intended Response
The measurement of "actual" production response involves the
estimation of the relationship between the variation in price and
realized output as observed in the past, irrespectiveof farm producers' intention in production activities. Since actual production is
subject to various factors which are beyond the control of decision
makers (farmers and policy makers), the degree of response thus
estimated reflects only a part of the behavioral intention in farm
investment. The "intended" production, however, emphasizes the
farm investors' efforts and plan as decision makers. This distinction
is important and the knowledge of the "intended" or "planned" response
of farmers appears to be more relevant in a country where the government policy is directed towards increasing total grain production by
inducing farmers to increase their efforts. In this context, the present study centers on the measurement of supply elasticities in the
latter sense with a view to gaining some information and insight as a
basis for foodgrain price policy.
Normative vs. Positive Approach
Normative analysis refers to what ought to exist under certain
conditions, while positive analysismeans prediction of quantitative
relationships among variables as they do exist at a point in time or
have existed over a period of time [12]. Normative analysis intends to
identify the optimum relation between the quantity of a product and its
price under the assumption of optimizing behavior of farm investors.
The normative supply function i& thus an estimate of the optimum
supply reaction to product price changes in terms of a given norm,
1. e., profit maximization or cost minimization.
On the other hand, positive analysis attempts to estimate the
production response to price changes from observations drawn from
the "actual operating world, "
Where subsistence farming is still the prevailing type and the
decision-making of the average farm producers deviates from the
concept of profit maximization in their resource allocation, the
equating of marginal values to prices could only be conczeived of as a
desirable goal but not as something accomplished in production
planning.
The analysis in this chapter thus follows one of the positive
approaches based on the actual observations in the past, ignoring the
derivation of the first order condition. In short, the present study
attempts, having in mind the above two sets of conceptuai differences,
to measure the "intended" response to price changes of farm decisionmakers by means of positive approach.
10
Methodological Basis
In his paper published in the Journal of Farm Economics, May,
1959, Zvi Grilliches [ii], presented the result of his analysis on
input demand and the estimates of derived supply elasticities for the
aggregate U.S. farm products. As he did not state the exact procedures for deriving aggregate supply elasticity there is no way of
making appropriate review of his analysis here. In any case, the
basic idea of the method used in this study owes its origin to Professor
Zvi Grilliches and it is well represented by the following statement in
his article!! [11, pp. 309-322]:
if inputs respond to price changes, so must also farm
output. This is the basic idea underlying the assertion
that a study of input behavior can also provide insight into
the supply response of farm products.
.
.
The elasticity of aggregate output with respect to its price is the
sum of the elasticities of planted acreage and that of yield per unit
acreage, both with respect to the product price. That is,
EQ
=
+ EAP
Heady and L. G. Tweeten [ 3], and L. G. Tweeten and
C. L. Quance [343 used essentially the same method in estimating the
aggregate supply elasticity of the U.S. farm products.
This can be shown as follows:
Q=YA
Where:
11
Where:
= Price elasticity of aggregate output.
= Price elasticity of yield.
Price elasticity of planted acreage.
EAP
If each component of total elasticity is to be estimated by fitting
stochastic models to the observed data, the above identity holds under
the following assumptions:
1. A decision to expand or contract the planted acreage of a
certain crop does not per se affect the efforts to achieve the
increased yield of that crop, or conversely the efforts to
increase yield does not affect the plan to plant a certain area
of land. In short, planted acreage
(A)
and yield
(Y)
not interdependent in decision making.
Aggregate output
Y = Yield per unit acreage
A Planted acreage
Differentiating both sides with respect to product prices we obtain
Q
dQaQdY+aQdA dYA+dAY
dP aA dP dP
dP
Multiplying through by P/Q, we get
dP
a
dPQdPYQ dPAQ
Which is
EQ
=
+ EAP
dPY dPA
are
12
2. Environmental condition is such that expansion or contraction
of land scale does not cause a change in yield, i. e. unitary
elasticity of production with respect to land.
The first assumption relates to the motivation of farm decision
makers and the second to the technical relationship between land scale
and yield. If they are interdependent, that is, if Y = F(P,A)
A = G(P, Y),
then
and
dY/dA, dA/dY / 0 and therefore the mathe-
matical derivation of the above identity cannot hold. In econometric
terminology the random error terms in the stochastic models for estimating each elasticity will be correlated, resulting in biased estimates.
As to the first assumption, it is very doubtful that Korean
farmers adjust intentionally in foodgrain production the amount of input
application simply because he plans to alter the planted acreage, or
vice versa. This could be true if farmers had a flexible choice of
planning planted acreage with a fixed amount of investment capital.
Considering, however, the limited availability of land, relatively
abundant labor supply, high proportion of non-purchased inputs, there
is no reason to believe that average Korean farmers substitute or
complement planted acreage for the efforts to achieve desired yield.
The existence of such substitution or complementary relation, both
technically and motivationally is only conceivable for some special-
ized crops which require intensive cultivation in terms of inputs.
But these crops are outside the interest of the present study.
13
The second assumption is also a realistic one in Korean agri-
culture. As shown in Table 2-1, the past trend of planted acreage
per farm-operator for major foodgrains does not indicate a variation
in sufficient scale as to cause the changing economy of size. Even
the study, made with the cross-sectional data, gives the estimate of
land productivity coefficient as large as . 8 to . 9 which is near unitary
elasticity.
Table 2-1. Per farm planted acreage for rice, common
barley and naked barley, by year, 1963-1970.
Unit: 1/10 Ha.
Common
Naked
Year
Rice
Barley
Barley
1963
1964
1965
1966
1967
1968
1969
1970
6.21
6.08
6.09
6.32
6.52
6.52
6.51
6.69
3.09
3.35
3.42
3.35
3.26
3.20
3.16
3.39
3.01
3.45
3.44
3.46
3.56
4.05
3.72
3.54
Source: Farm Household Economy and Cost of Production
Survey, 1963-19 70, Ministry of Agriculture and
Forestry.
The next procedure is to estimate each response coefficient
separately. The elasticity of yield with respect to the product prices
will be estimated by an indirect method in order to reflect the
fintendedu response of farm producers, whereas the elasticity of
planted acreage will be estimated by a direct method.
14
The response of yield to price changes is determined from the
response of yield to input application and the elasticities of demand
for each input factor with respect to "expected price" for the product.
In other words, yield is a function of productivities of inputs and the
level of inputs used. The level of input use in turn depend.s upon the
"expected price" of the product relative to input prices. This relation
can be formulated as:
ep !/
=
Where:
Elasticity of yield with respect to product price.
Elasticity of yield with respect to input X.
ep Elasticity of demand for input X. with respect to product
price.
Mathematical proof is given by Zvi Grilliches [ii, p. 319] as
follows:
Given production function
Y = f(X , .. .
n
1
Differentiating both sides with respect to product price, we get
dY
dXn
ay
dpaX dP
8Xn dP
1
Multiplying through by PlY,
dY P ay X1 dX1
we get
X dX
dPYaXYdPX '&XYdPX
n
n
+
1
that is,
=
ey
eXP
15
Hence, the procedure for estimating yield elasticity requires the
combination of two separate estimates: production function and input
demand functions from which to derive the relevant response coeffi-
cients.
Estimation Procedures
Yield Response (E)
The Cobb-Douglas type production function is fitted to the
cross-sectional input-output data of a selected normal crop year; normal in the sense that weather conditions and other uncontrollable fac-
tors were not unusually good or bad so as to cause a bumper crop or
crop failure. Production function thus estimated will reflect a relatively normal input-output relationship compared to one estimated
from the time-series data of different years. To put it differently,
the output of that year corresponds more closely to farmers' intention
or plan in regard to input use than other years. The underlying
as surnption is that region-to - region and individual- to - individual varia-
tion in uncontrollable factors are smaller than year-to-year variation.
TheCobb-Douglas production function is used, as it is generally
considered appropriate to agricultural production3 plus the fact that
its simplicity makes it easy to use.
Production function for yield, say rice, is expressed as:
16
=
L1 Fc2 FH3
Where:
= Rice yield per unit area.
L = Labor input in man-hours.
= Commercial fertilizer use in real value (deflated by
the Index of prices paid by farmers).
FH = Family supplied manure in (imputed) real value
(deflated by PPFI).
= Irrigation expenses (deflated by PPFI).
10
Other operating expenses including seed, pesticides,
repairs and depreciation on farm building and implements and stock labor (deflated by PPFI).
U
0. .
O5
Random error term reflecting unidentified factors.
Productivity parameters to be estimated.
Each of the estimated coefficients,
O,
gives the degree
of responsiveness of yield to each individual input factor, namely the
percentage change in yield corresponding to one percentage change in
the amount of each input factor, assuming all other factors remain
unchanged. The random error term,
U,
reflects the effect of
uncontrollable factors such as difference in weather condition, soil
conditions, managerial ability between different regions and/or
individuals. In short, this term represents the incomplete
17
specification of the model to be fitted to the observed data.
As the next step, input demand functions are estimated by relating the amount of input employed to the product price which farmers
expect to receive relative to the input prices paid by farmers during
the current production period. As the expected price for the current
product cannot be observed, the moving average price of the harvest
season in the past three years is used as a proxy.
Input demand functions are also of the form of power function.
(1) Demand for labor:
L = a0() U1
(2) Demand for commercial fertilizer:
Fc
b0(
Rt1)1 T2
±"Conventionally, the price of the immediately previous harvest
season has been used in estimating supply function under the assump-
tion that farmers anticipate to receive the same price for the current
output. This assumption is valid only if the previous harvest season
price had been a fairly stable one with normal crops. But where a
fluctuation in aggregate production causes corresponding fluctuation
in price, it is hard to believe that farmers will expect to receive that
abnormally high or low price for their products. The choice of moving average price in the preceding three years is based on the judgment that farmers are trend-conscious. The use of the three year
average is rather arbitrary, and each year's price is equally weighted
since appropriate weighting scheme is not found.
18
(3) Demand for family supplied
FH
manure:'
zc(Rt')l T2U3
(4) Demand for irrigation:
d
d1
Rt
IRdo(pPFI)
T
(5) Demand for other inputs:
I0e0(
Rt 1 e1
)
e
T
2U5
Where:
Rt...1
= Index of the moving ave-rage rice price of the harvest
and post-harvest season--November, December and
January--in the past three years.
WF = Index of therural wages in the current period.
= Index of the fertilizer prices in the current period.
PPFI
Index of the prices paid by farmers.
P0 = Index of the price of agricultural supplies in PPFI
excluding fertilizer.
T = Time trend.
'Although family supplied manure has no market price, it is of
analytical interest to see how its .ise is affected by the price of rice
relative to the price of commercial fertilizer which is thought to be
substitutes.
19
U1. . . U5
a1. . . e2
Random error term.
Elasticity coefficients to be estimated.
Time trend variable,
was introduced in order to reflect
T,
the effect of technological change owing to the extension service, etc.
Derivation of Yield Elasticity
Substitution of the estimated input demand functions into the
estimated production function will give
A(R1
a1e1
Rt-1
Rt-1
)
WF
x
b192
AA
d1B
c103
PPFI
)
Rt1)15
T2222
p0
Where:
A
A
o)2
A
A
A
A
(A)3 ()4 (A)5
Rearranging, we obtain
=
A(Rtl
PPFI
a1o1+b1e
A A
<
1e311e41e5
A A
-(b102103)
F
PPFI
WF
A
A
A
A
b2+c2+d2+e2
P0 -e105
T
PPFI
which gives the elasticity of "intended' yield with respect to product
price,
zo
A
A
A
A
A
a
a191 + b102 + Ac103 + d104 +Ae15
A
eLPe YL + eFP eYF
ee
=
+
+ eFF eYF
IP
+ e1
=
The coefficients,
give the estimated
.
elasticities of yield with respect to input prices.
Acreage Response (EAP)
The response to price changes of planted acreage of a given crop
is hypothesized to depend on the expected price of corresponding out-
put and the planted acreage in the previous year. The model adopted
is:
At = a0(
Rt
a1
a
A2i u
Where:
At = Planted acreage for the current output.
Ai = Planted acreage in the previous year.
Rt-1 = Index of the three-year moving average price of the
harvest and post-harvest season in the past.
a1, a2
Elasticity to be estimated.
The estimated elasticity is conceptually comparable with that of
yield in that the planted acreage reflects the farmers' intention or plan
21
for the current production. The estimated coefficient,
the short-run elasticity of acreage and
(l_.)
,
gives
the adjustment coef-
ficient of the current acreage to product price changes [5].
Throughout this study, it is assumed that farm
producerst
response to price of a given crop is not influenced by the price of
other crops both in input demand and planted acreage. The factors for
these rigidities which condition the
farmerst
decision-making are many
and complicated. Some of the major factors are:
1. Peculiar to subsistence farming, many small cultivators
grow grain crops to meet their family needs and therefore the
choice of crops is largely independent of the prices of other
crops.
2. Technical feasibility is also of a factor which limits the
elasticity of substitution between crops. For example. rice
is grown in paddy land where irrigation is required. Cornmon barley is grown upland (dry land) while naked barley is
planted in paddy land as a second crop after rice.
3. Although there is a certain degree of competition in labor
input between rice transplanting and common barley harvest
for a short period, it is not as strong as to preclude the
planting of either one or two crops, once planned to grow
both. Moreover, traditional labor exchange system among
farmers alleviates the labor shortage problem during this
22
peak demand season.
4. Possibility of diversified use of a given amount of fertilizer
is limited because a specific kind or type is generally used
for a specific crop and in case of mixed fertilizer the government sells only those of given formula already prepared.
Empirical Results
Production Function
The Cobb-Douglas production function was fitted to the cros s-
sectional data of 1970 for three major foodgrains. Data used are
individual farm records from the Farm Household Economy and the
Cost of Production Survey, 1963-7 1, conducted by the Ministry of
Agriculture and Forestry.6/ The number oI observations is 283 for
rice when irrigation expenses
'R is used as one of the independent
variable, but was increased to 500 when 'R was summed together
with other operating expenses
(I)
309 for common barley and 345
for naked barley. For each crop, two production functions were
The Farm Household Economy and the Cost of Production
Survey is a well-designed sample of 1,200 grain farmers who are
assisted by 80 enumerators in keeping daily records of their total
economic life. This is the best source of data available for understanding the Korean farmers as economic entities. These data are
frequently used for obtaining national estimates of certain economic
aspects of Korean agriculture in spite of the fact that there are limitations to these sample data.
23
estimated, one including family supplied manure
(FH)
and the other
without it and also without 'R in rice.
The estimated production functions are: (Figures in parenthesis
are t-values)
(1) Rice yield
C(2
11445
08164
0(5 157)
944) R(6 917)
03610
= 45. 96F 08141
713)
FH(2
.
288)
=
.
152)
=
.
257)
(R2
08972
52. l6L
(2)
9
)
F C3568
09146
(R2
°(5.531)
Common barley yield
Y
GB
= 14 66L 14447 F 19940 F 03793
(2. 884) C(5
H(2
073
097)
11346
07 783)
(R2
.16833
CB
(3)
18. 60L(3
.18802
.11262
435) F C(4 828)10
.247)
(R2
687)
Naked barley yield
.30888
NB
.20696
.09278
= 9.33L(7677)FCIO
(R2
= .
296)
Overall, the R2's are very low, indicating that a large portion of the variation in yield is associated with the variation in
unspecified factors. Nevertheless, inasmuch as all three crops are
24
believed to be commonly affected by the unspecified factors in the
same year, resulting estimates of the productivity coefficients are
still valid for the purpose of comparison among different crops. Since
the sum of the estimated coefficients,
A
0.,
shows asubstantial
1.
divergence from unity, it indicates that all crop-yields are subject to
decreasing returns to scale in input use. Noticeable is a fact that the
response of yield to commerciaL fertilizer is considerably lower in
rice and naked barley than in common barley, implying in some sense
that the absolute level of commercial fertilizer use is relatively closer
to its ceiling- -given the present technological status- -in rice and
naked barley.
Input Demand Function
In formulating demand function, it is hypothesized that the
amount of input demanded depends upon the three-year moving average
price in the preceding harvest and post-harvest season when the
largest portion of grain is sold. In Korea, approximately 40-45 percent of the total marketed rice is sold during the three
monthst
period
November through January, and 45-50 percent of the total marketed
barley is sold during the two months' period, July through August.
In the statistical demand function, if the quantity of input
demanded is used as dependent and price as independent variable, it
requires the assumption of infinitely elastic supply of input factors,
25
that is, the price of input is predetermined independently of demand
situation.
We will examine whether or not the above assumption is realistic by investigating briefly the supply situation of the factors involved.
1. Labor - Although accurate statistics are not available,
approximately one-fourth of total labor time available is
believed unutilized. Even if there is a seasonal shortage of
family labor during the peak demand periods it is well taken
care of, as already mentioned, by the traditional labor
exchange practices within the village. The situation may be
different in the suburban farming region where more employ-
ment opportunities are available. But as this type of region
cannot represent the Korean agriculture the assumption of
infinitely elastic labor supply largely holds at a point in time.
2. Commercial fertilizer - Supply and distribution of chemical
fertilizer areunder the strict control of the government in
Korea. Sale price is determined by the government set
criteria--such as manufacturing costs, importation, transportation and other handling costs plus subsidy. Owing to the
government emphasis, a sufficient amount has been always
available for the farmers who were willing to purchase at the
subsidized price. As shown in Table 2-2, total amount available was in excess of total consumption in all years.
Table 2-2. Total availability and actual consumption of government supplied fertilizer by
year, 1963-1970. Unit: 1,000 M/T (in plant nutrient).
Year
Nitrogen (N)
Availability Consumption
Phosphate (P205)
Availability Consumption
Potash (K20)
Availability Consumption
1963
200
192
118
94
29
21
1964
180
173
151
154
41
37
1965
237
218
163
123
79
52
1966
264
240
176
125
94
59
1967
297
278
188
133
117
76
1968
329
286
169
121
114
71
1969
365
320
188
131
128
84
1970
483
356
293
124
182
83
Source: Agricultural Statistics Yearbook, 1963-1970, Ministry of Agriculture and Forestry.
27
3. Pesticides and insecticides - Although these commodities are
not under government control, supply and demand situations
are similar to those of commercial fertilizer except that the
government
subsidize the price for only major items such as
calcium seresan and BHC dust that are most widely used by
farmers. Table 2-3 indicates that most items are made
available for farm users.
Table 2-3. Total supply and consumption of agricultural
chemicals, by year, 1963-1970. Unit: MIT.
Year
Supply
Consumption
1963
20,013
26,963
12,071
12,688
11,933
13,064
20,501
18,772
23,355
12,729
12,549
1964
1965
1966
1967
1968
1969
1970
31,325
9989
9,983
17,531
25,024
Source: Agricultural Statistical Yearbook, 1963 -1971,
Ministry of Agriculture and Forestry.
The evidence presented above thus justifies the form of the
statistical demand functions to be estimated with price as predeter-
mined variables. From these simplified equations with only a single-or two if time variable is introduced--independent variable, we cornpute the demand elasticity of inputs with respect to product price,
relative to each input price.
Each input demand function is computed from eight years of
data, 1963-1970, from the Farmhousehold Economy and the Cost of
Production Survey. In order to increase the number of observations
each year's data was divided into five classes on the basis of farm
size, resulting in 40 observations. Thus we have a combination of
time-series and cross-sectional data for each estimation, but only one
price for each year. This method of utilizing all individual observations available instead of class average for each year has been shown
to improve the efficiency of parameter estimates to a substantial
degree by William G. Brown and Farid Nawas [2, p.
1].
In their
research paper, "Improving the Estimation and Specification of Outdoor Recreation Demand Functions, they made the following conclu-
sion as the resulLof a Monte Carlo study;
The proposed method should give gains in efficiency
of several hundred percent over traditional procedures,
thus allowing sample several times smaller than those
.
presently used to yield parameter estimates at least as
reliable as those presently obtained.
As the result of using this method, the efficiency of the esti-
mated coefficient of the price elasticities were certainly improved,
2,
7/
though the R s are low. Since one is more interested in obtain.
.
ing the efficient elasticities in this study, low R2's
are not of great
7/ R. J. Freund [7] shows that the use of group means increase
2 but it does not represent an increase in the precision of the
B.
regression estimates,
29
concern. The estimated results are: (Figures in parenthesis are
t-values)
(1) Input for rice
Labor
Rtl
L
14925
(R
2
.088)
Commercial fertilizer
Rtl
Fc
65608
(R2 = .564)
Family supplied manure
01830
= 305. Z0(
129)
Other operating expenses
10
66. 14(
))
(R2
.004)
(R2
.341)
Irrigation
11064
Rt-1 -.24425 T2583)
= 731,00( PPFI (.588)
(R2
= .
153)
10619
= 244. OOT(2
540)
(R2
= .
145)
(2) Input for common barley
Labor
L
Btl
17696
(R2
.261)
30
Commercial fertilizer
Fc
=
98.sl(:t1)9)
(R2
=
.087)
(R2
=
.156)
(R2
=
.802)
(R2
=
.085)
Family supplied manure
FH
=
lz46,o(:t1)5:)5
Other operating expenses
10
Btl
69459
(3) Input for naked barley
Labor
L = 8O.5O(:):;)
Commercial fertilizer
Fc
Btl
(R2
.119)
Family supplied manure
FH = 1476. 0(
:t
1
(R2 = .465)
Other operating expenses
10 =
36.4o(:t1):o;)
(R2 = .684)
31
A few noticeable facts.in the above results are:
1. The elasticity of demand for labor in naked barley is consid-
erably lower than those in rice and common barley, indicat-
ing that farmers give higher priority to rice cultivation than
to naked barley that is mostly grown after rice in the same
paddy land.
2. The elasticity of demand for commercial fertilizer in rice is
much higher than that in barleys. This explains that Korean
farmers are in general partial to commercial fertilizer.
This high elasticity of demand with respect to rice price
makes a noticeable contrast with its low productivity coefficient in the estimated production function.
3. The elasticity for family supplied manure is negative in both
barleys and that in rice is not significantly different from
zero. As one would expect, if the price of commercial
fertilizer relative to product price falls, farmers will shift
to purchased fertilizer instead of bothering to make compost
or to grow green manure.
4. The elasticities for other inputs are high for all three crops.
The introduction of time dummy as an independent variable in
fertilizer and other input demand functions did not result in an
appreciable improvement. One possible explanation may be that, even
if the use of fertilizer and other inputs are steadily increasing over
32
time owing to the rural guidance services, increasing subsidies on
these inputs by the government have such a strong influence that
regression effects are mostly absorbed by the variation in relative
prices. But the reverse seems to be the case for irrigation. Once
the government supported irrigation facilities are established, it is
very likely that all farmers within the reach of capacity of the establishment make use of it regardless of product price changes.
When the demand elasticities for hired labor and family labor
are computed separately the results are very similar to those for
commercial and family manure in that those for family labor assume
negative sign in naked barley and rice. And the estimated elasticities
are considerably higher than those estimated with both categories
summed together. The results of separate estimation are:
(1) Rice
Hired labor
L
Familylabor
L
(2) Common barley
Hired labor
L=
Family labor
L
52382
657)
4. 36(
-.07708
l2(1
58.07(
WF
04991
)437)
(R2 = .067)
(R2 = .002)
172)
(R2
=
(R
= .005)
.
33
(3) Naked barley
Hired labor
L
3.96(1)l3)
(R2
Family labor
L
130,20(t1)1
(R2 = .035)
.130)
Deviation of PriceElasticity of Yield
Price elasticities of yield are computed by combining the estimated productivity parameters and demand elasticities of input factors. Family supplied manure for all crops and irrigation for rice are
excluded from computation.
(1) Rice yield
(. 14925)(. 14671) + (.65608)(. 08972) + (.41260)(. 09146)
p11850
(2) Common barley yield
(. 17696)(. 16833) + (.39477)(. 18802) + (.69459)(. 11262)
= .
18223
(3) Naked barley yield
EYP = (.04273)(. 30888) + (.34457)(. 09278) + (.60697)(. 20696)
=
.
17079
The result clearly indicates that the response of yield to changes
in relative product price is greater in barley than in rice cultivation.
34
In other words, the average Korean farmer tends to respond more
sensitively in his grain production plan for barley when he is faced
with price changes. This does not mean that greater emphasis was
placed on barley growing in the past. Fact is rather contrary, the
highest priority was given all the time to rice and it will continue to be
so. The result of the analysis reported above only implies that the
additional contribution made by additional use of input factors will be
greater for barley and at the same time changes in relative price of
product will induce farmers to add more productive factors to barley,
thus leading to greater proportional increases in yield.
Response of Planted Acreage
Adopted functional form is also that of power function. In order
to obtain the short-run (more suitably intermediate) and the long-run
elasticities, the current planted acreage is regressed on product
price and previous year's planted acreage. Data used are the aggregate planted acreage for 196 1-1971, resulting in 11 observations. The
results are:
(1) Rice acreage
A=
2174(P3z5:181 A4936
At 464.5())
(R2 = .724)
(R2 = .686)
35
(2) Common barley acreage
)°) A691
20(
At
(R2 = .679)
(R2
At
.534)
(3) Naked barley acreage
At
At
.
86(
168. 9(
(i) A7293
(R2
)1
(R2 = .162)
= .
824)
(4) All barley acreage
At
19. 1(
Bt1 .13448 A48084
PPFI (2. 102) t1(245Q)
(R2
.717)
The estimated price elasticity of rice acreage seems somewhat
high, considering the limited availability of land. As shown in Table
2-4, the area of fullyirrigated paddy land that is benefited by the
government established facilities has been steadily increasing, while
that of rain-fed paddy shows a downward trend. The main fluctuation
originates in the area of partially irrigated paddy which are irrigated
by the village constructed commoi facilities or indivdua1 farmers'
own facilities.
r.i
Table 2-4. Acreage of paddy land by irrigation status, by
year, 1961-1970. Unit: 1,000 Ha.
Partially
Irrigated
Fully
Year
Irrigated
1961
1962
1963
1964
1965
1966
1967
238
256
264
277
284
288
295
238
298
307
1968
1969
1970
Rainfed
Total
230
218
215
208
200
1132
1143
1158
1191
1209
1209
1214
1136
1208
1193
664
669
679
706
725
740
740
705
769
773
181
179
146
141
113
Source: Agricultural Statistics Yearbook, 1970, Ministry
of Agriculture and Forestry.
The estimated price elasticity of the acreage of partially
irrigated paddy land is computed as:
A
4.44( Rt-1 .18627 A64813
PPFI (1.652) t1(957)
(R2
.776)
The estimated short-run and long-run elasticities are . 19 and
53, respectively, for partially irrigated paddy land and those for all
barley acreage are . 13 and 26, respectively. The adjustment coef.
ficient for all barley acreage, however, is higher than that for rice
acreage, indicating a faster response to price changes. In the computation of aggregate supply response, the elasticity estimated with only
price variable is taken into account.
37
Aggregate Supply Response
Resulting aggregate response for rice is .3Z6, those for corn-.
mon barley and naked barley are .470 and .385, respectively.
Approximately, 10 percent increase in product price will induce
farmers to increase rice production by 3 percent, common barley by
5:percent and naked barley by 4 percent.
Conclusion
The empirical analysis supports the initial hypothesis that
average Korean farmers do respond in their grain production to
changes in relative product price and they tend to be more responsive
in barley cultivation than in rice. As stated above, this implies a
possibility that an appropriate price policy in favor of barley would
contribute relatively more to achieving increased foodgrain production. Especially, the higher yield response for barley indicates a
potential to improve the average productivity of land and other produc-
tive factors.
In interpreting the empirical results based on the past observations, however, one must not overlook the fact that, even if the fitted
function is accurate and the estimated coefficients are statistically
significant1 they can only serve as an indicator in the neighborhood of
the statistical population to which they refer. More specifically, the
I]
estimated elasticity may not be able to predict what the output
response would be if the relative price of product doubles, for such an
increase in price has never been experienced by Korean farmers and
is very unlikely to occur in the near future. In a statistical sense,
such an increase in price is too far from the population which an
empirical study intends to explain.
In measuring the response of farmers in production decisions,
the productivity of resources which
is
conditioned by the prevailing
level of technology was held constant by using the cross-sectional
production function approach. Therefore, the major responses are
revealed by the farmers' intention to increase the use of productive
factors when the price relationship between product and inputs
becomes favorable. From this we can conclude that even under a
static technological environment, the average Korean farmer does
respond to changes in price condition through changes in the quantity
of input factors used. In the growing agriculture however, the growth
of technology is not independent of the price condition for farm products. Farmers will become more willing to adopt the improved
methods of cultivation if the terms of trade for their products become
favorable than otherwise. In conjunction with other development
policy, price incentives play an important role in disseminating the
improved technology in the farm area. The improved technology
accompanies by definition an increase in productivity of farming
resources employed. Within the context of the empirical analysis
presented in this chapter, this has an effect of increasing the productivity coefficients estimated in the production function. This implies
that the true response of intendedT! output can possibly be greater
than those estimated here. Thus, price incentives have double effects
in increasing foodgrain production fast adoption of improved
tech-
nology by farm producers and increased use of productive factors,
both non-purchased and purchased.
40
MARKET ANALYSIS
In the preceding chapter, the analysis was centered on the effects
of grain prices upon the decisions of farm producers in their production activities. The present chapter will deal with various behavioral
phenomena that take place via the market mechanism for the realized
output of food grains in a given consumption period.
Ideally, the function of grain prices can best beanalyzed by
incorporating into one system all the behavioral decisions that govern
resource allocation in production, consumption and sales. As already
stated, however, there usually exists a divergence between production
decisions (or plan) and actual production due to uncontrollable factors
This fact alone gives a sufficient reason, from the analytical point of
view, to separate our grain analysis in this chapter from the production aspects and to carry out the study with the realized output (or
stock) as a starting point.
Given the output (or stock) of foodgrains, a change in the relative
level of grain prices- -whether relative to all other prices as a group
or to one another- -affects not only the total amount of grain consump-
tion but also causes a shift from one kind of grain to another. If grain
price policy is to be effectively used to influence the grain sector of
an economy, it is essential to know in what direction and to what
extent both farm and urban consumers adjust their consumption to
41
changing price condition and how it affects the decision of farm pro-
ducers in their market sales.
There has been a debate among a number of economists as well
as policy makers whether an increase in foodgrain prices in the
subsistence-oriented agriculture would induce farm families to
increase their consumption, resulting in the decrease in market
sales or it would cause an increase in market
sales,'
The underly-
ing argument for the former is that an increase in the prices of food-
grains makes it possible for the farm producers to satisfy their cash
requirements by selling a smaller quantity of foodg rains, leading to a
reduction in the amount of grain coming to the market. This reduction
in sales, in turn, causes an upward pressure on prices and again a
smaller amount on the market, and so on, resulting in the so-called
spiral phenomenon. Implicit in this argument is the assumption that
the farmers have minimum cash requirements and sell only that
quantity necessary to fulfill their minimum cash requirements.
The
latter argument, however, emphasizes the intersector demonstration
effect and hypothesizes an opposite phenomenon.
-'See, for example,P.N. Mathur and H. Ezekiel (28, pp. 399)
andV. Dubey(6, pp. 696).
'Mathur and Ezekiel hold that, although the farmer's demand
for cash income is not necessarily completely fixed, it is more nearly
fixed compared to demand for food consumption (28, pp. 398),
42
There is no a priori case for judging whether or not such a
phenomenon takes place. It can be posed only as an empirical question. 10/
In short, the present study attempts to answer the following
basic questions:
1) Do farm and urban consumers respond to an appreciable
degree to changes in the price of foodgrains in their foodgrain consumption?
2) What is the magnitude of the cross substitution effect between
the major foodgrains, rice and barley, when the relative
price of either one of the grains changes?
3) Do farm producers, when they are faced with an increasing
price for grains, consume more of their output (or stock)
because of an improvement in income, or do they sell more
in order to acquire a greater amount of cash receipts?
These questions are posed not as hypotheses of purely academic
interest but as practical policy questions which the Korean government
is facing in formulating foodgrain price policy. The questions are
especially highlighted by the recent movement of the Korean govern-
ment to reform the foodgrain price policy in the direction of a two
the writer's knowledge, essentially no empirical study
was made as to the response of farm marketings of foodgrains to
changes in cash income due to changes in foodgrain prices.
43
price system, aiming at a reduction in the consumption of rice, and
an increase in the consumption of barley, but at the same time an
increase in production of both grains.
In carrying out an empirical analysis concerning the subject,
special attention must be given to a peculiar feature emerging from
the dual role of farmers; as consumers on the one hand and as sellers
of products on the other. In Korean agriculture where the foodgrains
constitute major items in farm production and where nearly one-half
of the total grain output is consumed by farm families themselves, the
behavioral patterns regarding farm consumption and sales cannot be
studied in isolation from each other. The decision to consume from
the given stock of grain necessarily affects the opportunity to obtain
cash income that is required to purchase the non-agricultural corn-
modities and services, such as clothing, education, etc. Conversely,
an increase in market sales in order to increase cash receipts means
a decrease in family consumption of home-produced foodgrains. In
short, the farm household economy is characterized by the integration
of two decision units into one. Further, the market prices of food-
grains are not determined by the decision of farmers to sell alone.
They depend on the amount of foodgrains demanded by the urban con-
sumers, too.
Therefore, in understanding the impact of grain prices, it is
necessary to take into consideration the joint-dependence of farm
44
consumption, market sales and urban consumption)"
In constructing a simultaneous equation mode1 an appropriate
modification of the conventional theory of demand is attempted in
order to reflect the dualistic feature of the farm household in Korean
agriculture. Then the effects of foodgrain prices are analyzed on the
basis of the estimated results.
Model Construction
The model adopted is a simultaneous equation system based on
the market equilibrium concept. The system consists of eight equations: six behavioral equations comprising two farm demand, two
farm sales and two urban demand equations. each for rice and barley
respectively, and two market identity equations for rice and barley.
The system contains the total of 21 variables, of which eight are
endogenous and the rest are treated as exogenous variables.
The characteristics of each variable will be explained as we
proceed with the specification of each equation.
necessity of simultaneoas consideration of related variables is well stressed by M.A. Girshick and T. Haavelmo (10, pp.83)
it is impossible to derive
in the general context as follows: ".
statistically the demand functions from market data without specification of the supply functions involved.. More generally, if we wish to
estimate any particular economic relationship on the basis of market
data we are forced to consider, simultaneously, the whole system of
economic relations that together represent the mechanism that produces the data we observe in the market.
.
.
45
Farm Demand
The ordinary theory of demand hypothesizes that the demand for
a commodity is a function of the price of the commodity under consid-
eration, the prices of related goods and the disposable income of the
consumer. It is also shown that the total effect of a change in the
price of the commodity upon the quantity demanded, assuming constant
money income, can be analytically decomposed into two component
effects: the substitution effect involving a movement along the original
indifference curve if a price change is compensated by a simultaneous
income change and the real income effect producing a movement along
anEngel curve {14,36].
The following Slutzky equation represents the decomposition of
the total effect of price changes.
(3. 1)
aq
api = (-)
3q1
The first term on the right hand side is the substitution effect with the
Happarent real incomeT' unchanged and the second term the income
effect with price unchanged.
keep apparent real income constant implies that the consumer is always enabled to purchase the original bundle, and it is a
good approximation to real income where price changes are not so
great [8].
46
The basic assumption in the above analytical process is that the
consumer is the purchaser of the commodity with given money income
which is earned independently of the price of that commodity. The
level of money income has no direct relation to changes in the price
of the commodity demanded, except that the real income (as defined
by the purchasing power of money income) is liable to vary as a result
of price changes.
However, if the consumer is at the same time the seller of the
same commodity, as is the average Korean farmer, and a major portion of his money income comes from the sale of that commodity which
otherwise could be consumed bythe family, money income is no
longer independent of changes in the price.
Hence, in analyzing the consumption behavior of the seller-
consumer, it is necessary to take into accountthe effect of price
changes upon money income.
Given the following demand function,
p1
1
2
where
Y
and
q1
g(p1)
denotes the quantity demanded,
moneyincome,
I
p1
the price index excluding
the price,
Y the
and
the
p1
I
47
Lespeyres price index, the total effect of price changes can be decomposed as;
aq1
aq
q1()
i
(3. 2)
ap1
-
=
a q1
y
i
,
--'Mathematical derivation is given as follows; given demand
function
f(,
p1
q1
=
I
L
where
Y
g(p1)
Taking derivative for both sides with respect to
p1
Y
a()
a()
a q1
Ii
p1
ap1
a
f
1
a
f
a
Y
r
a(jX)
i
12
af
+
ap1
_L
we get
p1,
2
2
a,2
ap1
-_.
[ap1
'2
2
f.
p1
a()
Ii
+
af
12
12
Y
a
If we assume that apparent real income remains constant so that the
consumer can buy the original bundle of goods (following Slutzky),
then we can rewrite
aq1
af
ap1
a
+
q1
ap1
Y
Ii
Further, by setting the price indices
2
I
and
12
for the initial
The first two terms on the right-hand side represent the substitution effect and the income effect, both in the Slutzky sense, and the
third term represents the direct money income effect due to a change
in sale price. If the money income
price
p1
i.e. , if
Equation (3. 2)
aY
p1
0,
Y
is not dependent upon the
then the third term drops out and the
reduces exactly to the Slutsky Equation (3. 1).
Therefore, the Slutsky equation explains a special case of the purchaser with the given money income. It does not explain the behav-
ioral responses to price changes of the seller-consumer. The total
effect of a price change on consumption now depends not only on the
sign but also on the relative magnitude of each term on the right-hand
side of Equation (3. 2).
When faced with changing prices of grain at a given point in
time, the farmer has three alternative choices concerning the disposal of his grain stock, He has to decide how much to consume and
how much to sell, and/or how much to retain for future sales and
consumption. Suppose the price of rice rises due to a certain exter-
nal cause, then two opposite effects are conceivable. The increase in
cash receipts due to a rise in the price of grain may induce an
period to unity, we obtain
aq1
1.
8q1
aq1
= ()1 apparent
real income
= constant
q(
)
p1p10 +
1
p1-p1
I-
49
increase in the consumption of that grain. Or the higher cash receipts
due to the higher price may create a desire for a still higher cash
income which could only be acquired by reducing home consumption
and selling more on the market.
The case of the seller-consumer is illustrated in Figure 3-1.
M
M0
M2
p1
M1
0
q4
q3q2q1q0
q*
Figure 3-1. Effect of price changes on the consumption of the sellerconsumer.
For illustrative purpose, let us assume that the farmer disposes
of his grain stock Oq* in a given period by consuming and selling,
that is, he does not retain any grain at the end of the period. Let the
vertical axis OM represent all other goods lumped together. Given
the preference pattern between consumption and other goods, he will
consume
0q1
units of grain at the price of
units of other goods by selling
of grain rises from
0q2
p1
to
p1
and obtain
0M1
units of grain. Now the price
q1q*
p2. At this price he will consume
units of grain (the above diagram is drawn in such a way that the
net result is a decrease in consumption, but it could be an increase)
and sell q2q* units of grain to obtain 0M2
units of other goods.
The total movement from the initial consumption
sumption q2
q1
to the new con-
is attributable to a dualistic characteristic of this
consumer. Let us for a moment treat him as if he were a buyer of
grain with a given income 0M0,
then he would consume
of grain at the higher price p2. This movement from
can be decomposed into the substitution effect, i.e. ,
and the real income effect,
i. e. ,
q3
to
0q4 units
to
q1
q1
to
q4
q3,
q4.
These two effects are represented by the Slutzky Equation
(3.1).
or by the first two terms on the right-hand side of Equation (3.2),.
For the seller-consumer there is an additional effect due to the
increase in monetary receipts. This monetary income effect causes
the movement from
term ip1
q4
to
q2
which is explained by the third
in Equation (3. 2).
Whether or not the counteracting effect of the increase in mone-
tary receipts is dominant to the direct effect of price changes is
51
subject to an empirical test in this study.
Another point with which one must take special care in formula-
ting the statistical demand function for farm households is the vagueness of the concept of farm income. In semi-monetized and selfemployed farming a large portion of farm income consists of non-cash
items. The measurement of farm income is difficult especially when
an analytical study is made on the basis of monthly or seasonal
observations, for the largest part of income, whether in cash or in
kind, is realized only after the crop harvest. During the non-harvest
season they consume and sell from the stock of grain, partially
depending on the cash income they earn off-farm.
Seasonal characteristics of farm behavior should also be considered in specifying farm demand and sales equations. There is a
tendency for farm consumers to consume more grain when more is
available at the harvest and immediate post-harvest seasons and less
when less is available during the rest of the year. It is also reason-
able to assume that the farmers respond to price changes differently
in different seasons both in consumption and sales, that is, the slope
of demand and sales curve may vary depending on different
seasons'
The discussion thus far leads to the formulation of the farm
demand functions as follows:
Seasonal responses are not discussed in the main text, but
the estimated model is presented in Appendix B for reference.
52
(3.3)
q
FK
FD
b0 + bUPRt + blZPBt +bi3Pw + b14q1
FS
FD
(3. 4)
+ b16D3 + U1
FK
=
b20 + bZlPBt + bZZPRt + bZ3PWt + b24q1
+
FS
+ b26D2 + U2
Where
FD
=
Monthly farm per capita consumption of rice.
=
Monthly farm per capita consumption of barley.
=
Monthly farm per capita sales of rice.
Monthly farm per capita sales of barley.
FS
Rt
=
Monthly average wholesale price of rice deflated by the
Index of non-grain wholesale prices.
Monthly average wholesale price of barley deflated by
the Index of non-grain wholesale prices.
Monthly average wholesale price of wheat flour deflated
by the Index of non-grain wholesale prices.
FK
=
Farm per capita stock of rice on hand at the end of the
previous month.
qc1
Farm per capita stock of barley on hand at the end of the
previous month.
53
NRB
Monthly farm per capita income originating in the non-
rice-barley sources deflated by the Index of prices paid
by farmers.
= Random error terms.
b..
13
t
Behavioral parameters to be estimated.
Current period.
if February-May period
=1
= 0 otherwise.
D3 = 1 if June-September period (barley harvest affected
season)
= 0 otherwise.
All quantity variables are deflated by the. ratio of urban to farm
population
in order to maintain consistency with the
market identities (to be explained later).
The above two farm demand equations contain five endogenous
FD, FD
variables, namely,
R'
F5
FS
Bt
and five exogenous variables including seasonal dummies,
and
D3. 15/
FS +Pq FS
The composite term (Pq
15/
Though
FS
and
FS
)
which appears in both
are endogenous and 'NRB exogenous variables in the model, the whole composite. term is treated as
a single endogenous variable.
q
54
demand equations represents the cash receipts from sales of rice and
barley and it is obvious that the cash receipts is determined by the
level of prices for both grains and the quantity sold on the market.
The quantity sold of each grain in turn depends on the prices of grains
and other relevant variables as will be specified in the sales equation
later. Thus the measurement of the money income effect requires a
simultaneous consideration of all the variables in the system. It is of
analytical interest, however, to show how the estimated coefficient on
this composite term relates to the money income effect which is represented by the third term in Equation (3..2),
under a certain quali-
fication.
Assuming other variables remain unchanged the total response
of the quantity demanded, say for rice, can be measured by
FD
= b11 +b5q FS
A
A
one can clearly see, the estimated b
A
the ordinary demand curve, and b15 FS
represents the slope of
the response to a unit
change in money income due to price changes. To break it down, b15
aq1
corresponds to
()p1
p, and
term with the price remaining constant, i. e.
q
money income effect,
to
p1
in Equation (3. 2);
Whether the
(-)p1 p1 , offsets or reinforces the
direct effect of a price change depends on the sign and the magnitude
55
FS
of each coefficient, and
Farm Sales
The shortrun decision to sell from a given stock is hypothesized
as a function of the current price of the grain under consideration, the
size of stock on hand, the amount of liabilities as of the end of the
previous period, cash expenditures for non-farm source goods and
services, and the value of the stock of major grains rice and barley,
evaluated at the current prices.
FS
(3.5)
qRt
FK
L +b34E+b351
P +b3ZBt
P +b33t1
-b 30 +b3lRt
+
FK
+
FK
+ U3
1
FS
(3.6)
FK
b40 + b4JPBt + b42PR +b43L 1+ b44Et + b45J
+
FK
+
FK
+ U4
1
where:
L
= Farm per capita liabilities as of the end of the previous
month deflated by the Index of prices paid by farmers.
Farm per capita cash expenditures such as for childrens
education, clothing, etc. deflated by the Index of prices
paid by farmers.
tJ3, tJ4
Random error terms.
Other variables are the same as specified in the demand equations.
Insofar as the main objective of selling grains is to meet the
cash requirements such as for education of children and purchase of
goods and services from the nonfarm sector, an ideal thing to do is to
use the total cash demand as an explanatory variable in the sales
function.
But since it is almost impossible to identify the actual cash
demand from the observed time-series data, the farm debt and other
cash expenses are used as a proxy in order to reflect at least the
major part of farmers' monetary demand
Theoretically the outlay of cash expenses is determined by the
level of farm income rather than vice versa. But in the shortrun it is
reasonable to assume that the educational expenses and purchase of
clothing s, etc. are relatively stable
The introduction of the term
what analogous to that of the term
16 /
FK
FS
FK
FS
is somein
the demand equations. In the sales equations it was hypothesized that
farmers' decisions for market sales are affected by the value of the
--"This should not be taken to mean that farmers' demand for
cash for all purposes is fixed in the shortrun. It only means that the
cash spendings for certain purposes arerelatively stable. The fact
is that the total outlay varies even on a monthly basis depending on the
price level for farm products and off-farm earnings.
57
grain inventory, whereas the decision to consume was hypothesized to
depend on the direct cash receipts plus the off-farm earnings.JV
In understanding the sales decisions of the farmer, it is necessary to understand the saving pattern that prevails in the rural area.
Even though the monetization of the rural economy has proceeded
along with overall economic development, the average Korean farmer
still has a tendency to save in kind rather than in the form of cash,
whenever immediate cash need is satisfied.-
In the face of chang-
ing prices of grain plus some uncertainty about the next crop, the
!ZiSince PR and
are the averageprices during the curthe beginning stock of the curand
rent month while
FK
FK
does not represent the
rent month, the term
actual value of the current stock. A closer approximation could be
obtained by replacing the beginning stock by the average stock holding
FK
by
during the current month, that is,
1
FK
FTC
FD
FD FS
FS
or
and
FTC
2
FD FS
FTC
by
FTC
2
Considering, however, that a similar pattern of variation in all three
variables is repeated every year, the estimated coefficient may not
differ significantly.
immediate cash need does not mean the minimum cash
need. One may think that a continuous inflation is another factor which
discourages the monetary saving in general. But it is doubtful that
monthly sales of grain are affected by inflation. It may influence the
longer term saving patterns at least longer than a month.
1The
58
farmer may feel safer with foodgrain in storage. When the price of
grain, say rice, rises, he may expect a further rise in the future and
feel that his saving potentialities increase with the increased value of
his stock, thus selling a smaller quantity of rice. Or conversely, the
increased saving which is realized by the increased value of stock may
make the farmer feel richer and accelerate the sale on the market to
obtain more cash so that he can consume more of the non-agricultural
commodities and services. In which direction the farmer tends to
move depends on whether or not the response to an increase in the
value of his grain stock offsets the increased desire for more cash.
The total effect on rice sales of one unit change in the price of
rice can be measured by
FS
= b31 +
The estimated parameter
b3
1
curve (in the ordinary sense) and
A
FK
represents the slope o the supply
represents the effect of
the stock value. The rate of response in sales is affected by the size
of stock on hand. On the other hand, the size of stock which the
farmer can maintain is dependent upon the storage capacity. The
inadequacy of storage facilities is one of the important factors which
force the average Korean farmer to sell, particularly in the iminediate post-harvest season, a large portion of his output. Thus the size
59
of the stock itself tends to have a positive effect on the amount of
sales, probably offsetting the propensity to save in kind due to an
increase in stock value.
This counteracting effect of the grain stock is measured by
FS
A
FK
aq1
A
= b35 + b36PR
Notice that it is a function of the price level for rice.
This
implies that the sales of rice respond to a change in stock differently
at different price levels.
Urban Demand
The urban demand for foodgrairi is hypothesized to depend on its
price, the prices of related goods and disposable income. In order
for the demand function to be empirically useful, one must be able to
make statements about the preference function of the consumer at
varying level of prices and money income.-' More specifically the
urban consumer's preference pattern may be such that he may respond
differently to a given change in price at different levels of income and
!2i'Based on the lecture notes for Agricultural Economics 517:
Product and Factor Market taught by John A. Edwards, Department of
Agricultural Economics at Oregon State University, 1971.
also respond differently to a given change in income at different levels
of prices. An inference about such preference function is analytically
possible by introducing an interaction term between the price of the
foodgrain consumed and disposable income.
The urban demand equations for rice and barley would be written
(3.7)
b50 + b51PR + b5ZPBt + b53Pw+ bs4Yut
+ b55(PRtYUt) + TJ5
(3. 8)
q
UD
b60 + b6lPBt + b6zPRt + b63Pwt + b64Yut
+ b65(PBtYUt) + b66DG + U6
Where:
qTJD
UD
= Monthly urban per capita consumption of rice.
= Monthly urban per capita consumption of barley.
Monthly urban per capita income deflated by the Index of
urban consumer prices.
U5, U6
Random error terms.
1
if 1963-64 period
0
if otherwise.
The dummy DG
in the barley demand equation reflects a
special grain situation that lasted for two years, 1963 through 1964.
Due to the two successive, crop failures for 1962 rice and 1963 barley,
61
the Korean government initiated a nationwide campaign to encourage
the increased consumption of imported barley and wheat flour in the
urban area.
The estimated effect of a change in the price of rice, for
example, on urban consumption is given by
UD
A
aPR
b51 + bssYu
Which implies that, assuming other things remain constant, the
urban consurners response to price change varies depending on the
level of income Y.
Similarly the effect of income changes is estimated by
UD
A
aY
A
= b54 + b55PR
As already stated, the response of consumption to a change in
income dffers at a different level of the price of rice.
From the above results, we can approximate the level of income
and the level of the price of rice at which the urban consumer reaches
the HsatietyT point in rice consumption; the htsatietyu point defined as
the potential consumption at which the consumer does not respond to
changes in income and price. These levels of income and price can
be derived by setting eac1i of the above partial derivatives equal to zero.
62
b55
Market Identities
The total disappearance from the farm stock is the realized
supply of domestic grain available for both farm and urban consumers
during the period concerned, assuming that the grain held by whole-
salers and that in pipeline remain constant over time. This is a
somewhat rigid assumption because there are always some whole-
salers who buy a lump sum quantity from farmers at the harvest time
and sell in varying quantity during the period of several months
depending on the price condition. This inventory operation of whole-
salers causes a time lag between the release of grain from farmers
and the actual consumption by urban consumers. The inclusion of the
marketing function that specifies the operational behavior of wholesalers would improve the quality of our equation system. But it could
not be done due to a lack of statistical information. Besides, a distortion caused by the exclusion of such a marketing function may not
be big enough to make the whole system of equilibrium invalid.
63
Let
The total farm stock of rice at the beginning of the
MQ
current month.
MQK
= The total farm stock of rice at the end of the current
month.
The total consumption of rice by farm population
MQ
during the current month.
The total consumption of rice by urban population
MQ
during the current month.
= The total imports of foreign rice during the current
month.
= The total purchase of rice by government during the
MQ
current month.
GS
MQRt
= The total sales from the government stock during the
current month.
MQS
= The total salesof rice from the farm stock during the
current month.
MQ
The total exports of rice during the current month.
Then, the following identity relation must hold by definition:
MQ1 MQ
=
= MQ
+ MQ
+ MQ
Since MQ
+
MQS
= MQ
+ MQ
+ MQ
+ MQ
appears on both sides, we can write
FS
I
+ MQ
= MQ
MQRt + MQRt + MQ
+ MQt
Reducing to urban per capita basis, we obtain
FS
(3. 9)
I
+q
+
UD
GS
=
+q
GD
+
E
Likewise, for barley
FS
(3. 10)
+q
I
+
UD
GS
=
+
GD
Notice that the farm demand variables
E
+q
q
FD
and
q
FD
do not
appear in the market identities. This does not mean that the farm
demand is wholly unrelated to determining equilibrium condition in the
market. As already specified in the demand and sales equations, the
quantity demanded and that sold are jointly dependent upon the prices
of both grains and the size of grain stock on hand.
Complete Model
To rewrite the whole system of simultaneous equations:
(Farm demand)
FR
FD
(3.3)
b10 +bllPRt +blzPBt+bl3Pwt +b14q1
Is +YNRB) + b16D3 + U1
+
FD
FR
-b 20 +b21PBt +b22PRt +b23PWt +b 24Bt_1
IS
+
NRB
)+b26D 2+U
(Farm sales)
FS
(3.5)
b30+b3lPRt
FR
3ZPBt+b33Ltl +b34E+b351
+
FS
(3.6)
i+
FR
Bt i
+
b40 + b4lPBt + b4ZPRt + b43Lt
+
FR
FR
1
FR
+ b44E + b451
+ U4
(Urban demand)
(3.7)
= b50 + bSlPRt + bSZPBt + b53Pwt + bS4YUt
+ bsS(PRtYUt) + U5
(3.8)
q
UJD
Bt
b
60
+b
61PBt + b 62PRt + b63Pwt + b64Yut
+ b6S(PBtYU) + U6
I__
66
(Market identity)
FS
(3.9)
q
(3. 10)
q
FS
+q
+
I
+
I
+
GSUD
UD
GS
=
+
+
GD
GD
+
+
E
E
Since the structure is nonlinear in variables, though linear in
parameters, the reduced form obtained by solving it will be non-
linear in variables, and the solution may be complicated involving
multiple roots.
It has been suggested by Klein [16, pp. 120-121] that replace-
ment of these nonlinear terms by their observed sample means makes
it possible to obtain an explicit solution. The nonlinear terms in the
model were transformed into linear approximations according to the
formula given by Klein. The formula is:
xYYx+xY-xY
Linearized terms are:
FS
+
FS
+
NRB
=
FS
FS+ FS
+
+
FS FS---PB+YNRB
FK
FK
+ Pq1
= FK 1R +
FK
+
FK
1B
FK FK FK-
FS
The market price of rice,
can be determined by substitut-
ing the farm sales Equation (3. 5) and the urban demand Equation
(3.7) for rice into themarket identity (3. 9), and likewise that of barley,
by substituting the farm sales Equation (3.6) and the urban
B
demand equation for barley into the market identity (3. 10), and then
solving (3.9) and (3. 10) simultaneously. Next, we can solve for
FS
q
FS
,
q
UD
and
UD
by substituting the
obtained in the first step. Last step is to substitute
R'
pqFS
into Equations (3.3) and (3.4) in order to get
and
qFD
and
and
Hence, the system is complete as an econometric model.
Identification of the Model
Order Condition
The identification status of each behavioral equation is determined by the following rule [161.
Let
G
= Number of endogenous variables which appear in the
equation.
= Number of exogenous variables which are excluded from
the equation.
Then
(1)
If K* >G - 1,
we have overidentification.
(2)
If
K** =
- 1,
(3)
If
K** <
1,
we have exact identification.
we have unidentification.
The composite variable which appears in each equation must be
treated as a single endogenous variable, since they all contain multiplicative terms in one or more endogenous variables. The result of
identification status for each equation is given by:
Equation
-
3. 3
3. 4
3
3
3.5
3
Status
Over identified
Overidentified
Overidentified
Over identified
Overidentified
Overidentified
1
3. 6
3
3.7
3
6
6
6
6
7
3. 8
3
7
Rank Condition
The general rule is that an equation in a linear model- -linear in
parameters- -of
G
equations is identified if and only if at least one
nonzero determinant of G -
1
rows and columns is contained in the
array of coefficients formed by omitting the rows of coefficients of the
equation in question and omitting all columns not having zero in that
equation
[411.
Let
FS +
Yv
FK,
FS +
FK
NRB
OS
OS
GS
GD
E
I
GS
qB+qB
GD
E
I
Then, the array of all the coefficients in the model is as on the following page, each column being headed by its variable (the column of
constant terms is excluded).
For Equation (3.3), for example, the matrix of coefficients
relevant to the rank conditionsi.s shown on page 71. From this
matrix, eight 7 x 7 non-zero determinants can be formed. Hence,
the rank condition for the farm demand Equation (3.3) is satisfied.
By the same procedure one can easily show that the rank condition is
satisfied for the rest of the equations.
Empirical Results
Data
The price variables employed are the monthly wholesale price
series for rice, barley and wheat flour developed by the Bank of
Korea. The data are deflated by the Index of non-grain wholesale
prices so as to reflect the real prices relative to the prices of nongrain commodities.
The common use of the wholesale price series causes an upward
bias for thefarrn-level demand and sales equations, and downward
Equ
Fb FD FS
PS
TJD
LID
F1
Os
Os
b22
21
(3.5)
o
o
-1
o
o
(3.6)
o
o
o
-1
o
(3.7)
o
0
0
0
-1
(3.8)
0
0
0
0
0
(3.9)
(3.10)
0
0
o
o
b3
o
o
b3
b32
o
o
b
o
o
b42
b41
0
0
b
o
b5
-10
0
o
b
b
o
0
1
o
0_i
0
1
-1
23
0
6S
24
o
b
b3
o
b
b43
b
b52
b53
0
b
b
61
62
63
o
0
0
0
0
o
0
0
0
0
o
0
b3
o
0
0
o
o
o
o
o
o
0
o
0
o
b5
0
0
0
o
0
b
0
o
0
o
b
64
0
0
o
0
0
0
0
0
66
o
0
0
0
0_i
0
0
0
-1
0
FD
FS
FS
UD
UD
V
-1
o
0
o
0
-1
0
0
0
0
RU BU
0
Q
0
0
FK
as
L
E
0
0
0
0
b34
0
Y
q
as
D2
DG
0
1
0
0
0
0
0
0
0
0
b36
0
0
0
-1
0
0
b46
0
0
b45
b43
b44
0
0
0
0
0
0
-1
0
0
b55
0
0
0
0
b54
0
0
0
0
0
0
0
b66
0
0
0
0
-1
0
0
b65
0
0
0
b64
0
-1
0
1
0
0
0
0
0
0
0
0
-1
0
0
0
-1
0
1
0
0
0
0
0
0
0
0
-1
0
0
0
0
1
72
bias for those in the urban demand equations, for the prices actually
received by farmers tend to be lower and the prices actually paid by
urban consumers (consumer price) tend to be higher than the whole-
sale prices.
Provided that the marketing margin is constant over time, as
one can expect it to be, if there is no particular demand for marketing
services for foodgrain, a certain proportional relationship can be
derived between the wholesale prices, the prices received by farmers
and the urban consumer prices. Based on the hitorical trend from
1963 to 1971, the following relationships have been obtained.
= 1. O4P
(R2 = .995)
P
= .
(R2 = .991)
P
= .
(122. 79)
P
= 1. O4P
(15805)
96P
(R2
(334. 02)
94P
966)
.989)
(238.3)
Where the superscript W denotes the wholesale price,
prices received by farmer, and
(R2
.
U,
F the
the urban consumer price.
'Since the regression line was estimated forcing through the
origin, R2's were corrected in order to make comparable according to the following formula:
2
Y-bX.Y.
11
1
73
By using the above relationships, one can transform the original
equations expressed entirely in terms of wholesale prices to equations
expressed in terms of the prices received by farmer and the urban
consumer prices.
Per capita consumption data were drawn from the records of
The Grain Consumption Survey, 1963-71, conducted by the Ministry
of Agriculture and Forestry. Farm per capita sales of foodgrains,
stock on hand, liabilities, cash expenses and non-rice-barley income
data are based on the records of Farm Household Economy Survey,
1963-7 1.
Farm per capita liabilities, cash expenses and non-rice-
barley income are deflated by the Index of Prices Paid by Farmers.
Urban per capita income data were obtained from the records of the
Urban Household Expenditure Survey, 1963-71, of the Bureau of
Statistics. It was deflated by the Index of urban consumer prices.
In fitting the model to the observed data, the farm-level quantity
variables, i.e., per capita consumption, sales and stock, were
deflated by the ratio of urban to farm population so that all the quantity
variables contained in the behavioral equations were consistent with
those in the market identity equations. T1-ie model is fitted using
monthly data for the period from January 1963 through September
1971, totaling to 105 observations.
74
Estimation Procedure
Two-stage least squares (TSLS) was used to estimate the
behavioral (structural) equations under the following assumptions that
1) the observed data have no measurement errors, 2) the exogenous
variables in each equation are not correlated with the error term in
the equation, 3) the structural error term in each equation is serially
independent and 4) each structural error term follows a normal distribution and has common variance for all observed periods
TSLS method is a most widely used single-equation method for
estimating an overidentified structural equation in a joint-dependent
system of equations. The estimation process consists of two successive applications of the ordinary least squares method. In the first
stage the reduced form equations for each endogenous variable are
estimated and in the second stage the least squares method is again
applied using the predicted value of the endogenous variables obtained
in the first stage as predetermined variables [16]. Since each endoge-
nous variable which appears on the right-hand side of the structural
equations is regressed on all the exogenous variables in the system in
the first stage, this is one of the full-information methods
Possible Source of Errors
The error term in each structural equation may arise not only
75
due to the omission of relevant variables, both economic and noneconomic, but also to a failure in specifying the important economic
relationships in the model. There may be a number of such structural equations which might have improved the predictive accuracy of
the model but were omitted. Important of them are 1) the demand
equations for wheat flour for both farm and urban consumers, 2) the
marketing function that explains the behavior of wholesalers, espe-
cially their inventory operation and 3) the industrial demand for
grains. The omission was inevitable due to a complete lack or insufficiency of data for the period considered. The estimated structural
coefficients are presented in Table 3-1.
Predictive Tests
The ordinary R2 obtained in the second stage regression is
not a workable indicator of the usefulness of an estimated structural
equation.
H. Theil suggests three different methods for testing the
validity of the model--' [33, pp.
56-5811.
"The same ixiethods are also suggested by Christ [5, pp. 385-
408] defining the first method as the ex ante prediction, the second as
the expost reduced-form tests and the third the ex post structural tests.
Footnotes for Table 3-1.
The coefficients for
"B
bf
_FS
=
Cl
=
+
in the original equations are adjusted using the following relationships:
and
= 1,O4P
=
= L.04P
=
-PqFS
+
-
+
.96P
-
_FS
q8
FS _FS-
-
B
FK
+
,-FK
'1B "B +
-Bt-i Rt-1R- - FK
B +
NRB
FK
FK
*** denotes significance at 99% probability level
**
U
U
*
U
U
95%
90%
II
II
U
Figures in parentheses are V-values.
Simple correlation coefficient between observed series and predicted series.
Table 3-1. The estimated structural coefficients
Explanatory varab1e
Constant
Equation
term
(3> qD
(34)
(3.5)
(3.6,)
qFD
FS
19.515
(6.087)
(3,8)
q
R
B
W
Rt-1
qFK
Bt-1
L
t- 1
(NS)
.328850
(8.785)
.007342
(2.703)
-. 117110
.014225
(4.963)
.003991
(1.166)
.372224
(6,656)
(2.555)
.028372
(3.485)
*
(NS)
-21.360
(4.970)
.160111
(1.412)
***
-.048812
(.722)
.101249
(5.162)
(NS)
(NS)
4.707
(4.352)
-.034915
(.116)
-.003913
(.116)
B
qUD
-.35414
(6.277)
qFK
B
***
-12.895
(3.809)
qFS
(37)
R
***
.047675
(8.902)
***
31. 273
(10. 925)
R
*
UB
-
5,760
(1,611)
.00879S
(9.417)
-,443071
(11.014)
***
.237600
(12,995)
.105982
(4.360)
(NS)
-.073949
(.990)
-.008850
(4,589)
(NS)
.003014
(1.357)
*
-.000670
(1.786)
Table 3-1. (continued)
Explanatory variable
Equation
E
Y.E/
Y
PY
d/
D
D
D
-
(3)
(3 4)
(3,6)
FS
(3,7)
(3.8)
.000914
(1.869)
UD
UD
-2.169
.792
.843
765
861
.834
.913
.751
.855
.825
.822
.759
.782
(5.017)
***
qFD
FS
r!'
*
qD
(3.5)
R2
-. 002904
(6.383)
-1, 253
(3. 141)
.011425
(6,020)
-.001043
(2.549)
-.005386
(7.063)
.000091
(1.637)
,
*
-.002350
(3.152
.00006839
(4.984)
(NS)
*
.000295
(.389)
-.00003743
(1.722)
(NS)
.627
(1,055)
(i) One can take the predictions as they are and compare
them with the corresponding observed actual data.
(ii) One can separate the endogenous variables from the
exogenous ones, insert the observed values of the
latter category in the equation system, and derive
conditional predictions of the endogenous values by
means of this system.
(iii) One can insert, for each equation of the model, the
observed values of the right-hand (explanatory) variables, and compare the implied value of the lefthand variable with the observed value.
The first method is a straight forward forecasting by substituting the future year values of exogenous variables. The second
method is the one based on the reduced form equations and the third
is the structural equation tests.
In respect to the relative merits of these alternative methods,
C. Christ [4] states
the reduced form tests can testforecasts, whereas
the structural-equation tests cannot. . . . Thus for
checking forecasts, the reduced-form tests are appropriate; and for testing one or more structural equations
individually for validity in the postsample period, the
structural-equation tests are appropriate.
For the quantity variables the third method of testing was
adopted for the reasons that the main objective of estimating the
structural equations from (3.3) through (3. 8) is to investigate the
behavioral responses to changes in the prices of foodgrain in consurnp-
tion and sales decisions, not to forecast the future movement of the
endogenous variables. Moreover, as long as the predictive test is
79
performed on the basis of the sample period errors, thereduced
form equation and the structural equation testsareessentially the
same.
For the price variables,
and
B'
the second method
was used; that is, the reduced form equation for each price was
obtained by means of two market identities (3.9) and (3. 10) and then
the observed values of exogenous variables were inserted to calculate
the predicted series.
22/ Reduced form equations for
U
O579Ol
.272369
1
B
U
are:
.212583
1
R
and
.057901
V
whe r e:
.079523 +
U
OO00398ZY
V
=
.515732
X
=
-46.31 + .o08850P
+
Z
=
.
05Z635q
OOOO7lZ4Y
FK
.
9.92 + 003Ol4P
+
.
+ .00235Oy
03303Zq FK
-
+ .008795L
+
.011425E
E GS GD
I
+
.
000670L
.
0053 86E
E GSGD
004Z42q1+ 050557qT1- 627DG + I(qq+q
.
The predictive ability of each structural equation is summarized
in Figure 3-2 through Figure 3-9 where the corresponding observed
and the predicted series in the sample period are plotted. In order to
see the degree bf deviation between two series, simple correlation
coefficient,
r,
is given for each
set.-1
Interpretation of the Estimated Results
In interpreting the empirical results given in the preceding section, the main emphasis will be placed upon quantification of farm
seller-consumer and the urban consumer responses to changes in the
price of foodgrains. The impact of other variables such as farm debt
(Lti), urban income (Yu), etc. will be discussed only to the extent
that they are closely related to the effects of foodgrain prices.
First, the analysis will be made of the partial responses by
treating each behavioral equation as an independent single equation
under the usual assumption of ceteris paribus. Next, the total behav-
ioral responses will be examined by allowing simultaneous changes in
all endogenous variables within the system. The analysis of the
partial effects of foodgrain prices on farm demand, sales and urban
demand has a practical importance as much as it is of theoretical
Hooper [15] proposed a statistic called the "trace correlation" to measure the proportion of the total variance of the jointly
dependent variable as a group that is explained by the predetermined
variable as a group in a structural model.
N
1]
:30
20
Ui
Ui
-J
0
t'
1963
2
6
2
e
2
6
2
)2
6
f2
6
2
6
YEI-F C'1) MN'H
1964
1965
1966
1967
1968
1969
Figure 3-2. Monthly per capita farm demand for rice, 1963-71.
(FD); r =
Actual
. 843.
1970
1971
0
CL
in
j
-J
Si
I
6
12
6
12
6
12
6
12
6
12
6
12
6
12
6
6
YE(F 1129
1963
1964
1965
1966
1967
1968
1969
Figure 3-3. Monthly per capita farm demand for barley, 1963-71.
Actual (qFD); p p 0 Predicted (AFD) r = . 861.
1970
1971
03
NJ
0
30
2
20
-
Lr'
0
YER OND MSHTHS
1963
1964
1965
1966
1967
1968
1969
Figure 3-4. Monthly per capita farm sales of rice, 1963-71.
Actual (qFS);e n ePredicted (FS); r = .913.
1970
1971
-J
in
r
6
c:i
1963
2
6
.2
.12
6
.12
6
2
2
ñND MNTH1
1964
1965
1966
1967
1968
1969
Figure 3-5. Monthly per capita farm sales of barley, 1963-71.
Actual (qFS); p o opredicted (FS); r =
. 855.
1970
1971
CL:
2::;
a'
Di
s fl:
-J
1) D{.
nr
6
J2
6
1?
1?
6
:
6
Li
6
J2
6
Li
6
Li
YEcP D;'C MflH
1963
1964
1965
1966
1967
1968
1969
Figure 3-6. Monthly per capita urban demand for rice, 1963-7 1.
Actual (q)); c e 0 Predicted (CD); r = . 822.
1970
1971
Ui
N
JT
G
.
2
P
2
.P
YE
1963
1964
1965
1966
I?
2
42
1967
1968
1969
Figure 3-7. Monthly per capita urban demand for barley, 1963-71.
Actual (qUD); o o Predicted (AUD) r = . 782.
1970
1971
a.
..r
'0
./I
j/.
c
r_.
a
p
T
r
;r
1
2
--#----2
1963
1964
1965
--'2
1966
1967
1968
1969
1970
Figure 3-8. Monthly wholesale price of rice per kilo (polished), 1963-71.
Actual
R' o
p
e-Predicted
r = . 800.
1971
2
'Ic
2
1963
6
t
1964
1965
2
1966
1967
6
1968
2
6
1969
12
6
1970
Figure 3-9. Monthly wholesale price of barley per kilo (polished), 1963-71.
Actual B)0 o e-Predicted
r
851.
1?
6
1971
interest. Nearly all the past studies made of the foodgrain price
policy in Korea have been based on single-equation approaches under
the other-things-remain-constant as sumption. The policy suggestions
drawn from such analyses tended to be unrealistic and nonoperational
when brought into actual implementation. One good example is that in
the past the government decided to raise the price of rice through
manipulation of government stock in the hope that the urban consumers
might reduce the consumption of rice and increase that of barley or
wheat flour. The result was contrary to expectations because of the
simultaneous rise in the price of other grains. This experience gives
a lesson that certain conditions, whether institutional or behavioral,
must be fulfilled in order for the cross substitution between different
kinds of grains to take place.
By comparing the results of the partial response analysis against
those of the total response, one may be able to draw policy implications regarding the condition which is required for implementing food-
grain price policy.
Analysis of Partial Effects
Farm Level Demand. A unit increase in the price of rice,
other variables remaining unchanged, has the effect of reducing the
quantity of rice demanded at farm level by
90
aq
-(.354141-. 0009135)
-(.354141-. 008231)
R
aPR
(ES = 9. 01 kilo)
.345910 units
=
This change in per capita consumption is caused by two different
effects; the direct effect of price changes and monetary income effect
24/ The second effect of monetary
due to changes in sales proceeds.
income caused by price changes depends on the quantity of rice the
farmer can afford to sell, which in turn is affected by the price of
rice. Using the linearized equation, if the net effect is evaluated at
the mean value of the sales
ES
over the sample per-
= 9.01 kilos
iod, a unit increase in the price of rice received by farmer tends to
reduce its consumption by approximately . 35 units. Note that the
positive sign of the coefficient for the cash income term
(Pq FS + Pq IS
+
.
NRB
indicates that anincrease in cash income
has an effect of increasing farm consumption. But this positive effect
is too small to offset the direct price effect so that the result (in the
partial sense) is still a reduction in rice consumption when its price
rises. Instead, the consumer tends to increase barley consumption
by
'The substitution and real income effect due to price changes
in Slutzky sense are both entailed in the direct price effect.
91
3q
FD
F
aPR
= (.372224-. OOZ9O4
.346059
)
= (.372224-. 026165)
units
i. e., approximately .35 units (kilos) of barley. Notice that the effect
of the cash income is negative for barley. The net result is a substitution of barley for rice consumption by exactly the same amount.
The effect of a change in the price of barley can be approximated
in a similar manner. When the price of barley goes up by one unit,
the consumer reduces its consumption by
= -(.
ll7llo+.0029o4S) = -(.1l71l0+055466)
-. 172576
instead
a
,
units,
FS = 1.91 kilos)
increases rice consumption by
FD
=
(.32885O+.00O9l35qS)
= .330595
(.328850+. 001745)
units.
The difference of about . 16 units could be explained by a net increase
in total consumption of grain by that amount of rice or by a reduction
in the consumption of other grain. In either case, the above analysis
indicates that the average Korean farm family has a relatively strong
preference toward rice consumption.
92
The response to a change in the price of wheat flour presents a
somewhat interesting case: the quantity of rice demanded increases
whereas that for barley decreases when the price of wheat flour
decreases (the same phenomenon is seen in the case of the urban
demand function to be discussed later), Does this mean that wheat
flour is complementary to rice and substitute to barley? The answer
cannot be given solely on the basis of the sign of the response coeffi-
cient in the estimated equations.
A determination of the commodity relationship requires an
estimation of the demand for wheat flour in the same system so that
the estimated response coefficients can be compared to one
another-'
As already mentioned, this could not be done due to inaccurate data on
wheat flour consumption.
In view of the general consumption pattern in Korea, however, it
is more likely that rice and wheat flour are substitute goods for each
other. A possible explanation for this is that the price of wheat flour
(nearly all the marketed flour is imported) has been controlled by the
government and continuously lowered relative to the prices
of non-
grain items and other grains over the past several years, consequently
Henry Schultz [31, pp. 569-582) shows that in the estimated
and
demand functions for two commodities A and B if
are both positive, then A and B are in the substitute relationship, and if they are both negative, then A and B are complementary to each other.
93
leading to increased consumption both by farm and urban consumers.
In addition, the wheat flour furnished as wage goods in the rural area
through development projects has substantially increased its consump-
tion by farmers. Owing to this relatively abundant supply and its low
price in combination with a strong preference towards rice, the consumer could afford to consume more rice.
Farm Sales
The estimated results indicate that the average
farm producer is much less responsive to changes in the prices of his
products in sales decisions than in consumption. The difference in
response between the two decisions is attributable to the fact that the
farmer is an inventory operator as well as a seller and a consumer.
The quantity of grain he decides not to consume is not necessarily sold
in the same month, but can be held for future consumption or sales.
The partial effect of a change in the price of rice on the market
sale is measured by
FS
FK
(. 160111-. 001043q
)
(
.
160111-. 099753)
.060358
aPR
= 95.64 kilos)
As already pointed out, the slope of sales function, i.e. , the farmerTs
responsiveness to price changes, varies significantly depending on the
size of grain stock he can afford to retain at home. The negative sign
for the coefficient of the stock value implies that the farm seller tends
94
to reduce his market sales--or to put it differently, he tends to save
in form of rice--whenever the price of rice goes up. If the net effect
is measured by setting
at its mean value, a one unit change
in the price of rice induces the farmer to increase his market sales by
.06 units. The mean elasticityof sales with respect to the price is
given by
FSFR
F FS
aPR
48 47
(.160111-.099753)( 9.01
= .324696
(.861317- .536621)
95. 74 kilos)
Which means that a 10% increase in the price of rice will cause a
3. 2%
increase in the market supply of rice. More specifically, when the
price of rice rises by 10% the farmer is motivated to increase the
market sales by approximately 8. 6% in order to acquire more of other.
goods and services, but his feeling of uncertainty for future prices,
combined with a desire to increase his family consumption due to
improved savings position, has a counteracting effect of reducing his
sales by about 5.4%, resulting in 3. 2% net increase in sales.
This negative effect on the rice sales of changes in. stock value
confirms with the positive effect on consumption of an increase in
cash income.
One of the most significant results of the partial analysis is that
the average Korean farmer tends to increase family consumption and
95
to reduce market saleof rice, if the price of rice rises or is raised,
but this tendency is not sufficiently strong enough, as against the
expectation of some economists and policy formulators, to cancel out
the farmer's desire to earn more cash which would enable him to
26/
purchase better clothing and send hLs children to school. Another
explanation is that the need for cash income to purchase non-
agricultural goods and services is so great that in spite of his strong
preference toward rice compared to other grain, he is obliged to
restrict his consumption but increase the market sales.
As for the sale of barley, the net response to its price changes
is given by
FSU
B
B
F FS
aPB
= (-.0039l3+.0046l6)()
.012120
(FS
= 1.91 kilos)
which is smaller than for rice, indicating that the average Korean
farmer is much less sensitive to change in the price of barley.
One noticeable result is that a change in stock value due to a
rise in the price of barley has a positive effect whereas it has a negative effect in the case of rice. This fact again indicates, as already
'The response of farmers to price changes is different
depending on different seasons. The seasonal characteristics of
behavioral responses are not presented here, but the empirical results
based on the dummy response coefficient are given in Appendix B.
noted in the demand equations, that the Korean farmer is partial to
rice consumption.
The increase in farm liabilities and cash expenditures has a
positive effect on rice sales, as is expected, but they both have a
negative effect on barley sales. These opposIte effects of cash demand
(as proxy) are due to the fact that a major portion of cash needs is
met by the sale of rice. The elasticities of rice sales with respect to
farm debt and cash expenditure are 1. 64 and . 72 respectively, while
those of the barley sales are -. 59 and -. i6 respectively.
This gives an interesting sidelight on the possibility of increasing the market supply of rice through credit expansion.
Another interesting finding is that the further away from the
harvest season, the steeper is the slope of sales curve for both
grains, indicating that the farm producers become less sensitive to
market price conditions as their stock is drained. Contrary to this,
the slope of the demand curve becomes greater during the non-harvest
seasons, implying that the farmers respond to price changes more
sensitively when their grain stock is reduced.
Urban Demand. According to the estimated response coeffi-
cients, urban consumers appear to be more responsive to a change in
the price of rice than are farm consumers.
.
27/ In respect to seasonal differences,
See Appendix B.
97
The partial effects of an increase in the price of rice, the
barley price and other variables remaining unchanged, upon rice and
barley consumption are approximated by
&q
UD
XS
apu
R
=
-(.443071-. 00006839Yu)
= -(.443071-. 191150) = -. 251921
(at Yu = Yu
2795 won)
or
-(.443071-. 273560) = -. 169511
(at Yu
4000 Won)
UD
U
237600
3PR
As indicated, the degree of response to price changes varies at
different levels of income. As per capita income grows, price
changes have less influence on the quantity of rice demanded. For
example, at the mean level of income during the sample period,
Yu
2795 won
(unit of Korean currercy), a unit increase in the
price of rice causes about . 25 units decrease in rice consumption, but
at the higher level as 4000 won which is the average of the most recent
two years, the estimated response is approximately . 17 units decrease
On the other hand, a unit. rise in the price of rice tends to
increase the consumption of barley by approximately . 24 units.
The effect of barley price is measured by
UD
aq,
apU
B
073 949+. 00003 743 Yu)
= -(.073949+. 104617)
-. 178566
(at Yu
Yu
2795 won)
or
-(, 073 949+. l49720 = -. 223669
(at Yu = 4000 won)
a
UD
U
B
105982
If the price of barley goes up by one unit, the urban consumer
would decrease his barley consumption by . 18 units and instead
increase rice consumption by . 11
units.'"
Thus, it is obvious that the average urban consumer would
respond to price changes in his rice and barley consumption and that
he would substitute barley for rice or rice for barley whenever the
relative prices of two grains change, if all other variables considered
in our system stayed unchanged.
The estimated elasticities and cross elasticities of demand with
respect to the prices of rice and barley are as follows:
Yu
(all at
Yu)
-According to the criteria shown by H. Schultz (31, pp. 569582), rice and barley are substitute goQds in Korea for both urban and
>0 and
farm consumers, since
B
at
>0
UDU
U TJD
aq
= -1. 11173
48.70 won,
-2. 32273
(P1 = 33.69 won,
= 10.98 kilos)
UD PU
aPB
UD
= 2.59 kilos)
IJDU
U UD
= .325153
= 33.69 won,
= 4. 46688
= 48.70 won,
10.98 kilos)
UDU
U UD
aPR
= 2.59 kilos)
The cross substitution effect between rice and barley due to relative
price changes deserves special attention in the context of foodgrain
price policy in Korea.
The cross-elasticity of demand for barley with respect to the
price of rice is 4. 5, while that for rice with respect to the price of
barley is .33. The former is far greater than the latter, indicating
that the average Korean urban consumer shows a higher responsiveness in barley consumption when the price of rice changes.
From these facts we can hypothesize that there is a potential for
increasing barley consumption and reducing rice consumption in urban
areas, if the appropriate policy measures are taken.
The effect of growth in per capita income on per capita rice
100
consumption is estimated by
UD
-
aYu
-. 002350 + 00006839R
00098
(at
= -. 002350 + .00376 = .00141
(at
= -. 002350 + .00333 =
.
or
UU
=
48.70 won)
= 55.00 won)
Which explains that at different levels of the price of rice, the
consumer's response to income changes is different. The higher the
price level, the greater is the response to income changes, given the
initial income level. Using the elasticity concept, a 10% increase in
income would cause approximately 2. 5% increase in consumption when
the price of rice is
49
won per kilo, but at the higher price of 55 won
per kilo the same percentage increase in income would cause approximately 3.9% increase.
As the price of rice goes up, the consumer is forced to increase
his expenditure on rice purchase if he wants to maintain the same
level of consumption, or the same thing, he would become relatively
poorer due to price rise so that he naturally becomes more susceptible to changes in market condition.
As for barley consumption, the effect of income growth is given
by:
101
UD
B
DYu
000295
B
UU
33.69 won)
= .000295
.001260 = -.001001
(at
= .000295
.001497 = -.001202
(at P = 40.00 won)
or
B
'B
The income effect is negative both at the mean and the recent
level of barley prices and the responsiveness to income changes
becomes greater as the price of barley goes up (same as in rice consumption) but in negative direction. The estimated income elastici-
ties are -1.04 at the price level of 34.00 won and -1.39 at 40.00 won.
The positive response in rice consumption and negative response
in barley consumption indicate that rice is regarded as normal goods
while barley as inferior goods by the average urban consumers.
One important analysis can be made of the partial effect upon
rice consumption of both price and income changes shown previously.
By setting the following derivative at zero, we can approximately
estimate the potential income level at which the rice consumption of
the average urban consumer would not respond at all to changes in the
prices of rice, provided that the present consumption pattern continues. In other words, when such a level of income is attained, the
consumer would reach the "satiety" point in terms of rice consumption.
102
UD
*
= -(.44307 1-. 00006839Yu) = 0
ap
Y
= 6479
won
which is about 1. 5 times as high as the current level of monthly
income. A rise in average per capita income beyond this level would
rather cause a reduction in rice consumption and might accelerate a
shift to other high quality foods, such as meats and processed foods,
even if the price of rice falls.
We can also conceive of a certain price level which is so low
that the average urban consumer would be able to consume as much as
he wants with the present income, that is, the 'satiety'T point in terms
of price.
UD
R
(00235000006839U)
R
8Yu
0
34 . 36 won per kilo
The derivation of the potential "satiety' point is not only of theoretical
interest. The knowledge of such a boundary level of income and price
provide a landmark on the basis of which to judge the degree of urban
consumers' preferences toward rice. It is evident that there still
exists a possibility that the urban demand for rice will continue to
increase in the future as per capita income grows.
103
in the event that per capita income reaches such a level and at
the same time the supply of rice is such that its price is as low as
34. 00 won per kilo, then the growth of population would be the only
factor for increasing the demand for rice.
Analysis of Total Response
The preceding analysis of the partial responses on the basis of
each individual behavioral equation was possible only under the
assumption that other variables and relationships hold constant. But
once everything is left to be determined in the free market other
things cannot remain constant. When the price of rice changes, for
example, due to some external cause or by some kind of government
manipulation, the price of barley, farm level demand, sales and urban
demand will also change. Therefore, it is necessary to devise a
scheme to measure the price-quantity relationship when interactions
among the endogenous variables that occur in the market structure are
taken into account. Such a measurement will enable us to approxi-
mate the simultaneous responsiveness of farm demand, sales and
urban demand for rice and barley to a change, for example, in the
price of rice, the price of barley allowed to vary correspondingly as
the market system requires to reach a new equilibrium level.
Since the main focus of the study is on the effects of the prices
of rice and barley in the context of foodgrain price policy, the effects
104
of other exogenous variables are not analyzed here.
The total response to a change in the price of rice
for
example, on' the farm demand for rice is estimated as follows;"
Let
=
FS ES-- ESPB+YNRB)
ES P+Pq ES+qES
Then, from the farm demand Equation (3.3), we obtain
FD
ED
ED
+
dPR
dPB
8PB dPR
ED
aciR
+
dYF
aYE dPR
The process of the total response to a change in
can be
decomposed into three different component behavioral phenomena
according to the way in which the farm demand function was specified.
The first term
aql)/aP
represents the direct price effect
which induces the consumer to change his consumption, the second
term the repercussion due to simultaneous changes in the price of
barley, and the third term the effect of changes in monetary income.
term contains three endogenous variables
The
and
qFS
(other than
qFS
which are supposed to vary when
Calculation is first based on the estimated coefficients for
wholesale prices, R and B' in each equation, and at final stage
those corrected for farm and urban consumer prices are used.
105
R
changes. Hence,
dYF
FS
dPR
FS
+
R aPR
+
FS
FS
dPB
dPR
+
dPB
B aPB dPR
From the sales equation we can obtain
FS
RS
and
aPB
The procedure requires that the price of rice,
exogenous variable, since we change
R
R'
be treated as an
arbitrarily to measure its
effect. This implies now that the identity Equation (3.9) which repre-
sents the equilibrium condition in the rice market no longer holds. In
order to obtain dPB/dPR we need to express
A reduced form equation for
B
B
as a function of
as a function of
can be
obtained by substituting the barley sales Equation (3.6) and urban
demand equation for barley (3. 8) into the barley market identity
Equation (3. 10). The
dPB
dPR
dP
dP
thus derived is
R
.281072- 000091qK1
.074907 + 00003982Yu+ . OOOO91q'
!QiStrictly speaking, dPB/dPR should be written as
since other exogenous variables such as P' Li and E are held
constant. But as they are regarded as given at the point in time, the
total derivative seems relevant.
106
Note that the rate of change in the price of barley due to a unit change
in the price of rice, assuming other exogenous variables do not
change, is a function of
Yu, FK
q1 and
FK
which are
exogenous variables in the system. More specifically the higher the
level of income and the larger the stock of barley and rice, the
smaller is the influence of a change in the price of rice on the equi-
librium price of barley.
For analytical convenience, we will hold these three exogenous
variables at some constant level. For the size of rice and barley
stock, the mean values during the sample period were used. For the
/
per capita urban income the average of the most recent three years
31/
was used.
The estimated result is
dPB
272369
dPR
.238803
= 1. 140559
Since the monthly variation of the grainstock is repeated
every year with almost similar pattern, though slightly different in
quantity depending on the crop size, the mean value appears to be
reasonable. But the urban per capita income has been steadily
increasing in the future. Therefore, the mean value of the most
recent years seems more adequate.
The values used are:
FK
95.64 kilos
50.72 kilos
Yu
4000 won
107
We are faced with similar problems in obtaining dYF/dPR; this
time with endogenous variables
FS
FS
and
Again by
the same procedures, we set these variables at mean values. Since
the
dqlJ/dY
is as small as .0009135, the computed result is not
much affected by the term (aq/aY)(dY/dP) as shown by the
following:
a
FD
= -.354141
aq
FD
F
dPB
= (.328850)(L 140559) = .375073
F
aPB dPR
a q
FS
= .160111 - (.001043)(95.64)= .060358
FS
-.003913 + (.000091)(5072) = .000703
aPB
aq
FD
dYF
FdP
= .000913519. 01+(44. 81)(. 060358)+(1. 91)(1. 140559)
+ (30.45)(. 000703)(1. 140559)]
= .01274
Therefore,
FD
dP'R
= -.354141 + .375073 + .012714 = .033646
The result is opposite to that estimated in the partial analyses above.
Our estimates in the partial analyses showed that a unit increase in
the price of rice was supposed to cause a reduction of rice consump-
tion by .35 units. However, by dropping the ceteris paribus assumption, a unit increase in the price of rice has the effect of increasing
rice consumption at the farm level, even though by small quantity.
The result apparently seems to confirm the hypothesis of some
economists. Although it is true that an increase in cash income has a
positive effect upon rice consumption, a dominant reason seems to
lie elsewhere.
A careful examination of the joint-dependence between the price
of rice and barley will clarify this point. Let us decompose the total
response into three analytically different effects, each caused by a
unit increase in the price of rice.
1) The consumer tends to reduce his rice consumption by . 35
units and shift to other grain.
2) But a simultaneous increase in the price of barley forces the
consumer to shift back to rice consumption by .38 units,
more than offsetting the intended substitution.
3) A simultaneous increase in money income also causes an
increase in rice consumption by . 013 units.
Thus, the net result is a slight increase in total rice cons ump-
tion. We must make one point clear, that is, the positive net response
109
is not because of an increase in money income. It is because of the
dynamic price-to-price repercussion that occurs whenever either one
of the prices changes.
The following are the total responses of farm demand, farm
sales and urban demand for both rice and barley to a change in the
price of rice, estimated by the same procedures. As in the case of
the farm demand for rice, the first term represents the direct effect
of P,
the second the repercussion due to simultaneous changes in
and the third term represents the effect of change in money
income caused by a change in P.
dq
FD
F
dPR
dq
= -.354141 + .375073 + .012714
.033646
FD
.372224 - . 133505 - 040950 = . 197769
.
dP
dq FS
= .
160111
.055656 -
.
160059 = -.055604
dP
FS
-.034915- .004461 + .042954 = .003578
dPR
UD
-.443071 + .120819 + .273560 = -.048692
dP
(repeated)
110
dq,UD
=
.
237600 - .084302 -
.
149720 = .003578
dP
The estimated total demand and sales response have entirely
different meaning from the ordinary concept of demand and supply in
the sense that interaction among different variables is taken into
account. The ordinary concepts of demand and supply as defined in
the usual texts are based on the strict qualification in the form of
ceteris paribus assumptions which do not necessarily hold in the
market situation.
The total response coefficients given in the above, therefore,
must be interpreted as a slope of a locus of equilibrium points after
allowing all other variables (endogenous) to reach equilibrium at the
same time. The slopes of farm and urban demar1 curves in the
ordinary sense are each represented by the first term in
and
UD
U
IdPR
FD
F
/dPR
respectively,which are negative as expected. And
.
that of sales is represented by the first term in
FS
F
/dPR which is
positive as expected in the ordinary market supply curve. For
example, the positive total response in farm demand should not be
misjudged as the so-called Giffen goods case.
The cross substitution effect in barley demand with respect to
the price of rice was estimated as .35 in the partial analysis, but it is
only . 20 in the total response analysis. This is mainly due to the
111
"return shift" caused by the increase in theprice of barley. Even if
the consumer intends to shift to barley consumption in the first round
because of a rise in the price of rice, a simultaneous rise in the
price of barley forces him to return to rice consumption. The resulting increase in barley consumption is thus smaller than originally
intended.
As for rice sales, the total response is negative in spite of the
positive slope of the sales curve itself, as shown in the partial analy-
sis. The effect of an increase in the value of rice stock combined
with the repercussion that occurs inthe barley market seems to
offset the initial intention to sell more rice on the market.
A similar explanation can be given in regard to barley sales and
urban demand for rice and barley. Changes in income level and
changes
in
barley prices both tend to offset the direct effect of a
change in the price of rice. It is notable that the quantity of barley
demanded increases negligibly when the price of rice rises, in spite
of the fact that rice and barley are substitutes according to the conventional (or partial) analysis.
The total response to changes in the price of barley of each
variable is computed by the same procedure. This time we need to
treat the price of barley as an exogenous variable which can be varied
arbitrarily. The rate of change in the price of barley dPR/dPB
obtained by substituting the rice sales Equation (3. 5) and urban
112
demand for rice Equation (3. 7) into the rice market identity (3.9) and
expressing
as a function of
R
The computed total responses are:
O0l043q"
001043FK
6l5485-. 00006839Yu-.
dPR
.
dPB
159682+.
(at Yu = 4000 won)
FD
.328850 - .310864 + .013392
.004594
dP
dq
FD
-.117110 + .326738 - .042727 = .166901
dPB
dq
FS
= -.048812 + . 140545 - 134549 = -.042816
dP
dq
FS
-.003913 - .030648 + .014604 = -.019957
dPB
UD
= .
105982 - .388928 + .240130 = -.042816
dP
UD
= -.073949 + .208565 - .149720 = -.015104
dP
113
The results are quite different and opposite in many cases to
what would have been expected on the basis of partial analysis
The
reasons for these phenomena are about the same as already discussed
concerning the effect of the price of rice.
The cross substitution of rice for barley due to a rise in the
price of barley is very small in farm demand and negative in urban
demand whereas it is appreciably positive in the ordinary analysis.
The quantity of barley demanded by farm consumers moves in
the same direction as the price of barley while that by urban consumers responds negatively though by small amount.
One is again likely to think that barley is a Giffen good for
farmers. It was shown that barley is an inferior good but not a Giffen
good in the ordinary sense, as is clearly indicated by the negative sign
of the first term in each of the demand response equation. The reason for the positive slope of total demand response at farm level lies
again in the dynamic price-to-price relation which is liable to change
whenever either one of the prices of the two grains changes.
To use the elasticity concept a. 10% increase in the price of rice
causes a 17% increase in the price of barley while a 10% rise in
causes a 6% increase in
The estimated partial responses and total responses to both
prices are summarized in the following table.
114
Table 3-2. The coefficients of responses to price changes; partial
versus total.!'
Equation
Farm demand for
rice (3.3)
Farm demand for
barley (3.4)
Farm sales of
rice (3.5)
Farm sales of
barley (3.6)
Urban demand for
rice (3.7)
Urban demand for
barley (3.8)
Barley price
Response to Changes in
Total Response
Partial Response
Mean
Direct
Mean
Direct
Coefficient Elasticity
Coefficient Elasticity
-.346
-1.538
.034
.151
.346
2.497
.198
.143
.060
.325
-.056
-.278
-.026
-
.187
-.170
-
.719
4.484
.238
b/
.004-.049
.004-hi
1. 141
.094
-.207
.072
1.677
Response to Changes in
Farm demand for
rice (3.3)
Farm demand for
barley (3.4)
Farm sales of
rice (3.5)
Farm sales of
barley (3.6)
Urban demand for
rice (3.7)
Urban demand for
barley (3.8)
Rice price
1.000
.331
-. 173
-
.848
-.100
-
.338
.001
.012
.106
.325
-.224
-2.937
.005
.015
167
.819
-.043-b/
-.145
.
020
.319
-.043 b/
-.132
-.015
-.197
.
.
878
.
597
All calculations are based on the mean values for q1, q1,
50.72kilos, and Yu at 4000 won.
95.64 and qFK
The same magnitude of the total response coefficients in the farm
sales of and urban demand for barley is due to the fact that the
barley market is set at equilibrium while the rice market is not.
The same is the case for the rice sales and urban demand for rice.
115
Summary of Findings
The following is a summary of the major findings in this
chapter.
1) Overall, the analysis based on the total response approach
which takes into account the simultaneous changes in all
endogenous variables demonstrates a result which is entirely
different from that based on the ordinary (or partial) approach
under the usual ceteris paribus assumption. It was shown
that theresults obtained by the formermethod reflect the
quantity-price relationship in the real market condition.
Z) The demand for rice at the farm level is supposed to be
highly responsive to a change in the price of rice when the
price of barley is fixed. But if the price of barley is allowed
to change at the same time as the price of rice, the average
farmer tends to increaserice consumption becauseof the
Hreturn shift" caused by a simultaneous rise in the price of
barley.
An arbitrary change in the price of rice by 10% causes
a higher rate of increase in the price of barley; approximately 17%.
The cross substitution elasticity of rice for barley consumption due to a change in the price of barley is unitary if
116
the price of rice were fixed, indicating that a 10% increase in
the price of barley induces farm consumers to increase rice
consumption by 10%, but when the price of rice is allowed to
change, there is almost no substitution effect between the two
grains.
3) The demand for barley at the farm level is also negatively
related to its price if the rice price were fixed, but the total
response is positive in the real situation due to a simultaneous change in the price of rice. The rate of shift to
barley consumption, when the price of rice rises, is much
smaller than that widely believed under the other-thingsremain-constant assumption, because of a rise in the price
of barley at the same time.
4) The farm sales of rice shows a positive response in the
partial analysis, but negative response in the total response
analysis. The farm sales of barley follows the same pattern
as in the case of rice.
5) Urban consumers respond negatively to a change in the price
of rice in both the partial and total response analyses, but the
degree of responsiveness is substantially offset by a change
in the price of barley that occurs simultaneously with the
price of rice. The substitution effect between rice and
barley is either negative or almost negligible against the
117
general expectation based on the partial analysis.
One noti.''eable fact is that, given the present consumption pattern, the average urban consumer would show no
response at all when his income (real) reaches as high as
6800 won which is approximately one and a half times the
present level.
In the event that this situation arrives in the future, the
growth of per capita income alone would have no effect upon
the rice consumption and increases in income would be
absorbed in the consumption of higher quality foods or nongrain commodities.
6) Although the results of the total response analysis indicate
that farmers increase their consumption of rice and reduce
their market sales if the. price of rice rises, the primary
cause for this does not Li.e in the increase in cash income or
stock value resulting from the price rise as hypothesized by
a number of economists. The empirical results show that an
increase in cash income due to a rise in the price of rice does
have the effect of increasing family consumption, but this
effect is not strong enough to offset the farmer's desire to
shift to consumption of other grain. It is also shown that an
increase in the value of grain stock due to a rise in the market
price tendsto induce the farmer to increase his saving in kind
118
and reduce the market sale, but this effect alone does not
cause a reduction in sales. The net decrease in sales is
attributable mainly to the pr ice-to- price correspondence that
takes place via the market mechanism.
119
POLICY IMPLICATIONS
From the foregoing empirical analysis, one can draw the following policy implications which are addressed to improving the
present foodgrain price policy in Korea. Problems related to institutional or detailed operational aspects are not discussed.
The evidence is clear that Korean farmers respond in their
grain production to changes in relative product price and they tend to
be more responsive in barley cultivation than in rice as already
stated. This implies a possibility that the price raising measures for
barley would contribute relatively more to achieving increased foodgrain production.
A price policy which aims at increased production of major
foodgrains may have some conflicting effects on the production of
other agricultural products, especially cash crops, due to the competition in land use and family labor input. Such competition may be
relatively greater in common barley cultivation as most cash crops
are grown upland (dry land). An increase in foodgrain production is
not costless; nor can it be achieved automatically. In Korea, where it
is imperative to step up the rate of growth in foodgrain production, a
certain amount of expense is inevitable.
Since the empirical measurement of farmers' response is based
on the average iarm household data for three major foodgrains, the
120
relatively high elasticities of production should not lead one to judge
that the production responses are high for all situations and for all
farmers, or that the elasticity for aggregate agricultural production
is also high. As already pointed out, the production response differs
by different region and by different individuals. The relatively more
commercialized farmers will in general show a higher response to
changes in price relationships. Also farmers living in relatively
more progressive areas will be more-responsive. These differences
in response have important policy implication considering the limited
government resources. In many cases, it is more effective to con-
centrate on certain areas or certain classes of farmers, for example
in input distribution or farm credit program, rather than a little-bitfor-everyone approach.
From the above-reported results one might conclude that a
decrease in input price such as by increased subsidy, with product
price remaining unchanged, would have the same effect as an increase
in product price, leaving input price constant, as long as the result-
ing price ratio is the same. This may be only partially true but not
in general for foodgrain production. In agriculture where non-
purchased inputs constitute a large portion of the total cost of produc-
tion, a government subsidyon purchased inputs may not affect the
relative price sufficiently to influence farm decisions. Since most
farmers are more familiar with product price than input price,
121
especially when new inputs appear on the market, farmers are likely
to be more sensitive to changes in grain prices. Grain price-raising
measures will be more effective in increasing the use of, for example,
family labor, assuming the existence of substantial amount of idle
labor in rural areas. One of the drawbacks in using product price
policy is that the marginal or submarginal farmers may spend extra
income on consumption items rather than for improved cultivation.
But this kind of phenomena is limited to only small farmers whose
products do not add much to market supply. The aggregate effect is
still an increase in production and market supply. As agricultural
development proceeds, the purchased inputs will of course assume an
increasing importance. For this reason, input price policy (subsidy
policy) should be viewed in a long-run context. Where an achievement
of increased foodgrain production is viewed as an immediate goal,
more emphasis should beplaced upon foodgrain price policy, though
some combination with input price policy is necessary.
The results of the study shows that the higher price of rice
induces the average Korean farmers to increase family consumption
and reduce market sales when total responses on the open market are
taken into account. Superficially, it appears to confirm the Utarget
cash requirementT' hypothesis. But the net result of increasing con-
sumption and decreasing sales are only partially associated with the
increase in money income due to the higher price they receive.
122
Although marginal, or submarginal, farmers who are in the underconsumption status would show such reaction when the price of rice
rises, it is not the general case in Korea.
A knowledge of the real causes for an increase in family con-
sumption and resulting decrease in marketings is crucial from the
standpoint of policy formulation. It makes a great deal of difference
whether this reaction of farmers is due to an increase in money
income brought by a price rise or itjg attributable to other causes,
even if the result is exactly the same amount of reduction in marketings, If a reduction in market sales of rice were caused by an
increase in money income, thefl price-raising policy would serve to
foster the aggregate foodgrain production, but it would not help in
alleviating the overall market shortage because the additional output
would be absorbed by increased family consumption.
It was found in the analysis of consumers' responses to price
changes, however, that both farm and urban consumers have a
"potential" tendency to respond negatively to price changes in their
consumption of rice and barley and that they have a "potential" tend-
ency to substitute rice for barley or barley for rice whenever either
one of the prices changes, but that this negative response to price
changes and the tendency toward cross substitution between two grains
are offset by the simultaneous change in the price of the other grain
on the open market, resulting in positive changes in the quantity
123
demanded, or no substitution at all.
The fact that such potentials exist, though they are not actually
displayed, provides an optimistic view in respect to the effectiveness
of price policy in restructuring foodgrain consumption. If the government's objective is to reduce foreign exchange spending on rice
imports by reducing rice consumption and increasing the consump-
tion of barley or other grain, it can be done, through the use of price
incentives, by inducing the consumers to reveal their potential
responses on the market. This is equivalent to forcing the ceteris
paribus assumption, which is made in the partial analysis, to operate
in the real world, that is, the price relationship which leads to the
results obtained by the partial analysis must be maintained by the
government operation. More specifically, a policy designed to raise
rice prices and lower barley prices to consumers would decrease rice
consumption and increase barley consumption, and at the same time
higher prices for both grains at harvest time would stimulate increased
production of both grains, especially of barley. The actual operation
of this policy requires a two price system for barley as well as some
degree of manipulation of the price of rice. In order for such a
pricing policy to be effective, a sufficient stock of grain should be held
by the government.
The operation of a two price system for barley will create a
considerable amount of loss in the government budget. But if the
124
government sells rice in the urban area at higher prices, it may be
able to generate a substantial amount of net revenue with which a loss
in barley operation can be compensated
When the government used
low price policy for rice in the urban area in the past the government
cost was borne by farm producers and the urban tax payers.
The
higher rice price policy will shift this burden directly to consumers
so far as rice consumption is concerned, but if the net revenue generated by such operation is plowed back into barley stock manage-
ment, there will be little or no burden in aggregate on the urban
consumers. Individual consumers who still prefer to consume rice
even at higher price will bear the cost.
There is a movement within the Korean government to reform
price policy in this direction. Although the adoption of such a policy
is a step in the right direction which is consistent with the results of
empirical analysis in this study, determination of the price level for
rice and barley still remains as the problem. The instrument of
price policy can be successfully used in achieving the objectives only
when the prices are determined at the appropriate level.
Finally, the impact of wheat flour prices deserves special attention in formulating foodgrain price policy although the demand situa-
tion for wheat flour was not analyzed in detail due to a lack of data
As was already pointed out, the government policy to keep the price
of wheat flour at low level (relative to the prices of rice and barley)
125
caused a rapid increase in its consumption. There is no doubt that
such a low price policy served to reduce rice as well as barley con32/
sumption.
As discussed earlier, however, the relative price of wheat
flour has been lowered to such a level that further decrease in its
price would cause an increase in rice consumption, whereas it would
cause a decline in barley consumption.
The policy based on a simple judgment that a lower price for
imported wheat will continue to serve to reduce rice consumption does
not seem to be workable in all situations any longer. If the govern-
ment is determined to reform its foodgrain price policy with a view to
increasing barley production as well as increasing its consumption in
the urban area, a higher sales price for imported wheat flour would
better serve this purpose because it may promote ashift to barley consumption and reduce rice consumption. The consequence of such a
policy would also enable the government to create a considerable
amount of revenue which in turn can be injected to cover the cost
incurred for implementing two-price system for barley. Of course, a
change in this direction must be a step-wise one in relation to the
The overall assessment of the effect of imported wheat and
that of low price policy requires an extensive research. The discussion here is confined only to its impact in conjunction with the prices
of rice and barley.
126
rate of growth in rice and barley production
In any case, the pre-
sent ceiling price system for imported wheat should be reconsidered.
127
BIBLIOGRAPHY
1.
Bauer, P.T. and Yamey, B.S. ACaseStudyofResponseto
Price in an Under-Developed Country. Economic Journal
69:800-805, Dec. 1959.
2.
Brown, W. G. and Nawas, F. Improving the Estimation and
Specification of Outdoor Recreation Demand Functions. Corvallis,
Oregon, Agricultural Experiment Station Project 850, 1971.
3.
Buse, R. C. Total Elasticities A Predictive Device. Journal
of Farm Economics 40:881-891. Nov. 1958.
4.
Christ, C. F. Econometric Models and Methods. New York,
John Wiley, 1966. pp. 292-294, pp. 314-331, pp. 564-575.
5.
Aggregate Econometric Models: A Review
Article. The American Economic Review 46:385-408. June
1956.
6. Dubey, V. The Marketed Agricultural Surplus and Economic
Growth in Underdeveloped Countries. Economic Journal 73:689702.
Dec. 1963.
7. Freund, R. 3. Some Observations on Regressions with Grouped
Data. The American Statistician 25:29-30. June 1971.
8.
Friedman, M. Price Theory; A Provisional Text. Chicago,
Aldine, 1968. pp. 48-55.
9.
The Marshallion Demand Curve in Essays. In:
Positive Economics. Chicago, The University of Chicago Press,
1966. pp. 47-99.
10. Girshick, M. A. and Haavelmo, T. Statistical Analysis of the
Demand for Food; Examples of Simultaneous Estimation of
Structural Equations. Econometrica 15:79-110. April 1947.
11. Grilliches, Zvi. The Demand for Inputs in Agricultural and a
Derived Supply Elasticity. Journal of Farm Economics 4 1:309322. May 1959.
128
12. Heady, E.O. Uses and Concepts inSupplyAnalysis. In:
Agricultural Supply Functions - Estimating Technique and Interpretation, ed. by E.O. He.ady and others, Ames, Iowa State
Univ. Press, 1961. pp. 3-25.
13. Heady, E.O. and Tweeten, L.G. Resource Demand and Structure of the Agricultural Industry. Ames, Iowa State University
Press, 1963. pp. 433-435.
14. Henderson, J.M. and Quandt, R.E. Microeconomic Theory.
New York, McGraw-Hill, 1958. pp. 24-30.
15. Hooper, J. W. Simultaneous Equations and Canonical Correlation
Theory. Econometrica 30:324-331. April 1959.
16.
Johnston, J. Econometric Methods. New York, McGraw-Hill,
1972. pp. 352-356.
17.
Klein, L.R. A Textbook of Econometrics. Evanston, Ill., Row,
1953. pp. 120-121.
18. Korea, Government of. Bank of Korea, Monthly Economic
Statistics, 1963-71.
Bank of Korea, Price Statistics Summary,
19.
1963 -71.
Bureau of Statistics, Monthly Statistical
20.
Review, 1963-71.
Bureau of Statistics, Urban Living Expenditure
21.
Survey, 1963-71.
22.
Ministry of Agriculture and Forestry,
Agricultural Statistics Year Book, 1963-7 1.
23.
Ministry of Agricu1tcre and Forestry,
Foodgrain Consumption Survey, 1963-71.
24.
Ministry of Agriculture and Forestry, The
Farm Household Economy and Cost of Production Survey, 196371.
25. Korea, National Federation of Agricultural Coops. Monthly
Review, 1963-71.
129
26. Korea, National Federation of Agricultural Coops. Rural Prices
and Wage Survey, 1963-71.
27. Krishna, R. Agricultural Price Policy and Economic Development. In: Agricultural Development and Economic Growth,
ed. by H. M. Southworth and B. F. Bruce, New York, Cornell
University Press, 1967. pp. 540.
28. Mathur, P.N. andEzekiel, H. SurplusofFoodandPrice
Fluctuations. Kykios 14:396-408. 1961.
29. Mellor, J. W. The Economics of Agricultural Development.
New York, Cornell University Press, 1966. pp. 196-213.
30. Nerlove, M. and Addison, W.
Statistical Estimation of Longrun Elasticities of Supply and Demand. Journal of Farm Eco-
nomics 40:861-880. Nov. 1958.
31. Schultz, H. Theory and Measurement of Demand. Chicago,
The University of Chicago Press, 1938. pp. 565-582.
32. Schultz, T.W. Transforming Traditional Agriculture. New
Haven, Yale Univ. Press, 1964. pp. 162-174.
33. Theil, H.
Economic Forecasts and Policy. Amsterdam, North-.
Holland, 1961. pp. 56-58.
34. Tweeten, L.G. and Quance, C,L. Positivistic Measures of
Aggregate Supply Elasticities: Some New Approaches, The
American Economic Review 59:175-183, May 1969.
35. IJSAID/Korea. Foodgrain Demand and Supply Projection for
1967-1971. Mimeograph.
36. Vickrey, W.S.
Microstatics. New York, Harcourt, Brace and
World, 1964. pp. 59-66.
APPENDICES
130
APPENDIX A
Estimation of Foodgraiii Supply
and Demand
Footnotes to Table A-i.
aRY 1967 beginning stock as of November 1, 1966 estimated as 200,000 MT and those for RY 1967-70 based on net change in stock during the year
and those for 1971-76 assumed to be constant.
bActual production adjusted to Rice-Year for RY 1967-71 and those for RY 1972-76 projected based on linear trend (1960-71).
imports on the arrival basis for RY 1964-70 and those for RY 71-76 are shortages based on total distribution minus production and
beginning stocks.
dEstimation for RY 196 7-71 based on daily per capita consumption data surveyed by Ministry of Agriculture and Forestry (MAF) and those for
Population growth rate: urban 2 8%, farmer 1. 4%; Per capita income growth
1972-76 based on population growth and income growth
rate (1960-71): urban 5. 5%, farmer 2.8%; Income-elasticity of consumption (1960-71): urban 0. 35; farmer 0. 78.
effects
eimation for 1967-71 based on the residual (total distribution minus staple food use and feed/seed/waste) and those for 1972-76 assumed to
be constant at 100,000 MT.
lncludes all uses on farm, and estimating method same as d.
use based on MAF plan and waste estimated as 3% of total production.
hEstimated as carryover of "old graixl. J)es not include fall harvest, the total of which is included in production.
Data Source: I Agricultural Statistics Yearbook, 1963-1971, Ministry of Agriculture and Foresty
2 Grain Consumption Survey, 1963-1971, Ministry of Agriculture and Forestry
3 Monthly Statistical Review, 1963-1971, Bureau of Statistics.
Table A-i. Estimation of rice demand and supply.
1.
II.
Unit: 1,000 MT
RY 67
RY 68
RY 69
RY 70
RY 71
RY 72
RY 73
RY 74
RY 75
RY 76
200
452
239
140
450
450
450
450
450
450
3,919
3,603
3,195
4,090
2,939
3,998
4,190
4,254
4,378
4,472
113
216
760
540
535
629
596
666
791
824
4,232
4,271
4,194
4,770
4,924
5,077
5,236
5,400
5,569
5,796
3,780
4,032
3,899
4,320
4,474
4,627
4,786
4,950
5,119
5,296
1,717
1,904
1,834
2,014
2,079
2,146
2,216
2,288
2,362
1608
1,704
1,806
1,914
1,979
2,046
2,416
2,188
?262
2440
2340
109
200
28
100
100
100
100
100
100
100
1,910
1,986
2,065
2,148
2,234
2,317
2,403
2,494
2,584
2,680
153
142
155
158
161
164
167
170
173
176
452
239
140
450
450
450
450
450
450
450
4,232
4,271
4,194
2,770
4,924
5,077
5,236
5,400
5,569
5,746
Supply
A.
Beginning stocka
B.
Production
c.
ImportsC
D.
Total supply
Distribution
A.
Consumption
1.
Commercial
a. Food use
d
b. Non-staple
2.
On farm
3.
B.
Exports
C.
Ending stock
D.
Total distribution
fOOde
Data Source:
1 Agricultural Statistics Yearbook, 1963-1971, Ministry of Agriculture and Forestry
2 Grain Consumption Survey, 1963-1971, Ministry of Agriculture and Forestry
3 Monthly Statistical Review, 1963-1971, Bureau of Statistics.
use based on MAF plan and waste estimated as 3% of total production.
hFor RY 70-76 surplus represents the amount necessary to balance total distribution with total supply.
1This carry-in appears high in view of the short barley crop in 1963. No reasonable basis is available for making adjustments.
1Estimating methods same as d.
'1Estimation for RY 1967-71 based on daily per capita consumption data surveyed by Ministry of Agriculture and Forestry (MAF) and those for
2.8%, farm 1.4%; Per capita income growth
RY 1968-76 based on popuiation growth and income growth effect: Population growth rate: urban
(-)0.
925,
farm
(-)0. 560.
rate (1960-71): urban 5. 5%, farm 2, 8%; Income-elasticity of consumption: urban
This item reflects any errors
eEstjmation based on residual (total consumption minus staple food use and feed/seed/waste) for RY 67 to RY 70.
of estimation in production and other consumption categories during this period.
imports on the arrival basis for RY 1964-70.
year and those for RY 1971 to 1976 assumed to be constant.
bActual production estimates adjusted to Rice-Year for RY 1967-71 and those for RY 1972-76 projected based on linear trend (1960-1971).
based on net change in stock during the
aRY 1967 beginning stock as of November 1, 1966 estimated as 850,000 MT and those for RY 1967-70
Footnotes for Table A-2.
Table A-2. Estimation of barley dmand and supply
1.
II.
Unit: 1,000 MT
RY 67
RY 68
RY 69
RY 70
RY 71
RY 72
RY 73
850
853
914
973
913
913
913
2,002
1,940
2,037
2,045
1,917
2,375
105
64
-
-
2,852
2,898
3,015
3,018
1,999
1,984
2,042
Commercial
820
790
a. Food use
202
RY 74
RY 75
RY 76
913
913
913
2,470
2,565
2,660
2,755
-
-
-
-
-
2,830
3,288
3,383
3,478
3,573
3,668
2,105
2,166
2,226
2,287
2,349
2,413
2r478
834
878
922
966
1,010
1,054
1,098
1,142
199
196
193
190
187
184
181
178
175
618
591
638
685
732
779
826
873
920
967
987
998
1,009
1,020
1,031
1,040
1,049
1,058
1,068
1,078:
192
196
199
207
213
220
228
237
247
258
Supply
A.
Beginning stocka
13.
Production
c.
ImportsC
D.
Total supply
-
Distribution
A.
Consumption
1.
b. Non-staple
2.
Onfarxn
3.
fOOde
B.
Exports (surplus
-
-
-
(84)
(114)
(149)
(183)
(216)
(247)
(277)
C.
Ending stock
853
914
973
913
913
913
913
913
913
913
D.
Total distribution
2,852
2,898
3,015
3,018
2,830
3,288
3,383
3,478
3,573
3,668
Footnotes for Table A-3.
aRY 1967 beginning stock as of November 1, 1966 estimated as 200,000 MT and those for RY 1967-70 based on net change in stock during the
year and those for RY 1971-76 assumed to be constant.
bActual production estimates adjusted to Rice-Year br RY 1967-71 and those for RY 1972-76 projected based on linear trend.
cActual imports on the arrival
basis for RY 1967-70 and those for RY 1971-76 are differences between total distribution and supply.
dEstimation for RY 196 7-71 based on daily per capita consumption data surveyed by the Ministry of Agriculture and Forestry (MAF) and those
for RY 196 8-76 based on population growth and income effect. Income elasticity of consumption: 0. 24.
eEstimation for RY 1967-71 based on the residual (total consumption minus staple food use and feed/seed/waste) and those for RY 1972 -76
based on 12. 3% average annual increasing rate for 1960-67.
1A11 domestic production assumed to be consumed on farm, in addition to imported wheat as food use which is included in "commercial food
Increases in consumption on farm for RY 1968 and 1969 are due to relief program in drought-stricken area.
use based on MAF estimates, waste estimated as 3% of production and 0.3% for imported wheat.
Data Source: 1 Agricultural Statistics Yearbook, 1963-1971, Ministry of Agriculture and Foresty
2 Grain Consumption Survey, 1963-1971, Ministry of Agriculture and Forestry
3 Monthly Statistical Review, 1963-1971, Bureau of Statistics.
uses
Unit: 1,000 MT
Appendix Table A-3. Estimation of wheat demand and supply.
I.
II.
RY 67
RY 68
RY 69
RY 70
RY 71
RY 72
RY 73
RY 74
RY 75
RY 76
pply
A.
Beginning
200
400
391
423
475
475
475
475
475
475
B.
Production
310
324
356
360
352
361
370
380
389
398
c.
IniportsC
922
1,127
1,205
1,254
1,464
1,609
1,769
1.946
2,142
1)
Total supply
1,432
1,851
1,952
2,037
2,160
2,300
2,454
2,624
2,810
3015
1,032
1,460
1,529
1,562
1,685
1,825
1,979
2,149
2,335
2540
Commercial
660
959
1,041
1,139
1,244
1,367
1,504
1,656
1,825
2,013
a, Food use
359
372
382
399
413
434
456
479
503
528
301
587
659
740
831
933
1,048
1,177
1,322
1,485
310
433
414
342
352
361
370
380
389
398
62
68
74
81
89
97
105
113
121
129
400
391
423
475
475
475
475
475
475
475
1,432
1,851
1,952
2,037
2,160
2,300
2,454
2,624
2,810
3,015
Distribution
A.
Consumption
1.
b.
2.
Non-staple foodC
On farm
3.
B.
Exports
C.
Ending stock
D.
Total distribution
(J.)
134
APPENDIX B-i
Estimated Seasonal Response Modela!
(Farm Demand)
qFD
= 19.71818 - . 37891P
(4.429)
(4.634) R
+
*
***
+ .09112D2P
. 19285D1P
R
R
(3.317)
(1.515)
(NS)
(r'4S)
+ .28441P + .00504q1 + .00O4l(P
(6.324) B (1.290)
(.585)
**
FD
- 2. 29762D2 - . 00584P
(.799)
(1.832)
B
(NS)
(NS)
.
+ . 01967P
***
(. 283)
R
+
(R
+YNRB)
2
=
.
815)
(NS)
= 4.4200 - 02975P
(.854)
(.369) B
(NS)
FS
*
(NS)
- 5. 8340ZD
(2,095)
q
FS
-
.
O178OD1PB +
(.431)
0416;qFK
Bt-1
(6. 117)
2639lD3P
(5. 995)
***
00199(P qFSp
BB
(4 261) R R
.
(NS)
.08842D - 5.42871D + .00194P
(.063) 1 (3.786)
(.535)
NRB
(NS)
+ .46364D
(.444) G
(R2
= .
866)
135
(Farm Sales)
FS
*
= -16.42861 + . 10343P
(3.966)
(1.401)
+ .00662L
(NS)
(NS)
R
- .06336D P
(.638)
+ .01020E +
1 R
+ .02841D2P
R
(294)
.07998q<1
1
(7.306)
(4.963)
**
-
.
OOO7Z(Pq
FK 1+Pq FK
(5.462)
(NS)
l+YNRB) + 5. 86913D
(2.088)
1
(1.116)
(NS)
1.15583D2
(R2
.
855)
(.244)
(NS)
(NS)
(NS)
(NS)
FS
= .56891 + .01763P - .00508D P - .01446D3P
q
B
B
1
B
B
(.534)
(.508)
(.493)
(.175)
***
FTC
00479E + . 03720q
B-
(NS)
+ . 00023L
1
(.669)
(5.482)
(9.284)
';**
+
.
OOOl8(Pq FK 1+Pq FK l+YNRB)
(3.408)
(NS)
.
03374D
(.350)
(NS)
1
(NS)
+ 1. 21469D2 - 07429D
G
(1.217)
(1.531)
.
2
(R
.
802)
136
(Urban Demand)
qUD
R
=
* **
28. 84979 -
.
* **
40033P
(5.224) R
(7.729)
-F .05579P
(1.166) B
+
***
13043P
(3.060)
.
85843D
(1.098)
.00172Y
(1.898) U
-
.00663P
(2.859)
(R2
=
.770)
*
-
.
03494DP B
(1.523)
000019PBYU
(.784)
R
10288D2P
+
(4.380)
*
(NS)
.
-
00l35Y
(1.773) U
.
(NS)
-
00232P
(.911)
.
***
(NS)
+
67l35D P
(1.901) 2 R
.
(1.484)
*
***
5. 86330 - . 20029P
(1.365)
(2.499) B
.
+
- 2. 69047D2
(2.251)
+
-
*
*
- 4. O8876D
=
.
***
.000056PRYU
(3.233)
**
qUD
+
* **
10238D P
(2.764) 1 R
1
2. 57700D + . 166548D
(2.423) G
(3.365)
(R2
Figures in parenthesis under each coefficient are t-values.
* Denotes significance at 90% level of probabiflty.
** Denotes significance at 95% level of probability.
*** Denotes significance at 99% level of probability.
.751)
137
APPENDIX B-2
Estimated Nonlinearized Mode1
(Farm Demand)
qFD
***
= 19. 249
(6. 053)
+
.
345828P + . 322775P - OO714ZP
(6.409) R (8 956) R (2. 651)
.
.
+
003264qRt
+YNRB)
RR
.b d.
-p -p -p
- 2. 107D3
(4. 881)
qFD
B
(R2
-12. 050 - . 118751P + .356392P
(3.634)
(2.765) B (6.816) R
+ . 02729 1q
(3.429)
1.262D
(3. 254)2
1
=
.
795)
+
(4.869)
(6.887)
(R2
.775)
138
(Farm Sales)
q
*
FS
(NS)
= -20.456 + 147469P - .058110P + .008562L
R
(5552) (1.547) R (.916) B (10.029)
+ . 011960E + i9l7q"
Rt(6. 339)
(6. 027)
00ill2(Pq
1
1+Pq<
i
(3. 056)
(R2 = .838)
q
(NS)
FS
= 4.627
B
(4.257)
-
*
(NS)
. 002457P - . 033260P - . 000653L
B
(.077)
(1.010) R (1.733)
***
-
***
047835qFK
. 005449E +
(7.067)
(9.003)
1
*
+ . 000097(Pq
1+Pq13
1L
(1.713)
(R2 = .751)
(Urban Demand)
qUD
***
***
* **
= 31.273 - .461532P + .112747P
R
B
(10.925) (11.014)
OO235OY
+
(3. 152)
*
-5.769
(1.611)
(4.589)
00007124(P Y
***
(NS)
.078669P B
(.990)
+
(NS)
247500R
(12.995)
*
-
°0003982BU
(1.722)
.825)
(R2
R U)
(4. 984)
(NS)
(.389)
(4.360)
* *
* * *
-
UD
* **
+
.003014pw
(1.357)
(NS)
+
627D
(R
2
=
(1.055)
Figures in parenthesis under each coefficient are t-values.
* Denotes significance at 90% level of probability.
** Denotes significance at 95% level of probability.
*** Denotes significance at 99% level of probability.
.
759)
139
APPENDIX C
Basic Data
140
Notation for Table C-i
(1)
Rt-11'F
Index of the three-year moving average rice
price of the November-January period relative
to Index of fertilizer prices.
(2)
Rt1'O
Index of the three-year moving average rice
price of the November-January period relative
to Index of the prices of agricultural supplies
excluding fertilizer.
(3)
= PRt1/WF
Index of the three-yearmoving average rice
price of the November-January period relative
to Index of rural wages.
PRt1/PPFI = Index of the three-year moving average rice
(4)
price of the November-January period relative
to Index of prices paid by farmers.
(5)
LH
Hired labor per 1/10 Ha.
(6)
LF
Family labor per 1/10 Ha.
(7)=L
Expenses on commercial fertilizer per 1/10 Ha
deflated by Index of prices paid by farmers
excluding fertilizer (PPFI nf ).
(8)
=
FC/PPFIf
(9)
=
FH/PPFIf
=
(10)
=
FA/PPFInf
=
(11)
=
IR/PPFI
=
(12)
=
10/PPFI
=
Imputed value of home manure per 1/10 Ha
deflated by PPFI nf
(8) + (9).
Irrigation expenses deflated by Index of prices
paid by farmers (PPFI).
Other operating expenses including seed, pesticides, repairs, and depreciation on farm
building and implements, and stock labor, etc.
deflated by PPFI.
141
Source: 1) Farm Household Economy Survey and Cost of Production
Survey, Ministry of Agriculture, 1963-71.
2) Rural Prices and Wage Survey, Korean National Federation of Agricultural Cooperatives, 1963-71.
Table C-i
Relative prices of rice and input factors used for 1/10 Ha, by size of farm, 1963-70.
Year Size of Farm (1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(10)
(11)
(9)
Ha
rn/h rn/h rn/h won won
won
won
1963
.5 or less 53y
79.1
55.5
32.8
35
120
498
155
431
379
209
G57
.5
1.0
79.1
36.5
82.8
31
111
142
523
496
1319
229
1.0
1.5
1.5- 2.0
1964
2.0 or more
5 or less
.5
1.0
1i4.L.
1.0- 1.5
114.4
114.4
114.4
1.5-2.0
2.0 or
1965
more
.5 or less
.5
1.5
-
20
1.0
1.5
1.5- 2.0
2.0
.5
.5
1.0
ilS.i
115.1
3.15.1
3.32.5
86.5
36.5
86.5
30.4
90.+
40
10.2 lOi.2
82.8
82.8
82.3
92.5
32.5
92.5
92.5
92.5
130.2
22
103.2
133.2
130.2
103.2
lQo.2
132.6
132.6
102.6
102.6
102.6
35
48
56
1U.2
103.2
9.4
9.4
93.4
3.30.2
100.2
113.2
j3.3 1Jj.3
94.5
94.5
94.5
34.9
94.5
73.9
93.4
33.4
93.4
38.4
95.4
3j....
81.0
73.9
81.3
1.0- 1.5
lt,1.7
3.49.?
53.4
53.4
53.4
1.5
19.7
1'.7
53.
t5.2
53.4
73.3
73.3
/3.3
73.3
73.3
63.4
1.0
158.?
1.0- 1.5
i5.?
61.3
61.3
1??.
1.5 2.0
133.5
2.0 or more
.5 or less
3.497
3.5
1.0
-2.0
2.Oormore
.5 or less
.5
1.52.0
1970
ti.2
79.1
79.1
79.1
99.3
99.3
99.3
99.3
99.3
73.9
73.9
.5
1969
.o
or more 115.1
or less i22.
1.0- 1.5
1968
.i.
2.0 or more 1J.3
.5 or less 115.1
.5
1.0
1967
IOT.2
1.0
1.0- 1.5
1966
93.9
93.9
93.9
114.4
2.0 or more 3.0.0
.5 or less
i.''. 1
.5
1.0
-
1.0
1.5
1.5- 2.0
2.0 or more
i4.j
1+.1
IP..1
t.1
/3.9
61.3
51.3
,1.3
64.0
54.0
64.0
64
81..
6.4
61.4
58.4
9j.t
91.0
91.0
31.3
91.3
85.5
89.8
85.8
55.9
85.8
35.3
89.3
88.3
38.?
96
69
41
33
41
54
75
70
43
37
45
93
77
61
130
115
133
130
156
1.1.2
1.45
93
51
134
126
128
121
149
141
136
134
156
149
131
129
122
114
153
3.11
146
135
136
125
72
49
99
3.14
93
80
64
116
112
39
3.05
1.44
32
43
lb
76
141
133
132
122
129
138
131
124
56
118
959
811
846
957
395
534
53
45
62
27
12
34
45
92
38
63
102
132
98
92.1
92.1
29
16
18
32.1
31.
133
136
13?
131
38
36
124
32.1
53
55
115
Sb.IL
781.
73
43
86
74
66.
°2.1
730
729
96
32
55
55
71
3P
35
38.!
55.3
S5.
731
847
768
770
795
772
829
329
836
886
876
961
869
932
932
935
976
578
954
935
3M...
66...
513
575
563
657
612
624
712
741
754
52
33
'35
395
353
413
431
376
372
395
311
288
301
265
227
295
309
252
266
199
357
363
345
327
320
333
397
341
356
349
293
343
313
339
281
340
331
348
312
428
951
930
916
1067
1033
1373
1174
1096
1065
1319
1331
994
1008
1142
1377
1322
1061
971
1.1.86
1197
1181
1213
1196
1263
1266
1.273
1288
1284
1269
1221
1267
1274
124c
1151
1177
1235
1207
1332
241
297
267
188
267
265
304
315
212
211
261
276
302
263
245
312
283
310
283
292
340
383
416
239
237
344
340
366
229
228
286
325
31.2
358
243
266
123
360
(12)
won
394
385
360
366
381
486
486
473
496
513
433
448
420
485
560
358
342
363
349
385
389
361
374
379
380
250
293
277
330
306
393
375
391
425
405
483
413
435
447
433
A
143
Notation for Table C(1)
Bt-1'F
Index of the three-year moving average barley
price of the July-August period relative to
Index of fertilizer prices.
Bt-l"O
= Index of the three-year moving average barley
price of the July-August period relative to Index
of the prices of agricultural supplies excluding
fertilizer.
(3)
= PBtl/WF
Index of the three-year moving average barley
price of the July-August period relative to
Index of rural wages.
(4)
= PBt1/PPFI
Index of the three-year moving average barley
price of the July-August period relative to
Index of prices paid by farmers (PPFI).
(5) = LH
= Hired labor per 1/10 Ha.
(6)
= Family labor per 1/10 Ha.
LF
(7)=L
(8)
= FC/PPFIf
(9) = FH/PPFIf
(10)
= FA/PPFI
(11) = 10/PPFI
= Expenses on commercial fertilizer per 1/10 Ha
deflated by Index of prices paid by farmers
excluding fertilizer.
= Imputed value of home manure per 1/10 Ha
deflated by PPFInf
= (8) + (9).
= Other operating expenses including seed, pesticides, repairs, and depreciation on farm building and implements, and stock labor, etc.
deflated by PPFI.
Source: 1) Farm Household Economy Survey and Cost of Production
Survey, Ministry of Agriculture, 1963-71.
2) Rural Prices and Wage Survey, Korean National Federation of Agricultural Cooperatives, 1963-71.
Table C-2. Relative prices of common barley and input factors used per 1/10 Ha, by size of farm,
1963-70.
Year Size of Farm
(1)
(2)
(3)
(4)
Ha
1963
.5 or less
1.5
1.0
2.0
1.5
1964
L5
2.0
143.5
2.Oormore 1.43.5
.Sorless
1.0
1.5
134.6
134.6
134.6
134.6
2.0 or more
134.6
.5 or less 151.2
.5
1.0
lSe.2
15.2
1.5 2.0
15.2
2. 0 or more 15.2
1.5
.5 or less
.5' 1.0
1.0
1.5
1.52.0
2.0 or more
.5 or less
.5"' 1.0
1.0' 1.5
1.5
2.0
144.a
144.2
144.2
144.2
144.2
113.3
113.3
134.6
134.6
134.6
115.9
115.9
134.6
134.6
134.6
6
T346
134.6
128.4
128.4
128.4
128.4
134.6
133.8
133.8
133.8
133.8
2.0
139.2
139.2
11.4
2.0 or more 131.4
.5orless
.5
1.0
1SC.1.
1.0
15.1
5
3.50.1
1
1.5" 2.0
126.5
126.5
134.6
134.6
134.6
1
&T
134.6
123.3
123.3
123.3
123.3
1Z3.3I?84
80.4
80.4
80.4
80.4
8.4
13Z53.4
139.2
53.4
2.0 or more 139.2
.Sorless 131.4
1.0
.5
I31..4
1.0"' 1.5
131.4
1.5
1970
113.3
143.5126.5
1.0
1969
11.3.3
126.5
7.3
1:5Z.O
1968
1.26.5
51.5
81.5
81.5
115.9
115.9
97.3
1.0
5
1967
85.1
65.1
85.1
85.1
85.1
97.3
143.5
143.5
1.0
1966
77.3
77.8
77.8
77.8
77.8
973
2.0 or more
.5 or less
.5 1.0
1.5
1965
97.3
15.1
2.0 or more 1SC.i
53.+
53.4
53.4
51.0
51.0
51.3
51.0
51.3
52.2
52.2
52.2
52.2
53.2
88.3.
88.1
88.1
85.1
88.1
61.1.
68.1
68.1
68.1
66.1.
56.8
6.5
56.8
56.5
56.8
53.8
53.8
53.
53.8
53.8
81.5
8I.
(5)
(6)
(7)
(8)
rn/h
rn/h
rn/h
(9)
(10)
(11)
won
won
won
won
ii
99
110
595
1293
1502
1804
1726
1539
1474
349
374
400
379
341
523
537
1276
1293
1289
1288
1266
547
506
407
529
488
1.390
465
25
372
tl93
18
79
35
40
1.3
14
25
41
16
19
22
75
47
99
86
61
47
102
76
85
1
97
110
87
1.12
100
86
88
118
95
107
698
52677
604
898
IZ2
658
607
467
551
1146
1119
1072
923
532
559
553
744
734
736
586
705
680
670
746
736
641
564
1205
1325
1306
1284
387
386
53
41J
42
82
12
13
16
100
78
112
104
94
38
50
88
535
579
654
665
720
99.1
99.1
99.1
99.1
99.1
7
79.7
79.7
79.7
79.7
73.3
15
11
13
21
39
103
118
98
90
86
92
733
769
819
767
875
862
678
655
639
698
1595
1447
1474
1406
1573
354
311
320
293
270
8
78
70
86
81
85
79
91
799
726
658
604
667
884
1528
1453
1553
1639
257
240
247
217
309
73.3
73.3
73.3
75.1
75.1
75.1
75.1
75.1
7
1446
1348
1359
1675
1604
274
288
265
293
210
1315
1295
204
203
ii.
20
26
T37
6
91
81
77
65
53
65
51
85
80.3
849
886
755
rB,,,,,,',,85
85
66
61
81
76
74
92
93
85
775
781
814
777
816
18
60
23
51
78
74
760
860
22
34
1.2
9
8
88
95
52832
671
567
545
898
788
614
555
435
1.461
t4462fl5
145
Notation for Table C-3
Bt.1"F
= Index of the three-year moving average barley
price of the July-August period relative to
Index of fertilizer prices.
PBt1h1)O
= Index of the three-year moving average barley
price of the July-August period relative to
Index of the prices of agricultural supplies
excluding fertilizer.
(3)
PBt1/WF
= Index of the three-year moving average barley
price of the July-August period relative to
Index of rural wages.
(4)
PBt1/PPFI
(5)
L
= All labor per 1/10 Ha.
(6)
Fc/PPFI
= Expenses on commercial fertilizer per 1/10 Ha
deflated by Index of prices paid by farmers
excluding fertilizer.
(7)
= FH/PPFIf
(1)
Index of the three-year moving average barley
price of the July-August period relative to
Index of prices paid by farmers (PPFI).
= Imputed value of home manure per 1/10 Ha
deflated by PPFI nf
(8)=
(9) = I/PPFI
= Other operating expenses including seed, pesticides, repairs, and depreciation on farm
building and implements, and stock labor, etc.
deflated by PPFI.
(10) = LH
= Hired labor per 1/10 Ha.
(11)
= Family labor per 1/10 Ha.
LF
Source: 1) Farm Household Economy Survey and Cost of Production
Survey, Ministry of Agriculture, 1963-71.
2) Rural Prices and Wage Survey, Korean National Federation of Agricultural Cooperatives, 1963-71.
Table C-3. Relative prices of naked barley and
input factors used per 1/10 Ha, by size of farm,
Year Size of Farm
(1)
(2)
(3)
Ha
.5 or less
1963
.5
1.0
1.5
1.0
1.5
2.0
2.0 or more
.5 or less
1964
.5
1.0
97.3
Q7.3
7.3
97.3
97.3
1435
11+3.5
I
1.5 -2.0
1965
1966
1967
1968
1969
1970
143.5
77.8
85.1
77.8 35.1
77.8
85.1
77.8 85.1
77.8 85.1
126.5 113.3
126.5 113.3
126.5
12b.5
134.6
134.6
134.6
81.5
81.5
81.5
81.5
81.5
115.9
115.9
113.3 115.9
113.3 115.9
134.6 134.6
134.6 134.6
134.6 134.6
2.0 or more 1'+3.5
.5 or less
134.6
.5 1.0
134.6
1.0 1.5
134.6
I
2.0 or more 134.6 134.6 13k.6
.5 or less 1511.2 123.3 128.4
.5 1.0
150,2 123.3 128.4
1.0
1.5
150.2 123.3 128.4
1.5 -2.0
151.2
123.3 128.4
20
6tthbe1.2
.5 or less
144.2
811.4
88.1
.5 1,0
11.4.2
80.4 88.1
1.0- 1.5
144.2
80.4 88.1
1.5-2.0
11+4.2
83.4
88.1
2.Oormore 144.2 80.4 88.1
.5
53.4
.5
1.0
139.2
53.4 68.1
1.0 1.5
139.2
53.'. 68.1
1.5- 2.0
53L
139.2
68.1
2.0 or more 139.2 53.4
68.1.
.5 or less 131.4 51.0 56.8
I
1.0- 1.5
51.0
56.8
1.52.0 131.4
131.4
51.0
58.8
2.0 or more 141.1+ 51.11
56.8
.5 or less 151.1
52.2 53.8
.5 1.0
1,0_is 151.1 52.2 53.8
1.52.0 151.1 52.2 53.8
2.0 or more 151.1 52.2 53.3
oles1Z2
(4)
134.6
133.8
133.8
133.8
133.8
99.1
99.1
99.1
99.1
99.1
6.IT79.7
7
79.7
79.7
79.7
73.3
73.3
73.3
73.3
75.1
75.1
75.1
75.1
(5)
(6)
(7)
rn/h
(8)
won
won
won
127
116
106
103
838
1028
975
837
11+40
1J4
115
(9)
(10)
rn/h m /h
1866
620
Br4go5l71g5a5
791
858
1649
15101
17
109
447
522
20
44
49
91+7
19
15
86
58
55
96
481+
21+15
781
833
823
838
799
877
725
844
819
592
567
139164021
1441+
621
26
112
110
106
111
1u20
997
867
891
955
558
733
715
632
687
1578
1730
1582
1523
1642
769
540
484
518
539
107.
106
95
89
86
1053
933
997
1099
925
1804
1719
579
1121
1001
751
786
797
695
662
714
798
636
611
1.070
61+7
85
90
99
87
85
91
1tZ
93
81
84
80
95
87
86
83
102
97
102
126
(11)
won
1247
936
717
97
1963-70.
729
1.045
958
1088
2084
1665
1498
1558
1667
1717
953
91+7
842
677
39
46
18
46
22
82
46
41
81
66
59
z5
21
90
89
30
41
76
1791+
20
52421
521
25
87
85
70
1587
515
2160
27
57
655
1757
1612
1717
1743
342
366
391
366
395
671+
1689
381
402
384
16
1+76
1+36
16
71
62
53
86
11+
83
392
452
21
35
81
91
1794597
17538B
1756
1015
1033
974
1163
1125
711
696
670
61+9
1670
1830
1774
1168
1180
637
587
1805
1767
171+1+
70
2960
38
48
25'77
18
75
37
19
21+
30
76
147
Notation for Table C-4
(l)
=
(2)
Monthly farm per capita income originating in the
non-rice-barley sources deflated by the Index of
prices paid by farmers (PPFI).
NRB
(3)
(4)
Monthly urban per capita income deflated by the Index
of urban consumer prices (CPI).
Lti
Farm per capita liabilities as of the end of the previ-
E
Monthly farm per capita cash expenditures on nonfarm goods and services deflated by PPFI.
(5)
ous month deflated by PPFI.
=
Monthly average wholesale price of wheat flour per bag
(22 kilos) deflatedby the Index of non-grain wholesale
prices (WPIn).
Monthly average wholesale price of rice per kilo
deflated by WPL n
(7)
=
qD
(8)
Monthly urban per capita consumption of rice
Monthly urban per capita consumption of barley.
=
(10)
Monthly average wholesale price of barley per kilo
deflated by WPI
q
=
Monthly urban per capita supply of rice from non-farm
sources, i.e.,
qOS
(11)
=
Monthly urban per capita supply of barley from non-
farm sources, i.e.,
(12)
(13)
=
(14)
=
+
q
I
F
+
GS
GD
=
Monthly farm per capita sales of rice.
=
Monthly farm per capita sales of barley.
Farm per capita stock of rice on hand at theend of the
previous month.
148
(15) = q1 = Farm per capita stock of barley on hand at the end of
the previous month.
(16) =
= Monthly farm per capita consumption of rice.
(17) =
= Monthly farm per capita consumption of barley.
Source: 1) Price Statistics Summary, Bank of Korea, 1963-71.
2) Urban Living Expenditure Survey, Bureau of Statistics,
Government of Korea, 1963-7 1.
3) Foodgrain Consumption Survey, Ministry of Agriculture
and Forestry, Government of Korea, 1963-71.
4) Farm Household Economy and Cost of Production Survey,
Ministry of Agriculture and Forestry, Government of
Korea, 1963-71.
5) Rural Prices and Wage Survey, Korean National Federation of Agricultural Coops, 1963-7 1.
Table C-4. Data used for estimating simultaneous equation model, 1963-71.
(2)
(3)
(4)
(5)
(6)
(7)
won
won
won
won
won
won
won
1
181.2
530
1.313
1+11.
1313
1313
1817
1817
1817
1890
1833
1
1612
i812
1637
1637
1637
1705
1705
1705
1743
1743
1743
1565
40.83
42.91
43.01
43.16
50.59
60.39
81.63
77.01
67.81
2.26
32.0'.
2
2
3
(1)
Year Month
1963
522
597
1019
1112
523
189t,
2253
2253
2253
1453
156
592
594
388 600
331. 580
301
568
238
554
252
536
300
519
438 519
402
508
601
1+88
653 485
534 465
521
1453
681
483
4
1565
1491
411
5
1.1+91
31+9
6
1491
'+26
7
1496
3
4
5
6
7
8
9
10
11
12
1964
1965
+uo
481
49'.
8
i'+9
9
1496
393
364
360
10
1570
1+68
11
1.570
12
1571
1
1491
638
810
776
1453
1622
1622
1622
1645
16'+5
1645
1976
1976
1976
1246
521
124E
2
3
4
5
6
1491
1491
1524
1524
1521+
7
1821
8
1821.
9
1621
1773
1773
1773
1.890
10
11
12
1966
393
85
352
1
2
3
4
1393
1890
0O6
621
563
51+3
1+81
1+67
494
561
600
825
1.156
71'.
1+91
598
568
656
459
516
'+96
582
535
612
777
751
738
742
731
725
474737
146 537 730
1908
1908
1908
1978
415
44.85
'.48
3L+5
431
273
325
327
53u
391
648
673
731
44.11+
46.1+6
56.14
56.09
60.68
57.58
52.61+
44.06
39.1+8
38.69
'iJ.91
'+1.74
4± .E
39.45
40.70
41.50
34.00
34.08
'+9.06
41.61
1+5.29
53.15
53.74
52.88
1+7.36
42.03
43.52
45.19
52.72
55.22
56.60
53.22
40.39
36.33
32.42
31.28
32.63
33.25
31.76
(8)
Kg
13.73
8.88
9.14
5.99
7.91
7.34
5.47
5.05
6.15
10.37
9.85
10.61
10.80
(10)
(11)
(12)
(13)
(14)
Kg
Kg
Kg
Kg
Kg
Kg
2.30
3.11
8.83
7.22
8.13
7.96
5.99
4.08
2.70
3.29
3.63
4.30
4.81.
5.28
6.91.
1.9O
1.66
1.01
1.03
1.92
3.26
2.53
1.66
1.50
3.94
7.73
6.61
4.33
2.74 -5.83
1.95 -1.3.85
1.54
.62
1.1.691.20
3.36
10.89
10.50
9.83
8.30
7.33
1.79
2.36
3.24
4.45
5.61
1.52
1.32
2.93
3.60
3.93
8.54
9.89
11.11
11.14
11.73
4.67
3.19
2.91
2.49
1.62 -5.07
1.60 -14.15
1.70 -2.91
3±661 1.4s
31.19
31.62
31.76
28.77
(9)
10.97
10.90
1.
2.09
2.20
2.28
2.75
3.78
5
3.1.7
3.75
4.34
3.13
3.40
5.46
5.19
3.11
2.32
1.67
1.26
5.00
6.90
1.99
1.29
.97
15.39
23.60
13.09
.60
8.78
6.62
7.70
5.07
3.73
3.45
3.23
8.62
1.13
1.10
1.15
3.50
2.08
4.35
4.80
4.61
3.69
.03
1.32
1.65
1.35
1.15
-2.70
-1.26
.74
-1.05
-13.87
1.21
.08
1.18
1632
474
10.71+
669
37.18
2.33 -0.53
10.76
1.30
2.1+9
2.57
1.17
36.74
36.23
38.39
3.81.
2.91
2.12
1.83
1.94
28.391. ii32.3H
26.07
1.37
37.0
2.49
1.50
37.08 27.76
38.21+
4.34
2.15
3.51
2.27
1.42
.92
.42
.22
673
42.29
26.17
25.97
26.24
25.78
27.06
26.35
.1+7
6.44
4.39
3.17
498
1+4.83
27.L.1.
.55
95.8
74.5
49.6
39.8
1.98
2.91
1.13
1695
43.75
13.02
8.97
8.47
9.53
9.67
13.76
11.38
12.35
.1+6
.35
.30
3.03
4.93
i.07.7?
1.47
1978
2620
2620
2623
1695
1635
1.978
1+3.30
44.1+0
143.5
116.5
89.1
69.2
46.1
.26
297
285
319
562
460
597
773
588
661
1.69
3.39
4.65
6.43
15.68
24.52
9.50
1.87.6
27.6
18.3
13.1
126.0
257.9
201.7
729
716
703
7u3
731
699
661.
673
665
667
676
'+53
2.91+
.1+0
.18
.1+9
.77
6.71+
8.56
13.56
22.58
10.54
.31.
(16)
Kg
(17)
Kg
27.9
16.8
13.7
16.16
12.97
1.1.0
12.39
11.60
9.86
6.18
4.72
3.87
4.13
5.77
6.73
8.1
17.2
68.1
57.0
43.9
32.3
24.8
21.1.
17.8
1.2.94
5.9'.
15.36
17.63
15.7B
13.96
.12151.31.0.815.62
.29
125.2
7.91.
10.79
15.16
15.22
13.27
4.54
2.05
1.60
1.47
8.8
7.0
3.1
11.0
95.7
13.93
13.38
9.17
5.32
2.21
2.55
4.12
10.39
12.39
61.7
.1.3
20.2
14.0
121.2
248.2
.51+
1.99.7
25.7
6.90
13.61
14.77
13.38
13.13
10.69
4.73
2.59
2.61
3.03
.69
.50
142.3
118.9
92.3
73.1
53.6
42.1
33.3
23.5
79.5
212.4
164.0
19.9
16.9
13.6
10.?
111.4
99.6
80.9
66.8
53.8
47.5
39.3
11.53
3.98
4.59
4.95
.81+
1.43
5.80
5.00
2.56
2.17
.81
.95
.57
.678.36iI6
9.69
.89
7.04
(15)
Kg
1.13
143.3
119.2
96.1
34.9
2.7
14.1+6
11.?'.
11.23
9.19
6.64
6.29
10.13
13.00
12.51
12.31
10.29
10.01
4.14
4.87
5.0'.
35.10.19
31.0
24.8
7.81
10.42
11.23
10.15
6.36
3.61
3.25
2.94
341
.
'.0
Table C -4
Continued.
(1)
(2)
Year Month won
won
5
2316
536
6
216
4l
9
2135
2135
2135
451
10
21+37
11
.437
12
3
2437
2767
2767
2767
4
5
2506
6
28
609
719
525
702
689
619
639
662
535
7
2939
2939
2939
339k
7
8
1967
1
2
8
9
10
11
12
1968
60)
530
626
57
33q
1393
104
10
3394
3181
3181
3181
3159
3159
3159
3189
3189
3189
3293
11
1290
12
3293
3734
3704
3734
3497
1
2
3
4
5
6
7
8
9
1969
?86
480
524
1
2
3
4
5
6
7
8
3497
37
3948
3945
571
755
679
678
626
519
519
1+79
555
730
556
1176
775
671
644
685
717
642
567
517
(3)
(4)
(5)
won, won won
1632
1632
2356
2396
2396
2196
21%
415
298
1+09
371
576
415
673
743
639
696
536
512
2136
1353
1353
1383
1682
1682
1+45
1652
31.8
1+37
1903
1903
399
19u3
625
2377
452
377
721
2377
306
1397
589
1397
617
1397
576
1556
503
1556
439
1556 361
1870
328
1570
327
1870
458
1951
568
1981
618
1951 '824
1437
61.5
1437
733
1437
587
1394
580
1.394
1394
18+8
1348
654
638
632
637
642
638
63.
629
632
627
610
607
619
615
612
581
563
563
547
533
548
(6)
(7)
won
won
(8)
Kg
39.11
38.98
39.43
43.00
45.38
25.03
22.35
20.52
20.99
22.89
25.61
27.90
11.95
11.21
13.03
8.93
9.37
10.47
11.83
42.00
36.45
(12)
(13)
(14)
(15)
(16)
(17)
Kg
Kg
Kg
Kg
Kg
Kg
Kg
2.50
5.36
4.30
4.90
4.42
3.27
.65
-5.77
1.50
.49
-5.35
-1.59
.23
.97
1.07
5.67
5.94
4.41
3.85
5.24
8.44
15.13
.86
2.12
7.82
4.76
3.20
1.97
1.19
1.11
79.6
63.7
47.1
33.0
21.7
14.3
41.1
201.8
177.1
152.3
125.1
107.6
83.6
21.4
16.7
102.7
99.0
84.0
69.0
57.8
10.20
9.57
6.26
4.81
6.02
9.80
13.49
6.30
7.97
9.83
10.91
9.76
6.79
4.03
48.0
36.1
26.4
20.7
132.3
100.4
96.5
83.5
67.5
53.6
.9E
3.74
3.92
3.95
3.26
2.46
12.451.93-18.70
12.44
13.23
12.45
12.61
12.06
11.50
13.74
10.23
11.25
11.57
12.44
37.1.3
42.18
64.00
'4.96
44.04
43.25
42.39
39.71
38.83
27.36
27.51
28.77
30.75
31.45
26.78
24.81
24.85
26.05
26.27
27.25
25'28."9
39.33
559
41.13
41.34
42.50
(+2.53
1.2.33
42.05
42.01
45.95
49.14
46.21
28.05
12.45
13.47
2.69
1.65
1.79
1.71
1.84
26
2.73
2.87
2.68
2.09
1.54
1.44
1.2.41
1.43
27.87
26.93
25.82
25.27
24.51
25.75
24.43
25.05
28.01
28.60
12.53
12.47
12.13
11.91
11.38
13.95
11.15
1.65
1.82
2.00
2,11
2.38
2.57
12.117
1.98
1.73
1.62
12.21
12.26
12.04
12.58
11.71.
11.35
10.98
1i.'Oo
10.55
10.33
-3.15
1.25
-0.88
.67
4.49
.B1'
3.44
5.20
4.48
-0.74
-12.08
i'.3'18.72
28.07
49.53 29.23
53.06 28.98
55) 69.05 26.89
547 48.59 27.08
989 543 1.893 25.84
1+36
54j 49.39 25.36'
398 546 1.9.28 25.76
41.7 51+3
49.18 26.8?
555
553
(11)
Kg
35.83
36.69
41.88
563
565
565
565
565
563
562
62
(10)
35.5527.18
55?
56
(9)
Kg
2.1+8
-0.52
.6426.78
1.30
.12
.08
.14
.16
1.3.66
.70
13.50
11.68
10.46
6.64
1.34
6.39
4.41
5.94
10.75
21.49
8.09
4.79
2.79
1.4?
-0.504.93
-6.50
-2.63
-0.50
.42
.63
1.51.
1.39
1.48
.4
.80
7''ai3V'.
3
.45
13.05
.92
.62
.48
11.00
.31j
.74
11.41.
.32
5.37
6.52
8.41
7.18
5.84
2.28
-0.26
1.06
1.00
7.60
6.31
5.03
.59
-5.13
-1.33
-0.49
-0.38
.38
2.77
2.96
4.95
9.13
11.63
-11.68
.31 21.40
1.76 3.12
1.42
9.02
1.77
3.64
.93 9.03
2.38
3.1.1
.47
8.69
2.33 5.62
1.31 5.79
2.25
2.87
.24
8.19
2.44 4.30' 135' 6.77
2.66 6.34 -5.52
4.25
2.73 6.34 -0.51
4.03
'+8.611.76
3.61
42.0
37.9
33.0
27.9
23.0
3.70
3.21
4.47
4.98
5.43
63.8±?.?
2±o.5'"'' 65.
169.2
38.0
.89 147.5
34,0
.85
.71
.93
1.1+2
7.01
3.64
2.77
2.20
1.26
1.22
1.16.3
95.3
75.2
56.5
43.3
31.9
26.0
19.7
41.1
ils.6
29.0
24.9
21.0
17.2
111.5
1.07.1
93.1
77.6
67.3
51.1
.35 169.1
52.2
.85 149.1
48.1
1.62 1.30.1
43.9
.96 110.?
38.0
2.02
90.9 32.9
1.10
69.3 24.6
8.27 52.7 105.2
3.33
'#1.6 111.9
11.21
12.96
11.02
10.91
10.46
9.92
6.82
5.25
7.12
10.99
12.34
9.24
10.06
9.12
5.80
4.09
12.59
4.05
10.71
11.24
10.14
4.93
5.50
6.10
1.46
ii.3O'33''"'"
10.82
3.83
1U.36
7.27
7.20
5.58
5.57
9.03
9.39
11.36
10.51
9.71
11.33
9.69
9.25
9.17
9.93
7.02
5.75
9.18
9.62
7.10
4.67
4.16
4.49
4.27
4.94
5.99
6.50
6'+1
8.94
9.?'.
i
Ui
Table C-4. Continued.
(1)
(2)
(3)
Year Month won
won
won
3948
4177
4177
4177
+344
7F1.
757
18.8
9
10
11
12
1970
1
910
0i1t3
785
3
4344
809
4
3955
81.7
5
6
7
8
3955
747
608
601
657
873
2
9
10
3955
4190
4193
4190
4389
7'".
(5)
735
1870
512
1870
784
1870 li4S
1168
820
1168
833
1168 687
1276 67
1276
640
1276
417
1651
412
1651
460
1651
782
1839 590
1839
74'.
536
532
5311
528
518
510
504
499
495
495
1.95
'+95
492
(6)
(7)
won
won
(8)
Kg
48.85
48.50
43.23
49.64
26.13
10.54
26.4611.12
1.7.81
27.31.
11.1.7
2.1.8
47.54
47.10
27.65
30.03
10.93
12.98
49.15
1.8.46
4891+7.06
18J9
999
485
1305
483
479
3
1.259
769
1375
1375
1375
381
2
4259
4259
55.39
56.69
56.45
55.91
55.11.
4
4416
1.76
797
31.11
33.45
36.22
35.40
34.80
5
4416
'+69
55.28
1.0.73
6
'+'+16
7
8
4637
4637
772
710
685
613
4637
825
53.91
55.31
9
37.90
35.02
36.99
1
us
1443
1443
1443
1732
1732
1732
834
730
732
621
532
41555.6137.83
54.03
432
512
511
569
810
563
54.83
1+30
12.'.t
1 .5i
12.12
11.83
55:j
2.13
4.67
2.18
39.18
(11)
Kg
(12)
Kg
.1.2
1.27
.86
.69
1.07
.76
.02
.54
.52
.64
2.75
1.77
6.57
6.54
6.59
6.94
-2.49
-1.81
12.79
12.72
13.54
12.41
12.77
1.65
1.70
1.61
1.56
1.84
1.12
-5.79
3.02
5.62
4.23
.36
.31
1.05
.96
12.46
11.74
11.02
11.06
1.97
2.18
2.18
.71
3.t3t.1ia.eo
1371
12
11.98
12.24
12.41
12.58
(10)
Kg
2.95
5.79
2.45
i.'.9
1.79 -1.36
1.63 -11.91
1.65
.81
1.40
5.09
1.66
3.65
4s.44
48.35
47.89
1.9.U0
115
1.87
(9)
Kg
26.81
26.35
27.31
27.13
26.61
27. 1u
27.37
27.25
4389
4389
11
1971
11189
11115
(4)
won won
11.42
2.16
13.87
24.40
13.02
8.40
9.62
6.74
6.30
6.82
5.70
-0.02
1
.39
.49
-2.35
-1.84
.65
(14)
Kg
(15)
Kg
(16)
Kg
(17)
2.56
31.2
10.70
22.4
99.2
85.6
5.30
1.19
.95
227.7
181.6
161.3
140.0
75.3
65.3
56.8
53.1
49.1
8.57
12.49
12.06
11.35
11.72
10.15
4.23
3.84
3.89
3.12
4.36
'.1.7
10.475.0k
.94
.66
.72
2.11
73.5
59116.1
Kg
7.8'.
1.
8.36
5.93
5.53
4.84
6.77
1.81
1.73
5.58
5.11
1.80
97.1
8.6
62.9
51.4
38.7
35.8
29.0
79,7
104.9
92.7
9.76
9.51
7.16
5.08
6.43
5.62
6.21
7.59
9.31
8.03
13.09
20.76
12.49
1.45
1.56
.63
.68
2.09
62.7
208.1
175.5
153.7
132.8
70.7
63.1
57.6
52.0
47.1
11.92
10.94
10.83
9.74
3.82
3.12
3.57
3.53
4.53
1.78
1.90
5.08
4.85
'37.4
9.35
9.01
6.48
5.45
5.63
5.27
6.11
7.91
8.94
8.57
5.8
i43i35'
2.1.8
.17
Ls.80
9.73
(13)
Kg
8.07
9.43
0.8
.89i.5
8.02
5.94
5.61
5.04
6.79
1.69
114.1
kG.'.
78.3
64.8
54.9
35.1
27.6
87.6
97.6
'.4.3
85.'.
9s;l6
12.5'.
101k6
01