THE FACTORS AFFECTING THE LONG RUN SUPPLY
OF RUBBER FROM SARAWAK, EAST MALAYSIA, 1900-1990.
- An Historical and Econometric Analysis.
Timothy D. Purcell
A Report submitted for AG868 Research Project,
in partial fulfilment of the requirements for the degree of
Master of Agricultural Economic Studies
at the University of Queensland
Department of Agriculture
Supervisor: Dr. R.A. Cramb
Associate Supervisor: Dr. N.D. Karunaratne
8th November 1993
ABSTRACT
The factors affecting the supply of rubber from Sarawak,
East Malaysia were identified and reviewed in an historical
framework.
A methodological framework for the general analysis of
economic relationships between variables was reviewed and a
practical application of the methodology to the supply of
rubber from Sarawak was carried out.
An econometric analysis of the long run factors affecting
the production of rubber was carried out:
(1) Two log-differenced autoregressive models of the
rubber supply were formulated.
(2) The models were tested for parameter constancy to
identify structural breaks in the time series and for
structural invariance to determine whether they were suitable
for policy analysis, forecasting, and backcasting.
(3) The variables were tested for bivariate Granger
Causality to determine the relationships between the factors
of production and the output of rubber.
(4) Forecast Error Variance Decomposition analysis of
multivariate Granger Causality was carried out using a Vector
Autoregressive model.
The results confirm the a priori economic theory that
long run changes in supply have been affected primarily by
changes in area under rubber production and long term price
trends. The area planted to rubber has depended upon price
incentives and the availability of scarce labour resources.
Prices have been affected by the supply of rubber from Sarawak
but this is posited to be a reflection of global supply trends
affecting prices.
While the results generally confirm the economic theory,
caution is urged when interpreting the results. The severe
inadequacies of the data used highlights the need for more
accurate time series and the mainly methodological approach of
this study.
i
ii
DECLARATION
This thesis reports the original work of the author
except as otherwise stated. It has not been submitted
previously for a degree at any university.
Timothy D. Purcell
iii
iv
TABLE OF CONTENTS
Page
ABSTRACT............................................... i
DECLARATION............................................ iii
LIST OF TABLES......................................... vii
LIST OF FIGURES........................................ ix
LIST OF APPENDIXES..................................... xi
INTRODUCTION........................................... 1
CHAPTER 1. Factors Affecting Rubber Production.........
1.1. Introduction.................................
1.2. Bio-Physical Factors.........................
1.2.1. Climate...............................
1.2.2. Soils.................................
1.2.3. Land..................................
1.3. Socio-Cultural Factors.......................
1.4. Economic Factors.............................
1.5. Political Factors............................
3
3
4
4
5
6
8
13
20
CHAPTER 2. Historical Overview of the Rubber Industry
in Sarawak........................................
2.1. Introduction.................................
2.2. Rubber Production Before 1910................
2.3. Rubber Production from 1910 to the
International Restriction Scheme of 1934.....
2.4. International Restriction to the end of
Brooke Rule, 1934-1945.......................
2.5. Colonial Rule to Independence, 1946-1963.....
2.6. Independence to the Present, 1963-1990.......
2.7. Future Developments..........................
2.8. Conclusions..................................
32
CHAPTER 3. Econometric Methods for Testing Time Series
Relationships.....................................
3.1. Introduction.................................
3.2. Classical Cowles Commission Methodology......
3.3. Criticism of the Cowles Commission Approach..
3.4. New Approaches to Econometric Modelling......
3.4.1. The Procedure.......................
3.4.2. Data Transformations.................
3.4.3. Testing for Stationarity.............
3.4.4. Testing for Cointegration............
3.4.5. Determining Appropriate Lag Length...
3.4.6. Formulation of the Models............
3.4.7. Testing for Parameter Constancy......
3.4.8. Testing for Structural Invariance....
3.4.9. Testing For Granger Causality........
3.4.10. Formulation of a VAR Model...........
3.5. Conclusions..................................
77
77
80
82
84
85
87
89
93
95
97
99
103
110
113
130
v
23
23
24
43
53
64
73
76
Page
CHAPTER 4. Discussion and Conclusions..................
4.1. Introduction.................................
4.2. Qualitative Review of the Factors Affecting
the Supply of Rubber.........................
4.2.1. Short Run Factors.....................
4.2.2. Long Run Factors......................
4.3. Quantitative Review of the Factors Affecting
the Supply of Rubber.........................
4.3.1. Exogenous Shocks Affecting the
Supply of Rubber........................
4.3.2. Causality Tests for Rubber Production
Variables...............................
4.4. Conclusions..................................
133
133
133
133
134
135
136
140
144
APPENDIX 1............................................. 147
APPENDIX 2............................................. 150
ACKNOWLEDGMENTS........................................ 204
BIBLIOGRAPHY........................................... 205
vi
LIST OF TABLES
Page
1.1:
Comparison between Rubber and Padi
as Cash Crops, 1929-1933..................... 15
1.2:
Comparison between Rubber and Padi as
Means of Selling a Given Quantity of Rice,
Peninsular Malaysia, 1923-33................. 16
1.3:
Economics of Crop Production for an Average
Household at Batu Lintang, Saribas District,
Sarawak, 1979-80............................. 16
1.4:
Price Elasticities of Supply for Rubber in
Peninsular Malaysia (%)...................... 18
2.1:
Development of Rubber Gardens in Santubong
South - West Sarawak, 1905-1960.............. 25
2.2:
World Net Exports of Rubber and U.S.A.
Motor Vehicle Sales, 1906-1910............... 31
2.3:
Area Under Mature Rubber and Estimated
Output, Sarawak 1929-1933.................... 41
2.4:
Planting Density and Yield/Acre on
Smallholdings in Sarawak..................... 43
2.5:
Basic Quotas Under International Rubber
Regulation Scheme 1934-1938 and Renewed
Regulation Agreement 1939-1943, Sarawak...... 44
2.6:
Specifications for Standard Malaysian
Rubber....................................... 68
2.7:
Average Yields of Rubber (kg per ha),
Peninsula Malaysia........................... 71
2.8:
Efficiencies in Producing Latex.............. 72
2.9:
Commercial Yields of Some Major Clones
(kg per ha), Peninsula Malaysia.............. 72
3.1:
Diagnostic Statistics for Testing for
Stationarity................................. 92
3.2:
Akaike Information Criterion and Schwarz
Criterion for determination of the
appropriate lag length....................... 96
3.3:
Diagnostic Statistics for test of Exogeneity. 107
3.4:
Bivariate Granger Causality test for Rubber
Production Variables......................... 112
vii
Page
3.5:
Direction of Granger Causality for Rubber
Production Variables......................... 113
3.6:
Optimal Lag Length for Rubber Production
Variable Equation............................ 116
3.7:
Optimal Lag Length for Population Variable
Equation..................................... 116
3.8:
Optimal Lag Length for Rubber Price Variable
Equation..................................... 117
3.9:
Optimal Lag Length for Rubber Area Variable
Equation..................................... 118
3.10.
Decomposition of Forecast Error Variance by
Strength of Causality........................ 129
3.11.
Standard Errors of Forecasts for VAR System
Variables.................................... 130
viii
LIST OF FIGURES
1.1:
Net exports of Rubber from Sarawak,
1900-1990.................................... 3
1.2:
Bio-physical factors affecting the Rubber
Supply in Sarawak............................ 4
1.3:
Socio-Cultural factors affecting the Rubber
Supply in Sarawak............................ 9
1.4:
Population of Sarawak 1900-1990.............. 15
1.5:
Price of RSS 1 (London) 1900-1990............ 18
1.6:
Area of Land under Rubber Production,
Sarawak 1900-1990............................ 20
2.1:
Stages in Rubber Processing.................. 68
3.1:
Actual and fitted values for Model 1......... 98
3.2:
Actual and fitted values for Model 2......... 98
3.3:
Scaled recursive Chow test for Model 1 at
the 5 per cent significance level............ 101
3.4:
Scaled recursive Chow test for Model 2 at
the 5 per cent significance level............ 101
3.5:
Test for Structural Invariance of Model 1 Plot of Conditional and Marginal Processes... 109
3.6:
Test for Structural Invariance of Model 2 Plot of Conditional and Marginal Processes... 109
3.7:
Impulse-Response of Variables to a Shock
in AREA...................................... 123
3.8:
Impulse-Response of Variables to a Shock
in POPULATION................................ 124
3.9:
Impulse-Response of Variables to a Shock
in PRICE..................................... 124
3.10:
Impulse-Response of Variables to a Shock
in PRODUCTION................................ 125
3.11:
Forecast Error Variance Decomposition of a
Shock to AREA into other Variables........... 125
ix
Page
3.12:
Forecast Error Variance Decomposition of a
Shock to POPULATION into other Variables..... 126
3.13:
Forecast Error Variance Decomposition of a
Shock to PRICE into other Variables.......... 126
3.14:
Forecast Error Variance Decomposition of a
Shock to PRODUCTION into other Variables..... 127
x
LIST OF APPENDIXES
Page
APPENDIX 1.
A.1. Sarawak, East Malaysia Database, 1900-1990........ 147
APPENDIX 2.
A.2. Microfit 3.2 and RATS 4.01 Output................. 150
A.2.1. Testing For Stationarity........................
A.2.1.1. Testing for Stationarity of the Rubber
Production Variable..........................
A.2.1.2. Testing for Stationarity of the Rubber
Price Variable...............................
A.2.1.3. Testing for Stationarity of the Rubber
Area Variable................................
A.2.1.4. Testing for Stationary of the Population
Variable.....................................
A.2.2. Determination of the Appropriate Lag Length.....
A.2.2.1. Lag Length for Rubber Production
Variable.....................................
A.2.2.2. Lag Length for Rubber Price Variable.....
A.2.2.3. Lag Length for Rubber Area Variable......
A.2.2.4. Lag Length for Population Variable.......
150
150
151
152
154
155
155
156
157
159
A.2.3. Formulation of Models........................... 160
A.2.3.1. Formulation of Model 1................... 160
A.2.3.2. Formulation of Model 2................... 161
A.2.4. Test for Parameter Constancy.................... 162
A.2.4.1. Scaled Recursive Chow Test for Model 1... 162
A.2.4.2. Scaled Recursive Chow Test for Model 2... 163
A.2.5. Test for Structural Invariance.................. 163
A.2.5.1. Structural Invariance for Model 1........ 163
A.2.5.2. Structural Invariance for Model 2........ 164
A.2.6. Test for Granger Causality......................
A.2.6.1. Price GRANGER CAUSES Production..........
A.2.6.2. Production GRANGER CAUSES Price..........
A.2.6.3. Area GRANGER CAUSES Production...........
A.2.6.4. Production GRANGER CAUSES Area...........
A.2.6.5. Population GRANGER CAUSES Production.....
A.2.6.6. Production GRANGER CAUSES Population.....
A.2.6.7. Area GRANGER CAUSES Price................
A.2.6.8. Price GRANGER CAUSES Area................
A.2.6.9. Population GRANGER CAUSES Price..........
A.2.6.10. Price GRANGER CAUSES Population.........
A.2.6.11. Population GRANGER CAUSES Area..........
A.2.6.12. Area GRANGER CAUSES Population..........
xi
165
165
166
167
168
169
170
171
172
173
174
175
176
Page
A.2.7. Vector Autoregression Model.....................
A.2.7.1. Determination of Optimal Lag Length......
A.2.7.1.1. Optimal Lag Length for Production
Variable Equation.......................
A.2.7.1.2. Optimal Lag Length for Price
Variable Equation.......................
A.2.7.1.3. Optimal Lag Length for Area
Variable Equation.......................
A.2.7.1.4. Optimal Lag Length for Population
Variable Equation.......................
A.2.7.2. Formation and Estimation of VAR Model....
A.2.7.3. Estimation of Impulse-Response Mechanism.
A.2.7.4. Estimation of Forecast Error Variance
Decomposition of Variables...................
xii
177
177
177
182
188
195
200
202
203
INTRODUCTION
Malaysia is the biggest producer of rubber in the world.
At its production peak in 1977, Malaysia contributed 45% of
the world’s natural rubber production (World Bank 1981). In
1991, Malaysian rubber supplied 28% of the world market (World
Bank 1991).
In 1991 rubber contributed 30% of Malaysian GDP (World
Bank 1991). The magnitude of this percentage makes thorough
study of the factors affecting Malaysian rubber production
valuable. Unfortunately most writing in this area has focused
only on Peninsular Malaysia with very little written about
Sarawak’s rubber industry. This study will investigate factors
determining shifts in rubber production in Sarawak over time.
The extent of the influence of these factors will also be
examined.
After outlining in the first chapter some of the major
factors which economic theory suggests might affect rubber
production in Sarawak, the second chapter examines how these
factors
have
influenced
production
of
rubber
from
the
incentives to first plant rubber in the early 1900’s to the
present. In the third and fourth chapters, some of the more
quantifiable factors are subjected to econometric analysis to
determine
the
strengths
and
directions
of
impacts
those
factors have had on Sarawak’s rubber production.
The main aim of this study is to present a methodological
framework for the analysis of agricultural commodity supply
response to exogenous and endogenous factors. By formulating
several rubber supply models that incorporate those factors of
1
production
identified
in
chapters
1
and
2
as
being
quantifiable, and subjecting them to econometric analysis in
chapter 3, it is hoped that the procedure outlined can be
extended to other commodities.
The methodological framework used in this study differs
somewhat
from
Commission
what
is
termed
Methodology".
This
the
"Classical"
so-called
"New
or
"Cowles
Econometrics"
incorporates concepts such as stationarity and cointegration
into
analysis
procedures
like
structural
invariance
and
superexogeneity, Granger causality and vector autoregression.
These procedures are used to identify when and how exogenous
and
endogenous
shocks
to
the
rubber
affected production and its factors.
2
supply
system
have
CHAPTER 1
Factors Affecting Rubber Production in Sarawak
1.1 Introduction
The supply of rubber in Sarawak has exhibited the peaks
and troughs in production that are characteristic of most
agricultural commodities (See Appendix 1 and Figure 1.1). It
is
suggested
that
these
changes
have
been
the
result
of
rational producer response to the varied factors which affect
rubber production including the standard endogenous factors of
land, labour, capital and management and the exogenous factors
of price, government policy and infrastructure.
Figure 1.1: Net Exports of Rubber from Sarawak 1900-1990.
3
For the purposes of this discussion, these factors will
be grouped as follows; bio-physical, socio-cultural, economic,
and political.
1.2. Bio-Physical Factors
The biological and physical factors affecting the yield
of rubber (Hevea brasiliensis) include climate, soil, land,
area, and technology (See Figure 1.2).
Figure 1.2: Bio-physical factors affecting the rubber supply
in Sarawak
1.2.1. Climate
For the optimum growth of rubber, conditions must be
equatorial with
a temperature of around 28 EC, with relatively
4
little variation, and evenly distributed annual rainfall of
1,500-2,000 mm (EuroConsult 1989).
By
the
above
standards,
Sarawak
is
in
an
ideal
geographical position to benefit from optimal rubber growth.
Located in the humid tropics between 0E50N N and 5EN, Sarawak,
has an equatorial climate characterised by heavy rainfall,
averaging 4,000 mm in Kuching, and high relative humidity
(Jackson
occurs
1968; Sarawak Information Service 1960). Rainfall
throughout
the
year,
peaking
between
October
and
February. Temperatures in the area rarely exceed 37.8 EC and at
sea-level
they
range
between
22.2EC
and
31.1EC
(Sarawak
Information Service 1960). Temperatures vary little throughout
the year and average monthly temperatures showing a range of
only about 2.2EC (Jackson 1968). This small variation is due
to the high incidence of cloud which reduces the mean number
of sunshine hours received daily to between four and seven
hours over most of the state.
1.2.2. Soils
Soils in Sarawak are generally acidic and poor, being
deficient
in
nutrients,
and,
when
exposed,
suffer
rapid
erosion and oxidisation of organic material. Rubber has a wide
tolerance to different soil types and soil moisture factors
such
as
moisture
absorption,
moisture
holding
capacity,
drainage and resistance to flooding. A deep soil is required
to encourage deep penetration of roots and surface rooting,
which results from shallow soils or bad drainage, is a major
factor in wind damage (World Bank 1981).
5
1.2.3. Land.
In peninsular Malaysia, the concerted government policy
to
alienate
land
for
rubber
production
was
a
significant
factor in expanding the estate sector. By comparison, the
Sarawak government under Brooke and subsequent rule, inhibited
estate sector growth while favouring the smallholder sector.
The Sarawak governments under Brooke and later Colonial
rule adopted a Land Code that distinguished between Native
Area Land and Native Customary Land which could only be held
by "natives" of Sarawak and Mixed Zone Land that could be
acquired by non-natives - principally Chinese and Europeans.
This enabled successive Sarawak governments to restrict the
growth of the estate rubber industry (on Mixed Zone Land) by
limiting the transfer of title and thus the potential area of
land under estate rubber.
The adoption of this "smallholder" policy significantly
weakened
Sarawak’s
production
potential
due
to
the
inefficiency of smallholder practices compared to those of the
estate
holders.
This
inefficiency
arose
from
a
number
of
sources including:
(1) The selection of Hevea brasiliensis variety. It was
not
until
the
introduction
of
the
government
sponsored
planting schemes of the 1950’s and 1960’s that smallholders
moved from using unselected clones to using high yielding
clonal varieties such as RRIM 600.
(2)
The
intensity
of
tapping
and
the
use
of
yield
stimulants. Smallholders in Sarawak seemed to use a variable
tapping intensity, tapping heavily when prices were high and
6
cash was needed and reducing their tapping effort when prices
were low. In contrast, the estate sector with its large labour
force had a rigid system of alternate daily tapping that could
not be varied. During the 1950’s and 1960’s, experiments on
the Peninsula were carried out on latex yield under different
tapping intensities and yield stimulants. It was concluded
that the tapping intensity and the use of stimulants were of
short-run significance to yield, for changes in their levels
had immediate effects (Barlow 1978).
(3) The use of fertilisers varied between smallholder and
estates. Since the volume of latex extracted from the rubber
tree
is
small,
nutrient
requirements
are
low.
Fertiliser
applications on smallholdings were not generally carried out
due
to
the
high
cost
of
fertiliser
and
the
practice
of
maintaining a cover crop which increased nutrient cycling and
maintained
moisture
and
temperature
at
optimal
levels.
By
contrast, the estate sector used fertilisers on a wide scale
and, especially in the early years, carried out a policy of
clean weeding to reduce the spread of fungoid diseases and
make tapping easier.
(4) The approach to processing and marketing of rubber
varied from the smallholder to the estates. Smallholder rubber
production
and
marketing
is
not
as
advanced
as
that
for
estates. Most smallholder rubber is marketed through local
Chinese traders as distances to major distribution centres are
large and the difficulties in transport make such journeys
cost prohibitive. On the processing side, most smallholder
processing is done on-site using primitive equipment. Latex is
7
either concentrated to a dry rubber content of 60-70% or
coagulated
using
formic
or
acetic
acid.
Rubber
is
either
manufactured into ribbed smoked sheets (RSS), the major form
of rubber produced by smallholders, or, as is done on estates,
into crumb rubber
and block packaged. During times of high
prices, as in the 1950’s boom, Harrisson (1970) found that
some smallholders were selling rubber in a wet crepe form to
their local Chinese traders. Although they received a lower
price
for
this
form
of
rubber,
the
time
saved
by
the
smallholder in not having to dry the rubber could be used for
more productive purposes.
In conclusion, on both estates and small holdings there
was great upward flexibility in short run supply within the
capacity of the workers on hand and long run supply was only
restricted by the amount of new rubber that could be planted
which was dependent on available area and access to credit
(Barlow 1978; World Bank 1981).
1.3. Socio-Cultural Factors
The main socio-cultural factors that have affected rubber
production in Sarawak relate to the incorporation of rubber
cultivation
into
the
smallholder
lifestyle.
The
degree
to
which rubber is adopted by a smallholder is dependent upon the
smallholder’s goals and his attitudes towards risk. The desire
for cash for a particular use, such as a religious festival or
wedding feast may prompt a smallholder to move away from a
purely subsistence lifestyle to one that incorporates a cash
crop such as rubber. The degree to which the smallholder has
8
adopted rubber as a cash crop in preference to food crop
production
has
depended
on
the
farming
system
used.
The
incorporation of rubber into the agricultural cycle has varied
from smallholder to smallholder in response to his desires
(See Figure 1.3).
Figure 1.3: Socio-Cultural factors affecting the rubber supply
in Sarawak.
In Sarawak there are three basic types of agricultural
production: export crop production; peasant farming based on
wet-padi and other food crops for local consumption; and hillpadi farming on a bush-fallow system. The divisions between
these
are
blurred
and
there
is
widespread
integration
of
different crops and farm activities; thus wet-padi farmers
9
frequently
proportion
also
of
have
rubber
hill-padi
or
farmers
coconuts
own
rubber
and
a
rising
lots
or
plant
pepper (Harrisson 1970).
The production of rubber by smallholders in Sarawak is
unique in that it constitutes a significant departure from the
local way of life (Harrisson 1970). Rubber, unlike most other
agricultural crops grown in Sarawak, produces nothing edible
and nothing of use locally, it is not seasonal and it involves
the use of machinery and chemicals for processing (Harrisson
1970).
Rubber
cultivation
constituted
an
alien
form
of
agricultural production and the initial fear of adoption of
this
new
and
cultivation
unfamiliar
techniques
technology
such
as
(leading
stunting
of
to
trees
poor
due
to
shading out and poor tapping techniques leading to severe
wounding) was offset by its economic attractiveness.
The adoption of rubber by smallholders in Sarawak had
been faster than the adoption of other cash crops like coffee
and
pepper
which
required
substantial
departures
from
the
local way of life. However, the need for processing of the
latex into sheets or crepe required a level of skill from the
smallholder which was not readily apparent. Even though the
planting
and
cultivation
of
rubber
fitted
well
into
the
indigenous lifestyle of shifting cultivation and fruit tree
cultivation, the problems of new technology slowed the rate of
adoption by smallholders. The problems of new technology was
offset by:
(1) Using very simple and often improvised tools;
(2) Setting a very low production quality;
10
(3) Except in periods of high prices, treating rubber as
secondary to other activities; and
(4) Nearly always planting other crops like fruit trees or
vegetables amongst the rubber.
Harrisson’s (1970) survey of South-West Sarawak gave a
comprehensive description of the cultivation of rubber in a
bush fallow system. The approach adopted was:
(1) To get as much as possible in food crops out of the
fertile ground after felling.
(2) Plant durian and other fruit first.
(3) Remove any tapioca or banana surviving, once the rubber
has germinated.
(4) Weed germinated rubber out from around durian saplings as
required.
(5) Leave with minimum attention once rubber and fruit are
well established.
The process of developing a rubber garden from the virgin
or secondary forest stage was described:
The vegetation cover in the proposed plot is slashed and
cut with parangs. The cutting is left to dry for about a month,
before being burned during the relatively dry months from June
onwards. Within two or three days after the soil has been
sufficiently softened by a suitable shower, initial cash crops
such as padi maniok, bananas, or sugar cane are first grown. Seed
padi, Indian corn and vegetables are dibbled into the ground by
the tugal method, identical to that practised among primitive
cultivators in the Pahang delta and other parts of Malaya and
South-east Asia where parangs and dibbling sticks are used as
farming instruments rather than changkols (mattocks).
Except for the occasional weeding of grass and other weeds,
and sporadic eradication of pests such as monkeys, the farmers
seldom visit these initial plots until harvest time. The average
such field is a miscellany of food-crops fenced in by uncleared
secondary or primary jungle. The crops ripen at different periods
after planting; vegetables and maize are harvested within two to
three months, sweet potatoes and tapioca within about six months,
bananas 10 months or so. Thus not only is the labour of
harvesting spread out at succeeding stages during the year,
assuring the farmer of a continuous food supply throughout that
year; but also the storage requirements, which in the humid
climate are a necessary adjunct to any extensive successful
farming, are thereby minimised.
11
This productive process is only possible once. In the
second year the rubber seedlings are planted about nine to 12
feet apart. Still, subsidiary crops, particularly corn and
perhaps sweet potatoes, will be planted again; but not padi or
anything heavy. Such subsidiary cropping may continue for three
and even four years.
The young rubber trees, running at nearly 1,000 to the
acre, are here tappable from six to eight years after planting.
Poor drainage on the sandstone soil of the area, close planting,
considerable degree of carelessness about clearing stumps which
form favourite haunts for white ants, and crude methods of
tapping, all add up to poor yields by outside standards.
According to the Agricultural Department in Kuching, the average
holding in this locality produces half or less than half the
’cropper’ [sic] yield. Offsetting this, however, is the important
fact that to these rubber gardners the work is essentially
subsidiary and is not - as it is in many parts- the primary
economic job. (Harrisson 1970, pp 405-406).
The socio-cultural factors outlined above have affected
rubber
production
smallholders
have
by
creating
responded
an
environment
rationally
to
in
which
incentives
to
increase their welfare. The smallholders in Sarawak have been
quick to adopt a cash economy to satisfy their wants and the
adoption of rubber as an alternative cash crop to coffee and
pepper
has
been
quick
once
incentives.
12
there
was
enough
economic
1.4. Economic Factors
Both Boeke’s backward bending supply curve theory (Boeke
1953) and Myint’s (1964, 1968) enclave dualism
and vent-for-
surplus theory of trade attempt to explain the development of
a modern, cash crop system from a traditional, subsistence
farming system. While Boeke views the development of a modern
sector, including, for example, cash crop production, as an
outcome of smallholders fulfilling their cash needs, Myint
views the development of a cash economy as a result of surplus
productive
capacity
in
the
presence
of
low
population
pressure. The opening of a subsistence agrarian society to
external
trade
factors
of
greater
output
Sarawak
economy
creates
production
for
opportunities
like
the
seemed
labour
export
to
to
and
land
sector
follow
relocate
to
produce
(Todaro
this
surplus
a
1989).
The
with
the
pattern
development of a cash crop system incorporating crops such as
coffee, pepper and rubber. While surplus land and labour not
used for subsistence could be used for alternative activities,
this had to be tempered against the seasonality of labour and
land
resources
under
the
predominant
shifting
cultivation
system present.
Many researchers (Bauer 1948, Cramb and Reece 1987) claim
that the labour profile of most subsistence farming systems
exhibit seasonal demand patterns where there may be a labour
shortfall
in
evidence
from
some
months.
Sarawak
This
(Sarawak
is
supported
Gazette
by
1951)
anecdotal
where
the
Resident of the Second Division complained about the lack of
labour for road maintenance gangs when rubber prices were
13
high. The lack of labour (in addition to the socio-cultural
factors outlined above) in Sarawak contributed to a lower than
what would be expected adoption rate of rubber as a cash crop.
Reece (1987) reports of the concerted efforts of the Charles
Brooke administration to increase the size of the labour force
(See Appendix 1 and Figure 1.4) in Sarawak by reducing the
price of opium to attract migrant (especially Chinese) labour.
The
response
of
smallholders
to
changes
in
price
of
rubber and the rapid adoption rate compared to other cash
crops like coffee and pepper suggest that subsistence farmers
are very much price responsive. This was confirmed by Bauer
(1948) who has calculated the gross margins for rubber and
rice production and concluded that, when prices of rubber was
high relative to rice, it was more economic to produce rubber
and purchase rice (See Tables 1.1, 1.2). Bauer’s 1948 study
showing that rubber gave higher returns than rice and enabled
more rice to be purchased than would otherwise be grown has
been supported by Cramb’s (1987) study of the Iban in Sarawak
(See Table 1.3). Cramb’s 1987 study shows that the gross
margins
for
cash
crops
such
as
pepper
and
rubber
give
substantially higher returns than cultivating rice and the
excess cash obtained from cash crop production could be used
to purchase rice and other needs.
14
Figure 1.4: Population of Sarawak 1900-1990.
TABLE 1.1: Comparison between Rubber and Padi as Cash Crops,
Peninsular Malaysia, 1929-1933.
Year
Av. Yield
of Padi
per ac.
(gantangs
)
(1)
Price per
gantang
(¢)
(2)
Gross
proceeds
from padi
($ per
ac.)
(3)
Cash
equivalent of
cost of
padi production
($)
(4)
Assumed
net
proceed
s from
padi
($)
(5)
Estimated
net proceeds of
rubber as
Table 1
col. 6
(6)
Difference
in favour
of rubber
($)
(7)
1929
1930
1931
1932
1933
202
180
248
272
260
14.0
13.0
8.0
7.5
6.7
28
23
20
20
17
14
13
7
6
6
14
10
13
14
11
142
69
32
19
36
128
59
19
5
25
(Source: Bauer 1948)
15
TABLE 1.2: Comparison between Rubber and Padi as Means of
Securing a Given Quantity of Rice, Peninsular Malaysia, 192933.
Year
Av. yield
of
smallholder
s rubber
(lb per
mature ac.)
(1)
Singapore
price of
ribbed
smoked
sheet. (¢
per lb)
(2)
Assumed Av.
price received by
smallholder
s (¢ per
lb)
(3)
Estimated
gross proceeds per
ac.($)
(4)
Assumed
expenses
per ac.
($)
(5)
Estimated
net
proceeds
per ac. ($)
(6)
1929
1930
1931
1932
1933
485
460
445
385
465
34.5
19.3
10.0
7.0
10.2
30.5
16.8
8.0
6.0
8.7
150
77
36
23
40
8
8
4
4
4
142
69
32
19
36
Year
Av. retail
price of
No. 1
Rangoon
rice in
Malacca (¢
per
gantang; 1
gantang
rice = 8
lb)
(7)
Gantangs
rice
obtainable
with
proceeds of
rubber
(6)/(7)
(8)
Av. yield
of cleaned
rice
(gantangs
per ac.)
(9)
Rice
equivalent
of
expenses
(gantangs)
(10)
Net yield
of rice
(9)-(10)
(11)
Balance in
favour of
rubber in
gantangs of
rice
(8)-(11)
(12)
1929
1930
1931
1932
1933
52
46
28
22
23
273
150
114
86
156
83
73
101
110
106
30
30
30
30
30
53
43
71
80
76
220
107
43
6
80
(Source: Bauer 1948)
TABLE 1.3: Economics of Crop Production for an Average
Household at Batu Lintang, Saribas District, Sarawak, 1979-80
Crop
Planted area (ha)
Production (kg)
Yield (kg/harvested ha)
Gross income (M$)
Variable expenses (M$)
Gross margin (M$)
Gross margin/harvested ha (M$)
Current labour (work days)
Work days/harvested ha
Gross margin/work day (M$)
(Source: Cramb 1987)
16
Rice
Pepper
Rubber
1.3
794
611
750
36
714
549
274
211
2.59
0.2
445
2227
800
110
690
5219
125
950
5.49
10.5
474
236
673
12
592
1184
122
61
4.85
With
the
competitive,
market
even
for
small
natural
changes
in
rubber
the
being
supply
highly
and
demand
balance are reflected in prices. The main factors that affect
the short-term natural rubber prices are changes in stock
inventories
and
inflation
rates.
In
the
long
term
rubber
prices are largely determined by the trend in synthetic rubber
prices (World Bank 1981). Synthetic rubber prices set the
ceiling
and
floor
(polyisoprene
rubber
(IR)
and
styrene-
butadiene rubber (SBR) respectively). While changes in the
supply of rubber affect prices, the short-run response of
rubber production to changes in price is inelastic. The low
price elasticities for supply combined with the variability in
demand
and
supply
provide
an
explanation
for
the
sharp
instability in prices for natural rubber (World Bank 1981)
(See Appendix 1 and Figure 1.5).
While supply elasticities of rubber have been calculated
for Peninsular Malaysia (Behrman 1971; Chan
1962; Cheong
1971; Chow 1976; Tan 1984; Wharton 1963) (See Table 1.4), none
have been done for Sarawak. Cheong (1971), Wharton (1963), and
Chow (1976) have shown that smallholder rubber producers are
more responsive to price fluctuations than estates with short
run supply elasticities being 0.2-0.37% and Tan showing that
short
run
supply
elasticities
for
smallholder
were
0.6865
compared to 0.3007 for estates. While downward adjustments in
short
run
supply
on
estates
were
inflexible
due
to
high
overheads and a five to seven year lag in maturation of rubber
trees, on small holdings this was not the case due to low
17
Figure 1.5: Price of RSS 1 (London) 1900-1990
overheads
and
the
high
mobility
of
such
labour
into
alternative crop production (Barlow 1978; World Bank 1981).
TABLE 1.4: Price Elasticities
Peninsular Malaysia (%).
Year
Source
1949-59
1949-63
Chan 1962
Behrman 1971
1954-68
1954-61
1956-74
1956-78
(Source:
of
Supply
Producers
All Producers
All Producers
Estates
Small Holdings
Cheong 1971
Estates
Small Holdings
Wharton 1963
Estates
Small Holdings
Chow 1976
All Producers
Estates
Small Holdings
Tan 1984
Estates
Small Holdings
Barlow 1978, Tan 1984)
18
for
Short Run
0.12
0.14
0.09
0.18
0.05
0.25
0.04-0.1
0.20-0.37
0.15
0.03
0.29
0.3007
0.6368
Rubber
Long
Run
0.17
0.15
0.21
8.92
-1.0
in
Upward
adjustments
at
times
of
high
prices
were
encouraged by the low opportunity cost of labour; there was
then every incentive to increase the intensity of tapping. The
effects
of
these
economic
conditions
were
counteracted,
however, by the greater sluggishness in first-level prices,
which
were
those
relevant
to
smallholders,
as
opposed
to
inelastic
to
quotations in the primary markets.
Thus
price,
although
owing
to
the
the
supply
of
further
rubber
physical
was
factor
of
its
perenniality and to the economic condition that alternative
ventures
were
scarce,
in
the
short
run
this
aspect
was
probably more significant on estates than on smallholdings.
The high cost of establishing a smallholder rubber garden
was
an
initial
constraint
to
adoption
of
rubber
as
an
alternative cash crop to coffee, pepper and gambier. Cramb
(1987) reports that establishment costs in the 1910’s were in
the vicinity of M$25 per hectare and that only households with
sufficient capital reserves (those that had benefited from the
coffee boom of the 1880-90’s) were the ones that initiated
rubber planting in the smallholder sector (See Appendix 1 and
Figure 1.6). The high cost of capital obtained from local
Chinese
traders
inhibited
investment
in
rubber
by
those
without reserves.
The distance to markets and the high cost of transporting
the rubber sheets to distribution centres played an important
role in limiting rubber production to those households who had
access to transport routes, namely river transport. Even then
Masing (1987) reports that in the 1980’s it would cost M$150
19
Figure 1.6: Area of Land under Rubber Production, Sarawak
1900-1990.
in fees to transport M$280 worth of rubber from the upper
Rejang to market 150 km downstream.
1.5. Political Factors
From
initial
Brooke
government
reluctance
to
allow
European investment in estate production to the Colonial and
Malaysian governments attempts to increase rubber production,
successive
smallholder
government policies have attempted to influence
production.
Both
government
policies
and
international political events have played an important role
in structural change of the rubber industry in Sarawak.
20
The
change
government
to
in
governments
total
collapse
from
of
a
conservative
exports
under
Brooke
Japanese
occupation and then to more progressive Colonial and Malaysian
governments have caused changes in agricultural policies, as
from the rubber restriction scheme of the 1930’s to the FELDA
land
schemes
of
the
1960’s,
which
have
influenced
the
production of rubber.
On the international scene the changes in price, due to
automobile tyre demand in the western countries, have caused
rubber production to fluctuate. Further, international shocks
like the Great Depression, the occurrence of World War II, the
Korean War and the oil shock of the 1970’s have influenced
production to a greater extent than any endogenous factor
could. These exogenous factors have changed the structure of
the industry and the supply-demand relationships.
21
22
CHAPTER 2
HISTORICAL OVERVIEW OF THE RUBBER INDUSTRY IN SARAWAK
2.1. Introduction
The
rubber
industry
in
Sarawak
responded
in
varying
degrees to the factors outlined in chapter 1. This chapter
reviews historically how those factors interacted to cause
changes in the output of rubber from Sarawak.
The development of the Sarawak rubber industry up to 1960
is best summarised by a look at one particular representative
area, Santubong (See Table 2.1). This area was a starting
point for the fledgling Sarawak rubber industry.
The chapter is divided into sections, each dealing with a
significant period in the development of the rubber industry.
The first covers the beginnings of the rubber industry in
Sarawak up to the 1910 boom in rubber prices and explores the
interrelationships between prices, socio-cultural factors and
government policies.
The second section covers from the end of the 1910 boom
to the start of Sarawak’s participation in the International
Rubber Restriction Scheme in 1934. This section explores the
effect of periods of boom and bust in the international arena
on the production and demographics of the rubber industry in
Sarawak.
The third section covers from the start of Restriction to
the end of the World War II Japanese occupation. In this
section the effect of Restriction and government bureaucracy
on the production of smallholder rubber is outlined as well as
the effect on production of the War and the formation of the
23
strategic stockpile of rubber by the United States.
The fourth section covers the post war period up to the
formation of Malaysia in 1963. The policies of the Colonial
government affecting smallholder rubber production as well as
international factors affecting production such as the Korean
War boom and the rise of the synthetic rubber industry are
examined.
The fifth section covers the post-independence period
from
1963
to
1990
looking
at
the
effect
of
Malaysian
government policies on rubber production, exogenous shocks and
changes in planting, processing and marketing technology.
The last section looks briefly at what factors and trends
will affect the industry in the years to come.
2.2. Rubber Production Before 1910
When Henry Wickham brought a shipment of 70,000 seeds out
of Brazil in 1876 to establish a rubber industry in the East,
22 seedlings ended up in Singapore to form the basis of the
Malayan and Sarawak rubber industry. Rubber was first planted
in Sarawak in 1881 when two or three seedlings from Singapore
were planted in Kuching (Jackson 1968). Due to the lack of
interest
by
planters,
especially
smallholders,
commercial
planting did not get underway until 1905.
The general unwillingness of smallholders to undertake
rubber production at the beginning of the 1900’s was due in
part to problems encountered with other cash crops; the price
of coffee had crashed after the high prices of the mid 1890’s
(Barlow
1978).
In
addition,
the
24
considerable
clearing
and
maintenance work involved with planting rubber and the long
wait before returns were secured acted as a disincentive.
Accordingly, smallholders seemed to be unwilling to change
from a traditional farming system based on rice to a cash crop
economy based on rubber (Barlow 1978).
TABLE 2.1: Development of Rubber Gardens in Santubong, SouthWest Sarawak. 1905-1960.
Date
No. of
gardens
planted
Owners
1905
1
H.H. Everett planted 25 acres.
1908-10
4
Hajis Man, Aim, Gani and Taip, following H.H.
Everett; imported seeds from Malaya $4.50 per 100.
1911-16
0
Others watching above; the "war boom", price up to
$3.50 a kati.
1917
13
Seeds and trees purchased from above, $1.00 per
100.
1918-20
13
12 of these in 1920.
1921-5
25
All 25 completed in 1925.
1926-30
56
53 of these in 1926 (incl. several of the larger
gardens, 7-12 acres).
1931-5
5
All planted 1931-2; the "slump".
1936-40
1
Rubber restriction; government compulsorily
reduces tapping.
1941-4
3
Japanese Occupation period.
1945-50
10
8 in 1945-6 post-Occupation price boom.
1951-5
0
no activity and 12 gardens almost gone out of use.
1956-60
0
(Source: Harrisson 1970)
Another constraint on the rapid adoption of rubber as a
new
industry
was
the
deliberate
policy
of
the
Brooke
government to discourage foreign investment, especially of a
speculative nature (Reece 1987). James Brooke wrote in his
journal:
25
I am quite opposed to any rash scheme which would put money into
my own pockets, and leaving neither honour nor character behind,
ruin the country I am
so anxious to civilise, and turn out a
bubble. A small company and moderate capital are enough at first,
and from small beginnings we should so gradually advance, that at
length a vast country, whose resources are incalculable, would
surely be developed: but in your vast schemes backed by millions
never did, and never will, open a new country properly (Mundy
1848 p 55).
There are several theories about why this policy was
adopted. Reece (1987) suggested that Brooke policy was selfserving in that the Brookes apparently feared that their power
would
be
undermined
by
European
planter
interests.
Other
researchers such as Pringle (1970) suggest that it was more of
an
attempt
to
disruptive,
sensibly
develop
exploitative
the
investment
country
that
without
the
seemed
to
characterise European colonialism. Andrew McPherson, a Brooke
traditionalist,
argued
that
foreign-owned,
large-scale
agricultural industries had not benefited peasant populations
elsewhere
in
Southeast
Asia.
He
claimed
that
Sarawak’s
strength lay primarily with its smallholder proprietors. He
believed that Sarawak’s natural resources should be conserved
"until they can be exploited for Sarawak’s benefit and not for
the benefit of shareholders sitting in England" (Reece 1982 p
53).
In 1909, with the boom in rubber production in Malaya,
and companies paying dividends of at least 30% and some up to
325% to their shareholders, there was a great pressure for the
Brooke government to allow speculative investment by European
planters in the Sarawak rubber industry (Barlow 1978). In
spite of this, in May 1910 the Brooke government refused
26
concessions
"mania
at
on
the
the
grounds
present
that
which
rubber
did
not
speculation
suit
the
was
quiet
a
non-
speculative spirit of the country" (Sarawak Gazette 1910a).
Charles Brooke had carried on James’ aversion to speculative
investment by refusing all applications for the development of
plantation rubber. He told his son Harry, who had approached
him on behalf of a British consortium that:
I have had frequent applications of a similar kind from many
others within the last month. But not believing in the permanence
of the Rubber boom I don’t wish Sarawak to be a great producer of
this article - except it can be planted by natives who could
afford to sell it a 20th part less than European Companies, and
this is what it will come to another and not distant day... I
hate the name of rubber and look upon it as a very gigantic
gamble, as it now turned to account in making the fortunes of
many and another day will be the means of depriving the poor and
ignorant shareholders of their hard-earned savings (Pringle
1970 p 360).
The concern Charles Brooke felt over the pressures of
speculation
prompted
him
to
pass
legislation
to
prevent
indigenous persons allowing European planters to acquire their
land for rubber production. He issued a special Order in
November 1910 forbidding "native inhabitants of Sarawak, and
settlers
of
Chinese,
Indian,
Eurasian
or
any
Eastern
Nationality" from selling their rubber gardens to any European
firm or individual (Sarawak Gazette 1910b).
Despite
foreign
the
Brooke’s
investment,
the
government
early
fierce
development
opposition
of
the
to
rubber
industry did not totally exclude the establishment of estates.
Planting began on the Borneo Company’s Poak concession in 1902
with the formation of the 1000 hectare Dahan Estate (Cramb
27
1987). Five years later the Sungei Tengah Estate, which also
belonged
to
the
Borneo
Company,
was
planted
with
rubber
(Freeman 1970).
By 1906 research advances had enabled increased yields
and a reduction in production costs. It was found that:
(1) The yield of latex was directly related to the treegirth and since the latter increased more rapidly in the
wider-spaced blocks, less dense planting was recommended.
This
also
helped
to
prevent
the
spread
of
fungoid
diseases.
(2) The yield of dry rubber varied over the year and
there was a preference for morning rather than evening
tapping as this gave a better latex flow.
(3)
A
modified
farrier’s
knife
developed
for
tapping
allowed for speedier and more accurate work than the
chisel and mallet.
(4) Advances were made in tapping away from the Brazilian
methods of a "v" shaped incision into the inner cambium
to a method of cutting a thin layer of bark in a sloping
cut to the right. As opposed to the Brazilian method
which had low yields of rubber and caused damage to the
tree, this new method, with increased cutting, increased
yields and renewed the bark (Drabble 1973).
This increase in production provided further incentives
to plant rubber as profits were increased.
The first 200 kgs of rubber was exported from Sarawak in
1909 and a further 9.66 tonnes was exported in 1910 (See
28
Figure
1.1)
when
boom
prices
peaked
at
M$9697/tonne
(See
Figure 1.5). Only a very small acreage had been planted, but
the
continuation
of
high
prices
was
an
incentive
for
smallholders to plant the new crop.
The
excellent
high
prices
return
smallholders
being
with
offered
costs
being
for
low,
rubber
gave
especially
an
for
who did not need a large capital outlay. In
Malaya the total cost of bringing 1 ha of estate rubber into
tapping
from
administrative
the
jungle
overheads,
stage,
was
with
M$500-M$600
allowance
(Barlow
for
1978).
Rubber reached maturity after 5 years and yields in the first
year of tapping were usually around 250kg per hectare. With
the cost per kg landed in the United Kingdom rarely exceeding
M$1.50, and an average landed price of M$3.50 per kg, a net
return of $2.00 per kg could be obtained. This allowed the
initial capital outlay to be recovered in less than 3 years
(Barlow 1978).
In Sarawak it was comparatively expensive to establish a
rubber
garden
because
of
the
price
of
planting
material.
Rubber seed sold for five cents each, and in 1910 the Sarawak
Gazette was advertising rubber stumps for sale at $5 per 100
(Cramb 1987). The high startup cost meant that only households
with adequate reserves could plant extensively from the outset
and so make the most of the boom years.
It was only after the peak of the 1909-10 boom that
smallholders, after seeing the success of the big estates,
began planting rubber with zeal. The Iban of the Saribas were
one of the first group of smallholders to plant rubber (Cramb
29
1987). In the riverine zone at Stambak, below Betong, the
headman Budin planted over 4,000 seedlings in 1909, apparently
with
seed
(Pringle
brought
1970).
back
from
Another
Singapore
early
by
planter,
his
son
Penghulu
Lumpoh
Saang,
obtained seed from Stambak to plant at Pelandok in the Paku
branch of the Saribas in 1912 (Cramb 1987).
Initially, it was the Iban communities with a large land
base and an accumulated surplus from the gutta and coffee
booms which embarked on rubber planting (Cramb 1987). It was
attractive to the Iban for reasons other than price. Rubber
fitted easily into the Iban farming system and grew well in a
wide
range
of
soil
types.
The
rubber
seedlings
were
interplanted with the rice after weeding, or in the stubble
following
the
rice
harvest.
The
forest
regrowth
would
be
slashed periodically in subsequent years to promote growth,
and after eight to twelve years the rubber would be ready for
tapping. This low labour-input approach to crop establishment
was already used in cultivating indigenous tree crops and
similar to a managed forest-fallow system (Cramb 1987).
The high prices for rubber in the early 1900’s (See
Figure 1.5), resulting largely from demand for rubber for
pneumatic tyres in the United States, was the main reason for
the
expansion
(Barlow
1978;
M$7784/tonne
of
rubber
Bauer
for
RSS
production
1948).
1
in
Prices
London
throughout
in
and
1900
East
were
declined
Asia
around
slowly
to
M$6664/tonne by 1905.
The fall in the price of rubber in 1907-08 to a low of
M$4937/tonne was due to a sharp recession in America, whose
30
imports of rubber had been averaging a steady 40% of the
annual world total since 1900 (Bauer 1948). Following the
recession the prices for rubber rose rapidly in 1909 and 1910
to a peak of M$9697/tonne.
There were several factors responsible for the 1909-10
boom in prices. First, the demand for rubber in America for
the manufacture of vehicle tyres increased with the initiation
of mass-production by Henry Ford in 1907 (Drabble 1973). (See
Table 2.2). In addition, the average rubber tyre lasted only
about 2,000-3,000 miles at speeds of 20 mph or below thus
creating a steady demand for new tyres (Drabble 1973).
Second, in 1908 the principle exporter of wild rubber,
the Brazilian State of Para, authorised the Banco do Brasil to
make cash advances to producers to enable them to withhold
rubber from the market in order to raise prices. This was
successful up to the first months of 1910 by which time the
plantations
in
the
East
were
beginning
to
increase
their
output. Towards the end of the year the Brazilian interests
were obliged to liquidate stocks, causing the price to fall to
M$4957/tonne in 1911 (Drabble 1973).
TABLE 2.2: World Net Exports
Vehicle Sales, 1906-1910
Year
Wild Rubber
(tonnes)
of
Rubber
Cultivated Rubber
(tonnes)
1906
61,028
1907
97,317
1908
65,693
1909
74,066
1910
84,694
(Source: Drabble 1973)
2,936
7,940
5,247
5,667
11,198
31
and
U.S.A.
Motor
Vehicle Sales
(units)
34,000
44,000
65,000
127,287
187,000
2.3.
Rubber
Production
from
1910
to
the
International
Restriction of 1934
In Malaya, the big extensions which had been made in
planted area after 1904 began to affect rubber production from
1910 with massive increases in rubber production. The increase
in output led to steep declines in prices with the price of
RSS
1
in
London
falling
from
M$9697/tonne
in
1910
to
M$4957/tonne in 1911 and to M$4468/tonne in 1912 (See Figure
1.5).
In
July
1913
the
Rubber
Growers
Association
(RGA)
appointed a committee to look into the fall in price (Rubber
Growers Association 1913). It was concluded after talks with
brokers, dealers and manufacturers that the fall in price was
a normal one due to the increased output of plantation rubber.
The decline in price was also accentuated by general financial
stringency, together with floods and strikes in Akron, U.S.A.,
one
of
the
principle
manufacturing
centres.
The
committee
concluded that there was no over-production but exports had
risen faster than the channels of distribution could handle
smoothly and market feeling was affected by estimates of large
future production (Bauer 1948).
During 1913-14 the prices for plantation rubber declined
steadily from M$3023/tonne in 1913 to M$2295/tonne in 1914.
This
was
mainly
the
result
of
increasing
supplies
from
cultivated sources like Malaya and the Netherlands East Indies
(N.E.I.). Demand remained steady in spite of some doubt among
manufacturers about the quality and uniformity of plantation
rubber
as
opposed
to
wild
Brazilian
32
rubber
(Bauer
1948;
Drabble
1973).
market
to
Within
their
own
this
context
advantage
at
buyers
the
manipulated
large
the
fortnightly
auctions in London (Bauer 1948).
There
industry
was
with
a
rapid
the
loss
outbreak
of
of
confidence
WWI.
in
European
the
and
rubber
Eastern
markets fell into enemy hands and access to these markets was
cut off. However, the initial effects of the war were much
less adverse than expected. The loss of European and Eastern
markets was offset by larger imports by Britain and the United
States. By 1916 the United States was absorbing 77% of the
total international production of rubber (Lim 1967). In spite
of this enormous increase in rubber absorption, there was
still
a
drastic
decline
in
raw
rubber
prices,
from
M$2429/tonne in 1915 to M$1805/tonne in 1920. This was caused
simply by supply exceeding demand at the start of the war
(Drabble 1973; Lim 1967).
Exports from Sarawak slowly increased over this time from
29.28 tonnes in 1911 to 549 tonnes in 1915 as rubber gardens,
planted
before
the
boom
of
1909-10
had
just
begun
to
be
tapped. In Sarawak, those smallholders who had planted some
rubber
before
1910
had
good
returns
from
these
initial
planting. This stimulated a planting boom in the first few
years of the war as other smallholders tried to participate in
the returns. Planting extended among the Malays of the southwest and the coastal Melanaus began to abandon or neglect
their
sago
(Jackson
1968).
The
Foochow
settlers
at
Sibu,
augmented by the arrival of new immigrants, opened holdings on
the Rejang river and in the Sarikei area and rubber planting
33
spread to the Iban communities of the near interior (Jackson
1968).
Demand, which had as stated above, been initially high at
the
beginning
of
the
war,
dropped
off
as
the
American
automobile industry moved to the production of war goods with
the entry of the United States into WWI. This the decline in
demand for rubber goods from America prompted a slowdown in
planting
by
smallholders
in
Sarawak,
but
there
was
no
incentive to decrease the rate of tapping as costs were still
way below returns.
In Malaya, the economic setbacks proved beneficial to the
estate
sector
techniques
in
highlighting
the
need
for
more
economic
of production, processing and marketing (Barlow
1978; Drabble 1973). The two main advances during the first
world war were a reduction in the cost of management and a
great enhancement in the quality of the product. As a result
of the latter the premium of "fine hard para" from Brazil over
smoked sheet narrowed considerably (Barlow 1978).
The curtailment of rubber absorption with the entry of
the
United
States
into
WWI
and
the
move
to
restrict
the
shipment of rubber due to the shortage of shipping and to
prevent rubber falling into the hands of the enemy, caused the
accumulation of large quantities of stock in South-East Asia.
At
times,
with
the
large
stock
buildup,
Singapore
rubber
prices were only half those prevailing in London and New York
(Drabble 1973).
This accumulation of rubber stocks due to the decline in
demand
and
shortage
of
shipping
34
resulted
in
the
first
production restriction scheme curtailing output to 80 percent
of
the
1917
voluntary,
production
applicable
to
level
in
members
Malaya.
The
of
Rubber
the
scheme
was
Growers’
Association only, and had very limited success (Drabble 1973).
The end of hostilities prompted the abandonment of the
restriction scheme was and net imports from Malaya increased
greatly in 1919-20 to 278,000 tonnes. This was due to a mini
boom in the industrialised countries with the United States
and Britain increasing demand for consumer goods (Barlow 1978;
Bauer
1948).
Because
Sarawak
had
not
participated
in
the
restriction scheme there was no flush of increased production
and the 1,606 tonnes of rubber exports in 1920 was actually
down from the 2,261 tonnes in 1919.
Rubber manufacturers had a favourable long term view of
the prospects of their finished products and made forward
contracts for raw rubber 2 to 3 years ahead (Barlow 1978;
Drabble 1973). However, prices fell from M$2,003/tonne in 1919
to M$1,805/tonne in 1920 due to an increase in rubber supplies
as the stocks accumulated in the United States were released
and the rubber which had been planted between 1910 and 1913
started coming into production.
In April 1920 at a conference in Brussels, it was decided
to raise bank rates and restrict credit in order to check
inflation
caused
by
speculation
(Drabble
production
in
the
post-war
1973).
With
industrialised
boom
the
in
production
reduction
countries
in
declined
and
credit
and
a
depression occurred in the luxury goods sector, especially in
the United States. Accordingly, demand for rubber also fell
35
(Drabble 1973). As a consequence prices for rubber fell from
M$1,805/tonne in 1920 to M$767/tonne in 1921.
The
short
but
severe
post-war
depression
in
1920-21,
caused by the restriction of credit and the replacement of
fabric by harder-wearing cord as a base for vehicle tyres,
caused the absorption of rubber to decline (Barlow 1978).
Despite a sharp recovery in 1922, the increase in production
meant that stocks continued to rise and as a result prices
fell from M$1,805/tonne in 1920 to M$728/tonne in 1922 (See
Figure 1.5).
With
such
a
decline
in
the
fortunes
of
the
rubber
industry and the resultant decline in government revenues,
international
seriously
restraints
considered.
restriction
in
1918,
on
the
The
output
brief
being
largely
of
rubber
attempt
at
confined
to
were
voluntary
overseas
companies in the Malay States, the East Indies and Ceylon,
helped
show
producers
that
was
comprehensive
necessary
to
action
achieve
a
by
all
types
significant
of
impact
(Barlow 1978; Bauer 1948). Initial suggestions for universal
compulsory restriction were rejected by Malaya and Britain
which still clung to the notions of laissez-faire (Barlow
1978).
While about 40,500 hectares had been planted with rubber
in Sarawak during the early to mid 1920’s, much of this was
immature, and exports in 1924 totalled only 6,753 tonnes (See
Figure 1.1). The high prices for rubber during 1919 and 1920
had
brought
renewed
activity
with
widespread
planting
in
western Sarawak. The Chinese invested considerable amounts in
36
rubber around Sibu, Binatang and Sarikei whilst other Foochows
opened holdings at this time near Kapit and Sebauh and in the
Baram valley. Many Iban communities had also planted rubber
during the 1920’s (Cramb 1987).
The early 1920’s saw changes in production technology
that
led
to
increased
yields
and
reduction
in
costs.
In
Malaya, estates had favoured clean weeding in an effort to
maximise the supply of nutrients to the trees and reduce the
spread
of
diseases
(Bauer
1948).
However,
the
absence
of
covers led to serious erosion, the removal of nutrients, and
an
accelerated
productivity
spread
in
some
of
root
areas
diseases.
was
As
seriously
a
result
reduced.
the
This
explains the early inferiority of yields on estates compared
to those on smallholdings (Bauer 1948). The smallholdings,
with minimal weeding and the establishment of good cover-crops
under the rubber trees, managed to reduce the spread of fungal
diseases
reduction
and
in
smallholdings,
improve
losses
RGA
soil
of
condition.
trees
research
to
officers
In
addition
fungal
had
to
the
diseases
on
been
conducting
experiments which, by 1922, confirmed that alternate daily
tapping gave less rubber per acre, but a higher yield per
tapper. This permitted a lower tapping cost, and the rate of
bark consumption was reduced by half (Bauer 1948, Drabble
1973).
With the decline in price and the rise in stocks, there
was renewed pressure for supply controls. In late 1921 Sir
James Stevenson was appointed to chair a committee to advise
on remedies to the situation in the rubber industry. With four
37
of the eight on the committee being on the council of the
Rubber
Growers’
Association
and
a
further
member
having
financial interests in rubber, it was obvious even then that
this meant that the curtailment of rubber output would be
officially supported (Barlow 1978).
There was trouble getting the Dutch producers in the East
Indies to agree on restriction and eventually the idea of
their participation was abandoned (Barlow 1978). Restrictions
of output under the Stevenson Scheme came into effect on 1
November 1922.
In Malaya and Ceylon, exports under the Stevenson Scheme
were
limited
output"
of
scheme
the
September
to
a
each
plantation.
price
1922
stipulated
of
to
rubber
percentage
With
the
increased
M$1,100/tonne
at
of
the
"standard
announcement
from
the
end
of
the
M$610/tonne
of
the
in
year.
However, the crude formula by which the scheme was managed had
a seesawing effect on the price and output of rubber with
expectations of chronic shortages in 1925 causing a peak of
M$4,170/tonne in July 1925 (Barlow 1978).
Such
variation
in
supply
and
price
caused
the
main
consumers such as the US to stimulate further the manufacture
of reclaimed rubber and lead to the increased use of synthetic
rubbers. The first industrial production of synthetic rubbers
was in Germany during WWI. Some 2,500 tonnes of "Methyl"
rubber was made during this time and during the 1920’s and
1930’s better and faster processes were developed and by 1939
the German output of Styrene butadiene synthetic rubbers was
around 22,000 tonnes (Barlow 1978).
38
Reclaimed rubbers were made from rejected goods, notably
tyres, which were usually ground up and treated to obtain
partial
unvulcanization.
technically
inferior,
Although
they
were
their
often
properties
employed
in
were
blends,
especially in making the treads of tyres. By 1928, following a
rapid
expansion,
around
210,000
tonnes
of
reclaim
were
consumed in the United States; this was equivalent to over
half the domestic absorption of new rubbers (Barlow 1978).
With Sarawak and the Netherlands East Indies (N.E.I.) not
participating in the restriction scheme the smallholders of
Sarawak and the smallholders and estates in N.E.I. benefited
from the increased prices. By the end of the 1920’s the area
under rubber in Sarawak was about 105,300 hectares. Output
increased in N.E.I. and also in Sarawak from 3,801 tonnes in
1922 to 10,637 tonnes in 1928 (See Figure 1.1). As there was a
concomitant
decrease
in
exports
from
within
the
area
of
control, the effect of the restriction scheme in eroding the
competitive position of estate producers became more apparent.
Eventually the scheme was abandoned on 1 November 1928 with a
price of M$650/tonne, M$40/tonne above the price six years
previously when the scheme was introduced (Barlow 1978).
The end of restriction caused an increase in exports from
Malaya and Ceylon, the largest portion of which came from
smallholdings, whose potential production had been very much
under-assessed during the Stevenson period. Consumption also
increased too, offsetting the rise in output. While prices
rose to a peak of M$940/tonne in early 1929, in expectation of
more
buoyant
economic
conditions,
39
they
dropped
slowly
thereafter (Barlow 1978).
It had been expected that higher yields due to flush
production would occur following the end of the restriction
scheme. However, by the end of 1929 the much higher yields
that
had
occurred
could
not
be
explained
by
such
flush
production. The real reason lay in the technical progress and
the reduction in costs which had taken place between 1922 and
1928, and whose effects in Malaya and Ceylon were masked by
restriction (Barlow 1978; Bauer 1948).
The collapse of the Wall Street stockmarket on 29 October
1929 and the ensuing depression caused the price of rubber to
fall from M$818/tonne in 1929 to M$447/tonne in 1930 (See
Figure 1.5). The decline in price was due to a decline in
demand as consumption of rubber in the American automobile
industry fell and technological advances lengthened the life
of tyres. The output of automobiles reached 5,358,000 units in
1929 and fell to 1,371,000 units in 1932 as a result of the
depression (Bauer 1948). The fall in automobile sales affected
not
only
replacement
technical
tyre
sales
sales,
change.
for
which
In
original
were
1921
equipment
further
the
depressed
rubber
slump
but
also
by
rapid
had
been
accentuated by the displacement of the fabric tyre by the cord
tyre; in the great depression there was a replacement of the
cord by the balloon tyre. Balloon tyres contained more rubber
than cords, but lasted much longer, and their introduction
therefore
reduced
the
amount
of
rubber
required
for
replacement (Bauer 1948). Improvements in technology over the
decade
reduced
further
the
demand
40
for
rubber.
The
United
States in 1939 was producing 2,000 tonnes of Neoprene rubber
but at US$1.43/kg, compared to US 51¢/kg for RSS 1, its use
could only be justified in special circumstances. By 1940 a
rubber tyre was expected to have a useful life of 25,00030,000 miles at speeds of 50-60 mph, while the cost of a tyre
per mile had been reduced by over 95 percent (Barlow 1978).
From
1929
to
1933,
in
a
demonstration
of
the
low
elasticity of supply, the output of rubber from the Malaya
rubber estates did not adjust sufficiently to the reduction in
demand. Smallholders, on the other hand were able to reduce
output by not tapping their rubber trees and switching their
labour to other activities (Bauer 1948) (See Table 2.3). This
was evidenced in Sarawak where the volume of exports declined
from 11,262 tonnes in 1929 to 7,110 tonnes in 1932 (See Figure
1.1). The smallholders in Sarawak did not seem to reduce their
planting effort with a consistent 30-35% increase in planting
area from 1929 to 1932 (Bauer 1948) (See Table 2.3).
TABLE 2.3: Area under Mature Rubber and Estimated Output,
Sarawak 1929-1933.
Year
Area (ha)
Output kg/ha
1929
24,300
1930
36,450
1931
56,700
1932
81,000
1933
101,250
(Source: Bauer 1948)
In
1931
rubber
exports
from
77
41
31
16
19
Sarawak
totalled
10,450
tonnes (See Figure 1.1) as the large areas planted in the
previous five years reached maturity. The reduction in price
due
to
the
depression
had
severe
41
and
widespread
effects;
although
tapping continued, often at increasing intensity,
many holdings were neglected and others abandoned.
The total area under rubber in Sarawak in 1930-32 was
estimated at 86,000 ha of which 90 percent was planted by
smallholders. In Saribas, the Depression caused considerable
dislocation
to
the
economy
(Cramb
1987).
Administrative
reports from 1930 indicate that rubber tapping had practically
ceased and that the more commercialised areas were feeling the
greatest
impact
(Sarawak
Gazette
1930).
The
low
cost
of
tapping, about one-half cent per lb (Bauer 1948) meant that it
was
worthwhile
for
the
great
majority
of
smallholders
to
continue tapping throughout the slump in rubber price. Tapping
tasks on smallholdings averaged 390 trees and these would be
tapped in about 3.5 hours. This work secured the smallholders
adequate cash for their needs even in bad times (Bauer 1948).
In 1935 Major W.F.N. Bridges was commissioned by the
Sarawak
government
to
make
a
report
on
the
industry.
He
concluded that in 1936 there were an estimated 75,000 rubber
gardens covering about 81,000 hectares and involving 50,000
smallholders
(Bridges
1937).
Major
Bridges
found
that
smallholdings were tapped on average about 16 times per month
obtaining at least ten katis (6 kg) per day. At fourteen to
sixteen cents per katy, this meant a daily income of around
$1.50, enough to buy about 15 kg of unhusked rice (Bauer
1948). Most of the findings of the survey were very close to
those found by the Malayan smallholdings enquiry of 1931-33
with annual yields ranged from 169 lb to 944 lb per acre with
an average of 489 lb (Bridges 1937). The planting density
42
varied from 60 to 680 trees per acre, with an average of 239
trees (See Table 2.4).
TABLE 2.4: Planting Density and Yield/acre on Smallholdings in
Sarawak.
Stand/acre
Average Yield/acre
(lb/year)
1-100
101-150
151-200
201-300
301-400
over 400
320
451
470
507
564
630
(Source: Bauer 1948)
With
the
gradual
recovery
in
prices
after
1932
to
M$269/tonne in 1933 and M$495/tonne in 1934, rubber was still
an attractive alternative crop to smallholders in Sarawak. The
incentive to go on planting and tapping was still strong even
in the years of very low prices because there was no other way
to generate the desired cash income, the markets for pepper
and forest produce being equally poor (Cramb 1987).
2.4. International Restriction to the End of Brooke Rule,
1934-1945
With the resultant decrease in price and output under the
Great
Depression,
there
were
calls
for
Stevenson-like
restrictions on output again. Discussions were held between
the N.E.I. growers and the Rubber Growers’ Association to
discuss
issues
of
territorial
quotas,
the
extent
of
new
planting and the inclusion (along with Ceylon) of Siam and
Indochina in the agreement (Barlow 1978). The International
Rubber Regulation Agreement became effective on 1 June 1934
and controlled 98% of the international area under rubber
43
production. The agreement aimed at keeping "...world stocks at
a normal figure, adjusting in an orderly manner supply to
demand, and maintaining a fair and equitable price level which
will be reasonably remunerative to efficient producers" (Bauer
1948 p 84). The allocation of quotas to each country was based
on average exports over the years 1929-32 (See Table 2.5).
TABLE 2.5: Basic Quotas under International Rubber Regulation
Scheme 1934-38 and Renewed Regulation Agreement 1939-43,
Sarawak.
Year
Quota (tonnes)
1934
24,480
1935
28,560
1936
30,600
1937
32,130
1938
32,640
1939
43,860
1940
44,625
1941
44,880
1942
44,880
1943
44,880
(Source: Bauer 1948)
In Malaya the restriction was much better organised than
the previous Stevenson scheme. The standard outputs of the
estates were assessed according to their average performance
in 1929-32, with additional allowances for areas not tapped
during that period. The standards for smallholdings were fixed
on another arbitrary scale relating potential output to the
visual appearance of the trees (Barlow 1978; Bauer 1948).
The scheme from the outset was aimed at benefiting the
European planters at the expense of the smallholders who were
widely regarded as producing a product that was inferior in
quality and quantity to the European estates. The Chairman of
44
the British North Borneo Company, Sir Andrew McFadyean, a
member of the International Rubber Regulation Committee said
in 1936 that "one of the primary objects of the Rubber Control
Scheme was to protect European Capital in plantation companies
in
Malaya,
Borneo,
and
the
Netherlands
East
Indies
from
competition arising from the production of rubber by natives
at a fraction of the cost involved on European owned estates"
(McFadyean 1936).
It had generally been taken for granted in Malaya that
the smallholders were not as efficient as estates in producing
rubber and yields would be significantly depressed with trees
being
overtapped.
However,
surveys
by
the
Department
of
Agriculture, Straits Settlements and Federated Malay States
(1934) found that bark reserves on smallholdings were very
well conserved and that there was no possibility of a decline
in
output
in
the
foreseeable
future.
Further,
the
root
diseases that had caused a heavy loss on estates were not
present on smallholdings with only eight of the 9,000 trees
examined being dead. The spread of root diseases had been
found to be stimulated by clean weeding which was carried out
extensively on estates. The smallholdings, which were rarely
clean weeded, had kept their top soil which on estates had
been eroded away; and the dense cover, as well as supplying
organic matter and maintaining the water-holding capacity of
the soil, also helped maintain a low soil temperature combined
with a high humidity near the ground and thus gave ideal
conditions for bark renewal. Similar investigations in Sarawak
also
supported
these
findings
45
(Bridges
1937).
By
1932
conclusive evidence against clean weeding had been marshalled
at
the
Rubber
Research
Institute
and
the
onset
of
the
depression had already forced a relaxation of this expensive
policy on estates (Bauer 1948). By 1940 the use of legume
covers
between
weeded
strips
of
immature
rubber,
and
the
encouragement of some natural growth under mature rubber were
policies
estates
widely
there
adopted
was
a
by
the
operators
progressive
of
substitution
estates.
of
On
chemical
weedicide for clearing by hand (Drabble 1973).
The
success
of
the
smallholders
and
the
competitive
strength they had compared with the estate sector prompted
this article in The Planter in June 1930:
In the hands of the producers of budwood lies the decision
whether rubber planting will, in the far from remote future,
become a native industry or remain an asset of immense value to
those European races to whose administrative skill and financial
acumen the development of Malaya and of the Dutch East Indies has
been due... it is the honest, unbiased opinion of many leading
men outside the rubber industry that the less the smallholder has
to do with rubber the better it will be in the long run for
himself, and for all others engaged in rubber production (Cited
in Bauer 1948 p 285).
In addition to having to contend with a low price for
rubber,
the
limitation
on
cash-earning
opportunities
for
smallholders in Sarawak was exacerbated by the government’s
decision
to
participate
in
the
International
Rubber
Restriction Scheme (Bauer 1948). For Sarawak, the inadequacy
of
the
administrative
introduction
of
machinery
individual
did
not
restriction
permit
before
the
1938.
Restriction before 1938 involved Sarawak accepting an overall
export quota and imposing a ban on all new planting (See Table
2.5).
46
In the absence of detailed information, the quota for
smallholders
in
Sarawak
was
essentially
determined
by
bargaining and proved to be a serious underestimate of the
potential output (Barlow 1978). It was later discovered that
smallholder
yields
averaged
a
respectable
545
kg
per
ha
compared with 531 kg per ha for smallholders in Malaya in 1932
(Barlow 1978; Bridges 1937).
Replanting in Sarawak, even in periods of high price, was
not carried out to any great extent by the smallholders. There
were two principal reasons for the smallholder’s unwillingness
to replant. First there was the lack of capital required to
pay not only the heavy expenses involved, especially the cost
of manuring, which is necessary for successful replanting, but
also to bridge the loss of income from the felling of old
trees to the maturity of the new stand (Lim 1973). The Rubber
Research Institute of Malaya (RRIM) estimated in 1938 that the
loss of income and the cash expenses of replanting would be
recouped only twelve years after the felling of the old stand
(RRIM 1939). A rubber tree is tappable five or six years after
planting and is fully mature in another four or five years.
The replanting of a stand of trees thus involves at least five
years’ loss of income and possibly several more years of
reduced income, depending on the relative yields of the old
and new stand of rubber varieties. Replanting could thus be
undertaken only by producers with ample working capital, which
smallholders did not usually possess (Lim 1973). In addition,
given
the
extensive
nature
of
the
farming
system
under
shifting cultivation, it was feasible to plant new areas of
47
rubber after hill rice and leave the old stand to revert to
secondary forest rather than to lose income by replanting.
In the early days of the rubber industry smallholder
production was carried out with minimal capital expenditure
and maintenance costs and yet it obtained a yield that did not
compare too unfavourably with that obtained on estates under
the pre-war high-yielding clones (Lim 1973). When the rubber
was inter-planted with a catch crop it would have minimal
maintenance costs in the initial years as the growth of weeds
would be controlled. Little attention would then be needed as
the trees could be left on their own until they matured and
were ready for tapping. The annual yield obtained from these
smallholdings
was
around
350
lb
per
acre.
This
was
not
significantly smaller than the annual yield of 500 lb per acre
obtained on estates to justify the higher costs that would be
incurred in meeting the more exacting standards of planting,
rearing and maintaining the high-yielding clones (Lim 1973).
In
existing
addition,
the
opportunity
income
stream,
the
cost
inevitable
of
cutting
consequence
off
an
of
a
replanting programme, was large for the smallholders compared
to estates. An estate could manage the problem relatively
easily by replanting only 3 percent of its acreage every year
so that the cost relative to its total resources and earnings
would be small. On the other hand, a smallholder who depended
on
2
hectares
of
land
for
his
livelihood
would
find
the
minimum area technically practicable for replanting would be
nearer 40 percent than 3 percent of his area and this in terms
of labour resources and income foregone is more than many
48
smallholders could afford (Lim 1973).
The second reason for the smallholders unwillingness to
replant
is
the
technical
impossibility
of
replanting
successfully part of a holding of a few acres, as the area
replanted would be closely surrounded by mature trees which
would intercept the sunlight and whose roots would compete for
food with the undeveloped rootlets of the newly-planted trees.
In Malaya, from 1934 to 1938, individual producers were not
allowed to replant more than 10 percent of their area in any
one year, or more than 20 percent over the entire period, and
this rendered replanting on smallholdings uneconomical (Lim
1973).
In
1939-40,
the
failure
of
Malayan
smallholders
to
undertake new planting was due to the excessive restriction
placed on planting. The planting rights, issued in the form of
transferable share certificates in denominations and multiples
of 1/20th of an acre, entitled the owner to plant rubber to
the extent of five percent of his 1938 registered acreage
(Bauer 1948). Thus the owner of three acres would be entitled
to plant about one-seventh of an acre, while the owner of a
five acre holding would plant one-quarter of an acre. These
very small areas were reduced considerably by a ruling of the
restriction authorities by which one-twentieth of an acre was
deemed
to
be
the
equivalent
of
eight
trees.
As
the
smallholders usually planted over 200 trees per acre, this
arbitrary ruling based on past estate practice, reduced even
further
the
fractional
area
which
entitled to plant (Bauer 1948).
49
the
smallholders
were
In addition to the restrictions on planting, exports were
kept
within
the
permissible
limit
by
several
over-all
measures. In Sarawak there was insufficient labour to tap the
entire mature area, and the control of immigration restricted
the number of tappers and the quantity of rubber produced
(Bauer 1948). The collection, manufacture, storage and export
of lower grade rubber (scrap and lump) were also prohibited
from time to time. For a few months in 1934-35 no tree was to
be tapped
every
more than once a day on a quarter-circumference, or
other
day
on
one-half
circumference,
but
this
prohibition was subsequently removed. These measures proved
insufficient and tapping holidays, the total prohibition of
tapping for four-weekly periods, were soon introduced (Bauer
1948).
The "tapping holiday" scheme was initiated in June 1934
and was used to restrict production by specifying extended
periods during which rubber could not be tapped. In 1935 there
were two periods totalling 44 days, and in 1936, five periods
totalling
114
days
(Bridges
1937).
However,
production
continued to rise and in 1938, under the Rubber Regulation
Ordinance of February 1938, tapping holidays were abandoned in
favour of issuing transferable coupons to all smallholders
limiting the amount of rubber which could be sold to dealers
(Sather
1981).
This
scheme
was
still
in
force
when
the
Japanese invaded Sarawak in 1941. By that time the total area
under rubber amounted to 97,200 hectares of which only 7,290
hectares were large estates (See Figure 1.6). The remainder
consisted of no less than 97,000 smallholdings (Cramb 1987).
50
Although the International Rubber Restriction Agreement
of 1934 to which Sarawak subscribed was essentially designed
to control production in the interests of relatively highcost,
European-owned
and
managed
plantations
in
Malaya,
N.E.I., and Indochina, and was contrary to the interests of
Sarawak’s
smallholders,
government
regulation
at
least
prompted an official recognition of the importance of the
industry (Reece 1987).
Despite further problems, the restriction slowly began to
take effect and, although exports from Sarawak rose steadily
from 17,839 tonnes 1934 to 21,627 tonnes in 1936, a slow rise
in absorption began to achieve the result which producers
desired. By the close of 1936 international visible stocks had
dropped
to
only
five
months
consumption,
and
anticipated
shortages of rubber had raised the price to M$790/tonne for an
average over the year of M$611/tonne (Barlow 1978).
For a short time in 1937, with the reduction in stocks
and the increase in speculative demand, a further boom was
widely expected and the price reached a peak of M$1,080/tonne
in late March. But a new recession then developed in the
United States, and by the end of the year the price had again
fallen to M$590/tonne (Barlow 1978).
As world demand for rubber increased after 1939, and
export quotas were raised beyond the capacity of Sarawak and
other countries to fill them, the economic situation of the
smallholder eased further and rubber regained its position as
a major source of employment and income (Cramb 1987).
In June 1939, as the world edged towards war in Europe
51
and
Asia,
The
United
States
created
the
American
Rubber
Stockpile as a strategic insurance against possible loss of
supply (Barlow 1978). This stockpile, as it was increased
further in the early years of the war and later during the
Korean conflict, influenced the price of rubber substantially
and was a bone of contention with those countries producing
rubber as indiscriminate buying of rubber and disposal of the
stockpile played havoc with world rubber markets (Barlow 1978;
Lim 1967).
With the outbreak of war in September 1939 the price of
rubber rose to M$830/tonne in October and by December it had
risen to M$920/tonne. As the target for the American Rubber
Stockpile
grew,
international
rates
committee.
of
release
were
lifted
late
1941
the
By
by
the
exportable
percentage was 120 and the price had risen to M$1100/tonne
(Barlow 1978; Bauer 1948) (See Figure 1.5).
By this stage there was a shortfall in quotas from Malaya
and
Ceylon
due
to
the
rapid
increase
in
the
allowable
production and the imposition of a British wartime excess
profits tax imposed at 100% which discouraged tapping (Bauer
1948).
Following the attack on Pearl Harbour in December 1941
the
Japanese
25th
Army
invaded
the
Malay
peninsula
and
Sarawak. Production of rubber declined to negligible amounts
as the Japanese occupation forces encouraged food production
rather
than
cash
crop
production
and
the
Anglo-American
blockade hampered exports.
A major outcome of Japanese control over South-East Asia
52
was its stimulus of synthetic rubber manufacture. By 1944 the
United States had established a vast domestic industry, with a
capacity of over 950,000 tonnes a year that was more than
sufficient to meet the wartime requirements of the American,
British, and associated armies (Barlow 1978).
In Malaya some rubber trees were destroyed during the
Occupation, although there is no accurate estimate of the
number involved (Jackson 1968). But in Sarawak there seems to
have
been
little
effect
on
rubber
gardens,
indeed
some
additional areas were planted by smallholders (Cramb 1987). As
a
smallholder
enterprise,
the
rubber
industry
recovered
quickly from the effects of the Occupation and exports from
Sarawak of 36,119 tonnes in 1947 slightly exceeded that of
35,428 tonnes in 1940 (Barlow 1978).
2.5. Colonial Rule to Independence, 1946-1963
With the liberation of Malaya and Borneo from Japanese
occupation, rubber production rose swiftly. Despite a shortage
of labour and production inputs, exports from Sarawak in 1946
were 23,902 tonnes with smallholdings contributing the larger
share of this increase, chiefly on account of an expansion in
mature area. The increase in production from estates was due
to a greater yield from areas planted with better material in
the late 1930’s. In both areas increases in yield per hectare
occurred due to the larger girth of trees obtained during the
Occupation’s enforced rest from tapping (Barlow 1978).
The price of rubber had altered little over the war
period
from
1941-1946
as
the
53
British
government
purchased
rubber from its colonial territories for delivery primarily to
the
United
States
under
successive
bilateral
agreements.
Private trading was partly restored in mid 1946 and prices
then rose slowly to around M$920/tonne f.o.b. in Singapore but
fell to M$640/tonne in July 1947 as production outstripped
consumption (Barlow 1978; Bauer 1948).
Towards the end of the war the production of synthetic
rubber had reached a peak of 916,000 tonnes in 1944 but this
declined to 880,000 tonnes by 1946 (Barlow 1978) due to the
inferiority of synthetic rubber for a great many applications.
Following international discussions, the United States reduced
its
mandatory
goods
from
synthetic
42%
to
33%.
rubber
As
a
content
result
of
of
domestic
the
rubber
reduction
in
synthetic content, by the beginning of 1948, synthetic rubber
production was only 540,000 tonnes and prices for natural
rubber had hardened to over M$1,010/tonne in London (Barlow
1978). This was down slightly from the controlled market price
of
M$1,258/tonne
of
1946-47.
Exports
from
Sarawak
in
the
meantime had almost doubled from 23,902 tonnes in 1946 to
40,517 tonnes in 1948.
The beginning of the 1950’s saw the introduction of yield
stimulants which were applied to scraped bark below the cut
and to renewing bark above it. The application of stimulants
such as 2,4,5 Trichlorophenoxy acetic acid, coppersulphate,
and
ethephon
released
ethylene
gas
in
the
tissues
and
increased the yield of latex by 10-20% (Barlow 1978).
The increase in yields and the outbreak of the Korean war
in 1950 provided an incentive to smallholders to increase
54
production as the consumption of rubber rose and the price of
rubber climbed to levels near those of the 1909-10 boom period
(Barlow 1978). The high price level enabled many smallholders
and
especially
estates
to
replant
their
old
low-yielding
rubber trees with high-yielding materials. It was estimated
that almost 19,000 acres were newly planted between 1946 and
1951 and measures were taken to prevent planting on land
suitable for wet-padi (Jackson 1968). As prices fell this
interest
declined.
In
some
areas
tapping
ceased
and
one
European-owned estate was closed (Jackson 1968).
The price of rubber rose from M$1,280/tonne in March 1950
to a peak of M$5,410/tonne in February 1951 (See Figure 1.5).
The effect of an increase in consumption was reinforced by the
buildup of the American stockpile. However, the boom ended
after two years and the increase in the minimum synthetic
rubber content of rubber goods by the United States caused
international
consumption
to
drop
again
in
1951
and
1952
(Barlow 1978; Lim 1973). While prices fell from M$4,037/tonne
in 1951 to M$1,623/tonne in 1953, exports from Sarawak fell
from a peak of 54,887 tonnes in 1950 to 23,581 tonnes in 1953.
This drop in consumption was softened by a continuing
buildup of strategic reserves which absorbed excess production
and prevented much change in visible stocks (Lim 1973). With
the lifting of restrictions on synthetic rubber content of
rubber
goods
in
1953,
the
consumption
of
natural
rubber
increased in 1954 to a level higher than it had ever been.
Prices,
nevertheless,
declined
1954.
55
slowly
to
M$1,565/tonne
in
Like
their
smallholders
in
counterparts
Sarawak
had
elsewhere,
responded
the
immediately
rubber
to
the
marked fluctuations of natural rubber prices caused by the
Korean War. In September 1949 rubber was being sold at the
Kapit bazaar for 30 cents per kati while rice was available at
M$1.80 per gatang. With production of rubber at about 5 katis
per day, enough rice could be purchases for a family of 5-6
people. Under these conditions, rubber provided a very good
economic safeguard but did not seriously compete with farming
as a main activity (Freeman 1970). With the rubber boom of
1950 this balance was completely disturbed. In September 1950,
the price offered at Rumah Nyala in Kapit District was $1.50
per kati. With an average output of 5 katis per day, the
production of rubber had become at least three times more
profitable than padi (Freeman 1970). In the Saribas District
of the Second Division there was a marked tendency for farming
to be abandoned in favour of full-time rubber production.
However, as many smallholders did not have sufficient rubber
and
preferred
their
own
hill
rice
to
the
polished
rice
purchased from bazaars with revenue from rubber sales, there
was not a wholesale conversion to full-time rubber production
(Freeman 1970).
In South-West Sarawak, when crude rubber was fetching
$2.00 a kati and over in the 1949-51 boom, some fishermen
virtually abandoned fishing in favour of rubber tapping. Then,
by 1952, as prices fell after the Korean War, they steadily
returned to fishing. Harrisson wrote in his diary:
56
August 27, 1955: - Little fishing at present. Off period for
ranto. Inggian busy and some local jala for prawns etc. But
recent up in price for rubber ($1.20 to $1.25) has caused many to
work it temporarily - price went up suddenly a fortnight ago.
Small scale operation - 3 to 4 katis a day or rather more per
family (Harrisson 1970 p 429).
In
Sarawak,
government
policy
during
the
1950’s
was
directed towards finding an alternative cash crop to rubber
rather than the rehabilitation of the industry. Rubber was
viewed as a declining prospect that was too risky to have the
whole economy dependent on its success (Jackson 1968).
At the end of the second world war at least four-fifths
of the total rubber area comprised old, unselected seedlings
which
were
nearing,
or
past,
an
age
when
tapping
was
uneconomic except at times of high prices (Sarawak Annual
Report 1956). Much of this rubber was poorly planted, often on
unsuitable
soils.
Techniques
of
maintenance,
tapping
and
processing were of a low standard, yields were poor and most
of the product was exported as low grade sheet (Sarawak Annual
Report
1956). The Colonial government realised eventually,
however, that rubber had become the main cash stand-by for
many farmers and that, despite earlier hopes, there was no
easy alternative to rubber as the main smallholder cash crop
(Cramb 1987).
Consequently, a Rubber Development Scheme was approved in
1947 to "provide the smallholder with high yielding planting
material
for
both
replanting
and
new
planting."
(Sarawak
Annual Report 1947). The dearth of estates, a legacy of the
Brooke
era,
meant
that
the
substantial
57
external
private
capital investment in the planting of high-yielding rubber
that had occurred in post-war Malaya could not be expected in
Sarawak (Jackson 1968). The task of planting new land with
high-yielding
material,
the
replanting
of
old
rubber
and
improving the standards of maintenance, tapping and processing
was impossible without major government intervention.
In 1955-56 a Rubber Planting Scheme was set up by the
Department of Agriculture to subsidise replanting and newplanting
with
high-yielding
material.
The
department’s
allocation was M$7 million in 1955 (Sarawak Annual Report
1956). The initial aim was to plant 4,000 ha over five years
but applications for nearly 7,000 ha had been received for
1957 alone. The total area under rubber in 1957 had reached
107,600 hectares,
with the increase since the war largely
being in response to the Korean war boom and the incentives of
the replanting scheme (Jackson 1968). In 1957 the planting
target was increased to 16,200 hectares and in 1958 to 24,300
hectares. Large numbers of smallholders continued to take up
the scheme and in 1959 the target area increased to 36,450
hectares of high-yielding rubber to be planted before 1964
(Jackson 1968; Morrison 1987). In 1960 a supplementary scheme
was introduced for remote areas such as the upper Saribas and
met with an immediate response (Sarawak Annual Report 1960).
The planting scheme boosted production and in 1964 exports
were at 42,263 tonnes, up from 22,604 tonnes in 1954.
Although subsidies were also available to estates, the
prime object of the scheme was to "expand and diversify a
smallholding agricultural economy based on rubber as the cash
58
crop and not to replace traditional systems of agriculture
with
one
wholly
dependent
on
rubber"
(Department
of
Agriculture, Sarawak 1957). Smallholders received grants in
the form of cash, planting material and fertilisers totalling
M$200 per acre for those new-planting and M$450 per acre for
those replanting (Sarawak Annual Report 1956). Subsidies to
estates were of the same order but were made only in cash.
Since the beginning of 1959 a tax had been levied on all
rubber exports to help finance the scheme and this permitted
the grant for new planting to be raised to M$250 per acre. A
total
of
M$37.41
Development
Plan
million
for
the
was
set
purposes
aside
of
in
the
the
Scheme
1959-63
(Jackson
1968).
In addition to the incentives to plant rubber offered by
the government, there was further stimulus for smallholders to
increase both tapping intensity and area under production. The
years 1955 and 1959-60 saw peaks in the price of rubber as the
market
responded
to
demand
boom
conditions.
World
motor
vehicle production increased 34% in 1955; 22% in 1959 and by
18% in 1960 (World Bank 1981).
The
Rubber
Planting
Scheme
failed
to
encourage
smallholders to replant existing stands with high yielding
clones as the Scheme did not offer enough capital to make it
economical however, planting of new rubber in Sarawak was
taken up with vigour under the Scheme.
While in Sarawak smallholders were rushing to take up the
planting
scheme
offer,
in
Malaya
the
story
was
quite
different. The experience in Malaya highlighted the economic
59
acumen of smallholders and the need for offering adequate
finance
to
smallholders
before
they
would
take
up
the
replanting offer. In Malaya The Rubber Replanting Schemes’
Fund A and Fund B for estates and smallholdings respectively
actually slowed down the rate of replanting on smallholder
properties.
Finance for the Schemes was obtained from a flat-
rate replanting cess of 4.5 cents per lb on rubber exported
and was divided between the two funds in proportion to the
output of the respective sectors (Lim 1973). Under Fund A,
estates received an automatic rebate of their cess payments
upon proof of replanting. Under Fund B, smallholders received
direct financial assistance with the cost of the material and
labour required for replanting. The original scheme provided
for payments totalling $392 per acre to smallholders who had
replanted
up
to
one-third
of
their
holdings
to
specified
standards. This grant was increased up to $786 per acre under
certain circumstances (Lim 1973).
While
Fund
smallholders,
at
B
seemed
least
to
economically
the
Malayan
attractive
to
government,
the
implementation was fraught with obstacles. A smallholding was
officially defined as a plantation of less than one hundred
acres and no distinction was made between the medium holdings
of 25-100 acres and the very small peasant holdings of less
than 25 acres.
This failure to differentiate between the two categories
discriminated against the peasant holdings in a number of
ways.
The
practicable
replanting
would
of
deprive
the
the
60
minimum
peasant
area
technically
smallholder
of
his
income
for
tapping.
six
Fund
years
B
was
until
the
inadequate
new
to
trees
cover
were
ready
for
both
costs
and
sustenance so that many peasant smallholders were effectively
discouraged from replanting (Lim 1973). Participation in the
scheme came mainly from the medium holdings so that in reality
the operations of Fund B served to regressively redistribute
the real resources away from smaller to larger producers,
thereby worsening the relative position of peasant proprietors
(Lim 1973).
When Fund B was expanded under the Second Malaya Plan of
1961-65, smallholders began to undertake replanting to such an
extent that the government had to limit the annual replanting
as the resources in Fund B were not sufficient to cover the
demand from smallholders. This, as much as anything else,
showed that smallholders were not unduly slow in responding
rationally to their economic environment once they had been
given the opportunity (Lim 1973).
Throughout
shortage
of
the 1950’s in Sarawak there was a growing
domestically
produced
rice
as
the
population
increased (See Figure 1.4) and land was being used for rubber
and pepper production. Smallholders either shifted to a wholly
commercial
agricultural
operation
and
purchased
rice
or
continued to maximise rice production, tapping rubber only in
the slack periods of the hill rice cycle (Cramb 1987). Other
smallholders reduced the cultivated area, thereby releasing
more labour for tapping. The latter strategy was more common
when rubber prices were high. At such times there was little
demand for wage work (Cramb 1987). In 1951, at the height of
61
the post war rubber boom, the Resident of the Second Division
observed: "The difficulty in obtaining labour is reflected in
the small and debilitated Government maintenance gangs. Few
unskilled workers will work for less than M$10.00 per day"
(Sarawak Gazette 1951).
In Sarawak the quality of rubber from smallholders was
low and received low grades as most were ignorant of correct
processing techniques (Jackson 1968). Most of the rubber was
exported as low quality smoked sheets although towards the end
1958-59 smallholders began to produce wet sheets and leave the
drying up to the Chinese dealers (Harrisson 1970). In 1959 the
Department of Agriculture began a scheme to encourage the
establishment
of
communal
or
group
processing
centres
at
strategic points at which smallholders could learn improved
processing
methods.
The
scheme
had
marked
effects
on
the
quality of sheet produced although much more needed to be done
as only 8 percent of the ribbed smoked sheet exported from
Sarawak in 1965 received RSS grades 1 and 2 (Jackson 1968).
The beginning of the 1960’s was important for the rubber
industry as it heralded the end of the virtual monopoly it had
over
synthetic
rubber
in
rubber
goods
manufacture.
The
development of new synthetic "stereo-regular" rubbers in 1961
enabled
synthetic
rubbers
to
be
used
in
applications
previously held exclusively by natural rubber (Lim 1973). In
addition, the new technical qualities exhibited by the stereoregular rubbers allowed it to be used in applications for
which the natural rubbers and previous synthetic rubbers could
not be used. In the period 1962-69 the proportion of synthetic
62
rubber in the overall consumption of rubber went up from 44
percent to 57 percent (Lim 1973).
The production of synthetics surpassed that of natural
rubbers
for
the
first
time
in
1962,
and
grew
to
reach
7,295,000 tonnes in 1973. This was over two-thirds of the
total
world
synthetic
output
rubbers
of
10,788,000
(Barlow
1978).
tonnes
Whilst
of
natural
there
were
and
large
increases in rubber demand that could not be met by natural
rubber production, the output of synthetic rubber caused total
rubber production to exceed demand and force the price of
rubber down. In 1960 the price of rubber was M$2,529/tonne and
this declined steadily to a low point of M$1,270/tonne in
1968.
While the average yearly price figures
(See Figure 1.5)
give an indication of the general price trend over time, Lim
(1973) suggests that the large monthly deviations in price
exhibited over this period are indicative of first, a low
elasticity
of
supply,
second,
shifts
in
the
demand
curve
resulting from the erratic purchasing behaviour of the USSR
and China, and third, short-run speculative forces, especially
in Singapore.
Another important factor in the price fluctuations of
natural
rubber
accumulation
was
finally
the
American
ceased
in
May
Rubber
1954,
Stockpile.
it
had
When
reached
1,250,000 tonnes. Disposal commenced in late 1959, and was
subsequently varied at rates determined by domestic expediency
in the United States, the price of rubber, and the volume of
complaints from the major producing countries (Barlow 1978).
63
In March 1966 the United States announced that they were
going
to
conduct
stockpile.
As
"unlimited
the
price
of
releases"
rubber
of
the
continued
American
to
decline
steadily from M$1,562/tonne in 1966 to M$1,270/tonne in 1968
and consumption fell, complaints became more and more vocal
and stockpile sales were progressively limited (Barlow 1978).
As the price recovered to M$1,697/tonne in 1969, sales from
the stockpile were terminated for strategic reasons. Under
difficult
economic
conditions
and
accompanied
by
a
rising
overproduction of synthetics, prices fell further from the
peak
in
1969
to
a
low
of
M$1,048/tonne
in
1972.
Tapping
accordingly fell significantly in all districts of Sarawak and
exports dropped from 44,246 tonnes in 1960 to 19,502 tonnes in
1971 (Barlow 1978).
Sales from the stockpile were resumed again subsequent to
1969 and continued during the period of very low prices in
1971 and 1972. By 1974 the balance of stocks had fallen to
some 130,000 tonnes (Barlow 1978).
2.6. Independence to the Present, 1963-1990
Two
political
significance
for
events
the
rubber
of
the
1960’s
industry
in
had
particular
Sarawak.
Firstly,
"Confrontation" with Indonesia arose from the formation of
Malaysia in 1963 and continued until 1966. The security forces
implemented a policy of resettlement of a few communities for
security purposes. This enabled an increase in the efficiency
of rubber production, especially for those communities that
had been isolated from extension, processing and distribution
64
centres.
Secondly, the serious interracial disturbances in
Peninsula Malaysia emphasised to the government the need for
greater progress towards equality of economic status between
races. A recognition of this need was strongly reflected in
subsequent rural policies (Barlow 1978).
The
36,450
hectare
target
of
the
planting
scheme
in
Sarawak outlined in the 1959-63 development plan was not fully
met with 32,400 hectares being planted by the end of 1963
(Jackson 1968). By the end of 1962 the total rubber area in
Sarawak was estimated to be 145,613 ha of which 18% was under
high yielding rubber (Sarawak Annual Report 1962). The area
under
rubber
increased
over
the
decade
rising
to
186,987
hectares by the end of 1969 (See Figure 1.6).
This increase in area was due to the success of the
planting program in planting new areas. Under the 1st Malaysia
Plan M$61 million had been allocated to finance the planting
of a further 105,000 acres of rubber in the period 1966 to
1970. The area of rubber in the Second Division increased from
25,000
ha
yielding)
(14.2%
from
high-yielding)
1960
to
1972
to
36,000
(Department
of
ha
(44%
high
Agriculture,
Sarawak 1968, 1975). Cramb and Reece (1987) suggest that the
increase was due to the Rubber Planting Scheme as once the
scheme was suspended in 1972 almost no further planting took
place until its reintroduction in 1977. Replanting had proved
not to be as popular as new planting and over nine-tenths of
the area planted under the scheme by the end of 1963 were
newly-created holdings (Jackson 1968). To some extent this was
due
to
the
unwillingness
of
smallholders
65
to
forego
their
income stream from existing rubber due to replanting as there
was a general attitude that there was plenty of land available
for new planting. Replanting on estates was carried out under
the Rubber Planting Scheme "A" which, by the end of 1965, had
stimulated
the
planting
of
over
40,500
hectares
of
high-
yielding rubber (Jackson 1968).
Because
of
the
limitations
of
the
transport
infrastructure in Sarawak and the shortage of trained staff,
the Rubber Planting Scheme was restricted to accessible areas.
The planting schemes were successful with over one-third of
the
total
workable
rubber
area
in
1965
comprising
high-
yielding material. Exports did not rise in the 1960’s as a
result of these schemes because most planting had occurred
late in the previous decade and as such were not mature. As
the new planting matured in the late 1960’s early 1970’s
output
rose
sharply
and
average
yields
showed
marked
improvement (Jackson 1968). However, exports of rubber from
Sarawak declined as falling prices made tapping uneconomical
on many very old holdings. Exports from Sarawak declined from
44,246 tonnes in 1960 to 23,327 tonnes by 1968.
Technological
advances
during
the
1960’s
were
not
restricted to the synthetic rubber industry. Large reductions
in the cost of producing natural rubber were achieved with:
(1) The use of new tree clones with yields of 2700-3200
kg per hectare per year as compared to the yield of
around
1100
kg
of
earlier
high-yielding
clones
a
to
(Lim
1973).
(2)
The
development
of
66
rainguard
prevent
interference
by rain which in its absence was likely
either to stop tapping, or cause it to be undertaken late
in the day (Barlow 1978).
(3)
The
substitution
of
polythene
bags
for
the
conventional cups which allowed labour to be saved by
allowing latex from several tappings to coagulate before
collection (Barlow 1978).
One of the major reasons for the supremacy of synthetic
rubbers over natural rubbers even when technical and price
factors
were
in
natural
rubber’s
favour,
was
the
poor
marketing and processing of the natural rubber compared to the
synthetic (Barlow 1978).
The bulk of natural rubber exported was in the form of
ribbed smoked sheets badly packed in difficult to handle 250
lb "bare backed" bales. The sheets were graded visually from 1
to 6 and this was the source of much discrepancy (Lim 1973).
In contrast to this, synthetic rubbers were exported in easily
manageable
bales
of
70
to
80
lbs
held
together
in
1-ton
pallets that make lifting and transporting easy. The quality
was also properly specified and strictly maintained.
In
1960,
in
recognition
of
the
problem,
The
Malayan
rubber industry started introducing new processing techniques
to make "block rubbers" of homogenous quality (Barlow 1978)
(See
Figure
2.1).
This
development,
together
with
the
introduction of the "Standard Malaysian Rubber" grading scheme
in 1965 (See Table 2.6), had the potential to eliminate the
above advantages held by synthetic rubbers.
67
TABLE 2.6: Specifications for Standard Malaysian Rubber
Criterion
Dirt retained on 44 F
aperture (max % wt)
Ash (max % wt)
Nitrogen (max % wt)
Volatile matter (max %
wt)
Plasticity Retention
Index (min %)
Wallace Rapid
Plasticity (min initial
value)
Colour (Lovibond scale,
max)
(Source: Barlow 1978)
SMR Grade
EQ
5L
5
10
20
50
0.02
0.50
0.65
0.05
0.60
0.65
0.05
0.60
0.65
0.10
0.75
0.65
0.20
1.00
0.65
0.50
1.50
0.65
1.00
1.00
1.00
1.00
1.00
1.00
60
60
60
50
40
30
30
30
30
30
30
30
3.5
6.00
Figure 2.1: Stages in Rubber Processing (Barlow 1978)
68
The
new
grading
scheme
defined
important
technical
characteristics. It outlined a series of grades and ascribed
characteristics
to
those
grades
that
were
judged
to
be
appropriate to commercial requirements and aimed to secure a
uniform and consistent quality. Further, the rubbers had to be
prepared in small individual bales weighing 33.3 kgs, and
packed
in
together
polythene
weighing
of
1
a
tonne,
standard
had
to
grade.
be
Thirty
bales,
in
pallet
packed
a
adapted for convenient handling and of a standard dimension
(Barlow 1978). With the introduction of the SMR grades, the
natural rubber industry could compete with synthetics where
there was no technical difference in quality and only the ease
of handling and marketing differentiated the two products. The
natural
rubber
industry
did
not,
however,
take
up
the
offensive in this matter and in 1969, nine years after the
introduction of the new block rubber plant, only one-seventh
of
the Malaysian total output was exported in block form
(Barlow 1978).
In the 1970’s the shortage of crude oils and natural
gases,
due
to
the
formation
of
the
OPEC
cartel
and
the
subsequent restrictions on oil supplies, had the potential to
reduce the expansion in output of synthetic rubbers. As a
result
the
price
M$2000/tonne
in
of
rubber
in
1973
(See
late
London
Figure
rose
to
1.5).
well
Even
over
though
synthetic rubbers still commanded 60 percent of international
manufacturing
capacity
in
1974,
its
future
had
become
uncertain due to the scarcity and expense of styrene (Barlow
1978).
69
At the end of the 1960’s, in the face of the low prices
there was a new initiative by the Malaysian government to
directly intervene in the market for rubber. This occurred in
February 1968, and helped steady the price which had then
began to fall (Barlow 1978). Further attempts from November
1974 to restrict the supply of rubber were not so successful.
These attempts involved an enforced suspension of chemical
stimulation, the imposition of lower tapping frequencies on
estates, and the compulsory maintenance of minimum stocks by
dealers and exporters. It also involved the formation of an
official
buffer
government
of
stock
through
smallholding
direct
rubber,
purchases
and
the
by
the
mandatory
replanting of lower yielding areas on estates (Barlow 1978).
Whilst
the
two
latter
measures
had
some
economic
justification, the first two measures in particular seemed
likely to repeat important disadvantages of the schemes of the
1920’s and 1930’s.
Subsequently in 1976, all members of the Association of
Natural Rubber Producing Countries agreed on an international
buffer
stock to complement national supply rationalisation
policies; this buffer stock would be increased or decreased
appropriately when the international price of rubber moved
outside defined limits (Barlow 1978).
Despite the Malaysian attempts to restrict the supply of
rubber, with the oil price shock sending the price of rubber
skyrocketing, the international output of natural rubber rose
to 3,492,000 tonnes in 1973. Of this, Sarawak had contributed
41,824 tonnes from an area of around 193,000 hectares (See
70
Figures 1.1 and 1.6).
There
was
technologies
steady
of
progress
producing
and
in
the
processing
development
in
the
of
1970’s.
Systematic breeding with the selection of high-yielding clones
over the previous 50 years had led to increased yields per
hectare with trees producing up to six times as much as their
unselected predecessors (See Tables 2.7 and 2.8) with the most
popular
clone
in
the
early
1970’s
and
probably
the
best
available being RRIM 600 (Barlow 1978) (See Table 2.9).
High-yielding
clones
were
adopted
quite
readily
on
estates but there was slow adoption on smallholdings. Apart
from the problems relating to the economics of replanting, the
problem of breed selection was that clones were selected on
the basis of those that performed well under regimes of half
spiral tapping alternate daily, the process that was used on
the
big
estates,
rather
than
more
frequent
tapping
which
suited smallholders whose real cost of labour was low and
whose need for additional income was greater than that of the
estates. In addition, wide scale evaluation of clones took
place
on
estates
and
it
was
known
that
there
was
wide
variation in the performance of estates and smallholders who
used different inputs and intensity of tapping (Barlow 1978).
TABLE 2.7: Average Yields of Rubber (kg per ha) in Peninsula
Malaysia.
Overall Averages (Estates and Smallholdings)
rubber
1929-30 (two years)
1972-73 (two years)
of mature
Different Materials, 1973 (Estates only) of tapped
rubber
Unselected seedlings and mixed stands
High yielding material
(Source: Barlow 1978)
71
492
1,013
368
1,419
TABLE 2.8: Efficiencies in Producing Latex
Clone
Production of dry
rubber per tapping
(gr/tree)
RRIM 501
RRIM 600
RRIM 602
RRIM 605
RRIM 612
RRIM 613
RRIM 614
PR 257
AV 1191
(Source: Barlow 1978)
Total annual dry
matter production
(kg/tree)
31.0
29.1
21.4
31.3
14.5
38.1
39.3
31.1
28.3
Partition
ratio (%)
27.5
42.7
61.4
36.2
53.3
23.7
37.2
45.5
29.5
36.6
22.1
11.3
28.1
8.8
52.2
34.3
22.2
31.1
TABLE 2.9: Commercial Yields of Some Major Clones (kg per ha),
Peninsula Malaysia.
Year of
tapping
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
(Source: Barlow
RRIM 600
PR 107
848
1,112
1,451
1,674
1,802
2,056
2,106
2,025
1,814
1,861
2,777
492
746
1,004
1,231
1,361
1,476
1,574
1,704
1,800
1,835
1,875
1,890
1,925
2,117
2,156
RRIM 501
617
1,023
1,329
1,508
1,641
1,687
1,692
1,647
1,572
1,565
1,560
1,524
1,630
1,555
1,531
RRIM 605
910
1,148
1,372
1,482
1,506
1,637
1,602
1,491
1,590
1,691
1978)
In the 1980’s the external terms of trade of Malaysia
deteriorated, due mainly to the decline in world trade and the
recession experienced by the OECD countries. The surge in
commodity
prices
during
the
late
1970’s
was
expected
to
continue throughout the 1980’s and the Malaysian government in
particular saw the deterioration in the terms of trade as only
a temporary deviation from the long-term trend (Demery and
72
Demery 1992). Revenues increased as the price of Malaysian
crude oil rose from M$14 per barrel in 1979 to M$39 per barrel
in 1981. Similarly, the price of Malaysian rubber increased
from
M$2300/tonne
in
1978
to
M$3194/tonne
in
1980.
These
developments prompted Malaysia to embark on an expansionary
fiscal policy to alleviate the effects of the OECD recession
(Demery and Demery 1992).
When expectations were not met the continuation of the
recession with the prices of petroleum and rubber declining
simultaneously following the 1979-80 boom led the Malaysian
government
restore
to
the
undertake
macrobalance
austerity
(Demery
measures
and
in
Demery
1984-87
1992).
to
These
austerity measures reduced the exports of rubber from Sarawak
from a high of 35,207 tonnes in 1980 to a low of 16,248 tonnes
in 1986.
Concurrent with the decline in export volumes and values,
the costs of production of smallholder rubber increased over
the 1980’s with distance to markets, especially for the more
remote smallholders, being the major constraint to production
and cause of low net returns. For a smallholder in the upper
Rejanh river, the distance to market at Belaga is over 150km.
For such a distance, Masing (1987) calculated that the cost of
transporting
his
produce
would
be
around
M$150.
If
the
smallholder was selling about 4 pikuls (250 kg) then his net
revenue would be only M$130.
73
2.7. Future Developments
In
the
latter
half
of
the
1980’s,
in
response
to
increasing labour costs, a new labour saving tapping technique
was pioneered on Malaysian estates. Instead of the traditional
alternate
daily
Extraction
tapping
(HLE)
schedule,
tapping
technique
a
new
was
Hypodermic
trialled.
Latex
The
new
technique only requires a single puncture once a week and
involves
no
bark
removal.
The
combined
use
of
the
HLE
technique and continuous chemical stimulation causes gradual,
continuous dripping of latex for about 40 hours and the latex
can be collected on the third day (World Bank 1992).
The advantage of the HLE technique is that there is:
(1)
A
sharp
reduction
in
the
time
and
labour
skill
requirements at both tapping and collection stages;
(2) The elimination of crop loss during the rainy season
due to the air-tight design of the collection bags; and
(3) An increase in the flexibility of the timing of the
tapping and latex collection tasks instead of the fixed
dawn
and
midday
timing,
respectively,
of
traditional
tapping tasks (World Bank 1992).
Studies by the World Bank (1992) indicated that the new
HLE technique was expected to result in a 50% increase in
weekly yields but the benefits of higher yield and lower
labour input associated with the puncture tapping would be
partially
offset
by
lower
planting
density
for
efficient
puncture tapping as well as the higher initial cost of the
tapping equipment. The net effect was an estimated lowering of
production cost by 15-20% (World Bank 1992).
74
While this would be of benefit to the Estate sector,
smallholders
would
be
expected
to
adopt
this
technology
slowly. The high cost of the new tapping equipment, and the
additional
knowledge
needed
to
successfully
utilise
the
equipment would act as barriers to smallholder adoption.
Another technological change that will lower the cost of
rubber production in the 1990’s is the development of young
budgrafting. By budgrafting the seedlings at three to four
months, instead of the usual eight months, the seedlings can
be transplanted into the field in eight months and result in a
reduction of the gestation period from seven to five years.
The World Bank study forecasted that these technological
changes will impact positively on the rubber market over the
longer term, not only through reduced labour costs and the
elimination
of
latex
contamination,
but
also
because
the
shorter gestation period will lead to a shorter-term supply
response to price changes, thus raising the supply elasticity
and reducing market instability.
Into the next century the World Bank (1992) projects that
the
market
prospects
for
rubber
will
depend
on
rates
of
economic growth both in the producing countries like Malaysia
and in the industrialised countries. Economic growth in the
industrialised
countries
will
influence
the
demand
for
automobile tyres and the burgeoning demand for rubber goods
from the Eastern and Central European countries will have a
positive effect on demand. The outlook for supply is for
gradual expansion during the 1990’s, accelerating thereafter.
On the smallholder side, the World Bank projects that
75
they will continue to be competitive with estates and other
producing countries due to the availability of land, the long
tradition
in
technologies
rubber
from
cultivation
improved
and
the
extension
diffusion
services
of
new
enhancing
efficiency in labour and land utilisation.
2.8. Conclusions
Rubber
production
has
been
influenced
by
the
various
factors outlined in chapter 1. The main influences come from
price and government policies which impact on decisions to
increase tapping intensity and efficiency and plant new areas
respectively. In the short term, prices have an immediate
effect
on
production
by
increasing
tapping
intensity;
smallholders responded rapidly to any short term price change.
In the longer term the gradual rise and fall of average yearly
prices have impacted on producer decisions to extend their
productive
capacity.
Government
policies
have
impacted
on
production in the long term, mainly through incentives (Rubber
Planting Schemes) and disincentives (Restriction Schemes) to
plant new areas. Exogenous shocks like war and the oil price
shock have had both short and long term effects on the supply
of rubber by stopping the production of rubber for a few
years, as in the case of the Japanese occupation, and by
increasing the short and medium term price of rubber in the
case of the Korean war and the oil price shock.
The next chapter is concerned with the analysis of the
long
term
trends
in
rubber
production
theory.
76
through
econometric
CHAPTER 3
ECONOMETRIC METHODS FOR TESTING TIME SERIES RELATIONSHIPS
3.1. Introduction
The qualitative analysis of rubber production in Sarawak
has been examined inter alia using a causality hypothesis.
International and domestic shocks have presumably caused short
and long run changes in production, but no actual quantitative
study has been carried out to analyse the validity of this
hypothesis. The main aim of this chapter is to provide a
framework for such a "cause-effect" investigation through the
econometric theory outlined below.
The chapter first outlines the traditional methodology of
studying
economic
production
relationships
changes.
Next,
it
between
uses
policy
several
shocks
new
and
approaches
rather than the traditional methodology. In outlining these
new approaches, the chapter introduces the concept of:
(1) Stationarity (or absence of unit roots), which has to
be satisfied to avoid spurious regression results.
(2) Parameter constancy, to identify historical breaks in
a time series.
(3) Structural invariance, to determine whether regime
changes
have
affected
the
structure
of
the
model
to
render forecasting ineffective as alleged in the Lucas
Critique.
(4) Causality, where the predictions of a variable based
on its own past history can be improved by inclusion of
the
history
of
another
causal
Causality).
77
variable
(Granger-Sims
(5)
Vector
atheoretic
autoregression,
framework
which
analysing
is
cause
a
multivariate
and
effects
of
variables on other variables and on policy shocks in a
dynamic system.
The data1 to be used in the economic analysis of the
factors
that
affect
rubber
production
consist
of
five
variables:
(1) {Xt} Net exports of Rubber from Sarawak - as a proxy
for production.
(2) {Yt} Price of RSS 1 (London) - as a proxy for producer
prices.
(3) {At} Area of land in Sarawak under rubber.
(4) {Pt} Population of Sarawak - as a proxy for labour
force.
(5) {CPI t} Retail Price Index - as a proxy for CPI for
deflating price.
The data have been collated from various sources with
specific details given in Appendix 1. The main point to make
about
the
data
is
that
only
Net
exports
and
Prices
were
measured to any degree of accuracy. These time series are
complete, from 1900 to 1990 whereas the other two variables,
Area and Population figures are only census data and therefore
data points are missing for many of the years. Consequently,
1
The Analysis of the data excluding the section on the
VAR model was undertaken using the MICROFIT 3.2 Interactive
Econometric Software package. The estimation of the VAR model
was undertaken using RATS 4.01.
78
only the Area and Population data from 1960-1990 are used in
the subsequent analysis. Prior to 1960 the data points are too
spread out to justify the inclusion of such points.
Not only are the Area and Population figures census data,
but the degree of accuracy by which they have been measured
leaves much to be desired. Bauer (1948, p 6.) cites examples
of surveys of smallholdings done in the Federated Malay States
which used dubious methodology. Bauer claims that many surveys
had been done from the side of the road and thus smallholdings
which were in more remote areas were not surveyed. Not only
were the limitations of the area figures great, but most of
the statistics were prepared for business men who had a deep
mistrust of round figures, believing these to be evidence of
careless work. The pseudo-accuracy of many rubber statistics
sometimes became quite grotesque as, for instance, Bauer cites
an incident when in estimates of the approximate native rubber
acreage of a Netherlands East Indies residency the number of
trees in that area was given to the nearest digit. The worst
example of pseudo-accuracy occurred during regulation, when in
1934-36 a tree census in Sumatra and Borneo claimed to have
found 582,365,725 trees (Bauer 1948).
In addition, the potential yield from smallholdings was
consistently underestimated. It is conceivable that surveys
done
in
Sarawak
suffered
similar
shortcomings
with
many
smallholdings in remote regions not being assessed. It was not
until after Bridges’ (1937) survey that it could be claimed
that
smallholdings
had
been
assessed
to
any
degree
of
accuracy. Some of the data, particularly those after 1960,
79
have
been
collated
from
figures
published
by
the
Sarawak
Department of Agriculture. While the department has indicated
that their statistics should not be taken as accurate for any
rigorous analysis (Department of Agriculture, Sarawak 19601990 p 1), and a charge of garbage in - garbage out levelled
at this econometric study may well be justified, I feel that
inaccurate data showing some sort of underlying trend are
better than no data. Furthermore, the methodology outlined in
this chapter can be applied to more accurate time series
elsewhere.
3.2. Classical Cowles Commission Methodology
Historically,
most econometric studies of time series
data has concentrated on the Cowles Commission for Research in
Economics methodology (Haavelmo 1944; Tinbergen 1951, 1959).
In this methodology general assumptions about model building
and testing were formulated along the lines of:
(1) There were zero restrictions on the parameters in
equations.
(2)
The
parameters
were
invariant
in
time
(i.e.
unchanging for different values of t).
(3)
The
parameters
were
structurally
invariant
(i.e.
invariant with respect to changes in the variables of the
model).
(4) Causal ordering was known (i.e. it is known a priori
which variable is a cause and which is a result of the
relation)
and
thus
which
endogenous are known.
80
variables
are
exogenous
and
(5) There was no misspecification (i.e. there are no
significant variables omitted from the regression).
(6) The model could not be verified directly against
"rival" models but it could be tested using the classical
Neyman-Pearson type of tests like the R2, Student-t ratio,
Durbin-Watson d test and F test diagnostic statistics .
Within these guidelines model building and testing were
seen as a set of techniques necessary to handle the relaxation
of the assumptions of the Classical Normal Linear Model:
(1)
(1) Zero mean value of ,i
E(,i*Xi)=0
where the factors not explicitly included in the model,
and therefore subsumed in ,i do not systematically affect the
mean value of Y.
(2) No autocorrelation between the ,’s
cov(,i,,j)=E(,i,j)=0, iûj
where, given Xi, the deviations of any two Y values from
their
mean
value
do
not
exhibit
systematic
patterns
or
correlations.
(3) Homoscedasticity or equal variance of ,i
var(,i*Xi)=E(,2i)=F2
where the Y populations corresponding to various X values
have the same variance.
81
(4) Zero covariance between ,i and Xi)
cov(,i,Xi)=E(,iXi)=0
where the error term ,i and the explanatory variable Xi
are uncorrelated.
(5) No specification bias
where the regression model is correctly specified with
all relevant variables included and all irrelevant variables
excluded from the model.
(6) There is no multicollinearity between the explanatory
variables X1i and X2i in the multiple variable case of linear
regression.
Where there is no exact linear relationship between the X
variables.
(7) The ,i are distributed normally
,i-N(0,F2)
3.3. Criticism of the Cowles Commission Approach
The
almost
blind
adoption
of
the
Cowles
Commission
approach to model building during the 1950’s and 1960’s led to
growing scepticism by theorists and applied econometricians
like Christ (1975); Davidson et al (1978); Hendry (1974);
Leamer (1983); Lucas (1976); Pagan 1987; and Sims (1980).
Sims (1980) criticised the traditional approach on the
grounds that:
(1)
variables
The
was
imposition
arbitrary.
$=0
of
The
use
restrictions
of
on
Simultaneous
lagged
Equation
Models (SEMs) as advocated by the Cowles Commission are most
useful when substantial certainty exists regarding the true
82
economic structure generating data. But when the true economic
structure is highly uncertain, many of these restrictions may
be inappropriate (Myers et al 1990).
(2) Initial model was correctly specified. However, if
misspecification occurred originally, it would be carried on.
(3) Parameter constancy over time. Lucas (1976) claimed
that simulations with econometric models under alternative
hypothetical time paths of exogenous policy variables cannot
provide
guidance
for
policy
decision;
because
all
such
simulations are based on a single set of fixed parameters
estimated from the sample period, whereas the true parameters
may not in fact remain fixed but rather may vary with each
alternative
policy.
Lucas
concluded
that
such
simulations
provided no useful information as to the actual consequences
of alternative economic policies.
(4)
Structural
invariance
implied
that
policy
interventions do not change the model structure or parameters
unless
the
interventions
are
large.
The
Lucas
Critique
suggested that forecasts are adequate only if there are no
policy changes since if the parameters change due to a policy
change then the forecasts are invalid.
(5) Causal ordering or model closure. The dichotomisation
of variables into endogenous and exogenous failed to explain
the behaviour of those exogenous variables and the use of
expectations brings in identification problems.
(6) Testing vs. rivals and encompassing tests. The use of
mechanistic techniques to select an appropriate model for a
given set of data came under criticism as "data mining". The
83
practice
of
regressions
selecting
data
mining
using
the
involves
different
best
formulating
combinations
regression
based
on
of
a
number
variables
their
fit
to
of
and
the
diagnostic statistics rather than economic theory (Charemza
and
Deadman
1992).
The
use
of
diagnostic
statistics
for
hypothesis inference came under fire with what was called the
Lovell (1983) bias where the t-test statistic was used to
accept or reject the validity of including a variable in the
model based on nominal " = P(Type I error) = P(Rejecting true
H0) where the difference between the nominal and true " can be
quite large. Secondly, the Leamer (1983) critique was that R2
is a descriptive coefficient that does not lend itself to
inferential
testing
of
hypothesis.
Thus
selecting
a
model
using R2, t, F, DW is not theoretically acceptable.
3.4. New Approaches to Econometric Modelling
The ground breaking work of econometricians like Davidson
et al (1978) and Sims (1980) led to the development of new
approaches
to
alleviate
the
flaws
in
the
traditional
methodology.
The traditional methods of examining the relationships
between variables looked at estimated correlation coefficients
which suggested that if coefficients are positive then there
is a chance that the variables are related. However, the use
of correlation coefficients is flawed because they are not
based on stationary series and assume invariant parameters
(Lucas 1976; Granger 1980). Non-stationarity in a time series,
where the mean and variance of the series change over time,
84
can lead to spurious correlation and regression results as
tests of significance indicate a relationship when in fact
none exist. Moreover, the correlation coefficients do not give
an indication of the strength and direction of causalities
(Granger 1980).
The basic hypothesis that will be tested using the socalled "New Econometrics" is, firstly, that the factors Past
Production {Xt-n}, Price {Yt}, Area {At} and Population {Pt} have
all affected to some extent the long run production of rubber
in any given time period. That is:
(2)
Secondly, that exogenous shocks (factors which have not
been
explicitly
modelled)
like
government
policies
and
international factors such as World War I, World War II, the
Korean War, and the oil price shock have affected rubber
production by influencing Price, Area and Population.
3.4.1. The Procedure
Two
models
will
be
formulated
to
test
the
above
hypothesis. Firstly, as prior economic theory has suggested
that
prices
variable
and
model
production
incorporating
are
closely
Production
correlated,
and
Price
a
two
will
be
formulated along the lines of:
(3)
85
Secondly, to examine the effects of the other variables
in a whole system approach, an autoregressive AR(p) model will
be formulated along the lines of equation (2).
There
are
two
parts
to
the
second
part
procedure
that
will
be
followed:
(1)
To
test
the
of
the
hypothesis
that
exogenous shocks have affected rubber supply, the models will
be tested for parameter constancy and structural invariance.
(a) To test for parameter constancy, a scaled recursive
Chow test will be used to identify if and when the parameters
of the model have changed over time. That is, it will test the
significance
of
structural
breaks
in
the
models
due
to
exogenous shocks.
(b) To test for structural invariance in the parameters
of
interest,
conditional
the
(the
models
will
parameter
of
be
dichotomised
interest)
and
a
into
a
marginal
process. By identifying changes in the marginal process and
comparing them to changes in the conditional process, if the
structural breaks in both processes coincide over time then
the model is structurally invariant.
(2) To test the first part of the hypothesis that the
variables have affected rubber production, the models will be
tested for bivariate and multivariate causality.
(a) Firstly, each variable will be tested individually
against
each
variables.
other
Bivariate
to
determine
Granger
if
they
Causality
affected
will
be
other
used
to
determine if the predictive capability of a variable, using
its own history, or lagged variables, can be improved by using
86
the history, or lagged variables, of another variable. Simply,
Granger Causality tests whether, for example, an increase in
prices
results
in
production
increasing
when
otherwise
it
would not have altered, or whether the relation works in the
other direction, so that an increase in production causes an
increase (or decrease) in price.
(b) Secondly, to test the hypothesis that the variables
have
affected
other
variables
not
only
in
bivariate
relationships but in multivariate relationships as well, a
Vector
Autoregession
(VAR)
model
will
be
formulated
to
indicate how much of an exogenous shock is explained by each
variable and by how much the exogenous shock affects each
variable.
3.4.2. Data Transformations
Granger
and
Newbold
(1977b)
suggest
that
two
basic
differences between the General to Specific approach expounded
by Davidson et al (1978) (DHSY) and that undertaken by the
Cowles
Commission
involves
the
determination
of
the
lag
structure of the model and the handling of the residuals. DHSY
models typically contain more lags, and residual terms in the
traditional
approach
are
treated
as
casual
"add-ons"
to
equations to account for such things as model misspecification
and variable measurement error. The traditional approach has
been to assume first the residuals to be white noise and to
check
this
assumption
by
looking
at
the
Durbin-Watson
statistic, which effectively measures the first-order serial
correlation of the residuals. If evidence of significant first
87
order serial correlation is found, the residuals are assumed
to be first order autoregressive (Granger and Newbold 1977a).
If
the
integrated
variables
moving
relationships
are
random
average
will
(ARIMA)
often
be
walks
or
processes
found
by
autoregressive
then
using
spurious
classical
estimation procedures. A method that is frequently used by
econometricians to reduce the problem of a low d value is to
first
difference
the
data.
Both
first
differencing
and
seasonal adjustment are steps toward prewhitening the data.
i.e., producing a series having a generally flatter spectrum
than the original (Granger and Newbold 1977b).
If the data is initially in what is termed a "raw data in
levels" state, that is, it is not expressed in a natural
logarithm form, transforming the raw data into logged data
before analysis has been shown to be justified on the grounds
of statistical and economic theory (Charemza and Deadman 1992;
Engle and Granger 1987; Granger and Newbold 1977b; Sims 1980).
Most
agricultural
commodities
exhibit
seasonal
cyclical
trends, a Cobb-Douglas-like nonlinear production function, and
non-constant
logged
data
variance
dependent
overcomes
on
its
level.
heteroscedasticity
The
use
of
(non-constant
variance) of data in level form (Cryer 1986) and transforms
nonlinear relationships into linear relationships. Moreover,
differencing data enables trend and seasonal effects to be
modelled without the corresponding loss in degrees of freedom
which occurs when time trends and seasonal dummies are used
(Charmeza and Deadman 1992).
In consideration of the above theoretical argument, the
88
data was transformed into logs 2:
xt = ln{Xt}
yt = ln{Yt/CPIt}
At = ln{At}
Pt = ln{Pt}
and then differenced:
)xt = xt-xt-1
)yt = yt-yt-1
)At = At-At-1
)Pt = Pt-Pt-1
3.4.3. Testing for Stationarity
A
stochastic
process
{Xt}
is
said
to
be
covariance
stationary if:
(1) E(Xt)=constant=µ;
(2) Var(X t)=constant= F2; and
(3) Cov(X tXt+j)=Fj
If one or more of the conditions above are violated then
the series is said to be nonstationary. Nonstationarity in
time series, where the mean, variance and covariance of the
series change over time, can lead to spurious correlation and
regression
results
as
tests
of
significance
indicate
a
relationship when in fact none exist.
Since regression analysis makes sense only for data which
are not subject to a trend, and almost all economic data
2
While, strictly speaking, logged variables should appear
in lower case letters, I have retained upper case letters for
{At} Area and {Pt} Population so as not to confuse them with
other notation such as ", D, p etc.
89
series contains trends, it follows that these series have to
be detrended before any sensible regression analysis can be
performed (Charemza and Deadman 1992).
Trends can be either as a difference stationary process
(DSP) or a trend stationary process (TSP) (Nelson and Plosser
1982). If a series is DSP then it can be differenced to
achieve detrending. A series can be tested for DSP or TSP by
using a Dickey-Fuller (DF) test (Dickey and Fuller 1976) which
regresses the model:
(4)
and
"0=1, "1=0 (DSP) against the
tests the hypothesis H0:
alternative H1: TSP. The calculated F-statistic is compared
against the tabulated critical M3 values in Dickey and Fuller
(1981
pg.
1062
Table
VI)3.
The
Dickey-Fuller
values
were
computed for each variable and the results are shown in Table
3.1. The results show that the rubber production is DSP and
can
be
differenced
to
eliminate
the
trend
and
the
other
variables are TSP and do not need to be differenced.
As suggested in the previous sections, a convenient way
to get rid of a trend in a series is to first difference the
data: )yt=yt-yt-1. However it may be necessary to difference the
data more than once to achieve stationarity. The concept of
Integration (Engle and Granger 1987) defines a nonstationary
3
It is important to note that the DF and ADF values,
being simulated and not derived analytically, are sensitive to
the structure of the model used for the simulation and thus
differ substantially from those published by other researchers
(Charemza and Deadman 1992; Fuller 1976; Guilkey and Schmidt
1989; and MacKinnon 1991).
90
series that can be transformed to a stationary series by
differencing d times as being integrated order d: yt-I(d).
Thus, for example, if yt-I(2), then to achieve stationarity:
))yt=(yt-yt-1)-(yt-1-yt-2)-I(0)
To
test
for
the
order
of
integration
the
Augmented
Dickey-Fuller (ADF) test (Dickey and Fuller 1981) was used
which tests the hypothesis that, in the equation
yt=(yt-1+,t,
(<1 and thus y t-I(0).
The ADF test regresses the equation:
(5)
for a sufficient number of lags (k) until the residuals become
white noise, where the Ljung-Box (1978) Q statistic:
(6)
is non-significant and simultaneously the Akaike Information
Criterion (1970) (AIC):
(7)
gives a minimum value. At this point, examination of the
Student-t
ratio
for
(
(H0:
(=0,
nonstationary;
H1:
(<0,
stationary) using the critical J
^$J values in Dickey and Fuller
(1981 p 1062 Table III) determines whether or not the series
91
is stationary.
Congruent with the above the ADF test was computed for
each variable (See Appendix 2.1) and the results are shown in
Table 3.1.
TABLE 3.1: Diagnostic Statistics for testing for Stationarity.
Lags
Statistic
s
Productio
n {xt}
Price
{yt}
Area
{At}
Populatio
n
{Pt}
k=1
t-ratio
(df)
2.3182(89
)
3.0451
(89)
-7.15936
(29)
3.7209
(29)
Q (df)
71.1975(9
0)
77.8300(9
0)
26.8293(3
0)
27.9845(3
0)
AIC
12.0399
0.08903
0.0000289
43
0.0001055
t-ratio
2.3978(88
)
3.5821
(88)
-3.0356
(28)
4.2895
(28)
Q
123.9048(
90)
141.5427(
90)
45.9392(3
0)
51.1448(3
0)
AIC
12.3978
0.08665
0.0000296
64
0.0001004
M3 (df)
("0=0,
"1=1)
83.1139
(72)
5.4875
(87)
6.8186
(27)
4.5288
(27)
k=2
DF
Test
Critical Values
Ljung-Box Q
Statistic
P2(0.05,30)
P2(0.05,90)
43.7729
113.145
ADF test
^J$J ("=0.05 n=20)
^J$J ("=0.05 n=50)
^J$J ("=0.05 n=100)
2.85
2.81
2.79
DF test
M3 ("=0.05 n=25)
M3 ("=0.05 n=100)
7.24
6.49
The results show that the Price of rubber variable, the
Area under rubber variable, and the Population variable are
stationary and the other variable, Production of rubber, is
non-stationary
and
has
to
be
stationarity.
92
differenced
once
to
achieve
3.4.4. Cointegration
In economics there is a need to integrate short-run
dynamics
with
long-run
equilibrium
to
obtain
meaningful
results. While the analysis of short-run dynamics is often
done by differencing to eliminate trends in the variables,
this
procedure,
however,
loses
information
about
long-run
relationships. The concept of cointegration as expounded by
Engle
and
Granger
(1987),
which
uses
an
Error
Correction
Mechanism (ECM) to incorporate past period’s disequilibrium,
addresses this issue of integrating short-run dynamics with
long-run equilibrium without losing long-run information.
If there are two time series xt and yt and a graph of them
shows
the
variables
drifting
apart,
in
other
words,
the
difference between xt and yt does not seem to be stationary,
then it is possible that they may not be integrated of the
same order. If, on the other hand, the variables seem to be
floating in time together, they may be integrated of the same
order and this suggests that a linear combination of xt and yt
might be stationary or integrated order zero, I(0) (Charemza
and Deadman 1992).
The formal definition states that: If yt - I(1) and xt I(1) then yt and xt are said to be cointegrated if there exists
a $ such that y t - $xt is I(1). More generally if y t - I(d) and
xt - I(d), then yt and xt - CI(d,b) if yt - $xt - I(d-b) with b>0
(Pindyck and Rubinfeld 1991).
If the variables are CI(1,1) and have the cointegrating
vector [$,-1], so that the deviations of yt from its long run
path y*t are I(0), a model in first differences incorporating
93
an error correction mechanism (ECM) can be developed.
An ECM is of the form
(8)
To test for cointegration, the order of integration must
first be established for each variable. If there are only two
variables in the long run relation, both have to be of the
same order of integration for cointegration to occur. If there
are more than two variables the order of integration of the
dependent
variable
cannot
be
higher
than
the
order
of
integration of any of the explanatory variables (Pindyck and
Rubinfeld 1991). Moreover, there must be either none or at
least two explanatory variables integrated to an identical
order higher than the order of integration of the dependent
variable (Charemza and Deadman 1992).
If
the
dependent
presence
variable
is
of
cointegration
replaced
by
is
the
established,
appropriate
the
linear
combination of the variables which we expect to be stationary
and the appropriate ECM (Charemza and Deadman 1992). This
section on cointegration is included for completeness sake, as
one of the aims of this study is to provide a methodological
framework for analysis of commodities like rubber. In this
study
there
is
only
one
non-stationary
variable,
rubber
production, and thus no cointegration exists and an EC model
need not be formed.
94
3.4.5. Determining Appropriate Lag Length
After
testing
the
variables
for
stationarity
and
differencing the non-stationary production variable to achieve
stationarity,
before
the
the
two
models
can
be
models
can
be
formulated,
formulated.
the
However,
appropriate
lag
length for the autoregressive model must be determined.
When using an autoregressive AR(p) model it is useful to
determine
the
lag
length,
so
that
no
more
lags
than
are
necessary are included in the model. Calculation of the lag
length was carried out by determining the order of residual
serial correlation. That is, at what lag does the serial
correlation become non-significant.
The test regresses an AR(p) model of the form:
(9)
and calculates the AIC and the Schwarz (1978) Criterion (SC)
which is of the form:
(10)
The appropriate lag length is the one at which the AIC
and Schwarz criterion are at their minimum. Appendix 2.2. and
Table
3.2
show
the
results
of
variables.
95
the
test
for
each
of
the
The results show that no more than:
(1) 1 lag for the rubber production variable,
(2) 1 lag for the rubber price variable,
(3) 5 lags for the rubber area variable, and
(4) 1 lag for the population variable
is needed to adequately capture the underlying data generating
process.
TABLE 3.2: Akaike Information Criterion and Schwarz Criterion
for determination of the appropriate lag length.
Lags
Statistics
Production {xt}
Price {yt}
Area {At}
Population {Pt}
p=1
AIC
12.314
0.0888
2.01*10-4
1.451*10-4
SC
12.9879
0.0936
2.187*10-4
1.578*10-4
AIC
12.74
0.0913
2.93*10-5
1.527*10-4
SC
13.7871
0.0988
3.32*10-5
1.723*10-4
AIC
13.199
0.1656
2.63*10-5
1.677*10-4
SC
14.57
0.2656
3.069*10-5
1.956*10-4
p=2
p=3
p=4
p=5
p=6
AIC
1.646*10-5
SC
1.98*10-5
AIC
1.57*10-5
SC
1.95*10-5
AIC
1.803*10-5
SC
2.284*10-5
96
3.4.6. Formulation of the Models
Once the lag lengths are determined, the two models can
be formulated along the lines of equations (2) and (3).
The first model, equation (3) (See Appendix 2.3.1.), is a
two variable AR(1) model of production and price of the form:
(11)
The second model, equation (2) (See Appendix 2.3.2.),
incorporates all of the variables in an AR(p) process:
(12)
Actual
and
fitted
values
of
the
OLS
regression
are
plotted for each model (See Figures 3.1 and 3.2). Visually
they show how well the model captures the underlying data
generating process (DGP).
97
Figure 3.1: Actual and Fitted Values for Model 1.
Figure 3.2: Actual and Fitted Values for Model 2.
98
Model 1 does not seem to fit the DGP well with an R2 =
0.0049. Examination of Figure 3.1 indicates that the fitted
values fail to pick up any medium term deviations from the
trend although the long term trends, over 10 years, seem to
allow enough time for the model to respond to changes in the
variables. Model 2 gives a better fit to the DGP with an R2 =
0.79. The fitted values pick up the deviations from the trend
quite quickly. Since model 1 is a nested model of model 2, the
poor fit of model 1 to the DGP suggests that production does
not only respond to price changes but that changes in the area
and population variables play a significant role.
3.4.7. Testing for Parameter Constancy
Once the models have been formulated they can be tested
for parameter constancy since one of the assumptions of the
Cowles Commission methodology was that the parameters should
be constant over time. One of the main aims of this paper is
to consider the stability of the parameters in the sample
period and to be able to identify specific points within the
sample period where a structural break in the model may have
occurred.
A test for the statistical significance of a possible
break is provided by a recursive Chow test (Chow 1960). If the
residual sum of squares for the model fitted by OLS up to and
including period t-1 is RSS, and if the corresponding residual
sum of squares for the model fitted up to and including period
99
t is RSS *, then the Chow test statistic is calculated as:
(13)
Under the null hypothesis that there has been no structural
change in the model between periods t-1 and t, this statistic
has the F distribution with 1 and t-k degrees of freedom.
Dividing the Chow value by its 5% critical value from the F
tables yields a scaled recursive Chow test of the form:
(14)
which, under the null hypothesis should be less than unity
(one). Values greater than one imply the rejection of the null
hypothesis of no structural change between periods t-1 and t.
The models were tested for parameter constancy from 1902
to 1990 for the first model and 1965 to 1990 for the second
model and the Chow tests were calculated (See Appendix 2.4).
The Chow tests were divided by their five, per cent critical
values and plotted below (See Figures 3.3 and 3.4).
The first model looked at the period 1900 to 1990 and
indicated that several structural breaks occured, namely in
the years 1910-1911, 1920-1921, 1940-1941, and 1945-46. What
these indicate is that the model formulated to capture the
underlying DGP, namely a two variable model based on past
production
variables
and
over
prices,
this
failed
time
to
period.
explain
These
changes
changes
in
the
in
the
variables were of a great enough magnitude to actually change
100
Figure 3.3: Scaled Recursive Chow Test for Model 1 at the 5
per cent significance level.
Figure 3.4: Scaled Recursive Chow Test for Model 2 at the 5
per cent significance level.
101
the relationship between the variables, namely the values of
their coefficients. The nature of the Chow test is that it
detects shifts in the variable coefficients (the parameters)
which are not endogenous to the system. By comparing a ten
year period with the next five year period, the recursive Chow
test enables the long run structural breaks to be identified.
What is important to note is that the test for parameter
constancy undertaken by the Chow test identifies periods of
change in the exogenous variable, production, which is not
explained by shifts in the endogenous variables, in this case
past production and price. Normal changes in the endogenous
variables which affect production according to the underlying
DGP are not identified as having significant impact on the
constancy of the parameters.
The second model looked at the period 1960-1990 and the
Chow test indicated that there had been a structural break in
the model in 1983-1984 that could not be explained by the
model parameters of price, area, or population changes.
As this section has shown the two models have been unable
to
explain
several
long
run
exogenous
shocks
which
have
changed the model parameters This can be further evidenced by
examination of the actual and fitted values of the two models
shown in figures 3.1 and 3.2. In model 1 the fitted values
deviate from the actual values for all the structural break
periods identified by the scaled recursive Chow test. In the
second model, the fitted values deviate from the actual values
over the period 1984-86, when the scaled recursive Chow test
had indicated that a structural break had occurred.
102
3.4.8. Testing for Structural Invariance
While
well-specified
economic
models
allow
ex
ante
forecasting under various scenarios as specified under the
Lucas
Critique,
if
parameters
are
not
constant
over
the
historical period then analysis of the model may be spurious
(Engle and Hendry 1993).
Specification tests of the Chow (1960) form like that
used
in
structural
section
breaks
3.4.7.
and
are
commonly
parameter
used
constancy.
to
test
However,
for
these
tests provide no inference as to whether parameters are likely
to remain constant in the future or whether they are invariant
to changes in regimes (Engle and Hendry 1993).
This section is concerned with outlining a structural
invariance test to determine whether or not the parameters of
the variables in the rubber supply models have changed in
response to changes in the supply regime during the historical
period.
The concept of structural invariance implies that policy
interventions do not change the model structure or parameters
unless the interventions are large. If there is no variability
in the parameters of a model when there is changes in the
variables then the model is structurally invariant. The Lucas
Critique suggests that forecasts are adequate only if there
are no policy changes since if the parameters change due to a
policy change then the forecasts are invalid.
If a time series takes the form:
(15)
and we need to evaluate the expected response of y t if, in
103
period T, zt, a system of endogenous variables, is changed from
zT to zyT.
If:
(16)
then the model is structurally invariant and the parameter "
can be used for evaluating the response of the dependent
variable, y t to changes in z t. If, however:
(17)
and
"*û", the model is not structurally invariant and no
knowledge can be retrieved from the sample relevant to "*
In the above model, we are interested in identifying
variables which can be used safely as instruments in policy
analysis,
that
is;
which
are
at
least
weakly
exogenous
(without any feedback relations or cross restrictions with the
variable representing output) and whose changes do not affect
the
parameters
of
interest
(the
concept
of
structural
invariance) (Charemza and Deadman 1992).
More formally, if the model can be dichotomised into a
conditional and marginal process where:
(18)
(Charemza and Deadman 1992)
and
where
the
parameters
of
interest
in
the
conditional
process (CD) are independent of the parameters in the marginal
process (MD) so that 81 does not contain elements of 82 (the
104
condition of weak exogeneity), then, if )82 has no effect on
81, there is structural invariance in the conditional model and
the Lucas Critique does not hold.
The examination of structural invariance hinges on the
assumption that the model can be divided into a conditional
and marginal process and then tested for structural breaks in
both processes.
Hendry,
Muellbauer
"historical"
and
interventions
Murphy
(the
(1990)
variation
assumed
of
the
that
tested
variable within a sample) affect the parameters of interest if
there
is
no
structural
structural
break
correspond
to
a
in
invariance.
the
Hence,
conditional
structural
break
in
if
model,
the
there
it
marginal
is
a
should
model
(Charemza and Deadman 1992). The procedure is to test for
structural breaks in both conditional and marginal processes
and to visually compare the eventual occurrences of structural
breaks in both models. If there are structural breaks, it
means that the parameters of the processes are not constant
within
the
sample
and
If
those
structural
breaks
in
the
conditional and marginal processes coincide in time, that is;
they appear for the same time period, it is likely that the
structural break in the conditional model has be caused by
variability in the parameters of the marginal model. If this
is the case, the hypothesis of structural invariance can be
rejected (Charemza and Deadman 1992).
The test for structural invariance can be developed by
firstly
assuming
that
the
parameter
of
interest
can
be
regarded as a linear function of the variable tested for
105
structural invariance. In the case of the model:
(19)
One can assume that variability in " is not due to changes in
zt
itself,
conditional
but
rather
to
the
deviations
of
zt
from
its
expected value (that is from the mean of its
marginal process). Hence, it is assumed that:
(20)
where µt is the conditional expected value of the marginal
process of z t. If we combine (19) and (20) we get:
(21)
The expected value of (z t-µ t)z t is the same as E(zt-µt)2, which
is the variance of the marginal process of zt. This suggests
testing the squared residuals (ût2) from the marginal process
of zt as an omitted variable in (19). If the null hypothesis
that "1=0 is rejected in the regression:
(22)
the variable zt cannot be regarded as exogenous. The procedure
can be extended allowing for a dynamic process:
(23)
106
which eventually leads to testing the hypothesis that in the
equation:
(24)
"1="2=...="k=0. This can be done with the use of a Lagrange
Multiplier P2 test or the Wu-Hausman T2 (= F test) statistic
(Wu 1973).
The test for exogeneity was carried out for both models 1
and 2 by using the VARIABLE ADDITION option in MICROFIT 3.2
(See Appendix A.2.5.1. and A.2.5.2.). The squared residuals
were obtained for the marginal process
(25)
and added to the model. The LM and T 2 statistics were obtained
for the joint test of zero restrictions on the coefficients of
the added variables such that H0: "=0 (exogeneity) H1: "û0 (no
exogeneity) and the results are presented in Table 3.3.
TABLE 3.3: Diagnostic Statistics for Test of Exogeneity.
Statistics
Model 1
Critical
Values
Model 2
Critical
Values
LM P2(0.05,
0.2637
(1)
3.8415
1.7383
(3)
7.8147
0.2496
(1,84)
3.92
0.2627
(3,11)
3.59
T2 F test
(df))
(0.05, (df1,df2))
The results show that the null hypothesis of exogeneity
is not rejected for either model examined. The results suggest
107
that the parameters of interest in the conditional model are
independent of the parameters in the marginal model.
Since it has been determined that the conditional and
marginal
processes are independent, testing for structural
invariance can take place. To test for structural invariance
the
conditional
and
marginal
processes
are
plotted
and
visually compared over time (See Figures 3.5 and 3.6). If the
variations
in
the
processes
coincide
in
time,
then
the
hypothesis of structural invariance can be rejected as it is
likely that the variation in the conditional model has been
caused by variation in the marginal model.
The graphs show that for both models the variations in
the
marginal
variations
processes
in
the
do
not
conditional
coincide
in
processes
time
with
and
thus
the
the
hypothesis of structural invariance holds. The combination of
(weak)
exogeneity
and
structural
invariance
gives
the
composite property of superexogeneity (Engle et al 1983) where
xt is superexogenous for 2 if xt is weakly exogenous for 2 and
81 is invariant to changes in 82. The Lucas Critique does not
hold
under
the
condition
of
superexogeneity
and
thus
models can be used for policy prescription and analysis.
108
two
Figure 3.5: Test for Structural Invariance of Model 1 - Plot
of Conditional and Marginal Processes.
Figure 3.6: Test for Structural Invariance of Model 2 - Plot
of Conditional and Marginal Processes.
109
3.4.9. Testing for Granger Causality
The second part of the hypothesis that was tested related
to bivariate Granger Causality. Granger Causality determines
the
direction
of
the
relationship,
if
any,
between
two
stationary variables. A variable Wt is said to GRANGER CAUSE a
variable Zt, (Wt=>Zt), if the prediction of the value of Zt
based on its own history, or lagged variables, can be improved
by using the history, or lagged variables, of Wt (Granger
1969).
In
addition,
the
Granger
tests
also
indicate
bi-
directional causality or feedback effects (w<=>z) and indirect
causality is implied through a process of transivity where, if
w=>z and z=>v, then w->v.
In a stationary bivariate model:
(26)
the
past
values
of
wt
Granger
cause
zt
if
the
Lagrange
Multiplier F-test4 (LMF) for the past wt values are jointly
significant.
If
the
two
variables
in
the
bivariate
model
are
integrated of the same or higher order, and a cointegrating
vector exists, then an ECM should be included in the Granger
Causality test. In this particular study, as explained in
section 3.4.4., only one variable is stationary and an ECM
need not be formed for the Granger Causality test.
4
For statistical reasons beyond the scope of this study
the Lagrange Multiplier F statistic is more robust than the Ftest and LMP2 test when there are non-linearities and logged
data. (See Charemza and Deadman 1992 pp 87-95).
110
The hypothesis $1=$2=...=$k=0 can be tested using the LMF
test in the following way:
(1) Regress z t on the constant and z t-1,...,zt-k;
(2) Compute the residuals of the above regression, denoted by
u*t;
(3) Regress u*t on the entire set of explanatory variables
which appear in (26), that is, the variables used in step 1
and additionally w t-1,...,wt-k;
(4) Calculate the coefficient of determination for the above
regression, R 20;
(5) Test the hypothesis $1=$2=...=$k=0 using
(27)
Granger Causality tests can also be carried out using the
variable delete option in MICROFIT 3.2. By regressing the full
equation
(26)
and
then
deleting
the
variables
that
are
supposed to be GRANGER CAUSING, the Lagrange Multiplier P2
statistic and the F test can be calculated to test the null
hypothesis that $1=$2=...=$k=0; w Y
/ z
The two tests for Granger Causality were carried out for
each pair of variables and the results are shown in Appendix
2.6. and Tables 3.4 and 3.5. The Granger Causality tests are
carried out to the 25% significance level to cater for the
difference between the true and nominal ". This is necessary
due to the inaccuracy of the data which may cause a test to
111
indicate that no relationship exists when in fact at the true
" such a relationship may hold.
TABLE 3.4: Bivariate
Production Variables.
Granger
H0:
LMF(df
)
LMP2
(df)
F(df)
y Y
/
x
0.9512
(6,71)
6.2491
(7)
0.8037
(7,70)
y Y
/
A
1.5985
(6,12)
11.1056
(7)
1.2560
(7,11)
y Y
/
P
4.5102
(6,12)
17.3197
(7)
3.5437
(7,11)
x Y
/
y
1.2954
(6,71)
8.2877
(7)
1.0946
(7,70)
x Y
/
A
1.0720
(6,12)
8.7240
(7)
0.8423
(7,11)
x Y
/
P
1.1061
(6,12)
8.9024
(7)
0.8691
(7,11)
H0:
Causality
LMF(df
)
A Y
/ x
A Y
/ y
A Y
/ P
P Y
/ x
P Y
/ y
P Y
/ A
Tests
LMP2 (df)
for
Rubber
F(df)
3.2847
(6,12)
10.6743
(7)
1.170
9
(7,11
)
5.2767
(6,12)
13.9498
(7)
1.983
8
(7,11
)
0.7003
(6,12)
6.4835 (7)
0.550
2
(7,11
)
2.7296
(6,12)
8.9931 (7)
0.882
9
(7,11
)
2.4959
(6,12)
7.1147 (7)
0.625
1
(7,11
)
3.0954
(6,12)
15.1872
(7)
2.432
1
(7,11
)
Critical Values
F0.05,
F0.05,
F0.05,
F0.05,
F0.05,
F0.05,
= 2.17
= 2.09
(6,60) = 2.25
(7,60) = 2.17
(6,12) = 3.00
(7,11) = 3.01
2
P 0.05,(7) = 14.0671
(6,120)
F0.10,
(7,120)
(7,120)
= 1.82
F0.10,
= 1.77
F0.10, (6,60) =
1.87
F0.10, (7,60) = 1.82
F0.10, (6,12) = 2.33
F0.10,
P20.10,(7) =
(7,11) = 2.34
12.0170
(6,120)
F0.25,
F0.25,
F0.25,
F0.25,
F0.25,
F0.25,
= 1.33
= 1.31
(6,60) = 1.35
(7,60) = 1.33
(6,12) = 1.53
(7,11) = 1.54
2
P 0.25,(7) = 9.03715
(6,120)
(7,120)
At the 5 per cent significance level the results show
that
Price
GRANGER
CAUSED
Population,
Area
GRANGER
CAUSED
Production, Area GRANGER CAUSED Price, and Population GRANGER
CAUSED Area.
At the 10 per cent significance level the results show
that
Population
GRANGER
CAUSED
Production
and
Population
GRANGER CAUSED Price. In addition, at the 25% significance
level, the results show that Price GRANGER CAUSED Area. This
can be summarised in Table 3.5.
112
TABLE 3.5: Direction
Production Variables.
of
Granger
Causalities
for
Direct
Causality (Y)
Two Way
Causality
(])
Indirect
Causality (6)
yt
ytYPt; ytYAt
yt]Pt; yt]At
yt6xt via At, Pt
yt6At via Pt
At
AtYxt; AtYyt
At]yt
Pt
PtYxt; PtYyt;
PtYAt
Pt]yt
Impacted
Variable
(Direction)
Rubber
xt
Pt6At via yt Pt6xt
via At
While the Granger bivariate causality tests indicate the
direction of causality between two variables, to gain insights
on
the
degree
of
strength
of
causal
interactions
between
variables, it is necessary to use a multivariate framework.
3.4.10. Formulation of a VAR Model
Traditionally simultaneous equation modelling (SEM) was
used
to
determine
equations.
multivariate
Applications
of
the
relationships
SEM’s
were
focused
between
on
the
building of large and complicated models of economies, in some
cases containing several thousand endogenous variables, which
were used for forecasting and policy analysis. These models
did not perform very well and there was widespread criticism
of structural econometric modelling using SEM’s (Judge et al
1985;
Liu
1960;
Sims
1980).
With
the
imposition
of
zero
restrictions on parameters in order to achieve identification,
there
was
included
satisfy
a
danger
some
the
that
variables
models
and
identification
would
deleted
of
each
be
formulated
that
others.
This
was
to
equation
even
if
the
economic justification for such variables was weak (Charemza
113
and Deadman 1992).
Sims (1980) viewed that the restrictions imposed on SEM’s
were "incredible" and had no theoretical justification. Sims
(1980)
suggested
autoregressive
an
alternative
modelling
where
procedure
everything
of
vector
depends
on
everything else.
The vector autoregression (VAR) approach is atheoretic by
nature and allows the empirics to reveal the dynamics that
govern
the
unaffected
underlying
by
relationship
preconceived
theory.
between
The
variables
two
principle
assumptions of the Cowles Commission methodology, namely the
dichotomisation of the variables into endogenous and exogenous
and
the
zero
restrictions
imposed
on
the
parameters
are
abandoned in VAR modelling (Charemza and Deadman 1992; Cooley
and LeRoy 1985).
The model, in matrix format, can be shown as:
(28)
There are two potential problems in constructing a VAR
model. The first problem involves deciding on the order of
adding
the
variables
and
the
second
involves
choosing
an
optimal lag length.
The order of variables added to the VAR will critically
affect
the
outcome.
The
Caines’
Specific
gravity
criteria
(Caines et al 1981) of 1/AIC will indicate the order in which
to add the variables where, the higher the specific gravity,
the more priority a variable should be given.
114
The problems of a uniform lag length for all endogenous
variables and an optimal lag length will be solved by using
the AIC in conjunction with the Ljung-Box Q statistic in a
process similar to the one outlined in Section 3.4.5. The AIC
achieves an optimal trade-off between the risks of imposing a
shorter lag than is necessary to capture the data generating
process and a longer lag with a larger variance (Hsiao 1981).
The procedure is to regress, using OLS, the variable
against
its
successive
lags
and
obtain
the
AIC
and
Q
statistics:
(29)
The optimal lag length is the one that there is the
minimum AIC for a non-significant Q statistic (residuals are
white noise).
Once the optimal lag length has been obtained for the
first variable, the second and subsequent variables are added
with
their
successive
lags
using
the
procedure
above
and
obtaining the minimum AIC for a non-significant Q statistic.
The procedure outlined above was carried out for the four
rubber variables and the results are shown in Appendix 2.7.1.
and Tables 3.6 to 3.9 below.
115
TABLE 3.6: Optimal Lag Length for Rubber Production Variable
Equation.
Model
Q (df)
AIC
xt="0+xt-1
11.612 (20)
0.2152
xt="0+xt-1+xt-2
9.8381 (20)
0.0708
xt="0+xt-1+xt-2+xt-3
10.3429 (19)
0.0741
xt="0+xt-1+xt-2+yt
9.6276 (20)
0.0705
xt="0+xt-1+xt-2+yt+yt-1
13.2711 (20)
0.0481
xt="0+xt-1+xt-2+yt+yt-1+yt-2
14.5271 (20)
0.0494
xt="0+xt-1+xt-2+yt+yt-1+At
12.8687 (7)
0.0351
xt="0+xt-1+xt-2+yt+yt-1+At+At-1
12.9509 (7)
0.0382
xt="0+xt-1+xt-2+yt+yt-1+At+Pt
13.4957 (7)
0.0363
xt="0+xt-1+xt-2+yt+yt-1+At+Pt+Pt-1
10.2397 (7)
0.0384
Caine’s Specific Gravity
27.556
Critical Values
P2(0.05,
P2(0.05,
P2(0.05,
= 14.0671
19) = 30.1435
20) = 31.4104
7)
TABLE 3.7:
Equation.
Optimal
Lag
Length
for
Population
Variable
Model
Q (df)
AIC
Pt="0+Pt-1
2.3082 (7)
1.451*10 -4
Pt="0+Pt-1+Pt-2
1.7247 (7)
1.527*10 -4
Pt="0+Pt-1+yt
2.2195 (7)
1.551*10 -4
Pt="0+Pt-1+yt+yt-1
4.5191 (7)
1.515*10 -4
Pt="0+Pt-1+yt+yt-1+yt-2
4.2545 (7)
1.620*10 -4
Pt="0+Pt-1+yt+yt-1+xt
5.2361 (7)
1.588*10 -4
Pt="0+Pt-1+yt+yt-1+xt+xt-1
5.2695 (7)
1.700*10 -4
Pt="0+Pt-1+yt+yt-1+xt+At
7.3028 (7)
1.541*10 -4
Pt="0+Pt-1+yt+yt-1+xt+At+At-1
7.8291 (7)
1.621*10 -4
Caine’s Specific Gravity
6487.609
Critical Values
P2(0.05,
7)
= 14.0671
116
TABLE 3.8:
Equation.
Optimal
Lag
Length
for
Rubber
Price
Variable
Model
Q (df)
AIC
yt="0+yt-1
29.8613
(22)
0.0888
yt="0+yt-1+yt-2
26.1833
(22)
0.0913
yt="0+yt-1+xt
22.1616
(20)
0.0916
yt="0+yt-1+xt+xt-1
20.2466
(20)
0.0747
yt="0+yt-1+xt+xt-1+xt-2
12.0666
(20)
0.0637
yt="0+yt-1+xt+xt-1+xt-2+xt-3
13.7476
(19)
0.0587
yt="0+yt-1+xt+xt-1+xt-2+xt-3+xt-4
13.5674
(19)
0.0619
yt="0+yt-1+xt+xt-1+xt-2+xt-3+At
4.1713
(7)
0.0149
yt="0+yt-1+xt+xt-1+xt-2+xt-3+At+At-1
9.8296
(7)
0.0146
yt="0+yt-1+xt+xt-1+xt-2+xt-3+At+At-1 +At-2
8.4506
(7)
0.0144
yt="0+yt-1+xt+xt-1+xt-2+xt-3+At+At-1 +At-2+At-3
14.0870
(7)
0.0078
yt="0+yt-1+xt+xt-1+xt-2+xt-3+At+At-1 +At-2+Pt
9.7793
(7)
0.0146
yt="0+yt-1+xt+xt-1+xt-2+xt-3+At+At-1 +At-2+Pt+Pt-
8.9476
(7)
0.0150
1
Caine’s Specific Gravity
68.587
Critical Values
P2(0.05,
P2(0.05,
P2(0.05,
P2(0.05,
= 14.0671
19) = 30.1435
20) = 31.4104
22) = 33.9244
7)
117
TABLE 3.9:
Equation.
Optimal
Lag
Length
for
Rubber
Area
Variable
Model
Q (df)
AIC
At="0+At-1
8.0833 (7)
2.011*10 -4
At="0+At-1+At-2
8.6302 (7)
2.939*10 -5
At="0+At-1+At-2+At-3
14.0148 (7)
2.632*10 -5
At="0+At-1+At-2+At-3+At-4
5.5725 (6)
1.646*10 -5
At="0+At-1+At-2+At-3+At-4+At-5
0.6822 (6)
1.575*10 -5
At="0+At-1+At-2+At-3+At-4+At-5+At-6
1.3356 (6)
1.804*10 -5
At="0+At-1+At-2+At-3+At-4+At-5+yt
4.0605 (6)
1.368*10 -5
At="0+At-1+At-2+At-3+At-4+At-5+yt+yt-1
5.8626 (6)
1.362*10 -5
At="0+At-1+At-2+At-3+At-4+At-5+yt+yt-1+yt-2
6.2414 (6)
1.463*10 -5
At="0+At-1+At-2+At-3+At-4+At-5+yt+yt-1+xt
2.5461 (6)
1.223*10 -5
At="0+At-1+At-2+At-3+At-4+At-5+yt+yt-1+xt+xt-
2.9802 (6)
9.912*10 -6
2.9799 (6)
1.083*10 -6
At="0+At-1+At-2+At-3+At-4+At-5+yt+yt-1+xt
+xt-1+Pt
10.1894 (6)
8.537*10 -6
At="0+At-1+At-2+At-3+At-4+At-5+yt+yt-1+xt
+xt-1+Pt+Pt-1
10.7883 (6)
9.337*10 -6
Caine’s Specific Gravity
117137.168
1
At="0+At-1+At-2+At-3+At-4+At-5+yt+yt-1+xt+xt1
+xt-2
Critical Values
P2(0.05,
P2(0.05,
6)
7)
= 12.5916
= 14.0671
Once the order of variables and their lag lengths have
been determined, the VAR system can be built. Two points are
of interest here. Unlike univariate model building which was
undertaken
to
test
for
parameter
constancy,
structural
invariance and Granger Causality, VAR models generally do not
include
trends
or
differenced
variables.
While
the
debate
about whether or not to include differenced variables and
ECM’s is an academic one (See, for example, Fanchon and Wendel
118
1992; Johansen 1988, 1989; and Todd 1990 for a discussion), in
general, trends in VAR models are picked up by the constant
term and differencing in VAR models has been shown by Fuller
(1976) to reduce information which cannot be captured in a cointegrating relationship and produces no gain in asymptotic
efficiency in an autoregression. Thus, unlike the univariate
models which were discussed before, the VAR model does not
include a trend nor any differenced terms.
From the results in Tables 3.6 to 3.9, the Var model is
specified by the system of equations:
(30)
Once the VAR system has been specified, estimation of the
system to obtain the impulse-response mechanism can be carried
out. The impulse-response mechanism indicates how the impact
of an exogenous shock to the dependent variable is spread
between the variables of the system.
In (28) ,t is a vector of white noise errors such that
,t,’t=I and I is an identity matrix implying that the errors
are
contemporaneously
correlated
119
and
intertemporally
uncorrelated (Karunaratne 1992). Under these specifications
the system can be consistently estimated using OLS (Judge et
al 1985). However, because these estimates are difficult to
interpret, through a process called invertibility, the finite
order VAR process can be inverted into a infinite order Moving
Average (MA) process (Granger and Newbold 1977a; Pindyck and
Rubinfeld
1991)
which
is
easier
to
interpret.
A
matrix
polynomial B(L) can be obtained from:
(31)
Where the matrix polynomial B(L) defines the impulse response
matrix and its coefficients measure the dynamic response of
endogenous variables z(t) consequent upon a unit change in
innovations
emanating from a specified endogenous variable
(Brillinger 1975; Karunaratne 1992).
However,
the
correlation
of
innovations
across
the
equations confuses the diagnosis of distinct movement patterns
displayed by the system. In the conventional methodology the
issue
of
contemporaneous
correlation
among
innovations
(errors) is often suppressed by arbitrary restrictions that
constrain
the
correlation
between
two
variables
to
be
unidirectional. In the VAR framework, since all variables are
considered to be endogenous, and because contemporary righthand
variables
are
omitted
at
the
estimation
stage,
any
contemporaneous correlation shows up as a correlation between
the current innovations in the variables (Gordon and King
1982).
120
To get around the problems of contemporaneous errors the
variance-covariance
transformed
by
a
matrix
of
diagonal
residuals
matrix
to
needs
obtain
to
be
orthogonal
innovations thus getting statistically acceptable results with
the error terms no longer contemporaneously correlated. The
idea behind making the error terms orthogonal to each other is
to enable the equations of the VAR model to be used separately
for policy analysis of the impact of a known random shock on
the system (Charemza and Deadman 1992).
The technique of Choleski factorisation (Doan 1990) uses
orthogonalisation of the innovations to overcome the problem
of
contemporaneous
errors.
The
triangularised
innovation
matrix traces out in a recursive Wold causal chain the shock
movements
(Todd
1990)
and
the
VAR
system
after
Choleski
factorisation can be shown as:
(32)
where V(t) is generated through the Choleski factorisation
technique (Karunaratne 1992).
Not only does the transformed matrix enable statistically
acceptable results to be obtained from the impulse-response
matrix
but
it
also
enables
Forecast
Error
Variance
Decomposition (FEVD) analysis associated with each endogenous
variable to be done. FEVD analysis is used for forecasting and
policy analysis and indicates how the strength of the shock
effects different variables and how much of the shock is
explained by each of the variables (Charemza and Deadman 1992;
Karunaratne 1992).
121
The total innovations generated by an endogenous variable
on itself and other variables in the system can be regarded as
proportional
economic
to
the
forces,
FEV.
These
orthogonal
by
FEV’s
describe
construct,
fundamental
that
cause
the
endogenous variables z(t) in the system to shift over time
(Karunaratne
1992;
et
Myers
al
1990).
The
variance
decomposition divides the FEV of a given variable according to
the causal strength of the innovation effects of the variable
on
itself
and
other
variables
in
the
system.
An
optimal
forecast is obtained when the highest amount of innovations by
the variable is accounted for by itself. The magnitude of the
FEV provides an estimate of the Granger causal strength of the
innovating variable on the other variables in the VAR system
(Karunaratne 1992; Sims 1980).
Consequent on the above discussion, the VAR model was
transformed by Choleski factorisation of the matrix and the
Forecast Error Variance Decomposition was obtained along with
the
Impulse-Response
Mechanism.
As
explained
above,
the
Impulse-Response Mechanism shows how much a one unit shock
affects each variable and the FEVD shows how much of the shock
is explained by each of the variables. The results are shown
in Appendix 2.7.3., Appendix 2.7.4., and Figures 3.7 to 3.14.
Normally confidence intervals are computed for each of
the impulse-responses which show whether or not the response
of
a
variable
to
a
one
unit
shock
is
significant.
Since
impulse-responses are highly-non-linear functions, estimation
of the confidence intervals by linearization is infeasible
(Doan 1990). Confidence intervals can be computed for the
122
impulse-response functions by Monte Carlo integration where
multiple draws are taken for the coefficients and the response
changes are evaluated. While RATS 4.01 provides a program for
obtaining confidence intervals for VAR’s using Monte Carlo
integration, this is only for an unrestricted VAR model with
uniform lag lengths, unlike the structural VAR model presented
in
this
study.
Deriving
confidence
intervals
for
the
structural VAR model using the Monte Carlo technique over 100
draws proved to be too labourious given the time constraints
and thus the impulse-responses are presented without their
confidence intervals. Intuitively, responses over, say, a 5%
level for a one unit shock for the first few years and then
expanding rapidly after could be said to be significant.
Figure 3.7: Impulse-Response of Variables to a Shock in AREA.
123
Figure 3.8:
POPULATION
Impulse-Response
of
Variables
to
a
Shock
in
Figure 3.9: Impulse-Response of Variables to a Shock in PRICE
124
Figure 3.10:
PRODUCTION
Impulse-Response
to
Variables
to
a
Shock
in
Figure 3.11: Forecast Error Variance Decomposition of Shock to
AREA into other Variables
125
Figure 3.12: Forecast Error Variance Decomposition of Shock to
POPULATION into other Variables
Figure 3.13: Forecast Error Variance Decomposition of Shock to
PRICE into other Variables
126
Figure 3.14: Forecast Error Variance Decomposition of Shock to
PRODUCTION into other Variables
The
impulse-response
mechanism
results
show
that
the
response of Price and Production of rubber to a one unit shock
in each variable is significant (taking the confidence bands
at 5% as mentioned above) whereas Area and Population do not
seem
to
be
affected
by
shocks
to
the
system.
Normally
macroeconomic variables affected by a shock settle down after
a period of time but this does not seem to be the case for the
rubber supply system. Even after 10 years the shock is still
apparent
with
what
appears
to
be
cyclical
trends.
This
suggests that price and production of rubber are governed
endogenously and without any additional shocks the system does
not
change
variable
is
regime.
Figure
affected
by
3.8
shows
shocks
to
that
itself
the
population
which
raises
population by a set amount. The Area variable (Figure 3.7)
127
does not seem to be affected by a shock to itself and this
suggests that a shock greater than one unit is needed to
change its level significantly. Area and Population, being
static in the long run (only dependent on generational changes
in the case of population), would seem to remain at the higher
level until another shock appears, rather than decline to
original levels after the shock has passed. Due to the long
run nature of both Area and Population it is not surprising
that shocks to the system do not affect these two variables in
such a way.
The
forecast
error
variance
decomposition
results
presented in Figures 3.11 to 3.14 show that shocks to Area,
Population
variables
and
Production
themselves.
are
Shocks
to
explained
Price
mostly
seems
to
by
the
have
been
caused by changes in Production. Arbitrary significance levels
can be taken at around 0% to 5% for Low Causality, 5% to 30%
for Medium Causality, and greater than 30% for High Causality
(Karunaratne 1992) so that variables that explain over 30% of
a shock to a given variable can be said to significantly
Granger Cause that variable (See Table 3.10).
The
Granger
Causality
results
in
this
multivariate
framework suggest that:
(1) Production accounted for most of the innovations on
itself with high causal effects where as Prices and Area had
medium level causal effects on production.
(2) The innovations in Price had medium causal effects on
itself whereas Production seems to have had high level causal
effects on the innovations in Price.
128
(3) Area has high to medium causal effects on its own
innovations with Prices and Production having medium to high
causal effects of Area.
(4) The innovations in Population are accounted for by
itself
with
greater
than
70%
of
the
FEV
in
Population
accounted for by itself and Prices, Production, and Area only
having low to medium causal effects on Population.
TABLE 3.10: Decomposition
Strength of Causality.
Caused
Variable
of
Forecast
Years after
Shock
Prices
Area
Populatio
n
Variance
by
Causing Variable
Production
Productio
n
Error
Prices
Area
Population
1
High
Low
Low
Low
5
High
Medium
Low
Low
10
High
Medium
Medium
Low
1
High
Medium
Low
Low
5
High
Medium
Low
Low
10
High
Medium
Medium
Low
1
Medium
Medium
High
Low
5
Medium
High
Medium
Medium
10
Medium
High
Medium
Medium
1
Low
Medium
Low
High
5
Medium
Low
Low
High
10
Medium
Classification of Causality Strength
0 < 5% : Low Causality.
5 - 30% : Medium Causality.
30% >
: High Causality.
Low
Low
(Karunaratne 1992):
High
In addition, the standard errors of the forecasts are
given in Appendix 2.7.4. and the first two years are presented
in Table 3.11. These standard errors give a quick comparison
of the forecasting abilities of the VAR set of equations. The
129
set of variables that produces the lowest values are regarded
as those that give the best forecasting ability. The table
shows that the AREA set of equations give the best forecasts
with standard errors of only 0.002 - 0.004 compared with the
least
accurate
errors
of
forecasts
0.3
forecasting
-
of
0.34.
As
of
the
ability
PRODUCTION
which
Appendix
2.7.4.
VAR
model
has
standard
shows,
reduces
the
as
the
forecasting period gets larger, indicative of the inability of
forecasts to predict accurately as time lengthens.
TABLE 3.11:
Variables.
Year
Standard
Area
Errors
Population
of
Forecasts
Price
for
VAR
System
Production
1
0.001916
0.012519
0.203093
0.310934
2
0.004064
0.019839
0.214268
0.339848
3.5. Conclusions
This chapter has looked at an empirical framework for
analysing
changes
changes in the supply of rubber in response to
in
the
factors
of
Area,
Population,
Price,
and
Production itself. This chapter has used tests for
(1) Parameter Constancy to see whether or not the supply
system
has
been
stable
over
time
and
if
not
where
those
exogenous shocks leading to structural breaks have occurred;
(2)
Structural
Invariance
to
see
whether
or
not
the
system is unchanged when exogenous shocks to its endogenous
variables occur and if so then the specified models can be
used for prediction (both forecasting and backcasting) and,
finally;
130
(3) Granger Causality in a bivariate and multivariate
framework
to
see
if
(bivariate
Causality)
by
how
much
(multivariate Causality) each variable has affected the supply
system.
The
next
qualitative
chapter
is
concerned
with
reconciling
the
review of chapters 1 and 2 with the empirics
outlined in chapter 3.
131
132
CHAPTER 4
DISCUSSION AND CONCLUSIONS
4.1. Introduction
This study has looked at the factors affecting the long
run supply changes of rubber from Sarawak. This concluding
chapter ties in the historical factors outlined in chapters 1
and 2 with the empirical results from chapter 3. The first
section will deal with a qualitative review of the factors
which have affected the changes in rubber supply and the
second section will deal with the quantitative analysis of the
long run changes in rubber supply.
4.2. Qualitative Review of the Factors Affecting the Supply of
Rubber
4.2.1. Short Run Factors
Chapter 2 has reviewed in an historical framework how the
various factors outlined in chapter 1 have affected the supply
of
rubber. In general it appears that factors which have
affected production of rubber only in one year or part of a
year have had only a short run effect. A qualitative analysis
of this suggests that price has played an important role in
determining
short
run
production
of
rubber
through
the
increase in tapping intensity. In addition, as discussed in
section 2.8., ample qualitative evidence exists to suggest
that the exogenous shocks like short booms and recessions in
industrialised
countries
and
the
oil
price
shock
of
the
1970’s, although quite significant, have only played a short
133
run role as their effects on price and output have been short
lived.
The
physical
factors
of
climate
and
soil
characteristics have determined the geographical distribution
of production but not any long run departures of production
from its underlying trend. Climate has played an important
short term role in determining yield of latex through its
effects on the Hevea growth cycle and on the constraints of
tapping rubber during rain.
This study has not looked at the empirics of short run
changes to production of rubber due to the aggregated nature
of the data analysed. The use of average yearly data has meant
that
the
short
run
effects
on
production
of
the
factors
outlined in chapter 1 have been disguised. The use of long run
data has meant that only long run effects can be modelled.
4.2.2. Long Run Factors
In the long run, factors have only affected production if
the changes have been sustained over time.
The socio-cultural effects outlined in chapter 1 have
played
a
long
run
role
in
affecting
rubber
production.
Qualitative analysis suggests that the slower than optimum
adoption rate of rubber as an alternative cash crop to coffee,
pepper, and gambier was due to the unfamiliarity of rubber
production by the smallholders and the departure from the
indigenous way of life. Even though rubber fitted easily into
the
forest-fallow
constituted
system,
the
a
system,
significant
need
for
unlike
pepper
departure
changes
134
in
from
and
coffee
the
cultural
which
traditional
operations
-
revolving around the tapping and processing side of rubber
production - inhibited what should have been an extremely
rapid adoption of rubber.
Political
policies
have
factors
also
such
played
as
an
government
important
long
agricultural
run
role
by
providing incentives to plant additional areas of rubber. For
example, schemes reducing the cost structure of planting by
extending fertiliser subsidies and providing cash grants have
been a positive influence on smallholders decisions to plant
rubber. In the long run economic factors such as price changes
have affected production by providing stimulus to increase
area
planted
and
the
alleviation
of
labour
shortage,
especially in the Brooke era, by encouraging migration. This
has been empirically supported by this study as the next
sections will show.
Exogenous shocks like the Japanese occupation of Sarawak
during World War II, which had a medium term effect (over 4
years), did not cause any long term effect on production.
Although there was a structural break, the nature of rubber
production and the physiology of Hevea meant that there was no
deterioration in latex yield when tapping recommenced after
the war.
4.3. Quantitative Review of the Factors Affecting the Supply
of Rubber
This
section
deals
with
an
analysis
of
the
results
presented in chapter 3. First of all this section will examine
those exogenous shocks which have caused structural breaks in
135
the time series and have been picked up by the tests for
parameter constancy and structural invariance. Secondly, this
section
will
also
deal
with
the
results
of
the
Granger
Causality tests and VAR analysis.
4.3.1. Exogenous Shocks Affecting the Supply of Rubber
In Chapter 3 there were two models formulated for the
empirical
analysis
of
the
supply
of
rubber.
These
incorporated, in the first model, a simple price - production
relationship,
and
in
the
second
model,
a
price
-
area
-
population - production relationship.
Tests for parameter constancy for the first model using
the scaled recursive Chow test indicate that the years 19101911,
1920-1921,
1940-1941,
and
1945-1946
experienced
exogenous shocks which could not be explained by price changes
within
the
model
and
had
actually
changed
the
model
parameters. Reconciliation of these structural breaks with the
historical factors outlined in chapter 2 shows that:
(1)
The
years
1910-11
corresponded
to
the
rise
in
production as rubber, which had been planted at the beginning
of the Century, reached maturity and had begun to be tapped.
(2)
The
years
1920-21
corresponded
to
the
period
of
turmoil after the end of World War I. The rise in production
as smallholder rubber planted after the 1910 boom matured was
tempered with periods of boom and recession in the United
States and a release of rubber stocks.
(3) The periods 1940-41 and 1945-46 correspond to the
Japanese occupation of Sarawak and the resultant cessation of
136
production. The initial exogenous shock in 1941 corresponds to
the
changing
in
the
model
parameters
as
prices
on
the
international market still operated but production had ceased.
The model was "shocked" back to its pre-war price-production
relationship in 1945-46 with the liberation of Sarawak and the
tapping of rubber continued as before.
The second model looked at the period 1960-1990 and the
scaled recursive Chow test indicated that there had been a
structural break in the model in 1983-1984 that could not be
explained
by
the
model
parameters
of
price,
area,
or
population changes. As section 3.4.6 has shown, the R2 of model
2
was 0.79, meaning that the variation in the endogenous
variables of the model explained 79% of the variation in the
exogenous variable, production. This high R2 indicates that the
model was a good fit of the underlying DGP. This suggests that
changes in production due to normal changes in the model
variables (Area, Population, and Price) would not cause a
change in the model parameters, that is, the changes in the
endogenous variables would not cause structural breaks.
As chapter 2 has shown, from 1984 to 1987 the government
of Malaysia had undertaken austerity measures to reduce the
effect of macro-imbalances caused by its expansionary fiscal
policy of the late 1970’s to early 1980’s. In addition prices
for rubber on the international market fell from M$3,194/tonne
in 1980 to M$1,887/tonne in 1985 and the booms in coffee and
pepper in the late 70’s and early 80’s may have led to a shift
in land use.
While a shift in land use away from rubber may have
137
occurred prior to 1984-85 due to the booms in other cash
crops, this would not have affected existing area under rubber
to any great extent and the booms would have been too short to
effect any long run changes to area under rubber. Significant
long run changes to area under rubber would have had to occur
under the auspices of the Chow test, that is, the previous ten
years would have had to be significantly different from the
next five years. Similarly, changes in prices from 1980 to
1985 would have affected short run yield through changes in
tapping intensity but only over that actual period, and would
not have been significant enough to cause an actual shift in
the
parameters.
The
most
plausible
explanation
for
the
structural break that occurred in the 1980’s, keeping in mind
that the Chow test detects shifts in parameter constancy which
is not explained by the model structure, is the implementation
of austerity measures in 1984.
These austerity measures, along with declining prices and
shifting land use, had decreased the size of exports from
Sarawak, from 35,207 tonnes in 1980 to 16,248 tonnes in 1986
(See Figure 1.1) as many rubber gardens became uneconomical to
tap. This exogenous shock to the whole economy had impacted on
the supply of rubber from Sarawak and changed the parameter
relationships more than what would of occurred under price and
area changes alone.
As this section has shown the two models have been unable
to
explain
several
long
run
exogenous
shocks
which
have
changed the model parameters. This can be further evidenced by
examination of the actual and fitted values of the two models
138
shown in Figures 3.1 and 3.2. In model 1 the fitted values
deviate from the actual values for all the structural break
periods identified by the scaled recursive Chow test. In the
second model, the fitted values deviate from the actual values
over the period 1984-86, when the scaled recursive Chow test
had indicated that a structural break had occurred. The fall
in production due to the fall in prices over 1980-85 was
picked
up
by
the
fitted
values
but
the
actual
fall
in
production was greater than what should have occurred in the
absence of an exogenous shock.
The results of the structural invariance tests show that
the
hypothesis
of
normal
changes
in
the
parameters
not
affecting the model is supported and that exogenous shocks
have caused changes in the parameters leading to structural
breaks. The tests for structural invariance and exogeneity
show that changes in the parameters of the marginal process
(price, area, and population) do not affect the conditional
process
parameter
of
interest
(production)
and
that
the
invariant,
the
marginal and conditional process are independent.
Because
parameters
the
can
models
be
used
are
to
structurally
evaluate
the
response
of
the
dependent variable, production, to changes in the price, area,
and population variables. This finding has impact on both the
interpretation
of
the
parameter
Impulse-Response
results
Lucas
is
Critique
implications
derived
of
the
nullified,
from
the
constancy
VAR
we
139
analysis.
can
results
constancy and VAR analysis are valid.
results
and
the
Because
the
assume
of
the
that
the
Parameter
4.3.2. Causality Tests for Rubber Production Variables
Granger Causality tests were carried out on all four
variables and the results are summarised in Table 3.5.
The results show that price had direct long run effects
on both area planted with rubber (y t Y At) and population
changes (yt Y Pt). This suggests that a continued high price
for
rubber
provided
incentives
for
smallholders
to
plant
rubber. There are numerous examples given in chapter 2 of
smallholders responding to changes in price both in the short
run by changing tapping intensity and in the long run by
planting
more
rubber.
The
effect
of
price
changes
on
population has no a priori economic theory basis to suggest
that such a relationship over the measured time span actually
exists.
Population
increases
were
significant
during
the
Brooke period up to the 1920’s, as the Brooke governments
actively encouraged migration to alleviate a labour shortage and booming economic conditions with high prices certainly
encouraged migration. However, during the 1960’s to 1990’s,
the time period over which the Granger Causality tests were
conducted, the increase in population closely corresponded to
the rate of natural increase (Cramb 1987). While high prices
for rubber - indicative of overall economic conditions - would
have
had
some
minor
effect
in
encouraging
migration
and
natural increases in population, especially in a labour scarce
economy, a more plausible explanation lies in the dynamics of
the
population
variable.
Examination
of
the
growth
of
population (See Figure 1.4) indicates that increases followed
an exponential growth curve. As trend variables can also be
140
set to incorporate exponential growth, the implication is that
population acted as a Trend proxy for general technological
advances and other qualitative factors outlined in chapter 1.
If we assume that this is the case, that population not
only reflected the supply of labour but also acted as a proxy
for
technological
and
other
qualitative
changes,
then
the
Sarawak
had
results imply that:
(1)
The
chronic
shortage
of
labour
in
restricted the output of rubber by directly affecting rubber
yield (Pt Y xt) through inhibiting tapping (which is labour
intensive), and indirectly (Pt 6 xt, via At) through reducing
the amount of land physically able to be planted.
(2) The shortage of labour affected, through prices (Pt 6
At, via yt), the amount of land that was planted to rubber.
Where a low price meant that labour could be more profitably
directed towards other activities like fishing or food/cash
crop production (Harrisson 1970).
(3)
Technological
production,
yield
and
change
has
affected
prices.
The
advances
area
in
under
tapping,
processing and marketing have both reduced the cost structure
and improved yields, resulting in greater yields per tree and
providing incentives to increase tapping intensity in the long
run and plant more area to rubber. Technological change also
includes
international
factors
such
as
improvements
in
synthetic rubbers which provide a floor and ceiling for rubber
prices (World Bank 1981). Changes in synthetic rubbers may
have influenced changes in the production and price of rubber.
141
The output of rubber is a direct biological function of
the area of land under rubber and the Granger Causality tests
show
this
Y
(At
xt).
In
addition,
the
area
under
rubber
production seems to have influenced the price of rubber (At Y
yt). While Sarawak is by no means the largest rubber producer,
the changes in the area under rubber in Sarawak have moved in
synchrony with global changes in area which have influenced
price. Rubber producing countries like Peninsular Malaysia and
Indonesia as a group have been able to affect the price of
rubber by changing supply. This has been evidenced by the
success of the various restriction schemes outlined in chapter
2.
One of the important findings coming out of the Granger
Causality tests has been the long run effect of price on
production. Unlike the direct effects of price on short term
production through the change in tapping intensity, the long
run changes to production caused by price have been indirect
(yt 6 xt, via At and Pt). Price has caused changes in production
by
affecting
the
area
under
rubber
production
and
the
availability of labour and technology changes. By providing
sustained
economic
incentives
to
increase
the
area
under
rubber production, prices have been able to influence the
production of rubber. In addition to sustained high prices
increasing
the
production
of
rubber
by
increasing
the
availability of labour for rubber cultivation, low prices have
provided incentives for technological change to decrease costs
and maintain profit margins.
Whilst the Granger Causality tests have indicated that
142
such relationships between variables do exist, they provide no
information as to the strength of causality between those
variables. So while a priori economic theory can suggest what
the directions of causation might be (e.g. low prices causing
technological change to maintain profit margins versus high
prices
providing
incentives
to
increase
area
under
production), the impulse-response mechanism and FEVD analysis
of the VAR model provides indications as to what the strengths
of causation are.
The results of the VAR model presented in Figures 3.7 to
3.14 and Table 3.10 show that:
(1) Shocks to the system are exhibited in fluctuating
Prices and Production. The Area and Population variables are
very stable and require more than a one unit shock to exhibit
any
significant
response.
The
unusual
feature
of
the
VAR
system is that the responses of Prices and Production do not
settle down over time and this indicates that in the absence
of further exogenous shocks to change the supply regime these
two variables will continue to be unstable and result in a
variability of supply.
(2) The multivariate Causality tests using the Forecast
Error Variance Decomposition gave indications of the strengths
of causality between the variables. In general these findings
gave
support
to
the
bivariate
Granger
Causality
tests
undertaken in section 3.4.9. However, unlike the bivariate
tests, the multivariate test found that there was a causality
relationship
between
Price
and
Production.
Production
explained around 70% of the innovations in Price and Price
143
explained between 1% and 20% of the innovations in Production.
The bivariate test also failed to pick up the medium and lowto-medium causal strength of Production to Area and Population
respectively.
An
explanation
of
these
discrepancies
between
the
bivariate and multivariate tests centres around:
(1)
the
determining
use
of
Production
different
and
Price
estimation
relationships.
periods
in
While
the
bivariate test used the whole sample period, 1900-1990, the
multivariate test only used the sub-sample 1960-1990. The use
of the sub-sample period allowed the more accurate data of the
1960-1990 period to be isolated from the less accurate data of
the pre-1960 period, and
(2)
The
multivariate
test
allows
for
the
interaction
between all variables to bring out the full relationship that
might be disguised in a bivariate framework (Charemza and
Deadman 1992).
These findings give support to the qualitative review in
chapters 1 and 2 of the relationships between the various
factors of production.
4.4. Conclusions
The
results
obtained
by
the
parameter
constancy,
structural invariance and causality tests support the initial
hypothesis that the variables price, area, and labour (proxied
through population) have affected the production of rubber in
Sarawak. The a priori economic theory about how these factors
have
actually
influenced
production
144
is
confirmed
in
this
study.
In summary, the main points that this study has shown
are:
(1) There have been several structural breaks in the time
series that have not been explained by normal movements in the
factors of production modelled. These breaks have occured due
to the initiation of rubber exports (1910), international and
domestic macroeconomic shocks (1920, 1984), and the Japanese
occupation of Sarawak (1941-1945).
(2) Price has had medium to high causal strength on
production
by
influencing
smallholder
decisions
to
plant
rubber.
(3) Area under rubber has had a low to medium causal
influence on rubber production through a straight biological
relationship.
(4)
Production
has
had
high
causal
influence
the
international price of rubber through being tied to other
major producers of rubber who, as a group, have been able to
affect
the supply/demand relationship on the international
market.
(5) Labour supply proxied through Population has had a
small affect on rubber production by regulating the amount of
rubber that can physically be tapped by a finite labour force.
Labour supply has had a medium causal effect on the area
variable, influencing the amount of area that can physically
be planted. In addition, labour supply has affected the amount
of area that can be planted through the price variable for the
reasons given in (2) above.
145
The methodology outlined in this study can easily be
extended for the analysis of other commodities that can be
modelled
quantitatively.
While
the
results
of
this
study
should be interpreted with some caution, due to the reasons
given in section 3.1., the framework and methodology is sound;
(1) The historical analysis of any agricultural commodity
needs
to
incorporate
instability
and
tests
departures
that
can
identify
from
parameter
periods
constancy.
of
The
scaled recursive Chow test outlined in this study is just one
of many tests that can be used.
(2)
negate
Tests
the
economic
of
Lucas
structural
Critique.
relationship
is
invariance
If
not
the
are
specified
invariant
to
essential
to
model
an
changes
of
in
the
parameters, then the model is useless for forecasting and
backcasting.
(3)
Granger
Causality
Causality
tests
provide
using
a
bivariate
means
to
and
multivariate
identify
the
exact
relationship between factors of production and the dependent
variable, production itself. The VAR modelling provides an
ideal way to identify the exact relationship between variables
without any a priori economic theory which may or may not be
correct.
Unlike the classical Cowles Commission Methodology, the
methodology outlined in this study allows the data to speak
for itself and does not rely on classical assumptions which
are violated under real-world scenarios.
146
APPENDIX 1
SARAWAK, EAST MALAYSIA DATABASE 1900-1990
Data for Rubber Production.
____________________________________________________________________________
RUBBER PRODUCTION STATISTICS
POPULATION STATISTICS
____________________________________________________________________________
London
Retail
RSS 1
Area
Price
Net Export
Price Planted
Index
Year
(tonnes)
(M$/t)
(ha)
Pop. (1959=100)
____________________________________________________________________________
1900
0
7784
39.0
1901
0
7560
39.0
1902
0
7336
1000
39.0
1903
0
7112
40.0
1904
0
6888
40.0
1905
0
6664
40.0
1906
0
5639
41.0
1907
0
4937
41.0
1908
0
5101
416000
41.0
1909
0.02
7425
41.0
1910
9.66
9697
42.0
1911
29.28
4957
42.0
1912
94.14
4468
42.0
1913
152
3023
42.0
1914
273
2295
42.0
1915
549
2429
43.0
1916
1011
2643
43.0
1917
1746
2639
43.0
1918
1502
2177
43.0
1919
2261
2003
43.0
1920
1606
1805
43.0
1921
2119
767
41.0
1922
3801
728
40.0
1923
5751
1216
37.0
1924
6753
1095
36.0
1925
9216
3021
40500
36.0
1926
9937
1944
34.0
1927
11289
1439
33.0
1928
10637
892
32.0
1929
11262
818
105300
32.0
1930
10705
477
30.6
1931
10485
259
86000
23.1
1932
7110
195
86000
21.1
1933
11236
269
18.3
1934
17839
495
92300
18.0
1935
19991
478
20.8
1936
21627
611
81000
20.3
1937
26732
757
21.0
1938
18132
579
21.0
1939
24224
716
490585
25.0
1940
35428
952
97200
27.4
1941
0
1080
97200
27.5
1942
0
1082
42.0
1943
0
1416
43.0
147
____________________________________________________________________________
RUBBER PRODUCTION STATISTICS
POPULATION STATISTICS
____________________________________________________________________________
London
Retail
RSS 1
Area
Price
Net Export
Price Planted
Index
Year
(tonnes)
(M$/t)
(ha)
Pop. (1959=100)
____________________________________________________________________________
1944
0
1416
81.0
1945
0
1416
85.0
1946
23902
1258
88.0
1947
36119
1258
546385
89.0
1948
40517
1010
83.0
1949
39524
941
79.0
1950
54887
2620
85.0
1951
41851
4037
111.0
1952
30975
2688
113.0
1953
23581
1623
110.0
1954
22604
1565
102.0
1955
38736
2465
98.0
1956
40618
2249
99.0
1957
40328
2036
107600
104.0
1958
36841
1851
103.0
1959
40839
2359
100.0
1960
44246
2529
144790
744529
99.8
1961
42904
1947
141750
760373
99.6
1962
33094
1834
145613
778488
99.7
1963
42872
1711
154309
799246
102.8
1964
42263
1613
161508
818147
102.4
1965
39402
1687
168474
851888
102.3
1966
32965
1562
173612
877297
103.7
1967
27335
1341
178312
903107
108
1968
23327
1270
182736
925311
108.2
1969
39178
1697
186987
945947
107.1
1970
21659
1378
190316
971162
109.3
1971
19502
1116
193049
999324
110.9
1972
19895
1048
193049
1024273
114.2
1973
41824
1655
193049
1045941
125.4
1974
32577
1794
193052
1072290
145.8
1975
28989
1357
193052
1107323
152.0
1976
40313
1991
193052
1134938
155.7
1977
37582
2028
193714
1162998
162.8
1978
39551
2300
195007
1191429
170.4
1979
38569
2793
197482
1220673
176.3
1980
35207
3194
199881
1235553
187.5
1981
28157
2578
202226
1342397
189.2
1982
15919
2011
203667
1376256
195.5
1983
18918
2472
204959
1409724
199.8
1984
17970
2246
206313
1443178
204.1
1985
17415
1887
207965
1478096
204.6
1986
16248
2084
207965
1515325
205.3
1987
19906
2402
207965
1552869
206.3
1988
28332
2766
207965
1593101
209.5
1989
23461
2402
207965
1632974
213.2
1990
14974
2187
208750
1670344
217.3
____________________________________________________________________________
148
NOTES:
RUBBER EXPORTS:
Net exports of rubber from Sarawak assumed to be
equivalent to production of rubber.
Sources:
Cramb, R.A. (Unpublished) 1905-1940
Rubber Statistical Bulletins 1940-1945, 1950-1960
Sarawak Information Service 1946-1949
Agricultural Statistics of Sarawak 1960-1990
AREA OF PLANTED RUBBER:
Sources:
Bauer, P.T. (1948) The Rubber Industry (1900-1940)
Rubber Statistical Bulletin (1950-60)
Agricultural Statistics of Sarawak 1960-1990
RETAIL PRICE INDEX:
As a proxy for CPI
Source:
Linear extrapolation 1900-1920, 1921-30
Barlow (1985) Changes in the Economic Position of Workers
on Rubber Estates and Smallholdings in Peninsular Malaysia, 1910-85,
Barlow (1978) The Natural Rubber Industry 1930-1973
Bank Negara Malaysia Quarterly Economic Bulletin 1960-1990
1920
POPULATION OF SARAWAK:
Source:
Freeman (1970) 1900-1960
Agricultural Statistics of Sarawak 1960-1990
RUBBER PRICES:
London Spot Prices for RSS 1 used to approximate
prices received by producers as margins are small.
Assumed exchange rates 1900-1973
($malaysian per Pound Sterling)
_______________________________
Period
Rate
_______________________________
1900 - 19 Nov 1967
8.5714
20 Nov 1967 - 22 Jun
7.362
23 Jun 1972 - 31 Oct
6.8275
1 Nov 1972 - 30 Jun
6.6591
1 Jul 1973 - 31 Nov
5.625
1 Dec 1973 - 31 Dec
5.4546
_______________________________
Source:
Barlow (1978) The Natural Rubber Industry 1900-1973
Demery and Demery, OECD (1992) Adjustment and Equity in Malaysia 1979-87
World Bank (1992) Market Outlook for Major Commodities 1974-1978, 1988-1990
149
APPENDIX 2.
MICROFIT 3.2 and RATS 4.01 Output.
A.2.1. Testing For Stationarity:
A.2.1.1. Testing For Stationarity of the Rubber Production
Variable:
Variable LPROD
Sample from 1900 to 1990
**********************************************************************
Order
Autocorrelation
Standard
Box-Pierce
Ljung-Box
Coefficient
Error
Statistic
Statistic
**********************************************************************
1
.87488
.10483
69.6529[.000]
71.9747[.000]
2
.73900
.16677
119.3495[.000] 123.9048[.000]
3
.60049
.19953
152.1632[.000] 158.5830[.000]
4
.45824
.21849
171.2718[.000] 179.0094[.000]
5
.31390
.22881
180.2386[.000] 188.7060[.000]
6
.25537
.23349
186.1730[.000] 195.1989[.000]
**********************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
89 observations used for estimation from 1902 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.87688
.75698
1.1584[.250]
TREND
-.032417
.016919
-1.9160[.059]
LPROD
.13834
.059677
2.3182[.023]
DLPROD(-1)
-.025455
.10924
-.23301[.816]
*******************************************************************************
R-Squared
.067638
F-statistic F( 3, 85)
2.0554[.112]
R-Bar-Squared
.034731
S.E. of Regression
3.3944
Residual Sum of Squares
979.3877
Mean of Dependent Variable
.23738
S.D. of Dependent Variable
3.4550
Maximum of Log-likelihood
-233.0095
DW-statistic
1.7275
Durbin’s h-statistic
*NONE*
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
88 observations used for estimation from 1903 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.89784
.78409
1.1451[.255]
TREND
-.034738
.017495
-1.9856[.050]
LPROD
.15329
.063932
2.3978[.019]
DLPROD(-1)
-.029012
.11034
-.26293[.793]
DLPROD(-2)
-.074186
.11049
-.67142[.504]
*******************************************************************************
R-Squared
.073306
F-statistic F( 4, 83)
1.6414[.172]
R-Bar-Squared
.028646
S.E. of Regression
3.4245
Residual Sum of Squares
973.3813
Mean of Dependent Variable
.24008
S.D. of Dependent Variable
3.4747
Maximum of Log-likelihood
-230.6179
DW-statistic
1.7049
*******************************************************************************
150
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
87 observations used for estimation from 1904 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.90811
.81266
1.1174[.267]
TREND
-.037383
.018158
-2.0587[.043]
LPROD
.17182
.069320
2.4787[.015]
DLPROD(-1)
-.036941
.11184
-.33032[.742]
DLPROD(-2)
-.078987
.11168
-.70728[.481]
DLPROD(-3)
-.075725
.11239
-.67378[.502]
*******************************************************************************
R-Squared
.079472
F-statistic F( 5, 81)
1.3986[.234]
R-Bar-Squared
.022649
S.E. of Regression
3.4549
Residual Sum of Squares
966.8511
Mean of Dependent Variable
.24284
S.D. of Dependent Variable
3.4947
Maximum of Log-likelihood
-228.2016
DW-statistic
1.6718
*******************************************************************************
A.2.1.2. Testing
Variable:
For
Stationarity
of
the
Rubber
Price
Variable LPRICE
Sample from 1900 to 1990
**********************************************************************
Order
Autocorrelation
Standard
Box-Pierce
Ljung-Box
Coefficient
Error
Statistic
Statistic
**********************************************************************
1
.90977
.10483
75.3194[.000]
77.8300[.000]
2
.81855
.17082
136.2917[.000] 141.5427[.000]
3
.74464
.20954
186.7507[.000] 194.8687[.000]
4
.68496
.23684
229.4451[.000] 240.5075[.000]
5
.63114
.25769
265.6937[.000] 279.7066[.000]
6
.58439
.27415
296.7713[.000] 313.7091[.000]
**********************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPRICE
89 observations used for estimation from 1902 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-1.0424
.32619
-3.1958[.002]
TREND
.0067363
.0023013
2.9272[.004]
LPRICE
.21186
.069573
3.0451[.003]
DLPRICE(-1)
-.090926
.10862
-.83714[.405]
*******************************************************************************
R-Squared
.10254
F-statistic F( 3, 85)
3.2372[.026]
R-Bar-Squared
.070862
S.E. of Regression
.29191
Residual Sum of Squares
7.2428
Mean of Dependent Variable
-.033237
S.D. of Dependent Variable
.30283
Maximum of Log-likelihood
-14.6517
DW-statistic
1.6179
Durbin’s h-statistic
*NONE*
*******************************************************************************
151
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPRICE
88 observations used for estimation from 1903 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-1.2554
.33407
-3.7580[.000]
TREND
.0080837
.0023317
3.4669[.001]
LPRICE
.25593
.071447
3.5821[.001]
DLPRICE(-1)
-.10923
.10689
-1.0219[.310]
DLPRICE(-2)
-.23654
.10428
-2.2683[.026]
*******************************************************************************
R-Squared
.15674
F-statistic F( 4, 83)
3.8569[.006]
R-Bar-Squared
.11610
S.E. of Regression
.28634
Residual Sum of Squares
6.8054
Mean of Dependent Variable
-.033273
S.D. of Dependent Variable
.30457
Maximum of Log-likelihood
-12.2432
DW-statistic
1.6053
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPRICE
87 observations used for estimation from 1904 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-1.4468
.33880
-4.2703[.000]
TREND
.0092575
.0023419
3.9530[.000]
LPRICE
.29567
.072690
4.0675[.000]
DLPRICE(-1)
-.16059
.10714
-1.4989[.138]
DLPRICE(-2)
-.24578
.10234
-2.4015[.019]
DLPRICE(-3)
-.22773
.10241
-2.2238[.029]
*******************************************************************************
R-Squared
.20884
F-statistic F( 5, 81)
4.2763[.002]
R-Bar-Squared
.16000
S.E. of Regression
.28075
Residual Sum of Squares
6.3845
Mean of Dependent Variable
-.033007
S.D. of Dependent Variable
.30632
Maximum of Log-likelihood
-9.8240
DW-statistic
1.5083
*******************************************************************************
A.2.1.3. Testing For Stationarity of the Rubber Area Variable:
Variable LAREA
Sample from 1960 to 1990
**********************************************************************
Order
Autocorrelation
Standard
Box-Pierce
Ljung-Box
Coefficient
Error
Statistic
Statistic
**********************************************************************
1
.88701
.17961
24.3902[.000]
26.8293[.000]
2
.73602
.28813
41.1838[.000]
45.9392[.000]
3
.58335
.34347
51.7329[.000]
58.3720[.000]
4
.45272
.37406
58.0865[.000]
66.1376[.000]
5
.33941
.39134
61.6576[.000]
70.6701[.000]
6
.24324
.40072
63.4917[.000]
73.0911[.000]
**********************************************************************
152
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLAREA
29 observations used for estimation from 1962 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.9499
.26147
7.4576[.000]
TREND
.8019E-3
.2760E-3
2.9059[.008]
LAREA
-.16489
.023032
-7.1594[.000]
DLAREA(-1)
.52255
.069354
7.5345[.000]
*******************************************************************************
R-Squared
.90582
F-statistic F( 3, 25)
80.1533[.000]
R-Bar-Squared
.89452
S.E. of Regression
.0050433
Residual Sum of Squares
.6359E-3
Mean of Dependent Variable
.013347
S.D. of Dependent Variable
.015529
Maximum of Log-likelihood
114.4040
DW-statistic
2.0491
Durbin’s h-statistic
-.14247[.887]
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLAREA
28 observations used for estimation from 1963 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.6931
.54790
3.0901[.005]
TREND
.5886E-3
.3565E-3
1.6511[.112]
LAREA
-.14239
.046906
-3.0356[.006]
DLAREA(-1)
.63458
.18092
3.5074[.002]
DLAREA(-2)
-.14917
.12161
-1.2267[.232]
*******************************************************************************
R-Squared
.91178
F-statistic F( 4, 23)
59.4294[.000]
R-Bar-Squared
.89644
S.E. of Regression
.0050169
Residual Sum of Squares
.5789E-3
Mean of Dependent Variable
.012864
S.D. of Dependent Variable
.015590
Maximum of Log-likelihood
111.2821
DW-statistic
2.0203
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLAREA
27 observations used for estimation from 1964 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.51369
.53860
.95375[.351]
TREND
.2309E-3
.3181E-3
.72587[.476]
LAREA
-.043520
.045948
-.94716[.354]
DLAREA(-1)
.59139
.14885
3.9732[.001]
DLAREA(-2)
.18795
.17128
1.0974[.285]
DLAREA(-3)
-.079678
.095983
-.83013[.416]
*******************************************************************************
R-Squared
.93032
F-statistic F( 5, 21)
56.0778[.000]
R-Bar-Squared
.91373
S.E. of Regression
.0038421
Residual Sum of Squares
.3100E-3
Mean of Dependent Variable
.011192
S.D. of Dependent Variable
.013081
Maximum of Log-likelihood
115.2486
DW-statistic
1.5081
*******************************************************************************
153
A.2.1.4. Testing For Stationarity of the Population Variable:
Variable LPOP
Sample from 1960 to 1990
**********************************************************************
Order
Autocorrelation
Standard
Box-Pierce
Ljung-Box
Coefficient
Error
Statistic
Statistic
**********************************************************************
1
.90590
.17961
25.4404[.000]
27.9845[.000]
2
.81028
.29190
45.7935[.000]
51.1448[.000]
3
.71411
.35716
61.6021[.000]
69.7765[.000]
4
.61859
.40058
73.4643[.000]
84.2747[.000]
5
.52312
.43029
81.9475[.000]
95.0417[.000]
6
.43196
.45034
87.7318[.000] 102.6770[.000]
**********************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPOP
29 observations used for estimation from 1962 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-7.0962
1.9163
-3.7030[.001]
TREND
-.016601
.0044626
-3.7201[.001]
LPOP
.60298
.16205
3.7209[.001]
DLPOP(-1)
-.37884
.16276
-2.3276[.028]
*******************************************************************************
R-Squared
.38580
F-statistic F( 3, 25)
5.2345[.006]
R-Bar-Squared
.31210
S.E. of Regression
.0096288
Residual Sum of Squares
.0023179
Mean of Dependent Variable
.027137
S.D. of Dependent Variable
.011609
Maximum of Log-likelihood
95.6497
DW-statistic
1.1549
Durbin’s h-statistic
4.7267[.000]
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPOP
28 observations used for estimation from 1963 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-8.1857
1.9179
-4.2681[.000]
TREND
-.019190
.0044680
-4.2950[.000]
LPOP
.69619
.16230
4.2895[.000]
DLPOP(-1)
-.48221
.16397
-2.9408[.007]
DLPOP(-2)
-.31018
.16077
-1.9294[.066]
*******************************************************************************
R-Squared
.47891
F-statistic F( 4, 23)
5.2847[.004]
R-Bar-Squared
.38829
S.E. of Regression
.0092302
Residual Sum of Squares
.0019595
Mean of Dependent Variable
.027265
S.D. of Dependent Variable
.011801
Maximum of Log-likelihood
94.2114
DW-statistic
.94678
*******************************************************************************
154
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPOP
27 observations used for estimation from 1964 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-9.0882
1.8861
-4.8185[.000]
TREND
-.021326
.0043956
-4.8516[.000]
LPOP
.77362
.15972
4.8436[.000]
DLPOP(-1)
-.55081
.16034
-3.4354[.002]
DLPOP(-2)
-.41543
.16204
-2.5637[.018]
DLPOP(-3)
-.30247
.15270
-1.9807[.061]
*******************************************************************************
R-Squared
.56550
F-statistic F( 5, 21)
5.4662[.002]
R-Bar-Squared
.46205
S.E. of Regression
.0088196
Residual Sum of Squares
.0016335
Mean of Dependent Variable
.027301
S.D. of Dependent Variable
.012025
Maximum of Log-likelihood
92.8123
DW-statistic
.81476
*******************************************************************************
A.2.2. Determination of Appropriate Lag Length:
A.2.2.1. Lag Length for Rubber Production Variable:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
89 observations used for estimation from 1902 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.22526
.36878
.61084[.543]
DLPROD(-1)
.049984
.10710
.46671[.642]
*******************************************************************************
R-Squared
.0024974
F-statistic F( 1, 87)
.21782[.642]
R-Bar-Squared
-.0089681
S.E. of Regression
3.4704
Residual Sum of Squares
1047.8
Mean of Dependent Variable
.23738
S.D. of Dependent Variable
3.4550
Maximum of Log-likelihood
-236.0147
DW-statistic
2.0002
Durbin’s h-statistic
*NONE*
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
88 observations used for estimation from 1903 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.22651
.37607
.60231[.549]
DLPROD(-1)
.049651
.10848
.45768[.648]
DLPROD(-2)
.0056412
.10849
.051997[.959]
*******************************************************************************
R-Squared
.0025239
F-statistic F( 2, 85)
.10754[.898]
R-Bar-Squared
-.020946
S.E. of Regression
3.5109
Residual Sum of Squares
1047.7
Mean of Dependent Variable
.24008
S.D. of Dependent Variable
3.4747
Maximum of Log-likelihood
-233.8565
DW-statistic
1.9998
*******************************************************************************
155
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
87 observations used for estimation from 1904 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.22560
.38357
.58816[.558]
DLPROD(-1)
.049513
.10977
.45105[.653]
DLPROD(-2)
.0048363
.10991
.044002[.965]
DLPROD(-3)
.015231
.10978
.13874[.890]
*******************************************************************************
R-Squared
.0027492
F-statistic F( 3, 83)
.076271[.973]
R-Bar-Squared
-.033296
S.E. of Regression
3.5524
Residual Sum of Squares
1047.4
Mean of Dependent Variable
.24284
S.D. of Dependent Variable
3.4947
Maximum of Log-likelihood
-231.6840
DW-statistic
1.9998
*******************************************************************************
A.2.2.2. Lag Length for Rubber Price Variable:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LPRICE
90 observations used for estimation from 1901 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.22289
.11924
1.8692[.065]
LPRICE(-1)
.92265
.034773
26.5335[.000]
*******************************************************************************
R-Squared
.88889
F-statistic F( 1, 88) 704.0288[.000]
R-Bar-Squared
.88763
S.E. of Regression
.29466
Residual Sum of Squares
7.6408
Mean of Dependent Variable
3.2776
S.D. of Dependent Variable
.87903
Maximum of Log-likelihood
-16.7204
DW-statistic
1.9193
Durbin’s h-statistic
.40536[.685]
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LPRICE
89 observations used for estimation from 1902 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.24300
.12383
1.9624[.053]
LPRICE(-1)
.95991
.10770
8.9130[.000]
LPRICE(-2)
-.043483
.10525
-.41313[.681]
*******************************************************************************
R-Squared
.88267
F-statistic F( 2, 86) 323.4875[.000]
R-Bar-Squared
.87994
S.E. of Regression
.29726
Residual Sum of Squares
7.5991
Mean of Dependent Variable
3.2552
S.D. of Dependent Variable
.85790
Maximum of Log-likelihood
-16.7886
DW-statistic
1.9904
*******************************************************************************
156
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LPRICE
88 observations used for estimation from 1903 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.24050
.12803
1.8784[.064]
LPRICE(-1)
.96118
.10828
8.8771[.000]
LPRICE(-2)
-.16142
.14978
-1.0777[.284]
LPRICE(-3)
.11578
.10569
1.0954[.276]
*******************************************************************************
R-Squared
.87726
F-statistic F( 3, 84) 200.1224[.000]
R-Bar-Squared
.87288
S.E. of Regression
.29805
Residual Sum of Squares
7.4620
Mean of Dependent Variable
3.2327
S.D. of Dependent Variable
.83594
Maximum of Log-likelihood
-16.2962
DW-statistic
2.0311
*******************************************************************************
A.2.2.3. Lag Length for Rubber Area Variable:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LAREA
30 observations used for estimation from 1961 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.0125
.26790
3.7792[.001]
LAREA(-1)
.91758
.022074
41.5689[.000]
*******************************************************************************
R-Squared
.98405
F-statistic F( 1, 28)
1728.0[.000]
R-Bar-Squared
.98348
S.E. of Regression
.013730
Residual Sum of Squares
.0052787
Mean of Dependent Variable
12.1484
S.D. of Dependent Variable
.10684
Maximum of Log-likelihood
87.1109
DW-statistic
.70741
Durbin’s h-statistic
3.5661[.000]
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LAREA
29 observations used for estimation from 1962 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.2124
.12515
9.6874[.000]
LAREA(-1)
1.2844
.071046
18.0788[.000]
LAREA(-2)
-.38354
.065722
-5.8358[.000]
*******************************************************************************
R-Squared
.99718
F-statistic F( 2, 26)
4605.0[.000]
R-Bar-Squared
.99697
S.E. of Regression
.0051616
Residual Sum of Squares
.6927E-3
Mean of Dependent Variable
12.1583
S.D. of Dependent Variable
.093746
Maximum of Log-likelihood
113.1626
DW-statistic
1.4520
*******************************************************************************
157
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LAREA
28 observations used for estimation from 1963 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.0029
.25713
3.9004[.001]
LAREA(-1)
1.5530
.18618
8.3413[.000]
LAREA(-2)
-.85954
.24870
-3.4561[.002]
LAREA(-3)
.22465
.094744
2.3712[.026]
*******************************************************************************
R-Squared
.99676
F-statistic F( 3, 24)
2464.4[.000]
R-Bar-Squared
.99636
S.E. of Regression
.0047986
Residual Sum of Squares
.5526E-3
Mean of Dependent Variable
12.1679
S.D. of Dependent Variable
.079534
Maximum of Log-likelihood
111.9319
DW-statistic
1.9139
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LAREA
27 observations used for estimation from 1964 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.30054
.26108
1.1512[.262]
LAREA(-1)
1.5388
.16118
9.5469[.000]
LAREA(-2)
-.37119
.28762
-1.2906[.210]
LAREA(-3)
-.29931
.23656
-1.2653[.219]
LAREA(-4)
.10723
.081758
1.3115[.203]
*******************************************************************************
R-Squared
.99745
F-statistic F( 4, 22)
2155.1[.000]
R-Bar-Squared
.99699
S.E. of Regression
.0037269
Residual Sum of Squares
.3056E-3
Mean of Dependent Variable
12.1761
S.D. of Dependent Variable
.067949
Maximum of Log-likelihood
115.4423
DW-statistic
1.4115
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LAREA
26 observations used for estimation from 1965 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.44198
.26172
1.6887[.107]
LAREA(-1)
1.8136
.20864
8.6925[.000]
LAREA(-2)
-.77873
.35559
-2.1900[.041]
LAREA(-3)
-.38343
.28692
-1.3364[.196]
LAREA(-4)
.38708
.23515
1.6461[.115]
LAREA(-5)
-.074647
.081520
-.91569[.371]
*******************************************************************************
R-Squared
.99699
F-statistic F( 5, 20)
1324.1[.000]
R-Bar-Squared
.99624
S.E. of Regression
.0035768
Residual Sum of Squares
.2559E-3
Mean of Dependent Variable
12.1832
S.D. of Dependent Variable
.058294
Maximum of Log-likelihood
112.9838
DW-statistic
1.8984
*******************************************************************************
158
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LAREA
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.41641
.30006
1.3878[.182]
LAREA(-1)
1.8421
.24174
7.6201[.000]
LAREA(-2)
-.86166
.49064
-1.7562[.096]
LAREA(-3)
-.33538
.41910
-.80023[.434]
LAREA(-4)
.46123
.31533
1.4627[.161]
LAREA(-5)
-.17464
.26574
-.65716[.519]
LAREA(-6)
.034365
.088270
.38932[.702]
*******************************************************************************
R-Squared
.99591
F-statistic F( 6, 18) 729.9751[.000]
R-Bar-Squared
.99454
S.E. of Regression
.0037538
Residual Sum of Squares
.2536E-3
Mean of Dependent Variable
12.1891
S.D. of Dependent Variable
.050815
Maximum of Log-likelihood
108.2574
DW-statistic
1.9756
*******************************************************************************
A.2.2.4. Lag Length for Population Variable:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LPOP
30 observations used for estimation from 1961 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.034615
.12441
.27823[.783]
LPOP(-1)
.99945
.0089430
111.7578[.000]
*******************************************************************************
R-Squared
.99776
F-statistic F( 1, 28)
12489.8[.000]
R-Bar-Squared
.99768
S.E. of Regression
.011663
Residual Sum of Squares
.0038090
Mean of Dependent Variable
13.9368
S.D. of Dependent Variable
.24232
Maximum of Log-likelihood
92.0056
DW-statistic
2.4083
Durbin’s h-statistic
-1.1195[.263]
*******************************************************************************
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LPOP
29 observations used for estimation from 1962 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.053882
.13187
.40862[.686]
LPOP(-1)
.78742
.19110
4.1205[.000]
LPOP(-2)
.21107
.19144
1.1025[.280]
*******************************************************************************
R-Squared
.99767
F-statistic F( 2, 26)
5555.1[.000]
R-Bar-Squared
.99749
S.E. of Regression
.011764
Residual Sum of Squares
.0035985
Mean of Dependent Variable
13.9504
S.D. of Dependent Variable
.23462
Maximum of Log-likelihood
89.2716
DW-statistic
2.0525
*******************************************************************************
159
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is LPOP
28 observations used for estimation from 1963 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.073551
.14310
.51398[.612]
LPOP(-1)
.75413
.20257
3.7229[.001]
LPOP(-2)
.13411
.25301
.53008[.601]
LPOP(-3)
.10912
.20226
.53950[.595]
*******************************************************************************
R-Squared
.99746
F-statistic F( 3, 24)
3144.1[.000]
R-Bar-Squared
.99714
S.E. of Regression
.012113
Residual Sum of Squares
.0035215
Mean of Dependent Variable
13.9642
S.D. of Dependent Variable
.22669
Maximum of Log-likelihood
86.0046
DW-statistic
2.0305
*******************************************************************************
A.2.3. Formulation of Models:
A.2.3.1. Formulation of Model 1:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
89 observations used for estimation from 1902 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.41523
1.4812
-.28033[.780]
DLPROD(-1)
.046018
.10883
.42286[.673]
LPRICE
.22598
1.2765
.17702[.860]
LPRICE(-1)
-.028634
1.2452
-.022996[.982]
*******************************************************************************
R-Squared
.0049162
F-statistic F( 3, 85)
.13998[.936]
R-Bar-Squared
-.030204
S.E. of Regression
3.5068
Residual Sum of Squares
1045.3
Mean of Dependent Variable
.23738
S.D. of Dependent Variable
3.4550
Maximum of Log-likelihood
-235.9067
DW-statistic
2.0004
Durbin’s h-statistic
*NONE*
*******************************************************************************
160
A.2.3.2. Formulation of Model 2:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is DLPROD
26 observations used for estimation from 1965 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
3.6548
27.4569
.13311[.896]
DLPROD(-1)
-.12491
.16000
-.78065[.448]
LPRICE
1.2283
.28490
4.3113[.001]
LPRICE(-1)
-1.2606
.35904
-3.5110[.003]
LAREA
-3.4777
16.0015
-.21734[.831]
LAREA(-1)
-13.1843
27.0666
-.48711[.634]
LAREA(-2)
28.4940
22.9195
1.2432[.234]
LAREA(-3)
-12.5940
17.5723
-.71670[.485]
LAREA(-4)
3.6212
15.0265
.24099[.813]
LAREA(-5)
-3.0240
5.4932
-.55050[.591]
LPOP
2.8987
3.7230
.77861[.449]
LPOP(-1)
-3.0164
3.6685
-.82224[.425]
*******************************************************************************
R-Squared
.79299
F-statistic F(11, 14)
4.8754[.003]
R-Bar-Squared
.63034
S.E. of Regression
.18268
Residual Sum of Squares
.46718
Mean of Dependent Variable
-.039908
S.D. of Dependent Variable
.30045
Maximum of Log-likelihood
15.3563
DW-statistic
2.2824
Durbin’s h-statistic
-1.2453[.213]
*******************************************************************************
161
A.2.4. Test For Parameter Constancy:
A.2.4.1. Scaled Recursive Chow Test For Model 1:
Year
SRC5%
Year
SRC5%
****************************************************
1910
1951 0.004337
1911 1.45698
1952 0.006645
1912 0.016513
1953 0.004521
1913 0.016243
1954 0.001153
1914 0.027451
1955 0.001177
1915 0.426851
1956 0.0012
1916 0.232916
1957 0.001224
1917 0.045401
1958 0.001247
1918 0.000535
1959 0.001271
1919 0.004762
1960
0
1920 0.049447
1961 0.001317
1921 1.581397
1962 0.004023
1922 0.031343
1963
0
1923 0.560072
1964 0.001388
1924 0.002953
1965 0.001439
1925 0.54607
1966 0.001463
1926 0.001119
1967 0.002974
1927 0.0044
1968 0.001511
1928 0.018037
1969 0.001534
1929 0.001424
1970 0.009349
1930 0.048737
1971
0
1931 0.030886
1972
0
1932 0.000128
1973 0.006517
1933 0.000413
1974 0.003305
1934 0.074419
1975
0
1935 1.3E-05
1976 0.0017
1936 0.034252
1977
0
1937 0.011764
1978
0
1938 0.014408
1979 0.001772
1939 0.009587
1980
0
1940 0.006621
1981 0.003639
1941 75.83722
1982 0.005529
1942 0.21845
1983 0.001866
1943 0.001827
1984
0
1944 0.011258
1985
0
1945 0.012189
1986
0
1946 8.448515
1987
0
1947 0.019844
1988 0.001986
1948 0.001014
1989
0
1949 0.002075
1990 0.0061
1950
0
****************************************************
162
A.2.4.2. Scaled Recursive Chow Test For Model 2:
Year
SRC5%
********************************
1976
1977 0.001105
1978 0.011395
1979 0.132672
1980 0.129812
1981 0.084937
1982 0.052369
1983 0.000242
1984 2.414206
1985 0.394359
1986 0.30841
1987 0.000422
1988 0.549264
1989 0.000495
1990 0.62758
********************************
A.2.5. Test of Structural Invariance:
A.2.5.1. Structural Invariance of Model 1:
Variable Addition Test (OLS case)
*******************************************************************************
Dependent variable is DLPROD
List of the variables added to the regression:
RYSQ
89 observations used for estimation from 1902 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.44577
1.4891
-.29936[.765]
DLPROD(-1)
.050189
.10963
.45781[.648]
LPRICE
-.046805
1.3936
-.033585[.973]
LPRICE(-1)
.21888
1.3453
.16270[.871]
RYSQ
1.2131
2.4282
.49960[.619]
*******************************************************************************
Joint test of zero restrictions on the coefficients of additional variables:
Lagrange Multiplier Statistic
CHI-SQ( 1)=
.26368[.608]
Likelihood Ratio Statistic
CHI-SQ( 1)=
.26407[.607]
F Statistic
F( 1, 84)=
.24960[.619]
*******************************************************************************
163
A.2.5.2. Structural Invariance of Model 2:
Variable Addition Test (OLS case)
*******************************************************************************
Dependent variable is DLPROD
List of the variables added to the regression:
RYSQ
RASQ
RPOPSQ
26 observations used for estimation from 1965 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-9.9241
36.5139
-.27179[.791]
DLPROD(-1)
-.14053
.18516
-.75898[.464]
LPRICE
1.4385
.42998
3.3456[.007]
LPRICE(-1)
-1.1775
.41067
-2.8672[.015]
LAREA
1.8028
18.8900
.095437[.926]
LAREA(-1)
-19.5766
32.1866
-.60822[.555]
LAREA(-2)
30.2239
26.4840
1.1412[.278]
LAREA(-3)
-11.8133
20.0280
-.58984[.567]
LAREA(-4)
3.9198
16.9804
.23084[.822]
LAREA(-5)
-3.7120
6.2683
-.59219[.566]
LPOP
10.0798
13.7879
.73106[.480]
LPOP(-1)
-10.1758
14.0028
-.72670[.483]
RYSQ
.42571
1.4887
.28595[.780]
RASQ
1683.5
2149.3
.78327[.450]
RPOPSQ
-190.3525
329.5967
-.57753[.575]
*******************************************************************************
Joint test of zero restrictions on the coefficients of additional variables:
Lagrange Multiplier Statistic
CHI-SQ( 3)=
1.7383[.628]
Likelihood Ratio Statistic
CHI-SQ( 3)=
1.7991[.615]
F Statistic
F( 3, 11)=
.26271[.851]
*******************************************************************************
164
A.2.6. Test of Granger Causality:
A.2.6.1. Price GRANGER CAUSES Production:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPD
84 observations used for estimation from 1907 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-2.4002
1.6806
-1.4282[.158]
DLPROD(-1)
-.018011
.11947
-.15076[.881]
DLPROD(-2)
-.028702
.10731
-.26747[.790]
DLPROD(-3)
-.3385E-3
.10766
-.0031441[.998]
DLPROD(-4)
-.048664
.10796
-.45074[.654]
DLPROD(-5)
.012696
.10820
.11734[.907]
DLPROD(-6)
-.020758
.12033
-.17250[.864]
LPRICE
.35158
1.3052
.26936[.788]
LPRICE(-1)
.51917
1.7639
.29433[.769]
LPRICE(-2)
-2.1069
1.7661
-1.1930[.237]
LPRICE(-3)
2.1282
1.7772
1.1975[.235]
LPRICE(-4)
-1.8147
1.7887
-1.0146[.314]
LPRICE(-5)
1.1915
1.7769
.67056[.505]
LPRICE(-6)
.45967
1.2587
.36520[.716]
*******************************************************************************
R-Squared
.074394
F-statistic F(13, 70)
.43278[.952]
R-Bar-Squared
-.097504
S.E. of Regression
3.3058
Residual Sum of Squares
764.9912
Mean of Dependent Variable
.0000
S.D. of Dependent Variable
3.1556
Maximum of Log-likelihood
-211.9708
DW-statistic
1.9982
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is DLPROD
List of the variables deleted from the regression:
LPRICE
LPRICE(-1)
LPRICE(-2)
LPRICE(-3)
LPRICE(-4)
LPRICE(-5)
LPRICE(-6)
84 observations used for estimation from 1907 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.32207
.36377
.88536[.379]
DLPROD(-1)
.075051
.11385
.65923[.512]
DLPROD(-2)
.010011
.10143
.098694[.922]
DLPROD(-3)
.016197
.10139
.15975[.874]
DLPROD(-4)
.026893
.10139
.26525[.792]
DLPROD(-5)
-.45961
.10143
-4.5312[.000]
DLPROD(-6)
.051542
.11385
.45272[.652]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)=
6.2491[.511]
Likelihood Ratio Statistic
CHI-SQ( 7)=
6.4938[.483]
F Statistic
F( 7, 70)=
.80374[.587]
*******************************************************************************
165
A.2.6.2. Production GRANGER CAUSES Price:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPR
84 observations used for estimation from 1907 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.026776
.15179
.17640[.860]
LPRICE(-1)
-.0087296
.11975
-.072898[.942]
LPRICE(-2)
.029319
.16267
.18024[.857]
LPRICE(-3)
-.018339
.16430
-.11162[.911]
LPRICE(-4)
-.045323
.16491
-.27484[.784]
LPRICE(-5)
.096550
.16298
.59239[.555]
LPRICE(-6)
-.063402
.11513
-.55068[.584]
DLPROD
.0029451
.010934
.26936[.788]
DLPROD(-1)
.0064654
.010925
.59181[.556]
DLPROD(-2)
-.015480
.0096475
-1.6046[.113]
DLPROD(-3)
.010642
.0097722
1.0890[.280]
DLPROD(-4)
.016432
.0096871
1.6962[.094]
DLPROD(-5)
.0072956
.011009
.66268[.510]
DLPROD(-6)
-.4090E-3
.011019
-.037121[.970]
*******************************************************************************
R-Squared
.098667
F-statistic F(13, 70)
.58945[.855]
R-Bar-Squared
-.068723
S.E. of Regression
.30256
Residual Sum of Squares
6.4080
Mean of Dependent Variable
-.2508E-3
S.D. of Dependent Variable
.29267
Maximum of Log-likelihood
-11.1138
DW-statistic
2.0311
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LPRICE
List of the variables deleted from the regression:
DLPROD
DLPROD(-1)
DLPROD(-2)
DLPROD(-3)
DLPROD(-4)
DLPROD(-5)
DLPROD(-6)
84 observations used for estimation from 1907 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.25766
.14737
1.7484[.084]
LPRICE(-1)
.91530
.11322
8.0843[.000]
LPRICE(-2)
-.14700
.15378
-.95591[.342]
LPRICE(-3)
.4612E-3
.15483
.0029788[.998]
LPRICE(-4)
.053131
.15483
.34316[.732]
LPRICE(-5)
-.034497
.15416
-.22378[.824]
LPRICE(-6)
.11684
.11029
1.0594[.293]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)=
8.2877[.308]
Likelihood Ratio Statistic
CHI-SQ( 7)=
8.7256[.273]
F Statistic
F( 7, 70)=
1.0946[.376]
*******************************************************************************
166
A.2.6.3. Area GRANGER CAUSES Production:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPD
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
71.8489
37.1179
1.9357[.079]
DLPROD(-1)
-.64143
.28600
-2.2428[.046]
DLPROD(-2)
-.70458
.31404
-2.2436[.046]
DLPROD(-3)
-.57180
.35640
-1.6044[.137]
DLPROD(-4)
-.47063
.38380
-1.2262[.246]
DLPROD(-5)
.10585
.32921
.32153[.754]
DLPROD(-6)
-.48303
.30317
-1.5933[.139]
LAREA
9.5875
23.2806
.41182[.688]
LAREA(-1)
-50.7561
50.3791
-1.0075[.335]
LAREA(-2)
34.6400
58.1508
.59569[.563]
LAREA(-3)
-8.8438
48.8600
-.18100[.860]
LAREA(-4)
16.6043
32.3546
.51320[.618]
LAREA(-5)
-13.6996
23.8809
-.57366[.578]
LAREA(-6)
6.5658
7.9126
.82980[.424]
*******************************************************************************
R-Squared
.62155
F-statistic F(13, 11)
1.3897[.296]
R-Bar-Squared
.17429
S.E. of Regression
.30146
Residual Sum of Squares
.99966
Mean of Dependent Variable
-.37291
S.D. of Dependent Variable
.33175
Maximum of Log-likelihood
4.7667
DW-statistic
1.9838
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is DLPROD
List of the variables deleted from the regression:
LAREA
LAREA(-1)
LAREA(-2)
LAREA(-3)
LAREA(-4)
LAREA(-5)
LAREA(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.060195
.066622
-.90353[.378]
DLPROD(-1)
-.23522
.23394
-1.0055[.328]
DLPROD(-2)
-.21583
.24102
-.89547[.382]
DLPROD(-3)
-.20657
.24824
-.83216[.416]
DLPROD(-4)
.13033
.25418
.51276[.614]
DLPROD(-5)
-.10116
.25487
-.39691[.696]
DLPROD(-6)
-.28838
.24313
-1.1861[.251]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)= 10.6743[.153]
Likelihood Ratio Statistic
CHI-SQ( 7)= 13.9206[.053]
F Statistic
F( 7, 11)=
1.1709[.391]
*******************************************************************************
167
A.2.6.4. Production GRANGER CAUSES Area:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDAR
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.33826
.55249
-.61224[.553]
LAREA(-1)
.10860
.33468
.32448[.752]
LAREA(-2)
-.12586
.69853
-.18018[.860]
LAREA(-3)
.19892
.62756
.31698[.757]
LAREA(-4)
-.25038
.41596
-.60194[.559]
LAREA(-5)
.14888
.31139
.47812[.642]
LAREA(-6)
-.052488
.10469
-.50137[.626]
DLPROD
.0015837
.0038456
.41182[.688]
DLPROD(-1)
.2707E-3
.0042804
.063238[.951]
DLPROD(-2)
.0031391
.0047581
.65973[.523]
DLPROD(-3)
-.0014407
.0050428
-.28569[.780]
DLPROD(-4)
.0053122
.0049723
1.0684[.308]
DLPROD(-5)
.0053361
.0041465
1.2869[.225]
DLPROD(-6)
.0035831
.0041001
.87389[.401]
*******************************************************************************
R-Squared
.34896
F-statistic F(13, 11)
.45354[.912]
R-Bar-Squared
-.42045
S.E. of Regression
.0038745
Residual Sum of Squares
.1651E-3
Mean of Dependent Variable
-.1817E-6
S.D. of Dependent Variable .0032509
Maximum of Log-likelihood
113.6221
DW-statistic
2.0792
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LAREA
List of the variables deleted from the regression:
DLPROD
DLPROD(-1)
DLPROD(-2)
DLPROD(-3)
DLPROD(-4)
DLPROD(-5)
DLPROD(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.41641
.30006
1.3878[.182]
LAREA(-1)
1.8421
.24174
7.6201[.000]
LAREA(-2)
-.86166
.49064
-1.7562[.096]
LAREA(-3)
-.33538
.41910
-.80023[.434]
LAREA(-4)
.46123
.31533
1.4627[.161]
LAREA(-5)
-.17464
.26574
-.65716[.519]
LAREA(-6)
.034365
.088270
.38932[.702]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)=
8.7240[.273]
Likelihood Ratio Statistic
CHI-SQ( 7)= 10.7296[.151]
F Statistic
F( 7, 11)=
.84229[.576]
*******************************************************************************
168
A.2.6.5. Population GRANGER CAUSES Production:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPD
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.12374
4.5982
-.026910[.979]
DLPROD(-1)
-.72940
.33798
-2.1581[.054]
DLPROD(-2)
-.74581
.38935
-1.9155[.082]
DLPROD(-3)
-.46629
.37232
-1.2524[.236]
DLPROD(-4)
-.11399
.36299
-.31404[.759]
DLPROD(-5)
.46090
.31239
1.4754[.168]
DLPROD(-6)
-.070127
.28679
-.24452[.811]
LPOP
-10.1935
6.3282
-1.6108[.136]
LPOP(-1)
-7.3578
7.4136
-.99246[.342]
LPOP(-2)
5.2261
7.3555
.71050[.492]
LPOP(-3)
-.59923
7.6640
-.078188[.939]
LPOP(-4)
5.3767
7.6820
.69991[.499]
LPOP(-5)
.21373
7.7201
.027685[.978]
LPOP(-6)
7.4486
7.1381
1.0435[.319]
*******************************************************************************
R-Squared
.57713
F-statistic F(13, 11)
1.1548[.410]
R-Bar-Squared
.077380
S.E. of Regression
.31866
Residual Sum of Squares
1.1170
Mean of Dependent Variable
-.37291
S.D. of Dependent Variable
.33175
Maximum of Log-likelihood
3.3796
DW-statistic
1.9529
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is DLPROD
List of the variables deleted from the regression:
LPOP
LPOP(-1)
LPOP(-2)
LPOP(-3)
LPOP(-4)
LPOP(-5)
LPOP(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.060195
.066622
-.90353[.378]
DLPROD(-1)
-.23522
.23394
-1.0055[.328]
DLPROD(-2)
-.21583
.24102
-.89547[.382]
DLPROD(-3)
-.20657
.24824
-.83216[.416]
DLPROD(-4)
.13033
.25418
.51276[.614]
DLPROD(-5)
-.10116
.25487
-.39691[.696]
DLPROD(-6)
-.28838
.24313
-1.1861[.251]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)=
8.9931[.253]
Likelihood Ratio Statistic
CHI-SQ( 7)= 11.1463[.132]
F Statistic
F( 7, 11)=
.88286[.550]
*******************************************************************************
169
A.2.6.6. Production GRANGER CAUSES Population:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPOP
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.051746
.19708
-.26257[.798]
LPOP(-1)
-.46653
.32367
-1.4414[.177]
LPOP(-2)
-.016575
.32144
-.051565[.960]
LPOP(-3)
.047904
.32811
.14600[.887]
LPOP(-4)
.17457
.33119
.52708[.609]
LPOP(-5)
.094751
.32887
.28811[.779]
LPOP(-6)
.17310
.30683
.56416[.584]
DLPROD
-.018724
.011624
-1.6108[.136]
DLPROD(-1)
-.028440
.014415
-1.9729[.074]
DLPROD(-2)
-.027677
.017298
-1.6000[.138]
DLPROD(-3)
-.015015
.016370
-.91725[.379]
DLPROD(-4)
-.0080868
.015406
-.52492[.610]
DLPROD(-5)
.0098483
.013055
.75436[.466]
DLPROD(-6)
.0084625
.012026
.70368[.496]
*******************************************************************************
R-Squared
.35610
F-statistic F(13, 11)
.46795[.903]
R-Bar-Squared
-.40488
S.E. of Regression
.013657
Residual Sum of Squares
.0020517
Mean of Dependent Variable
.6403E-8
S.D. of Dependent Variable
.011522
Maximum of Log-likelihood
82.1259
DW-statistic
1.8358
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LPOP
List of the variables deleted from the regression:
DLPROD
DLPROD(-1)
DLPROD(-2)
DLPROD(-3)
DLPROD(-4)
DLPROD(-5)
DLPROD(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.059067
.18797
.31424[.757]
LPOP(-1)
.70643
.23161
3.0501[.007]
LPOP(-2)
.099096
.27986
.35409[.727]
LPOP(-3)
.0090103
.28079
.032089[.975]
LPOP(-4)
.022651
.28077
.080677[.937]
LPOP(-5)
.025955
.27973
.092785[.927]
LPOP(-6)
.13647
.23118
.59035[.562]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)=
8.9024[.260]
Likelihood Ratio Statistic
CHI-SQ( 7)= 11.0052[.138]
F Statistic
F( 7, 11)=
.86905[.558]
*******************************************************************************
170
A.2.6.7. Area GRANGER CAUSES Price:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPR
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
16.1867
12.7734
1.2672[.231]
LPRICE(-1)
-1.0907
.29360
-3.7148[.003]
LPRICE(-2)
-.41633
.25047
-1.6622[.125]
LPRICE(-3)
.098645
.25983
.37965[.711]
LPRICE(-4)
-.20959
.27043
-.77503[.455]
LPRICE(-5)
-.10696
.26020
-.41106[.689]
LPRICE(-6)
-.58932
.25522
-2.3091[.041]
LAREA
21.0016
10.2215
2.0547[.064]
LAREA(-1)
-16.6158
22.0384
-.75395[.467]
LAREA(-2)
-6.2508
25.3040
-.24703[.809]
LAREA(-3)
-.51342
19.5948
-.026202[.980]
LAREA(-4)
2.4105
14.0235
.17189[.867]
LAREA(-5)
-4.3052
11.9553
-.36011[.726]
LAREA(-6)
3.4103
3.8838
.87807[.399]
*******************************************************************************
R-Squared
.72515
F-statistic F(13, 11)
2.2325[.095]
R-Bar-Squared
.40033
S.E. of Regression
.14276
Residual Sum of Squares
.22418
Mean of Dependent Variable
-.047262
S.D. of Dependent Variable
.18435
Maximum of Log-likelihood
23.4535
DW-statistic
1.9175
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LPRICE
List of the variables deleted from the regression:
LAREA
LAREA(-1)
LAREA(-2)
LAREA(-3)
LAREA(-4)
LAREA(-5)
LAREA(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.5763
.65237
2.4163[.027]
LPRICE(-1)
.50533
.22765
2.2198[.040]
LPRICE(-2)
-.29802
.25547
-1.1665[.259]
LPRICE(-3)
.29232
.26847
1.0888[.291]
LPRICE(-4)
.097205
.26073
.37282[.714]
LPRICE(-5)
-.13120
.25851
-.50755[.618]
LPRICE(-6)
-.096770
.20596
-.46986[.644]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)= 13.9498[.052]
Likelihood Ratio Statistic
CHI-SQ( 7)= 20.4106[.005]
F Statistic
F( 7, 11)=
1.9838[.149]
*******************************************************************************
171
A.2.6.8. Price GRANGER CAUSES Area:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDAR
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.33422
.34269
-.97528[.350]
LAREA(-1)
-.40674
.36591
-1.1116[.290]
LAREA(-2)
.49641
.62668
.79213[.445]
LAREA(-3)
.033639
.48288
.069665[.946]
LAREA(-4)
-.21851
.34444
-.63440[.539]
LAREA(-5)
.20601
.30140
.68351[.508]
LAREA(-6)
-.091832
.099244
-.92532[.375]
LPRICE
.013206
.0064273
2.0547[.064]
LPRICE(-1)
.0064430
.0072233
.89197[.392]
LPRICE(-2)
.0089943
.0070874
1.2691[.231]
LPRICE(-3)
-.0025713
.0065126
-.39482[.701]
LPRICE(-4)
.0072062
.0065315
1.1033[.293]
LPRICE(-5)
.0033463
.0065343
.51212[.619]
LPRICE(-6)
.0050546
.0071687
.70508[.495]
*******************************************************************************
R-Squared
.44422
F-statistic F(13, 11)
.67632[.751]
R-Bar-Squared
-.21260
S.E. of Regression
.0035798
Residual Sum of Squares
.1410E-3
Mean of Dependent Variable
-.1817E-6
S.D. of Dependent Variable .0032509
Maximum of Log-likelihood
115.5998
DW-statistic
1.6671
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LAREA
List of the variables deleted from the regression:
LPRICE
LPRICE(-1)
LPRICE(-2)
LPRICE(-3)
LPRICE(-4)
LPRICE(-5)
LPRICE(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.41641
.30006
1.3878[.182]
LAREA(-1)
1.8421
.24174
7.6201[.000]
LAREA(-2)
-.86166
.49064
-1.7562[.096]
LAREA(-3)
-.33538
.41910
-.80023[.434]
LAREA(-4)
.46123
.31533
1.4627[.161]
LAREA(-5)
-.17464
.26574
-.65716[.519]
LAREA(-6)
.034365
.088270
.38932[.702]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)= 11.1056[.134]
Likelihood Ratio Statistic
CHI-SQ( 7)= 14.6848[.040]
F Statistic
F( 7, 11)=
1.2560[.353]
*******************************************************************************
172
A.2.6.9. Population GRANGER CAUSES Price:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPR
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.48919
5.5425
.088261[.931]
LPRICE(-1)
-.45382
.28318
-1.6026[.137]
LPRICE(-2)
.052228
.33625
.15532[.879]
LPRICE(-3)
.60197
.40310
1.4934[.163]
LPRICE(-4)
.081153
.32862
.24695[.809]
LPRICE(-5)
.25502
.40466
.63020[.541]
LPRICE(-6)
-.54396
.31741
-1.7138[.115]
LPOP
-7.8699
5.2979
-1.4855[.166]
LPOP(-1)
-.33906
4.6579
-.072792[.943]
LPOP(-2)
2.3392
4.3515
.53757[.602]
LPOP(-3)
-.86786
4.5189
-.19205[.851]
LPOP(-4)
3.6996
4.6017
.80396[.438]
LPOP(-5)
1.6848
4.4982
.37455[.715]
LPOP(-6)
1.3830
3.9551
.34968[.733]
*******************************************************************************
R-Squared
.55515
F-statistic F(13, 11)
1.0559[.470]
R-Bar-Squared
.029408
S.E. of Regression
.18162
Residual Sum of Squares
.36285
Mean of Dependent Variable
-.047262
S.D. of Dependent Variable
.18435
Maximum of Log-likelihood
17.4344
DW-statistic
1.8954
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LPRICE
List of the variables deleted from the regression:
LPOP
LPOP(-1)
LPOP(-2)
LPOP(-3)
LPOP(-4)
LPOP(-5)
LPOP(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.5763
.65237
2.4163[.027]
LPRICE(-1)
.50533
.22765
2.2198[.040]
LPRICE(-2)
-.29802
.25547
-1.1665[.259]
LPRICE(-3)
.29232
.26847
1.0888[.291]
LPRICE(-4)
.097205
.26073
.37282[.714]
LPRICE(-5)
-.13120
.25851
-.50755[.618]
LPRICE(-6)
-.096770
.20596
-.46986[.644]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)=
7.1147[.417]
Likelihood Ratio Statistic
CHI-SQ( 7)=
8.3724[.301]
F Statistic
F( 7, 11)=
.62511[.727]
*******************************************************************************
173
A.2.6.10. Price GRANGER CAUSES Population:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPOP
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-.42593
.26603
-1.6011[.138]
LPOP(-1)
-.44743
.22904
-1.9535[.077]
LPOP(-2)
-.051297
.22851
-.22448[.826]
LPOP(-3)
.12239
.23174
.52812[.608]
LPOP(-4)
.22594
.23424
.96456[.355]
LPOP(-5)
.17717
.22700
.78047[.452]
LPOP(-6)
-.0039611
.20267
-.019545[.985]
LPRICE
-.021231
.014292
-1.4855[.166]
LPRICE(-1)
.010352
.016092
.64329[.533]
LPRICE(-2)
.018464
.016620
1.1109[.290]
LPRICE(-3)
.044427
.018653
2.3818[.036]
LPRICE(-4)
.0052440
.017125
.30622[.765]
LPRICE(-5)
.041524
.017232
2.4096[.035]
LPRICE(-6)
-.039096
.013325
-2.9340[.014]
*******************************************************************************
R-Squared
.69279
F-statistic F(13, 11)
1.9081[.145]
R-Bar-Squared
.32972
S.E. of Regression
.0094335
Residual Sum of Squares
.9789E-3
Mean of Dependent Variable
.6403E-8
S.D. of Dependent Variable
.011522
Maximum of Log-likelihood
91.3760
DW-statistic
1.7261
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LPOP
List of the variables deleted from the regression:
LPRICE
LPRICE(-1)
LPRICE(-2)
LPRICE(-3)
LPRICE(-4)
LPRICE(-5)
LPRICE(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.059067
.18797
.31424[.757]
LPOP(-1)
.70643
.23161
3.0501[.007]
LPOP(-2)
.099096
.27986
.35409[.727]
LPOP(-3)
.0090103
.28079
.032089[.975]
LPOP(-4)
.022651
.28077
.080677[.937]
LPOP(-5)
.025955
.27973
.092785[.927]
LPOP(-6)
.13647
.23118
.59035[.562]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)= 17.3197[.015]
Likelihood Ratio Statistic
CHI-SQ( 7)= 29.5054[.000]
F Statistic
F( 7, 11)=
3.5437[.030]
*******************************************************************************
174
A.2.6.11. Population GRANGER CAUSES Area:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDAR
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
1.0124
.50041
2.0231[.068]
LAREA(-1)
-.26649
.25602
-1.0409[.320]
LAREA(-2)
.42557
.45377
.93785[.368]
LAREA(-3)
-.33229
.37528
-.88545[.395]
LAREA(-4)
-.20057
.27635
-.72577[.483]
LAREA(-5)
.42658
.24606
1.7336[.111]
LAREA(-6)
-.17100
.088230
-1.9381[.079]
LPOP
.015058
.061760
.24381[.812]
LPOP(-1)
-.015058
.067133
-.22429[.827]
LPOP(-2)
.079571
.067588
1.1773[.264]
LPOP(-3)
.030487
.069133
.44100[.668]
LPOP(-4)
-.028841
.067059
-.43009[.675]
LPOP(-5)
-.15433
.065506
-2.3560[.038]
LPOP(-6)
.10359
.063746
1.6250[.132]
*******************************************************************************
R-Squared
.60749
F-statistic F(13, 11)
1.3096[.331]
R-Bar-Squared
.14361
S.E. of Regression
.0030084
Residual Sum of Squares
.9956E-4
Mean of Dependent Variable
-.1817E-6
S.D. of Dependent Variable .0032509
Maximum of Log-likelihood
119.9472
DW-statistic
2.3864
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LAREA
List of the variables deleted from the regression:
LPOP
LPOP(-1)
LPOP(-2)
LPOP(-3)
LPOP(-4)
LPOP(-5)
LPOP(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.41641
.30006
1.3878[.182]
LAREA(-1)
1.8421
.24174
7.6201[.000]
LAREA(-2)
-.86166
.49064
-1.7562[.096]
LAREA(-3)
-.33538
.41910
-.80023[.434]
LAREA(-4)
.46123
.31533
1.4627[.161]
LAREA(-5)
-.17464
.26574
-.65716[.519]
LAREA(-6)
.034365
.088270
.38932[.702]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)= 15.1872[.034]
Likelihood Ratio Statistic
CHI-SQ( 7)= 23.3798[.001]
F Statistic
F( 7, 11)=
2.4321[.091]
*******************************************************************************
175
A.2.6.12. Area GRANGER CAUSES Population:
Ordinary Least Squares Estimation
*******************************************************************************
Dependent variable is RESIDPOP
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
-2.9303
3.0961
-.94644[.364]
LPOP(-1)
-.15500
.28228
-.54911[.594]
LPOP(-2)
.0085192
.34768
.024503[.981]
LPOP(-3)
.072249
.33867
.21333[.835]
LPOP(-4)
-.11523
.32804
-.35125[.732]
LPOP(-5)
.021490
.39095
.054968[.957]
LPOP(-6)
.11400
.33727
.33801[.742]
LAREA
.35695
1.4641
.24381[.812]
LAREA(-1)
.25896
2.6263
.098601[.923]
LAREA(-2)
.35141
2.2978
.15293[.881]
LAREA(-3)
-1.1446
2.0445
-.55983[.587]
LAREA(-4)
.66058
1.3846
.47708[.643]
LAREA(-5)
-.45031
1.2464
-.36128[.725]
LAREA(-6)
.27006
.46705
.57823[.575]
*******************************************************************************
R-Squared
.25934
F-statistic F(13, 11)
.29628[.980]
R-Bar-Squared
-.61598
S.E. of Regression
.014647
Residual Sum of Squares
.0023600
Mean of Dependent Variable
.6403E-8
S.D. of Dependent Variable
.011522
Maximum of Log-likelihood
80.3760
DW-statistic
2.2159
*******************************************************************************
Variable Deletion Test (OLS case)
*******************************************************************************
Dependent variable is LPOP
List of the variables deleted from the regression:
LAREA
LAREA(-1)
LAREA(-2)
LAREA(-3)
LAREA(-4)
LAREA(-5)
LAREA(-6)
25 observations used for estimation from 1966 to 1990
*******************************************************************************
Regressor
Coefficient
Standard Error
T-Ratio[Prob]
INPT
.059067
.18797
.31424[.757]
LPOP(-1)
.70643
.23161
3.0501[.007]
LPOP(-2)
.099096
.27986
.35409[.727]
LPOP(-3)
.0090103
.28079
.032089[.975]
LPOP(-4)
.022651
.28077
.080677[.937]
LPOP(-5)
.025955
.27973
.092785[.927]
LPOP(-6)
.13647
.23118
.59035[.562]
*******************************************************************************
Joint test of zero restrictions on the coefficient of deleted variables:
Lagrange Multiplier Statistic
CHI-SQ( 7)=
6.4835[.485]
Likelihood Ratio Statistic
CHI-SQ( 7)=
7.5054[.378]
F Statistic
F( 7, 11)=
.55023[.781]
*******************************************************************************
176
A.2.7. Vector Autoregression Model.
A.2.7.1. Determination of Optimal Lag Length.
A.2.7.1.1.
Optimal Lag Length for Production
Equation.
Variable
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1910:01 To 1990:01
Usable Observations
75
Degrees of Freedom
73
Total Observations
81
Skipped/Missing
6
Centered R**2
0.932405
R Bar **2
0.931479
Uncentered R**2
0.997766
T x R**2
74.832
Mean of Dependent Variable
9.3965431710
Std Error of Dependent Variable 1.7490032812
Standard Error of Estimate
0.4578265558
Sum of Squared Residuals
15.301176329
Regression F(1,73)
1006.9683
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.303122
Q(20)
11.612000
Significance Level of Q
0.92878360
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
2.7070009930 0.2173362293
12.45536 0.00000000
2. LPROD{1}
0.7262598965 0.0228867520
31.73276 0.00000000
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1911:01 To 1990:01
Usable Observations
73
Degrees of Freedom
70
Total Observations
80
Skipped/Missing
7
Centered R**2
0.972585
R Bar **2
0.971802
Uncentered R**2
0.999292
T x R**2
72.948
Mean of Dependent Variable
9.4791530117
Std Error of Dependent Variable 1.5538788941
Standard Error of Estimate
0.2609312439
Sum of Squared Residuals
4.7659579835
Regression F(2,70)
1241.6877
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.042398
Q(20)
9.838143
Significance Level of Q
0.97101276
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.341256431 0.220763662
6.07553 0.00000006
2. LPROD{1}
0.871130713 0.067485948
12.90833 0.00000000
3. LPROD{2}
-0.003510586 0.050717562
-0.06922 0.94501316
177
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1912:01 To 1990:01
Usable Observations
71
Degrees of Freedom
67
Total Observations
79
Skipped/Missing
8
Centered R**2
0.965185
R Bar **2
0.963626
Uncentered R**2
0.999289
T x R**2
70.950
Mean of Dependent Variable
9.5491801161
Std Error of Dependent Variable 1.3886458783
Standard Error of Estimate
0.2648433884
Sum of Squared Residuals
4.6995153646
Regression F(3,67)
619.1443
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.952427
Q(19)
10.342872
Significance Level of Q
0.94397462
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.317042649 0.282586587
4.66067 0.00001548
2. LPROD{1}
0.811913797 0.126044306
6.44150 0.00000001
3. LPROD{2}
0.086084636 0.128504732
0.66989 0.50522722
4. LPROD{3}
-0.028030366 0.051603384
-0.54319 0.58880267
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1911:01 To 1990:01
Usable Observations
73
Degrees of Freedom
69
Total Observations
80
Skipped/Missing
7
Centered R**2
0.973476
R Bar **2
0.972323
Uncentered R**2
0.999315
T x R**2
72.950
Mean of Dependent Variable
9.4791530117
Std Error of Dependent Variable 1.5538788941
Standard Error of Estimate
0.2585115085
Sum of Squared Residuals
4.6111458011
Regression F(3,69)
844.1332
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.020465
Q(20)
9.627601
Significance Level of Q
0.97443292
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.7989089759 0.4181021675
1.91080 0.06018601
2. LPROD{1}
0.8853700559 0.0675114884
13.11436 0.00000000
3. LPROD{2}
0.0063729547 0.0506650996
0.12579 0.90026720
4. LPRICE
0.1043517130 0.0685609613
1.52203 0.13257196
178
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1911:01 To 1990:01
Usable Observations
73
Degrees of Freedom
68
Total Observations
80
Skipped/Missing
7
Centered R**2
0.982377
R Bar **2
0.981340
Uncentered R**2
0.999545
T x R**2
72.967
Mean of Dependent Variable
9.4791530117
Std Error of Dependent Variable 1.5538788941
Standard Error of Estimate
0.2122628842
Sum of Squared Residuals
3.0637761758
Regression F(4,68)
947.6255
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.231424
Q(20)
13.271141
Significance Level of Q
0.86544975
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.559297775 0.367003932
4.24872 0.00006696
2. LPROD{1}
0.882810276 0.055435165
15.92510 0.00000000
3. LPROD{2}
-0.031420928 0.042097844
-0.74638 0.45801148
4. LPRICE
0.492143772 0.086878724
5.66472 0.00000032
5. LPRICE{1}
-0.510372361 0.087089175
-5.86034 0.00000015
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1911:01 To 1990:01
Usable Observations
73
Degrees of Freedom
67
Total Observations
80
Skipped/Missing
7
Centered R**2
0.982423
R Bar **2
0.981111
Uncentered R**2
0.999546
T x R**2
72.967
Mean of Dependent Variable
9.4791530117
Std Error of Dependent Variable 1.5538788941
Standard Error of Estimate
0.2135597439
Sum of Squared Residuals
3.0557202032
Regression F(5,67)
748.9564
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.275292
Q(20)
14.527127
Significance Level of Q
0.80279618
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.453891151 0.446367145
3.25716 0.00176789
2. LPROD{1}
0.898825761 0.067548783
13.30632 0.00000000
3. LPROD{2}
-0.040205222 0.047231371
-0.85124 0.39766835
4. LPRICE
0.499513815 0.089151200
5.60300 0.00000043
5. LPRICE{1}
-0.548910480 0.126829265
-4.32795 0.00005137
6. LPRICE{2}
0.042512062 0.101151556
0.42028 0.67562516
179
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1960:01 To 1990:01
Usable Observations
31
Degrees of Freedom
25
Centered R**2
0.805394
R Bar **2
0.766472
Uncentered R**2
0.999774
T x R**2
30.993
Mean of Dependent Variable
10.245056927
Std Error of Dependent Variable 0.355094199
Standard Error of Estimate
0.171598149
Sum of Squared Residuals
0.7361481162
Regression F(5,25)
20.6929
Significance Level of F
0.00000004
Durbin-Watson Statistic
2.476588
Q(7)
12.868730
Significance Level of Q
0.07537209
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
15.05065792
6.88736471
2.18526 0.03845274
2. LPROD{1}
0.66097250
0.15764256
4.19286 0.00030151
3. LPROD{2}
0.00050743
0.13109037
0.00387 0.99694224
4. LPRICE
1.06366532
0.21250961
5.00526 0.00003677
5. LPRICE{1}
-1.08812228
0.24409890
-4.45771 0.00015196
6. LAREA
-0.94820896
0.50489876
-1.87802 0.07209140
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
23
Centered R**2
0.800880
R Bar **2
0.748935
Uncentered R**2
0.999774
T x R**2
29.993
Mean of Dependent Variable
10.229974816
Std Error of Dependent Variable 0.350920443
Standard Error of Estimate
0.175833388
Sum of Squared Residuals
0.7110997473
Regression F(6,23)
15.4180
Significance Level of F
0.00000049
Durbin-Watson Statistic
2.415174
Q(7)
12.950925
Significance Level of Q
0.07331318
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
15.92229183
7.14264142
2.22919 0.03585559
2. LPROD{1}
0.63224151
0.16489214
3.83427 0.00084824
3. LPROD{2}
0.00664356
0.13925848
0.04771 0.96236186
4. LPRICE
1.13176493
0.23147437
4.88937 0.00006138
5. LPRICE{1}
-1.06046710
0.25356455
-4.18224 0.00035751
6. LAREA
-2.17225898
2.47898063
-0.87627 0.38994314
7. LAREA{1}
1.15267235
2.32299450
0.49620 0.62446139
180
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1960:01 To 1990:01
Usable Observations
31
Degrees of Freedom
24
Centered R**2
0.812162
R Bar **2
0.765203
Uncentered R**2
0.999782
T x R**2
30.993
Mean of Dependent Variable
10.245056927
Std Error of Dependent Variable 0.355094199
Standard Error of Estimate
0.172064028
Sum of Squared Residuals
0.7105447130
Regression F(6,24)
17.2950
Significance Level of F
0.00000012
Durbin-Watson Statistic
2.441668
Q(7)
13.495683
Significance Level of Q
0.06091349
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
11.33954490
7.97616017
1.42168 0.16798489
2. LPROD{1}
0.60980173
0.16737411
3.64335 0.00129051
3. LPROD{2}
-0.02152973
0.13356526
-0.16119 0.87329097
4. LPRICE
1.09324211
0.21544704
5.07430 0.00003444
5. LPRICE{1}
-0.98608655
0.26822967
-3.67628 0.00118871
6. LAREA
-0.25868190
0.89782180
-0.28812 0.77572720
7. LPOP
-0.30511243
0.32809615
-0.92995 0.36165799
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
22
Centered R**2
0.813358
R Bar **2
0.753972
Uncentered R**2
0.999788
T x R**2
29.994
Mean of Dependent Variable
10.229974816
Std Error of Dependent Variable 0.350920443
Standard Error of Estimate
0.174060854
Sum of Squared Residuals
0.6665379761
Regression F(7,22)
13.6961
Significance Level of F
0.00000104
Durbin-Watson Statistic
2.347964
Q(7)
10.239703
Significance Level of Q
0.17539178
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
14.21623487
8.51390200
1.66977 0.10913437
2. LPROD{1}
0.59842221
0.17271049
3.46489 0.00220184
3. LPROD{2}
-0.06045504
0.13915673
-0.43444 0.66820132
4. LPRICE
1.16467167
0.23073899
5.04757 0.00004696
5. LPRICE{1}
-1.03705661
0.28820165
-3.59837 0.00159774
6. LAREA
-0.48789192
0.93593405
-0.52129 0.60737385
7. LPOP
2.65368890
3.18549929
0.83305 0.41376789
8. LPOP{1}
-2.93735513
3.14436456
-0.93416 0.36035958
181
A.2.7.1.2. Optimal Lag Length for Price Variable Equation.
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1901:01 To 1990:01
Usable Observations
90
Degrees of Freedom
88
Centered R**2
0.888893
R Bar **2
0.887630
Uncentered R**2
0.992622
T x R**2
89.336
Mean of Dependent Variable
3.2775849753
Std Error of Dependent Variable 0.8790276792
Standard Error of Estimate
0.2946639934
Sum of Squared Residuals
7.6407644744
Regression F(1,88)
704.0288
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.919327
Q(22)
29.861314
Significance Level of Q
0.12187439
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.2228866388 0.1192422777
1.86919 0.06492228
2. LPRICE{1}
0.9226530801 0.0347730854
26.53354 0.00000000
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1902:01 To 1990:01
Usable Observations
89
Degrees of Freedom
86
Centered R**2
0.882670
R Bar **2
0.879941
Uncentered R**2
0.992460
T x R**2
88.329
Mean of Dependent Variable
3.2552312691
Std Error of Dependent Variable 0.8578969894
Standard Error of Estimate
0.2972568795
Sum of Squared Residuals
7.5991021084
Regression F(2,86)
323.4875
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.990372
Q(22)
26.183333
Significance Level of Q
0.24389399
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.243000902 0.123826935
1.96242 0.05294637
2. LPRICE{1}
0.959908225 0.107697185
8.91303 0.00000000
3. LPRICE{2}
-0.043483052 0.105252329
-0.41313 0.68053855
182
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1909:01 To 1990:01
Usable Observations
77
Degrees of Freedom
74
Total Observations
82
Skipped/Missing
5
Centered R**2
0.836778
R Bar **2
0.832366
Uncentered R**2
0.991619
T x R**2
76.355
Mean of Dependent Variable
3.0961991253
Std Error of Dependent Variable 0.7250725901
Standard Error of Estimate
0.2968672199
Sum of Squared Residuals
6.5216308212
Regression F(2,74)
189.6846
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.794537
Q(20)
22.161626
Significance Level of Q
0.33180528
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.063816247 0.400053604
2.65918 0.00959617
2. LPRICE{1}
0.782762735 0.069660675
11.23680 0.00000000
3. LPROD
-0.044331667 0.022561099
-1.96496 0.05317291
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1910:01 To 1990:01
Usable Observations
75
Degrees of Freedom
71
Total Observations
81
Skipped/Missing
6
Centered R**2
0.857462
R Bar **2
0.851440
Uncentered R**2
0.993236
T x R**2
74.493
Mean of Dependent Variable
3.0739780683
Std Error of Dependent Variable 0.6907323139
Standard Error of Estimate
0.2662325337
Sum of Squared Residuals
5.0324631009
Regression F(3,71)
142.3714
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.745391
Q(20)
20.246565
Significance Level of Q
0.44260387
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.084875542 0.508413283
-0.16694 0.86789021
2. LPRICE{1}
0.845957837 0.071239665
11.87481 0.00000000
3. LPROD
0.279527955 0.074218745
3.76627 0.00033878
4. LPROD{1}
-0.226988532 0.051396261
-4.41644 0.00003518
183
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1911:01 To 1990:01
Usable Observations
73
Degrees of Freedom
68
Total Observations
80
Skipped/Missing
7
Centered R**2
0.862370
R Bar **2
0.854274
Uncentered R**2
0.994267
T x R**2
72.581
Mean of Dependent Variable
3.0473672717
Std Error of Dependent Variable 0.6397325207
Standard Error of Estimate
0.2442121691
Sum of Squared Residuals
4.0554916801
Regression F(4,68)
106.5193
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.971958
Q(20)
12.066663
Significance Level of Q
0.91376236
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.306057353 0.473542792
-0.64631 0.52025141
2. LPRICE{1}
0.851203074 0.066745621
12.75294 0.00000000
3. LPROD
0.651446078 0.115000549
5.66472 0.00000032
4. LPROD{1}
-0.611426565 0.117222133
-5.21597 0.00000187
5. LPROD{2}
0.031863466 0.048478551
0.65727 0.51322634
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1912:01 To 1990:01
Usable Observations
71
Degrees of Freedom
65
Total Observations
79
Skipped/Missing
8
Centered R**2
0.865661
R Bar **2
0.855327
Uncentered R**2
0.994816
T x R**2
70.632
Mean of Dependent Variable
3.0308177388
Std Error of Dependent Variable 0.6115055825
Standard Error of Estimate
0.2325913870
Sum of Squared Residuals
3.5164189660
Regression F(5,65)
83.7702
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.953112
Q(19)
13.747581
Significance Level of Q
0.79820208
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.275325384 0.457222777
-0.60217 0.54915653
2. LPRICE{1}
0.817967314 0.065639589
12.46149 0.00000000
3. LPROD
0.672778251 0.110279501
6.10066 0.00000006
4. LPROD{1}
-0.360070695 0.145233577
-2.47925 0.01576844
5. LPROD{2}
-0.272308756 0.115054541
-2.36678 0.02093237
6. LPROD{3}
0.037350541 0.045846059
0.81469 0.41822204
184
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1913:01 To 1990:01
Usable Observations
69
Degrees of Freedom
62
Total Observations
78
Skipped/Missing
9
Centered R**2
0.849695
R Bar **2
0.835149
Uncentered R**2
0.994645
T x R**2
68.630
Mean of Dependent Variable
3.0151237337
Std Error of Dependent Variable 0.5837933208
Standard Error of Estimate
0.2370307257
Sum of Squared Residuals
3.4833810241
Regression F(6,62)
58.4157
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.912029
Q(19)
13.567419
Significance Level of Q
0.80829959
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.318519490 0.479934819
-0.66367 0.50936042
2. LPRICE{1}
0.802679597 0.070509659
11.38397 0.00000000
3. LPROD
0.684286747 0.114135675
5.99538 0.00000011
4. LPROD{1}
-0.358375201 0.149744723
-2.39324 0.01974151
5. LPROD{2}
-0.226684405 0.144455581
-1.56923 0.12168373
6. LPROD{3}
0.000872827 0.122617950
0.00712 0.99434335
7. LPROD{4}
-0.013705754 0.047159955
-0.29062 0.77230981
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1960:01 To 1990:01
Usable Observations
31
Degrees of Freedom
24
Centered R**2
0.835681
R Bar **2
0.794602
Uncentered R**2
0.998592
T x R**2
30.956
Mean of Dependent Variable
2.5715522726
Std Error of Dependent Variable 0.2430059911
Standard Error of Estimate
0.1101324437
Sum of Squared Residuals
0.2910997240
Regression F(6,24)
20.3430
Significance Level of F
0.00000003
Durbin-Watson Statistic
2.087142
Q(7)
4.171293
Significance Level of Q
0.75984996
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-2.179874321 4.965548789
-0.43900 0.66458874
2. LPRICE{1}
0.651713926 0.169581284
3.84308 0.00078259
3. LPROD
0.460476164 0.090913807
5.06498 0.00003526
4. LPROD{1}
-0.244817295 0.122913562
-1.99178 0.05789137
5. LPROD{2}
-0.113709864 0.096352899
-1.18014 0.24951003
6. LPROD{3}
0.141649244 0.083899814
1.68831 0.10430484
7. LAREA
0.046699370 0.352312030
0.13255 0.89565343
185
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
22
Centered R**2
0.808588
R Bar **2
0.747685
Uncentered R**2
0.998713
T x R**2
29.961
Mean of Dependent Variable
2.5495236463
Std Error of Dependent Variable 0.2133691313
Standard Error of Estimate
0.1071774816
Sum of Squared Residuals
0.2527142765
Regression F(7,22)
13.2765
Significance Level of F
0.00000136
Durbin-Watson Statistic
2.004140
Q(7)
9.829612
Significance Level of Q
0.19843662
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-3.385225289 4.892482668
-0.69192 0.49622349
2. LPRICE{1}
0.572252140 0.170867682
3.34910 0.00290293
3. LPROD
0.440595698 0.089201303
4.93934 0.00006103
4. LPROD{1}
-0.204196211 0.121665010
-1.67835 0.10742948
5. LPROD{2}
-0.099052325 0.094818517
-1.04465 0.30752729
6. LPROD{3}
0.135952070 0.083809113
1.62216 0.11901309
7. LAREA
0.641978493 1.545137362
0.41548 0.68181274
8. LAREA{1}
-0.505308229 1.445418863
-0.34959 0.72996853
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1962:01 To 1990:01
Usable Observations
29
Degrees of Freedom
20
Centered R**2
0.806886
R Bar **2
0.729641
Uncentered R**2
0.998831
T x R**2
28.966
Mean of Dependent Variable
2.5349250533
Std Error of Dependent Variable 0.2013216814
Standard Error of Estimate
0.1046794092
Sum of Squared Residuals
0.2191555744
Regression F(8,20)
10.4457
Significance Level of F
0.00001157
Durbin-Watson Statistic
2.039919
Q(7)
8.450617
Significance Level of Q
0.29453903
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-10.27268707
6.80461822
-1.50966 0.14676661
2. LPRICE{1}
0.35736943
0.24095919
1.48311 0.15363251
3. LPROD
0.39920796
0.09223007
4.32839 0.00032648
4. LPROD{1}
-0.08056902
0.15053460
-0.53522 0.59839968
5. LPROD{2}
-0.13769298
0.09532650
-1.44444 0.16410135
6. LPROD{3}
0.16712260
0.08407598
1.98776 0.06070320
7. LAREA
8.51729094
5.31769262
1.60169 0.12490169
8. LAREA{1}
-11.99914473
7.00989489
-1.71174 0.10240847
9. LAREA{2}
4.16324268
2.38463665
1.74586 0.09617946
186
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1963:01 To 1990:01
Usable Observations
28
Degrees of Freedom
18
Centered R**2
0.895124
R Bar **2
0.842686
Uncentered R**2
0.999421
T x R**2
27.984
Mean of Dependent Variable
2.5214549133
Std Error of Dependent Variable 0.1912453018
Standard Error of Estimate
0.0758532818
Sum of Squared Residuals
0.1035669666
Regression F(9,18)
17.0701
Significance Level of F
0.00000044
Durbin-Watson Statistic
2.164549
Q(7)
14.086964
Significance Level of Q
0.04965606
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-35.44104620
7.79397778
-4.54723 0.00024961
2. LPRICE{1}
0.04189319
0.19039730
0.22003 0.82832364
3. LPROD
0.50193482
0.07162628
7.00769 0.00000153
4. LPROD{1}
0.03046008
0.11187196
0.27228 0.78850833
5. LPROD{2}
-0.14036403
0.07151047
-1.96285 0.06530836
6. LPROD{3}
0.13618018
0.06226901
2.18697 0.04218919
7. LAREA
16.99564713
4.41287540
3.85138 0.00116952
8. LAREA{1}
-15.06334808
6.76616507
-2.22628 0.03900715
9. LAREA{2}
1.90809420
5.26430518
0.36246 0.72122995
10. LAREA{3}
-1.18824875
1.74598705
-0.68056 0.50481076
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1962:01 To 1990:01
Usable Observations
29
Degrees of Freedom
19
Centered R**2
0.818414
R Bar **2
0.732400
Uncentered R**2
0.998901
T x R**2
28.968
Mean of Dependent Variable
2.5349250533
Std Error of Dependent Variable 0.2013216814
Standard Error of Estimate
0.1041439303
Sum of Squared Residuals
0.2060732060
Regression F(9,19)
9.5148
Significance Level of F
0.00002309
Durbin-Watson Statistic
2.034671
Q(7)
9.777933
Significance Level of Q
0.20151100
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-5.389129933 8.099535981
-0.66536 0.51381518
2. LPRICE{1}
0.339139019 0.240300579
1.41131 0.17431872
3. LPROD
0.416220352 0.093056579
4.47277 0.00026082
4. LPROD{1}
-0.082794142 0.149778259
-0.55278 0.58686071
5. LPROD{2}
-0.120729271 0.096088417
-1.25644 0.22418572
6. LPROD{3}
0.176691873 0.084098469
2.10101 0.04921567
7. LAREA
5.468838284 5.974421949
0.91538 0.37146668
8. LAREA{1}
-7.965156673 7.882156349
-1.01053 0.32493815
9. LAREA{2}
2.426811829 2.851002097
0.85121 0.40525147
10. LPOP
0.277984945 0.253111601
1.09827 0.28580979
187
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1962:01 To 1990:01
Usable Observations
29
Degrees of Freedom
18
Centered R**2
0.827214
R Bar **2
0.731222
Uncentered R**2
0.998954
T x R**2
28.970
Mean of Dependent Variable
2.5349250533
Std Error of Dependent Variable 0.2013216814
Standard Error of Estimate
0.1043728057
Sum of Squared Residuals
0.1960862864
Regression F(10,18)
8.6175
Significance Level of F
0.00005002
Durbin-Watson Statistic
2.056212
Q(7)
8.947591
Significance Level of Q
0.25644062
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-5.930967706 8.137038255
-0.72889 0.47544940
2. LPRICE{1}
0.427233890 0.257805732
1.65719 0.11480681
3. LPROD
0.430964424 0.094523837
4.55932 0.00024306
4. LPROD{1}
-0.115528235 0.153951448
-0.75042 0.46269926
5. LPROD{2}
-0.099015160 0.098933935
-1.00082 0.33017855
6. LPROD{3}
0.177495756 0.084287473
2.10584 0.04952050
7. LAREA
4.470003800 6.077748837
0.73547 0.47152852
8. LAREA{1}
-6.435061083 8.059499189
-0.79844 0.43502147
9. LAREA{2}
1.904546889 2.908866566
0.65474 0.52091564
10. LPOP
-1.571601945 1.948313731
-0.80665 0.43039944
11. LPOP{1}
1.865234608 1.948072193
0.95748 0.35101104
A.2.7.1.3. Optimal Lag Length for Area Variable Equation.
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
28
Centered R**2
0.984054
R Bar **2
0.983485
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
12.148421861
Std Error of Dependent Variable 0.106842633
Standard Error of Estimate
0.013730451
Sum of Squared Residuals
0.0052787083
Regression F(1,28)
1727.9718
Significance Level of F
0.00000000
Durbin-Watson Statistic
0.707407
Q(7)
8.083343
Significance Level of Q
0.32530211
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.0124525372 0.2679037032
3.77917 0.00075720
2. LAREA{1}
0.9175808545 0.0220737450
41.56888 0.00000000
188
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1962:01 To 1990:01
Usable Observations
29
Degrees of Freedom
26
Centered R**2
0.997185
R Bar **2
0.996968
Uncentered R**2
1.000000
T x R**2
29.000
Mean of Dependent Variable
12.158304677
Std Error of Dependent Variable 0.093745890
Standard Error of Estimate
0.005161650
Sum of Squared Residuals
0.0006927083
Regression F(2,26)
4605.0163
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.452000
Q(7)
8.630196
Significance Level of Q
0.28030759
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.212396812 0.125151299
9.68745 0.00000000
2. LAREA{1}
1.284419640 0.071045713
18.07878 0.00000000
3. LAREA{2}
-0.383542119 0.065721749
-5.83585 0.00000376
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1963:01 To 1990:01
Usable Observations
28
Degrees of Freedom
24
Centered R**2
0.996764
R Bar **2
0.996360
Uncentered R**2
1.000000
T x R**2
28.000
Mean of Dependent Variable
12.167933140
Std Error of Dependent Variable 0.079534254
Standard Error of Estimate
0.004798608
Sum of Squared Residuals
0.0005526394
Regression F(3,24)
2464.4089
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.913899
Q(7)
14.014794
Significance Level of Q
0.05091886
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.002901261 0.257127244
3.90041 0.00067744
2. LAREA{1}
1.552963224 0.186177989
8.34128 0.00000001
3. LAREA{2}
-0.859543364 0.248700564
-3.45614 0.00205321
4. LAREA{3}
0.224653525 0.094743731
2.37117 0.02610260
189
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1964:01 To 1990:01
Usable Observations
27
Degrees of Freedom
22
Centered R**2
0.997454
R Bar **2
0.996992
Uncentered R**2
1.000000
T x R**2
27.000
Mean of Dependent Variable
12.176126502
Std Error of Dependent Variable 0.067948697
Standard Error of Estimate
0.003726887
Sum of Squared Residuals
0.0003055732
Regression F(4,22)
2155.1432
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.411531
Q(6)
5.572488
Significance Level of Q
0.47273910
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.300541415 0.261075465
1.15117 0.26202175
2. LAREA{1}
1.538774035 0.161180495
9.54690 0.00000000
3. LAREA{2}
-0.371193911 0.287622481
-1.29056 0.21025557
4. LAREA{3}
-0.299309436 0.236555397
-1.26528 0.21900770
5. LAREA{4}
0.107226580 0.081757897
1.31151 0.20320744
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
20
Centered R**2
0.996988
R Bar **2
0.996235
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.003576803
Sum of Squared Residuals
0.0002558703
Durbin-Watson Statistic
1.898433
Q(6)
0.682239
Significance Level of Q
0.99486636
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.441982244 0.261724943
1.68873 0.10680320
2. LAREA{1}
1.813645483 0.208644966
8.69250 0.00000003
3. LAREA{2}
-0.778727868 0.355585119
-2.18999 0.04054652
4. LAREA{3}
-0.383431418 0.286923017
-1.33636 0.19643886
5. LAREA{4}
0.387075798 0.235145549
1.64611 0.11536546
6. LAREA{5}
-0.074646907 0.081519663
-0.91569 0.37073787
190
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1966:01 To 1990:01
Usable Observations
25
Degrees of Freedom
18
Centered R**2
0.995907
R Bar **2
0.994543
Uncentered R**2
1.000000
T x R**2
25.000
Mean of Dependent Variable
12.189142756
Std Error of Dependent Variable 0.050814572
Standard Error of Estimate
0.003753822
Sum of Squared Residuals
0.0002536412
Durbin-Watson Statistic
1.975600
Q(6)
1.335603
Significance Level of Q
0.96965825
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.416409016 0.300057917
1.38776 0.18215038
2. LAREA{1}
1.842098533 0.241741268
7.62012 0.00000049
3. LAREA{2}
-0.861659197 0.490636260
-1.75621 0.09605304
4. LAREA{3}
-0.335380433 0.419102435
-0.80023 0.43400984
5. LAREA{4}
0.461228483 0.315328134
1.46269 0.16079328
6. LAREA{5}
-0.174636086 0.265742256
-0.65716 0.51939108
7. LAREA{6}
0.034364751 0.088269664
0.38932 0.70161024
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
19
Centered R**2
0.997590
R Bar **2
0.996829
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.003282726
Sum of Squared Residuals
0.0002047496
Regression F(6,19)
1310.7450
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.024120
Q(6)
4.060468
Significance Level of Q
0.66849354
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.227777127 0.259560251
0.87755 0.39114968
2. LAREA{1}
1.764076521 0.192838358
9.14795 0.00000002
3. LAREA{2}
-0.652864291 0.331426600
-1.96986 0.06360791
4. LAREA{3}
-0.445763233 0.264883418
-1.68287 0.10876182
5. LAREA{4}
0.419820929 0.216335479
1.94060 0.06729220
6. LAREA{5}
-0.105759850 0.076168814
-1.38849 0.18105177
7. LPRICE
0.009542921 0.004381445
2.17803 0.04220639
191
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
18
Centered R**2
0.997794
R Bar **2
0.996935
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.003227036
Sum of Squared Residuals
0.0001874477
Regression F(7,18)
1162.8448
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.909250
Q(6)
5.862622
Significance Level of Q
0.43875401
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.199711630 0.256084251
0.77987 0.44560335
2. LAREA{1}
1.625208709 0.218042654
7.45363 0.00000066
3. LAREA{2}
-0.436972313 0.366335804
-1.19282 0.24842726
4. LAREA{3}
-0.502632918 0.264101195
-1.90318 0.07313438
5. LAREA{4}
0.396193744 0.213453950
1.85611 0.07988532
6. LAREA{5}
-0.100985685 0.074968193
-1.34705 0.19468024
7. LPRICE
0.008304792 0.004412926
1.88192 0.07611790
8. LPRICE{1}
0.006225477 0.004829816
1.28897 0.21372917
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
17
Centered R**2
0.997825
R Bar **2
0.996801
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.003297152
Sum of Squared Residuals
0.0001848106
Regression F(8,17)
974.7049
Significance Level of F
0.00000000
Durbin-Watson Statistic
1.824339
Q(6)
6.241409
Significance Level of Q
0.39669784
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.198596654 0.261658109
0.75899 0.45825213
2. LAREA{1}
1.580312143 0.240707885
6.56527 0.00000481
3. LAREA{2}
-0.394926945 0.383906798
-1.02871 0.31803325
4. LAREA{3}
-0.477769992 0.274520628
-1.74038 0.09986106
5. LAREA{4}
0.367559928 0.225707453
1.62848 0.12181262
6. LAREA{5}
-0.094840802 0.077606479
-1.22207 0.23835493
7. LPRICE
0.009050706 0.004756357
1.90287 0.07413440
8. LPRICE{1}
0.005771938 0.005019935
1.14980 0.26614398
9. LPRICE{2}
0.002564509 0.005206824
0.49253 0.62864632
192
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
17
Centered R**2
0.998181
R Bar **2
0.997325
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.003014766
Sum of Squared Residuals
0.0001545098
Durbin-Watson Statistic
2.302441
Q(6)
2.546096
Significance Level of Q
0.86327352
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
1.154180392 0.555533443
2.07761 0.05321277
2. LAREA{1}
1.344618402 0.251432942
5.34782 0.00005328
3. LAREA{2}
-0.294146454 0.350365825
-0.83954 0.41281701
4. LAREA{3}
-0.495137403 0.246760340
-2.00655 0.06097381
5. LAREA{4}
0.478275221 0.204021360
2.34424 0.03147195
6. LAREA{5}
-0.123749500 0.071050348
-1.74172 0.09962109
7. LPRICE
0.021879318 0.008236662
2.65633 0.01662038
8. LPRICE{1}
0.005052138 0.004554018
1.10938 0.28271360
9. LPROD
-0.011586532 0.006086376
-1.90368 0.07402137
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
16
Centered R**2
0.998652
R Bar **2
0.997893
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.002675547
Sum of Squared Residuals
0.0001145368
Regression F(9,16)
1316.8408
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.249493
Q(6)
2.980173
Significance Level of Q
0.81133117
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
2.592065339 0.783156395
3.30977 0.00442726
2. LAREA{1}
1.186535509 0.232954249
5.09343 0.00010845
3. LAREA{2}
-0.572679614 0.332534284
-1.72217 0.10431036
4. LAREA{3}
-0.215339488 0.248955473
-0.86497 0.39983756
5. LAREA{4}
0.507503814 0.181487046
2.79636 0.01293804
6. LAREA{5}
-0.102994862 0.063664579
-1.61777 0.12525318
7. LPRICE
0.025830150 0.007498646
3.44464 0.00333104
8. LPRICE{1}
0.022334653 0.008356108
2.67285 0.01667276
9. LPROD
-0.014554035 0.005545601
-2.62443 0.01840359
10. LPROD{1}
-0.015120625 0.006398807
-2.36304 0.03112186
193
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
15
Centered R**2
0.998656
R Bar **2
0.997760
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.002758717
Sum of Squared Residuals
0.0001141578
Durbin-Watson Statistic
2.199484
Q(6)
2.979870
Significance Level of Q
0.81136906
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
2.544088781 0.835631820
3.04451 0.00819472
2. LAREA{1}
1.180750227 0.241590786
4.88740 0.00019713
3. LAREA{2}
-0.555644944 0.351266179
-1.58183 0.13453985
4. LAREA{3}
-0.212247247 0.257068120
-0.82565 0.42194292
5. LAREA{4}
0.494130938 0.196490119
2.51479 0.02380041
6. LAREA{5}
-0.100557442 0.066546142
-1.51109 0.15154282
7. LPRICE
0.025813118 0.007732122
3.33843 0.00449070
8. LPRICE{1}
0.022036535 0.008718818
2.52747 0.02321101
9. LPROD
-0.014451053 0.005736581
-2.51911 0.02359813
10. LPROD{1}
-0.015076734 0.006600648
-2.28413 0.03735158
11. LPROD{2}
0.000554894 0.002486605
0.22315 0.82642675
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
15
Centered R**2
0.998941
R Bar **2
0.998235
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.002449326
Sum of Squared Residuals
0.0000899880
Durbin-Watson Statistic
2.631575
Q(6)
10.189426
Significance Level of Q
0.11689822
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
2.783719847 0.723172759
3.84932 0.00157613
2. LAREA{1}
1.199696325 0.213356928
5.62295 0.00004854
3. LAREA{2}
-0.632896570 0.305870138
-2.06917 0.05621645
4. LAREA{3}
-0.223842358 0.227944771
-0.98200 0.34167329
5. LAREA{4}
0.583213512 0.170305517
3.42451 0.00376363
6. LAREA{5}
-0.160047125 0.064747161
-2.47188 0.02590259
7. LPRICE
0.022606068 0.007047223
3.20780 0.00586938
8. LPRICE{1}
0.019570109 0.007770710
2.51845 0.02362894
9. LPROD
-0.012141800 0.005214886
-2.32830 0.03429326
10. LPROD{1}
-0.013585542 0.005906731
-2.30001 0.03622357
11. LPOP
0.016539027 0.008176009
2.02287 0.06128810
194
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1990:01
Usable Observations
26
Degrees of Freedom
14
Centered R**2
0.998947
R Bar **2
0.998120
Uncentered R**2
1.000000
T x R**2
26.000
Mean of Dependent Variable
12.183196369
Std Error of Dependent Variable 0.058293938
Standard Error of Estimate
0.002527592
Sum of Squared Residuals
0.0000894421
Durbin-Watson Statistic
2.622320
Q(6)
10.788295
Significance Level of Q
0.09514400
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
2.854105308 0.784167194
3.63966 0.00267920
2. LAREA{1}
1.193537144 0.221180547
5.39621 0.00009425
3. LAREA{2}
-0.653459796 0.323388430
-2.02067 0.06287038
4. LAREA{3}
-0.200827577 0.248056080
-0.80961 0.43171269
5. LAREA{4}
0.583169045 0.175747550
3.31822 0.00507493
6. LAREA{5}
-0.161483486 0.066996553
-2.41033 0.03026419
7. LPRICE
0.023504689 0.007895513
2.97697 0.00999752
8. LPRICE{1}
0.019094608 0.008182353
2.33363 0.03503874
9. LPROD
-0.012775712 0.005802065
-2.20192 0.04493784
10. LPROD{1}
-0.013736529 0.006117323
-2.24551 0.04140260
11. LPOP
0.032084733 0.053848561
0.59583 0.56079702
12. LPOP{1}
-0.015577645 0.053292727
-0.29230 0.77434173
A.2.7.1.4.
Equation.
Optimal
Lag
Length
for
Population
Variable
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
28
Centered R**2
0.997763
R Bar **2
0.997683
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011663459
Sum of Squared Residuals
0.0038090158
Regression F(1,28)
12489.8022
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.408282
Q(7)
2.308205
Significance Level of Q
0.94083435
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.0346151687 0.1244138626
0.27823 0.78288321
2. LPOP{1}
0.9994478210 0.0089429818
111.75778 0.00000000
195
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1962:01 To 1990:01
Usable Observations
29
Degrees of Freedom
26
Centered R**2
0.997665
R Bar **2
0.997486
Uncentered R**2
0.999999
T x R**2
29.000
Mean of Dependent Variable
13.950424580
Std Error of Dependent Variable 0.234618291
Standard Error of Estimate
0.011764482
Sum of Squared Residuals
0.0035984787
Regression F(2,26)
5555.0892
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.052470
Q(7)
1.724668
Significance Level of Q
0.97349890
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.0538821523 0.1318652883
0.40862 0.68616818
2. LPOP{1}
0.7874215083 0.1910993499
4.12048 0.00034124
3. LPOP{2}
0.2110681654 0.1914369747
1.10255 0.28032555
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
27
Centered R**2
0.997764
R Bar **2
0.997598
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011875321
Sum of Squared Residuals
0.0038076278
Regression F(2,27)
6024.0691
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.403659
Q(7)
2.219541
Significance Level of Q
0.94669684
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
0.046690368 0.175671203
0.26578 0.79242555
2. LPOP{1}
0.998809800 0.011147491
89.59952 0.00000000
3. LPRICE
-0.001255301 0.012652943
-0.09921 0.92170431
196
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
26
Centered R**2
0.997958
R Bar **2
0.997722
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011564814
Sum of Squared Residuals
0.0034773679
Regression F(3,26)
4235.4204
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.540289
Q(7)
4.519077
Significance Level of Q
0.71841890
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.068762629 0.186187080
-0.36932 0.71488001
2. LPOP{1}
1.005398909 0.011637668
86.39179 0.00000000
3. LPRICE
-0.012357135 0.014203764
-0.86999 0.39226718
4. LPRICE{1}
0.020192890 0.012850180
1.57141 0.12817907
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
25
Centered R**2
0.997962
R Bar **2
0.997636
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011781966
Sum of Squared Residuals
0.0034703682
Regression F(4,25)
3060.5629
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.534981
Q(7)
4.254549
Significance Level of Q
0.75003589
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.087619107 0.207439574
-0.42238 0.67635557
2. LPOP{1}
1.006482569 0.012800701
78.62715 0.00000000
3. LPRICE
-0.011951285 0.014582901
-0.81954 0.42022272
4. LPRICE{1}
0.018271450 0.015639814
1.16827 0.25372041
5. LPRICE{2}
0.002958265 0.013173979
0.22455 0.82415177
197
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
25
Centered R**2
0.998002
R Bar **2
0.997682
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011666456
Sum of Squared Residuals
0.0034026547
Regression F(4,25)
3121.5932
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.534354
Q(7)
5.236129
Significance Level of Q
0.63117177
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.199002394 0.257251442
-0.77357 0.44643783
2. LPOP{1}
1.009869203 0.013199649
76.50728 0.00000000
3. LPRICE
-0.023075284 0.020361362
-1.13329 0.26784508
4. LPRICE{1}
0.024303828 0.014100677
1.72359 0.09712905
5. LPROD
0.008287147 0.011185239
0.74090 0.46565658
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
24
Centered R**2
0.998003
R Bar **2
0.997587
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011902963
Sum of Squared Residuals
0.0034003327
Regression F(5,24)
2399.0239
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.518539
Q(7)
5.269481
Significance Level of Q
0.62711406
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.196952124 0.262954722
-0.74900 0.46113783
2. LPOP{1}
1.010002044 0.013507155
74.77533 0.00000000
3. LPRICE
-0.024154203 0.022418587
-1.07742 0.29200424
4. LPRICE{1}
0.026150362 0.020372114
1.28364 0.21152534
5. LPROD
0.009391903 0.014307502
0.65643 0.51779116
6. LPROD{1}
-0.001676743 0.013097659
-0.12802 0.89920100
198
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
24
Centered R**2
0.998190
R Bar **2
0.997812
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011333655
Sum of Squared Residuals
0.0030828419
Regression F(5,24)
2646.5851
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.742277
Q(7)
7.302849
Significance Level of Q
0.39804293
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.908691808 0.514538142
-1.76603 0.09010981
2. LPOP{1}
0.983831775 0.020898029
47.07773 0.00000000
3. LPRICE
-0.012766329 0.020831566
-0.61284 0.54574887
4. LPRICE{1}
0.032789550 0.014716275
2.22811 0.03550197
5. LPROD
0.005435313 0.011015450
0.49343 0.62619649
6. LAREA
0.086666562 0.054925453
1.57789 0.12768099
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1961:01 To 1990:01
Usable Observations
30
Degrees of Freedom
23
Centered R**2
0.998225
R Bar **2
0.997762
Uncentered R**2
0.999999
T x R**2
30.000
Mean of Dependent Variable
13.936795906
Std Error of Dependent Variable 0.242321761
Standard Error of Estimate
0.011464812
Sum of Squared Residuals
0.0030231642
Regression F(6,23)
2155.3906
Significance Level of F
0.00000000
Durbin-Watson Statistic
2.770798
Q(7)
7.829056
Significance Level of Q
0.34790874
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.928409515 0.521314505
-1.78090 0.08814206
2. LPOP{1}
0.989921713 0.022990868
43.05717 0.00000000
3. LPRICE
-0.016306918 0.021717883
-0.75085 0.46035531
4. LPRICE{1}
0.032785817 0.014886578
2.20237 0.03793191
5. LPROD
0.007108787 0.011416347
0.62268 0.53961695
6. LAREA
0.188235667 0.160651664
1.17170 0.25331238
7. LAREA{1}
-0.107692449 0.159825543
-0.67381 0.50714628
199
A.2.7.2. Formation and Estimation of VAR Model.
Dependent Variable LAREA - Estimation by Least Squares
Annual Data From 1965:01 To 1987:01
Usable Observations
18
Degrees of Freedom
7
Total Observations
23
Skipped/Missing
5
Centered R**2
0.999298
R Bar **2
0.998296
Uncentered R**2
1.000000
T x R**2
18.000
Mean of Dependent Variable
12.175382743
Std Error of Dependent Variable 0.064768410
Standard Error of Estimate
0.002673372
Sum of Squared Residuals
0.0000500284
Durbin-Watson Statistic
2.651058
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
3.996279812 1.134396073
3.52283 0.00969077
2. LAREA{1}
1.079773767 0.285126157
3.78700 0.00682955
3. LAREA{2}
-0.921671481 0.358391984
-2.57169 0.03691705
4. LAREA{3}
-0.007629005 0.312443596
-0.02442 0.98120129
5. LAREA{4}
0.695520249 0.218972321
3.17629 0.01557079
6. LAREA{5}
-0.190622091 0.079087553
-2.41027 0.04675568
7. LPRICE
0.013218281 0.010306493
1.28252 0.24049421
8. LPRICE{1}
0.028320700 0.010502919
2.69646 0.03079640
9. LPROD
-0.010206331 0.007880795
-1.29509 0.23636458
10. LPROD{1}
-0.018156166 0.007594153
-2.39081 0.04811160
11. LPOP
0.028717612 0.011821802
2.42921 0.04547339
Dependent Variable LPOP - Estimation by Least Squares
Annual Data From 1965:01 To 1987:01
Usable Observations
18
Degrees of Freedom
12
Total Observations
23
Skipped/Missing
5
Centered R**2
0.996582
R Bar **2
0.995158
Uncentered R**2
0.999999
T x R**2
18.000
Mean of Dependent Variable
13.954072703
Std Error of Dependent Variable 0.212134782
Standard Error of Estimate
0.014760548
Sum of Squared Residuals
0.0026144854
Durbin-Watson Statistic
2.816776
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-0.650701632 1.518694168
-0.42846 0.67590540
2. LPOP{1}
1.027073511 0.055892177
18.37598 0.00000000
3. LPRICE
-0.061541667 0.055997180
-1.09901 0.29332274
4. LPRICE{1}
0.048548546 0.022850431
2.12462 0.05507986
5. LPROD
0.036692696 0.034924168
1.05064 0.31412290
6. LAREA
-0.003037744 0.175038586
-0.01735 0.98643886
200
Dependent Variable LPRICE - Estimation by Least Squares
Annual Data From 1965:01 To 1987:01
Usable Observations
18
Degrees of Freedom
8
Total Observations
23
Skipped/Missing
5
Centered R**2
0.907733
R Bar **2
0.803933
Uncentered R**2
0.999435
T x R**2
17.990
Mean of Dependent Variable
2.5273861924
Std Error of Dependent Variable 0.2041544990
Standard Error of Estimate
0.0903985243
Sum of Squared Residuals
0.0653751456
Durbin-Watson Statistic
2.386821
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
-18.45446974 21.89299135
-0.84294 0.42374635
2. LPRICE{1}
0.02309936
0.34312705
0.06732 0.94797895
3. LPROD
0.60370642
0.12218850
4.94078 0.00113395
4. LPROD{1}
0.03692114
0.19781644
0.18664 0.85658645
5. LPROD{2}
-0.04675018
0.11839875
-0.39485 0.70326944
6. LPROD{3}
0.05993749
0.10537336
0.56881 0.58509478
7. LAREA
4.51183288
7.66310100
0.58877 0.57225798
8. LAREA{1}
-0.64856510 12.95468276
-0.05006 0.96129868
9. LAREA{2}
-3.00820815
7.92028720
-0.37981 0.71397055
10. LPOP
0.27150742
0.33408701
0.81268 0.43991133
Dependent Variable LPROD - Estimation by Least Squares
Annual Data From 1965:01 To 1987:01
Usable Observations
18
Degrees of Freedom
11
Total Observations
23
Skipped/Missing
5
Centered R**2
0.910233
R Bar **2
0.861270
Uncentered R**2
0.999904
T x R**2
17.998
Mean of Dependent Variable
10.121598514
Std Error of Dependent Variable 0.340841339
Standard Error of Estimate
0.126951612
Sum of Squared Residuals
0.1772838305
Durbin-Watson Statistic
2.222138
Variable
Coeff
Std Error
T-Stat
Signif
*******************************************************************************
1. Constant
5.89894234 13.40723141
0.43998 0.66846991
2. LPROD{1}
0.20605217
0.25991403
0.79277 0.44467401
3. LPROD{2}
0.02444230
0.14454820
0.16909 0.86879170
4. LPRICE
1.39300851
0.20705946
6.72758 0.00003254
5. LPRICE{1}
-0.53202631
0.38570473
-1.37936 0.19517993
6. LAREA
0.60285858
1.47922727
0.40755 0.69142353
7. LPOP
-0.54694971
0.43087126
-1.26940 0.23049649
201
A.2.7.3. Estimation of Impulse-Response Mechanism.
Responses to Shock in LAREA
Year
LAREA
LPOP
1
0.001646
0.000641
2
0.002078
0.002148
3
0.001299
0.003897
4
-0.00043
0.002596
5
-0.00079
0.001292
6
0.000566
0.001243
7
0.001939
0.001435
8
0.001463
0.00331
9
-0.00024
0.003944
10
-0.00132
0.001402
Responses to Shock in LPOP
Year
LAREA
LPOP
1
0.000253
0.0119
2
0.000753
0.01309
3
0.001333
0.013507
4
0.001386
0.013348
5
0.00126
0.014499
6
0.001252
0.014061
7
0.001423
0.013263
8
0.001833
0.014464
9
0.002029
0.014381
10
0.001696
0.013992
LPRICE
0.046808
0.033875
-0.04154
-0.03297
0.002071
0.016132
0.047255
-0.00107
-0.06813
-0.01306
LPROD
0.064486
0.035649
-0.06832
-0.03872
0.009596
0.022063
0.062408
-0.01416
-0.09803
-0.00405
LPRICE
LPROD
0.029588
0.047045
0.009494
0.000472
0.001262
-0.00863
0.024393
0.025076
-0.01595
-0.03742
-0.01452
-0.02577
0.027832
0.033873
-0.00536
-0.02273
-0.00702
-0.01742
0.016718
0.016246
Responses to Shock in LPRICE
Year
LAREA
LPOP
1
0.000633
-0.00383
2
0.002773
-0.00082
3
0.002748
-0.00284
4
0.000497
-0.00011
5
-0.00145
-0.00089
6
-0.0014
-0.00688
7
0.000551
-0.00542
8
0.002273
-0.00171
9
0.000745
-0.00382
10
-0.00206
-0.00208
LPRICE
0.08259
-0.03748
0.03714
-0.02862
-0.13849
0.057903
0.081865
-0.04522
0.03166
-0.05406
0.034111
-0.087
0.057792
-0.04892
-0.18674
0.117584
0.106183
-0.07949
0.056916
-0.08246
Responses to Shock in LPROD
Year
LAREA
LPOP
1
-0.0007
5.94E-05
2
-0.00052
0.007762
3
0.000779
0.006483
4
0.00088
0.002998
5
0.001139
0.011456
6
0.001002
0.008466
7
-4.9E-05
0.003875
8
0.000601
0.011445
9
0.001777
0.007178
10
0.00127
0.004405
LPRICE
0.177085
-0.04496
-0.04018
0.191347
-0.09585
-0.0757
0.162769
-0.11619
-0.02416
0.185135
0.29857
-0.09988
-0.0484
0.274398
-0.18554
-0.09001
0.241783
-0.20673
-0.01138
0.261706
202
LPROD
LPROD
A.2.7.4. Estimation of Forecast Error Variance Decomposition
of Variables.
Decomposition of Variance for Series LAREA
Year Std Error
LAREA
LPOP
LPRICE
1 0.001916168 73.80035
1.73913 10.92979
2 0.004063702 42.55472
3.82236 48.98472
3 0.005304226 30.97148
8.56256 55.58797
4 0.005591098 28.46017 13.85001 50.82110
5 0.006072568 25.83625 16.04871 48.77313
6 0.006459605 23.60202 17.93749 47.79954
7 0.006914981 28.45990 19.88628 42.34573
8 0.007671009 26.76576 21.86633 43.18941
9 0.008169146 23.68895 25.45115 38.91505
10 0.008787212 22.73121 25.72247 39.13490
LPROD
13.53074
4.63820
4.87800
6.86873
9.34190
10.66095
9.30809
8.17850
11.94484
12.41143
Decomposition of Variance for Series LPOP
Year Std Error
LAREA
LPOP
LPRICE
1 0.012519122
0.26245 90.36066
9.37464
2 0.019839567
1.27699 79.51168
3.90422
3 0.025323994
3.15167 77.24839
3.65097
4 0.028922723
3.22154 80.52113
2.95582
5 0.034357765
2.42435 74.86977
2.16185
6 0.038713937
2.01248 72.16090
4.86366
7 0.041484087
1.87240 73.06703
5.93137
8 0.045552276
2.08095 70.68043
5.06084
9 0.048615784
2.48520 70.80405
5.05999
10 0.050842507
2.34837 72.31178
4.79347
LPROD
0.00225
15.30710
15.94896
13.30151
20.54402
20.96295
19.12919
22.17777
21.65075
20.54638
Decomposition of Variance for
Year Std Error
LAREA
1 0.203093385
5.31200
2 0.214268355
7.27174
3 0.225014840 10.00206
4 0.299577298
6.85434
5 0.344050605
5.20046
6 0.357666399
5.01548
7 0.405127763
5.26970
8 0.423914912
4.81360
9 0.431254784
7.14685
10 0.472892667
6.01995
Series LPRICE
LPOP
LPRICE
2.12240 16.53783
2.10311 17.91743
1.91017 18.97120
1.74064 11.61546
1.53474 25.00853
1.58484 25.76156
1.70723 24.16238
1.57524 23.20627
1.54854 22.96200
1.41284 20.40306
LPROD
76.02778
72.70773
69.11657
79.78955
68.25627
67.63812
68.86069
70.40489
68.34260
72.16416
Decomposition of Variance for
Year Std Error
LAREA
1 0.310933525
4.30121
2 0.339848232
4.70079
3 0.354853778
8.01803
4 0.453581209
5.63601
5 0.525856909
4.22651
6 0.547360918
4.06341
7 0.611866175
4.29215
8 0.651270414
3.83576
9 0.661389152
5.91618
10 0.716244782
5.04787
Series LPROD
LPOP
LPRICE
2.28921
1.20353
1.91643
7.56097
1.81691
9.58738
1.41768
7.03110
1.56102 17.84176
1.66237 21.08219
1.63683 19.88297
1.56654 19.03950
1.58835 19.20194
1.40582 17.69889
LPROD
92.20606
85.82181
80.57768
85.91520
76.37071
73.19202
74.18806
75.55820
73.29353
75.84742
203
ACKNOWLEDGMENTS.
The assistance of:
(1) Dr. Rob Cramb and Dr. Neil Karunaratne for their
supervision and helpful comments, and
(2) Mr. Phil Bodman and Mr. Nigel Williams for their
assistance with RATS ver 4.01.
is gratefully acknowledged.
204
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