A0b& New Tea Mod&3 INTERNATIONAI. BANK
RECONSTRUCTION NI) DEVEl.OPMENT
Specification, Estimation, and Simulation
MAY 4 1987
Takamasa Akiyama and Pravin K. Trivedi
WORLDBAN_K STAFF _COMM_ _D_n_ WO_NG_ _
9198 H ***iD9198 .A2 A424 1987 c.2
aA2 A new global tea model speciication, estimation, and simu
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WORLD BANK STAFF COMMODITY WORKING PAPERS ' f}
Number 17
C,Z
A New Global Tea Model
Specification, Estimation, and Simulation
Takarnasa Akiyama and Pravin K. Trivedi
The World Bank
Washington, D.C., U.S.A.
The International Bank for Reconstruction
and Development / THE WORLD BANK
1818 H Street, N.W.
Washington, D.C. 20433, U.S.A.
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First printing March 1987
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Takamasa Akiyama is a senior economist in the Commodity Studies and Projections
Division of the World Bank. Pravin Trivedi is a professor of economics at Indiana
University; this work was completed while he was on sabbatical leave and working as
a consultant for the World Bank.
Library of Congress Cataloging-in-Publication Data
Akiyama, T. (Takamasa), 1944-
A new global tea model.
(World Bank staff commodity working papers,
ISSN 0253-3537 ; no. 17)
Bibliography: p.
1. Tea trade--Mathematical models. I. Trivedi,
P. K. II. Title. III. Series: World Bank staff
commodity working paper ; no. 17.
HD9198.A2A424 1987 382'.41372 87-6238
ISBN 0-8213-0868-8
- iii -
ABSTRACT
This econometric model of the world tea economy represents an advance on
previous models for perennial crops in several respects: (i) the use of a
conceptual framework based on the vintage production model; (ii) the detailed
indelling of the supply side to incorporate new planting decisions in three
laading producing countries (this specification makes it possible to
distinguish explicitly between the long-run and short-run producer responses
t) changes in exogenous variables); and (iii) the use of a market-clearing
rational expectations approach to modelling the "world price" of tea, which
lsads to a "forward-looking" price equation for tea. The specification of the
supply side is more detailed for the four leading producing and/or exporting
c,untries, viz., India, Sri Lanka, Kenya, and Malawi, since there is some
attempt to model long-run decisions, such as new planting, replanting, and
uprootings. For the remaining producer countries in the model the specifica-
tion is simpler. There are sound a priori reasons for expecting that the
laading producers/exporters will show substantial divergences in their long-
run responses to external stimuli. The empirical results support these a
priori expectations in that the long-run response in "newer" producer
countries, like Kenya and Malawi seems to be different in kind and magnitude
from that in the "older" producing countries such as India and Sri Lanka.
Specification of demand is based on fairly conventional demand equations
for tea. Compared with previous models, this paper has greater disaggregation
by country or geographicaL zone. There is also a greater attempt to use the
appropriate retail price variable in place of the producer price that is often
used in demand equations.
Price determination is based on a simplified linear rational expectations
model in which a market clearing price is established in each period. The
model consists of a supply and demand relation and an inventory demand
equation which closes the model. Inventory demand comprises speculative and
transactions components, both of which involve expectations of future prices.
The insight provided by the analog model--that price depends upon expectations
of future values of exogenous variables--provides the basis for specifying and
estimating a "world price" equation which plays an extremely important role in
the model. The "world price" is linked to the producer prices and retail
prices in individual countries through price linkage equations.
The results of simulation exercises are presented to exhibit the
properties and weaknesses of the model. In the estimated model, equilibrium is
established very rapidly following an initial shock. This characteristic
reflects the market clearing assumption and the absence of lags in price
determination. More significantly, the dynamic simulation of the model based
on such an assumption portrays the historical behavior of price's reasonably
accurately, in the specific sense that the spike-like behavior of the price of
tea can be reproduced by the model.
TABLE OF CONTENTS
ABSTRACT ............................................................. iii
]. INTRODUCTION ......................................................... 1
I.1 An Overview of Some Published Tea Models ........................ 1
I.2 Distinguishing F'eatures of the Present Model .................... 4
TI. STRUCTURE OF THE MODEL ............................................... 8
--II. SUPPLY BEHAVIOR ........................................................ 11
III.1 General Considerations ........................................ 11
III.2 Definitions, Assumptions and Basic Concepts ................... 12
III.3 Specification of New Plantings and Replantings ................ 20
III.4 The Supply Equation ........................................... 29
III.4.1 The Basic Specification ........ :t .................... 29
III.4.2 Alternative Specifications for Q (t) .... ............ 31
III.4.3 A Special Case of (3.29) ............................. 33
III.5 Empirical Results .. 34
III.5.1 Compultation of the Index of Feasible Production ...... 34
III.5.2 New Planting and Replanting Equation ................. 35
III.5.3 Supply Equations ..................................... 49
IV. FINAL DEMAND FOR TEA ................................................. 54
IV.l The Demand Specification ........................................ 54
IV.2 Empirical Results ................................................ 58
V. PRICE DETERMINATION ......................................... I ......... 67
V.1 The Specification of the Price Equation .......................... 67
V.2 Linear Analog Model ............................................. 69
V.3 Solution of the Model ........................................... 72
V.4 Derivation of the "Structural" Equation ......................... 74
V.5 Empirical Application ........................................... 75
V.6 Price Linkage Equation .......................................... 79
VI. MODEL SIMULATIONS .................................................... 85
VI.l Results of Ex-Post Simulation .................................. 85
VI.2 Results of Base Ex-Ante Simulation .............................. 97
VI.3 Evaluation of Some Key Elasticities ............................ 98
VI.4 Simulation of a One Time Supply Shock .......................... 110
VII. CONCLUSIONS AND SUGGESTIONS FOR FURTHER RESEARCH ..................... 114
GLOSSARY OF VARIABLES ..................................................... 118
REFERENCES ............................................................ 129
- vi -
LIST OF TABLES AND FIGURES
Table
1. A Tabular Summary of Some Tea Models ................................. 2
2. Base Period Age-Yield Profiles ...................................... 35
3. Estimated Equations for Supply Block ................................ 40
4. Short-Run Price Elasticities ........................................ 53
5. Estimated Equations for Consumption Block ........................... 59
6. Price and Income Elasticities of Demand for Tea ..................... 65
7. Price and Linkage Equations ......................................... 80
8. Results of Ex-Post Simulation ....................................... 86
9. Results of Ex-Ante Simulation ....................................... 99
10. Changes in Key Variables in case World
Price is Increased by 10% During 1990-2000 .......................... 111
11. Simulation Results with 200,000 mt World Production Decline in 1990. 112
Figure
1. A Schematic Representation of Production Process and Decisions
in a Price-Taking Producing Country ................................. 18
I. INTRODUCTION
The model of the world tea economy described herein has a number of
g;eneral features which are shared by other econometric commodity models. The
*tylized version of an econometric agricultural commodity market model usually
contains supply equations for the major producers, demand equations for major
consumers and either inventory demand equations or price equations. However,
within this general structure there are usually many variations. To give the
reader some appreciation of these in the specific case of globaL econometric
tea models the paper begins with a brief overview of the relevant literature.
1.1 An Overview of Published Tea Models
The tea models hitherto estimated and published tend to have the
!;tructure of market-clearing commodity models. Some of these models are
briefly discussed here and their main characteristics are summarized in TabLe
which is adapted from Ramanujam (1984).
One of the earliest model for tea was developed by Murti (1966). In
this model the demand for tea was disaggregated and equations were estimated
for eight countries or regions. On the supply side only India and Sri Lanka
were considered separately and the rest grouped together. An equation
explaining the average price of tea in the London market was specified and
estimated. Two price-linkage equations modelling the relationship between the
London auction price and the internal price of tea in India and the unit value
of imports of tea in the United States were also estimated. Finally, the model
included identities for total demand, total supply and stocks.
Table 1: A TABULAR SUMMARY OF SOME TEA MODELS
Model & Year of Murti Behrman & Adams Tyler UNCTAD/FAO Cheong7Hoy & Ukpong
Publication 1966 1976 1975 1978 1981
1. Type of data Annual Annual Annual Annual Annual
period covered or
used in estimation 1948--961 1956-1971 1958-1971 1960-1977 1957-1978
method of estimation OLS OLS OLS OLS & OLS &
Cochrane Orcutt Cochrane Orcutt
Procedure Procedure
2. Main equations Supply, Demand & Supply, Demand & Exports, imports, Supply, Demand, Stocks Acreage Response, Yield, Response
Price Price Price & Stocks Price Linkages Supply, Demand, & Price
3. Countries covered India, Sri lanka & Developed Countries, India, Sri Lanka, India, Sri Lanka, Kenya, Total Industrialized Countries,
in supply Rest of World Developing Countries Indonesia, Kenya, Other Africa, Bangladesh, Total Centrally Planned, Total
Centrally Planned East Africa & Argentina, Indonesia, & Developing Countries, Asia, India,
Rest of World Rest of World Sri Lanka, Indonesia, Other Asia,
Africa, Kenya, Tanzania, Uganda,
Other Africa & Latin America
4. Countries covered UK, US, Canada, EEC Same as above 37 Countries UK, US, India, Other UK, US, Canada, Japan, Australia,
Developing Countries & Other Industrialized Countries,
Eastern Europe India, Sri Lanka, Indonesia, Iran,
Pakistan, Kenya, Latin America,
Other Developing Countries, China,
USSR, Other Centrally Planned
5. Price variable London Auction Price London Auction London Auction Average Price of four London Auction Average Price
employed in for Indian & Ceylon Average Price Average Price Auction centers, Colombo,
Tea Calcutta, Cochin & Mombasa
(a) Supply
(b) Demand London Auction Same as above Same as above Same as above Same as above
Average Price
6. Market structure Competitive Competitive Competitive Competitive Competitive
7. Any other features Lagged adjustments Arbitrary selection Price lagged one year Price determined by Fixed gestation period. Supply
of supply with of different lags on in the supply equilbrating supply estimated in two ways: Supply
adaptive expectation different supply ftnctions and demand Acreage & Yield Response; and
of price functions Supply separately
-3-
Adams & Behrman (1976) analyzed the world tea economy in a regional
framework. This was one of the seven models they estimated for different
commodities with a general specification. They specified three supply and
three demand functions for three groups of countries. The price equation was
estimated in two ways, in terms of the actual price and the deflated price--
both prices were related to stocks and with an assumption of lagged
adjustment.
Tyler (1975) designed his world tea model to have all imports and
exports determined only by the average London price of tea and a time trend.
The equilibrium price of tea for any year was specified, as that price which
ensures total import demand equals total available exports. The inclusion of a
time trend as the only other explanatory variable is clearly a simplification.
The trend in exports represents the combined effects of assumed steady trends
in the productivity of existing estates and smallholdings and in the extension
of acreage. The import trend represents secular influences on the demand side.
FAO and UNCTAD (1979) jointly constructed an econometric model of the
world tea economy to analyze the prospective supply/demand balance of tea and
the implications of an international buffer stock arrangement and an export
quota system. The model consists of eight supply equations, six demand equa-'
tions, eight price-linkage equations and two inventory equations (one for the
supply side and one for the demand side). Total supply is defined as sum of
total production and carry-over stocks, while total demand is defined as the
sum of world consumption and the demand for inventories. There is no explicit
equation for price, which is determined by equilibrating total demand and
total supply. Supply is considered to be a function of real price and a time
trend. Demand is a function of own price, the price of substitute and a time
trend.
-4-
Cheong-Hoy and Ukpong (1981) developed an econometric model of the
world tea economy for the Worid Bank. They estimated supply functions for some
individual tea producing countries and others were aggregated according to
geographical and/or economic regions. The supply functions were somewhat
different from the earlier models. The authors attempted to distinguish
between long-term effects of investment and short-term price effects on
yields. The model included 14 country- or region-specific demand functions.
The demand for tea was postulated in per capita terms as a function of the
relative price of tea with respect to coffee and per capita GDP. The price
equation was estimated with the London auction average price as a function of
the proportion of implied stocks to total world tea consumption, the ferti-
lizer price and the price of coffee.
I.2 Distinguishing Features of the Present Model
In this section, some of the important differences between the model
estimated in this paper and previous tea models are summarized. Several of
these differences also apply to previous models of perennial crops. Broadly
speaking, the major differences of conceptual and operational nature between
the present model and earlier work is in the specification of the producers'
supply decisions and in the specification of the price equation. The treatment
of the demand equations is largely conventional.
A major difficulty with earlier models of perennial crops is their
failure to distinguish clearly between the long-run and the short-run dimen-
sions of the producers' supply decisions. Conventionally, in dealing with the
short-run decision the capital stock is taken as given and attention is con-
centrated on the producers' decision concerning the changes in utilization of
variable inputs (and consequently output) induced by changes in prices. Such
ain analysis yields a measure of the short-run elasticity of production. To
obtain a measure of long-run elasticity, it is necessary to model the response
of fixed and quasi-fixed factors to changes in prices. Few global models of
?erennial crops have attempted this despite the fact that the literature is
Eull of conjectures about the size of the long-run elasticity. The usual
approach of specifying an area equation with a distributed lag on prices and
of deriving from it both a short-run and a long-run price response has some-
what vague conceptual foundations (Trivedi 1985). In this paper the issue is
resolved in the following way:
(i) The conceptual framework is based on the vintage prc.duction model
(See Trivedi (1985)). Within this framework it is possible to
distinguish between actual output, feasible output and potential
output in an empirically useful way. Such a framework also explains
the components of short-run price response.
(ii) For the major producing countries equations are developed for new
plantings and replantings which highlight the role of producer price
expectations in the determination of investment decisions. These
equations also are potentially valuable for analyzing long-run supply
responses. They already incorporate, or given additional data can be
made to incorporate, very important iocal institutionel features and
incentives that have a key role in determining long-run responses.
Moreover, they are consistent with the theoretically more flexible
notion of time-variant, long-run supply elasticities.
(iii) For the major producing countries data on new plantings and average
age-yield profiles are combined to construct measures of feasible
- 6 -
output which play an important role in the short-run supply equa-
tions. Moreover, the measures of feasible output contribute to the
ease of interpretation of the supply equations. By contrast,
(planted) area equations comprising distributed lags on producer
prices are difficult to interpret.
(iv) Both theory and empirical observation suggest that there are impor-
tant differences between old established producers and newly emerging
ones in their supply response to prices. The specification of the tea
model exploits this feature at several levels. For example, the new
planting and replanting equations and short-run supply equations
allow for the differences between countries and, in a few cases,
between different types of producers in the same country.
Finally, it is to be noted that the disaggregation by countries is
much more extensive than in most previous work.
Coming now to the issue of price determination, a major limitation of
previous modelling has been the lack of emphasis on the role of forward-
looking variables. The main reason for this lies in the conventional treatment
of inventories. In a typical econometric inventory equation the role of
expected future prices is not emphasized. It can be shown (see Section V
below) that if inventory demand is comprised of a speculative component which
depends upon the difference between the expected future price and the spot
price and on a transactions component which depends upon expected future
demand, and if the market-clearing price is established within each period,
this price will depend upon the expected future values of exogenous variables
that drive aggregate demand and supply. The derivation of the price equation
exploits this feature. The issues involved in estimating such an equation are
- 7 -
discussed below and this discussion makes clear the role of factors such as
inflation and exchange rates in commodity price determination. Global commo-
dity modelling has to date not paid adequate attention to these important
variables.
Although the treatment of demand for tea is fairly conventional, it
needs to be said that previous work in the area has been somewhat cavalier in
the choice of the price variable. Often the London auction price has been used
in all demand equations whereas a more appropriate variable is the local
retail price which would reflect local taxes, margins and the exchange rate.
In this model demand for tea is disaggregated to a greater extent than pre-
viously. This is desirable because of the important changes in t:he pattern of
consumption that are currently under way. For example, per capita consumption
is growing relatively rapidly in India and the Middle East - a factor that has
important implications for price behavior.
The rest of the paper is organized as follows. Section II provides an
overview of the model and an outline of important features of the data used in
estimation. Section III sets out the theoretical specification and the empiri-
cal estimates of the supply side. Sections IV, V and VI deal, respectively,
with the demand side, price determination and the simulation properties of the
model. Section VII concludes. The estimated econometric equations appear in
two places, first in the relevant parts of the main body of the paper and
again in the Appendix where they have been collected together. Definitions of
the variables are also in the Appendix.
-8-
II. STRUCTURE OF THE MODEL
The basic structure of the model consists of supply, demand, price
and stock blocks. A schematic view of the model is given in Figure 1. The
supply block covers production of 15 countries/regions--India, Sri Lanka,
Turkey, Bangladesh, Indonesia, Iran, exports of black tea from China, rest of
Asia, Kenya, Malawi, Uganda, rest of Africa, USSR, Argentina and rest of Latin
America. Of the 15 countries/regions, behavioral equations were estimated for
eight regions and the rest were treated as exogenous. The demand block covers
demand for 24 countries/regions--United Kingdom, rest of Western Europe, USSR,
Eastern Europe, United States, Canada, Australia, New Zealand, South Africa,
Pakistan, Saudi Arabia, Arab countries (Abu Dhabi, Bahrain, Oman, Qatar,
Dubai, Kuwait and other Arabian states), Iran, Iraq, Syria, Turkey,
Afghanistan, North African countries (Algeria, Libya and Tunisia), Egypt,
India, Sri Lanka' Indonesia, Chile and rest of world. Of the 24 countries/
regions, behavioral equations have been estimated for all except Indonesia,
Afghanistan, Iran and Iraq which are treated as exogenous variables. There is
one behavioral price equation which determines the world price and 13 price-
linkage behavioral equations linking the world price to the major auction
prices in producing countries and retail prices in major consuming countries.
All the statistics used are from the various issues of International.
Tea Council (ITC) publications except for the data on stocks, retail prices,
subsidies and'other s'uch variables. Efforts have been made to exclude green
and other teas from the statistics.
(i) Production: Data for each country are from ITC. From the world total,
Indonesian smallholders production' in Java and Sumatra, and produc-
tion in China and Japan has been excluded from the world totals, as
-9-
most of this output is of green tea. Exports of black tea from China
are treated as part of the world production in the model. This
convention allows exclusion of production and consumption of tea in
China from the model without causing distortions.
(ii) Demand: The statistics used are those of ITC "Tea Imports for
Consumption" and "Consumption of Tea in Producing Countries" with few
exceptions. For the United Kingdom, "Apparent Consumption" is used.
For Pakistan and the United States, net imports of green and other
teas are excluded. Morocco is excluded from the model- as it mainly
imports green tea for consumption.
(iii) Prices: The "world" price is a value-share weighted sum of 4 major
auctions (Calcutta, Cochin, Colombo and Mombasa) including sales tax,
cesses and export duties. Prices used in supply equations are either
auction prices excluding sales tax, cesses and export duties or the
"world" price adjusted by exchange rates. Prices used in demand
equations are retail prices for the United States, United Kingdom,
India, Australia and Canada. In those cases where retail price data
are not available the "world" price adjusted by exchange rates has
been used.
(iv) Stocks: "World stocks" used in the price equation is a simple sum of
stocks held in United Kingdom, India and Sri Lanka, and denoted by
TWS. In the simulation runs of the model, however, the world stocks
are calculated as:
WK = WK-1 + QW - CW
where WK = End-of-year World Stocks
QW = World Production
CW = World Consumption
- 10 -
The calculated values of WK for the period 1971-83 showed a correla-
tion coefficient of 0.83 against TWS suggesting that treating TWS as world
stocks is not inappropriate. In the ex-post simulation run, an error correc-
tion term has been added to adjust the historical discrepancies between WK and
TWS. For the ex-ante simulation runs, the discrepancy in 1983, the last year
for which data are available, is assumed to persist throughout the period
simulated.
- 11 -
III. SUPPLY BEHAVIOR
III.1 General Considerations
This section deals with issues relating to the specification and
estimation of relationships which jointly determine the production of tea in
the world on an annual basis. The material is divided into three subsections.
Sections III.2 to III.4 deal with, respectively: the general issues of speci-
fication without emphasis on country-specific detail; the empirical results,
on a country basis; and finally the comparison of behavior across countries--
at least in respect of certain key parameters. The empirical results are
contained in Subsection III.5.
In the case of tea, as also in the case of most tree-crops, when
modelling the supply side careful attention has to be paid to four features of
the production process: (i) the existence of a biologically-determined
gestation lag between planting and obtaining output (ii) the dependence of
current production on current as well as on previous levels of inputs; (iii)
the existence of significant costs of adjustment in respect of the planting
and removal of trees; and (iv) the constraints on planting and removal
resulting not only from past decisions but also from the existence of binding
non-negativity constraints. 1/ Features (i) - (iv) imply, individually and
jointly, that investment behavior of the productive firm cannot be myopic.
Features (i) and (ii) imply that the relevant supply theory is intrinsically
dynamic. More specifically, if the productivity of trees varies with the age,
for given levels of other variable inputs, then the age distribution of the
1/ When dealing with aggregate data, feature (iv) may not be ELS important as
it would be in a microeconometric study.
- 12 -
trees becomes important in determining feasible levels of production. The
average yield curve for tea shrubs approximates a logistic curve, with the
asymptote corresponding to maximum yield obtainable approximately 10 years
after planting in the case of the traditional hybrid variety (slightly earlier
for vegetatively propagated (VP) clones). There is no significant output in
the first four years and yields gradually increase subsequently. Thus, in
general, the capital stock should be regarded as heterogeneous-with respect to
yield. Since the productivity of a tea tree declines very slowly with age
(Etherington 1973)--the biologically-productive period can be 90-100 years--to
achieve minimum differentiation the stock of trees should be classified into
three categories; less than five years old, between five and ten years old and
more than ten years old. Furthermore, for countries like India and Sri Lanka
which have been growing tea for a long time it would be helpful to disaggre-
gate the last category further into trees less than and more than (say) 60
years old. A further source of heterogeneity in the stock of trees derives
from the introduction of VP varieties which have been increasingly-adopted
since the 1960s. Given these sources of heterogeneity in the capital stock, a
major potential misspecification may be avoided by adopting the vintage
capital approach to investment and production behavior. This point has been
discussed at length in Trivedi (1985). In the next sub-section some of the
implications of this approach are spelled out.
III.2 Definitions, Assumptions and Basic Concepts
Production possibilities are characterized by a vintage production
function F[K(t,v),L(t,v)] where K(t,v) denotes "capital" of vintage v used at
time t and L(t,v) denotes "labor" combined with K(t,v). "Capital" means
- 13 -
homogeneous land planted with trees with some specified density and requiring
fixed levels of other inputs such as fertilizers and pesticides. 1/ The
variable "labor" refers to all non-capital inputs which are used in fixed
proportion to labor. Output is produced only by mature vintages and is assumed
to be homogeneous. 2/ Total output Q(t,v) is defined by
Q(t,v) =Iq(t,v) (3.1)
v
where q(t,v) = F[K(t,v), L(t,v)]. (3.2)
Average productivity or yield per unit of capital is given by q(t,v)/K(t,v).
Assuming constant returns to scale, this can be derived from (3.2),
6(t,v) K(t,v) K(t,v) (3e3)
In general 6(t,v) would depend upon the wage-rental ratio. Two interesting
special cases are
6(t,v) = 6(t-v) (3.4)
and
1/ In practice the issue is complicated by the existence of mixed stands of
trees resulting from infilling of existing area. Removal and replacement
of aged or damaged trees by younger trees means that a "capital" unit
cannot be regarded as homogeneous.
2/ In the case of tea the assumption of homogeneous output is a simplifica-
tion. Q(t,v) in equation (3.1) should be thought of as measured in units
of "standard" quality. If the relative prices of different types of tea
vary a lot, aggregation into "standard" units will be difficult.
- 14 -
6(t,v) = X(t)6(t-v). (3.5)
In the case of (3.4) productivity depends only on the age of the trees,
denoted t-v, and not upon the time at which they were planted.. In the case of
(3.5) the productivity of trees of age (t-v) changes smoothly with time at a
rate determined by the function X(t) which may be given a specific parametric
form.
Capital stock of vintage v at the end of period t, denoted K(t,v),
obeys the capital depletion equation
K(t,v) = K(t-1, v) - U(t,v) (3.6)
where U(t,v) denotes uprootings or removals of vintage v capital in period t.
By definition,
K(t,t) = N(t) (3.7)
where N(t) denotes new plantings (or new investment). One may distinguish
between additions to the capital stock from new plantings from those which
come about from replanting currently uneconomic area under the same crop or
under a different crop. Taking account of replantings leads to a modified
version of (3.7), viz.
K(t,t) = N(t) + R(t) (3.7a)
where R(t) denotes replantings. U(t,v), N(t) and R(t) are all non-negative.
- 15 -
Trivedi (1985) presents a model of a competitive firm which chooses
levels of U(t,v), N(t), R(t) and L(t,v) to maximize net discounted revenue.
The optimization also involves the choice of the subset of ma >0, a2<0 provided N(t) is always positive.
The fact that N(t) can be zero whenever a corner solution is optimal is a
p)roblem. N *(t) denotes the desired level of new plantings in period t. The
objective of new plantings is to obtain some desired level of capacity output
i.n period t+G where G is the-gestation lag. Denote this level by QP(t+G) and
assume that it has been calculated conditionally on expectations about future
profitability. If no new planting were undertaken between period t and period
t.+G, the feasible level of production would depend upon the current feasible
production level, Qf(t), and on the additional production from currently
planted but immature vintages. The profitable level of production would be at
nost equal to Qf(t), and the profitable level anticipated at t would depend
upon expected future prices. Denote this level by QP(t). Specify that if
QP(t+G) > QP(t), then
N (t) = O[Qp(t+G) - Qr(t)], 6>0 (3.13)
which means that the rate of new plantings is an increasing function of the
eKpected shortfall in profitably-usable capacity. Once again, the presumption
L/ See Nickell (1985).
- 22 -
that N*(t) is non-negative is troublesome, since the expected shortfall in
capacity can be negative. If the shortfall is negative there may be an
incentive for uprooting or replanting of unprofitable trees.
Let QP(t+G) be linear in two sets of variables Z, and Z2 which are
determinants of expected future profitability and which will be specified
later, and let Qp(t) be linear in ZI; 1/ that is,
Qp(t+G) = f Z (t) + g Z ( (3.14)
1 1 g1Z2(t
Qp(t) = f2ZI(t) (3.15)
where fl, f2 and g, are vectors of parameters. Combining (3.12) - (3.15) leads
to,
N(t) = aI[6(f -f2) (Z1(t) - z (t-1)) + og1(z2(t) - Z(t-)
+ (ai-a2) K[(f -f2) Z1(t-l) + g Z2( t-)]
+ (1-ac +a2) N(t-1). (3.16)
which is the type of equation estimated for Sri Lanka.
1/ Slightly greater generality can be achieved by allowing QP and Qp to
depend upon, respectively, Z1 and Z2 and Z, and Z3. That is, Z1 is the
common subset of variables and Z2 and Z3 are specific toQP and QP,
respectively.
- 23 -
As usual in error correction models, the right-hand side variables
appear in first difference and also in (lagged) level form. Notea also that if
corresponding elements of vectors f, and f2 have the same sign a priori, the
coefficients of levels as well as the first differences of Z, are ambiguous in
sign. Those of (Z2(t) - Z2(t-1)) and Z2(t-1), however, are not. The broad
concept of variables which influence expected future profitability subsumes a
variety of specific factors and can accommodate a number of ways in which
those specific variables can be introduced into the equation. Two determinants
of expected profitability are the expected real price of the product received
by the producer and the expected real unit cost of production. The first will
be positively related to both the desired level of future production and to
the profitable level of production from the existing capital stock. That is,
an increase in future expected product price will raise desired capacity
output and thereby stimulate new plantings, but it may also make the existing
capital more profitable and cause previously unprofitable capital to become
profitable and hence lead to postponement of uprootings and replantings. Thus
the net effect on the level of new plantings may be unclear. If the existing
zapital stock is large relative to the level of new plantings, the negative
effect may well dominate. In the same way an increase in the unit real cost of
production will depress both the required future capacity and the level of
output from the existing capital stock. This could lead to scrapping of exist-
ing capacity to such an extent that the net effect may well be to stimulate
new plantings.
There are some factors, subsumed under Z2, which will affect QP(t+G)
but not .QP(t). Examples are new planting subsidies and embodied technological
progress, both of which will reduce the marginal cost of new planting and
hence stimulate it.
- 24 -
Some empirical short-cuts may be necessary if the data on real unit
cost of production are not available. One possibility is to assume that the
unobservable variable Qp(t) is proportional to Q (t)--e.g., Qp(t) is k times
Qf(t). The latter denotes estimated feasible output which could be measured
using (3.8), given 6(t,v), although in general this would lead to a measure-
ment error. If this assumption is employed, the estimating equation will have
the following form:
N(t) 1f(zl(t) - Yt-1)) + g1(Z2(t) - Z2(t-1))
+ (a1 1Z(t-1) + g Z2(t-1)]
- a1Ik(QV(t) - Qf(t-l)) - (Ct1-a2 )kQ (t-l)
+ (l-a 1+a2)N t-l (3.17)
(This is the type of equation used for India for which no data are available
on cost of production.) If a >a2' both Q (t) and AQ (t) will enter the
equation with a negative sign. The use of Q in place of Qf (3.17),
however, will involve a misspecification error (due to the use of the
proportionality assumption) whenever the scrapping of capacity moves in a
highly variable fashion.
Before proceeding to another variant of the new planting equation it
is worth noting that equation (3.17) incorporates a complex dependence between
the existing capital stock and new plantings. This is captured directly via
the proxy for the QP variable in (3.17); in (3.16) it is captured indirectly
- 25 -
via variables which influence the rate of discarding of old capacity. However,
since the magnitude of the unprofitable productive capacity is not directly
Dbservable, it seems desirable to include variables which would capture the
effects of both physical and economic obsolescence. Equation (3.16) does not
adequately capture the first and equation (3.17) does not adequately capture
the second.
Consider now the variants of the new planting equation which have
ibeen used for Kenya. In this case the specification for the smallholders is
different from the rest. Since the new plantings of smallholders in Kenya
;hows a strong trend-type behavior, the ECM specification developed above
iieeds to be modified. An ECM model for the rate of growth of new plantings
expressed as a proportion of existing smallholder area, denoted r(t), seems
riore appropriate (see below for the rationale)
r(t) -r(t-1) = y(r'(t)-r(t -1)) + y (r(t - 1)-r* (t-1))
or r(t) = yI(r*(t)-r*(t - 1)) + (y -y ) r(t-l)
+ (1-y1+y2)r(t-1) (3.18)
where a priori y>?O, Y2<0 and r(t) = N(t)iA(t-1), with A(t-1) being the total
area under smallholder production at t-l. r (t) denotes the desired rate of
expansion; its precise specification is not needed at this juncture. Given the
strong trend in smallholders' new plantings the choice of r(t) has the merit
that it ensures the model has the property of trend neutrality. This would not
be so if N(t) was chosen to be the dependent variable. If the previous
- 26 -
specification for N(t) were adopted, wherein N*(t) incorporated a trend
component, the standard partial adjustment model would imply that once N(t)
and N*(t) begin to diverge the discrepancy would never be made up. In contrast
the model of (3.18) applies the partial adjustment principle only to the
deviations from the trend growth rate. See Pagan (1985) for a discussion of
higher-order error correction models.
Note that this specification implies a different type of costs-of-
adjustment specification from the one which implicitly underlies (3.16). In
the present case, costs of adjustment arise from growth of new plantings
exceeding or falling short of the optimal rate r (t), whereas in the former
case they arise from N(t) being different from N (t). This latter specifica-
tion seems appropriate in certain cases such as for Kenya smallholders.
Theoretical analysis shows that planned uprootings and replantings
are interrelated decisions not only in the sense that uprooting (U) precedes
replanting (R) but also in the more substantial sense that they are jointly
determined--i.e., a sequence of planned {R(t)} implies a corresponding
sequence of {U(t)}. On the other hand, actual {R(t)} is likely to be closely
related to previous actual levels of uprootings. For this reason the preferred
strategy would be to develop a behavioral model for {U(t)} and relate {R(t)}
to {U(t)} through a simple distributed lag model. Unfortunately, however, data
on both U(t) and R(t) are available only for Sri Lanka. For India, data are
available only for R(t). For relatively new tea producers such as Kenya and
Malawi, for which only the data on net new plantings are available, uprootings
and replantings are not thought to be important.
An estimating equation for either U(t) or R(t) can be derived from a
vector error correction model (VECM) as follows:
- 27 -
AU(t) U7 1 2 l(t)-U(t-l)l l 91 1V U(t-l)-U*(t-l)"
1 ~~12 1~~ 1(3.19)
AR(t) 21 I22 it (-R(t-l) *21 22 R(t-l-R (t-l)
where U and R denote, respectively, the desired rate of uprooting and
replanting. Planned uprooting followed by replanting depends upon the stock of
unieconomic capital and on the expected future profitability of production. As
before, these variables- are subsumed in the vector Z(t) and it is assumed
that U (t) and R*(t) are!both linear in Z(t), i.e., U (t) = h Z(t) and
R (t) = hZ(t). Substituting these into (3.19) and expanding, the following
2
ecuation for U *(t) is obtained:
U(t) [ 11 h+ p12h21 (Z(t) - Z(t-1)) +
I I
11 1 -12h2 v 11h1 1 v12h2] Z(t-l)
+ (1-v11 - "ll) U(t-l) + (v12 12) R(t-). (3.20)
A similar equation can be obtained for R(t) if desired. The main difference
between this and the scalar ECM is the appearance of R(t-l) in the equation
with an ambiguous coefficient. The expected future profitability variables
subsumed in Z :appear in the level form and as rates of changes. If the off-
diagonal terms in the p- and v- matrices are not too large, and ifp.11 and v 1
are positive, the coefficients of Z(t) and AZ(t) have their signs determined
* I
by the signs of the corresponding elements of h and h which (it is expected)
1 2
- 28 -
share the same sign. For example, an increase in the expected future price or
in the uprooting-replanting subsidy will affect U(t) and R(t) in the same
direction.
Now consider the determinants of U (t) and R*(t). As in the case of
N (t) the ultimate objective of uprooting and replanting is to eliminate the
gap (Qp(t+G) - QP(t)). Therefore, variables which enter the U(t) and R(t)
equations should be the same as those in the N(t) equation, with the qualifi-
cation that the subsidy variable in the former case would be specific to
uprooting and replanting. Furthermore, to eliminate the unobserved variable
QP(t) will require approximations as is the case in the N(t) equation. (See
discussion immediately preceding equation (3.17).)
- 29 -
I]I.4 The Supply Equation
III.4.1 The Basic Specification
The supply equation for the model is based on (3.11). However, more
detailed discussion needs to be provided about the calculation of QP(t) and
the choice of a suitable functional form. Both these steps involve important
simplifications and approximations. Details vary considerably even for the
three major produ'cers--India, Sri Lanka and Kenya. For the remaining countries
the specification of the model is rather crude.
First, it is assumed that actual and potential output are related by
the following equation:
Q(t) = A'(Qp(t)) (P(t)/Pe(t))8u(t) (3.21)
where A is an unknown scale factor and 0 is the unknown elasticity of Q with
respect to P. If Pe(t) in (3.21) is the same Pe(t) that determines QP(t), then
Pe(t) = P(t) would imply equality of Q(t) and QP(t) apart from the scale
factor A and the supply shock u(t). Since neither QP(t.) nor Pe(t) are
directly observable, additional assumptions are required to reduce (3.21) to a
suitable form for estimation. Begin with the identity
QP(t) = Qf(t)QP(o/Qf(t)- (3.22)
Let Q (t) denote measured feasible output calculated under the assumption of
a given (not necessarily profit-maximising) age-yield profile and assume that
(i) Q (t) = kl(Qf(t)) , k1, c>O (3.23)
- 30 -
and
GO fP = k 2IT P(t-i) i, B. > O. (3.24)
Q (t) i=O
f f
The difference between Qf and Q arises from the possible error in calculating
Q from an "average" age-yield profile. The justification for (3.24) is that
the profit-maximising level of output is an increasing function of the pro-
ducer price whereas Qf(t) is definitionally determined, given past decisions.
Furthermore, lags are introduced in (3.24) to take account of the dependence
of current yield on past inputs such as fertilizers. Whereas it is expected
that the unknown value of m would be a small integer such as 1 or 2, the issue
is empirical.
Combining (3.21) - (3.24) and taking logs we obtain the basic supply
equation:
ln Q(t) = ln(A k k2) + e ln Q (t) + (a0+e) ln P(t)
m
- ln Pe(t) + l in P(t-i). (3.25)
i=l1
To convert this equation to a form suitable for estimation the following steps
are necessary: (a) either make specific assumptions about the relation
between Q (t) and observable variables such as past new plantings or precal-
culate Q (t); (b) make> a specific assumption about how expectations are
formed; and (c) fix the value of m. With respect to (a) the premise approach
on takes will depend on the data constraints; with respect to (b) it is
assumed *that In pe is a linear function of past values of P which is,
strictly speaking, inconsistent with the approach elsewhere in the paper
- 31 -
end, finally, with respect to (c) an empirical approach is taken and m is
fixed at 1, 2 or 3. 1/
III.4.2 Alternative Specifications for Q (t)
Since it is desired to take into consideration the differences in the
average productivity of capital (trees) of different ages and the effects of
disembodied technological change on the age-specific yields, a distinction is
made between known total productive capacity (feasible output) existing at
some arbitrary origin, denoted Q(O), and the subsequent additions to that
capacity arising from new planting and replanting in subsequent periods. Let
Q)f(t) denote the former and Qnf(t) the latter; then
If ~ f t 4 nf
Q(t) (t) + Qnf(t) . (3.26)
A:;sume that old capacity Q(O) is changing at an unknown proportionate rate
X>, which reflects the joint effects of disembodied technological change and
oi the reduction in productivity due to aging;
Qof (t) = ex2tQ(O). (3.27)
Given data on total newly planted and replanted areas from t=l onwards, and
given the normalized age-yield profile 6(t-v) known up to a proportionality
ccnstant B(0) (i.e. 8(0)&(t,v) gives the productivity in physical units of
capital of age (t-v) in period 0), it is possible to construct an index of the
ptoductive capacity contributed by additions to the planted area since time 0.
t
Denote this by B(0) I 6(t-v)N (t-v) where N (t-v) is the cumulated sum of new
t-v=l
1/ A consequence of this approach is that coefficients of P(t-l)*, P(t-2),...
P(t-m) could be ambiguous in sign. Empirically, it is found that these
coefficients are usually positive when statistically significant.
- 32 -
plantings and replantings of age (t-v) at time t. I/ O(0) is the productivity
of mature vintages in period 0. Finally, assume that &(t-v) is also subject to
disembodied technical change at a constant proportionate rate X (>O if pro-
ductivity is increasing). That is,
Q (t) = B(O)exlt(I 6(t-v)NW(t-v)). (3.28)
Combining (3.25) - (3.28) yields Variant 1 of the "vintage" supply function.
In Q(t) = In A + In t(0) I(t_v)N+(v) X e Q()
m
+ (s +0)ln P(t) - 0 ln P (t) + . ln P(t-i) + u(t) (3.29)
which is nonlinear in the unknown parameters .(A,B(O), X1, X2, 0). The expres-
sion inside the square bracket is analogous to the shift term representing the
heterogeneous stock of capital. The remaining terms are analogous to the price
variable of the textbook supply function. Given data on N , 6(t-v), Q(O) and
an appropriate proxy for Pe(t), (3.29) can be estimated by nonlinear least
squares. Data permit such estimation for two major producers, India and Sri
Lanka, whose age-yield profiles appear to have changed through time. Though
straight-forward in principle, the estimation of (3.29) in practice poses
difficult problems of identifiability, as will be seen in Section TT1.5.
1/ There could be an important aggregation problem here if different kinds of
trees are planted at different times.
- 33 -
III.4.3 A Special Case of (3.29)
If (a) Q(O) = 0 and (b) the age-yield profile is approximately
constant such that Xi= 0, (3.29) simplifies to the log-linear equation which
is Variant 2 of the vintage supply function:
ln Q(t) = ln A + In [E 6(t-v)N (t-v)]
+ (8+0) ln P(t) - 0 ln Pe(t) + Ea. In P(t-i) + u(t). (3.30)
I
In certain cases (e.g., Kenya estates) it was necessary to allow for
changing yields through time. It was possible to construct with Kenyan data
ttiree time series corresponding to planted area in three age-classes--less
than 5 years old, between 5 and 10 years and more than 10 years old. Using
data on normalized yields (=1 in the age-class 'older than 10 years'), a
weighted area measure, denoted WAREA, was constructed. Then, variant 3 of the
vintage supply function which may be thought of as the 'yield equation', was
sDecified as follows:
Q(t) Ae 1 (P(t)/pe(t))Ou(t). (3.31)
WAREA(t (pe 3.1
(Note that this assumes that all age-groups share equi-proportionally in the
increase in yield.)
For the remaining countries the quality and quantity of data do not
permit estimation of anything much more than simple variants of (3.25),
u:;ually based on the assumption that Qf(t) is a function of a linear or
quadratic time trend. This specification will be referred to a Variant 4 of
the basic specification. It makes essentially no use of any data of a vintage
nature.
- 34 -
III.5 Empirical Results
III.5.1. Computation of the Index of Feasible Production
To calculate Qf(t) as defined in (3.26) - (3.29) involves the unknown
parameters B(0), X1 and X2.The first task is to construct. an index of the
productive capacity added by new plantings and replantings, denoted ZE(t-v)
N+(t-v). The coefficients in the sequence R(t-v)denote the proportion of peak
yield obtainable at different ages prior to full maturity. This profile will,
of course, vary between countries and over time. Available information
concerning the base period, derived from World Bank project reports, is
summarized in Table 2. It is assumed that in those countries such as India,
Kenya and Malawi where the yields are rising 1/ the increase is spread over
all age groups. The base period profile is taken to be the same in India and
Sri Lanka.
In India and in Sri Lanka, the rate of new planting and replanting as
a proportion of total planted area has varied considerably over the last 20
years. The range of variation is from 0.8 to 1.8 percent in India and from 0.9
to 1.4 percent in Sri Lanka. (The latter figure should be treated with caution
because of uncertainties about the planted area.) In contrast, the annual
average growth rate of planted area for Kenya smallholders has been at nearly
17 per cent over the period 1963-83; though the rate has declined to around 4
per cent in the last five years.
1/ Such information as is available fails to distinguish between the
productivity of new VP varieties and the older hybrids.
- 35 -
Table 2: BASE PERIOD AGE-YIELD PROFILES
0 i 2 3 4 5 6 7 8
India 1952-(a) 0 0 .074 .299 .449 .599 .749 .899 1
-(b) 0 0 70 281 422 563 704 845 939
Sri Lanka -(a) 0 0 .074 .299 .449 .599 .749 .899 1
L956 -(b) 0 0 76 303 455 608 760 912 1014
Kenya -(a) .199 .399 .399 .798 .899 .899
-(b) 0 0 0 206 412 412 824 928 928
(a): Proportion of peak output at different ages prior to full rnaturity.
(b): Yield in Kg/hectare at different ages.
k: Applies to smallholders only.
III.5.2 New Planting and Replanting Equations
In both India and Sri Lanka new planting and replanting are subsi-
dized. Information about the subsidies in India remains very sketchy and it is
hoped that the estimated new planting and replanting equations cam be revised
at a later date when more detailed and accurate information has been obtained.
There are indications that such respecification and reestimatiort is required.
In the case of Sri Lanka, uprooting and replanting subsidies are more
important relative to the new planting subsidies. However, there are a number
of subsidy schemes in existence and they have changed considerably over the
years, so the computation of total value of subsidies is a somewhat involved
matter.
In Kenya it is important to distinguish between the behavior of
smallholders, who account for nearly two-thirds of the area but only one-third
of the production, and that of the. estates which have been producing tea for
several decades. (Etherington 1973; Schluter 1982; Lamb and Muller 1982). Net
new plantings have thereEore. been disaggregated into smallholder and estates
- 36 -
categories. The former (but not the latter) shows a strong trend-like behavior
which cannot be readily understood without reference to the historical and
institutional factors. These have been studied by Etherington (1973) and by
Lamb and Muller (1982). The former author has stressed the stimulative effects
of the removal of legal restrictions on the cultivation of tea by smallhoLders
at a time when tea was an alternative cash crop; the latter have detailed the
important role played by the Kenya Tea Development Authority (KTDA) in the
provision of extension services and a network of factories which provided the
necessary infrastructure and increased the profitability of tea production to
the smallholder. The specification used here, therefore, pays. attention to
these factors. 1/
A measure of this role is KTDA development expenditure (including the
expenditures on nurseries, tea stumps, field and factory development) per
hectare of smallholder-planted tea area. This variable has a role similar to
but not identical with that of subsidies in other countries.
For Kenya smallholders the model used for replanting is equation
(3.18). To complete it a specification of r (t) is needed. Specify r'(t) to be
linear and increasing in (i) real per hectare investment expenditure by the
KTDA, denoted E(t), and (ii) the real producer price PR(t). That is, r (t) =
a1E(t) + a2PR(t) + ao, and
r(t) = y1[alE(t) - E(t-l)) + a2(PR(t) - PR(t-l))]
1/ From an analytical viewpoint such factors are akin to subsidies which
increase the net producer price and hence the actual and expected
profitability.
- 37 -
+ Y2) [a1E(t-L) + a2PR(t-1)]
+ (l-y1, 4 y2)r(t-1) + (y1-Y2 0. (3.32)
rhe main justification for this specification is that the Kenya. smaliholders
probably did not face an area constraint in this period and that: the critical
Limitation arose from access to planting material, credit facilities, fac-
tories for processing tea leaves and expertise in the marketing of the leaf.
ro the extent that the KrDA provided these facilities, it made it easier for
the smallholder to take up growing tea. It is postulated that by maintaining a
constant real rate of development expenditure 1/ per hectare the KTDA would
enable more smallholders t,o undertake tea production and to maintain a steady
growth in new plantings. Of course, this assumption is reasonable only as long
as the availability of suitable land is not a binding constraint. Eventually,
such a constraint will become binding and this would imply a different kind of
adjustment cost function.
Consider now the net new plantings of Kenya estates. In absolute
terms new planting activity of the estates is small compared with that of the
Kenya smallholders. It is also highly variable. The main difference between
the behavior of the estates and smallholders in the specification is that it
is hypothesized the former to be active at the intensive margin and the latter
at the extensive margin. Specifically, any improvement in expected profit-
ability, arising from (say).an increase in the real price of tea, causes more
1/ Actual real rate of expenditure per hectare (KEXPPH) declirned after 1969
but stabilized after 1980.
- 38 -
smallholders to enter tea production, whereas, it causes estates to increase
production at the intensive margin by greater use of yield-increasing agricul-
tural practices. (The yield-price relationship has been discussed elsewhere.)
A general model of production and demand for inputs clearly does not rule out
the possibility that a firm may respond to higher product prices by raising
its production through more intensive utilization of variable inputs. Given
sufficiently high adjustment costs of fixed inputs this may be an optimal
response. 1/ The empirical fact that in the case of the estates in Kenya
yields per hectare have increased in a spectacular fashion, while the area
under production has increased rather slowly, suggests that the hypothesis
proposed is reasonable. To obtain the estimating equation, a variant of (3.12)
has been combined with the following specification of new plantings N (t),
N (t) = c0 + c YLD(t) (3.33)
0 1
clO, (4.3)
where p reflects the per period reduction in the unit price. The second effect
is that the new technology is more efficient in the sense-that it 'enables the
same cuppage to be produced from a smaller amount of loose tea 1/ which is
I/ Goradia (1977, pp. 9-10) estimates that over the period 1951-70 the global
consumption of liquid tea rose by 145% while the consumption of tea leaves
increased by only 92%.
- 57 -
also akin to a reduction in the effective price of tea. The parameter p cap-
tures this to some extent:. Similar arguments could apply to the variable PS
However, it.will be assumed that PS = PS An important qualification is that
such changes need not be well proxied by a trend. Combining (4.2) and (4.3)
leads to
C = b1(PT-pt) + b2PS + b3t+b X
= b PT + b PS + (b b p)t+bX. (4.4)
1 2 (3-b1p 4~
Note now that if b3<0 and also b1<0, then (b 3-bp) is ambiguous in sign. The
price reduction effect due to "technological" changes raises consumption, but
the pure taste effect reduces it. On the other hand, if b3>0, so tastes are
-hanging in favor of tea, then the effective price reduction increases
*:onsumption and the coefficient of the trend variable will be positive.
Jnfortunately, however, since b3 and p cannot be identified, a clear inter-
?retation of the trend variable is not possible.
The variables X will usually include per capita income. In the case
of some developing countries where tea is usually consumed with milk and sugar
:complementary commodities) the price of sugar has also been included as an
aidditional variable. There are, however, some problems associated with the use
of the income variable. The first is that for some countries per capita income
shows a strong trend and hence its effects cannot be distinguished satis-
i'actorily from those of the taste-cum-price variable. In several cases, where
per capita tea consumption has been rising (as in the United States), a
- 58 -
negative coefficient was obtained on the income variable. As this results was
thought to be unacceptable a priori, it was omitted from the specification and
the trend variable was retained. Also for some other countries and country
groupings such as the USSR, the EEC and. North Africa (Algeria, Libya and
Tunisia), the trend variable has been preferred to the per capita GDP.
IV.2 Empirical Results
The estimated demand equations are given in Table 5. Table 6-contains
a summary of the price and income elasticities. Some estimates from
Ramanujam's recent study are also included for purposes of comparison.
As expected, most of the price elasticities are less than 0.4 in
absolute value. Some of the lowest values have been found for relatively large
consumers such as Sri Lanka, and the United Kingdom. Some of the largest
income elasticities are found for large consumers like India and Pakistan and
for some Middle East countries where per capita consumption has grown fast. It
is plausible that, in conformity with Engel's law, the income elasticity for
tea should be specified to be a declining function of income. A linear or
semi-logarithmic demand function can capture this feature. The use of a double
logarithmic functional form, by contrast, may lead to an overestimate of the
income elasticity which may produce over-estimates of consumption in the
simulation period. Thus, even if two functional forms yield similar results in
the sample period, they may produce rather different simulation results
outside the sample. It would be desirable, therefore, to use a priori
information to a greater extent in the selection of the functional-form. As a
specific example, consider the fact that the double logarithmic equation for
India produced an estimate of income elasticity of around 2, whereas the semi-
logarithmic equation implies an estimate of about 1.7 at the sample mean and
- 59 -
Table 5: ESTIMATED EQUATIONS FOR CONSUMPTION BLOCK *
SOUTH AFRICA - CONSUMPTION
0)145: LN ClSO = 7.1601 -- 0.0622 LN PTWSO(-1) - 0.0277 LN ET2
(17.0161 (-0.6067) (-5.6328)
R-SQUARED(CORR.): 0.696 SEE: 0.73452E-01 DW: 1.21
:?ERIOD OF FIT: 1971 1984
l?( 2, 11): 15.873
U.S. - CONSUMPTION
0101: ClUS2 = 349.1718 + 0.1104 ATEMP12 - 0.4660 PTUS2 + 1.8351 12
(9.35.94) (1.8787) (-2.4651) (3.3393)
R-SQUARED(CORR.): 0.720 SEE:
L6.216 DW: 1.32
?ERIOD OF FIT: 1954 1983
?( 3, 26): 25.880
U.K. - CONSUMPTION
)104: LN ClUK = 8.5546 - 0.0173 LN ET2 - 0.0295 LN PTUKCH
(370.187 (-li.7797) (-0.9677)
R-SQUARED(CORR.): 0.915 SEE: 0.38976E-01 DW: 1.68
?ERIOD OF FIT: 1960 1984
i?( 2, 22): 130.479
INDIA - CONSUMPTION
0160: CIN = 140.1597 + 697.5458 GDPCINL - 418.9983 PTIN2
(1.9218) '(10.6003) - (-1.9380)
R1-SQUARED(CORR.): 0.886 SEE: 25.997 DW: 1.41
PERIOD OF FIT: 1960 1983
?( 2, 21): 90.697
t See Glossary of Variables for definition of variables.
continued...
- 60 -
...continued
AUSTRALIA - CONSUMTPION
0506:
LN CAU = 3.8946 - 0.9015 LN TR51 - 0.0740 LN PTAU(-1)
(9.3872) (-12.7538) (-1.2250)
R-SQUARED(CORR.): 0.920 SEE: 0.44409E-01 DW: 1.81
PERIOD OF FIT: 1970 1984
F( 2, 12): 81.329
CANADA - CONSUMTPION
0108: LN CCA = 0.8935 - 0.1437 LN PTCA - 0.0580 LN PTCA(-1) - 0.0199 LN ET2
(2.8038)(-1.9086) (-0.9010) (-16.1241)
+ 0.1023 LN PCWCA
(2.8267)
R-SQUARED(CORR.): 0.912 SEE:
0.49682E-01 DW: 1.97
PERIOD OF FIT: 1954 1984
F( 4, 26): 79.039
TURKEY - CONSUMPTION
0112: ClTUR = + 0.9673 QCTUR
(104.8762)
R-SQUARED(CORR.): 0.987 SEE:
52.817 DW: 1.12
PERIOD OF FIT: 1976 1984
F( 1, 8): 625.395
continued...
--61 -
...continued
USSR - CONSUMPTION
0513:
IN C1USSR = -5.6556 + 1'.3624 LN TR51 - 0.1569 LN WPRICEDF(-IL)
(-18.2152 (14.8110) (-1.5216)
El-SQUARED(CORR.): 0.960 SEE: 0.63600E-01 DW: 1.95
EERIOD OF FIT: 1967 1984
F( 2, 15): 206.998
EGYPT - CONSUMPTION
C(125: LN CIEG = - 5.7449 + 0.9846 LN GDPC2EG - 0.0609 LN PSWEG
(-4.2247) (4.2108) (-0.8899)
E.-SQUARED(CORR.): 0.428 SEE: 0.21692 DW: 1.53
E'ERIOD OF FIT: 1962 1983
F( 2, 19): 8.868
SAUDI ARABIA - CONSUMPTION
(1127:
I.N C1SA2 = 1.2282 + 0.4974 LN GDPC2SA2 - 0.2990 LN PTWSA2 - 0.2357 LN PSWSA2
(0.3472) (1.1742) (-0.8088) (-3.5073')
R-SQUARED(CORR.): 0.765 SEE: 0.13792 DW: 1.67
IPERIOD OF FIT: 1969 1983
V( 3, 11): 16.231
SRI LANKA - CONSUMPTION
0128:
LN C1SL 1.1775 + 0.8208 LN ClSL(-l) - 0.0114 LN PRS(-1) - 0.0375 LN PRS
(1.1823) (6.0094) (-0.3704) ' (-1.2725)
R-SQUARED(CORR.): 0.701 SEE: 0.21731E-01 DW: 2.11
]?ERIOD OF FIT: 1965 1982
17( 3, 14): 14.271
continued...
- 62 -
... continued
REST OF WESTERN EUROPE - CONSUMPTION
0140:
LN CNlRWEl = 10.5726 + 0.0179 LN ET2 + 0.1115. LN PCWORLD
(74.5790) (5.2353) (2.8144)
- 0.1521 LN PTLOND(-1)
(-2.0857)
R-SQUARED(CORR.): 0.937 SEE: 0.45174E-01 DW: 2.14
PERIOD OF FIT: 1961 1983
F( 3, 19): 110.638
EASTERN EUROPE - CONSUMPTION
0130:
CN1EE = 6769.7065 - 276339.2500 WPRICEDF + 962.8294 T2
(1.0277) (-2.1912) (4.6623)
R-SQUARED(CORR.): 0.949 SEE: 2098.9 DW: 2.15
RHO(1): 0.756
PERIOD OF FIT: 1960 1983
F( 2, 20): 204.402
PAKISTAN - CONSUMPTION
0133:
LN C1PAK2 = -1.1317 - 0.0753 LN PSWPA2 + 0.8635 LN GDPC2PA2
(-4.6962)(-2.0039) (6.0697)
- 0.0518 LN PTWPA2
(-0.3862)
R-SQUARED(CORR.): 0.806 SEE: 0.63683E-01 DW: 1.52
PERIOD OF FIT: 1972 1983
F( 3, 8): 16.191
continued...
- 63 -
...continued
CHILE - CONSUMPTION
0134:
LN CICH = 7.3810 + 0.7215 LN EDM74 + 0.6256 LN PCWCH
(16.1212 (6.2948) (4.9271)
- 0.6024 LN PTLCH - 0.1407 LN PSWCH(-1)
(-3.1206) (-3.3640)
R-SQUARED(CORR.): 0.764 SEE: 0.10647 DW: 2.26
PERIOD OF FIT: 1965 1983
F( 4, 14): 15.562
SYRIA - CONSUMPTION
0535:
LN ClSYR2 = -1.3676 + 0.4249 LN GDPC2SYR2 + 0.6690 LN ClSYR2(-1)
(-1.5203) (2.6496) (6.0528)
R-SQUARED(CORR.): 0.869 SEE: 0.13877 DW: 1.91
PERIOD OF FIT: 1957 1983
F( 2, 24): 86.867
NORTH AFRICA - CONSUMPTION
0138:
LN CN1NA = 7.3428 + 0.0258 LN ET2 - 0.5258 LN WPRICEDF
(10.1954 (2.8081) (-2.2482)
R-SQUARED(CORR.): 0.778 SEE: 0.16665 DW: 2.01
PERIOD OF FIT: 1960 1983
F( 2, 21): 41.403
continued...
- 64 -
continued
NEW ZEALAND - CONSUMPTION
0142:
LN ClNZ 7.4567 - 0.0259 LN ET2 - 0.2423 LN PTWNZ(-1) + 0.0192 LN PCWNZ(-1)
(18.5655 (-5.6188) (-1.8997) (0.2961)
R-SQUARED(CORR.): 0.784 SEE: 0.81175E-01 DW: 1.91
PERIOD OF FIT: 1955 1984
F( 3, 26): 36.024
OTHER ARAB COUNTRIES
0158:
CN1ARAB = 8342.0508 - 709063.2500 PTWORLD + 4388.8398 PCWORLD
(0.4389) (-1.7307) (1.5107)
+ 771.4318 TRARA - 9237.0986 DM82
(1.2180) (-2.3802)
R-SQUARED(CORR.): 0.754 SEE: 4340.8 DW: 0.99
RHO(1): 0.698
PERIOD OF FIT: 1970 1983
F( 4, 8): 10.183
REST OF WORLD - CONSUMPTION
0161:
LN CNIROW = 11.9103 - 0.6049 LN PTLOND - 0.3321 LN PTLOND(-1)
(76.2005) (-3.0176) (-2.1167)
+ 0.2338 LN PCWORLD - 0.1782 LN EDM80
(2.3421) (-1.8418)
R-SQUARED(CORR.): 0.624 SEE: 0.90744E-01 DW: 2.49
PERIOD OF FIT: 1970 1983
F( 4, 9): 6.403
- 65 -
Table 6: PRICE AND INCOME ELASTICITIES OF DEMAND FOR TEA
This study Ramanujam
Retail Price Income Retail Price Income
I,ustralia (*) -.07 - -.09
Canada -.20 0.10 -0.09 .66
Chile -.60 -
E:astern Europe - -.40 -
Egypt - 0.98
]:ndia (*A) -.23 -1.71 -.09 .09
2lew Zealand -.24 -
2lorth Africa -.53
Other Arab Countries -.73 -
Pakistan -.05 0.86
IRest of W. Europe -.15 - - -.09/-.31
Saudi Arabia -.30 0.50
South Africa -.06 - -.18
Sri Lanka - 0.42 -.26
';yria -.14 0.76
IJ. K. -.03 - -.18
UJS () -.34 - -.41
IJSSR -.16
REST OF WORLD -.93
Notes: *Indicates a linear specification, ** indicates a semi-logarithmic
specification. In the remaining cases, the specification is double
logarithmic.
- 66 -
about 1.3 for 1983. The two estimates have vastly different implications for
the behavior of prices in view of the fact that India is such an important
consuming country.
Turning to the time trend variable, it is worth noting that the
United States, USSR, Eastern Europe, North Africa and Rest of Western Europe
all show positive coe-fficients while the remaining countries have negative
coefficients. In all these cases, the positive coefficient could reflect
exogenous growth in the taste for tea, or the effect of income growth, or the
trend component of effective price reduction. But it is also not realistic to
suppose that the trend variable can satisfactorily capture all these effects.
- 67 -
V. PRICE DETERMINATION
V.1. Specification of the Price Equation
The general question of price determination is a complex one since it
irvolves questions-concerning the structure of markets in which a commodity is
traded and the question of whether these conditions can lead to disequili-
brium. In the specific case of commodities like tea and coffee, and perhaps
also other tree crops, there is a widespread presumption that price determina-
t:.on conforms to the basic paradigm of competitive markets, at least as a good
f:.rst-order approximation. If this position is accepted, the "price equation"
o' the model should be one that corresponds to the standard "law of supply and
demand" in which the change of price is a function of the gap between market
demand and supply.
In the context- of the elementary static market model consisting of
tharee equations, viz. supply, demand and the equilibrium condition, the model
will determine three endogenous variables, quantity demanded, quantity
supplied and the price. If an'inventory accumulation identity is added to the
model, so that equality of production and demand period by period is no longer
required, the basic picture does not change much since a new equilibrium
cond'ition 'would replace replacing the old one. This equilibrium condition
Would stipulate that the price must. be such that the excess of production over
demand must -be willingly held. In a model with inventories no price equation
is required to close the model.
In extending these basic. ideas to.a global dynamic model of a peren-
rial crop, several complications need to be considered. First, there is the
complication arising from the existence of a production lag or the gestation
lag. Production 'is determined in part by price expectations held in the past
- 68 -
about the future price level. The second complication arises when the exis-
tence of demand for inventories (consisting of a transactions component and a
speculative component) is introduced. The latter component, in the usual
specification, depends upon the difference between the prevailing market price
and the expected future price of that commodity. There is an intimate
connect-ion between the current price and the expected, past, current and
future prices. The third complication arises from the hypothesis of rational
expectations which implies certain restrictions on the equation of the model
and on the errors of expectation.
If commodity market disequilibria last for relatively short periods
of time only, and hence the market clearing model applies, the assumption of
rational expectations has considerable appeal. It is then necessary to
consider how prices will behave along, the rational expectation equilibrium
path. To characterize the response of the.equilibrium price level to stochas-
tic demand and supply shocks in this case involves a derivation of the
rational expectations solution to the model. Such an exercise can involve
considerable complications in a global commodity model which would necessarily
have a large number of exogenous variables (both on the demand and on the
supply side) and which is highly likely to be nonlinear. Furthermore, within a
global model the demand and supply equation would be specified in terms of
loca-l consumer and producer prices which iwould reflect the effects of local
taxes, exchange rates and so forth. "The price" which clears the market,
-however, is the average world price. In that sense "the price equation" of the
model should be specified to explain the variation in the average world price.
If this approach is accepted, then two issues 'have to be tackled, viz., (i)
the specification of a model-closure equation which, in conjunction with the
- 69 -
rEst of the model, determines the average world price; and (ii) the specifica-
tion of linkage equations which connect the average world price with various
local prices.
The approach taken to this problem is to begin with a linear anolog
model of the global, model specified in terms of average world prices. This
model is solved to yield a rational expectations solution and to derive the
theoretical price equation. The insights provided by the analog model are used
to specify the empirical price equation. However, given the gap between the
ILnear analog model and the actual nonlinear empirical model, the connection
between the theoretical and empirical price equations remains a loose one.
V.2 Linear Analog Model
The linearized model. of world demand and world supply consists of the
following equations:
Q = y1Pt + y2Pe + Yiit l+ t (production) (5.1)
Q = _B p + ' X + u (demand) (5.2)
t 1 t 2 2t 2t
p = E[P| IIt1] (expectations) (5.3)
Hd = (e P) + 6QdJe (inventory demand) (5.4)
t 1 t+ t 2 t+1 netr
Ht .=.H + Q Q (market clearing). (5.5)
d
H = H .(market clearing) (5.6)
t t
- 70 -
The current meaning of the symbols is as follows:
Q : World production
Qd : World demand for consumption
P : World price
pte : World price for period t expected in period t-l
t
Xlt, X2t : Vectors of exogenous variables
Ht : Inventories at the end of period t
Qtd+le World demand for period t+l expected in period t
Hd : World demand for inventories
t
ult,u2t Stochastic disturbances
It : Information set at time t
It is not necessary to provide a detailed justification of equations
(5.1) and (5.2) which are consistent with the production and demand equations
developed earlier in this paper. We assume 6lI 62 %19 y1, Y2, to be positive.
To preserve the homogeneity property of the demand and supply functions the
price variable appearing in such equations should be the real price. In the
context of a world demand and supply model the appropriate deflators to use in
(5.1), (5.2) and (5.4), respectively, will be different and will be functions
of the general world price level and exchange rates in the producing,
consuming and stockholding countries. At the theoretical level the simplifying
assumption is made that the deflators are all the same, implying that the
price variable(s) in different equations as the same. An alternative
possibility is to interpret P as the nominal price but to include the relevant
deflators and exchange rates as exogenous variables in all equations where the
price variable appears. To ensure the-homogeneity property the parameters of
the equation would be subject to additional restrictions.
- 71 -
Now consider equation (5.4) which is a variant of the standard
inventory equation used in the supply of storage literature. See Weymar
(1968). The first term on the right-hand side is the speculative component.
Strictly speaking this component should depend upon the expected price change
net of the cost of storage (usually proxied by an interest rate variable), but
for simplicity storage costs are ignored. It is assumed 6 1>0. Some derivations
of the demand for speculative inventories show the coefficient 61 to be a
nonlinear function of higher moments of the distribution of P but this
pcssibility is also ignored. The second term in (5.4) reflects transactions
dEmand for inventories. That is, it is hypothesized that inventories produce a
positive convenience yield and that the transations component reilects this.
The demand for inventories for transactions purposes depends upon expected
future consumption demand, denoted Qdt+e
Equation (5.4) may be thought of as the price equation of- the model.
Ccmbining (5.4) and (5.6) and normalizing on Pt leads to
p H + e +_2 , ,
Pt ^ IHt t+l a1 Qt+l
- e + 2
= H +P + _( e + %Yx + u ) 5. 7.)
= t t+l+ 6 .1 t+l 2 2,t+l 2,t+l
wlience it is seen that the period t price will depend, inter alia, upon
ecpected future price and on the expected future values of X2. Equation (5.7)
i3 "structural" in the sense that its parameters have a behavioral interpreta-
tion. However, if additional assumptions are made about the process generating
fiture values of X2, and if these are substituted into (5.6), then the
r2sulting equation contains some parameters which are not "structural" in one
- 72 -
sense. If an acceptable proxy for P e is available, as will be the case when
t+1
a futures market exists, and if a proxy variable for X2,t+l can also be found,
then (5.7) can be estimated directly. However, as.no futures price variable is
available -for tea, estimation is approached indirectly by first solving the
model to obtain a reduced form solution for P e and then substituting it back
t4 I
into (5.7) to produce a pseudo-structural equation for Pt.
V.3 Solution of the-Model
Combining (5.1) - (5.6) and solving for Pt yields the following
linear equation:
t XPt-l + vPt+l + BPt+ Vt (5.8)
where A, a, B andyv are defined as follows:
X = 5d
i
d 1 + B1 + 61
a= 2 1 i d1
)-l
= 281 - r22I)d
v. .[ ue -ue +u -IF X
t 2 .2,t+l- 2 2t 2t ult 1 lt
2Y2(Xe - X2t) + Y X Idl-
+ 22 2,t+1 2t ) 2 2t I.
- 73 -
The superscript e denotes the value expected in the previous period.
Nlote that the stochastic demand and supply disturbances, the current and
expected future values of X2 and the current value of Xl aLl appear as
components of vt. The explicit solution of the equation (5.8) depends upon the
roots of the quadratic equation
ap 2 (l-6)" + x = 0 (5.9)
klthough there are several cases to consider, a special case of some interest
is one in which equation (5.9) will have two real roots pl and p!' l < 1 and
02 > 1. This is sometimes. referred to as the 'regular' case.
The general solution for Pt involves a "backward" and "forward"
infinite series in vt. Also, if no further restrictions are imposed, such a
solution need not be unique. There are a number of ways of achieving a unique
solution. One of these is to assume that the stochastic process generating
input variables in the difference equation (5.8) is stationary. In this case a
unique stable "forward" solution will exist. To show this explicitly define
the process
V = (l-ac ) E E(v II ) (5.10)
t~ ~ t+i t
and assume that this is a realizable stationary process. Then using the theory
of martingale difference, processes. it can be shown that the reduced form
(unique) forward solution for Pt is given by
Pt iPlt-l Vt - wE[Vt ItI1] (5.1L)
- 74 -
where = (6261 - Y2 1)/[(21 1 2
(5.10) and (5.11) show that Pt is determined by current and expected future
values of the Xi and X2 and of u1t and u2t. If. the stochastic process
generating the exogenous variables X1 and X2 can be specified explicitly, then
it may be possible to replace the last term in equation (5.11) by an expres-
sion in terms of observable variables only. Some commonly used assumptions
include the following: Xt is generated by (i) random walk with a drift, (ii) a
first order autoregression, and (iii) random walk with a first order moving
average process. 1/ Under assumption (i) the future values of exogenous
variables are obtained by adding a constant to the known current value. So the
reduced form equation ends. up with only current dated exogenous variables in
it. Under assumption (ii) also the future dated values of X1 and X2 can, be
reexpressed in terms of the current values. 2/
V.4 Derivation of the "Structural" Equation
The "structural" version of the price equation is obtained as
follows. Using (5.11)
1/ Given assumption (iii) the optimal forecasting procedure is to use the
adaptive expectations formula.
2/ Given assumption (iii) lagged values of Xi and X2 will appear in the
equation though the usual transformation may be applied to-eliminate them.
- 75 -
P e+, E[Pt+ I It
1 t (1-w)E[Vt+lIItI (5.12)
which is then substituted into (5.7) and the equation rearranged. This yields
at forward-looking price equation:
Pt= (1-u 1- I 1 2 _ 1 [-6 Ht + (1-u) (1 + 2 )] E(Vt+iIIt)
2x2,t+l + u2,t+1] (5.13)
To turn (5.13) into an estimatable equation the' simplest procedure would be to
replace the. last three terms by a linear function of the current: values of Xi
and X2 and any other vari'able which is useful for forecasting future demand
and supply shocks. Clearly this procedure still leaves scope for
'experimentation'.
V.5 Empirical Application
In the empirical implementation of (5.13) the first task to be
tackled concerns the definition of *the price variable and the choice of the
deflator. The empirical counterpart of nominal P is WPRICE which denotes the
weighted average of the price of tea at four auction centers--Calcutta,
Cochin, Colombo and Mombasa--denominated in US dollars per Kg. The weights
used are shares in the total. value of tea traded with the qualification that
dust tea is excluded. The price includes export taxes and cesses which are
important in Sri Lanka and which have been imposed periodically in India.
- 76 -
The next question concerns the appropriate deflator for WPRICE. The
chosen deflator is the ratio of weighted CPI for the- United Kingdom, India and
Sri Lanka divided by the weighted exchange rate for the same three countries.
The weights used in each case are the total tea stocks held in each of the
three countries, expressed,as a proportion of the total stocks in the three
countries combined. The rationale for this index is as follows. Firstly, it is
broadly true that proportionally the largest amount of tea stocks are held in
India, Sri Lanka and the United Kingdom. Since the stocks data for other
countries are somewhat fragmentary the simplifying assumption is adopted that
total 'world' stocks consist of the stocks in the United Kingdom, India and
Sri Lanka; which implies that the equation (5.4) represents inventory demand
in those three countries alone. It follows that the appropriate deflator is
the three-country weighted CPI, denoted CPI3. Secondly, since WPRICE is
expressed in US dollars an exchange rate conversion is required. Therefore,
CPI3 is divided by EXW3, which denotes a weighted average of exchange. rates in
the three countries--the weights being the same as those used for constructing
CPI3.
The dependent variable in the price equation is (log of) WPRICE and
not WPRICE x EXW3/CPI3. If the latter were used, the equation would explain
the real price of tea. This would be equivalent to specifying a homogeneous-
of-degree-zero inventory equation. However, given the approximations implicit
in the construction of CPI3 and EXW3 it does not seem appropriate to. impose
the homogeneity assumption. Instead CPI3/EXW3 has -been included as an
independent variable on the right-hand side. - (The estimates show that the
elasticity of WPRICE with respect to this deflator is 1.18 with a standard
- 77 -
error of. 0.16, somewhat greater than the value of unity which would apply if
the homogeneity postulate was completely consistent with the data.)
Turn now to the proxy variables for the future-dated exogenous
variables in the price equation. Clearly these should be the aggregate world
counterparts of the exogenous variables which enter the demand and production
equations for individual countries. On the supply side the most important
exogenous variables are time trend, various country. specific price deflators
and exchange rates, and subsidies. On the demand side, time trend has also
been found to be an important explanatory variable, being a proxy for, inter
Ilia, growth in per capita income and changes in taste. Another exogenous
vrariable included in some demand equations is the price of coffee, though this
variable has not been found to be particularly important. Nevertheless, it may
have special value in forecasting future demand disturbances, i.e., in
calculating E(u +II). That is, the importance of the coffee price
2,t+l t
variable may derive not only from its power to predict the future coffee
price, which probably has a rather small direct impact on tea consumption, but
also from its usefulness as-a predictor of future demand disturbances
(u 21+, i >1)
(2,t+i'
The WPRICE equation estimated for the model is log-linear. The
exogenous variables included CPI3/EXW3, TYT, TYT_1, lagged values of CN4W and
a time trend. The variable CPI3/EXW3 is included because of its role as a
deflator in the inventory equation. Its inclusion can also be justified on the
argument that its current value may be useful for predicting future values
which belong in. the price equation. (This gives yet another reason for not
imposing the homogeneity constraint.) TYT denotes the coffee price variable
denominated in US dollars. CN4W denotes the total world consumption of tea.
- 78 -
The price equation. was estimated by ordinary least squares and the
Hausman-Wu test of exogeneity of the 'world' stock variable (TWS) was made.
The results indicated (as expected) that TWS should be treated as an
endogenous variable. The equation was then estimated by the' generalized
instrumental variabl.e method. Whether, ordinary least squares or instrumental
variables are used, it is found that the current and lagged coffee price
variable is extremely important in explaining WPRICE. The same is also true
for the deflator CPI3/EXW3. The total 'world' inventory variable TWS has the
expected negative coefficient with' a t-ratio between 2.6 and 3.8, depending
upon the estimation procedure. When the predicted value of TWS is used, based
on a first stage log-linear regression of TWS on a number of exogenous
variables (including the trend and lagged value of CN4W), the stock variable
was highly significant in the equation, but not the trend variable. The reason
seems to be that the predicted value of TWS is quite smooth and hence highly
correlated with the trend variable and with CN4W which is also trend-like. If,
on the other hand, the ordinary least squares procedure is used only the TWS
variable has a small coefficient (about 0.6 rather than 1.0) while the trend
variable has a positive coefficient and t-ratio approaching 1.8.
In conclusion, although the basic specification derived in this
section gets substantial empirical support, the final point estimates reported
are sensitive to the choice of the estimation method and the exogenous and
lagged endogenous variables used as proxies for their future values. It is
hard to see how to avoid a degree of arbitrariness in choosing a
specification.
- 79 -
V.6 Price Linkage Equations
From the view point of model closure it is essential to relate WPRICE
to local prices.
The "world" price in the model is a value-weighted average of auction
prices from Calcutta, Cochin, Colombo and Mombasa in US dollars, inclusive of
sales tax, cesses and export duties, i.e.
E PA2*EXi*QAi
W PAi*EX *QA.
where PAi Auction price in local currency
EX. = Exchange rate between the US dollar and local currency
QAi = Quantity of tea sold at i
WPRICE is linked to five major auction prices (4 auction places plus
l.ondon) through equations given in Table 7. As the equations show, the price
novements in all auctions closely follow the "world" price. Any deviation
could be due to differences among auctions in quality, transportation costs
alnd relative availabilities of tea. The high correlation among major auction
prices suggests that the assumption of tea being a homogenous commodity is
aicceptable.
An equation linking the Calcutta and Cochin auction prices to the
lnit value of tea produced in India also estimated. The unit value of tea was
taken as a proxy for producer prices in India. Before this price linkage
aquation was finalized, two intermediate steps were taken. First, Calcutta and
Zochin auction prices (in US dollars), which refer only to leaf, were linked
to the unit value of tea inclusive of dust in the two auctions. The weighted
average of these unit values were linked to unit value of tea in India.
- 80 -
Table 7: PRICE AND LINKAGE EQUATIONS '
WORLD PRICE
9969: WPRICEL = 4.7486 + 0.2479 TYT + 0.4162 TYT(-1)
(4.9879) (4.5334) (6.4177)
+ 1.2722 III - 1.3359 TWSLHAT3 + 0.0152 TR61
(8.2859) (-6.7953) (1.4579)
R-SQUARED(CORR.): 0.974 SEE: 0.57394E-01 DW: 1.64
PERIOD OF FIT: 1964 1983
F(5, 14): 143.496
PRICE LINKAGE EQUATIONS
Calcutta auction price in US dollars
LN PRICCALC2$ = 0.0662 + 1.0813 LN WPRICE
(6.3680) (43.7006)
R-SQUARED (CORR.): 0.991 SEE: 0.38195E-01 DW: 1.30
PERIOD OF FIT: 1965-1983
Cochin auction price in US dollars
LN PRICOCH2$ = -0.0639 + 1.0492 LN WPRICE
(-6.4656) (36.9010)
R-SQUARED (CORR.): 0.978 SEE: 0.46204E-01 DW: 2.02
PERIOD OF FIT: 1952-1983
Colombo auction price in US dollars
LN PRICOLO$ = -0.0197 + 0.9346 LNWPRICE
(1.9299)(31.8995)
R-SQUARED (CORR.): 0.970 SEE: 0.47614E-01 DW: 1.02
PERIOD OF FIT: 1952-1983
See Glossary of Variables for definition of variables.
continued...
- 81 -
..continued
oG..ibasa auction price in [JS dollars
LN PRIMOMB$ = -0.0442 + 0.9650 LNWPRICE - 0.1613 LN EDM78
(-2.1465)(20.3521) (-2.2558)
R-SQUARED (CORR.): 0.966 SEE: 0.65942E-01 DW: 2.11
PERIOD OF FIT: 1965-1983
London auction price in US dollars
LN TPLOND = 4.8041 + 0.8974 LN WPRICE
(256.725) (20.1394)
R-SQUARED (CORR.): 0.957 SEE: 0.68779E-01 DW: 1.39
PERIOD OF FIT: 1965-1983
Linkage between leaf atction price and average of leaf and dust auction price
in Calcutta and Cochin.
LN PRICALC = -0.2258 + 1.0441 LN PRICALC3
(-5.8880)(64.9806)
R-SQUARED (CORR.): 0.996 SEE: 0.31885E-01 DW: 0.74
LN PRICOCH = -0.1285 + 1.0257 LN PRICOCH3
(-5.8087)(98.3440)
R-SQJARED (CORR.): 0.998 SEE: 0.24017E-01 DW: 1.31
PERIOD OF FIT: 1965-1983
Linkage between weighted average auction prices of leaf and dust in Calcutta
and Cochin to unit value of tea produced in India.
LN PRIINDI = 0.0107- + 0.9956 LN PRIINDW
(1.3749) (253.3529)
R-SQUARED (CORR.): 1.000 SEE:.0.10922E-01 DW: 1.44
PERIOD OF FIT: 1952-1983
continued..
- 82 -
...continued
Linkage between Mombassa auction price and price paid by KTDA to Kenya
smalIholders.
LN PRIKTDA = -1.7205 + 0.9808 LN PRIMOMB
(-10.4500) (14.3009)
R-SQUARED (CORR.): 0.940 SEE: 0.11614 DW: 1.04
PERIOD OF FIT: 1970-1983
Retail Price in the US
LN TPUS = 1.7877 + 0.4601 LN WPRICE + 0.6432 LN TPUS(-1)
(3.8038) (4.2863) (6.8342)
R-SQUARED (CORR.): 0.940 SEE: 0.94048E-01 DW: 1.64
PERIOD OF FIT: 1960-1983
Retail Price in UK
LN TPUK = -0.5287 + 0.6811 LN TPUK (-1) + 0.4936 LN TPLONDUK
(-1.7797) (7.9504) (4.8186)
R-SQUARED (CORR.): 0.957 SEE: 0.12610 DW: 2.24
PERIOD OF FIT: 1965-1983
Retail Price in Australia
LN TPAU$ = -2.3246 + 1.4980 LN TPLOND
(-3.4368)(11.1121)
R-SQUARED (CORR.): 0.872 SEE: 0.19061 DW: 1.11
PERIOD OF FIT: 1965-1983
Retail Price in Canada
LN TPCA = 0.8311 + -0.5176 LN TPLONDCA + 0.6440 LN TPCA (-1)
-(-3.7215) (5.6044) (7.5587)
R-SQUARED (CORR.): 0.968 SEE: 0.85377E-01 DW: 1.38
PERIOD OF FIT: 1960-1983
continued...
- 83 -
...continued
letail Price in India
LN TPIN = 0.8443 + 0.8330 LN PRIINDW
(8.2495)(18.2237 )
It-SQUARED (CORR.): 0.948 SEE: 0.94211E-01 DW: 0.67
i?ERIOD OF FIT: 1965-1983
- 84 -
An equation linking the price paid by KTDA to Kenya smallholders and
the Mombasa auction price was also estimated.
These linkage equations give a very good fit and the coefficients are
well within the expected range, i.e. close to unity.
The "world price" and some auction prices were linked to retail
prices in the United States, United Kingdom, India, Australia and Canada. In
the United States, United Kingdom and Canada evidence was found of delayed
adjustments of retail prices to "world" or auction prices.
Prices used in the demand and supply equations for other countries
are the "world" or auction prices adjusted by corresponding exchange rates and
consumer price indices.
- 85 -
VI. MODEL SIMULATIONS
Dynamic ex-post. and ex-ante model eimulations were carried out. The
?urposes of this simulation exercise we.re to examine how the model performs in
:3imulating the past and in forecasting, and to investigate its prominent
,:haracteristics.
7I.1 Results of Ex-Post Simulation
An ex-post simulation with the model was carried out for the period
L975-83. The simulated values can be compared with the actual historical
ialues to evaluate the model's performance. Some statistics on the simulation
results for selected variables are given in Table 8.
As Table 8 shows, the dynamic ex-post simulation results are satis-
Eactory.. The nominal and real world prices yield root-mean-squa.re percentage
errors of 11% and for world consumption and production they are about 1%. The
*mnodel captures all the significant turning points of the key variables.
3ignificantly, the model captures the sharp rise in the world price in 1977
and 1983. The rise in 1977 was, in the main, caused by low stock levels and
the boom in coffee prices; the sharp rise in the world price in 1983 was
zaused by a decline in stocks which in turn was the cumulation of steadily
increasing demand and stagnant production in the preceding 3-4 years.
- 86 -
Table 8: RESULTS OF EX-POST SIMULATION
ACTUAL AND PREDICTED VALUES OF SELECTED VARIABLES: 1975-83
WORLD DEMAND
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( * ) ( .+ ) (TIE = X)
197501 1217453.625 1200485.625 16968.000 1.394
197601 1277629.875 1288592.375 -10962.500 -0.858
197701 1316020.625 1296932.375 19088.250 1.450
197801 1319398.250 1310620.875 8777.375 0.665
197901 1407339.500 1397360.750 9978.750 0.709
198001 1457335.000 1448943.125 8391.875 0.576
198101 1453270.000 1434582.8,75 18687.125 1.286
198201 1457848.000 1456025.750 1822.250 0.125
198301 1535750.000 1538569.875 -2819.875 -0.184
DATE GRAPH RANGE OF VALUES: 1200485.625 TO 1538569.875
..............................................................
197501 . *
197601 .*+
197701 . + *
197801 .+*
197901 . +*
198001 +:
198101 . + *
198201 . X
198301 . x.
..............................................................
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 10832.8887
MEAN ABSOLUTE % ERROR 0.8052
ROOT MEAN SQUARED ERROR 12382.6631
ROOT MEAN SQUARED % ERROR 0.9287
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued...
- 87 -
..continued
DEMAND IN PAKISTAN
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( * ) ( + ) (TIE = X)
:97501 52000.000 46045.344 5954.656 11.451
:97601 49100.000 53418.250 -4318.250 -8.795-
:97701 60700.004i 60374.637 325.367 0.536
:97801 57500.000 60896.848 -3396.848 -5.908
:.97901 65330.000 64979.285 350.715 0.537
.98001 63599.000 64020.789 -421.789 -0.663
.98101 73252.000 69691.766 3560.234 4.860
L98201 69136.000 69282.742 -146.742 -0.212
'L98301 83406.000 78645.336 4760.664 5.708
DATE GRAPH RANGE OF VALUES: 46045.344 TO 83406.000
...............................................................
197501 .+
L97601 . *# +
l97701 +*
l97801 . * +
L97901 +-
L98001 .+
L98101 . +
L98201 . X +
L98301 +
..........................................................
J3UMMARY STATISTICS:
M4EAN ABSOLUTE ERROR 2581.6963
A4EAN ABSOLUTE % ERROR 4.2967
ROOT MEAN SQUARED ERROR 3356.7725
ROOT MEAN SQUARED % ERROR 5.7797
A4CTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued ...
- 88 -
...continued
DEMAND IN UNITED KINGDOM
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( -A- ) ( + )(TIE =X)
197501 193153.000 196615.672 -3462.672 -1.793
197601 206356.000 192327.391 14028.609 6.798
197701 181738.000 187481.984 -5743.984 -3.161
197801 165883.000 185143.625 -19260.625 -11.611
197901 179002.000 183907.250 -4905.250 -2.740
198001 182882.000 181493.281 1388.719 0.759
198101 180467.000 177797.578 2669.422 1.479
198201 173706.000 173630.172 75.828 0.044
198301 167992.000 169957.563 -1965.563 -1.170
DATE GRAPH RANGE OF VALUES: 165883.000 TO 206356.000
............................. ..............................................................----v*--
197501 e +
197601 . +
197701 . * +
197801 .: +
197901 * +
198001 . + A
198101 . + *
198201 . X
198301 . * +
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 5944.5190
MEAN ABSOLUTE % ERROR 3.2839
ROOT MEAN SQUARED ERROR 8496.6797
ROOT MEAN SQUARED % ERROR 4.7828
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
- 89 -
.-continued
DEMAND IN INDIA
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( -:; ) ( + ) (TIE = X)
197501 272000.000 280498.469 -8498.469 -3.124
197601 287000.000 279657.906 7342.094 2.558
197701 302000.000 292612.750 9387.250 3.108
197801 320000.000 312350.000 7650.000 2.391
197901 332000.000 320213.406 11786.594 3.550
198001 346000.000 343171.656 2828.344 0.817
198101 360000.000 356969.969 3030.031 0.842
118201 372000.000 371089.531 910.469 0.245
1)8301 386000.000. 394409.375 -8409.375 -2.179
D)ATE GRAPH RANGE OF VALUES: 272000.000 TO 394409.375
19750...............................................................
137501 .* +
137601 . + *
197701 . + *
137801 . + *
137901 . + *
198001 +
198101 +
198201 +*
198301 . +
SJMMARY STATISTICS:
MEAN ABSOLUTE ERROR 6649.1807
MEAN ABSOLUTE-% ERROR 2.0905
ROOT MEAN SQUARED ERROR 7456.7217
RDOT MEAN SQUARED % ERROR 2.3681
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued...
- 90 -
... continued
WORLD PRODUCTION
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( : ) ( + ) (TIE = X)
197501 1200494.000 1198910.750 1583.250 0.1.32
197601 1246042.000 1236819.250 9222.750 0.740
197701 1370261.000 1330406.250 39854.750 2.909
197801 1407164.000 1411555.875 -4391.875 -0.312
197901 1439309.000 1447506.500 -8197.500 -0.570
198001 1439347.000 1432909.000 6438.000 0.447
198101 1426039.000 1404363.125 21675.875 1.520
198201 1456660.000 1446046.125 10613.875 0.729
198301 1553967.000 1538400.500 15566.500 .1.002
DATE GRAPH RANGE OF VALUES: 1198910.750 TO 1553967.000
..............................................................
197501 .X
197601 . +*
197701 . + *
197801 . *+
197901 * +
198001 . +*
198101 . + *
198201 . +*
198301 . + *-
...............................................................
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 13060.4863
MEAN ABSOLUTE % ERROR 0.9289
ROOT MEAN SQUARED ERROR 17090.3359
ROOT MEAN SQUARED % ERROR 1.2244
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued...
- 91 -
...continued
PRODUCTION IN INDIA
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( * ) ( + ) (TIE = X)
197501 487137.000 496078.063 -8941.063 -1.835
197601 511817.000 504903.688 6913.313 1.351
197701 556267.000 538680.750 17586.250 3.161
197801 563846.000 576420.750 -12574.750 -2.230
197901 543776.000 576122.313 -32346.313 -5.948
198001 569550.000 577079.688 -7529.688 -1.322
198101 560041.000 574561.313 -14520.313 -2.593
198201 560732.000 574227.125 -13495.125 -2.407
198301 587795.000 599219.813 -11424.813 -1.944
DATE GRAPH RANGE OF VALUES: 487137.000 TO 599219.813
197501 .* +
197601 . + *
197701 . + *
197801 . * +
197901 . +
198001 . * +
198101 . * +
198201 . * +
198301 . * +
..............................................................
SlMMARY STATISTICS:
MEAN ABSOLUTE ERROR 13925.7363
ME:AN ABSOLUTE % ERROR 2.5324
ROOT MEAN SQUARED ERROR 15711.2891
ROOT MEAN SQUARED % ERROR 2.8594
ACTUAL COLUMN: ZERO SECTOR
PEEDICTED COLUMN: TEA100.XP
continued...
- 92 -
...continued
PRODUCTION IN KENYA
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( * ) ( + ) (TIE = X)
197501 56730.000 60632.945 -3902.945 -6.880
197601 61984.000 70204.508 -8220.508 -13.262
197701 86291.000 85944.086 346.914 0.402
197801 93373.000 96199.594 -2826.594 -3.027
197901 99275.000 100309.461 -1034.461 -1.042
198001 89893.000 92876.219 -2983.219 -3.319
198101 90941.000 93879.570 -2938.570 -3.231
198201 96033.000 97110.539 -1077.539 -1.122
198301 119738.000 110158.570 9579.430 8.000
DATE GRAPH RANGE OF VALUES: 56730.000 TO 119738.000
19750..............................................................
197501 +
197601 . * +
197701 . X
197801 +
197901 *+
198001 +
198101 +
198201 .+
198301 . + *
..............................................................
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 3656.6865
MEAN ABSOLUTE % ERROR 4.4762
ROOT MEAN SQUARED ERROR 4742.8447
ROOT MEAN SQUARED % ERROR 5.9661
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued...
- 93 -
...continued
PRODUCTION IN SRI LANKA
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( - ) ( + ) (TIE = X)
197501 213679.000 209108.484 4570.516 2.139
197601 196606.000 200857.688 -4251.688 -2.163
197701 208572.000 207749.672 822.328 0.394
197801 198981.000 204465.813 -5484.813 -2.756
197901 206417.000 204997.469 1419.531 0.688
198001 191375.000 196162.922 -4787.922 -2.502
198101 210148.000 199283.391 10864.609 5.170
198201 187816.000 192542.969 -4726.969 -2.517
198301 179287.000 173295.031 5991.969 3.342
DATE GRAPH RANGE OF VALUES: 173295.031 TO 213679.000
197501 . +
197601 . * +
197701 +*
197801 . +
197901 . + *
198001 . * +
198101 . + *
198201 . * +
198301 .+ *
....... .................... . . . . .. . . . . . .. . . . . .
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 4768.9272
MEAN ABSOLUTE % ERROR 2.4079
ROOT MEAN SQUARED ERROR 5486.8813
ROOT MEAN SQUARED % ERROR 2.7481
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued...
- 94 -
...continued
WORLD STOCKS
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( -.*: ) ( + ) (TIE = X)
197501 310.900 326.285 -15.385 -4.948
197601 315.600 310.799 4.801 1.521
197701 318.800 293.233 25.567 8.020
197801 361.600 349.202 12.398 3.429
197901 415.500 421.278 -5.778 -1.391
198001 459.800 467.532 -7.732 -1.682
198101 435.900 440.644 -4.744 -1.088
198201 356.600 352.552 4.048 1.135
198301 338.200 315.766 22.434 6.633
DATE GRAPH RANGE OF VALUES: 293.233 TO 467.532
............................................................
197501 . *
197601 . + -
197701 .+
197801 . + *
197901 +
198001 +
198101 *+
198201 .+
198301 . + *
. ... .... ......... .......... .... ..... . .... ..... .. ........... ................................................ -- ......--
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 11.4319
MEAN ABSOLUTE % ERROR 3.3163
ROOT MEAN SQUARED ERROR 13.7537
ROOT MEAN SQUARED % ERROR 4.1380
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued...
- 95 -
...continued
WORLD NOMINAL PRICE
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( -'' ) ( + ) (TIE = X)
l97501 1.243 1.077 0.165 13.310
L97601 1.296 1.237 0.059 4.551
L97701 2.199 2.432 -0.234 -10.637
L97801 2.023 2.275 -0.252 -12.473
L97901 1.744 1.673 0.071 4.043
L98001 1.870 1.669 0.201 10.745
L98101 1.635 1.577 0.058 3.532
198201 1.657 1.996 -0.339 -20.446
198301 2.336 2.439 -0.102 -4.387
DATE GRAPH RANGE OF VALUES: 1.077 TO 2.439
197501 .+ *' '.v
197601 . + *
197701 . +
197801 . * +
197901 . + *
198001 . + -
198101 . + *
198201 . +
198301 . +
..............................................................
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 0.1646
MEAN ABSOLUTE % ERROR 9.3470
ROOT MEAN SQUARED ERROR 0.1895
ROOT MEAN SQUARED % ERROR 10.7916
ACTUAL COLUMN: ZERO SECTOR
PREDICTED COLUMN: TEA100.XP
continued...
- 96 -
...continued
WORLD REAL PRICE
DATE ACTUAL PREDICTED DIFFERENCE % DIFFERENCE
( * ) ( + ) (TIE = X)
197501 0.019 0.017 0.003 13.282
197601 0.020 0.019 0.001 4.635
197701 0.030 0.034 -0.003 -10.603
197801 0.024 0.027 -0.003 -12.392
197901 0.018 0.018 0.001 3.971
198001 0.018 0.016 0.002 10.778
198101 0.016 0.015 0.001 3.572
198201 0.016 0.020 -0.003 -20.430
198301 0.023 0.024 -0.001 -4.375
DATE GRAPH RANGE OF VALUES: 0.015 TO 0.034
197501..............................................................
197601 . + *
197701 . *+
197801 . * +
197901 . + *
198001 . +
198101 .+*
198201 . * +
198301 . +
..............................................................
SUMMARY STATISTICS:
MEAN ABSOLUTE ERROR 0.0019
MEAN ABSOLUTE % ERROR 9.3374
ROOT MEAN SQUARED ERROR 0.0022
ROOT MEAN SQUARED % ERROR 10.7759
- 9 7-
V[.2 Results of Base Ex-Ante Simulation
Ex-ante simulations were made for the period 1984-2000. Assumptions
aout future values of important exogenous variables were:
(i) Worldwide inflation of 4% p.a. Purchasing power parity is assumed to
hold for the period 1986-2000. To implement this assumption in the
model all exchange rates were kept at the value of the average of the
first 10 months of 1985 while all CPI's increased at 4% p.a.
(ii) GDPs of developing countries appearing in the model increased at 5%
p.a.
(iii) Subsidies on new plantings in Sri Lanka and Kenya and real
development expenditure and cost of production in Sri. Lanka were
assumed to stay at the same level in real terms as the last available
data in real terms.
(iv) Population increases at the rate given in the World Development
Report of 1985.
(v) Projections of coffee and sugar prices were those made by the World
Bank.
(vi) Exogenized tea consumption and production variables increase at the
following rates:
Production: % p.a.
Argentina 3.0%
Turkey 3-0%
Iran 1.0%
South America, excluding Argentina 0.5%
Uganda 8.5%
Asia, excluding India, Sri Lanka
Indonesia, Iran, Bangladesh, China, Japan 1.0
Consumption:
Afghanistan 1.0%
Iraq 2.0%
Indonesia 2.0%
Iran 3.0%
- 98 -
When the simulation was first performed, the model gave a much lower
world price level in 1984 than was actually realized. The model failed to
project the panic buying in 1984 caused by India's imposition of export
limitations. Therefore, a dummy variable was put into the model to adjust the
1984 price. The ex-ante simulation results with this adjustment are given in
Table 9.
The model predicts the sharp price fall in 1985 well. Nevertheless,
it still underestimates the nominal world price by about 20%. This forecast
error is mainly due to the actual high prices of the early months of 1985,
when prices were still influenced by the boom.
An interesting aspect of the model is the ability of the price to
return quickly to $2.00/Kg in 1984 U.S. dollar prices which the model gives as
the stable price level of the 1980s after the 1983-84 shock. This feature of
the model is examined in more detail in Section VI.4.
VI. 3 Evaluation of Some Key Elasticities
As discussed in Section III, long-term price elasticities of supply
cannot be calculated easily from the estimated equations for major producing
countries. A way to evaluate the implied long-term price elasticities is by
simulating the whole model. For this purpose the "world" price was exogenized
and increased by 10% from the base run simulation during the period 1990-2000.
Table 10 shows the differences in the key variables between the base run and
the run with higher world prices.
Table 9: RESULTS OF EX-ANTE SIMULATION FORECAST VALUES OF SELECTED VARIABLES: 1984-200
DEMAND IN PAKISTAN
VARIABLE GRAPHED : CN1PAK
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 187657.094
( + ) (TIE = X)
198401 81497.664 -81497.664 * +
198501 90606-.617 -90606.617 +
198601 97463.539 -97463.539 +
198701 109049.633 -109049.633 +
198801 107430.391 -107430.391 +
198901 120007.586 -120007.586 +
199001 117583.906 -117583.906 +
199101 130848.648 -130848.648 . +
199201 128901.039 -128901.039 . A +
199301 143332.453 -143332.453 .* +
199401 141306.234 -141306.234 .* +
199501 156737.984 -156737.984 . +
199601 155120.219 -155120.219 +
199701 171661.469 -171661.469 +
199801 170044.391 -170044.391 . +
199901 187657.094 -187657.094
200001 186343.234 -186343.234 +
continued...
... continued
DEMAND IN U.K.
VARIABLE GRAPHED : CN1UK
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 179854.656
(+) TIE = X)
..............................................................
198401 179854.656 -179854.656 . +
198501 157659.078 -157659.078 +
198601 162541.047 -162541.047 . +
198701 159831.375 -159831.375 +
198801 157344.578 -157344.578 +
198901 154703.594 -154703.594 +
199001 151938.328 -151938.328 +
199101 149443.750 -149443.750 + +
199201 146936.984 -146936.984 . +
199301 144523.703 -144523.703 +
199401 142077.047 -142077.047 +
199501 139723.641 -139723.641 .A +
199601 137398.078 -137398.078 .A +
199701 . 135141.813 -135141.813 +
199801 132858.625 -132858.625 . +
199901 130647.070 -130647.070 .* +
200001 128399.273 -128399.273 +
..............................................................
continued...
...continued
DEMAND IN INDIA
VARIABLE GRAPHED : CONIN
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 765367.188
( + ) (TIE = X)
..............................................................
198401 396811.938 -396811.938 ,A +
198501 419689.906 -419689.906 .* +
198601 457006.719 -457006.719 .* +
198701 470261.500 -470261.500 . +
198801 492660.875 -492660.875 .- +
198901 516398.563 -516398.563 .A +
199001 535535.875 -535535.875 .A +
199101 554175.125 -554175.125 .* +
199201 575197.688 -575197.688 .: +
199301 597981.938 -597981.938 .A +
199401 620038.375 -620038.375 .* +
199501 642576.625 -642576.625 .* +
199601 666742.875 -666742.875 .A +
199701 691552.063 -691552.063 .A +
199801 715828.250 -715828.250 . +
199901 740215.625 -740215.625 .A +
200001 765367.188 -765367.188 . +
continued...
continued
WORLD DEMAND
VARIABLE GRAPHED : CN4W
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 2509368.750
(+) (TIE = X)
..............................................................
198401 1596509.625 -1596509.625 +
198501 1640176.375 -1640176.375 +
198601 1770166.500 -1770166.500 +
198701 1770058.250 -1770058.250 +
198801 1837755.500 -1837755.500 +
198901 1878252.375 -1878252.375 +
199001 1937444.750 -1937444.750 +
199101 1966275.125 -1966275.125 +
199201 2032940.125 -2032940.125 + .
199301 2076846.000 -2076846.000 .* +
199401 2148491.750 -2148491.750 +
199501 2189954.000 -2189954.000 +
199601 2264451.250 -2264451.250 +
199701 2312160.750 -2312160.750 ** +
199801 2387170.250 -2387170.250 .* +
199901 2432494.500 -2432494.500 .* +
200001 2509368.750 -2-509368.750 *+ .
continued ...
... continued
PRODUCTION IN INDIA
VARIABLE GRAPHED : QIND
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 916563.750
( + ) (TIE = X) ..............................................................
198401 645742.438 -645742.438 +
198501 663975z375 -663975.375 +
198601 671537.750 -671537.750 . +
198701 704563.438 -704563.438 .: +
198801 712016.813 -712016.813 +
198901 703449.688 -703449.688 +
199001 720885.875 -720885.875 +
199101 744910.500 -744910.500 +
199201 776140.063 -776140.063 + . o
199301 791738.438 -791738.438 + Li
199401 812070.938 -812070.938 +
199501 832492.750 -832492.750 .: +
199601 848955.625 -848955.625 +
199701 864050.438 -864050.438 .* +
199801 880743.625 -880743.625 .* +
199901 898439.250 -898439.250 . + .
200001 916563.750 -916563.750 + .
continued...
... continued
PRODUCTION IN KENYA
VARIABLE GRAPHED : QKEN
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 212434.609
( + ) (TIE = X)
198401 113790.375 -113790.375 .A +
198501 145994.016 -145994.016 .* +
198601 137901.922 -137901.922 .* +
198701 133514.766 -133514.766 +
198801 140340.750 -140340.750 ** +
198901 142895.688 -142895.688 .* +
199001 147367.422 -147367.422 .A +
199101 154715.344 -154715.344 .* +
199201 162691.719 -162691.719 .A +
199301 168219.016 -168219.016 .* +
199401 174648.750 -174648.750 , +
199501 180599.484 -180599.484 +
199601 186973.766 -186973.766 .* +
199701 191837.484 -191837.484 ** +
199801 198404.281 -198404.281 .* +
199901 204709.016 -204709.016 . +
200001 212434.609 -212434.609 +
continued...
continued
PRODUCTION IN SRI LANKA
VARIABLE GRAPHED : QSL
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 228338.125
( + ) (TIE = X)
..............................................................
198401 208700.406 -208700.406 +
198501 215093.344 -215093.344 .- +
198601 210313.938 -210313.938 .- +
198701 217854.547 7 -2178 54.547 .* +
198801 212836.563 -212836.563 . +
198901 218527.531 -218527.531 .* +
199001 214022.844 -214022.844 .* +
199101 219844.500 -219844.500 .A +
199201 215882.156 -215882.156 .A +
199301 221335.844 -221335.844 .* +
199401 217514.250 -217514.250 .* +
199501 223023.094 -223023.094 .A +
199601 219596.328 -219596.328 .* +
199701 225207.516 -225207.516 +
199801 222327.609 -222327.609 .* + *
199901 228338.125 -228338.125 +
200001 225990.328 -225990.328 +
........................................................c.....
continued...
...continued
WORLD PRODUCTION
VARIABLE GRAPHED : QW
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 2538232.750
( + ) (TIE = X)
..............................................................
198401 1650433.125 -165.0433.125 +
198501 1749152.250 -1749152.250 .- +
198601 1776665.500 -1776665.500 +
198701 1840020.000 -1840020.000 +
198801 1870630.875 -1870630.875 +
198901 1896264.000 -1896264.000 +
199001 1941836.375 -1941836.375 +
199101 2009314.000 -2009314.000 +
199201 2075236.125 -2075236.125 + +
199301 2132423.500 -2132423.500 .* +
199401 2186828.500 -2186828.500 .A +
199501 2250880.000 -2250880.000 .* +
199601 2302258.500 -2302258.500 +
199701 2360414.750 -2360414.750 .* +
199801 2414284.750 -2414284.750 .* +
199901 2479133.250 -2479133.250 * + .
200001 2538232.750 -2538232.750 .*+ .
..............................................................
continued...
continued
WORLD STOCKS
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 452.029
( + ) (TIE = X)
..............................................................
198401 355.824 -355.824 .A +
198501 428.499 -428.499 . +
198601 398.698 -398.698 .A +
198701 432.360 -432.360 . +
198801 428.936 -428.936 .6 +
198901 410.647 -410.647 .A +
199001 378.739 -378.739 .A +
199101 385.478 -385.478 +
199201 391.474 -391.474 .* +
199301 410.751 -410.751 . +
199401 412.788 -412.788 . +
199501 437.414 -437.414 . +
199601 438.921 -438.921 . + *
199701 450.875 -450.875 .* +-
199801 441.690 -441.690 .* +
199901 452.029 -452.029 .* +
200001 444.593 -444.593 +
.........................................................e....
continued...
...continued
WORLD NOMINAL PRICE
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 4.490
(+) (TIE = X)
.............................................................
198401 3.315 -3.315 .* +
198501 1.694 -1.694 . +
198601 2.173 -2.173 +
198701 2.289 -2.289 . +
198801 2.206 -2.206 +
198901 2.272 -2.272 .-A +
199001 2.560 -2.560 +
199101 2.708 2.708 +
199201 2.881 -2.881 . +
199301 2.945 -2.945 +
199401 3.238 -3.238 .* +
199501 3.317 -3.317 .- +
199601 3.547 -3.547 . +
199701 3.625 -3.625 . +
199801 3.977 -3.977 . +
199901 4.115 -4.115 .- +
200001 4.490 -4.490 +
...............................................................
continued...
...continued
WORLD REAL PRICE
DATE PREDICTED DIFFERENCE GRAPH RANGE OF VALUES: 0.000 TO 0.033
(+) (TIE = X)
..............................................................
198401 0.033 -0.033
198501 0.016 -0.016 +
198601 0.020 -0.020 +
198701 0.020 -0.020 +
198801 0.019 -0.019 +
198901 0.019 -0.019 +
199001 0.020 -0.020 +
199101 0.021 -0.021 +
199201 0.021 -0.021 +
199301 0.021 -0.021 + *
199401 0.022 -0.022 +
199501 0.022 -0.022 .* +
199601 0.022 -0.022 .* +
199701 0.022 -0.022 +
199801 0.023 -0.023 .* +
199901 0.023 -0.023 +
200001 0.024 -0.024 .* +
..............................................................
- 110 -
As can be seen in Table 10, the supply response to the price change
of the world and the major producing countries is low in 1990 when the price
change is introduced. In the following year, the supply response is much
higher, although still not very high. Except for India (where they decline
after the fourth year), the price elasticities remain relatively stable
through the second to fifth or sixth year then increase thereafter. This
increase is due to the maturing of the plantings undertaken in response to
higher prices. The deviation from this time pattern of supply elasticities in
India is due to the coefficient of the relative price term in the estimated
supply equation for India being larger than the coefficient on the price level
term. The inference from these estimates is that the long-run elasticity of
supply response to a permanent price change is about 0.1 for the old-
established producers (India and Sri Lanka) and 0.5 for newer producers such
as Kenya. In global terms the elasticity is about 0.2. A negative response of
a similar size is observed in world consumption.
VI.4 Simulation of a One Time Supply Shock
In the base ex-ante simulation run, there was a sharp price decline
in 1985 and a return of the price level quickly to the $2.00/kg level. To
investigate this particular characteristic of the model, a simulation was
carried -out wherein world production was reduced by 200,000 mt in 1990. In
Table 11 the results of this scenario are compared with the results from the
base run simulation. The comparison is made in terms of level and percentages
changes.
Table 10: CHANGES IN KEY VARIABLES IN CASE WORLD PRICE IS
INCREASED BY 10% DURING 1990-2000
(shown as comparison with Base Run in percentage terms)
Production Consumption
YEAR India Sri Lanka Kenya World World
1990 0.356 0.005 1.644 0.388 -0.740
1]991 1.387 0.383 3.429 1.089 -1.366
1992 1.604 0.213 4.569 1.311 -1.396
1993 1.572 0.477 4.528 1.358 -1.401
1)94 1.531 0.331 4.519 1.345 1.429
1395 1.349 0.600 4.545 1.303 -1.418
1996 0.800 0.530 4.645 1.107 -1.425
1997 0.557 0.811 4.771 1.048 -1.406
1998 0.540 0.788 4.973 1.064 -1.412
1999 0.723 1.071 5.210 1.170 -1.395
2000 0.593 1.079 5.511 1.149 -1.389
Source: World Bank, Economic Analysis and Projections Department.
Table 11: SIMULATION RESULTS WITH 200,000 MT WORLD PRODUCTION DECLINE IN 1990
Year World Production World Consumption World Stocks World Price (Nominal)
(mt) (%) (mt) (%) (mt) (%) (US$/kg) (%)
1990 -162865 -8.481 -62895 -3.296 -99970 -26.362 1.290 50.503
1991 62738 3.168 -48107 2.483 10875 2.818 -0.099 -3.644
1992 8950 0.436 7708 0.384 12117 3.054 -0.111 -3.939
1993 56 0.003 6229 0.304 5944 1.432 -0.055 -1.882
1994 -4015 -0.186 1547 0.073 382 0.091 -0.004 -0.122
1995 -10069 -0.453 -3398 -0.157 -6289 -1.416 0.063 1.924
1996 -8491 -0.373 -7051 -0.315 -7730 -1.730 0.082 2.359
1997 -2042 -0.088 -5711 -0.250 -4061 -0.887 0.042 1.198
1998 8851 0.371 -454 -0.019 5245 1.172 -0.060 -1.544
1999 8271 0.338 5542 0.231 7974 1.754 -0.092 -2.296
2000 1138 0.045 5209 0.210 3903 0.875 -0.051 -1.158
Source:-----World----Bank,----Economic-----Analysis-----and---Projections----__Department.-----
Source: World Bank, Economic Analysis and Projections Department.
- 113 -
The exogenously imposed supply shortage of 200,000 mt is reduced to
lCO,000 mt in 1990 as world, production increases by 37,000 mt (therefore the
"effective" supply shock is 163,000mt) and world consumption reduces by 63,000
mt. The world price increases by 51%. This large price increase is due to the
lcw short-run price elasticities of supply and demand. In the folLowing year,
wcrld production increases by 63,000 mt and world consumption declines by
4E.,000 mt compared with the base run. The combined effect of 111,000 mt is
mcre than enough to fill the reduction in stocks of 100,000 mt of 1990. Thus
the eventual stock in 1991 is higher than the base run and the price is lower.
I'le effects in following years are small.
This simulation gives an insight into the "spike" type of price
movements observed in tea and in many other commodities. A prime example is
sugar. Because short-run price responses in demand and supply are so low, an
exogenous supply shock (which could, for example, be caused by weather) causes
price to change by a large amount. Although the supply and demand elasticities
i: the following year are still low the product of the small elasticities and
the large price change is fairly large, because the price change is very
Large. As a consequence, t:he price declines sharply in the year following an
exogenous supply shock, often to a lower level than the price level preceding
the shock.
- 114 -
VII. CONCLUSIONS AND SUGGESTIONS FOR FURTHER RESEARCH
In the introductory section several distinguishing features of this
new global tea model were described. It seems appropriate to begin with an
assessment of their, usefulness in the light of the results of the various
simulation exercises.
(i) Structure of the model: The results of estimated equations and
several simulation runs indicate that the basic assumptions made
about the world tea market, i.e., perfect competition, market
clearing equilibrium price and homogeneity of the commodity are
fundamentally correct. Noteworthy, however, is the model's inability
to simulate the market in 1984 when the impact of India's export
limitations led to panic buying. Further detailed research on what
happened in 1984 is necessary to improve the model.
(ii) Vintage production framework: In the case of the major producers the
use of constructed measures of feasible output, in conjunction with
the theoretical concepts of potential output, is a very useful
innovation. (It should be mentioned that some of these ideas have
previously been incorporated in World Bank models of cocoa and
coffee; see Akiyama and Bowers (1984), Akiyama (1982).) But it is
clear that to properly exploit this idea, reliable additional
information is needed on: (a) the average yield-age profiles for
hybrids and vegetatively propagated clones; (b) the age-structure of
the existing capital stock; and (c) current practices with respect to
the application of fertilizers and other inputs. The nonlinear supply
function for India and Sri Lanka assumed a constant rate of
disembodied technological change with respect to yields. This is a
- 115 -
strong assumption. Information is needed to improve understanding of
this issue.
(iii) New plantings and replantings: The low long-run elasticities implied
by the model were a surprise. Except for Kenya they turned out not a
great deal larger than the short-run elasticities. The main reason
for the low long-run elasticities is that new plantings and
replantings in India and Sri Lanka are relatively small and,
furthermore, not very price sensitive. At present there cannot be
complete confidence on this point because adequate data on planting
subsidies in India are lacking and it is suspected that the new
planting equation may be misspecified. However, the conclusion that
new planting is relatively price insensitive in old established pro-
ducing countries is not entirely surprising. The finding may reflect
the role of land constraints or it may reflect the importance of non-
price exogenous factors in determining new investment. Once again,
the matter needs more investigation.
(iv) Intercountry differences: It is evident that the three major produ-
cers are quite different in their behavior. Institutional factors and
the structure of the capital stock both contribute to this. A clear
implication is that there should be more attempt to distinguish
between the smallholders and estates. The analysis of Kenyan
production highlights this point. It is believecl that this
distinction is highly relevant to Indonesia but we lack the data to
investigate it thoroughly. Thus disaggregation by country alone may
not be enough.
- 117 -
Kvii) Econometric Methodology: There is much scope for improving the
estimation of the model. There was excessive reliance on the use of
ordinary least squares. The sensitivity of the conclusions to other
estimation methods remains a matter for future investigation.
GLOSSARY OF VARIABLES
VARIABLE FROM/TO DESCRIPTION UNITS SOURCE
AIEXT 1952-1983 ALL INDIA EXTENSIONS HA ITC
AIREPL 1952-1983 ALL INDIA REPLANTINGS HA ITC
AIREPM 1952-1983 ALL INDIA REPLACEMENTS HA ITC
ATEMPI 1953-1984 DeflatedRETAIL PRICE OF COFFEE, US US$/KG ICO
ATEMP12 1954-1984 TWO PERIOD MOVING AVERAGE OF DEFLATED REAL PRICE OF COFFEE, US US$/KG ICO
ATEMP12 = (ATEMP1 + ATEMP1(-1))/2
C1CH 1952-1984 APPARENT TEA CONSUMPTION PER CAP, CHILE MT/1000 HEAD ITC & IFS
CICH = CNICH/POP2CH
CIEG 1962-1983 APPARENT TEA CONSUMPTION PER CAP, EGYPT MT/HEAD ITC & IFS
CIEG = CNIEG/(POP2EG * 1000)
CINZ 1952-1983 APPARENT TEA CONSUMPTION PER CAP, NEW ZEALAND MT/1000 HEAD ITC & IFS
ClNZ = CNINZ/POP2NZ
C1PAK 1950-1984 APPARENT TEA CONSUMPTION PER CAP, PAKISTAN MT/1000 HEAD ITC & IFS
CIPAK. = CN1PAK/POPPAK
C1PAK2 1951-1984 TWO PERIOD M A OF APPARENT TEA CONSUMPTION PER CAP, PAKISTAN MT/1000 HEAD ITC & IFS X
C1PAK2 = (C1PAK + CIPAK(-1))/2.0
CISA 1968-1984 APPARENT TEA CONSUMPTION PER CAP, SAUDI ARABIA MT/1000 HEAD ITC & IFS
CISA = CN1SA/POP2SA
C1SA2 1969-1984 TWO PERIOD M A OF APPARENT TEA CONSUMPTION PER CAP, SAUDIA ARABIA MT/1000 HEAD ITC & IFS
C1SA2 = (CISA + C1SA(-1))/2
C1SL 1961-1983 APPARENT TEA CONSUMPTION PER CAP, SRI LANKA MT/1000 HEAD ITC & IFS
C1SL = CN2SL/POP2SL
CiSO 1952-1984 APPARENT TEA CONSUMPTION PER CAP, SOUTH AFRICA MT/1000 HEAD ITC & IFS
CISO = CNISO/POP2SO
CISYR 1952-1984 APPARENT TEA CONSUMPTION PER CAP, SYRIA MT/1000 HEAD ITC & IFS
CISYR = CN1SYR/POP2SYR
C1SYR2 1953-1984 TWO PERIOD M A OF APPARENT TEA CONSUMPTION PER CAP, SYRIA MT/1000 HEAD ITC & IFS
CWSYR2 = (CISYR + CISYR(-1))/2
CITUR 1953-1984 APPARENT TEA CONSUMPTION PER CAP, TURKEY MT/1000 HEAD ITC & IFS
CITUR = CN1TUR/POP2TU
ClUK 1953-1984 CONS PER CAP IN UK MT/1000 HEAD ITC & IFS
ClUK = CNlUK/POPUK
ClUS 1953-1984 APPARENT TEA CONSUMPTION PER CAP, US MT/1000 HEAD ITC & IFS
CIUS = CN1US/POPUS
ClUS2 1954-1984 TWO PERIOD M A OF APPARENT TEA CONSUMPTION PER CAP, US MT/1000 HEAD ITC & IFS
ClUS2 = (CIUS + CIUS(-]))/z
ClUSSR 1955-1984 APPARENT TEA CONSUMPTION PER CAP, USSR MT/1000 HEAD ITC & IFS
CIUSSR = CNlUSSR/POPUSR
CAU 1953-1984 APPRENT TEA CONSUMPTION PER CAP, AUSTRALIA MT/1000 HEAD ITC & IFS
CAU = CONAU/(POPAU * 1000)
CCA 1953-1984 APPARENT TEA CONSUMPTION PER CAP, CANADA MT/1000 HEAD ITC & IFS
CCA = CONCA/(POPCA * 1000)
CIN 1953-1984 APPARENT TEA CONSUMPTION PER CAP, INDIA MT/1000 HEAD ITC & IFS
CIN = CONIN/POPIN
CNIAFG 1952-1984 APPARENT TEA CONSUMPTION, AFGHANISTAN MT ITC
CNIARAB 1970-1984 APPARENT TEA CONSUMPTION, ABU DHABI, BAHRAIN,
DUBAI, KUWAIT, OMAN, QATAR, OTHER ARABIAN STATES MT ITC
CNICH 1952-1984 APPARENT TEA CONSUMPTION. CHILE MT ITC
CN1EE 1952-1984 TOTAL CONSUMPTION OF TEA, EASTERN EUROPE LESS USSR MT ITC
CNIEG 1952-1984 APPARENT TEA CONSUMPTION, EGYPT MT ITC
CN1IR 1952-1984 APPARENT TEA CONSUMPTION, IRAN MT ITC
CN1IRQ 1952-1984 APPARENT TEA CONSUMPTION, IRAQ MT ITC
CNINA 1952-1984 APPARENT TEA CONSUMPTION, ALGERIA, LYBIA, TUNISIA MT ITC
CNINZ 1952-1984 APPARENT TEA CONSUMPTION, NEW ZEALAND MT ITC
CNIPAK 1950-1984 APPARENT TEA CONSUMPTION, PAKISTAN MT ITC
CNIROW 1970-1983 APPARENT TEA CONSUMPTION, REST OF WORLD MT ITC
CNIRWEI 1952-1984 APPARENT TEA CONSUMPTION, WESTERN EUROPE EXCLUDING U.K. MT ITC
CNISA 1968-1984 APPARENT TEA CONSUMPTION, SAUDI ARABIA MT ITC
CNISO 1952-1984 APPARENT TEA CONSUMPTION, SOUTH AFRICA MT ITC
CN1SYR 1952-1984 APPARENT TEA CONSUMPTION, SYRIA MT ITC
CNITUR 1952-1984 APPARENT TEA CONSUMPTION, TURKEY MT ITC
CNIUK 1950-1984 APPARENT TEA CONSUMPTION, UK MT ITC
CNIUS 1953-1984 APPARENT TEA IN US MT ITC
CNIUSSR 1955-1984 APPARENT TEA CONSUMPTION, USSR MT ITC
CN4W 1970-1983 APPARENT TEA CONSUMPTION, WORLD MT ITC
CN1W = CN1UK + CNIUS + CNIRWEI + CNlUSSR + CN1EE + CONCA;
+ CONAU; CNINZ + CNISO + CNIPAK + CNISA + CNITUR + CNIIR;
+CNiiRQ + CNiSYR + CNiCH T CNIAFG + CN1NA : CN1ARAB 1CN2INDO;
+ CN2SL + CN1EG + CONIN; + CNIROW
CN2INDO 1952-1984 APPARENT TEA CONSUMPTION, INDONESIA MT ITC
CN2SL 1961-1983 APPARENT TEA CONSUMPTION, SRI LANKA MT ITC
CONAU 1953-1984 APPARENT TEA CONSUMPTION, AUSTRALIA MT ITC
CONCA 1953-1984 APPARENT TEA CONSUMPTION, CANADA MT ITC
CONIN 1953-1984 APPARENT TEA CONSUMPTION, INDIA MT ITC
COPSL 1963-1983 COST OF PROD. OF TEA -BASED ON SAMPLE SURVEY IN SRI LANKA RS/KG CENT.BANK OF SRI LANKA
(Glossary continues on the following page.)
GLOSSARY (continued)
COPSLCHR 1964-1983 CHANGE IN REAL COST OF TEA PRODUCTION IN SRI LANKA
COPSLCHR = COPSL/CPISL - COPSL(-I)/CPISL(-I)
COPSLR 1963-1983 REAL COST OF TEA PRODUCTION
COPSLR = COPSL/CPISL
CPIIAU 1953-1984 CPI, AUSTRALIA IFS
CPIICA 1953-1984 CPI, CANADA IFS
CPIIUK 1953-1984 CPI, UK IFS
CPIIUS 1953-1984 CPI, US IFS
CP12ARG 1950-1984 CPI, ARGENTINA IFS
CP12BAN 1972-1984 CPI, BANGLADESH IFS
CP12CH 1950-1984 CPI, CHILE IFS
CP12EG 1948-1984 CPI, EGYPT IFS
CP121NDO 1957-1984 CPI, INDONESIA IFS
CP12NZ 1954-1984 CPI, NEW ZEALAND IFS
CP12PA 1953-1984 CPI, PAKISTAN IFS
CP12SA 1954-1984 CPI, SAUDI ARABIA IFS
CP12SO 1948-1984 CPI, SOUTH AFRICA IFS
CP12SYR 1950-1984 CPI, SYRIA IFS
CP12TU 1953-1984 CPI, TURKEY IFS
CP13 1957-1985 STOCK SHARE WEIGHTED CPI
CP13 = CPIUK * TSUK/TWS + CPIIND TSIND/TWS + CPISL * TSSL/TWS
CPIIND 1952-1985 CPI, INDIA IFS
CPIKEN 1952-1984 CPI, KENYA
CPIMAL 1968-1984 CPI, MALAWI IFS
CPISL 1952-1985 CPI, SRI LANKA IFS
CPITIN 1957-1984 CPI, INDIA IFS
DM82 1960-1985 DUMMY VARIABLE FOR 1982
DM83 1960-1985 DUMMY VARIABLE FOR 1983
DM84 1960-85 DUMMY VARIABLE FOR 1984
DUMINDO 1961-1985 INDONESIA VOLCANIC ERUPTION DUMMY
DX 1957-1985 POST 1968 YIELD GROWTH DUMMY VARIABLE FOR INDIA
EDM74 1955-1985 DUMMY VARIABLE FOR 1974
EDM74 = EXP(DM74)
EDM78 1960-1985 DUMMY VARIABLE FOR 1978
EDM78 = EXP(DM78)
EDM80 1960-1985 DUMMY VARIABLE FOR 1980
EDM80 = EXP(DM80)
ET2 1953-1984 EXPONENTIAL TREND VARIABLE 1953 = 1.0
ET2 = EXP(T2)
ETRCH 1961-1984 CHILE EXPONENTIAL TIME TREND
ETRCH = EXP(TRCH)
EX2ARG 1957-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, ARGENTINA IlFs
EX2BAN 1971-1984 NATIONAL CURRENCY/US S EXCHANGE RATE, BANGLADESH IFS
EX2CH 1957-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, CHILE IFS
EX2EG 1948-1983 NATIONAL CURRENCY/US $ EXCHANGE RATE, EGYPT IFS
EX21NDO 1967-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, INDONESIA IFS
EX2NZ 1954-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, NEW ZEALAND IFS
EX2PA 1953-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, PAKISTAN IFS
EX2SA 1954-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, SAUDI ARABIA IFS
EX2SO 1948-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, SOUTH AFRICA IFS
EX2SYR 1950-1984 NATIONAL CURRENCY/US S EXCHANGE RATE, SYRIA IFS
EX2TU 1953-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, TURKEY IFS
EXAU 1953-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, AUSTRALIA IFS
EXCA 1953-'984 NATI'NAL C'URRENCY/UIS iEXCHANGE RATE, CANADA IFS
EXRIND 1952-1985 NATIONAL CURRENCY/US $ EXCHANGE RATE, INDIA IFS
EXRKEN 1952-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, KENYA IFS
EXRMAL 1952-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, MALAWI
EXRMK 1960-1983 RELATIVE EXCHANGE RATE OF MALAWI TO KENYA IFS
EXRMK = EXRMAL/EXRKEN
EXRSRI 1952-1985 NATIONAL CURRENCY/US $ EXCHANGE RATE, SRI LANKA IFS
EXUK 1953-1985 NATIONAL CURRENCY/US S EXCHANGE RATE, UK IFS
EXW3 1957-1985 WEIGHTED AVERAGE EXCHANGE RATE, WEIGHTED BY SHARES OF STOCKS IN
UK, SRI LANKA, INDIA IFS
EXW3 = (TSUK * EXUKX +TSSL * EXRSRIX + TSIND * EXRINDX)/TWS
EXWG 1953-1984 NATIONAL CURRENCY/US $ EXCHANGE RATE, WEST GERMANY IFS
GDP2EG 1948-1983 REAL GDP - 1980 PRICES, EGYPT
GDP2PA 1953-1984 REAL GDP - 1980 PRICES, PAKISTAN IFS
GDP2SA 1954-1984 REAL GDP - 1980 PRICES, SAUDI ARABIA IFS
GDP2SYR 1956-1983 REAL GDP - 1980 PRICES, SYRIA IFS
GDPC2EG 1948-1983 PER CAP REAL GDP - 1980 PRICES, EGYPT IFS
GDPC2EG = GDP2EG/POP2EG
GDPC2PA 1953-1984 PER CAP REAL GDP - 1980 PRICES, PAKISTAN IFS
GDPC2PA = GDP2PA/POP2PA
GDPC2PA2 1972-1984 TWO PERIOD MOVING AVERAGE PER CAP REAL GDP, PAKISTAN
GDPC2PA2 = (GDPC2PA + GDPC2PA(-1)/2
GDPC2SA 1954-1984 PER CAP REAL GDP - 1980 PRICES, SAUDI ARABIA IFS
GDPC2SA = GDP2SA/POP2SA
GDPC2SA2 1955-1984 TWO PERIOD MOVING AVERAGE PER CAP REAL GDP, SAUDi ARABIA
GDPC2SA2 = (GDPC2SA + GDPC2SA(-1))/2
(Glossary continues on the following page. )
GLOSSARY (continued)
GDPC2SL 1950-1984 PER CAP REAL GDP - 1980 PRICES, SRI LANKA IFS
GDPC2SL = GDP2SL/POP2SL
GDPC2SYR 1956-1983 PER CAP REAL GDP - 1980 PRICES, SYRIA IFS
GDPC2SYR = GDP2SYR/POP2SYR
GDPC2SYR2 1957-1983 TWO PERIOD MOVING AVERAGE PER CAP REAL GDP, SYRIA
GDPC2SYR2 = (GDPC2SYR + GDPC2SYR(-1))/2
GDPCIN 1960-1984 PER CAP REAL GDP - 1980 PRICES, INDIA IFS
GDPCIN = GDPIN/POPIN
GDPCINL 1960-1984 LOG OF PER CAP GDP, INDIA
GDPCINL = LOG(GDPCIN)
GDPCPAK 1956-1984 PER CAP REAL GDP - 1980 PRICES, PAKISTAN IFS
GDPCPAK = GDPPAK/POPPAK
GDPIN 1960-1984 REAL GDP IN INDIA IFS
HADJ 1972-1983 CORRECTION FACTOR FOR THE CALCULATED WORLD STOCKS
HADJ = TWS - TWS(-1) - (QW - CN4W)/1000.O
III 1957-1985 PRICE DEFLATOR FOR WORLD TEA PRICE EQUATION
III = LOG(CP13/EXW3)
INM8TRS 1953-1983 TOTAL TREES OF VINTAGE 8 YEARS AND OLDER IN INDIA HA ITC
INNEWTRS 1953-1983 TOTAL NEW TREE AREA IN INDIA HA ITC
INNEWTRS = AIEXT + AIREPL + AIREPM
KEXPPH 1963-1983 PER HECTARE KTDA DEVELOPMENT EXPENDITURE SHILLINGS KTDA
KEXPPH = KTDDER/KSHAREAT(-1)
KEXPPHD 1964-1983 FIRST DIFFERENCE IN KEXPPH
KEXPPHD = KEXPPH - KEXPPH(-1)
KMIOTRS 1963-1983 TOTAL TREES OF VINTAGE 10 YEARS AND OLDER IN KENYA HA KTDA
KSHAREAT 1900-1983 TOTAL SMALLHOLDER AREA IN KENYA HA KTDA
KSHAREAT = KSHAREAT + 2497.5
KSHNP 1963-1983 KENYA SMALLHOLDER NEWPLANT - CALENDER YR HA KTDA
KSHNPRT 1963-1983 RATIO OF NEW PLANTINGS TO SMALLHOLDER AREA
KSHNPRT = KSHNP/KSHAREAT(-1)
KTDDE 1963-1983 KTDA DIRECT EXPENDITURE LESS DEPRECIATION KTDA
KTDDER 1963-1983 KTDDE IN REAL TERMS SHILLINGS KTDA
KTDDER = KTDDE/CPIKEN
LEDM 1965-1983 EXPONENTIAL OF DUMMY VARIABLE FOR 1983 DROUGHT IN SRI LANKA
MUV 1960-1983 MANUFACTURES UNIT VALUE INDEX WORLD BANK WORLD BANK
PC1UK 1962-1983 DEFLATED RETAIL COFFEE PRICE, UK PENCE/KG ICO
PC1UK = RPCUK/CPI1UK
PCWCA 1953-1984 DEFLATED WORLD COFFEE PRICE, CANADA CENTS/KG ICO
PCWCA = PGUT * EXCA/CPIICA
PCWCH 1965-1984 DEFLATED WORLD COFFEE PRICE, CHILE PESO/KG ICO
PCWCH = PGUT * EX2CH/CPI2CH
PCWNZ 1954-1984 DEFLATED WORLD COFFEE PRICE, NEW ZEALAND NZS/KG ICO
PCWNZ = PGUT * EX2NZ /CPI2NZ
PCWORLD 1960-1983 WORLD COFFEE PRICE, DEFLATED BY MUV US$A'T iCv
PCWORLD = PGUT/MUV
PGUT 1950-1984 GUATEMALAN COFFEE PRICE $/MT WORLD BANK
POP2CH 1950-1984 POPULATION, CHILE MILLION IFS
POP2EG 1948-1983 POPULATION, EGYPT MILLION IFS
POP2NZ 1954-1984 POPULATION, NEW ZEALAND MILLION IFS
POP2PA 1953-1984 POPULATION, PAKISTAN MILLION IFS
POP2SA 1954-1984 POPULATION, SAUDI ARABIA MILLION IFS
POP2SL 1950-1984 POPULATION, SRI LANKA MILLION IFS
POP2SO 1948-1984 POPULATION, SOUTH AFRICA MILLION IFS
POP2SYR 1950-1984 POPULATION, SYRIA MILLION IFS
POP2TU 1953-1984 POPULATION, TURKEY MILLION IFS
POPAU 1953-1984 POPULATION, AUSTRALIA MILLION IFS
POPCA 1953-1984 POPULATION, CANADA MILLION IFS
POPIN 1953-1984 POPULATION, INDIA MILLION IFS
POPPAK 1950-1990 POPULATION, PAKISTAN MILLION IFS
POPUK 1953-1984 POPULATION, UK MILLION IFS
POPUS 1953-1984 POPULATION, US MILLION IFS
POPUSR 1950-1990 POPULATION, USSR MILLION IFS
PRCHS 1953-1983 FIRST DIFFERENCE OF LOG OF REAL PRODUCER PRICE OF TEA, SRI LANKA SL RUP/KG ITC
PRCHS = LOG(PRS) - LOG(PRS(-1))
PREI 1953-1983 THREE YEAR MOVING AVERAGE OF PRODUCER PRICE OF TEA IN INDIA, LAGGED THREE YEARS
PREIREV 1956-1983 FIRST MOVING AVERAGE DIFFERENCE OF PRODUCER PRICE OF TEA IN INDIA
PREIREV = PREI - PREI(-l)
PRES 1955-1984 THREE YEAR MOVING AVERAGE OF PRODUCER PRICE OF TEA IN SRI LANKA, LAGGED THREE YEARS
PRESREV 1956-1983 REVISION TO PRICE EXPECTATIONS
PRESREV = PRES - PRES(-1)
PRI 1953-1983 DEFLATED PRODUCER TEA PRICE, INDIA RUP/KG INDIA TEA BOARD
PRI = PRIIND/CPIIND
PRIARGE 1957-1983 DEFLATED WORLD TEA PRICE, ARGENTINA LOCAL CURR/KG WORLD BANK
PRIARGE = WPRICE * EX2ARG/CPI2ARG
PRIBAN 1972-1983 DEFLATED WORLD TEA PRICE, BANGLADESH LOCAL CURR/KG WORLD BANK
PRiBAN = wPRiCE ' EX2BANiCPi2BAN
PRICALC 1952-1984 WEIGHTED PRICE OF ALL TEA -CALCUTTA RS/KG ITC/WB
PRICALC2 1952-1984 PRICE AT CALCUTTA AUCTION WITH CESSES & EXPORT DUTY RS/KG ITC
PRICALC2$ 1952-1984 PRICE AT CALCUTTA AUCTION WITH CESSES & EXPORT DUTY IN US$ US/KG ITC
PRICALC2$ = PRICALC2/EXRIND
PRICALC3 1953-1983 PRICE AT CALCUTTA AUCTION WITHOUT EXPORT DUTY RS/KG ITC
PRICOCH 1953-1983 VALUE WGTD. PRICE OF ALL TEA -COCHIN RS/KG ITC
(Glossary continues on the following page.)
GLOSSARY (continued) I
__R _________ _9_-19 PRICE AT_______ COCH_N AUCTION WITH EXPORT DUTY RS__G ______ _______TC_______
PRICOCH2 1952-1984 PRICE AT COCHIN AUCTION WITH EXPORT DUTY RS/KG ITC
PRICOCH2S 1952-1984 PRICE AT COCHIN AUCTION WITH EXPORT DUTY IN US$ US$/KG ITC
PRICOCH3 1953-1983 PRICE AT COCHIN AUCTION WITHOUT EXPORT DuTA RS/KG ITC
PRICOLO 1952-1984 TEA PRICE AT COLOMBO AUCTION WITH SALES CESS & EXPORT TAX RP/KG ITC & FAO
PRICOLO$ 1952-1984 TEA PRICE AT COLOMBO AUCTION WITH SALES CESS & EXPORT TAX, IN US$ US$/KG ITC & FAO
PRICOLOI 1952-1984 TEA AUCTION PRICE AT COLOMBO, NET OF SALES TAX RS/KG ITC
PRIIND 1953-1983 PRODUCER PRICE OF TEA IN INDIA RS/KG ITC
PRIINDO 1967-1983 REAL TEA PRICE IN INDONESIA
PRIINDO = WPRICE * EX21NDO/CP121NDO
PRIINDOL 1967-1983 LOG OF PRIINDO
PRIINDOL = LOG(PRIINDO)
PRIINDW 1952-1983 WEIGHTED AVERAGE OF CALCUTTA AND COCHIN AUCTION PRICES
PRIINDW=0.33*PRICOCH + 0.67*PRICALC
PRIKTDA 1963-1983 PRODUCER PAYMENT FOR GREEN LEAF - KTDA SHILL/KG KTDA ANN. RPTS
PRIKTDAR 1963-1983 REAL PRODUCER PAYMENT FOR GREEN LEAF - KTDA SHILL/KG KTDA ANN. RPTS
PRIKTDAR = PRIKTDA/CPIKEN
PRIKTDARL 1963-1983 LOG OF PRIKTDAR
PRIKTDARL = LOG(PRIKTDAR)
PRIMALR 1968-1983 REAL TEA PRICE IN MALAWI DERIVED FROM PRIMOMB LOCAL CURR/KG ITC
PRIMALRD 1969-1983 FIRST DIFFERENCE OF PRIMALR
PRIMALRD = PRIMALR/PRIMALR(-1)
PRIMOMB 1958-1984 TEA AUCTION PRICE AT MOMBASA SH/KG ITC
PRIMOMB$ 1958-1984 DEFLATED TEA AUCTION PRICE AT MOMBASA IN US$ SHAG ITC
PRIMOMB$- PRIMOMB/EXRKEN
PRITUR 1953-1983 DEFLATED WORLD TEA PRICE, TURKEY LOCAL CURR/KG WORLD BANK
PRITUR = WPRICE * EX2TU/CP12TU
PRK 1958-1983 DEFLATED AUCTION PRICE AT MOMBASA SHAG ITC
PRK = PRIMOMB/CPIKEN
PRKD 1959-1983 DEFLATED MOMBASA AUCTION PRICE RATIO
PRKD = PRK/PRK(-1)
PRLSL 1958-1983 THREE YEAR MOVING AVERAGE PRODUCER PRICE, LAGGED THREE YEARS, SRI LANKA
PRLSL = 3.0 * PRS/(PRS(-4) + PRS(-5) + PRS(-6))
PRS 1953-1983 DEFLATED TEA PRODUCER PRICE, SRI LANKA
PRS = PRICOLO1/CPISL
PSWCH 1957-1984 ADJUSTED, DEFLATED WORLD SUGAR PRICE, CHILE LOCAL CURR/KG WORLD BANK
PSWCH = WPRSU * EX2CH/CPI2CH
PSWEG 1950-1983 DEFLATED WORLD SUGAR PRICE, EGYPT LOCAL CURR/KG WORLD BANK
PSWEG = WPRSU * EX2EG/CPI2EG
PSWPA 1953-1984 DEFLATED WORLD SUGAR PRICE, PAKISTAN WORLD BANK
PSWPA = WPRSU * EX2PA/CP12PA
,SW-A21x^-9A 2 VP MnVIWNI AV DEFLATED WORLD SUGAR PRICE, PAKISTAN WORLD BANK
PSWPA2 = (PSWPA + PSWPA(-1))/2
PSWSA 1954-1984 DEFLATED WORLD SUGAR PRICE, SAUDI ARABIA WORLD BANK
PSWSA = WPRSU * EX2SA/CP12SA
PSWSA2 1968-1984 2 YR MOVING AV DEFLATED WORLD SUGAR PRICE, SAUDI ARABIA WORLD BANK
PSWSA2 = (PSWSA + PSWSA(-I))/2
PSWSYR 1950-1984 DEFLATED WORLD SUGAR PRICE, SYRIA WORLD BANK
PSWSYR = WPRSU * EX2SYR /CP12SYR
PSWSYR2 1951-1984 2 YR MOVING AV DEFLATED WORLD SUGAR PRICE, SYRIA WORLD BANK
PSWSYR2 = (PSWSYR + PSWSYR(-1))/2
PTAU 1953-1983 DEFLATED RETAIL TEA PRICE, AUSTRALIA LOCAL CURR/KG NZ OFFICIAL YRBK
PTAU = TPAU/CPI1AU
PTCA 1953-1984 DEFLATED RETAIL TEA PRICE, CANADA LOCAL LOCAL CURR/KG NZ OFFICIAL YRBK
PTCA = TPCA/CPiiCA
PTIN 1957-1983 DEFLATED RETAIL TEA PRICE, INDIA LOCAL CURR/KG INDIA TEA BOARD
PTIN = TPIN/CPITIN
PTIN2 1958-1983 2 YR MOVING AV DEFLATED RETAIL TEA PRICE, INDIA LOCAL CURR/KG ITC
PTIN2 = (PTIN + PTIN(-1))/2.0
PTLCH 1957-1984 DEFLATED LONDON RETAIL TEA PRICE, CHILE LOCAL CURR/KG ITC
PTLCH = TPLOND § EX2CH/CPI2CH
PTLOND 1960-1983 LONDON TEA AUCTION PRICE DEFLATED BY MUV LOCAL CURR/MT ITC/WB
PTLOND = TPLOND/MUV
PTUK 1953-1984 DEFLATED RETAIL TEA PRICE, UK LOCAL CURR/KG NZ OFFICIAL YRBK
PTUK = TPUK/CPI1UK
PTUKCH 1954-1984 DEFLATED UK RETAIL TEA PRICE RATIO
PTUKCH = PTUK/PTUK(-1)
PTUS 1953-1984 DEFLATED RETAIL TEA PRICE, US LOCAL CURR/KG NZ OFFICIAL YRBK
PTUS = TPUS/CPlIUS
PTUS2 1954-1984 2 YR MOVING AV DEFLATED RETAIL TEA PRICE, US LOCAL CURR/KG
PTUS2 = (PTUS + PTUS(-1)) /2
PTWCH 1957-1983 DEFLATED WORLD TEA PRICE, CHILE LOCAL CURR/KG ITC
PTWCH = WPRICE * EX2CH/CPI2CH
PTWNZ 1954-1983 DEFLATED WORLD TEA PRICE, NEW ZEALAND LOCAL CURR/KG ITC
PTWNZ = WPRICE * EX2NZ/CPI2NZ
PTWORLD 1960-1983 WORLD TEA PRICE DEFLATED BY MUV
PTWORLD = WPRICE/MUV
PTWPA 1953-1983 DEFLATED WORLD TEA PRICE, PAKISTAN LOCAL CURR/KG ITC
PTWPA = WPRICE * EX2PA/CP12PA
PTWPA2 1954-1983 2 YR MOVING AV DEFLATED WORLD TEA PRICE, PAKISTAN LOCAL CURR/KG ITC
PTWPA2 = (PTWPA + PTWPA(-1))/2
(Glossary continues on the following page.)
GLOSSARY (continued)
PTWSA 1954-1983 DEFLATED WORLD TEA PRICE, SAUDI ARABIA LOCAL CURR/KG ITC
PTWSA = WPRICE * EX2SA/CPI2SA
0
PTWSA2 1955-1983 2 YR MOVING AV DEFLATED WORLD TEA PRICE, SAUDI ARABIA LOCAL CURR/KG ITC
PTWSA2 = (PTWSA + PTWSA(-1))/2
PTWSO 1953-1983 DEFLATED WORLD TEA PRICE, SOUTH AFRICA LOCAL CURR/KG ITC
PTWSO = WPRICE * EX2SO/CP12SO
QIINDO 1952-1984 TEA PRODUCTION, INDONESIA
QIMALAY 1970-1983 TEA PRODUCTION, MALAYSIA MT ITC
QIROW 1970-1984 TEA PRODUCTION, REST OF WORLD MT ITC
QARGE 1954-1984 TEA PRODUCTION, ARGENTINA MT ITC
QBAN 1972-1984 TEA PRODUCTION, BANGLADESH MT ITC
QBANL 1972-1984 LOG(QBAN)
QCTUR 1965-1984 PER CAPITA TEA PRODUCTION, TURKEY MT/1000 HEAD ITC 6 IFS
QCTUR = QTUR/POP2TU
QIND 1952-1984 TEA PRODUCTION, INDIA MT ITC
QINDO 1952-1984 TEA PRODUCTION, INDONESIA MT ITC
QINDOL 1952-1984 LOG OF TEA PRODUCTION
QIRAN 1970-1984 TEA PRODUCTION, IRAN MT ITC
QKEN 1954-1984 TEA PRODUCTION, KENYA MT ITC
QKES 1963-1983 ESTATE TEA PRODUCTION, KENYA MT KTDA
QKSE 1967-1983 KENYA SMALLHOLDER PROD. ESTIMATED 1000 KG
QKSEL 1967-1983 LOG KENYA SMALLHOLDER PROD. ESTIMATED 1000 KG
QKSH 1971-1983 SMALLHOLDER TEA PRODUCTION, KENYA 1000 KG KTDA
QKSHL 1971-1983 LOG KENYA SMALLHOLDER PRODUCTION KG
QMAL 1954-1984 TEA PRODUCTION, MALAWI MT
QOAS 1961-1984 TEA PRODUCTION, OTHER ASIA MT ITC
QPNG 1970-1984 TEA PRODUCTION, PNG MT ITC
QROAFRI 1970-1984 TEA PRODUCTION, REST OF AFRICA (AFRICA LESS KENYA,MALAWI,UGANDA) MT ITC
QROAFRIL 1970-1984 LOG OF REST OF AFRICA PRODUCTION MT ITC
QSAREST 1970-1984 TEA PRODUCTION, SOUTH AMERICA LESS ARGENTINA MT ITC
QSL 1954-1984 TEA PRODUCTION, SRI LANKA MT ITC
QTAFR 1970-1984 TEA PRODUCTION, AFRICA MT ITC
QTSAM 1970-1984 TEA PRODUCTION, SOUTH AMERICA MT ITC
QTUR 1961-1984 TEA PRODUCTION, TURKEY MT ITC
QUG 1952-1984 TEA PRODUCTION, UGANDA MT ITC
QUSSR 1955-1984 TEA PRODUCTION, USSR MT ITC
QVIET 1970-1984 TEA PRODUCTION, VIET NAM MT ITC
QW 1972-1984 TEA PRODUCTION, WORLD MT ITC
QW = QIND + QSL + QKEN + QINDO + QBAN + QOAS + QMAL + QTUR+ QUSSR;
+ QROAFRI + QARGE + QSAREST + QIRAN + QUG + XBTCHT
RATPC 1963-1983 RATIO OF PRODUCER PRICE TO COST OF PRODUCTION, SRI LANKA
RAIPC = R'HS/WP L
RPCUK 1962-1984 RETAIL COFFEE PRICE, UK PENCE/KG ICO
RSSL 1966-1984 REPL SUBSIDY PAID -SL '68 '69 ESTIMATES SLRS, MILL CEN. BANK OF SRI LANKA
RSSLREAL 1966-1983 RSSL DEFLATED BY CPI MILL. SL RP
RSSLREALCH 1967-1983 RSSLREALCH = RSSLREAL - RSSLREAL(-1)
SQTR61 1961-1984 SQUARE-ROOT OF TIME TREND
SQTR61 = SQRT(TR61)
SRINEWP 1956-1983 SRI LANKA- NEW PLANTINGS HA ITC
SRIREPL 1956-1983 SRI LANKA REPLANTINGS HA ITC
SRIUP 1956-1983 SRI LANKA - UPROOTINGS HA ITC
SRIUPMA 1958-1983 M A OF SRIUP(-1) AND SRIUP(-2)
SRM8TRS 1954-1983 SRI LANKA - AREA OF TREES MORE THAN 8 YEARS OLD
SRNEWTRS 1956-1983 TOTAL-NEW PLANTINGS IN SRI LANKA HA ITC
SRNEWTRS = SRINEWP + SRIREPL
STXSL 1952-1984 TEA SALES TAX IN SRI LANKA RPS/KG
STXSL = PRICOL02 - PRICOLOI
T2 1953-1984 TREND VARIABLE BEGINNING YEAR=1953
TPAU 1953-1983 RETAIL TEA PRICE, AUSTRALIA CENTS/KG NZ OFFICIAL YRBK
TPAU$ 1953-1983 RETAIL TEA PRICE, AUSTRALIA IN US$ US$/KG ITC
TPCA 1953-1984 RETAIL TEA PRICE, CANADA CENTS/KG ITC
TPIN 1953-1983 RETAIL TEA PRICE, INDIA RUPEES/KG INDIA TEA BOARD
TPLOND 1953-1984 LONDON AUCTION PRICE IN US $
TPLONDCA 1953-1984 LONDON AUCTION PRICE, CANADA LOCAL CURR/KG ITC
TPLONDCA = TPLOND * EXCA
TPLONDDF 1960-1983 LONDON AUCTION PRICE, DEFLATED BY MUV US $/KG WORLD BANK
TPLONDDF = TPLOND/MUV
TPLONDUK 1953-1984 LONDON AUCTION PRICE, UK LOCAL CURR/KG ITC
TPLONDUK = TPLOND * EXUK
TPRODCI 1957-1983 PRODUCTION CAPACITY IN INDIA
TPRODCI = 1.1942 * (1.0 * TPRODI + 283500.0) * EXP(0.00994 *;
DX * TXIN)
TPRODCICH 1958-1983 TPRODCICH = TPRODCI - TPRODCI(-1)
TPROuI 1l JJ-O l 98n3uf NIA NEW L AINT 'G PR ODU.CT'N CA PA C TY HECTARES !TC
TPRODSL 1956-1983 SRI LANKA NEW PLANTING PRODUCTION CAPACITY KG
TPUK 1953-1983 TEA RETAIL PRiCE IN UK PENCE /KG NZ OFFICIAL YRBK
TPUS 1953-1984 TEA RETAIL PRICE IN US CENTS/KG ITC
TR51 1951-1984 LINEAR TIME TREND - START 1951
TR51L 1951-1984 LOG OF TR51
TR61 1961-1984 LINEAR TIME TREND - START 1961
TR65 1965-1983 LINEAR TIME TREND - START 1965
(Glossary continues on the following page.)
GLOSSARY (continued)
-------)-------------------------________________
TRARA 1965-1985 LINEAR TIME TREND FOR OTHER ARAB COUNTRIES-START 1851
TRCH 1961-1984 TIME TREND CHINA- START 1961
TRINDO 1961-1984 TIME TREND INDONESIA - START 1961
TSIND 1957-1983 TEA STOCKS IN INDIA END YR. lOOO MT FAO
TSSL 1957-1983 TEA STOCKS IN YR. SRI LANKA END YEAR 1000 MT FAO
TSUK 1953-1983 TEA STOCKS IN UK END YR. 1000 MT ITC
TWS 1957-1983 TOTAL WORLD TEA STOCK MT ITC/FAO
TWS = TSUK + TSSL + TSIND
TWSL 1957-1983 LOG TOTAL WORLD TEA STOCK
TWSLHAT3 1964-1983 ESTIMATES OF TOTAL WORLD TEA STOCKS MT
TXIN 1957-1984 TIME TREND FOR INDIA SUPPLY EQUATION
TXSL 1958-1984 TIME TREND FOR SRI LANKA SUPPLY EQUATION
TYT 1957-1983 LOG OF ADJUSTED WORLD COFFEE PRICE
TYT = LOG(PGUT * EXW3/CP13)
WPRICE 1952-1984 WORLD PRICE INCLUSIVE OF EXPORT TAXES & CESSES USS/KG
WPRICEDF 1960-1983 WORLD PRICE INCLUSIVE OF EXPORT TAXES & CESSES DEFLATED BY MUV US$/KG
WPRICEDF = WPRICE/MUV
WPRICEDFL 1960-1983 LOG DEFLATED WORLD PRICE INCLUSIVE OF EXPORT TAXES & CESSES
WPRICEDFL = LOG(WPRICEDF)
WPRICEDFLD 1961-1983 CHANGE IN LOG DEFLATED WORLD PRICE AS GIVEN ABOVE
WPRICEDFLD = WPRICEDFL - WPRICEDFL(-1)
WPRICEDL 1953-1983 LOG RATIO OF WORLD PRICE INCLUSIVE OF EXPORT TAXES & CESSES
WPRICEDL = LOG(WPRICE/WPRICE(-1))
WPRICEL 1953-1983 LOG OF WORLD PRICE INCLUSIVE OF EXPORT TAXES 6 CESSES US$/KG
WPRICEL = LOG(WPRICE)
WPRSU 1950-1984 WORLD SUGAR PRICE CENTS/KG WORLD BANK
WPRSUDF 1960-1983 WORLD SUGAR PRICE DEFLATED BY MUV CENTS/KG WORLD BANK
WPRSUDF = WPRSU/MUV
XBTCHT 1972-1984 EXPORT OF BLACK TEA FROM CHINA & TAIWAN MT ITC
XDCSL 1952-1984 CESSES & EXPORT DUTY IN SRI LANKA RPS/KG ITC
XTCALC 1953-1983 EXPORT TAX IN CALCUTTA RPS/KG FAO
XTCOCH 1953-1983 EXPORT TAX IN COCHIN RPS/KG FAO
YLDKES 1972-1983 KENYA ESTATES TEA PRODUCTION YIELDS MT/HA ITC
YLDKES = QKES/ZKEAW
YLDMAL 1965-1983 MALAWI TEA PRODUCTION YIELDS MT/HA ITC
YLDMAL = QMAL/ZMALAW
ZKEAW 1972-1983 KENYA ESTATE TOTAL AREA EQUAVALENT MATURITY AREA HA KTDA/WB
ZKEAW .12 * ZKEA5 + .76 * ZKE51 .+ ZKEA1O
ZKEAWL 1972-1983 ZKEAWL = LOG(ZKEAW)
ZMALAW 1965-1983 AGE WEIGHTED TEA AREA IN MALAWI
ZMALAW .12 * ZM5 + .76 * ZM510 + 2M10 HA ITC
ZMALAWL 1965-1983 ZMALAWL = LOG(ZMALAW)
…-- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -_ - - - - - - - - - - - -_ _ - - - - - - - - - - - - - - - - - - - - -
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Adams, G.F., and J.R. Behrman (1976). Econometric Models of the World
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Etherington, D.M. (1973). Smallholder Tea Production in Kenya: An Econometric
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Goradia, P. (1977). "The Horizons of Growth", Proceedings of National Seminar
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Lamb, G., and L. Muller (1982). Control, Accountability and Incentives in a
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! c.2
Ž Akiyama, T. (Takamasa),
1944-
A new global tea model :
specificat,onl..-
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