f sS"i /7S7 3 •

A Dynamic Simulation Model of theWorld Jute Economy SWP-391

World Bank Staff Working Paper No. 391

May 1980

Prepared by: Jock Anderson, Development Research CenterCharles Blitzer, Development Research CenterTom Cauchois, Development Research CenterEnzo Grilli, Economic Analysis-and ProjectionsDepartmeiitDevelopment Policy Staff

Copyright 0 1980The World Bank1818 H Street, N.W.Washington, D.C. 20433, U.S.A.

The views and interpretations in this document are those of theauthors and should not be attributed to the World Bank, to itsaffiliated organizations, or to any individual acting in their behalf.

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The views and interpretations in this document are those of the authorsand should not be attributed to the World Bank, to its affiliated organi-zations, or to any individuals acting in their behalf.

WORLD BANlK

Staff Working Paper No. 391

May 1980

A DYNAMIC SIMULATION MODEL OF THE

WORLD JUTE ECONOMY

Jute and jute products have been steadily losing markets to syn-thetic substitutes since the 1960s. One of the causes of this loss in mar-ket shares has traditionally been identified in the supply instability ofraw jute; large market price fluctuations have stimulated the search fordomestically produced substitutes for jute in consuming countries. Pricestabilization for jute has been intensely discussed in international fora,within the framework of the UNCTAD Integrated Program for Commodities.This paper investigates both the sources of jute supply instability andthe potential impact of an internationally managed buffer stock to stabi-lize market prices.

The analysis is carried out utilizing a rather simple dynamicmodel of the markets for raw jute and jute goods. The model combineseconometric estimates of the relevant parameters with a priori informa-tion derived from industry studies. It integrates the behavior of jutefarmers in the principal jute growing countries with that of jute goodsmanufacturers and consumers using a series of region-specific demand andsupply functions. Expected price variance is an explicit factor indetermining jute acreage.

This simple modelling approach should be quite useful in study-ing commodity price stabilization. By focusing on the dynamics of supplyand demand, the adjustment to the stabilization policy itself can beexamined. By reducing the number of parameters to the minimum, it ispossible to identify the important ones and, therefore, those which needmore careful estimation. This methodology may also offer considerablepromise for medium-term analyses of other commodity markets.

Prepared by: Jock Anderson, Development Research CenterCharles Blitzer, Development Research CenterTom Cauchois, Development Research CenterEnzo Grilli, Economic Analysis and Projections DepartmentDevelopment Policy Staff

Copyright Q 1980The World Bank1818 H Street, N.W.Washington, D.C. 20433

ACKNOWLEDGMENTS

The authors wish to thank their former colleagues in the Develop-ment Research Center and the Economic Analysis and Projections Departmentof the World Bank who commented on this paper. They also wish to expresstheir appreciation to the participants in the Seminar on Planning Issues onNational Resources, held at the World Bank in December 1977, and in theCommodity Modeling Seminar, held in Aarhus, Denmark, in December 1979, whoreviewed this paper. Finally they wish to acknowledge the usefulness ofthe comments received from three anonymous referees who reviewed the paperbefore its publication in the World Bank Staff Working Paper series.

None of the authors of this paper is any longer employed by theWorld Bank. Mr. Jock Anderson is with the University of New England, Armi-dale, Australia, Mr. Charles Blitzer is with the U. S. International Develop-ment and Cooperation Agency, Mr. Tom Cauchois is at the Massachusetts Insti-tute of Technology, Mr. Enzo Grilli is with the General Confederation ofItalian Industries in Italy.

Introduction

1. One of the key objectives of the New International Economic Order

has been to strengthen the position of developing countries in markets for

traded primary products. In international fora these countries, often

speaking as the Group of 77, have demanded increased stability, equitable

prices, and meaningful control over their resources. As the principal in-

ternational mechanism for achieving at least market stability a Common

Fund financing a series of buffer stock programs,was proposed. 1/

2. During drawn-out discussions during the past five years, technical

discussions have been carried out for a core group of commodities, jute

among them. Jute and jute products have been steadily losing markets to

synthetic substitutes since the 1960s and the accepted wisdom is that over

time supply instability has generated,large market price fluctuations,which

have stimulated the search for domestically produced substitutes in the in-

dustrialized countries. 2/ In this paper, we investigate both the sources of this

supply instability and the potential impact of an internationally managed

buffer stock to stabilize prices.

3. In particular, alternative buffer stocking rules are examined in

terms of their impact on:

- export revenue;

- direct costs and benefits of the buffer stock operation;

- required maximum funding to insure acceptable risk-targets.

1/ UNCTAD, "An Integrated Program for Commodities: The Role of InternationalBuffer Stocks". (Report by the Secretary General of UNCTAD), Docs.TD/B/C.7/166 Supp. 7 and TD/B/C.7/Add. 7, December 1974.

2/ See Grilli and Morrison [1974] and IBRD World Jute Economy Report [1973].

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In addition, we make some comparisons of alternative policies which might

also achieve similar stability objectives. These include:

- compensatory financing;

- tariffs and taxation;

- acreage limitations.

4. The analysis is carried out utilizing a rather simple dynamic model

of the markets for raw jute and jute goods. It is our view that any meaning-

ful stabilization policy would affect the whole dynamic behavior of jute mar-

kets by altering expectations. With reduced risks, supply can be expected

to expand more than demand leading to increased price pressure, and ulti-

mately to further instability. Given the various ways that farmers, manu-

facturers, and customers react, some of the objectives of stabilization may

not be feasible except at unreasonable cost.

5. The model has expected price variance as an explicit factor determin-

ing jute acreage. In addition, the specific functional forms were chosen to

allow maximum flexibility in terms of dynamic adjustments. The model inter-

grates the behavior of jute farmers in the principal jute-growing countries

with that of jute goods manufacturers and consumers using a series of region-

specific demand and supply functions. The model has been kept as simple as

possible for two reasons. First, the underlying data base is not extensive,

especially regarding the markets for jute goods. Since, data did not allow

econometric estimation of behavioral parameters in that set of markets, in-

formal estimates were required. These were derived from industry-wide studies

previously conducted at the World Bank and referred to previously. Only by

keeping the model simple could this be meaningfully done. Second, with a

simple model, having specific theoretical, behavioral properties-it is re-

latively easy to understand what is important in determining the characteristics

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of simulated policy scenarios. It would be rash to claim that any model

could predict with great accuracy the'impact on the jute (or other) mar-

kets of a buffer stock operations. 'Nonetheless models can and should, be

able to isolate which factors will play the important roles and how they

inter-relate.

6. The paper is divided into four sections. We begin with a brief

review of the jute markets, highlighting those "stylized" facts which are

later included in the model. Section I describes the supply response of

jute farmers; econometric evidence is presented showing the importance of

risk in the planting decision. The rest of the model is outlined in

Section II. In SectionIII results from the numerical simulations are

reviewed. Our conclusions are in SectionIV.

I. WORLD JUTE ECONOMY -- A REVIEW

7. Jute fiber is produced in Asia, South America and Africa. The

plant -- which grows annually -- requires high temperature, deep soil, and

rainfall distributed in such a way as to assure adequate moisture to the

young plant and more abundant moisture during its growth. Commercial pro-

duction of jute fiber is concentrated in the Asian Sub-Continent; India

and Bangladesh account for over 80% of the volume of world trade. Thailand

is the third largest producer, 1/ followed by Burma, Nepal and Brazil.

8. Jute is mostly used to manufacture cloth (hessian or burlap) for

bags and sacks (including woolpacks and cotton-bale covers), backing for

tufted carpets and industrial wrapping material. Jute yarn is also used in

woven carpet backing and cordage. Sacks and bags are by far the most

1/ Thailand produces mostly a jute-like fiber, kenaf, which is coarser thanjute and it is only suitable in sack-making. Substantial amounts ofthis fiber are also produced in India where it is known as mesta.

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important end-use for jute in all major consuming areas. In industrialized

countries, however, backing for both tufted and woven carpets is a very

important market for jute.

9. Practically all the jute fiber grown in India is processed

locally and about 60% of it is exported in manufactured form. Bangladesh,

on the contrary, processes only about 45% of its annual jute production.

The remainder is exported as raw fiber. Until very recently, Thailand

exported production in fiber form well over 50% of its production. Burma

and Nepal are almost exclusively raw jute fiber exporters. Bangladesh

dominates the jute fiber export market: its exports account for 85% of

total world export of jute and for 60% of total world exports of jute and

jute-like fibers. Despite the growth of Thai kenaf exports during the mid-

and late 1960s, the traditional quasi-monopoly position enjoyed by Bangla-

desh in world jute export markets has not been seriously challenged. Burma

and Nepal are too small as exporters to influence the raw jute market to

any considerable extent. The world market for jute manufactures -- tra-

ditionally dominated by India -- is now almost evenly shared by India and

Bangladesh. Thailand only very recently became a sizeable exporter of jute

manufactures.

10. Industrialized countries account for about 30% total world jute

consumption, developing countries for about 40% and centrally planned

economies for the remainder. However, while the latter are largely self-

sufficient in jute, 1/ industrialized countries do not have any indigenous

supply and are, therefore, the main importers of raw jute (about 60% of

1/ The centrally planned economies of Europe import some raw jute as wellas finished products. China is totally self-sufficient.

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world total) and manufactures (55% of world total). Among developing coun-

tries, India is the largest single consumer of jute (over 60% of the total).

Among the industrialized countries, both Western Europe and Japan have a

sizeable jute industry that relies on imports from Bangladesh and Thailand.

The US and Canada, on the contrary, have practically no domestic processing

industry and rely almost exclusively on imports of finished products from

India and Bangladesh. Imports of raw jute are duty free in all industria-

lized countries. Imports of manufactures are duty free in the US, but sub-

ject to tariff and some quantitative restrictions in Japan, Western Europe

and Oceania.

11. The position of jute as the dominant fiber input for basic cloths

and cordage products began to be challenged in the mid-1960s by textile poly-

olefins. Polypropylene, a synthetic resin obtained by the polymerisation of

propylene, soon emerged as the most important substitute for jute. It had

very desirable technical properties -- high tensility stiffness, impact

resistance, and low weight, as well as low production costs, since its basic

raw material -- propylene -- was a by-product of oil refineries. Polypropy-

lene could easily be extruded into tapes and filaments from which suitable

cloth for bags and carpet backing was made. Economies of scale and tech-

nological improvements progressively reduced polypropylene resin costs (and

prices) in the 1960s. The polypropylene cloth industry expanded rapidly in

Western Europe, Japan and the US and penetrated very successfully almost all

the traditional jute markets (bags, sacks, carpet backing and cordage).

12. Polypropylene resin is now almost a perfect substitute for jute

fiber in most uses. Its supply is abundant in all industrialized countries

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and its processing technology is well established and readily available. Jute

prices, relative to those of polypropylene resin are the most important

factor affecting the substitution process between these two textile materials.

Jute (and for that matter polypropylene) demand, however, is for the most

part derived demand. Substitution, in response to changes in relative prices,

takes place slowly. Technical constraints -- as well as price expectations --

slow down the substitution process. Demand for jute as an input in textile

manufacturing is, therefore, price inelastic in the short term. In the

longer run, as capital stock is replaced, substitution takes place quite

easily and jute demand is very sensitive to relative price changes. 1/

13. In developing countries, the demand picture is more varied. In

the main jute producing countries -- India, Bangladesh and Thailand --

synthetic substitution is virtually non-existent. In Latin America, other

Asian countries and to a lesser extent in Africa, polypropylene resin is

available, but it is often imported and subject to import duties and other

restrictions. The processing technology, moreover, is less accessible and

the structure of demand for end-products (absolute dominance of demand for

sacks and bags) is such that price elasticities -- in both the short and

the long run -- are much lower than in industrialized countries.

14. Raw jute supply -- which is geographically very concentrated --

depends to a large extent on jute prices and on those of alternative crops.

In both Bangladesh and West Bengal jute competes for land with rice and the

annual planting decisions is strongly influenced by expected (relative)

1/ There are very few recent econometric studies of jute and even fewereconometric estimates of price elasticities of demand, given the noveltyof the substitution process and the enormous difficulties that are en-countered by researchers in getting the necessary consumption and pricedata by end-use. The little econometric evidence that is available --particularly for the US market -- indicates that jute demand is quitesensitive to relative price changes. See Grilli and Morrison [1974].

- 7 -

prices. In the Northeast of Thailand, where most of the fiber is grown,

kenaf competes for land with cassava, sugarcane, groundnuts and maize.

Cassava has become in recent years the single most important substitute

crop in kenaf growing areas. Jute yields are notoriously subject to weather-

induced random fluctuations.

15. Jute and jute goods prices fluctuate considerably from year to

year. 1/ The accepted wisdom is that supply instability has generated

over time large market price fluctuations that have stimulated the search

for and development of substitutes for jute in consuming countries. This

diagnosis has added impetus to the request made by jute producing countries

for market price stabilization measures. While the two countries most de-

pendent on jute exports (Bangladesh and India) did in the past call for

price stabilization on familiar macro.economic grounds, in recent times

they have stressed the importance of a greater degree of price stability for

jute and jute products as a disincentive to further synthetic substitution.

II. SUPPLY OF RAW JUTE

16. There have been many studies addressed to the question of the re-

sponsiveness of jute (and kenaf) producers to changing prices. Fortunately,

most of these studies have been reviewed recently by Askari and Cummings

[1976]. Producers have been shown (in the fraipework of the Nerlove adap-

tive expections model) to be strongly, but generally inelastically, re-

sponsive to changing observed, real or relative prices of jute.

1/ Raw jute accounts for at least 50% of the production costs of hessianand carpet bgcking cloth whose prices are directly affected by varia-tions in the costs of the fiber. This applies to both jute producingand jute importing countries. Jute mills in India, Bangladesh and Thai-land compete with overseas mills for their fiber supply. See Grilli andMorrison [1974]

- 8 -

17. The available results are generally deficient in treating the

producer's responsiveness to changing risk -- a consideration intuitively

important in an exploration of stabilization. Exceptions are the studies

of Behrman [1968] on Thai kenaf and the preliminary investigation of

Sengupta and Sen [19691 on Indian jute. These studies employed, respec-

tively, arbitrary moving-average standard deviation variables, and a port-

folio selection model to grapple with the issue of farmers' adjustments

to risk.

18. The recent work of Just [1974, 1975, 1977] has provided a fresh

general methodology for dealing with supply behavior under risk. His

methodology seems the one available that is pertinent to the present study.

Accordingly, we have attempted to glean a consistent set of data bearing

on jute and kenaf supply and have applied the Just methods to these data.

19. Essentially, the Just approach is to add second moments of ex-

planatory variables (such as prices) to the first moments that feature in

the traditional Nerlove model of adaptive expectations (i.e. geometrically

distributed lag functions). The first moment, or subjective expected value,

E, in this model depends on a stable adjustment parameter a and can be viewed

as a geometrically weighted sum of past observations on a random variable

X as in

(la) Et(a) k aC (-a)xt--t ~ kOt-1-k

or as an exponentially weighted moving average, as in

(lb) Et(a)= a X + (1 - a) E (a)

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20. The analogous second moment, or subjective variance, V, depends

not only on a (and E(a)) but also on a further stable adjustment parameter

$ for the geometrical weighting of past "observations" on variance

(squared deviations from means) as

co

(2a) Vt(a,a) = (1 - k) k(Xt-lk Et-l-k)2

or equivalently,

(2b) V (a,c) = E(X _ 2 + (1 2 (a,O

Covariances can be formulated in an identical way. Such first and second

moments can then be used as explanatory variables in estimating equations

which relate a decision variable to previous observed values of influential

variables.

21. For example, suppose for the moment that planned jute output

is represented by acreage planted, Ht, and that producers are influenced in

this decision only by the mean and variance of "jute" (kenaf in Thailand) price

AP. Then one simple model for a jute supply function is

(3a) H = a + a1 Ejt (a) + a2 Vjt ( ) + ut

where E and V. represent observed means and variances ofjt Jt

the price of jute and ut is a well-behaved disturbance term. This par-

ticular equation is linear in the parameters ai and so, for given a

and a, can be estimated by ordinary least squares (OLS) regression analysis.

Indeed, one estimation method is to search the feasible space, 0 < a

6< 1, for those values of the adjustment parameters that give the minimum

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sum of squares in the OLS equation. If ut is assumed to be normally dis-

tributed, this is a maximum likelihood search procedure. More generally,

non-linear least squares regression analysis can be used to estimate

a 1, and the ai simultaneously.

22. However, there is another difficulty to be confronted in empirical

work. Sample data will only be available for a finite (probably all too

brief) series of observations whereas the above equations implicitly in-

volve infinite series. Just has investigated several methods of dealing

with this difficulty. We review only that one we used empirically

to derive estimates for the base-year means and variances. There is no

way to avoid assigning some years as "prior" and later ones as "for regres-

sion". The prior year observations give the initial estimates of mean, E , as

r N 1-_lNkk ~~~k(4) E (1- (1-) X (+k)

K=O K=O

where the N observations are X 1 X 2 X-(N+1)

used as "priors" on the observed explanatory variables.

23. It is best to estimate the prior variance in the process of estimat-

ing the other parameters and this can be accomplished by adding an additional

variable to, for example, (3a), namely

(3b) Ht =a + a E. (ax) + a V (cx,~) + a (l - t + t(3) t O 1 jt 2 jt 3

where Ejt now depends on Eo , and the additional coefficient may be inter-

preted as embodying the prior variance, since a = V . The estimation is

similar to Dhrymes' [1971, p.98] truncation remainder procedure.

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24. This approach was applied to data from Bangladesh, India and

Thailand 1/ in several variants depending on: the surrogate assumed for

the decision variable (i.e. quantity supplied or area harvested since

neither planned output nor area sown is available); the particular series

of prices; the periods taken as "observation" and "prior to observation";

and the functional specification of the supply equation.

25. The specification finally selected is about the simplest one

consistent with the objectives of this study. It recognized substitution

with the major competing crop, "rice", (rice in Bangladesh and India, sugar-

cane, maize and cassava in Thailand -- designated by the subscript r in

what follows), while restrictively focusing on the price variability of

jute growing. However, preliminary results suggested that variance of

"rice" price and covariance of jute and "rice" prices were unimportant in

determining jute supply. The chosen specification also minimizes problems

associated with the failure to deflate the observed prices to account for

inflation. The dependent variable, is acreage of jute. The key exploratory

variables are the ratio of subjective expected prices (which avoids infla-

tion accounting), and the dimensionless measure of variability that depends

on first and second moments of jute price. Specifically the acreage equation

is : (4) t a0 + a 1 (E jt/Ert) + a 2 Cjt + a3 (1-) + ut

where, for brevity, the adjustment parameters and regional subscripts are

dropped, and C. is the coefficient of variation of jute price, C + (V ) /E21t t t t

1/ Supply of raw jute from the smaller producers is considered as exogenousin this model. Lack of data prevented its explicit modeling.

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26. The econometric estimates are summarized in Table 1. 1/ For all

three countries, the coefficients have the expected signs. Acreage responds

positively to expected price of jute relative to "rice" and negatively to

increased variance in jute prices. In terms of adjustment speeds, the mean

time lag is longer for price variance than for relative expected prices in

each country.

27. The separate modeling of yields leads to a multiplicative risk

specification which, as argued by Hazell and Scandizzo [1975], is a most

natural and appropriate specification for a risky supply function. For un-

known reasons, jute yields early in the observation period exhibit irregular

patterns that seem of dubious relevance to present-day production. The pat-

tern of the most recent 14 years of yields (up to 1974/75) is summarized by

a small positive trend and a disturbance term, N(S), that is normally and

independently distributed with zero mean and standard deviation S. Thus,

defining YEAR as the calendar year of the first half of the crop year minus

1900, we estimated the following yield equations:

(5a) ylt = 1.100 + (2.75) 10 YEAR + N(0.109) (India)

(5b) y2t = 0.779 + (6.67) 10 YEAR + N(0.117) (Bangladesh)

2t ~ ~ ~ ~ ~~-

(5c) y3 = .199 + (.7) 10 YEAR + N(O.0121) (Thailand)

where Yit is yield in tons/hectare of raw jute.

28. In implementing them in the model, the residual variation in the

price response functions was also modeled explicitly by assuming the dis-

turbance term, ut, in (4) to be normally distributed with mean zero and

standard deviation equal to the standard error of the estimate of the

respective equations.

1/ Data on production and prices used in estimating the supply equations forthe three jute exporting countries are given in Tables 6, 7, and 8.

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Table 1 Estimated Parameters of .Jute Acreage Response Equations

INDIA BANGLADESH TIIAILAND

Dependent Variable Acreage Acreage Acreage

Number of Observations

Pre-observation sample size-/ 2 2 4

(years) (1950/51- (1950/51- (1953/54-1951/52) 1951/52) 1956/57)

Observation sample size 23 23 19

(years) (1952/53- (1952/53- (1956/57-1974/75) 1974/75 1975/76)

Estimated Coefficients and(Standard Errors)

Mean adjustment a .793 .372 .708(.104) (.078) (.331)

Variance adjustment .287 .081 .133(.077) (.051) (.113)

Coefficients a 422 612 24250 (107) (243) (1009)

a 1 275 670 411(71) (244) (234)

a2 -992 -2742 -7200(276) (1139) (5552)

*3 -22 -602 -32223 (77) (272) (1211)

Coefficient of determination .72 .72 .74

Standard error of estimate 73 100 511

Durbin-Watson Statistic 2.34 1.79 2.18

Final Values of Variables Used asStarting Values in Simulations

Mean of jute prices 128.7 122.3 3529.5

Mean of competing crop prices 109.3 121.8 2093.4

Ratio of-means 1.178 1.004 1.686

Variance of jute prices 266.2 441.6 453285.0

Coefficient of variation of .1268 .1718 .1908jute prices

1/ Used inl estimating the prior mean.

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III. THE MARKETS FOR JUTE AND JUTE'GOODS

29. Manufacturers of jute goods represent both the demand side of the raw

jute market and the supply side of the goods market. Ideally, we would con-

sider separately each class of jute goods. But, since the source data are

not available at this level of detail, we were forced to aggregate all jute

goods into one. Data was available to differentiate country-specific in-

put ratios, y's defining the quantity input of raw jute per unit of aggregate

jute goods.

30. Since jute goodsmanufacturing has been a competitive industry, it is

reasonable to postulate a supply function relating prices to manufactured

quantities of jute goods. Here, we are concerned with prices of raw jute,

p and jute goods, P. For simplicity, we assume a linear form which treats

the two prices symmetrically. 1/ Thus defining X as output of jute goods

and using the subscripts "i" and "t" to denote regions and time,

(6) Xi O a + a (P (1+t) P^ (1'+^ )

In each region,relative domestic prices of raw jute and jute goods differ

from the "world" price by fixed wedges which can be a combination of taxes

and tariffs. In equations (6), ti and ti represent the implied ad valorem

rate on goods and raw jute respectively. 2/

1/ The linear form used is implied whenever variable costs (other than rawjute) are a quadradic function of total production volume, and the y'sremain constant.

2/ The region breakdown and indexing scheme is:

i = 1 India2 Bangladesh3 Thailand4 North America5 Western Europe6 Japan7 Rest of World (excluding the centrally planned economies)

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31. As with any industry-wide supply function, scale is determined

by implicitly holding some factor of production fixed. In our model, capital

stock is fixed in each region, a convenient simplification based on the ob-

servation that net investment in this industry has been effectively nil for quite

a few years, a trend likely to continue. Since investment decisions are not being

modeled, it is reasonable to assume rapid adjustment of production to prices,

and in (6), no distinction is made between long- and short-run response.

32. Demand for jute goods is also modeled on a regional basis. The

main determinant of demand (given future economic growth rates) is the

relative price of jute goods and their synthetic substitutes. As noted in

Section I, the short-run relative price elasticity is low in comparison with the long-

run elasticity. The central reason is that demand is embedded in installed

capital, only part of which turns over in any one year. Here, we assume

linear demand functions whose short-run position relative to the origin is

determined by past investments and by long-run expectations. That is,

P (1+t )(7) D = A (a+tib

it ~ ~ ~ ~ ~~~i

Ai scale of demand, region i , year tit

Pi = price of synthetics, region i , year tit

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33. In modeling the dynamics of the demand functions, we assume that

in each year there is exogenous growth in the market for fibers in general

(jute and the synthetics), related to expected general economic growth. In ad-

dition, a certain proportion of lute good consumers-is faced'with an investment

decision -- whether to re-invest in jute-using equipment or not. The pro-

portion of these users who opt for jute goods (and thus shift the future

demand schedule) depends on the expected future relative price of jute to

synthetics. For simplicity, we assume the growth rate of the'demand func-

tion to be a linear function of the growth rate in demand for fibers in

general and the expected future price structure. That is,

(8) i,t+l i,t 2 g (i,l i- t

Ai t i pil pi,2

where gi represents the expected growth in total fiber demand in region i.

This functional form makes use of two benchmarks, Pi l and P i2 '

which we have independently estimated for each region. Pi 1 represents an

upper bound on relative prices (between jute goods and synthetic substitutes),

such that demand will shrink if the expected relative price Pit, is above

this benchmark. On the other hand, Pi,2 represents a neutral expected price at

which jute's share of the i'th market would remain constant. We use a

adaptive expectations formulation to represent how these are formulated over

time.' 1/

1/ A similar formulation of dynamic change has been used for a model ofworld oil markets by Blitzer, Meeraus, and Stoutjesdijk [1975].

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e ~ (iX e P (1+t)g9) Pi t = (J-X) pi,tl + Xi i

34. In the numerical simulations, we take the price of synthetics

as exogenous and do not explicitly consider the competitive reaction of

synthetics producers to jute goods stabilization policy. Lack of data

prevented a more sophisticated treatment. However, synthetics are pro-

duced in process industries where costs of production generally determine

price. The price of oil is thus likely to have a more crucial impact on

the price of polypropylene than buffer stock action in jute markets.

35. In closing the model there are two world markets which must be

considered -- those for raw jute and jute goods. Within a reasonable

degree of approximation, buffer stock operations occur in the market for

raw jute only. Thus, equilibrium in the raw jute market implies:

(10) LSt +y X~ + E. = -(10) A t ZL Yi Xit +Ej t = 4_ Qi t where

E. represents the exogenous exports of raw jute to centrallyj,t planned economies and

ASt , stands for stocks purchases of raw jute in year t,

Similarly, equilibrium in markets for jute goods requires that

(11) ZDit+ Eg,t it

Where E represents the exogenous exports of jute goods to centrallyg,t planned economies in year t.

36. Equations (6) - (11) define a complete dynamic system given

values for ASt, and Qi t. The latter are provided by the stochastic supply

functions (4) and (5). That is,

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(12) Qit ~ Yit Hit '

where the bars above the right-hand symbols signify the actual randomly

selected values.

37. Traditionally, stocking behavior has been difficult to

model. In most econometrically estimated commodity models, some function,

often with price as the dependent variable, is estimated on the basis of

goodness-of-fit. This procedure would not work for this study. In the first place,

we are concerned with a 10-15 year horizon, and in that period it is quite un-

likely that any estimated stocking parameters would remain constant. More-

over, past data yields behavioral information on a period without pre-

known outside intervention. Introduction of an international stabilization policy

would almost certainly affect significantly private stocking behavior.

38. There is no entirely satisfactory way out of this dilemma.

The procedure chosen in our simulations is to introduce buffer stocking

rules which generally take the form:

(13) p C p^ <_t - t - t

where the upper and lower bounds are determined by past prices and pre-set policies.

With this formulation, stock changes take place only if one of the inequalities

in (13) is binding. While admittedly this is a mechanical procedure, so are

most internationally negotiated stabilization rules.

IV. SIMULATION RESULTS

39. A base case simulation, covering a fifteen year horizon for a deterministic

situation (no uncertainty in yield or acreage response) was first conducted to assess

at least qualitatively the behavior of the model against existing expectations

- 19 -

concerning the future of the world jute economy. 1/ The results for the

deterministic base case are summarized in Table 2 and details are found

in Tables 9, 10 and 11. 2/ They broadly conform to the current IBRD assess-

ment of the future of jute: total demand for jute goods is likely to grow

at only 1% per year in the medium-term and almost 70% of the increased demand

comes from the producing countries themselves. The long-run average price

for jute fiber ($298/ton in real terms), though roughly competitive with

that of polypropylene resin, still implies a loss in jute's share of the

world fiber market on both technical and non-price economic grounds (e.g.

self-sufficiency and import substitution).

40. Somewhat more surprising is the need for some price stabilization

even in the deterministic simulation. Although the magnitudes involved

are relatively small, it seems clear that, quite apart from stochastic dis-

turbances, the dynamic adjustment process of the market is a source of price

instability (Table 2). This is generally the case with adaptive expecta-

tions. If all expectations were rational, the cause of instability could be

put more exclusively on the stochastic variables.

1/ This requires that equation (13) be modified to reflect existing sta-bilization behavior. Indeed, the inelasticity of supply and demand inthe short-run necessitates that some stock adjustment exist -- and inreality stock trading and holding does occur. Rather than try to esti-mate a complex function, we merely try to replicate the observationthat prices rarely vary more than 15% per year.

2/ The simulation results highlight an interesting aspect of raw jute tradebetween India and Bangladesh: the unrecorded imports of raw jute intoIndia. A comparison between Indian needs of raw jute for the manufac-turing of jute goods -- as shown by Table 9 -- and the domestic supplyof fiber -- shown in Table 10 -- well illustrate the order of magnitudeof the "informal" trade between the two sides of Bengal.

- 20 -

41. The major elements of the picture are changed very little when

the stochastic elements are introduced (see Table 2). Here, dynamic simu-

lations are calculated for 100 sets of random disturbances. Not surpri-

singly, considerably more stocking activity is required to maintain the

bounds on yearly price movements. On average, traders remain net sellers,

obtaining some discounted profits after deducting discounted costs. More-

over, the values of stocks-on-hand implied are numerically consistent with

known initial conditions. On balance, uncertainty leads to slightly higher

average prices, slightly lower production levels, and marginally higher dis-

counted revenue.

42. The mean prices and quantities are calculated by averaging the

average of each of the 100 stochastic simulations. As one welfare measure,

the discounted export revenues are calculated for the three main producers.

Yearly revenues are discounted by 10% annually, noting that inflation is

not otherwise counted.

43. During each dynamic simulation, no checks are made on whether

the buffer stock of jute has been emptied. After the last year's simula-

tion is run, the model calculates using a backward recursion what the year

0 stock would have to have been to avoid ever reducing it below zero. Using

this initial value, the time path of jute stock is calculated. The means

of these across each experiment are shown in Table 2. Starting from fairly

high initial stocks, sales are made to reduce to the mean simulation stock

size, which is the average value of stocks in all periods in each simulation.

44. The model can be used to simulate almost any buffer stock policy.

In the context on any international agreement on jute price stabilization

- 21 -

Table 2 Summary Results of Deterministic and

Stochastic Simulations of the Model

Deterministic Stochastic

Mean world price of raw jute ($/MT) 298 307(24) /1 (38)

Mean world price of jute goods ($/MT) 613 620(27) (40)

Mean raw jute production ('000 MT) 2429 2404

Mean goods production ('000 MT) 2130 2107

Total discounted revenue of ('000 $) 10,533,998 10,862,855exporters

Mean initial buffer stock size ('000 MT) 868 1169(44)

Mean simulation stock size ('000 MT) 191 631(227) (490)

Mean change in stocks ('000 MT) -56 -57(120) (271)

Mean absolute change in stocks ('000 MT) 72 199(111) (193)

/1 Standard deviations in parentheses where applicable or computed.

- 22 -

based on an internationally held and financed buffer stock, the stabiliza-

tion bounds as well as the secular trend around which prices are to be

stabilized become a matter of producer-consumer bargaining and group nego-

tiations. The potential impact of a range of reasonable stabilization rules

that an international agency might try to adopt is examined here. The alter-

natives are:

Case 1 -- Stabilize world price of raw jute within + 10% ofprior year's price;

Case 2 -- Stabilize world price of raw jute within + 7.5% of$300 per ton;

Case 3 -- Stabilize world price of raw jute within + 7.5% of$330 per ton;

Case 4 -- Stabilize world price of raw jute within + 7.5% ofthe trend price (Pt = $315 (1.05 ).

45. Case 1 would simply represent an attempt to reduce the amplitude

of fluctuations by following, in essence, the dynamics of the jute market

without trying to interfere with it. Case 2 can be viewed as an attempt to

stabilize prices around a long-term average of $300 per ton. The rationale

of this alternative rests, on the fact that without continued increases in

crude oil prices driving up polypropylene costs, the long term competitive

price for raw jute is likely to remain fairly constant in real terms. Cases

3 and 4 represent possible examples of stabilization together with higher

prices that are advocated by primary commodity exporting countries and

endorsed -- at least in principle -- by UNCTAD. The first can be viewed as

stabilization around a long term average price 10% above trend, while Case 4

is an attempt to stabilize prices around a moderatly increasing long-term

trend. The results of these stochastic simulations are compared with the

basic case in Table 3.

- 23 -

46. In addition to the previous calculations, in each experiment

the profits of the buffer stocking operation, net of initial financing,

are determined. Letting r denote the discount rate, cl, the transactions

cost of buying or selling jute, and c2 the annual storage costs, discounted

profits = N ^

t= (l+r) (tl)E[_P tASt-C 2AStl-C St]

47. Case 1 illustrates quite clearly the fact that lower price vola-

tility (with respect to the basic case) induces a higher and more stable

average supply of raw jute despite the slightly reduced market prices. Pro-

duction of jute goods is only marginally affected. The export revenue of

India, Bangladesh and Thailand taken together remains virtually unchanged

with respect to the basic case, but is it more stable. The buffer stock

operation is marginally profitable as on average a net seller of jute.

48. The simulation results of Case 2 are similar, because the long-

term average price around which market prices are stabilized is very similar

to the mean price that prevails under Case 1 rules. Production of both raw

jute and jute goods and discounted total export revenue are substantially

the same as in Case 1 and so is buffer profitability; a smaller volume of

transactions reduces gross revenue, but a lower stock carryover reduces

costs by almost the same amount. The only substantial benefit that arises

to the producers is the reduction in revenue instability. The coefficient

of variation drops to 6.2%, as compared to 8.8% in Case 1 and 9.5% in the

Basic Case.

49. Cases 3 and 4 show clearly that stringent limits exist in the

jute market to "strong" UNCTAD-type stabilization rules, i.e. price sta-

bilization accompanied by attempts to raise average prices. The simula-

T.hle 3 S!n,ulated ITMaet of Different St.bilfration Rules

CASE I CASE 2 C'SZ 3 CASZ 4

STABLIZA11ON 7.ST 5 75%StbiSztinu.5il.bltatoationMI: %~~~~~~~~~~~~~~17 Saiiain .7 tbl Stabil izatiStbiiztn arc-nd .n initial

SATISTI ' : 5 CASE around prior around a co..tant arourd a constant i3 .' Arng.t'veer's price ~~~$300/NT $330/MT 5Z A,,i1y

I Wor1d Price Ran Jute (S/1IT) 307 295 299 316

P.^:CES 2 .(38) (28) (33) (26) :65)

3 World Price Jute ($/MT) 620 611 613 626 651

4 Goods (40) (40) (25) (28) (29)

S InitIl. Sirc ('000 MT) 1169 1142 950 535 549

6 (44) (60) (40) (44) ('.3)

7 Mean Si=ulation Size ('000 Mr) 631 572 442 673 1058

3':-F'R 3 (490) (464) (314) (599) (1037)

STOCK 9 Chanrge In Stocks ('000 MT) -57 -47 -30 98 23S:0 (271) (262) (223) (248) (34')

11 Absolute Chan 3 e in ('000 Mr) 19'i 196 164 201 326

12 Stocks (193) (ISO) (154) (176) (253)

13 Tot.1 DIscouoted ('000 MT) 151921 119522 114726 -372260 -997701Er.ol6ts

14 Supply of Rav Jute ('000 UT) 917 940 932 955 99S

i15 (IZ6) (119) (115) (122) (141)

16 Su.plv of Jute Goods ('000-ET) 1079 1095 1090 1063 1051

*ID:D'A I 17 (30) (33) (2.9) t21) (10)

13 -Nrxrt Rcrox,e ('000 S) 302205 309797 306098 311600 33393315 ~~~~~~~~~~~~~~(4i91) (367221) 12) (141)

19 (4991) (36722) (32694) (35456) (39'79)

20 Total Disceunted ('002 S) 3738747 3823675 3776975 3340895 4052051-ot - --

21 S-nny of R.. Jute ('00 i ME) 1118 1121 1137 1180 1232 -

-:2 (187) (188) (14) (196) (233)

23 I S_;'ly of Jute Coods ('000 MT) 475 403 484 462 j4 t9

':;0'"DES : I (26) (25) (21) (17) (9)

25 Ex-,ort Reverue ('000 S) 465018 458998 465062 488820 5237!3

'61 (60799) (58359) (52965) (59167) (87972)

27 Total Di,counted ('000 S) 5670508 5593884 5663808 5952411 6326131E'~pott Raevene

| 2 | SJnply of Rn. Jute ('000 mET) 369 385 384 390 413

29 (92) (94) (91) (92) (93)

30 Supply of Jute Goods ('000 ME) 158 158 158 158 159

T`'), .UND 31 (4) (3) (3) (3) (3)

32 Ex'ort Revenue ('000 5) 118435 120113 120340 127655 140889

33 (27795) (263SI) (24123) (25647) (33149)

|34 E'ota rt Di e-teue (000 5) 1453599 1469023 1471847 1560004 1703197

35 Su-, ?y of Raw Jute ('000 MT) 2404 2446 2453 2525 2640

36 Supplv of Jute Goods ('000 MT) 1712 1741 1732 1683 1659

TOtALS- i37 Total Revenue ('000 S) 885659 888909 891502 928076 995534383 (84218) (78400) (55601) (67050) (120567)

39 Ceefficient of (.095) (.088) (.062) (.072) (.121)j VarIation 1 _ _

40 f Total 5;ocverue ('000eI 10Z955 10886583 10912631 11353310 12031330

Standard dev,atlrns ln parentheses.

: oer all 3 cr-ntr:Cs.

- 25 -

tions indicate that the price-induced higher supply of jute is not readily

absorbed by the market either in raw form or as manufactured goods, unless

synthetic prices were to rise significantly. Price stabilization in Case 3

turns the modest bufferstock agency profit shown under the previously exa-

mined rules into a substantial loss. The agency turns from an average

seller to an average buyer and loses about $370 million over the period,

in addition to required initial capital. The losses to the buffe. stock

authority are almost exactly equal to the increment in the discounted value

of the export revenues of the three major producing countries. Bangladesh

and Thailand appear to gain relatively more than India in terms of export

revenue from this type of price intervention, mostly on account of greater

responsiveness of jute supply to the higher market prices sustained through

the action of the bufferstock authority.

50. Stabilization of raw jute prices,even around a modestly rising

trend (1.5% per annum), implies staggering losses to the buffer-

stock authority -- almost $1 billion over the period due to heavy stock ac-

cumulation. Again the producer gains,in terms of joint export revenue,are

almost exactly equal to the losses that the bufferstock agency would incur.

This type of stabilization with rising prices appears totally un-

feasible in the jute market, unless, of course, consumers accepted should-

ering the cost and considered it a vehicle for transferring resources

to jute producers or if synthetic prices changed dramatically. 1/

51. In addition to bufferstock policy, stochastic simulations were

conducted to assess the effects of alternative production and trade policy

options open to the jute producers and to the international community. These

alternatives are:

1/ These runs were made in 1978; the recent oil price increases indicatethat revisions in polypropylene prices would change these results some-what.

- 26 -

Case 5 -- Raw jute costs to the mills lowered by 20% inBangladesh;

Case 6 -- Positive exchange rate differential of 20% grantedto jute goods in Bangladesh;

Case 7 -- Jute acreage restrictions in Bangladesh;

Case 8 -- Compensatory financing applied to jute exporters.

The results of the stochastic simulations for Cases 5, 6 and 7 are compared

to those of the Basic Case in Table 4. Simulations of Case 8 are summarized

in Table 5.

52. Case 5 implies a subsidy of 20% to Bangladesh jute mills on their

raw jute costs. Such input subsidy stimulates production of jute goods in

Bangladesh and lowers their prices in world markets. Production of jute

goods is reduced substantially in India and marginally in Thailand. On a

world basis, however, supply of jute goods is increased. Higher overall de-

mand for raw jute in producing countries reduces export availability and

raises prices. In terms of export revenue (which increases by about $300

million over 15 years), producers as a group would gain marginally from such

a policy implemented by Bangladesh. India, however, would lose competitive-

ness in the market for jute goods and suffer a loss in export revenue, while

Bangladesh would reap almost all the benefits. The effects on Thailand are

small; her revenue position remains virtually unchanged with respect to the

Basic Case.

53. Case 6, which is a variation of the previous one, can be thought

as an atteuptby Bangladesh to subsidize even further its production of jute

goods, and at the same time to exploit to some extent its near monopoly

Table 4A Sieulated Im>act of Different Productlon and Trade Policies

CASE 5 CASE 6 CASE 7

POLICY ZtASZ Raw Jute Cost to 20. Positive

S'.'.VOARY STATIS CASF Mills LoercEd 207 in Differential Exchange Ac-nage Re,t, 7tic.B-gladesh ~~Rate for Goods in In Bangla.!,ch

W.orld Price Raw Jute (S/.MT) 307 316 320 34

PRICES (38) (38) (38) (SI) I

W.orld Price Jute (S/l-T) 620 610 603 65iGoods (40) (38) (37) (5S'

Supply of Rae Jute (000 MT) 917 929 934 982(126) (129) (187) (14:)

Supply of Jute Coods ('000 MT) 1079 996 950 1039

INDi'A (30) (42) (44) (31)

Export Revenue ('000 $) 302205 273028 257264 3279F4(4891) (42003) (41964) (4829'3)

Total Discounted ('000 5) 3738747 3374713 3179483 4037438

Export Revenue

Supply of Raw Jute ('000 MT) 1118 1152 1168 93'(187) (191) (192) (132)

Supply of Jute Goods ('000 MT) 475 626 710 43c

BLACSLADESH (26) (21) (29) (32)

Export Revenue ('000 5) 465018 520695 549061 425771(60799) (64458) (64753) (45903)

Total Discounted ('000 5) 5670508 6348718 6694344 5524819

Export Revenue

Supply of Raw Jute ('000 Mr) 369 379 384 395(92) (91) (90) (96)

Supply of Jute Goods ('000 Ki) 158 152 149 159

T!:.% Z W7 (4) (3) (3) (4)

Export Revenue ('000 S) 118435 120700 121442 139722(27795) (28812) (28781) (35857)

TotEa t Discounted (000 5) 1453599 1482967 1493533 1698042

Supply of Raw Jute ('000 iE) 2404 2460 2486 2309

Supply of Juta Goods ('000 K?) 1712 1774 1809 1536

Total Revenue ('000 $) 885659 914423 927768 893479TOTALS* (84218) (88055) (87220) (79130)

Coefficfent of (.095) (.096) (.094) (.OM9)Variation

Total DiscountedExport levenue S 0=2.e2 108678559 1137361 109503e2

* Standard deviations in parentheses.

Totals over all 3 countries.

- 28 -

position in raw jute exports.iI The overall impact of such a policy is

not very different from Case 5: total discounted export revenue is raised

only marginally since the fall in jute good prices almost completely off-

sets the increase in raw jute prices. The distribution of revenue gains,

however, is further changed in favor of Bangladesh and against India.

Thailand remains virtually unaffected.

54. Case 7 showsthat jute acreage restrictions by Bangladesh 2/ would

increase the prices of both raw jute and jute goods without raising the

joint revenue of the three major producers with respect to that of the Basic

Case. Bangladesh would shoulder the cost of such a policy, with its ex-

port revenues decreasing as a result of reduced raw jute exports. India

and Thailand, on the other hand, would both gain in terms of revenue since

their production and exports of raw jute and jute goods increase in response

to the higher prices caused by acreage restrictions in Bangladesh.

55. In Case 8, a compensatory financing scheme for jute exporters is

tested. Here the producing countries borrow from some lender (presumably

the IMF) whenever their export revenues fall short of the average revenue

of the Basic Case by more than 10%, and pay back in years when export re-

venue is above average. As shown in Table 5, at a modestly subsidized in-

terest rate of 3% the total discounted cost-of the plan would be very small

requiring total financing of $235 million and average drawings of $85 mil-

lion. Bangladesh would be the likely heaviest and India the likely lightest

user of the facility. At an interest rate of 8% the facility would become

self-financing, but the total financing required would increase to $275 million

1/ A differential exchange rate in favor of goods exports implies a"tariff" on raw jute exports.

2/ Acreage is held to the 1972-75 average.

- .29 -

Table 5 Compensatory Financing for Jute Exporters(Million $)

Rate of Interest

.03 .08

Percent PercentMen Change Men Change

Mean Main in Mean Mean Man in MeanMaximum Maximum Renu

Debt Debt Revenue Debt Debt Revinthewith wt

Respect to Respect toBasic Case Basic Case

INDIA 15.3 61.6 1.2 16.9 68.8 1.2

BANGLADESH 47.9 110.8 -. 12 64.9 133.4 -. 3

THAILAND 22.2 62.6 1.3 27.7 72.5 1.1

TOTAL 85.4 235.0 .5 109.5 274.7 .4

AverageProfits of -4.88 1.13FinanceFacility

BeforeCoefficient of Compensatory .095 .095Variation of SchemeMean ExportRevenue ofJute Exporting Compensatory .063 .061Countries Scheme

- 30 -

and average drawings to $110 million. The impact of the scheme on total

export revenue is on balance positive, but while India and Thailand would

marginally benefit from it, Bangladesh would experience a marginal loss

over the period. There is no straightforward way to evaluate the benefits

from increased revenue stability. If this is considered important, com-

pensatory finance would seem to be the cost effective strategy. 1/

IV. CONCLUDING REMARKS

56. In this study, we sought to investigate international policy to

stabilize jute markets using a simple and easily estimatible dynamic model.

Since the model has not been exhaustively tested, our conclusions are ten-

tative but we feel encouraging and reasonable.

57. Since risk plays a quantitatively significant role in determin-

ing the acreage that farmers plant, it is important in analyzing price sta-

bilization policies to explicitly model this part of farmer reaction. As

expected, price variance carries a negative coefficient in all cases.

While further econometric improvements can be made and alternative expec-

tations behavior tested, the variance factor will likely remain an impor-

tant endogenous variable in modeling commodities like jute. The simple

formulation represented by equation (4) has worked satisfactorily for our

simulations in terms of reproducing past behavior. 2/

1/ We hasten to note that these calculations are based on particularstochastic assumptions. If the underlying probability distributionsare much different from these, the results could be different. Spe-cific numbers reported here should be interpreted cautiously, beingexpected values of random variables.

2/ Details available from authors.

- 31 -

58. Jute is seen as a classic example of markets with low short-

run and high long-run elasticities. The internal dynamics are never in

complete equilibrium, as demonstrated in the deterministic version of

the base case, because the expectations of buyers, manufacturers, and

farmers differ and adjust at different speeds to new prices. 1/ Not

only will a jute buffer stock agency have to deal with agricultural

uncertainties and business cycles, but also with the internal cycles of

the jute markets themselves. This implies that a complete market model

is needed to calculate the costs and benefits of any proposed policy.

59. Cases 1 and 2 clearly indicate that a certain amount of sta-

bilization is feasible and might even be profitable for the buffer stock

agency. However, the net financial gains to the major jute producers is

quite modest, especially when viewed in light of the high level of buffer

stocks which would be required as an up-side risk hedge. It would seem

more difficult, and perhaps impossible, to both stabilize and raise prices

above the comparable synthetic substitute price. The effects of reduced

price risk and higher actual prices can easily lead to much over-production.

Moreover, jute goods would begin to lose their share of the total fibers

market quite rapidly. While the specific numbers would change if redone

at 1979 oil prices, the qualitative conclusion remains.

60. In terms of alternative forms of policy, the choices are in-

numerable. The cases we chose for illustration indicate that there is modest

scope for collective action in reducing output a bit, but the total effects

1/ We do not wish to debate whether a perfectly rational speculator couldachieve stability, nor why this has not occurred. We take for grantedthat it has not.

- 32 -

are hard to measure, since higher prices lead eventually to reduced mar-

ket shares. From the narrow point of view of export revenue, compensatory

financing appears cost-effective when compared with direct market inter-

vention.

61. While far from being fully satisfactory, we feel the simple

modeling approach used here, combining econometrics with industrial exper-

tise and bit of mdoel builders' glue, is a useful way to approach commodity

stabilization. By focusing on the dynamics of supply and demand, the

adjustment to the stabilization policy itself can be examined. In reduc-

ing the number of parameters to the minimum, we were able to see which

were important and in need of more careful estimation. Since a model like

this can be developed from existing statistics in only a brief time and

easily can be updated and modified, we feel the methodology offers great

promise for medium-term analysis of other commodity markets.

- 33 -Table 6 Production and Prices in India

(2) (3) (4) (5)Agricultural Area Quantity Harvest Harvest Aus Jute/Paddy

Year Harvested Ilarvested Jute Prices Paddy Prices Price Ratio

Units: ('000 ('000 (Rupees Per (Rupees PerHectares) Metric Tons) Quintal) Quintal)

1950-51 561.4 586.6 99.55 38.32 2.6

1951-52 762.2 829.8 114.12 37.93 3.0

1952 53 701.0 810.6 56.99 35.18 1.6

1953-54 484.0 567.6 59.42 29.91 2.0

1954-55 470.1 508.4 58.73 25.80 2.3

1955-56 684.5 749.3 60.36 30.24 2.0

1956-57 772.2 756.7 61.81 31.08 2.0

1957-58 705.0 734.9 61.37 35.71 1.7

1958-59 732.9 935.8 57.18 34.23 1.6

1959-60 681.5 825.3 58.06 34.32 1.7

1960-61 618.6 726.9 104.65 33.23 3.1

1961-62 923.3 1144.0 89.25 35.11 2.5

1962-63 852.8 982.4 73.94 36.44 2.0

1963-64 867.6 1090.9 82.59 39.07 2.1

1964-65 838.5 1083.6 94.97 44.19 2.1

1965-66 753.3 803.4 110.69 56.65 2.0

1966-67 796.7 964.2 114.31 70.76 1.6

1967-68 885.2 1146.4 98.53 88.26 1.1

1968-69 527.6 531.5 131.23 73.51 1.8

1969-70 762.0 1009.0 127.50 71.88 1.8

1970-71 743.2 879.9 145.22 73.90 2.0

1971-72 818.1 1027.9 130.37 77.86 1.7

1972-73 700.2 896.1 146.27 81.45 1.8

1973-74 792.8 1119.7 124.82 116.82 1.1

1974-75 664.3 804.7 -- -- --

1975-76 586.8 803.1 __ _ _--

Sources: (1) Indian Jute Manufacturers Association.(2) Intcrnational Jute Manufacturers Association.(3) IBRD (Weighted by State Jute Acreage).(4) IBRD (Weighted by State Jute Acreage).(5) Column (3) * Colulmn (4).

- 34 -

Table 7 Production and Prices in Bangladesh

(1) (2) (3) (4) (5)Agricultural Area Quantity Farm Gate Paddy Jute/Paddy

Year |larvested Harvested Jute Prices Prices Price RatioUnits: ('000 ('000 (Tk. Per (Tk. Per

Hlectares) Metric Tons) Quintal) Quintal)

1950-51 692.4 807.8 50.91 34.28 1.5

1951-52 719.9 1148.7 69.00 39.12 1.8

1952-53 771.6 1401.5 27.45 37.14 .7

1953-54 390.6 454.0 41.53 27.19 1.5

1954-55 503.1 845.9 41.91 19.46 2.2

1955-56 661.5 1014.6 50.56 36.59 1.4

1956-57 497.9 1000.4 64.55 55.82 1.2

1957-58 632.4 1034.3 53.75 46.76 1.2

1958-59 618.5 1088.7 42.87 44.99 1.0

1959-60 556.5 973.0 56.00 46.90 1.2

1960-61 614.2 1020.5 106.27 43.40 2.4

1961-62 833.3 1264.3 66.66 44.91 1.5

1962-63 697.3 928.5 58.81 47.95 1.2

1963-64 688.0 997.8 60.34 42.18 1.4

1964-65 671.0 976.1 84.32 44.32 1.9

1965-66 845.8 1154.6 73.39 52.46 1.4

1966-67 875.6 1161.1 96.54 68.52 1.4

1967-68 970.9 1242.7 73.90 61.27 1.2

1968-69 878.2 1043.9 91.13 68.20 1.3

1969-70 997.2 1301.0 79.79 67.49 1.2

1970-71 917.8 1242.6 94.18 66.43 1.4

1971-72 694.0 777.9 103.96 83.99 1.2

1972-73 896.3 1172.5 140.88 120.27 1.2

1973-74 888.9 1088.6 141.50 175.32 .8

1974-75 607.0 720.0 -- -- --

1975-76 526.0 816.0 __ __ __

Sources: 1950 to ].960; Statistical Abstract of East Pakistan

(1)>.(2); 1960 to 1970: Statistical Digest of Bangladesh1970 on: IBRD

(3): Pakistan Jute Board(4): IBRD(5): Column (3) Colunin (4)

- 35 -

Table 8: Production and Prices in Thailand

(1) (2) (3) (4) (5)

Average Composite Ratio of

Agricultural Area Quantity Price Good Competing Kenaf toYear Harvested Harvested Grade Kenaf Crop Price Composite

Bangkok__ _ _ _ _ _ _

('000 (BAHT Per (BAHT PerUnits: ('000 RAI) Metric Tons) Metric Ton) Metric Ton) l

1954-55 -- __ 2850.0 569.6 5.0

1955-56 __ __ 3040.0 542.9 5.6

1956-57 __ __ 2590.0 509.9 5.1

1957-58 -- -- 2300.0 596.9 3.9

1958-59 127.5 32.0 2243.0 630.8 3.6

1959-60 278.3 55.0 3195.7 679.4 4.7

1960-61 877.0 187.0 3592.5 764.7 4.7

1961-62 1720.4 351.0 2342.6 654.0 3.6

1962-63 711.7 148.0 3732.6 695.0 5.4

1963-64 957.5 220.0 2849.8 621.0 4.6

1964-65 1365.4 310.0 3018.3 650.4 4.6

1965-66 1870.0 374.0 3311.0 701.7 4.7

1966-67 3314.1 672.0 1974.6 885.4 2.2

1967-68 1400.0 280.0 2525.8 809.0 3.1

1968-69 750.0 150.0 3040.0 971.8 3.1

1969-70 1931.5 350.0 2966.7 1023.6 2.9

1970-71 1500.0 300.0 3427.6 1106.5 3.1

1971-72 1750.0 350.0 4690.1 952.3 4.9

1972-73 2950.0 590.0 4111.9 1296.4 3.2

1973-74 2205.0 441.0 3270.0 1967.2 1.7

1974-75 1800.0 360.0 4064.7 2237.0 1.8

1975-76 1250.0 250.0 4230.5 1973.3 2.1

Sources:(1) : IBRD.(2) : IBRD.(3) : National Bank of Thailand.(4) : Composite of Maize and Cassava weighted by acreage in

Northeastern Thailand -- IBRD.(5) : Column (3) - Column (4).

Table 9 Results of Deterministic Simulation (Basic Case)

Supply of Supply of Supply of World Price World PriceSimulation Raw Jute in Raw Jute in Raw Jute in of Jute Goods of Raw Jute

Year India Bangladesh Thailand('000 MT) ('000 MT) ('000 MT) ($/MT) ($/MT)

1 940 935 463 556 260

2 835 911 264 611 299

3 889 982 311 654 344

4 955 1088 361 623 314

5 946 1132 368 592 282

t- -- - - - --- - - - - - - - - - - - - - - - - - - - - - - -- - - - - -6 888 1106 348 636 324

7 954 1174 386 588 276

8 876 1132 360 631 317

9 945 1193 394 584 269

10 869 1145 367 627 310

11 939 1202 400 612 293

12 948 1220 408 598 278

13 936 1209 406 643 319

14 993 1274 433 595 271

15 912 1236 406 639 312

- 37 -

Table 10 Rcsults of Deterministic Simulation (Basic Case)

Manufacturing of Jute Goods by Region(' 000 rr)

Simulation INDIA BANGLADESH THAILAND WESTERN JAPAN USAYear EUROPE

1 1032 459 149 290 78 13

2 1078 476 157 300 80 14

3 1052 446 160 297 78 13

4 1062 462 158 298 79 14

5 1078 482 156 299 80 14

6 1065 460 159 299 79 14

7 1090 491 156 301 81 14

8 1079 472 160 301 79 14

9 1105 502 156 304 81 14

10 1097 485 160 304 80 14

13. 1110 499 160 306 81 14

12 1124 512 159 308 82 14

13 1117 495 163 308 81 14

14 1142 525 160 311 83 15

15 3.135 509 164 311 82 14

- 38 -

Table 11 Results of Deterministic Simulation (Basic Case)

Demand for Jute Goods by Region('000 MT)

Simulation INDIA BANGLADESH THAILAND WESTERN JAPAN USAYear EUROPE

1 447 34 64 365 203 416

2 452 34 63 372 217 463

3 458 35 63 356 208 410

4 470 37 65 354 211 407

5 482 38 67 356 216 408

______~ -' ___ ____ -___ -- __ -___ -_____-----_ --- ----- ---

6 488 39 67 346 211 371

7 503 41 70 351 219 383

8 509 41 70 341 213 349

9 524 44 73 347 222 360

10 530 44 73 338 217 330

______--_____--_____--_____.-_______-______. ______

11 543 46 74 338 220 325

12 555 48 76 338 223 321

13 562 48 76 328 217 291

14 578 51 79 332 225 299

15 585 51 79 322 219 272

- 39 -

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[ 2] Belrman, J.R. [1968]. Surp?y Response in UnderdeveZoped Agri-culture. Norti llolland: Amsterdam.

3] Blitzer, C.; A. NIeeraLls; and A. SLoutjesdijk. [1975]. "ADynamic Model of Opec Trade and Production." JournaZof DeveZopment Economics, 2(4) , pp. 319-336.

[ 4] Dhrynies, P.J. [1971]. Distributed Lags: Probleems of Estimationand Formulation. Holden Day: San Francisco.

5] Grilli, E. and R. Morrison. [1974]. "Jute and the Synthetics."Weorld Bank Staff Working. Paper, No. 171. Washington,D.C.

[ 6) HIazel1, P.B.R. and P.L. Scandizzo. [1977). "Market In.erventionPolicics when Production is Risky." American JournaZof Agric!ultural Economics, 59(l), pp. 204-9

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[ 8) _ [1975]. "Risk Response Models and Their Use inAgricultural Policy Evaluation," American Journal ofAgricultural Econom£ics, 57(5) , pp. 836-43.

[ 9] __ _ [1977]. "T'e Existence of Stable Distributed Lags,"Econcmetrica 45(6) , pp. 1467-1480.

[10] Sengupta, J.K. and A. Sen [1969]. "Econometric Supply Functionsfor Rice and Jute," Arthaniti 12(1 & 2), pp. 1-40.

[11] UNCTAD, [1974). "An Integrated Programme for Commodities: TheRole of 'thnternational Buffer Stocks." (Report by theSecretary General. of UNCTAD.) Documents TD/B/C.7/166SupplemT!ent 7 and TD/B/C.7/Addecdum 7

[12) \lorld Bank [1973]3. To. rVWor2id Jute Economy. Volumes I and II.Confidentia1 Report 114-BD, Washingzon, D.C.