THE FUTURE OF INDIAN COTTON SUPPLY AND DEMAND:
IMPLICATIONS FOR THE U.S. COTTON INDUSTRY
by
JAGADANAND CHAUDHARY, M.Sc.
A DISSERTATION
IN
AGRICULTURAL AND APPLIED ECONOMICS
Submitted to the Graduate Faculty of Texas Tech University in
Partial Fulfillment of the Requirements for
the Degree of
DOCTOR OF PHILOSOPHY
Approved
Samarendu Mohanty Co-Chairperson of the Committee
Sukant Misra Co-Chairperson of the Committee
Jaime Malaga
Robert Paige
Accepted
John Borrelli
Dean of the Graduate School
AUGUST, 2005
ACKNOWLEDGEMENTS
I would like to express my gratitude to my committee co-chairmen Dr.
Samarendu Mohanty and Dr. Sukant Misra for their advice, guidance and patience in the
completion of this work. I would also like to thank my committee members Dr. Jaime
Malaga and Dr. Robert Paige for their suggestions and time on this project. In addition I
want to thank Dr. Don E. Ethridge for the financial assistance provided by him. I wish to
express my appreciation to Dr. Eduardo Segarra for his advice, encouragement and help
throughout the study period. I would also like to thank Dr. Suwen Pan for his expertise
and time on this project. I am grateful to the faculty, staff and colleagues of Agricultural
and Applied Economics Department for their kind support.
Finally, I appreciate my family for their support and sacrifices throughout the
study period. Without their support, I could not have been able to complete the work.
ii
TABLE OF CONTENTS
ACKNOWLEDGEMENTS ii
LIST OF TABLES v LIST OF FIGURES vii
I. INTRODUCTION 1
1.1. General Problem 6 1.2. Specific Problem 8 1.3. General Objective 9 1.4. Specific Objectives 9
II. LITERATURE REVIEW 11
2.1. Partial Equilibrium Cotton Models 11
2.2. Studies Related to Cotton Supply Estimation 20 2.3. Studies Related to Cotton Demand Estimation 23 2.4. Studies Examining Competitiveness of Cotton 30 2.5. Summary 32
III. CONCEPTUAL FRAMEWORK 34
3.1. Supply Response 38 3.1.1. Cotton Acreage and Yield Response 41 3.1.2. Man-made Fiber Production Response 44 3.2. Demand Specifications 46 3.2.1. Demand for Textile Products 49 3.2.2. Cotton Demand 50 3.3. Competitiveness of U.S. Cotton 57
3.3.1. Cotton Import Demand 59 3.4. Summary 59
IV. METHODS AND PROCEDURES 61 4.1. Model Specification 64 4.1.1. Fiber Supply Estimation 64 4.1.1.1. Cotton Supply Model 64 4.1.1.2. Man-made Fiber Supply Model 66 4.1.2. Fiber Demand Estimation 67
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4.1.3. Cotton Ending Stocks and Trade Equations 69 4.1.4. Market Clearing Condition 70 4.2. Policy Simulations 72 4.3. Competitiveness of U.S. Cotton 73 4.4. Model Validation 74 4.5. Data Requirements 78 4.6. Summary 80 V. RESULTS AND DISCUSSIONS 82 5.1. Fiber Supply Model 82 5.1.1. Cotton Acreage Model 82 5.1.2. Cotton Yield Model 87 5.1.3. Man-made Fiber Supply Model 90 5.2. Fiber Demand Models 92 5.2.1. Per Capita Textile Consumption 92 5.2.2. Fiber Demand 94 5.3. Fiber Trade and Cotton Ending Stocks Equations 99 5.4. Model Validation 105 5.5. Policy Simulation 107 5.5.1. Baseline Projections 109 5.5.2. Simulation Results 113 5.6. Competitiveness of U.S. Cotton 124
VI. SUMMARY AND CONCLUSIONS 132
6.1. Summary of the Results 132 6.2. Conclusions 138 6.3. Limitations of the Study 140 REFERENCES 142 APPENDIX: List of Variables and their Unit of Measurement 145
iv
LIST OF TABLES
5.1. Regression Results of Indian Regional Cotton Acreage Models 83 5.2. Elasticities of Indian Regional Cotton Acreage Model at mean level 86
5.3: Regression Results of Indian Regional Cotton Yield Models 88
5.4. Regression Results of Manmade Fiber Capacity and Utilization 91
5.5. Regression Results of Per Capita Textile Consumption 93
5.6. Regression Results of Fiber Demand System 96
5.7. Estimated Uncompensated Fiber Price and Income Elasticities 97
5.8. Estimated Compensated Fibers Price Elasticities 98
5.9. Regression Results of Cotton Trade Equations 100
5.10. Regression Results of Man-made Fiber Net Trade 103
5.11. Regression Results of Cotton Ending Stocks 104
5.12. Model Validation Statistics 106 5.13. Summary of Baseline Projections for Fiber Demand, Cotton Price, Polyester Price, Fiber Production, and Fiber Trade in India, 2004/05-2014/15. 112 5.14. Effects of MFA Quota Elimination on Indian Fiber Consumption and Domestic Fiber Prices 115 5.15. Effects of MFA Quota Elimination on Indian Cotton Area 116 5.16. Effects of MFA Quota Elimination on Indian Cotton Yield 117 5.17. Effects of MFA Quota Elimination on Indian Fiber Supply 118 5.18. Effects of MFA Quota Elimination on Fiber Trade and World Price 119 5.19. Wald Chi-Square Statistic test for the Results of Unrestricted and Restricted Models 125
v
5.20. Estimated Coefficients of the Restricted AIDS model 126 5.21. Estimated Uncompensated Elasticities of the Restricted Model 127 5.22. Estimated Compensated Elasticities of the Restricted Model 129
vi
LIST OF FIGURES
1.1. Market Shares of the United States in the Indian Cotton Market 2 3.1. Impacts of MFA Quota Elimination on World Cotton and Indian Textile Markets 35 4.1. Schematic Representation of the Indian Fiber Model 62 5.1. Baseline Projections for Textile Consumption in India 110 5.2. Baseline Projections of the Domestic Fiber Prices 111 5.3. Baseline Fiber Net Trade Projections 114 5.4. Indian Man-made Fiber Net Trade Projections (Baseline vs. Scenario) 122
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CHAPTER I
INTRODUCTION
India has the largest cotton-producing area in the world, accounting for 25 percent
of the world acreage, but contributes only 14 percent to the world production. China and
the United States produce more cotton than India with substantially less area. In the last
decade (between 1993/94 and 2002/03), Indian cotton production has increased by only 8
percent (average annual growth of less than one percent). Consumption in India,
however, has grown by around 35 percent during the same period, primarily fueled by
rapid expansion in textile consumption and exports. Currently, India is the second largest
textile producer in the world after China, accounting for about 15 percent of world
production, with export exceeding 12 billion U.S. dollars.
Disparity of growth in cotton production and consumption in the last decade has
transformed India from a net exporter to a net importer of cotton. As recently as 1996,
India exported more than 4 percent of world's cotton. Since 1999, India has instead
accounted for about 6 percent of world imports with the record amount of 480 thousand
metric tons in 1999/00. The U.S. share of India’s cotton market, however, remains
highly unstable. For example, the U.S. share of the total Indian cotton imports has
decreased from 30 percent in 1995 and 1996 to about 5 percent in 2000 (Figure 1.1).
Since then, the U.S. exports to India have recovered accounting for around 30 percent of
the market share in 2001 and 2002. While the U.S. has emerged as an important supplier
over the last two seasons, prices will have to remain competitive in order to offset the
1
0
5
10
15
20
25
30
35
40
1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Percent
2
Figure1.1. Market Share of the United States in the Indian Cotton Market
lower freight and shorter delivery periods offered to Indian buyers by Egypt, West
Africa, and Australia.
India’s reemergence as a major cotton importer has occurred mainly because of
external and internal constraints. The external constraint was the Multi-Fiber
Arrangement (MFA), which provided a framework under which developed countries
abided by a quota on the export of yarn, textiles and apparel from the developing
countries. The history of quantitative restrictions goes back to 1930’s when it was first
imposed by developed countries against the increasingly competitive Japanese cotton
textile industries. Later on, it expanded into a system of voluntary export restrictions on
almost all significant suppliers of textiles or clothing. The Long Term Cotton
Arrangement governed the period 1962-1973 and the MFA was established for the period
1974-1994. Gradually, many importing countries (Sweden, Switzerland, and Australia
among them) left the MFA. By 1994, the MFA included only four importers (the US, the
EU, Canada, and Norway) and some 30 developing exporting countries with a total of
1,300 bilateral quotas on textiles and clothing.
Quotas are applied typically on a bilateral basis, under the threat of unilateral
restraints to be imposed by the importing country. The quotas are determined through
bilateral negotiations and are specific to particular product categories, as defined by fiber
and by function. The MFA allowed for discrimination not only against specific fibers
and products, but also among exporting countries. The quotas for apparel exports were
announced for three-year periods and were subject to specific criteria.
The MFA system is a departure from two of the most fundamental principles of
3
the multilateral trading system. These are: (i) the ban on quantitative restrictions, and (ii)
the prohibition of discrimination between suppliers of textiles and apparels. In the
Uruguay Round of the General Agreement on Tariffs and Trade (GATT), the Agreement
on Textiles and Clothing (ATC) negotiated the phase-out of the MFA over a ten-year
period beginning in 1995. The ATC stipulated liberalization to occur in four stages and
in two forms: (i.) integration, and (ii.) an acceleration of quota growth. At the end of
fourth and the final stage, i.e., by January 1, 2005, all bilateral quotas between developing
exporters and developed importers ceased to exist.
Internal constraints included a mandate to sustain the small-scale traditional
handloom sector, export constraints on yarn, government fixing of cotton ginning and
pressing fees, subsidization of raw cotton production, and an overvalued exchange rate.
These policies have generally kept domestic cotton producer prices well below the world
prices. Cotton production policies in India historically have been oriented towards
promoting and supporting the textile industry. Government of India (GOI) announces
minimum support prices for cotton every year and the Cotton Corporation of India (CCI),
a government-owned organization, sets the minimum support prices for each cotton
variety. The minimum support price is fixed by the Textile Commissioner for the Fair
Average Quality (FAQ) grade of each variety of seed cotton on the basis of
recommendations from the Commission for Agricultural Costs and Prices (CACP). The
CCI is responsible for procuring cotton from the free market to support these prices. In
addition, the GOI also heavily subsidizes fertilizers, electricity, and water to producers.
This is illustrated by the increase in the fertilizer subsidy from 60 billion rupees in
4
1992/93 to 140 billion in 2001/02 (Mohanty et al., 2002). The food and input subsidies
have accounted for approximately 5 percent of all government expenditures in 2002,
exceeding more than $12 billion dollars (Landes, 2004).
The GOI also intervenes in the cotton market from storage, movement and credit
controls, to the fixing of ginning fees, and restrictions on the scale of operations in the
ginning sector. On the trade front, the GOI controls cotton exports to provide cheap
cotton to the textile mills by announcing an annual export quota. Historically, the quota
has ranged from 8,000 Metric Tons (MT) to 303,600 MT depending on the local supply
and demand situation (World Bank, 1999).
India’s internally-imposed constraints extend across its entire development policy,
which until the 1990’s looked to internal markets and investment, spurning the
opportunities for transformation offered by foreign investment and competition. In 1991,
the GOI initiated significant economic reforms and structural adjustment polices. The
policies were targeted primarily at industry and the international trade regime, affecting
agriculture only indirectly through reductions in input subsidies. More recently, the GOI
announced its intent to reform the cotton and textile sector, but there were no specifics as
to what would be done or when. These reforms also included research, education,
irrigation development programs, an institutional framework for land ownership, and
plans to improve technology (Worden and Heitzman, 1999). In addition to these
unilateral reforms, India, as a member of World Trade Organization (WTO), is
committed to open its agriculture market to the world market. However, in the early
years of GATT, India under Article XVIII-B imposed quantitative restrictions on imports
5
because of problems in the Balance of Payments (BOP) account. Since 1997, India has
been removing many import licensing and quota restrictions and replacing them with
high tariffs as part of its WTO commitments (Gulati and Kelly, 2000).
Although the GOI, under pressure from trading partners, has removed quantitative
restrictions such as licensing and quota restrictions on imports of most agricultural
products, higher tariffs and other non-tariff barriers continue to shield the farm sector.
For example, wheat, cotton, and corn, which formerly carried no duty, are now subject to
tariffs, while the tariffs on edible oils, wine, poultry, and sugar have been sharply
increased (USDA, 2001).
1.1. General Problem
The effects of India’s unilateral liberalization on its cotton industry have been
significant. As the invigoration (revitalization) of textile exports drove cotton
consumption well above the pace of the rest of the world, India’s share of world
consumption has increased from 10 percent in 1990/91 to 15 percent in 1999/2000.
Virtually, all of the 50 percent increase in India’s cotton consumption to date has been
met through increased production. India’s cotton area was already the world’s largest by
a significant margin in 1990, but by 1997 India’s cotton area has increased by another 2
million hectares; with increase occurring in each of India’s three main growing regions1.
1 The regions include: (i) the northern zone (Haryana, Punjab, and Rajasthan); (ii)
the central zone (Maharashtra, Gujarat, and Madhya Pradesh); and (iii) the southern zone (Karnataka, Tamil Nadu and Andhra Pradesh). The northern region primarily grows short and medium staple cotton while the southern states primarily grow long staples. The central zone grows mostly medium and long staples cotton (Mohanty, et al., 2002).
6
Even with the increase in area and yield, India has emerged as a growing net importer of
cotton in recent years, and it is unclear how successfully India’s cotton sector will keep
pace with its burgeoning textile industry.
In the next few years, state interventions will be eliminated, and the external trade
constraints originally imposed under the MFA have already been eliminated.
Consequently, textiles and apparel were incorporated into the WTO structure that governs
world trade in general. As the world moves into the post-MFA era, a number of
questions arise about how the various segments of India’s textile industry will be
impacted, given that global competitors including China, Pakistan, and Southeast Asia
would no longer be constrained by quotas. India is also in the process of removing its
own import restrictions in order to meet its WTO obligations, which would likely further
impact cotton and textile production and trade patterns in both India and the rest of the
world.
In addition, India itself is a growing market for textile consumption. Following
the liberalization of 1991, India’s one billion consumers have increased their purchase of
apparel and textiles produced both domestically and abroad, with increasing implications
for the world market.
The overall picture of the textile and apparel sectors in India is one of great
potential with many unknowns. This potential is particularly important in light of the
liberalization in textiles and apparel trade foreseen under the Uruguay Round agreement
of the GATT, as well as ambitious export-led growth and liberalization programs
7
undertaken by the Indian Government since 1991 (Bhide et al., 1996). Full
implementation of the Uruguay Round Agreement and recent developments in the global
economy will also expose India’s textile and clothing sectors to more intense
competition, both at home from synthetics, and abroad from other major exporters such
as China. However, India has launched a series of initiatives such as further
liberalization of foreign investment restrictions on textiles, easy credit availability to
upgrade textile facilities, and the launch of a cotton technology initiative to respond to
upcoming challenges and opportunities. The ability of the cotton industry in India to
keep pace with changes in the textile industry will determine whether India would once
again become an important raw cotton exporter, or would remain a major source of world
import demand.
1.2. Specific Problem
Many studies project that India will be a major beneficiary of MFA textile quota
eliminations, with textile exports expanding by as much as 25 percent. In addition,
domestic textile consumption is also expected to increase rapidly in the future insofar as
the International Monetary Fund (IMF) and the World Bank project the Indian economy
to grow at 6-8 percent annually in the medium-term. The projected strong growth in
textile exports and domestic consumption would lead to the expansion of mill demand for
cotton, necessitating an increase in cotton production at a much faster pace than the
historical rate of less than one percent annually. Since cotton acreage is unlikely to
expand in the future, production growth will have to come through yield improvements.
8
Very few studies have examined the future of the Indian cotton market under the
scenario of MFA quota elimination and its effects on the world fiber market. However,
these studies have either failed to take into account substitutability between cotton and
man-made fibers, or appropriate linkage between cotton and textiles, thus producing
incomplete assessment of the elimination of MFA quota on the Indian cotton market and
the world fiber market. More specifically, a clear understanding of the effects of MFA
quota elimination on India’s cotton and apparel trade, and the subsequent competitiveness
of the U.S. cotton in the Indian market is still lacking.
1.3. General Objective
The general objective of this study is to analyze the demand for and the supply of
cotton in India in the Post-MFA era and its effects on the world fiber market, including
the United States.
1.4. Specific Objectives
The specific objectives are to:
1. Develop an empirical framework that incorporates regional supply response,
substitutability between cotton and man-made fibers, and appropriate linkage
between cotton and textile sectors to quantify demand, supply, and prices of
cotton and manmade fibers in India.
2. Assess the impacts of MFA textile quota elimination on the Indian and world
cotton market.
9
3. Identify factors influencing the competitiveness of U.S. cotton in the Indian
market.
10
CHAPTER II
LITERATURE REVIEW
This chapter reviews previous studies in the areas of cotton demand and supply
and is divided into four sections. The first section deals with the estimation of demand
and supply of cotton in a partial equilibrium framework. Studies related to cotton
demand estimation are dealt with in the next section, followed by crop supply response,
and competitiveness of U.S. cotton.
2.1. Partial Equilibrium Cotton Models
Hitchings (1984) developed an integrated supply-demand model in India to
analyze various policy issues relating to the cotton industry. The model consisted of
three stochastic equations – one for supply of lint, another for mill consumption of lint,
and a third for cotton textile consumption, as well as two identities to account for
adjusted trade and utilization balance, for cotton and for cotton textiles.
Cotton lint production was specified as a function of the lagged real lint and food
grain price indices, and the lagged proportion of cotton area under irrigation. Cotton mill
consumption was specified as the function of current and lagged real lint prices, lagged
real cotton textile price, and a time trend. Cotton textile consumption was dependent on
real textile prices and income. The difference between cotton production and lint
consumption captures the changes in the cotton ending stocks and net trade. Similarly,
the difference between cotton textile production (cotton mill consumption converted to a
11
cloth equivalent) and cotton textile consumption represents the cotton textile net trade
and the change in the ending stocks. The five-equation simultaneous model included lint
production, mill consumption of lint, textile consumption, the real lint price index, and
the real textile price index as the five current endogenous variables. The other nine non-
stochastic variables are exogenous, lagged endogenous, or constant and trend variables.
The elasticities of the structural form were derived from the reduced-form
coefficients. The production elasticities with respect to lagged real lint price, lagged real
food grain price, and lagged irrigation proportion were estimated to be 0.074, -0.567, and
0.421, respectively. Elasticities of cotton mill demand with respect to the lint and textile
prices were estimated to be -0.449 and 0.893 respectively. Similarly, cotton textile
consumption was found to be price inelastic with estimated elasticity of -0.69. On the
other hand, the income elasticity of cotton textile consumption was found to be 0.4,
implying the income inelastic nature of textile consumption.
This study is important for current research because it provides supply and
demand elasticities for both cotton and textile markets in India. However, it fails to take
into account the possible effects of other fibers such as man-made fibers on cotton
demand and prices. In addition, cotton production is estimated at the national level, and
thus fails to reflect and capture the regional differences in cotton production.
Naik and Jain (1999) developed a detailed econometric simulation of the Indian
cotton textile sector, with proper linkages between cotton lint, yarn and fabrics, to help
understand and quantify the magnitudes of relationships between major variables. Cotton
lint production was specified as the function of the lagged real price of cotton, percentage
12
area under hybrid cotton, real price of fertilizers, and the trend variable. All the variables
were statistically significant and had the expected signs. Supply of cotton lint was
specified as the sum of the cotton lint production, lint imports, and the beginning stocks.
Both imports and ending stocks were treated as exogenous variables in the model.
The model included the behavioral equations for cotton yarn production, exports
and ending stocks. Production of cotton yarn was specified as the function of cotton
price, yarn price, and lagged yarn production. The yarn model was closed with domestic
consumption of cotton yarn as the difference between the supply of cotton yarn and the
sum of ending stocks and exports. The ending stocks of cotton yarn were exogenous in
the model. All the variables except price of cotton yarn were statistically significant.
For the weaving sector, the authors estimated demand, exports, and production of
cotton fabrics at the mill level. Price of cotton fabrics (inverse demand function) was
specified as the function of quantity demanded for mill cotton fabrics, one year lagged
price of mill cotton fabrics, and price of blended and mixed nylon fabrics. The R-squared
value for this equation was found to be 0.92 and only the lagged price of mill cotton
fabrics variable was statistically significant. Cotton fabric exports were specified as the
function of world income, price of fabrics, and a time trend. The use of world per capita
income in the logarithmic form, instead of its level form, was for the purpose of avoiding
multicollinearity among the variables in the equation. All variables except export price
of mill cotton were found to be statistically significant.
The estimated model was used to conduct various policy analyses. One of the
simulations included the effects of five and 10 percent increases in the hybrid cotton
13
acreage. The simulation results showed that an increase in area under hybrid cotton
would have positive impacts on all endogenous variables of cotton farming, spinning
sectors, and decentralized weaving sectors (power looms and handlooms), while
endogenous variables of mill weaving units are unchanged.
The simulation that increased cotton exports by one-half of a million to one
million bales of cotton revealed insignificant changes in the weaving sector. At the same
time, minimal changes were noticed in the spinning and cotton sectors. However,
increase in yarn exports were found to have statistically significant impacts on cotton and
spinning sectors. The consumption and production of cotton fabrics would go down but
prices of cotton, cotton yarn, and cotton fabrics would increase. The final simulation that
increased fertilizer price by 10 percent was found to have no effect both on the spinning
and the weaving sectors. As expected, the rise in fertilizer price decreased cotton
production by less than one percent on average.
The main shortcoming of this study was the failure to allow for inter-fiber
competition at the mill level. In addition, the study did not incorporate regional
differences in cotton production in India. Finally, the estimated supply and demand
elasticities of cotton lint, cotton yarn, and cotton fabrics were not provided to assess the
accuracy of the simulation results. However, this study provides useful information on
proper linkages among cotton lint, yarn and fabric sectors in India.
A study by Kondo (1997) examined the political economy of cotton and textile
export policy in India by developing a multi-market simulation. The author advocated
the use of a multi-market model approach over the partial equilibrium model, because in
14
the latter, income changes of suppliers and consumers in the cotton markets, yarn
markets, and textile markets are estimated independently. As a result, there was no
linkage among the three markets. These three markets are related, however, to each other
in the sense that cotton is consumed by the cotton yarn market and cotton yarn is
processed in the cotton textile market. Cotton spinning mills, for example, are consumers
in the cotton market and suppliers in the yarn market. Similarly, cotton textile weavers
are consumers in the yarn market and are suppliers in the textile market. Consequently,
the effects of liberalization policies in India cannot be measured correctly without
considering the linkages among these three markets.
The simulation model consisted of six interrelated markets - cotton, cotton yarn,
and cotton textile, for India as well as for the rest of the world. Each market had a supply
and a demand function, and the equilibrium flows depended on initial quantities, initial
prices, and price elasticities in the market. For example, cotton lint and yarn supply were
specified as the function of their respective producer prices, whereas cotton textiles
depended on both yarn and textile prices. On the demand side, cotton demand included
cotton mill price and yarn producer price, cotton yarn included yarn retail price and
textile producer price, while textile demand included only textile consumer price.
Due to the vertical integration nature of the cotton sector markets, elasticities
were computed endogenously. The author argued that there should be a linkage between
the demand elasticity of cotton in the cotton market and the supply elasticity of yarn in
the yarn market, because both of the elasticities depend on the behavior of the mill
industry. In addition, simulations were carried out for both short-run and long-run
15
periods using different sets of price elasticities. The short-term elasticities were
determined from long-run elasticities, assuming that capital is a fixed cost in the short-run
(i.e., capital is treated as an exogenous value, which cannot be changed by the
manufacturers) and variable (i.e., capital becomes endogenous) in the long-run. A total
of 20 short-run and long-run elasticities were estimated for supply and demand of cotton
fiber, yarn, and textiles with respect to cotton, yarn and textile prices. The estimated
elasticities for these interrelated sectors are extremely useful for this study for the
purpose of comparison.
Coleman and Thigpen (1991) developed an econometric model of the world
cotton and non-cellulosic fibers markets to forecast fiber production, consumption and
prices for major world players. The representative country model included standard
supply estimation through acreage and yield, and fiber demand estimation using a two-
step process. The first step included the estimation of per capital fiber consumption, and
the second step included the estimation of the share of each fiber at the mill level. Cotton
acreage was estimated as the function of cotton and competing crop prices, whereas
cotton yield was explained by rainfall, temperature, fertilizer price and technology. Per
capita textile fiber consumption was estimated as the function of per capita income and
textile and food price indices. In the next step, shares of each of the fibers were
dependent on relative fiber prices.
Li (2003) developed a partial equilibrium structural econometric model of
Chinese fiber markets to analyze the effects of MFA elimination on the Chinese and
world cotton markets. The model included behavioral equations of supply, demand, and
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trade for cotton and man-made fibers. One of the unique characteristics of this study is
the use of a two-step approach to estimate fiber demand and specifically connecting
textile outputs with fiber inputs. In the first step, total textile production is estimated
after incorporating textile imports and exports into textile consumption. Cotton, wool,
and man-made fibers’ shares are estimated from the textile production depending upon
their relative prices in the second step. Moreover, the use of a translog model system to
interpret the relationship between textile consumption and fiber demand is unique.
On the supply side, cotton production is estimated in a regional framework to
capture the heterogeneity in growing conditions arising out of climatic differences,
availability of water, and other natural resources that influence the mix of crops in each
of the regions. The four regions include the Xinjiang, the Yellow River valley, the
Yangtze River valley, and the rest of China. In the acreage equations, the coefficient for
the cotton net return variable was statistically significant with positive sign implying that
cotton area increased with the increase in its net return. For competing crops, inverse
relationships between acreage and competing crops net return were observed for all the
regions. Similarly, regional cotton yields were explained by lagged net return of cotton
and time trend to capture technological development. Interestingly, cotton return was
found to be statistically significant in explaining yield only for Xinjiang region.
Man-made fiber production was also modeled by estimating production capacity
and utilization. Man-made fiber production capacity was dependent on previous years’
capacity and 3 to 7 years lagged prices of polyester and crude oil. Length of lag to be
included was determined using Akaike Information Criterion (AIC). The coefficients for
17
the prices of polyester and crude oil variables were not statistically significant in
explaining production capacity of man-made fiber implying that the factors of input and
output prices play lesser role in capacity building. Man-made fiber capacity utilization,
on the other hand, was explained by previous year’s utilization and the ratio of polyester
to oil prices. The coefficients for the ratio of polyester to oil prices was statistically
significant with a positive sign, which indicates that as the ratio of polyester to oil prices
increases, the utilization rate also increases due to higher profit margins. However, the
coefficient of this variable was not significant.
On the demand side, per capita textile demand was explained by income, textile
and food price indices. The estimated parameter for income was found to be positive and
statistically significant at 5 percent level. Neither of the price indices was statistically
significant in explaining per capita textile demand. Fiber demands for cotton, man-made
fibers, and wool were estimated using a non-linear seemingly unrelated regression
method with symmetry and homogeneity restrictions imposed. All the estimated
parameters were statistically significant. The sign associated with textile output for
cotton was negative, while that for man-made fiber and wool was positive. Own-price
elasticities at the sample mean level ranged from -0.07 to -0.33, the highest for cotton and
the lowest for wool. Cross-price elasticities between cotton and wool were negative,
suggesting both fibers to be complement; in contrast, those between man-made fibers and
cotton and man-made fibers and wool were positive indicating them to be substitutes.
18
Cotton exports and imports were estimated separately in the model. Import
demand was explained by variables such as per capita rest of the world income, and
current and past ratios of domestic to imported cotton prices. Cotton exports, on the
other hand, were estimated using the ratio of current domestic to world cotton prices.
The price ratio variable was found to be statistically significant with a negative sign
suggesting that either higher domestic price or lower world price would cause the export
to decline. In case of man-made fibers, because of the non-availability of data, net trade
was estimated instead of separate import and export equations. Finally the ending stocks
equation was estimated as the function of beginning stocks, production, and cotton farm
price. All the parameters were statistically significant and had the expected signs.
The estimated model was used to simulate the effects of MFA eliminations on
Chinese and world fiber markets. The simulation results indicated that the rise in textile
exports due to quota eliminations as part of ATC would increase domestic mill use of
cotton and man-made fibers. A rise in fiber mill use increased domestic fiber prices, with
cotton and man-made fiber prices rising by an average of 4 and 7 percent per year,
respectively. Since domestic fiber production, particularly cotton, was projected to grow
at a slower pace than demand, the excess demand was met by higher imports. In the case
of cotton, imports were expected to be approximately 50 to 60 percent higher than the
baseline level, whereas man-made fiber imports were projected to rise by 8 to 13 percent
due to textile quota eliminations.
Although this study deals with the Chinese fiber model, it is very important for
the current study in the sense that the model specification and estimation methods in the
19
current research will be borrowed from this study. The study deals with the partial
equilibrium model of cotton demand and supply, allowing inter-fiber competition in the
model and reasonable elasticity estimates. The only weakness of this study is that
separate export and import trade equations were not specified for the man-made fiber;
the use of a single net trade equation for man-made fibers may have distorted results
because exports and imports are not generally believed to be explained by the same
variables.
2.2. Studies Related to Cotton Supply Estimation
Coleman and Thigpen (1991) estimated cotton production by specifying a
separate behavioral equation for yield and another for acreage to avoid loss of important
information. An analysis of cotton data from1964 to 1988 provided justification for this
argument because while yield increased from 338 kilogram/hectare (kg/ha) to 545 kg/ha
during that time period, the area planted remained almost constant at about 30 million
hectares. The countries included in the study were Argentina, Australia, Brazil, Central
Africa, EEC, Egypt, India, Japan, Korea, Mexico, Pakistan, Peoples’ Republic of China,
USSR, and The United States.
Coleman and Thigpen estimated cotton yield and cotton area equations using the
Ordinary Least Squares (OLS) method for the period 1964-1988. In India, cotton is
produced in three different regions, northern, southern and western India. Cotton farmers
in each region get different prices and have different choices of alternate crops (rice,
jowar, bajra, maize, and groundnut). Climatic conditions, rainfall and temperature, also
20
varies from region to region, resulting in varying yields across regions. Therefore, to
capture the variability of these factors the authors used a regional disaggregated model in
this study.
Cotton yields in southern and northern India were specified and estimated as the
function of the planted acreage, rainfall, and time. The time variable was in logarithmic
form because the rate of increase declined over the study period (1964-88). Estimated
results showed that the explanatory variables in the northern and southern regions yield
equations accounted for 86 and 94 percent of the variation in yield, respectively. The
coefficient for the acreage variable was statistically significant and negative in both the
regions, indicating that it captured the decline in average yields as production expanded
to marginal land. The coefficient for the annual rainfall variable was statistically
significant implying that cotton yield depends on rainfall. Prices were not found to have
a statistically significant effect on yield in either region. However, the cotton yield in
western India was explained by a time trend and rainfall in the summer months and a
dummy variable for 1983 that accounted for the severe pest damage that was experienced
that year. These explanatory variables combined explained about 88 percent of the total
variation in cotton yield of the region.
The study specified cotton acreage in each region as the function of producer
prices of cotton and competing crops. In addition, northern and southern acreage
equations included lagged area and time, respectively. Estimated coefficients of all the
variables except for competing crop prices were found to be statistically significant in
21
each of the regional acreage equations. The estimated price elasticities ranged between
0.07 and 0.17, which indicated that planted acreage were price inelastic in the short run.
This study is very useful for this research because it provides theoretical and
procedural insights regarding the use of estimation methods and explanatory variables in
the disaggregated area and yield equations. This study provides the basis for estimating
the regional cotton production models using disaggregated data.
Reddy and Bathaiah (1990) estimated the supply response of major agricultural
crops such as rice, groundnut, sugarcane and cotton for Andhra Pradesh, a southern state
in India. The objectives of the paper were twofold. The first objective was to develop
the relationships between the agricultural output and prices, as well as some important
non-price variables affecting supply response. The second objective was to explore the
relative impacts of various factors on crop output and to examine whether any particular
pattern exists in planting methods among producers.
The supply model was dependent on acreage, expected prices of own and
competing crops, and rainfall. The expected price was used instead of actual observed
price because it was hypothesized that farmers base their production decision in a given
year upon the prices they expect to receive in that year, rather than by the past year’s
price. The output equation was estimated in linear as well as logarithmic forms using
data for the period of 1963/64 to 1982/83. The estimated parameter for acreage and
relative prices were statistically significant in the output equation, but rainfall was not
found to be statistically significant. This study is relevant for the current research as it
provides estimates of supply elasticities, which can be used as a basis for comparison.
22
Kaul (1967) conducted a study to estimate short- and long-run supply responses
for various crops in the northern state of Punjab. The objective of that study was to
measure the effects of price changes on the farmers’ decision to allocate land to different
crops. To get a better understanding of the reaction of farmers to price changes, the study
was conducted at the district level, and each district was further divided into irrigated and
non-irrigated zones using two cotton varieties: native cotton and American cotton.
Kaul used the Nerlove’s adjustment model and regressed the acreages under each
crop against the price of the crop (lagged by one year and deflated by the indices of
competing crop prices), lagged yield, lagged acreage and a time trend. R-squared values
were found to be 0.73 for native cotton and 0.85 for American cotton. In terms of
elasticities, American cotton was more price elastic than native cotton. The short-run and
long-run elasticities were estimated to be 0.34 and 2.84 for the American cotton and 0.29
and 1.19 for native cotton, respectively. The trend variable revealed a statistically
significant positive trend in acreage, as this is likely due to the expansion of canal
irrigation in the districts.
2.3. Studies Related to Cotton Demand Estimation
Coleman and Thigpen (1991) argued that modeling cotton demand is different
from that of other agricultural products because the demand for cotton is a derived
demand. First, raw cotton is demanded by the processors (mills and others), and then
finished textile products are demanded by the final consumers. Therefore, the authors
23
used two behavioral equations and an identity to estimate regional demand for cotton in
India.
The first behavioral equation was the cotton share of total fiber use in India and
was expressed as the function of cotton and polyester price ratio and a lagged dependent
variable. Ratio of cotton and polyester prices was used instead of two independent price
variables to avoid multicollinearity between these two prices. A double-log functional
form was found to fit the data better than the linear form. Data used in the model were
for the period 1964 to 1986, and were obtained from World Apparel Fiber Consumption
Survey, Food and Agricultural Organization (FAO).
A two-stage least squares procedure was used to estimate the cotton share
equation because the current endogenous variable appears on the right-hand side. The R-
squared value was found to be 0.95, stating that 95 percent of the variation in the cotton
share of total fiber use is explained by the explanatory variables. The coefficient for the
lagged cotton share variable was statistically significant with a positive sign implying
asset fixity in cotton milling. The calculated price elasticity of demand was - 0.016,
which suggests that cotton mill use was not very responsive to price changes.
The second behavioral equation estimated by Coleman and Thigpen (1991) was
the per capita textile fiber consumption and was specified as the function of per capita
deflated gross domestic product, a time trend, and a binary dummy variable for 1982. All
estimated parameters were found to be statistically significant. Income elasticity of
demand for textile was estimated to be 0.28.
24
The major weakness of this study was that it did not impose the theoretical
restrictions of homogeneity, symmetry and adding up in the demand estimation.
Additionally, wool price was not included in the cotton share equation and the reasons for
including dummies for specific years in both of the behavioral equations were not
discussed. This study, however, is extremely relevant for the proposed research because
it provides useful procedural insights regarding the use of two-step estimation method for
estimating cotton demand.
Meyer (2002) conducted a study with the objective of analyzing inter-fiber
competition in the United States and the three major textile producing countries in Asia –
China, Japan and Taiwan. For that purpose, detailed models of the textile markets such
as fiber production, intermediate textile trade, and finished textile goods markets were
constructed for the United States, followed by less detailed models for the three Asian
countries. For the three Asian countries, one or more of the textile markets (mostly
intermediate textile trade) were not considered in the model. Only the cotton market was
modeled for the rest of the regions of the world including India.
The model for the United States included cotton and synthetic as well as minor
fibers (cellulosics and wool) in order to determine the supply and demand for aggregate
fiber categories. Domestic finished textile goods markets were also incorporated into the
model to estimate fiber demand by types. This study estimated the effect on world and
U.S. textile and fibers markets of changes in income and exchange rate, as well as the
liberalization of textile quotas.
25
The structure of the Indian cotton model was not demonstrated in Meyer’s study.
However, Japanese and Taiwanese, as well as Chinese fiber model structure flow
diagrams were developed, followed by graphical representation of their synthetics and
cellulosics equations in a price and quantity space. In their fiber models, competing fiber
prices entered the consumption equations with the cross price weighted by consumption
to create a cross price index. The graphical model demonstrated the variables that caused
the demand and supply curves to shift. The fiber models of the United States were
explained at the most disaggregated level. Separate models were developed for man-
made fibers, wool fibers, cotton fibers and finished goods.
The world cotton model was constructed by considering cotton fiber of only
nineteen countries (including India) other than United States, China, Japan and Taiwan.
The model endogenously solved A-Index price (adjusted for exchange rates) by
balancing world trade, i.e. equating world exports with world imports. The net trade for
all these countries and regions was then added to the net trade positions of the United
States, China, Japan and Taiwan, and was constrained by an identity to clear world trade
markets.
The Indian cotton model in this study consisted of three behavioral equations
(cotton area harvested, per capita cotton domestic consumption, and ending stocks) and
two identities, one for cotton production and another for supply and total demand. In the
first equation, cotton area harvested in India was explained by oil price, A-Index price,
wheat price, and lagged cotton acreage. All the prices were deflated by Gross Domestic
Price (GDP) deflator. All the estimated coefficients were statistically significant at the 5
26
percent level, except for the A-Index price. This suggests that the extent of cotton
acreage in India is not considerably influenced by international price fluctuations. The
elasticities of cotton acreage with respect to oil price, A-Index price and wheat price
were estimated to be -0.0967, 0.416, and -0.122, respectively.
Per capita cotton consumption was estimated directly as a function of cotton
price, polyester price, per capita income, and lagged per capita consumption. All the
coefficients except for the fiber price ratio were statistically significant at the 5 percent
level. The demand elasticities with respect to price and income were found to be -0.106
and 0.221, respectively.
The model structures used in this study should provide some insight for
developing an Indian fiber model. However, these models estimated mill demand for
cotton as a final consumer product rather than an input for the finished product. More
important, inter- fiber substitution at the mill level was not accounted for in the cotton
demand equation. However, the study is recent and the variables used in the equations
are useful for the proposed research.
Clements and Lan (2001) estimated fiber demand for major consuming countries
to examine the effects of consumers’ income and prices on international consumption
patterns of fibers. They used disaggregated data for three fibers - cotton, wool, and
chemical fiber for the ten largest fiber-consuming countries in the world at two points in
time, 1974 and 1992. The use of a system-wide approach, cross-country data, and pure
numbers (without any units) to avoid exchange rate conversion problems were some of
the important features of their studies. The system-wide approach captured the
27
interrelationship between fibers in conformity with the theory. Cross-country data are
more variable than time-series data, and as a result, demand equations using this data
could be estimated more precisely. With the international data, however, problems arise
in expressing them in common currency. This has been handled by using logarithmic
changes over time and consumption shares, thus divesting them of currency units and
making them comparable across countries.
Prior to estimating the systems of equations, per capita quantity data was
converted to annual log-change form, which represented the long-run trends in
consumption. A divisia volume index was then created as the quantity-share-weighted
average of the growth in all the individual fiber. The divisia volume index can be defined
as the growth in the volume of per capita fiber consumption as a whole. Since domestic
prices were not available for cotton and wool, international prices were used in the
demand equations. Like other demand models, separability of preferences was the
necessary condition, and accordingly, it was assumed that the three fibers form a different
group from all other goods.
The Rotterdam model, Working model and E.A. Selvanathan’s model were used
to estimate the fiber demand. All the equations were estimated using maximum
likelihood estimation methods, where disturbances were assumed to be normally
distributed and the covariance matrix to be constant. In addition to the above three
models, two more composite models, Working’s and Selvanathan’s model with income
coefficients suppressed and Working’s and Selvanathan’s with intercept only, were
estimated in this study. Stress test was done in order to assess the performance of these
28
five models in terms of their ability to predict the consumption shares. Three out of five
models predicted negative shares for the richest and poorest countries and thus failed the
test and were therefore discarded. The two models to pass the stress test were the
Rotterdam and the combination of Working’s and Selvanathan’s with intercept. The
Strobel test was performed to detect outliers (information inaccuracy) in the data, and the
test results showed that data from the former USSR were suspicious and were therefore
dropped from the study.
The coefficients estimated from the nine countries were used to project the
consumption shares of the 63 out-of-sample countries. Further, Clements and Lan (2001)
formulated a composite model, which differed from the Rotterdam model in the sense
that the share in the former was the weighted average of the shares from the latter plus
the no-change extrapolation of the quantity shares. The no-change extrapolation was a
naïve approach, which assumed that fiber shares remained unchanged for the estimated
points of time, 1974 and 1992. It was found that the quality of predictions was improved
with the use of the composite model. The estimated conditional income elasticities for
cotton, wool and chemical fibers were found to be 0.8, 0.5, and 1.3, respectively,
implying that first two goods are necessity and the third is a luxury. The conditional
own-price elasticities are -0.14, -0.02, and -0.16 for cotton, wool, and chemical fibers,
respectively, indicating that all fibers are price inelastic.
The major weakness of this study is the use of international prices of cotton and
wool rather than domestic prices in estimating fiber share equations. Despite this
shortcoming, the study provides a unique approach for estimating fiber demand.
29
2.4. Studies Examining Competitiveness of Cotton
Chang and Nguyen (2002) examined the competitive position of Australian cotton
in the Japanese markets. Since Australia and the United States were the major cotton
suppliers to the Japanese market, the study primarily analyzed the factors that could
provide an edge to Australian cotton relative to U.S. cotton. In recent years, the Japanese
textile industry has been facing fierce competition from other Asian countries such as
China, India, Pakistan, and Indonesia. This has led to a decline in Japanese cotton
imports both from Australia and the United States. This study employed a non linear
version of the Almost Ideal Demand System (AIDS) model developed by Deaton and
Muellbauer to estimate import demand for cotton in Japan by country of origin.
They developed the model on the assumption that decisions on imports by the
Japanese textile industry are based on a two-stage budgeting process. Total expenditures
are allocated to a broad group of commodities such as cotton, wool and synthetics in the
first stage. In the second, expenditure on cotton is allocated over individual commodities
(countries in this case) such as cotton from United States, Australia, and other sources.
The results suggested that Australian cotton is an inferior good while U.S. cotton
is a normal good. Australian cotton was also found to be a strong substitute for U.S.
cotton. The study concluded that the U.S. had a relatively strong market position and
suggested that Australia needs to improve its cost competitiveness and quality image to
better its market standing.
Similarly, Alston et al. (1990) estimated import demand elasticities using the
Armington Trade Model. Import demand elasticites were used mainly to estimate the
30
effects of trade barriers and to examine trade policy options. The Armington Trade
Model is a disaggregate model, which differentiates commodities by country of origin
with import demand estimated in a separable two-step procedure. In the first stage of the
two-stage budgeting process, the importer decides how much to import. In the second
stage, given the total amount imported, the importer decides how much to import from
each supplier. The Armington model states that in the second stage of budget allocation,
market shares do not vary with expenditures and different import sources are separable as
well. Assumptions used for this model were homotheticity and separability, which
ensures restrictions on demand. The restrictions state that trade patterns within a market
change only with changes in relative prices, and the elasticities of substitution between all
pairs of products are identical and constant.
They argue that ease of use and flexibility are the two important reasons to use
this model in international agricultural markets. France, Italy, Japan, Taiwan, and Hong-
Kong were the five leading cotton importing countries chosen for the cotton import
analysis. Together they accounted for 37% of total cotton import in 1983/84. Three
approaches were used for the empirical analysis considering restrictions on the second
stage of a two-stage budgeting process. The first approach was the nonparametric
method, which tested whether data are consistent with a stable system or well behaved
demand equations, and whether Armington restrictions hold. The Armington model was
the second approach which was estimated and tested as a nested model. This model was
explained by a set of parametric restrictions on a double-log import demand model,
31
which incorporated the complete set of relative prices. AIDS was the third approach used
to estimate the parameter of the import demand equations.
The test of the Armington trade model’s assumptions in the context of cotton
revealed that this model is comprehensively rejected with data from the five leading
importing countries. This suggests that the Armington model should not be applied in the
analysis of import demand for cotton.
2.5. Summary
This chapter reviews literature that is most relevant to the proposed research and
is pertinent to the studies of Indian cotton supply and demand and the competitiveness of
the U.S. cotton. Hitchings (1984), Naik and Jain (1999), and Li (2003) estimated supply
and demand of cotton in a partial equilibrium framework, while Kondo (1998) developed
a multi-market model for cotton and textile markets. All these studies modeled India’s
cotton market independent of the effects of important fibers like man-made fiber and
wool, thus ignoring the effect of inter-fiber competition at the mill level. On the supply
side, these studies failed to incorporate regional differences in cotton production. The
proposed research attempts to address all these shortcomings of modeling cotton in a
partial equilibrium framework and proposes to develop a robust model consistent with the
economic theory.
In the current study, the partial equilibrium structural econometric model of the
Indian fiber sector is developed after taking into account the shortcomings of the existing
literature. Cotton supply response is estimated in a regional framework to account for
32
heterogeneity in growing conditions arising out of climatic differences and availability of
water and other natural resources that influence the mix of crops in each of the regions.
Similarly, man-made fiber production is modeled separately as production capacity and
utilization rate. Unlike most of the past studies, mill demand for cotton and other fibers
are modeled as an input for the finished product rather than a final consumer product.
33
CHAPTER III
CONCEPTUAL FRAMEWORK
This chapter focuses on the theoretical construction of various components of the
Indian cotton model that are critical to a conceptual analysis of the evaluation of the
elimination of MFA. The first section of this chapter includes a graphical representation
of the potential impacts of the MFA quota elimination on the Indian and world cotton
markets. In the next section, theoretical constructs for the supply and demand response
functions of cotton and man-made fibers are developed. Following this, theoretical
derivation of measuring competitiveness is presented.
The graphical analysis presented in Figure 3.1 shows the expected directional
changes to the Indian and world cotton markets, in a price-quantity space, due to MFA
quota elimination. As shown in Figure 3.1, panels (a) and (b) represent Indian textile and
cotton markets, respectively. Panel (d) represents the rest of the world cotton market, and
panel (c) shows the market clearing mechanism at the world level by equating excess
supply with excess demand. Transportation cost effects are ignored for simplicity.
Indian cotton demand is derived from the textile market in panel (a). AGBI and
HCDEI are the export demand and total demand for Indian textiles, where total demand is
a horizontal summation of export demand and domestic demand (HCF). As shown in the
diagram, export demand for textiles is zero at or above the price PT. In this range, total
textile demand is same as the domestic consumption. However, as the price falls below
34
S
a. Textile Market in India
b. Cotton Market in India c. World Cotton Market d. Rest-of-the-World Cotton Market
ES
ED
A
B
CD
EF
I
J
K
price
Quantity
Quantity
price price price
Quantity Quantity
INS
G
INX IINX INY I
INY WX IWX
IED
RWDR WS
IRWY RWY RWX I
RWX
IE
IB
IWP
IK
H
WP
PT
PRW
PIN
IINP
35
Figure 3.1. Impacts of MFA Quota Elimination on World Cotton and Indian Textile Markets
PT, export demand becomes positive and is added to the domestic demand; thus the total
textile demand curve is kinked and is represented by HCDEI.
The presence of MFA quotas limit textile exports to certain markets causing the
textile export demand kinked at G to become AGB. This, in turn, results in a kinked total
textile demand, HCDE. Since the domestic cotton demand in panel (b) is derived from
the total textile demand and the latter is kinked, this results in a kinked cotton demand
curve represented by IJK. Supply function of cotton for India is represented by SIN in the
same panel. Recent studies show that India is a net cotton importing country, implying
that domestic demand of cotton is more than production potential. In the absence of
trade, domestic price of cotton in India would be PIN. For prices below PIN India would
demand more cotton than producers produce. However, above price PIN, India would be
an exporter. As price falls below PIN, the difference between cotton supply and demand
would expand, thus, excess demand function, ED, is drawn as shown in panel (c). The
excess demand function is the demand function for imports from the world market.
Contrary to India, rest of the world in panel (d) is assumed to be cotton exporting
country. In the absence of trade, its domestic price would be PRW. For price above PRW,
quantity supplied in rest of the world would exceed quantity demanded. As the price
rises, this difference would expand, thus tracing out an excess supply function, ES, in
panel (c). However, for price below PRW, rest of the world would be an importer.
36
Panel (c) displays the world market equilibrium with excess supply, ES, derived
from the rest of the world in panel (d), and excess demand, ED, from the Indian cotton
market in panel (b). Equilibrium in the world market exists where excess demand, ED is
equal to excess supply, ES, yielding a world cotton price of Pw. At this world price,
India’s cotton imports are (YIN-XIN), and rest of the world cotton exports are (XRW-YRW),
and both are equal to XW, the volume traded in world cotton market.
With the elimination of MFA, the textile export demand shifts to AGBI in panel
(a), an increase in export demand, resulting in an outward shift in the total textile demand
from HCDE to HCDEI. The rise in textile demand, in turn, increases the mill demand for
cotton in India with the cotton demand curve shifting from IJK to IJKI in panel (b). Due
to the increase in demand, the domestic cotton price in India rises to (panel b). The
rise in cotton demand in India causes excess demand for cotton derived from panel (b) to
shift from ED to EDI (panel c). This results in an increase in the world cotton price from
PW to , and an increase in the volume of world cotton trade to . Price rise in rest
of the world results in declining cotton consumption, increasing cotton production, and
therefore, widening exports to ( - ), (panel d). Higher prices results in an
expansion of cotton production in India from to ; however, due to increase in
textile consumption cotton demand increases more than its production, thus, cotton
imports expands to ( - ), (panel b).
IINP
I I
IX IY
I
I I
WP WX
RW RW
INX INX
INY INX
If the representation of the markets depicted in Figure 3.1 is reasonably accurate
then the expected effects of textile quota elimination would be to increase Indian textile
37
exports, increase Indian cotton imports, increase world price, and increase production and
exports in the rest of the world. However, the conceptual analysis does not, and cannot,
reveal the magnitude of these expected effects. The magnitudes, however, can be
determined by the various supply and demand elasticities in these markets. Slopes of the
excess supply and demand functions in panel (c) depend upon the slopes of the domestic
supply and demand functions. For example, if the domestic cotton demand in India is
perfectly inelastic, then the slope of the new excess demand function is equal to the
negative of the slope of Indian cotton supply function. Similarly, if domestic cotton
supply in rest of the world is perfectly inelastic, then the slope of the excess supply
function in panel (c) is equal to the absolute value of the slope of the rest of the world
cotton demand function. For this research, an econometric model of the Indian fiber
market is developed and linked to an existing world fiber model developed by Pan et al.
(2004) in order to endogenize world fiber prices. The theoretical analysis begins with a
derivation of a fiber supply response model followed by fiber demand derivations.
3.1. Fiber Supply Response
Following Henderson and Quandt (1980), a generalized production function for a
firm can be expressed implicitly as:
1 s 1 nF(q ......q , x ...x ) = 0
s n
, (3.1)
where qi ( ),1q ...q and xi ( ) represent output and input (fixed and variable both)
use in the production process respectively. The function in (3.1) is single valued,
continuous, twice differentiable, and defined only for non-negative inputs and outputs.
1x ...x
38
The producers maximize their profit by processing the inputs into finished goods. The
output price pi and input price wj are exogenous because the producers encounter
perfectly competitive input and output markets and therefore cannot influence prices, and
as such, use prices as given. The profit function of the firm is explained as:
, (3.2) s n
i i j ji =1 j =1
π = p q - w x∑ ∑
The profit function is assumed to be non-negative, monotonically increasing in
prices of output, pi, and decreasing in prices of inputs, wj, convex, and homogenous of
degree zero in pi and wj. The profit function is maximized subject to the production
function constraint given by (3.1). The associated Lagrangian for the constrained profit
maximization problem is depicted as:
n
s n
i i j j 1 s 1i =1 j =1
L = p q - w x - λF(q ...q , x ...x )∑ ∑ , (3.3)
Taking the partial derivatives of (3.3) with respect to each input (x1…xn), each output
(q1…qs), and the Lagrangian multiplier (λ), and setting them equal to zero ensures a local
extremum.
i ii
δL = p + λF = 0δq
, (3.4)
j jj
δL = w + λF = 0δx
, (3.5)
1 s 1 nδL = F(q ,...,q ,x ,...,x ) = 0δλ
, (3.6)
39
Solving (3.4), (3.5) and (3.6) simultaneously yield a system of optimum
Marshallian output supply and factor demand functions that ensure a local maximum and
have output and input prices as arguments. These are expressed as follows.
*i i 1 s 1 nq = f (p ...p , w ...w ) , (3.7)
*j j 1 s 1 nx = f (p ...p , w ...w ) , (3.8)
However, this is only a necessary condition for profit maximization. To ensure
that the local extremum is a maximum, second order conditions require that the relevant
bordered Hessian determinants alternate in sign. This is the sufficient condition for profit
maximization.
11 1n+s 111 12 1
n+s21 22 2
n+s1 n+s, n+s n+s1 2
1 n+s
λF λF FλF λF F
λF λF F > 0,...(-1) > 0λF λF F
F F 0F F 0
, (3.9)
The necessary and sufficient conditions when satisfied yield a solution, which
ensures profit maximization. The output supply equation (3.7) is determined by input
costs, cotton and competing crops prices, and output supply is the products of optimal
acreage of crop i and their yields, which can be mathematically stated as:
, (3.10) * *i iq = A .Y*
i
where is the optimum area and represents the optimum yield. *iA *
iY
As in the output supply quantity equation, the acreage planted and the yield both
have cotton price, competing crop prices, and input prices in the arguments, implying
both depend on these variables. For theoretical purposes, yield and area will be modeled
40
separately to avoid information loss. The reason for this is that India’s increased
production in previous years is mainly due to yield, while area remains almost constant.
3.1.1. Cotton Acreage and Yield Response:
Most supply constructs, including the one mentioned above oversimplify the
complex micro-level decision framework, and do not include important features such as
risk aversion, imperfect markets, incomplete information, dynamic adjustments, and
sequential decision making (Sadoulet and Janvry, 1995). According to Nerlove (1956,
1958), two problems emerge when estimating a supply response equation. First, the
observed prices, which are either market or farm-gate prices, are realized only after
harvesting, while farmers make planting decisions based on their expectation of prices to
be received after harvesting. Thus a time lag occurs in agricultural production which
makes the modeling of formation of expectations a key issue in the area of agricultural
supply response analysis. Second, the observed acreage and desired acreage differ
because of adjustment lags in the reallocation of variable factors. It may take several
years for farmers to reach their desired acreage level once the price changes. Therefore,
specifying adjustment lags clearly becomes necessary in the model.
In order to address these two dynamic processes, Nerlovian supply models are
used in the analysis of the Indian acreage model. The reason for using Nerlovian model
is that it assumes a more realistic farmer’s adjustment behavior. Nerlove (1956, 1958)
argued that the dynamic approach explains the data better, coefficients are more
reasonable in sign and magnitude, and the residuals indicate a lesser degree of serial
41
correlation than in the static approach. The reason for this can be attributed to the fact
that actual acreage cannot adjust immediately to the desired or planned level due to the
fixity of land assets.
The desired area to be allocated to cotton in period t in the Nerlovian model (also
called partial adjustment model) is specified as a function of expected relative prices:
, (3.11) * et 0 1 tA = α + α P + Ut
where is the desired cultivated area, and is the expected price, in general it can be
said to be a vector of relative prices including the cotton price and prices of competing
crops, is the error term capturing the effects of variables not accounted for in the
model but affecting the area under cultivation, and has an expected value of zero, and
is the coefficient associated with the expected price.
*tA e
tP
tU
1α
As discussed earlier, farmers cannot observe the actual price at harvest time.
Therefore, expectations are formed in which expected price is represented by a weighted
moving average of past prices. Based on this, Nerlove hypothesized that each year
farmers adjust their expectations as a fraction γ of the magnitude of the mistake they
made in the previous year, i.e. of the difference between the actual price and expected
price in period t-1. The hypothesis can be stated mathematically as:
e e et t-1 t-1 t-1P - P = γ( P - P ), 0 γ 1≤ ≤ , (3.12)
where is the price expected this year, is the price expected last year, and is the
actual price last year.
etP e
t-1P t-1P
42
Because of techno-economic and socio-institutional constraints confronted by the
farmers in India, full adjustment to the desired allocation of land may not be possible in
the short run. Consequently, the actual adjustment in area will be only a fraction δ of the
desired adjustment. That means the process of realizing desired change may be spread
over a number of years. This is also called the Nerlovian partial adjustment model, and
can be mathematically expressed as:
*t t-1 t t-1A - A = δ(A - A ), 0 δ 1≤ ≤ , (3.13)
where is actual change in acreage, is desired change in acreage and δ
is the coefficient of adjustment. The value of δ near to one implies that farmers have no
constraint in adjusting their acreage to the desired level in the short term. However, if
this value is close to zero then it suggests that acreage level will take a long time to
adjust.
t tA - A -1 -1*t tA - A
Substituting equation (3.11) into equation (3.13) and rearrangement gives the
reduced form:
, (3.14) t 0 1 t-1 2 t-1 tA = b + b P + b A + V
where 0b = 0α δ
1b = 1α δ
2b = (1-δ)
= tV tδU
Cotton yield and cotton acreage both are derived from the same cotton supply
response function. Therefore, cotton yield, like cotton acreage, depends upon expected
43
prices of cotton and competing crops, as well as input prices. Additionally, the previous
studies, for example by Coleman and Thigpen (1991), and Reddy and Bathaiah (1990),
show that cotton yield in India is influenced not only by economic factors but also by
non-economic variables such as rainfall, and percentage of area under irrigation.
Therefore, these variables are also incorporated in the cotton yield model.
3.1.2. Man-made Fiber Production Response:
In the case of man-made fiber, the total productive capacity is almost fixed in the
short period. It may take several years to expand the current capacity, which is affected
by the expectations of market price for several periods before construction actually
begins (Meyer, 2002). Following Li (2003), this study separately conceptualizes the
production capacity and capacity utilization components of the man-made fiber
production. The output level associated with the tangent point of short-run average cost,
and long-run average cost which occurs at the minimum of the average cost curve, is
defined as capacity.
Supply of man-made fiber will be examined using cost function in general form
as:
, (3.15) c (W,y) = WX (W,y)
where c (W,y is the cost function, X is the vector of input factors, W is the vector of
input prices and y is the output. The cost function is assumed to be concave, continuous,
non-decreasing, and homogeneous of degree one in input prices, W. The theory
pertaining to total cost is used for analytical purposes instead of that of the average cost.
)
44
The reason for this is that if the short-run and long-run total cost curves are tangent to
each other then average costs in short-run and long-run will also be tangent. If Xf is the
vector of fixed factors then the short-run and long-run cost functions can be expressed as:
, (3.16) fSRTC = c (W, y, X )
, (3.17) LRTC = c (W, y)
where SRTC and LRTC are sort-run and long-run total costs respectively. The envelope
theorem states that as the short-run cost minimization problem is the constrained version
of the long-run cost minimization problem, the short-run and the long-run cost curves
must be tangent at the cost minimizing output yc. This suggests that solution for the
equation (3.18) exists. Output yc is the capacity which satisfies its definition given
earlier.
fc (W, y, X ) c (W, y) =
y yδ δ
δ δ, (3.18)
, c cy = y (W, X)
Capacity utilization was theoretically conceptualized in two ways (Li, 2003).
First, the optimal output y* was determined by solving the Lagrangian for profit
maximization problem constraining with input limitation. The Lagrangian can be stated
as:
(3.19) Lπ = py + λ(C-WX)
Second, the capacity utilization rate (CU) was specified as the ratio of optimal
output and capacity.
45
c
y*CU = y
, (3.20)
Equations (3.19) and (3.20) provide structure which can be used to estimate man-made
fiber capacity and the utilization rate being explained by the input price and output price.
3.2. Demand Specifications
Unlike other agricultural products, consumer demand for cotton cannot be
estimated directly because raw cotton as such is not used directly by the consumer. Raw
cotton is demanded by the processors in response to final consumer demand for apparel
and other manufactured textile products. Therefore, cotton demand is derived using a
two-step process - first the fiber equivalent quantity of the textile is conceptualized
followed by the cotton demand in the second stage
Assumptions about consumer behavior are presented through the specification of
a utility function. The utility function is expressed as:
, (3.21) 1 nu = u (x ,..., x )
nwhere 1x ,..., x is an n-element vector of the commodities consumed per unit of time.
The utility function is assumed to be strictly increasing, strictly quasi-concave,
continuous, and twice differentiable. These conditions all imply behavioral consistency
of choice by the consumer they represent (Johnson, Hassan, and Green, 1984).
The utility function (3.21) is maximized subject to a budget constraint, which
specifies that available income is exactly spent:
46
, (3.22) n
i ii =1
p x = y∑
where is the price of iip th commodity and y is consumer income. p and y both are
assumed to be positive and consumers take it as given. Maximization of the utility
function (3.21) subject to the budget constraint (3.22) applying Lagrangian method can
be expressed as:
n
1 n i ii =1
L(x, λ) = u (x ,..., x ) - λ( p x - y)∑ , (3.23)
where is the Lagrangian multiplier representing the marginal utility of income which
depends upon commodity prices and the existing income. Differentiating the Lagrangian
equation (3.23) with respect to each of the arguments, x
λ
i and λ , and solving uniquely for
x1,…, xn and in terms of prices and income yield the system: λ
i i i nx = x (p ,..., p , y)
, (3.24)
1 n λ = λ(p ,..., p , y) , (3.25)
The demand function xi also known as unconditional demand function, or
Marshallian demand function indicates how the consumer will behave when confronted
with alternative sets of prices and a particular income. According to Deaton and
Muellbauer (1980a), these demand functions add up, are homogeneous of degree zero,
symmetric, and show negativity. The former two properties appear due to linear budget
constraint, while the latter two are derived from the presence of consistent preferences.
The Marshallian demand system can also be obtained via another approach.
Using duality theory, the consumer’s problem of maximizing utility for a given budget or
47
cost can be reformulated as one of selecting goods to minimize the budget necessary to
reach utility level u:
, (3.26)
n
i ii =1
1 n
min y = p x
s.t. u = u(x ,..., x )
∑
In both problems, the optimal values of x are determined. However, in dual
problems the determining variables are u and p not x and p, and the same solution as for
primal is obtained but as a function of u and p. The new cost-minimizing demand
functions are known as Hicksian or compensated demand functions and are denoted as
h (u, p). The term ‘compensated’ indicates how x is affected by prices holding u
constant. Since primal and dual problems have the same solutions the following holds
true:
, (3.27) i i ix = f (y, p) = h (u, p)
The two solutions obtained above can be substituted back into their respective
primal and dual problems to get the maximum attainable utility and minimum attainable
cost (Deaton and Muellbauer, 1980a). Therefore,
, (3.28) u = g(x) = g(f(y, p)) = ψ(y, p)
, (3.29) i i i i y = p x = p h (u, p) = c(u, p)∑ ∑
The function is known as the indirect utility function and defined as the
maximum attainable utility given prices p and outlay y. The next function is the
cost function defined as the minimum cost of attaining u at prices p.
ψ (y, p)
c(u, p)
48
3.2.1 Demand of Textile Products
This and the following sections of the study are adapted from Li (2003). The
theory of maximizing consumers’ utility subject to the budget constraint as discussed
earlier is applied to analyze the fiber equivalent demand. Under the separability
assumption, the fiber equivalent as a group is distinct from all other goods, thus two
groups of consumption goods are available: fiber equivalent and other products
equivalent. The consumption function of an individual can be expressed as:
, (3.30) F NF
F F NF NF
max U = f (X , X )
subject to P ×X + P ×X = I
where
U = total utility
FX = the fiber equivalent available to the individual
NFX = other products equivalents available to the same individual
FP = price of fiber equivalent
NFP = price of other products equivalents.
The budget constraint is the sum of expenditure on textile and non textile products
and is equal to real personal disposable income (I).
The utility function with the budget constraint, expressed in equation (3.23) may
be re-written as:
49
2
F NF i ii =1
φ = U (X , X ) + λ (I - p q )∑ , (3. 31)
solving for the per capita fiber equivalent demand ( ): Fq
, (3.32) F Fq = f (P , P , I)NF
OP , (3.33) F FQ = q * P
where
QF = total fiber equivalent demand
POP = population
Since it is assumed that textile products in India operate in a perfectly competitive
market, an individual’s consumption is hypothesized to be not influenced by another.
Therefore, it is assumed that the total domestic demand for fiber equivalents is the sum of
each individual’s demand given a specific price level.
3.2.2. Cotton Demand
An important assumption in estimating cotton demand is that the fiber mill
demand is greater than or at least equal to the amount of textile products (total fiber
equivalent demand) derived from the first step. The mill’s broad group allocation
problem can thus be expressed as:
50
n
F F i ii =1
F 1 n
Max π = P Q - p q
s.t. Q = f(q ,.., q ) i = 1,..., n
∑ , (3.34)
where
ip = price of ith fiber
= quantity demanded of the iiq th fiber
QF = total fiber equivalent demand
PF = price of fiber equivalent
The solution for (3.34) yields group input demands that have the general form:
* *i i F i jq = q (P , p , p ) i = 1...n, j=1,...,n i j≠ . (3.35)
The share of cotton, wool and man-made fibers among total textile consumption is
estimated using LA/AIDS model.
The Indian fiber market is mainly made up of cotton, wool and man-made fibers,
and textile products are distinct from all other goods indicating that the textile cost
function is weakly separable into two sub-cost functions:
, (3.36) F F NF NFc (W, X) = c (X, c (W , X), c (W , X))
where c (.) is cost function, W denotes the vector of input prices, X the vector of inputs, F
refers to fibers, and NF denotes non-fibers. Applying Shephard’s lemma to the sub-cost
function provides the demand function for a particular fiber. For example, cotton, which
51
itself is a function of total textile production and fiber prices can be expressed by this
equation:
F F
Fcot
cot
δc (W , X) = q (W , X)δW
, (3.37)
LA/AIDS (Linear Approximation/ Almost Ideal Demand System) specified by
Deaton and Muellbauer (1980b) is used to represent the sub-cost function for fibers, and
differentiation of which yield a set of cost-minimizing factors demands. This model
presents a first-order approximation to an arbitrary demand system and uses Price
Independent Generalized Logarithmic (PIGLOG) preferences. Since these preferences
allow exact aggregation across consumers, they represent market demands reflecting the
decisions made by a rational representative consumer. PIGLOG preferences are
represented through the cost or expenditure function, which states the minimum
expenditure necessary to attain a specific utility level at given prices (Deaton and
Muellbauer, 1980b). This cost function is denoted by c (u, p), and PIGLOG is defined by
log c(u, p) = (1 - u) log {a(p)} + u log {b(p)}. , (3.38)
u lies between 0 (subsistence) and 1 (bliss) so that the positive linearly homogeneous
functions of a (p) and b (p) can be regarded as the cost of subsistence and bliss,
respectively. The functional forms for log a (p) and log b (p) are given as,
*0 k k kj k
k k j
1log a (p) = a + α logp + γ logp logp2∑ ∑∑ j , (3.39)
kβ0 kk
log b(p) = log a (p) + β Π p , (3.40)
and, thus AIDS cost function is written as
52
kβ*0 k k kj k j 0 kk k j
1log c (u, p) = α α logp + γ logp logp + uβ Π p2
+∑ ∑∑ k , (3.41)
where , and iα iβ*kjγ are parameters. It can be shown that c (u, p) is linearly
homogeneous in p, which shows it to be a valid representation of preferences. The
demand functions can be derived directly from equation (3.41). The price derivative of
any cost function gives quantity demanded i.e. ii
δc(u,p) = qδp
. Multiplying both sides by
ipc(u,p)
, we get
i ii
i
p qlogc(u,p) = = wlogp c(u,p)
δδ
, (3.42)
wi is the budget share of good i. Logarithmic differentiation of (3.41) provides the
budget shares as a function of prices and utility:
kβi i ij j i 0 kj
w = α + γ logp + β uβ Π p∑ k , (3.43)
where * *ij ij ji
1γ = (γ + γ )2
, (3.44)
Combining these gives Hicksian demand (demand in terms of utility rather than the
income),
kβi i ij j i 0 kji
c(u,p) q = α + γ logp + β uβ Πpp
⎛ ⎞⎜⎝ ⎠
∑ k ⎟ , (3.45)
Inverting the cost function and expressing u as a function of p and y, and the fact that
income is equal to expenditure gives
53
kβ*0 k k ij k j 0 i kk k j
1log y = α + α logp + γ logp logp + uβ β Π p2∑ ∑∑ k , (3.46)
k k
*0 k k ij k
k k jβ β
0 k 0 kk k
1α + α logp + γ logp logp2logyu = -
β Π p β Π p
∑ ∑∑ j
, (3.47)
Substitution of u into the share equations allows the expenditure shares to appear as a
function of income and all prices. This is also the equation that is usually estimated.
k
k k
*0 k k ij k j
k k jβi i ij j 0 i k β βkj 0 k 0 kk k
1α + α logp + γ logp logp2logyw = α + γ logp + β β Π p -
β Π p β Π p
⎛ ⎞⎜ ⎟⎜ ⎟⎜ ⎟⎜ ⎟⎝ ⎠
∑ ∑∑∑ ,
(3.48)
AIDS demand function in the budget share form can thus be written as
i i ij j ij
w = α + γ logp + β log{y/p}∑ , (3.49)
where p is a price index defined by
*0 k k kj k
k j k
1log p = α + α logp + γ logp logp2∑ ∑∑ j
k
, (3.50)
If p is approximated by the Stone geometric price index p*,
, (3.51) kk
log p* = w logp∑
The model that uses Stone’s price index is known as the “Linear Approximate AIDS”
(LA/AIDS) (Green and Alston, 1990). From equation (3.49) we get the share of cotton,
54
man-made and wool fiber. To represent a system of demand equation, equation (3.49)
must hold the following restrictions:
Adding up: , (3.52) n n n
i ij ii =1 i =1 i =1α = 1 γ = 0 β = 0∑ ∑ ∑
Homogeneity: , (3.53) ijjγ = 0∑
Symmetry: , (3.54) ij jiγ = γ
Adding-up and homogeneity restrictions show that the demand equation follows a
linear budget constraint. Symmetry is a guarantee of and a test of the consumer’s
consistency of choice. Inconsistent choices are made in the absence of symmetry.
Following Green and Alston (1990), the Marshallian (uncompensated) and the
Hicksian (compensated) elasticities, as well as the expenditure elasticities in the
LA/AIDS model can be computed from the estimated coefficients as follows.
Uncompensated elasticities:
The Almost Ideal Demand System (AIDS) is derived in budget share form as:
( )i ii ij j i
j
p q = α + γ logp + β log y/Py ∑ ,
Own-price elasticities:
Rearranging the above equation, keeping only qi on the left side and then taking partial
derivative with respect to pi:
( ) jii ij j i ii i2 2
ji i i
ywq y y = - α + γ logp + β log y/P + γ - βp p p p
⎛ ⎞∂⎜ ⎟∂ ⎝ ⎠
∑ 2i
, and
55
iiii i
i
γη = -1 + - β ,w
(3.55)
Cross-price elasticities:
jiii i
i j i j
wq y 1 y = γ - βpj p p p p
⎛ ⎞∂⎜ ⎟⎜ ⎟∂ ⎝ ⎠
ij i jij
i i
γ β wη = - ,
w w (3.56)
Expenditure elasticities:
( )i ii ij j i
ji i
q β p q β1 1 = α + γ logp + β log y/P + = + p py⎛ ⎞ ⎛ ⎞∂⎜ ⎟ ⎜ ⎟∂ ⎝ ⎠⎝ ⎠
∑ i i i
i ip y p,
ii
i
βη = 1 + w
, (3.57)
Hicksian elasticities:
Own-price elasticities:
, (3.58) *ii ii i iη = η +w η
Cross-price elasticities:
, (3.59) *ij ij j iη = η + w η
A good can be categorized according to the signs and magnitudes of the
elasticities. If the absolute value of the own price elasticity is less than 1, the demand of
that commodity is inelastic, while if it is greater than 1, the demand is elastic. On the
other hand, the own price elasticity is positive for the Giffen good. Positive cross price
elasticity suggests that the commodities are substitutes, while the commodities are
complements if it is negative. Similarly, the commodity is said to be normal when the
56
expenditure elasticity is positive, while the commodity is inferior if it is negative. The
normal good is said to be a luxury when expenditure elasticity is more than one and a
necessity when it is between zero and one.
3.3. Competitiveness of U.S. Cotton
It could be conceptualized that the demand for U.S. cotton in the Indian market is
price inelastic because of quality differential between cotton imported from the United
States and Australia/rest of the world. India primarily imports the extra long staple (ELS)
cotton from the United States, which is a premium cotton and is preferred for apparel.
On the other hand, the demand for cotton from Australia/rest of the world are likely to be
price elastic because cotton imported from Australia and the rest of the world are
medium/short staple, which is preferred for denim manufacturing and can be substituted
with domestic cotton. Thus, it is hypothesized that import demand for the U.S. cotton
will be expenditure elastic, implying that U.S. market share in the Indian market will go
up as the import demand for cotton in India increases.
The model used for this study is based on the assumption that decisions on
imports by the Indian textile industry are made based upon a two-stage budgeting
process. In the first stage, the importer (India) decides how much cotton to import. In
the second stage, given the total amount imported, the importer decides how much to
import from each supplier (U.S., Australia, and the rest of the world).
57
Let the consumer’s utility function be expressed as in the equation (3.21). Then
in the presence of separability of preferences, the utility function can be partitioned into a
set of sub-utility functions which can be depicted as
1 2 3 C 1 M 2 W 3u = v (x , x , x ) = f [v (x ), v (x ), v (x )] , (3.60)
where x1, x2, x3 are cotton, man-made fibers and wool respectively. The three groups can
be considered separable and f (.) is some increasing function and vC, vM, vW are the sub-
utility functions associated with cotton, man-made and wool fibers respectively.
Following the weak separability in this utility function, the elements of x are allocated
among the xi in such a way that the preference structure within any subutility function can
be determined independently of the quantities of goods consumed in other utility
functions. The utility tree suggests the idea of two-stage budgeting. In the first stage
consumers allocate expenditures to the commodity groups. This allocation is obtained by
maximizing the utility function
subject to ,
1 2 3 C 1 M 2 W 3u = v (x , x , x ) = f [v (x ), v (x ), v (x )]
i ii
p x = y∑
where pi = a price index for commodity group i,
y = total consumer income.
This problem is solved to determine yi which is the proportion of income
allocated to each commodity group. This gives the basis for estimating per capita fiber
equivalent demand, and when multiplied by population gives total fiber equivalent
demand.
58
3.3.1. Cotton Import Demand
In the second stage, consumers’ group expenditures are allocated to the individual
commodities (in this study the U.S., Australia and ROW). This is done by maximizing
sub-utility function for each group subject to the amount of expenditure determined in the
first stage of budget allocation (Colmen and Thigpen, 1991). This is given by,
Max i iv (x )
Subject to i i ii
p x = y∑ ,
where pi is a vector of individual commodity prices associated with xi. The solution to
this problem gives the demand function for each element of xi, in this case, the demand
for U.S., Australia and the ROW which can be shown as:
i i i ix = x (p , y ) , (3.61)
3.4. Summary
This chapter graphically presents the effects of the MFA quota elimination on
Indian and world cotton markets. The analysis provides the directional change of the
effects and suggests that the expected effects of textile quota elimination would be to
increase Indian textile exports, increase Indian cotton imports, increase world price,
increase rest of the world production, and exports. Cotton supply response
conceptualizes two dynamic processes. First, farmers make planting decision based on
their expectation of prices to be received after harvesting, which occurs several months
after planting. Thus, a time lag occurs in agricultural production, which makes the
59
modeling of expectations formation a key issue in the area of agricultural supply response
analysis. Second, the observed acreage and desired acreage differ because of adjustment
lags in the reallocation of variable factors. It may take several years for farmers to reach
their desired acreage level once the price changes. Therefore, specifying adjustment lags
clearly becomes necessary in the model. This dynamic behavior has been captured by the
Nerlovian model where desired acreage is conceptualized to depend upon expected
prices.
Man-made fiber capacity is determined at the point where short-run and long-run
cost functions are tangent to each other. Optimal output for man-made fiber, on the other
hand, is determined by solving the Lagrangian for the profit-maximization constrained by
input limits. Man-made fiber capacity utilization is obtained as the ratio of optimal
output and man-made fiber capacity.
Cotton demand is derived using a two-step process - first the fiber equivalent
quantity of the textile is conceptualized, followed by the cotton demand in the second
stage. Similarly, competitiveness of U.S. cotton is conceptualized on the assumption of
two-stage budgeting procedures, where the importer (India) decides how much cotton to
import in the first stage, while in the second, given the total amount imported, the
importer decides how much to import from each supplier.
60
CHAPTER IV
METHODS AND PROCEDURES
For the purpose of this study, a partial equilibrium structural econometric model
of Indian fiber markets was developed to measure the effects of MFA quota elimination
on Indian cotton trade. Schematic representation of the Indian fiber model, which depicts
the relationships among different components of the model, is presented in figure 4.1.
The framework includes supply, demand, and price linkages equations for cotton and
man-made fibers.
The discussion of the conceptual framework in the beginning of the last chapter
revealed that the expected effects of MFA quota elimination would be to increase Textile
exports, cotton imports, world price, and cotton production. However, the analysis only
revealed the directional change; the change in magnitudes will be determined by the
demand and supply elasticities for which econometric model of the Indian fiber is
developed, which is depicted in Figure 4.1. As shown in the diagram, acreage and yield
levels contribute to cotton production, which builds up total domestic supply after
incorporating beginning stocks and imports. Indian cotton supply responses are
estimated in a regional framework. Cotton-producing area in India is segregated into four
regions in order to account for heterogeneity in growing conditions arising out of climatic
differences, availability of water, and other natural resources that influence the mix of
crops in each of the regions. This is important because liberalization of domestic
agricultural policies is likely to have varying effects on the different cotton-producing
61
Cotton Area
CottonYield
Cotton Production
BeginningStocks
Cotton ImportsA-Index
Domestic Cotton Supply
Cotton Consumption
MarketEquilibrium
CottonExports
Cotton EndingStocks
Apparel Demand
IndustrialDemand
Total Fiber Consumption
Cotton Mill Use Wool Mill
UseMan-madeFiber Mill
Use
Polyester Price
Textile Trade
MarketEquilibrium
Man-madeFiber
Utilization
Man-made Fiber
Capacity
Man-madeFiber
Production
A-Index
Domestic CottonPrice
Man Made Fiber Net Trade
Exogenous Variable
Endogenous Variable
62
Figure 4.1. Schematic Representation of the Indian Fiber Model
regions. Thus, the disaggregated supply model, i.e., four regional models, will avoid the
aggregation bias. The four regions are comprised of northern, central, southern, and rest
of India.
The structural Indian fiber model also takes into account inter-fiber competition
among cotton, wool, and man-made fibers at the mill level. This allows for substitution
between cotton and man-made fibers based on their relative prices. Mill utilization of
each fiber is estimated in two steps (Figure 4.1): (1) total textile consumption, and (2)
allocation of textile consumption among various fibers such as cotton, man-made fibers,
and other fibers based on the relative prices. Thus, the second step yields domestic mill
use demands of cotton, wool, and man-made fiber in the Indian fiber model. Cotton
ending stocks and cotton trade must also be taken into account in the cotton component in
order to close the model. Total cotton supply and total cotton demand in equilibrium
determine the domestic cotton price. Total cotton demand includes domestic cotton mill
utilization, ending stocks, and exports.
Similarly, man-made fiber production (Figure 4.1) is estimated as the product of
capacity and utilization rate. Man-made fiber production and man-made fiber demand,
domestic mill use and net trade combined, determine market clearing conditions for the
man-made fiber sector. This enables to solve the man-made fiber price endogenously in
the model to allow inter-fiber substitution at the mill level. Finally, world cotton price
(A-Index Price) enters into the model through cotton trade equations.
63
After choosing the appropriate framework according to the stated objectives, the
next step is the specification and estimation of cotton, man-made fiber and textile supply,
demand, and trade equations. The specified equations are estimated using ordinary least
squares (OLS), two-stage least squares (2SLS), and seemingly unrelated regression
(SUR), as appropriate. Policy simulation is discussed next, followed by competitiveness
of U.S. cotton. The next section discuss the validation, where the models are evaluated
using signs and magnitude of the parameters, and are also tested for their statistical
validity using t-test, F-test, R-squared value, Theil’s inequality coefficients, and
decompositions of mean squared errors. Finally, data sources are discussed; and the
chapter ends with a summary.
4.1. Model Specification
In this section, each behavioral equation is specified based on the conceptual
framework developed in the previous chapter. In addition, proper functional forms and
appropriate lag structure of the variables, which provide a good fit as well as reasonable
elasticity measures, are chosen.
4.1.1. Fiber Supply Estimation:
4.1.1.1. Cotton Supply Model:
In this study, the Indian cotton-producing area is segregated into four regions in
order to account for heterogeneity in growing conditions arising out of climatic
differences, availability of water, and other natural resources that influence the mix of
64
crops in each of the regions. The four regions include north, central, south and the rest of
India. The ith region acreage response is specified as:
, (4.1) i t i, t i, t i, t-1AC = f(EPC , EPCM , AC )
where i=north, central, south, and the others regions in India. is the cotton acreage
in i
i tAC
th region in time t; represents expected cotton price in ii, tEPC th region in time t;
are the expected prices of competing crops in the ii, tEPCM th region in time t.
Competing crops for the northern region include wheat, rice and rapeseed; for the
central region, include sugarcane, groundnut, rapeseed, and wheat, while groundnut,
corn, sugarcane, and rice are the competing crops for the southern region. The area
devoted to cotton is expected to be positively related to the price of cotton and negatively
related to the prices of competing crops. Expected prices are calculated as the weighted
average of the current year’s minimum support price and last year’s average market
prices for the corresponding crops. Weight for the support price is based on the
proportion of cotton procured by the government (e.g., 18.8 and 19.5 percent in 2002, and
in 2003).
Cotton yield is specified as follows.
, (4.2) i t i, t i, t-1 t t tYC = f (EPC , YC , RF , FA , T )
where represents cotton yield in the ii tYC th region; is cotton yield lagged one year
in the i
i, t-1YC
th region; represents rainfall in millimeter; and is the fertilizer application
in Kilogram/Hectare. Cotton is very sensitive to rainfall. Heavy or scanty rainfall at
tRF tFA
65
critical times of the growth period may cause decline in yield. Additionally, an optimum
application of fertilizer may increase the yield.
Once cotton area and yield have been estimated, cotton production can be
calculated by multiplying area by yield and sum over all four regions:
, (4.3) 4
t i t i=1
CPR = AC *YC∑ i t
where represents total cotton production in India in time period t. tCPR
4.1.1.2. Man-made Fiber Supply Model:
In this model, man-made fiber mill use, the components of man-made fiber
production, man-made fiber capacity, and utilization, are all determined endogenously.
The specifications of man-made fiber production capacity, utilization, and net trade
equations are similar to Li (2003). Following the conceptual framework (3.1.2) derived
in the previous chapter, man-made fiber production capacity is specified as a function of
polyester and crude oil prices lagged three to six years, and man-made fiber production
capacity lagged one year.
, (4.4) t t - k t - k t-1MMPC = f (PP , PO , MMPC )
where represents the man-made fiber productive capacity at time t; is the
lagged price of polyester; is the lagged price of petroleum crude oil;
represents the man-made fiber productive capacity at time t-1, and k = 3, …, 6.
tMMPC t - kPP
t - kPO t-1MMPC
Man-made fiber capacity utilization is specified as the function of polyester and
crude oil prices, and utilization rate lagged one year:
66
, (4.5) t t t t-1MMCUZ = f (PP , PO , MMCUZ , T)
where MMCUZt is the man-made fiber capacity utilization in period t.
4.1.2. Fiber Demand Estimation:
As discussed in the conceptual framework, fiber demand is derived using a two-
step process. In the first step, consumers allocate expenditures to a broad group of
commodities, i.e., per capita textile consumption in fiber equivalent in India is estimated
and then allocated among various fibers in the second stage.
Textile consumption (per capita textile demand in fiber equivalent) is specified as
the function of textile price index, food price index, per capita real income, and time
trend:
t t t tTXPC = f (PTX , PFD , I , T) , (4.6)
where TXPCt is the per capita textile demand in fiber equivalent in time t; is the
textile price index; is the food price index; represents per capita income, and T
represents time trend. Because of economic expansion, consumers will have higher
disposable income resulting in elevated purchasing power, and higher demand for various
products, including textiles and clothing. Thus, textile consumption is hypothesized to be
positively related to income. In addition, economic theory suggests that consumption of
a good is inversely related to its own price and positively related to the prices of
competing goods. Thus, textile consumption is expected to have an inverse relationship
with the textile price index and a direct relationship with the food price index. Equation
(4.6) is estimated by using the OLS method.
tPTX
tPFD tI
67
In the second stage, the share of each of the fibers in the textile consumption
framework is estimated using a LA/AIDS model. The empirical LA/AIDS model for the
cotton, man-made fiber, and wool shares for Indian textiles is specified as follows:
t 1 11 t 12 t 13 t 1 1tYSC =α + γ log(PC ) + γ log(PMF ) + γ log(PW ) + β log + uP*
⎛ ⎞⎜ ⎟⎝ ⎠
, (4.7)
t 2 21 t 22 t 23 t 2 2 tYSMF = α + γ log(PC ) + γ log(PMF ) + γ log(PW ) +β log + uP*
⎛ ⎞⎜ ⎟⎝ ⎠
, (4.8)
t 3 31 t 32 t 33 t 3 3 tYSW = α + γ log(PC ) + γ log(PMF ) + γ log(PW ) + β log + uP*
⎛ ⎞⎜ ⎟⎝ ⎠
, (4.9)
where is the price of cotton; is the price of man-made fiber; is the price of
wool; Y is the total fiber expenditure; is the stone price index; is the share of
cotton fiber in the textile; is the share of manmade fiber, and represents the
share of wool.
tPC tPMF tPW
P* tSC
tSMF tSW
Total Mill demand of the individual fiber in India can be estimated by multiplying
respective fiber shares with total textile consumption and population as
, (4.10) t t tDC = TXPC *SC *PO tP
t
tP
t t tDMF = TXPC *SMF *POP , (4.11)
t t tDW = TXPC *SW *PO , (4.12)
where , , are the total mill demand of cotton, man-made fiber, and
wool, respectively, and POP
tDC tDMF tDW
t represents the total population of India in time t.
68
4.1.3. Cotton Ending Stocks and Trade Equations:
Cotton ending stocks can be specified as the function of domestic cotton price,
cotton production, and beginning stock.
, (4.13) t t t-1CES = f(PC , CES , CPR )t
t
where is the cotton ending stocks. Both beginning stocks and cotton production
variables are expected to have a direct relationship with ending stocks; at the same time,
the cotton market price is expected to have an inverse relationship with ending stocks.
Equation (4.13) is estimated using the OLS method.
tCES
Cotton export in India is specified to depend on relative prices between domestic
and world markets, and cotton supply. The exchange rate is not modeled explicitly here
but it is captured in the model by expressing international price in domestic currency after
adjusting for export tax:
, (4.14) t t t t tCEX = f (PC , PA * (1 - ET )*XR , CSU )
where is the export of cotton; PAtCEX t is the international cotton price represented by
A-Index c.i.f. North Europe; is the cotton supply; the export tax on cotton in
percentage, and is the exchange rate of U.S. dollars into Indian currency. Cotton
exports should increase with a rise in international cotton prices relative to domestic price
and vice-versa. In addition, a greater supply of cotton means that more cotton can be
exported owing to high international price, thus a direct relationship is hypothesized
between supply of cotton and export of cotton. The cotton export equation is estimated
using the OLS method.
tCSU tET
tXR
69
The cotton import equation is specified as the function of the relative domestic
price of cotton with respect to the international price adjusted for the import tariff, and
Income.
t t t t tCIM = f (PC , PA *(1+IT )*XR , I )t , (4.15)
where CIMt is the import of cotton at time t; is the import tariff rate in India in
percentage, and gross domestic product represents the proxy for income ( ) in India. It
is assumed that as increases and the international prices of cotton relative to domestic
price decreases, cotton import demand should increases; that is, more cotton fiber will be
imported. The cotton import equation is estimated by the OLS method.
tIT
tI
tI
4.1.4. Market Clearing Condition:
Finally, an identity equation is added to solve the system of demand and supply
equations for equilibrium prices and quantities of cotton. The equilibrium condition
exists at the point where total cotton consumption is equal to total cotton supply in India.
This can be specified as in the following equation:
t t-1 t t tCPR + CES + CIM = DC + CES + CEX . t
tR
(4.16)
Similarly, the man-made fiber market clearing condition includes production,
consumption, and net trade. Since ending stocks data is not available, it is assumed that
stock does not change from year to year.
, (4.17) t tMMPR = DMF + MMT
70
where represents man-made fiber production, which is obtained through
multiplying man-made fiber capacity utilization with its capacity; is the man-made
fiber consumption, and is the man-made fiber net trade (exports - imports).
tMMPR
tDMF
tMMTR
All the above equations except those of LA/AIDS models are estimated using
OLS. A battery of statistical methods is used to test whether the assumptions of normal,
independent, and identically distributed (NIID) error terms are violated and whether these
violations cause a bias in their estimators. The assumption of homoskedastic conditional
variance is tested using the White’s test and Cook-Weisburbg test. For testing whether
the errors are linear with respect to the conditioning variables, the Respecification Error
Test (RESET) developed by Ramsey is used. To test the assumption of a normal
conditional distribution, the skewness-kurtosis (SK) test, the Shapiro-Wilk (SW) test, and
a graph of the conditional distribution with a normal curve overlay are used. If
heteroskedasticity, autocorrelation, and non-normality are detected, these are corrected
before estimation.
The LA/AIDS model for share of fiber is estimated first without any restrictions
and later, imposing adding-up, homogeneity and symmetry restrictions combined. The
error terms across the equations in LA/AIDS models are correlated by the fact that the
dependent variables need to satisfy the budget constraint. Consequently, the OLS
estimates of these equations would be inconsistent and biased. Therefore, Seemingly
Unrelated Regressions (SUR), which provide more efficient estimates are used instead.
In the first stage, OLS is used to estimate the variance-covariance matrix among the
residuals, while in the second stage, the estimated matrix is used in a generalized least
71
squares estimation. Imposing of the adding-up restriction makes the variance-covariance
matrix for the disturbances singular, thus, non-singularity is maintained by deleting the
equation of Rest of the World (ROW) from the fiber share empirical LA/AIDS model.
The parameters associated with the omitted demand equation are recovered later on by
making use of the restrictions. Correction for autocorrelation is done in the estimation of
parameters, and a non-linear algorithm is used for the estimation of the LA/AIDS model.
All the equations are estimated using SAS (SAS Version 9.1, 2002-2003).
4.2. Policy Simulations
In this study, the results of the estimated econometric model were used to
develop baseline projections for supply, demand, and prices of cotton, man-made fibers,
and textiles, assuming the continuation of the current policies including MFA quotas in
textile and apparel exports. Projections for macroeconomic variables such as real GDP,
consumer price index (CPI), exchange rates, and population were obtained from the 2004
World and U.S. Agricultural Outlook published by Food and Agricultural Policy
Research Institute (FAPRI). The baseline projections for India and the world cotton
market were developed for a span of ten years ranging from 2004/05 to 2014/15 by
linking the Indian fiber model to the existing world fibers model at Texas Tech
University. Linking the Indian model with the world model allowed the world cotton and
man-made fiber prices to be determined endogenously.
Once the baseline projections were developed, scenario projections were
generated by removing MFA quotas. Three scenarios were performed, and these were
72
10, 20, and 30 percent increase in textile exports from India. In the baseline projection,
all the exogenous variables were assumed to be constant, whereas scenario projections
were run by changing exogenous variables of interest (textile exports) in the model. In
both projections, simulation was done until the values of endogenous variables do not
change any more. The policy effects are measured by comparing the differences between
the scenario and the baseline projections.
4.3. Competitiveness of U.S. cotton
After measuring the effects of MFA quota eliminations on the Indian cotton market,
particularly on its cotton imports, the next step was to determine whether U.S. cotton is
competitive enough to capture additional market shares. Competitiveness of U.S. cotton
in Indian market was determined by estimating Indian cotton import demand by origin
using a LA/AIDS model. The LA/AIDS model was estimated first without any
restrictions and later imposing adding-up, homogeneity, and symmetry restrictions
combined. The empirical LA/AIDS model for the USA, Australian and the rest of the
world cotton import demand was specified as follows and are estimated using SHAZAM
(SHAZAM, 2002):
vUSA 1 11 USA 12 AUS 13 ROW 1 1t
v
YW = µ + δ log(P ) + δ log(P ) + δ log(P ) + λ log + uP *
⎛ ⎞⎜ ⎟⎝ ⎠
(4.18)
vAUS 2 21 USA 22 AUS 23 ROW 2 2t
v
YW = µ + δ log(P ) + δ log(P ) + δ log(P ) + λ log + uP *
⎛ ⎞⎜ ⎟⎝ ⎠
(4.19)
vROW 3 31 USA 32 AUS 33 ROW 3 3t
v
YW = µ + δ log(P ) + δ log(P ) + δ log(P ) + λ log + uP *
⎛ ⎞⎜ ⎟⎝ ⎠
(4.20)
73
where are, respectively, the value of market share of the United
States, Australia, and the rest of the world, while are the unit import
values of cotton from the United States, Australia, and the Rest of the World,
respectively. is the Stone price index, is the value of total cotton imports in
India, and are the error terms, associated with the share equations for the
USA, Australia, and the rest of the world, respectively. The error terms,
are assumed to be normally distributed with constant means and
variances, and may be contemporaneously correlated.
USAW , AUSW , ROWW
USAP , AUSP , ROWP
vP * vY
1tu , 2 tu , 3tu
21t 2t 3tu , u , u ~N(µ, σ )
The estimated import demand elasticities of Indian cotton will be used to
determine whether U.S. cotton is competitive enough to capture additional market shares
when situations arise. Own-price, cross-price, and expenditure elasticities for the U.S.
will be compared with that of Australia and the rest of the world. Positive cross-price
elasticities indicate that cotton is substitute for those countries, while negative value
implies it is complement. Large expenditure elasticity suggests the cotton of that country
is of premium quality and is preferred for apparel.
4.4. Model Validation
Model validation is undertaken by using the standard t-tests, F-tests, and R2
procedures where applicable in this analysis. Mean Squared Error (MSE) and Theil’s
inequality coefficient techniques are applied to assess the overall reliability of each
74
model. Theil’s inequality coefficient test is formulated and discussed as follows
(Pindyck and Rubinfeld, 1998):
( )
( ) ( )
T 2s at t
i=1
T T2 2s at t
i=1 i=1
1 Y -YTU =
1 1Y + YT T
∑
∑ ∑, (4.21)
The numerator of the U-statistic is the Root-Mean-Squared Error (RMSE). However, the
denominator is scaled in such a way that U is always between 0 and 1. U = 0 indicates a
perfect fit because = for all t, while U = 1 suggests the model is a poor fit. Thus
the Theil’s inequality coefficient measures the RMSE in relative terms.
stY a
tY
The MSE measures the mean of the squared deviation between simulated and
actual variables, and is expressed as:
T
s at t
t=1
1MSE = (Y -Y )T∑
2 , (4.22)
Where Yst = simulated value of the endogenous variable at time t,
Yat = actual value of the endogenous variable at time t, and
T = number of periods in the simulation.
MSE depends upon the units in which the variable is expressed. The magnitude of the
error does not give any indication of how large the error is, therefore, this error can be
assessed only by comparing it with the average size of the variable in question.
However, the main advantage of MSE is that it can be decomposed into various
components, which show the deviation between the simulated and actual values. Two
methods of decomposition exist: first, by Theil, and second, by Maddala.
75
Pindyck and Rubinfeld (1998) mention the Theil’s decomposition of MSE as
follows:
( ) ( ) ( ) ( )2 2 2s a s a
t t s a s a1 Y -Y = Y -Y + σ -σ + 2 1-ρ σ σT∑ , (4.23)
where sY , are the mean and standard deviation of the series ; and sσstY aY , are the
mean and standard deviation of the series ; and ρ is the correlation coefficient for the
two series. Rearranging, equation 4.23 can be written as,
aσ
atY
( )( )
( )
( )( )
( )
2 2s as a s a
2 2s a s a s at t t t t t
Y -Y σ -σ 2 1-ρ σ σ1 + + 1 1 1Y -Y Y -Y Y -Y
T T T
=∑ ∑ ∑
2 (4.24)
where
( )( )
2s a
2s at t
Y -YUM = 1 Y -Y
T∑, (4.25)
( )
( )
2s a
2s at t
σ -σUS = 1 Y -Y
T∑ , (4.26)
( )
( )s a
2s at t
2 1-ρ σ σUC = 1 Y -Y
T∑ , (4.27)
where , , are the bias, variance and covariance proportions of U,
respectively; and the sum of these three is equal to one.
UM US UC
The represents the systematic error and it is expected to be equal to zero. A
larger value of (above 0.1 or 0.2) suggests the presence of systematic bias. A large
value of US , which is the variance proportion, implies that the actual series has more
UM
UM
76
fluctuation than the simulated series or vice versa, and thus it becomes necessary to
revise the model. Finally, the covariance proportion, , measures the unsystematic
error and is less worrisome than the other two.
UC
According to Coleman and Thigpen (1991), the second decomposition of MSE by
Maddala, consists of bias (UM), regression (UR), and disturbance (UD) terms, and they
are derived as follows:
( ) ( ) ( ) ( )2 2 2s a s a 2t t s a
1 Y -Y = Y -Y + σ -ρσ + 1-ρ σT∑
2a (4.28)
Equation 4.28, after rearrangement, can be written as:
( )( )
( )
( )( )( )
2 2s a 2 2as a
2 2s a s a s at t t t t t
Y -Y 1-ρ σσ -ρσ 1= + + 1 1 1Y -Y Y -Y Y -Y
T T T∑ ∑ ∑2
(4.29)
where
( )( )
2s a
2s at t
Y -Y UM = 1 Y -Y
T∑ (4.30)
( )
( )
2s a
2s at t
σ -ρσ UR = 1 Y -Y
T∑ (4.31)
( )( )
2 2a
2s at t
1-ρ σ UD = 1 Y -Y
T∑ (4.32)
where UM, UR, UD are the bias, regression, and disturbance components of U. The
sum of UM, UR, and UD equals one. The UM and UR components capture the
systematic divergence of the prediction from actual values. Therefore, for a model that
77
fits the data well, the proportion of UM and UR should approach zero. The UD
component, which captures the random divergence of the prediction from the actual
values should approach one.
4.5. Data Requirements
Data were collected from various sources. Macroeconomic variables for India
such as GDP, population, exchange rate, GDP deflator and the average spot price of
crude oil were obtained from “International Financial Statistics” published by the
International Monetary Fund (IMF). Cotton A-Index price, US 1.5 denier polyester price,
and US farm price sheer wool (greasy basis) were collected from Cotton and Wool
Yearbook of Economic Research Service, United States Department of Agriculture (ERS,
USDA). Prices of polyester staple fiber and cotton fiber, and cotton tariff/duty in India
were obtained from Foreign Agricultural Service, USDA and the Textile Commissioner’s
Office, Government of India (GOI). Minimum Support Price for cotton and competing
crops were obtained from the web site of India’s Ministry of Agriculture (available on-
line at http:// agricoop.nic.in/statistics/). The Textile Price Index was gathered from the
Handbook of Industrial Policy and Statistics 2001, India; at the same time, Wholesale
Price Index for food was obtained from the Handbook of Statistics on Indian Economy,
2001 on CD-ROM. Both indices were originally available on 1970/71 and 1981/82 base
years, which were converted to1993/94 base year for consistency. The Textile Price
Index for the year 1982-84 was missing and had to be interpolated. The producer price
for cotton and competing crops was obtained from the data base of Food and Agricultural
78
Organization (FAO). The Consumer Price Index was gathered from the Ministry of
Finance, GOI. Total fiber consumption, total cotton consumption, and total manmade
fiber consumption were obtained from the Foreign Agricultural Services of the United
States Department of Agriculture (FAS/USDA), and the Textile Commissioner’s Office,
GOI. Wool and other fiber consumption were calculated by subtracting cotton and man-
made from total fiber consumption. Similarly, man-made fiber capacity, utilization and
man-made fiber production were also collected from the same sources.
Data for cotton supply and demand was obtained from the FAS/USDA. The
database consists of cotton area, yield, production, imports, exports, ending stocks, and
total domestic consumption. The area, yield, and production data for cotton at the state
level were obtained from the FAS/USDA, Indiastat.com and the Ministry of Agriculture,
India on-line. The yield data for competing crops at the state level were gathered from
the Centre for Monitoring Indian Economy, and the Directory of Indian Agriculture,
1997. Fertilizer consumption was obtained from the Centre for Monitoring Indian
Economy. The percentage of coverage under irrigation was collected from the
Department of Agriculture and Cooperatives, India (available on-line at
http://agricoop.nic.in/statistics/), while rainfall data was collected from Indiastat.com.
Although the data were obtained from various sources, these were cross-checked for
consistency.
Data used in the study of the competitiveness of United States cotton in India
are the annual data for import value and net weight for the period of 1988 to 2002. Data
for the United States, Australia, and the other countries, which are consistent and major
79
suppliers of raw cotton to India, were collected from the website of “Commodity Trade
Statistics” published by the United Nations. The unit import values (equivalent to c.i.f.
prices) were calculated by dividing the total import value by total import volume (net
weight) for each country. Average cotton market shares (in value terms) during the study
period of 1988-2002 are 13.61 percent, 11.56 percent, and 74.83 percent for the United
States, Australia, and the rest of the world, respectively.
4.6. Summary
Supply, demand, and trade equations are specified for cotton and manmade fibers
based on the conceptual analysis developed in the previous chapter. An identity equating
cotton supply with cotton demand cleared the cotton market and determined the cotton
price endogenously. The man-made fiber markets were closed with an identity, which
determines man-made fiber net trade. An expected price was used in the area equation of
cotton in line with the Nerlovian model. This expected price was expressed as the
weighted average of the current year’s minimum support price and the last year’s market
prices for the corresponding crops. Similarly, to estimate individual fiber share in the
textile, textile consumption was estimated in the first stage, whereas individual fiber
share was estimated from textile consumption in the second stage using the LA/AIDS
model. Per capita fiber demand was calculated as the product of textile consumption
with individual fiber share.
A battery of statistical methods were used to test whether the assumptions of
normal, independent, and identically distributed (N.I.I.D.) of error term are violated and
80
whether these violations cause a bias in their estimators. All the equations except those
of the LA/AIDS models were estimated using OLS.
The estimated model was used to develop ten-year (2004-2014) baseline
projection for supply, demand, prices, and trade flows of cotton and man-made fibers. In
the next step, a MFA elimination scenario was developed and compared with the baseline
projections to measure the policy effects. Projections for exogenous parameters were
taken from the study of FAPRI, 2004.
The competitiveness of U.S. cotton in the Indian market was specified and
estimated to determine whether U.S. cotton was competitive enough to capture additional
market shares. Validation of the models was done to assess the overall reliability of each
model using MSE and Theil’s inequality coefficient. Various prices and income data
were deflated using appropriate deflators and were adjusted for exchange rates before
estimation. As data were collected from various sources, they were tested for
consistency.
81
CHAPTER V
RESULTS AND DISCUSSIONS
The results of the various components of the partial equilibrium model along with
simulation results are presented and discussed in this chapter. It opens with the reporting
and discussion of the estimated parameters of supply, demand, and trade equations as
well as model validation statistics. The chapter then proceeds with the discussion of
macroeconomic assumptions required for developing ten-year baseline projections for the
period of 2004/05 to 2014/15. The baseline model is then used to conduct policy
simulation by removing textile quotas. This section provides the comparison of the
baseline and scenario results and highlights the effects of MFA quota eliminations.
Finally, competitiveness of the U.S. cotton in the Indian market is reported, and policy
implications are discussed.
5.1. Fiber Supply Model
5.1.1. Cotton Acreage Model:
The regional cotton acreage models were estimated using OLS for the period
1970-2003. The adjusted R2 for the cotton acreage models for northern, central, and
southern regions are 0.73, 0.62 and 0.49, respectively. This implies that 73, 62 and 49
percent of variations in the acreage in the respective regions are explained by the
explanatory variables included in the equations. The estimated parameters along with the
diagnostic statistics are presented in Table 5.1. The cotton acreage was specified as the
82
Table 5.1: Regression Results of Indian Regional Cotton Acreage Models Northern Central Southern
Intercept 587.06* (289.98)
4115.38*** (593.03)
1431.15*** (185.73)
EPC 0.88* (0.46)
2.04** (0.92)
1.71*** (0.53)
EPCM1 -6.97* (3.91)
-7.94** (3.77)
EPCM2 -19.71*** (5.83)
T 27.34* (15.38)
LAC1 0.64*** (0.14)
LYC2 4102.86** (1911.69)
D02 -309.94** (133.11)
D86
-604.96*** (166.81)
DW Statistic
1.70 2.15
DH Statistic
1.92
Adj. R2 0.73
0.62 0.49
F-Stat 17.44***
10.22*** 8.97***
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
83
function of the expected prices of cotton, expected prices of competing crops, and lagged
acreage.
For the northern region, in addition to expected price of cotton (EPC), expected
price of rice (EPCM1) as the competing crop, and lagged cotton acreage (LAC1), a
dummy variable (D02) was included to represent drought in 2002. The estimated
coefficients of own and competing crop price (rice) have the expected signs and are
statistically significant at the 10 percent significance level, implying that cotton acreage
tends to increase with an increase in the expected price of cotton and cotton acreage tends
to decrease with an increase in the price of rice. The statistically significant coefficient
on the lagged cotton acreage (LAC1) demonstrates fixity in cotton production. Farming
in the northern region is highly mechanized relative to the other parts of India. Since
cotton is a capital intensive crop, it makes it difficult for the producers to switch to other
crops in the short-run based on relative returns. The dummy variable D02 captured the
decline in cotton acreage due to severe drought in 2002.
Similarly, cotton acreage for central region was specified as the function of
expected price of cotton, price of groundnut (EPCM2), a time trend (T), and previous
year’s cotton yield (LYC2). The coefficients of expected prices of cotton and groundnut
are statistically significant with hypothesized signs. The estimated coefficients for lagged
yield is found to be statistically significant with a positive sign, which suggests that
producers in the central region base their cotton acreage decision on yield realized in the
previous year in addition to other economic variables. The negative coefficient on time
84
trend captures the shift in acreage from cotton to more drought resistant oilseeds crops
such as groundnut and soybean.
The specification of cotton acreage in southern region includes the expected
prices of cotton and rice and a dummy variable for 1986 (D86). The coefficients for the
expected prices of cotton and rice are statistically significant and have the expected signs.
The dummy variable was included to account for the drought that was experienced in that
region in 1986.
To obtain further insight, the point estimates for own and cross prices were
converted into elasticities at the sample means and the standard errors associated with the
corresponding elasticities were estimated using the delta method2. The estimates for
regional own- and cross-price acreage elasticities are reported in Table 5.2. As expected,
all the own-price elasticities are found to be positive and range from 0.15 to 0.36, with
the highest for the southern region and the lowest for the central region. These
elasticities are comparable with the own-price elasticities estimated by Coleman and
Thigpen (1991), which ranged from 0.07 to 0.17 for the same three regions. The
estimated own-price elasticity of the northern region is also comparable with the
εε
2 Delta method is a statistical procedure used to quantify the uncertainty associated with estimates obtained from a model. Delta method quantifies how the variance transfers from the parameters (β ) that are estimated directly by the statistical model and those parameters ( ) that are derived from the application of mathematical formulations. In this case,β ’s are the estimated parameters for the price variables, and ’s are the own- and cross-price elasticities. Mathematically, it can be expressed as:
2
XVariance(ε )= Variance(β ) Y
∧ ∧⎛ ⎞⎜ ⎟⎝ ⎠
85
Table 5.2: Elasticities of Indian Regional Cotton Acreage Model at Sample Mean Region Acreage Elasticities with respect to the Expected Price of
Cotton Rice Groundnut Northern
0.20 (0.10)
-0.21 (0.11)
Central 0.15 (0.07)
-0.35 (0.10)
Southern 0.36 (0.11)
-0.22 (0.10)
Notes: Standard errors are reported in the parentheses below the coefficients.
86
findings of Kaul (1967), who reported the own-price elasticity of 0.29 for the northern
state of Punjab. Generally, the estimated own-price acreage elasticities in Table 5.2
suggest that a 1 percent increase in the price of cotton will result in an increase in 0.2,
0.15 and 0.36 percent increases in the acreage of cotton in the northern, central and
southern regions, respectively. On the other hand, the estimated cross-price acreage
elasticities in the same table indicate that a 1 percent increase in the price of rice will
result in the 0.21 and 0.22 percent decline in the acreage planted of cotton in northern and
southern regions, respectively. A 1 percent increase in the price of groundnut will result
in a 0.35 percent decrease in the cotton planted acreage in the central region.
5.1.2. Cotton Yield Model:
The estimated parameters for regional cotton yield equations are reported in Table
5.3. The yield equations were specified as the function of expected prices of cotton and
time trends. Since cotton in the northern region is mostly irrigated, the percentage of area
under irrigation (PERCVIRG) was also included as an explanatory variable for the
northern region yield model. In addition, lagged cotton acreage (LAC1) was included in
the northern yield equation to capture the effects of rising cotton acreage on average
yield. Lagged yield was also included in the northern and southern region yield models,
suggesting that yield realized in the previous year influences the current cotton yields in
these regions. A dummy variable (D01) was included in the central region yield model to
capture the effect of sudden decrease in yield in the 2001 crop season. The adjusted R2
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Table 5.3: Regression Results of Indian Regional Cotton Yield Models Northern Central Southern
Intercept 0.098 (0.058)
-0.083 (0.048)
0.019* (0.105)
EPC 0.0002* (0.0001)
PERCVIRG 0.006* (0.003)
LAC1 -0.0001* (0.0000)
T 0.006*** (0.001)
0.004*** (0.001)
LYC1 0.649*** (0.159)
LYC3 0.493*** (0.138)
D01
0.062* (0.031)
DW Statistic 1.383 DH Statistic
-2.345
-1.28
Adj. R2 0.371
0.723 0.911
F-Stat 7.68***
22.85*** 200.76***
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
88
values range from 0.37 to 0.91, with the highest for the southern region and the lowest for
the northern region. The possible cause of the low Adjusted R2 value in the northern
region may be that weather variability is more severe, and monsoon rainfall is erratic,
which are not being captured by the model.
The coefficient of expected price of cotton was found to be positive and
statistically significant only for the central region. For the other two regions, results
suggest a lesser role of price in influencing the yield level. For the northern region, the
percentage of the irrigated cotton acreage was found to have a statistically significant and
positive effect on yield. The coefficient for the lagged cotton acreage in the northern
region yield equation was found to be negative and statistically significant. This might
suggest that the average yield in the northern region has declined as the marginal lands
were brought into the cotton production. The strong upward trend in yield in the northern
region is evident from statistically significant positive coefficient for lagged cotton yield
(LYC1) variable.
In addition to the expected price of cotton, a time trend (T) and dummy variable
for 2001 (D01) were also included in the cotton yield equation for central region. The
estimated coefficient on time trend (T) is statistically significant and positive, implying
that yields have increased over time and it is likely due to varietal and technological
improvements in the central region. D01 captures the effect of sudden decrease in yields
in the 2001 crop season in the central region.
Similarly, cotton yield equation in the southern region included time trend (T),
and yield lagged one year (LYC3). The coefficients on the both these variables are
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statistically significant with positive signs, which imply that improved technology and
yield realized in the previous year influence the current cotton yields in this region.
5.1.3. Man-made Fiber Supply Model
Man-made fiber production is calculated as the product of production capacity
and utilization rate. The man-made fiber production capacity is specified as the function
of the ratio of three to six year lagged prices of polyester and crude oil (LDEFF) and
production capacity lagged one year (LLMMPC). Similarly, the man-made fiber
capacity utilization rate is explained by the ratio of prices of polyester and crude oil
(PRPET), as well as one year lagged capacity utilization (LMMCUZ). The lag structure
in the production capacity equation was determined using Akaike Information Criterion
(AIC). The estimated parameters along with the diagnostic statistics are reported in
Table 5.4.
The lagged dependent variables (LLMMPC for capacity and LMMCUZ for
utilization rate) are found to be statistically significant in both of these equations,
implying high degree of fixity in man-made fiber production process. The coefficient for
the time trend (T) variable in the capacity utilization equation is statistically significant
with negative sign, which captures the slightly declining trend of the utilization rate over
the study period. This seems plausible because plant size and levels of modernization in
man-made fiber sector are still below the international standards and therefore utilization
rate has been declining over time. The dummy variable for 2002 and 2003 (D0302) was
included in the production capacity equation to capture the sudden decline of capacity
90
Table 5.4: Regression Results of Manmade Fiber Capacity and Utilization Capacity Utilization Rate
Intercept 2.915*** (0.938)
0.857*** (0.277)
LDEFF -0.942** (0.433)
LLMMPC 0.616*** (0.127)
PRPET 0.619** (0.286)
LMMCUZ 0.356* (0.185)
T -0.008** (0.004)
D0302 -0.359* (0.187)
DH Statistic -0.304
-0.630
Adj. R2 0.937
0.443
F-Stat 94.85*** 7.09***
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
91
during those years. The adjusted R2 value for the capacity equation is 0.94, suggesting
that most of the variation in the capacity is explained by the variables included in the
equation. The low R2 value for capacity utilization equation suggests that capacity
utilization is mostly determined by non-market forces and the reasons for these are not
clearly understood.
5.2. Fiber Demand Models
As explained in the previous chapter, the fiber demand was derived using a two-
step process. In the first step, Indian per capita textile consumption in fiber equivalent
was estimated, and then it was allocated among the raw fibers in the second stage to
derive the mill use of individual fibers.
5.2.1. Per Capita Textile Consumption
The per capita textile consumption was estimated using OLS for the period 1970-
2003. The consumption equation was specified as the function of per capita real GDP
(GDPIND), the ratio of food price index to textile price index (CTWH1), and a time trend
variable (T). Time trend variable was added in the equation to capture the rise in textile
consumption over time. The estimated parameters along with the diagnostic statistics are
presented in Table 5.5. The adjusted R2 for the per capita textile consumption equation is
0.96, suggesting that 96 percent variations in textile consumption is explained by the
explanatory variables included in the equation. Although the DW statistic signaled the
problem of autocorrelation, no corrective measures were undertaken.
92
Table 5.5: Regression Results of Per Capita Textile Consumption Per Capita Textile Consumption
Intercept 0.846*** (0.276)
CTWH1 5.238*** (1.263)
GDPIND 0.215*** (0.069)
T 0.077** (0.030)
DW Statistic 1.286
Adj. R2 0.959
F-Statistic 197.48***
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
93
The coefficient for the GDPIND variable is found to be statistically significant at
the one percent level with the hypothesized sign. This suggests that textile consumption
increases with the rise in standard of living and vice-versa. In order to avoid
multicollinearity between textile and food price indices, the ratio of food price to textile
price was included in the equation rather than individual variables. The coefficient for
the price ratio is statistically significant with positive sign, suggesting that an increase in
the ratio, either due to rise in food price index or decline in textile price index increases
textile consumption and vice-versa.
In addition, the coefficient for the trend variable (T) is found to be statistically
significant with a positive sign. This may suggest that part of the increase in textile
consumption can be attributed to factors other than income and population. Some
possible factors may include greater choices of textile products and brand names
availability because of textile and apparel trade liberalizations, and shift in consumer
preferences in India.
5.2.2. Fiber Demand
In the second stage, total textile demand was allocated among competing fibers,
i.e. cotton, wool, and man-made fibers using the AIDS demand system. The share
equation for individual fiber is specified as the function of prices of cotton, man-made
fiber, and wool, as well as total fiber expenditure. The demand system was estimated
using non-linear SUR with symmetry and homogeneity imposed. The wool equation was
omitted from the estimated system and was later obtained through the adding-up
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constraint. The estimated parameters along with standard errors are presented in Table
5.6. All the estimated parameters are statistically significant at the 1 percent level.
The coefficients for the estimated parameters of the total fiber expenditures are
found to be negative for cotton and wool, and positive for man-made fibers. This implies
that an increase in total fiber expenditure results in the decline of cotton and wool shares
in textile but causes an increase in the share of man-made fiber. The estimated
parameters converted into elasticities are discussed below.
The estimated parameters were converted into price and expenditure elasticities at
the sample mean and are reported in Tables 5.7 and 5.8. As expected, all the expenditure
elasticities are positive and range between 0.47 and 1.39. Interestingly, expenditure
elasticity for man-made fiber was found to be highly elastic as compared to cotton and
wool. This suggests that one percent increase in total expenditure at the mill level will
increase the demand for man-made fibers by 1.39 percent, for cotton by 0.82 percent, and
for wool by 0.47 percent. The expenditure elasticity of 0.82 for cotton is higher than that
of Meyer (2002), who used the income elasticity of 0.22 for cotton.
All uncompensated own-price elasticities are found to be negative and range from
-0.04 to -0.63, with the lowest for wool and the highest for the man-made fibers (Table
5.7). Own price elasticity of cotton is more or less the same as that of man-made fiber.
On the other hand, Hicksian own price elasticities in Table 5.8 are much smaller in
magnitude, and range between -0.03 and -0.18. The estimated own price elasticity for
cotton in this study is comparable with the findings of Meyer (2002) who estimated it to
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Table 5.6: Regression Results of Fiber Demand System Share Intercept
( ) iαCotton Price
( i1γ ) Man-made Fibers
Price ( i2γ ) Wool Price
( ) i3γExpenditures
( ) iβ
Cotton ( ) tSC
2.307*** (0.299)
0.157*** (0.0148)
-0.144*** (0.0148)
-0.013 -0.118*** (0.021)
Manmade Fibers ( tSMF )
-1.44*** (0.30)
-0.144*** (0.0148)
0.147*** (0.015)
-0.003 0.127*** (0.022)
Wool ( ) tSW 0.133 0.133
-0.013 -0.12
-0.009
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
96
Table 5.7: Estimated Uncompensated Fiber Price and Income Elasticities Demand Elasticities with respect to the Price of
Expenditure Elasticities
Cotton Man-made Fibers
Wool
Cotton -0.63
-0.18 -0.02 0.82
Man-made Fibers -0.73
-0.63 -0.02 1.39
Wool -0.36
-0.08 -0.04 0.47
97
Table 5.8: Estimated Compensated Fibers Price Elasticities Demand Elasticities with respect to the Price of
Cotton Man-made Fibers Wool
Cotton -0.09 0.09 -0.01
Man-made Fibers 0.18
-0.18 0.01
Wool -0.05 0.08 -0.03
98
be -0.11. The positive compensated cross price elasticities between cotton and man-made
fibers demonstrate that they are net substitute at the mill level. Similarly, cross-price
elasticities between man-made fibers and wool are found to be positive and small,
suggesting weaker substitutional relationship between these two fibers at the mill level.
However, compensated cross-price elasticities between cotton and wool, and wool and
cotton are small and negative, implying weaker complementary relationship between
cotton and wool at the mill level.
5.3. Fiber Trade and Ending Stocks Equations
The equations for cotton exports and imports were estimated separately using
OLS. Both of these equations were specified as the function of domestic and
international cotton prices. In addition, cotton supply was also included as an
explanatory variable in the cotton exports equation to account for the government
restrictions on the cotton exports. Historically, the Indian government has allowed cotton
exports in the surplus years and restricts exports in the years when the supply is tight.
The world cotton price, represented by the A-index price, was converted into the local
currency using the market exchange rate, and the import/export tariffs were added to the
international prices. In order to avoid multicollinearity between the domestic and the
international prices, the ratio of domestic to world price was included as the explanatory
variable rather than individual prices. The regression results along with the diagnostic
statistics are reported in Table 5.9. The low R2 values for both the cotton imports and
cotton exports equations suggest the role of excluded variables in determining the level of
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Table 5.9: Regression Results of Cotton Trade Equations Cotton Exports Cotton Imports
Intercept 174188** (76321)
52489 (44954)
CSU 0.011 (0.014)
WW -145900** (67850)
PRCTAI 27854 (25953)
D9901 96421*** (16840)
DW Statistic 1.826
1.834
Adj. R2 0.113
0.590
F-Stat 2.970*
15.640***
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
100
imports and exports. The excluded variables may include competition from man-made
fibers, lack of modernization of spinning and weaving sectors, low quality and
contamination of cotton, slow transportation and handling facilities, and oligopolistic
influence on trade and price of cotton trading companies.
As would be expected, the coefficient on the domestic to A-Index price ratio
(WW) was found to be negative and statistically significant in the cotton exports
equation. This implies that the cotton exports from India would increase as the domestic
price of cotton decreases relative to the world price and vice-versa. However, the
estimated coefficient for the cotton supply variable (CSU) was not statistically different
from zero, indicating a lesser role of domestic supply in determining export level.
Cotton imports in India are explained by the ratio of domestic cotton market price
to the world cotton price (PRCTAI) and a binary variable for 1999 and 2001 (D9901)
jointly to capture the effects of change in trade policy and price effects. In constructing
the PRCTAI variable, A-index price (international cotton price) was converted into the
local currency using the market exchange rate, and the import tariffs were added to the
international price; while in the WW variable in the cotton exports equation, no tariffs
were added because of zero export tax on cotton exports from India. In the initial
regression results, the coefficient on PRCTAI was found to be negative. However, after
correcting for first order autocorrelation using maximum likelihood estimation, the sign
of the coefficient for PRCTAI changed to positive and DW statistic and adjusted R2 value
also comparatively improved. The coefficient on PRCTAI, is however, not statistically
significantly different from zero but has the hypothesized positive sign. The binary
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variable D9901 captures the effects of change in trade policy on cotton imports for 1999
and 2001.
In the case of man-made fibers, the net trade equation was estimated rather than
separate equations for exports and imports, primarily because of non-availability of data.
For the estimation period, the net trade data was calculated by taking the difference
between production and consumption of man-made fibers. The man-made fiber net trade
equation was specified as the function of the international price represented by the U.S.
1.5 denier polyester price (DUSPP), domestic polyester price (DPP), and man-made fiber
net trade lagged one year (LMMTR). Since the share of cellulosic fiber is negligible, the
polyester price was used as the representative price for the man-made fibers. The
regression results reported in Table 5.10 indicate that the coefficient for DUSPP is
positive and statistically significant, while that for DPP is not statistically significantly
different from zero but has the expected negative sign. This implies the importance of
the international price rather than the domestic price in determining the man-made fiber
trade level. The coefficient for the lagged man-made fiber net trade (LMMTR) is
statistically significant with positive sign, which indicates that man-made fiber net trade
in the previous year influences the current man-made fiber net trade in India. The
Adjusted R2 value of 0.88 suggests that majority of the variation in man-made fiber net
trade is explained by the explanatory variables included in the equation.
Finally, the estimated parameters and the diagnostic statistics for the cotton
ending stocks equation are reported in Table 5.11. The ending stocks was explained by
cotton price (DPC), beginning stock (LCES), and a dummy variable for 1995 (D95) to
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Table 5.10: Regression Results of Manmade Fiber Net Trade Manmade Fiber Net Trade
Intercept -166.386 (160.293)
DUSPP 457.850** (208.362)
DPP -13.646 (65.765)
LMMTR 0.916*** (0.158)
DH Statistic 1.222
Adj. R2 0.882
F-Stat 58.35***
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
103
Table 5.11: Regression Results of Cotton Ending Stocks Cotton Ending Stocks
Intercept 636077*** (208293)
DPC -511311 (308920)
LCES 0.399** (0.153)
D95 505539** (176136)
DW Statistic 1.795
Adj. R2 0.396
F-Stat 7.78***
Notes: Standard errors are reported in the parentheses below the coefficients. * indicates significance at the 10 percent level, ** indicates significance at the 5 percent level, and *** indicates significance at the 1 percent level.
104
capture the effect of sudden rise in stocks in 1995. The coefficient for DPC is found to
be negative and statistically significant at the 10 percent level, suggesting an inverse
relationship between price and carryover stock. As hypothesized, the LCES is
statistically significant and directly related with the cotton ending stocks. D95 captures
the effect of sudden rise in stocks in 1995. The low R2 value for the cotton ending stocks
equation is not surprising in the presence of the government procurement program.
Historically, more than half of the carryover stock is held by the government.
5.4. Model Validation
After estimating all the equations, the model was solved simultaneously in a
simulation program using SAS (Statistical Analysis System). Historical simulation of the
model’s equations was used to validate the estimated model using the components of the
Mean Squared Error (MSE) and the Theil inequality coefficients. Table 5.12 presents the
decomposition of the MSE and Theil U coefficient. The decomposition of MSE provides
two sets of statistics. The first decomposition suggested by Theil gives bias (UM),
variance (US), and covariance (UC) statistics. The second decomposition, as suggested
by Maddala, consists of bias (UM), regression (UR), and disturbance (UD) components.
An adequate model produces projections in which UM approaches zero, i.e. the model is
without consistent bias; US approaches zero, implying variability of the predicted series
closely resembles the variability of the actual series; and the random deviation (UC) is a
large number. In the second decomposition, the bias and regression components capture
the systematic divergence of the prediction from actual values. Therefore, for a model
105
Table 5.12: Model Validation Statistics Variable Bias
(UM) Reg (UR)
Dist (UD)
Var (US)
Covar (UC)
U
AC1 0.00
0.05 0.95 0.35 0.65 0.05
AC2 0.01
0.07 0.93 0.05 0.94 0.02
AC3 0.11
0.01 0.88 0.14 0.75 0.05
YC1 0.12
0.00 0.88 0.44 0.44 0.09
YC2 0.01
0.02 0.98 0.39 0.61 0.08
YC3 0.44
0.12 0.44 0.32 0.23 0.13
TXPC 0.04
0.17 0.79 0.05 0.91 0.02
CES 0.66
0.12 0.23 0.00 0.34 0.12
CIM 0.41
0.22 0.36 0.49 0.10 0.53
MMPC 0.84
0.01 0.16 0.05 0.12 0.13
MMCUZ 0.87
0.00 0.12 0.11 0.01 0.07
PC 0.68
0.22 0.10 0.04 0.28 0.21
PP 0.03
0.16 0.82 0.08 0.89 0.17
DC 0.00
0.68 0.32 0.22 0.78 0.05
DMF 0.65
0.16 0.19 0.18 0.17 0.03
MMTR 0.39
0.02 0.59 0.53 0.08 0.16
AC 0.06
0.00 0.94 0.16 0.78 0.02
CPR 0.13
0.38 0.49 0.04 0.83 0.05
EXPEND1 0.02
0.68 0.30 0.36 0.62 0.14
106
that fits the data well, the proportion of UM and UR should approach zero. The UD
component, which captures the random divergence of the prediction from the actual
values, should approach one. The Theil U coefficient should approach zero when the
predicted series is close to the actual series.
The MSE and its decomposition reported in Table 5.12 show that the majority of
the UM and UR values are close to zero. This suggests that those simulated values are
close to their actual values. However, the UM values for a few variables, such as cotton
ending stocks, man-made fiber production capacity, capacity utilization, and consumption
are quite high. Consequently, disturbance terms are low, which implies that errors of
these simulated variables are not captured by the randomness contained in the actual data
series. Contrary to UM and UD, most of the UR values are close to zero. In the second
decomposition, US component performs well; however, UC values in some instances are
fairly low. Compared to the decomposition of MSE statistic, almost all the Theil’s U-
Statistic are close to zero for the endogenous variables for the model. The only variable
whose value is high is the cotton import variable. This suggests that overall the
simulation model has reasonably good forecasting ability.
5.5. Policy Simulations
Once the specified model was estimated, the next step was to use the estimated
model to develop baseline projections for Indian fiber demand, supply, and prices for ten-
year period under a set of assumptions for exogenous variables. Some of the exogenous
variables include per capita GDP, exchange rate, consumer price index, crude oil price,
107
and population. Projections for macro economic variables such as real GDP, consumer
price index (CPI), exchange rates, and population were obtained from the 2004 World
and U.S. Agricultural Outlook published by the Food and Agricultural Policy Research
Institute (FAPRI). The GDP is projected to grow at an annual rate of 5.5 percent between
2003 and 2015. During the next ten years, the exchange rate is projected to appreciate in
the initial years and depreciate towards the later period. The population growth is
projected to decline from 1.50 percent in 2003 to 1.23 in 2014. Projections for
international prices for crude oil, wheat, rice, corn, and groundnut were also taken from
the same source. Projections for competing crop prices were obtained by regressing
domestic prices on their respective international prices. Projections for textile and food
price indices were obtained by the regression of these indices on consumer price index.
Baseline projections generally assume the continuation of the current policies including
MFA quotas in textile and apparel exports.
The baseline projections were developed by linking the India model to the world
fiber model of Texas Tech University (Pan et al., 2004). The fiber trade from the India
model was used in the world model to solve for world fiber prices, and the world fiber
prices entered the India model through the trade equations. Once the baseline projections
for fiber demand, supply, and prices were developed, policy simulations were conducted
by running the baseline model at different levels of textile exports to measure the effects
of MFA quota elimination on the Indian cotton market. Since the effects of MFA quota
elimination on the Indian textile exports are not known, three scenarios were conducted
by increasing textile exports 10, 20, and 30 percent, above the baseline level.
108
5.5.1. Baseline Projections:
As shown in Figure 5.1, the total textile consumption is projected to rise by 30
percent from 4803.45 thousand metric tons (TMT) in 2004 to 6224.36 TMT in 2014,
partly due to rising per capita consumption. Per capita textile consumption is projected to
increase by about 14 percent in the next ten years, primarily driven by strong income
growth. Rising textile consumption will result in higher mill use of raw fiber, with cotton
and man-made fiber mill use increasing by 13 and 72 percent, respectively (Table 5.13).
During the baseline period, the domestic fiber prices are projected to rise steadily.
As shown in Figure 5.2 and in Table 5.13 the cotton price is expected to rise from 52.50
rupees per kilogram in 2004/05 to 64.65 rupees per kilogram in 2014/15, whereas the
polyester price increases from 77.78 rupees per kilogram to 88.25 rupees per kilogram
during the same period. The cotton production, on the other hand, is projected to rise by
19 percent from 3,180 TMT in 2004/05 to 3,778 TMT in 2014/15 ( Table 5.13). As
expected, most of the production growth is projected to come from yield improvements
rather than area expansion. Although total cotton area is projected to be flat in the next
decade, shift in the cotton area among the regions is likely to happen based on relative
profitability among crops. An increase in cotton area in the central region is projected to
be offset by decline in area in the other two regions.
Average cotton yield, one of the lowest in the world, is projected to increase by
around 18 percent in the next decade, primarily because of expected adoption of Bt
(Bacillus thuringiensis) cotton at a larger scale. This is fairly conservative considering
the findings of ICAC (2004), which concluded that Bt cotton adoption could increase
109
2000
2500
3000
3500
4000
4500
5000
5500
6000
6500
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15
000 MT
110
Figure 5.1. Baseline Projections for Textile Consumption in India
30
40
50
60
70
80
90
100
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15
Cotton Man-made Fiber
Rupees per Kilogram
111
Figure 5.2. Baseline Projections for Domestic Fiber Prices
Table 5.13: Summary of Baseline Projections for Fiber Demand, Cotton Price, Polyester Price, Fiber Production, and Fiber Trade in India, 2004/05-2014/15. 2004/05 2009/10 2014/15 Cotton Consumption at the mill (000 Metric Ton)
3397.16 3575.55 (5%)
3849.48 (13%)
Man-made Fiber Consumption (000 Metric Ton)
1348.15 1952.73 (45%)
2316.72 (72%)
Price of Cotton (Rupees/Kilogram)
52.50 60.59 (15%)
64.45 (23%)
Price of Polyester (Rupees/Kilogram)
77.78 77.34 (-0.6%)
88.25 (13%)
Cotton Production (000 Metric Ton)
3180.48 3499.42 (10%)
3777.90 (19%)
Man-made Fiber Production (000 Metric Ton)
1640.02 2174.96 (33%)
2537.98 (55%)
Cotton Acreage (000 Hectare)
7932.50 8045.15 (1%)
8064.59 (2%)
Cotton Yield (Metric Ton/Hectare)
0.40 0.43 (8%)
0.47 (18%)
Net Import of Cotton (000 Metric Ton)
155.62 167.95 (8%)
197.06 (27%)
Net Export of Man-made Fiber (000 Metric Ton)
291.88 222.22 (-24%)
221.26 (-24%)
* Notes: Figures in parenthesis indicate percentage change compared to 2004/05.
112
yield as much as 50 percent. However, the Government of India has approved
commercial planting of only four genetically engineered Bt varieties for the central and
southern states, and currently less than 10 percent of total planted cotton area is under Bt
cotton (USDA/FAS, 2004).
Currently, India is a net importer of cotton and net exporter of man-made fibers.
Strong domestic fiber demand in the future, fueled by rising textile exports, is likely to
make India a growing net importer of cotton and a declining net exporter of man-made
fibers. Cotton net imports are projected to rise by 27 percent from 156 TMT in 2004/05
to 197 TMT in 2014/15 (Table 5.13). Similarly, man-made fibers’ net exports are
projected to decline by 24 percent from 292 TMT to 221 TMT during the same period
(Figure 5.3).
5.5.2. Simulation Results
After developing the baseline, alternate scenarios were performed for three
different levels of textile exports at 10, 20, and 30 percent above the baseline level.
Scenarios 1, 2, and 3 refer to 10, 20, and 30 percent increases in textile exports,
respectively, due to MFA quota elimination. Simulation results, expressed as a
percentage change from each year’s baseline level, are summarized in Tables 5.14-5.18.
As shown in Table 5.14, the increase in textile exports due to quota eliminations
raises the domestic mill use of raw fibers in India. Cotton and man-made fiber mill use
are projected to increase each year by an average of 1.2 and 5.1 percent, respectively,
113
-200
-150
-100
-50
0
50
100
150
200
250
300
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15
Cotton Man-made Fiber
000 MT
114
Figure 5.3. Baseline Fiber Net Trade Projections
Table 5.14: Effects of MFA Quota Elimination on Indian Fiber Consumption and Domestic Fiber Prices
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 AverageCotton Consumption Thousand Metric Tons
Baseline 3397.16
3459.06
3453.83 3506.99 3544.42 3575.55 3609.59 3677.39 3729.88 3786.31
3849.48 3599.06Percentage Change
Scenario 1 0.15 1.79 1.41 1.43 1.36 1.20 1.18 1.15 1.15 0.97 1.09 1.17Scenario 2 0.19 2.37 1.78 1.67 2.32 2.62 2.69 2.77 2.55 2.57 2.70 2.20Scenario 3
0.36 3.35 2.62 2.49 2.41 2.60 2.70 3.53 3.76 3.20 3.43 2.77
MMF Consumption
Thousand Metric Tons Baseline 1348.15 1511.23 1635.70 1759.64 1863.98 1952.73 2076.35 2148.92 2210.48 2259.29
2316.72 1916.65
Percentage Change Scenario 1 8.53 4.23 4.89 4.71 4.74 4.95 4.86 4.87 4.84 5.13 4.90 5.15
Scenario 2 17.34 11.22 11.98 11.79 10.25 9.51 9.16 8.95 9.26 9.20 8.94 10.69Scenario 3
25.81 17.29 18.08 17.73 17.41 16.69 16.06 14.50 14.01 14.90 14.45 16.99
Indian Cotton Price
Rupees per Kilogram Baseline 52.50 56.58 59.26 60.07 60.54 60.59 61.75 62.75 63.96 64.55
64.65 60.65
Percentage Change Scenario 1 7.99 5.07 5.52 5.55 5.17 5.44 5.53 5.76 4.81 5.71 6.85 5.76
Scenario 2 10.77 6.58 6.33 9.84 11.68 12.53 13.63 12.82 13.31 14.52 15.59 11.60Scenario 3
15.13 10.04 9.93 9.95 11.74 12.49 17.41 19.13 16.30 18.32 21.70 14.74
Indian Polyester Price
Rupees Per Kilogram Baseline 77.78 80.34 84.48 83.89 81.41 77.34 77.20 81.13 85.46 87.93
88.25 82.29
Percentage Change Scenario 1 14.63 12.34 8.43 8.58 10.02 13.74 13.95 14.55 15.73 15.82 16.01 13.07
Scenario 2 18.98 16.53 11.96 17.28 21.74 29.72 31.25 30.36 24.57 22.64 23.11 22.56Scenario 3 25.35 25.70 20.82 22.07 25.91 30.51 34.63 35.86 36.53 39.34 42.50 30.84
115
Notes: Scenario 1 indicates a 10 percent increase in textile exports, scenario 2 indicates a 20 percent increase in textile exports, and scenario 3 indicates a 30 percent increase in textile exports.
Table 5.15: Effects of MFA Quota Elimination on Indian Cotton Area
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 AverageNorthern Region Thousand Hectares
Baseline 1303.59
1328.22
1350.50 1365.07 1366.76 1358.87 1343.57 1327.76 1311.43 1295.89
1279.18 1330.08Percentage Change
Scenario 1 0.00 1.45 1.90 2.28 2.51 2.59 2.67 2.73 2.79 2.67 2.72 2.21Scenario 2 0.00 1.96 2.51 2.83 3.64 4.46 5.11 5.71 5.96 6.19 6.51 4.08Scenario 3
0.00 2.75 3.68 4.27 4.63 5.15 5.58 6.64 7.60 7.79 8.19 5.12
Central Region
Thousand Hectares Baseline 5021.77 5069.67 5076.31 5127.70 5158.05 5182.99 5205.51 5238.57 5273.41 5310.98
5345.00 5182.72
Percentage Change Scenario 1 0.00 0.91 0.77 0.75 0.74 0.67 0.65 0.65 0.65 0.55 0.59 0.63
Scenario 2 0.00 1.23 1.01 0.88 1.23 1.45 1.50 1.58 1.47 1.46 1.52 1.21Scenario 3
0.00 1.73 1.51 1.37 1.32 1.46 1.50 1.95 2.14 1.85 1.91 1.52
Southern Region
Thousand Hectares Baseline 1526.13 1546.98 1457.96 1456.73 1440.32 1422.29 1402.49 1391.69 1380.55 1371.69
1359.42 1432.39
Percentage Change Scenario 1 0.00 2.49 1.71 1.86 1.83 1.66 1.69 1.69 1.73 1.42 1.65 1.61
Scenario 2 0.00 3.35 2.22 2.13 3.24 3.74 3.89 4.16 3.84 3.93 4.19 3.15Scenario 3
0.00 4.71 3.39 3.34 3.28 3.76 3.88 5.32 5.73 4.81 5.29 3.96
Total Acreage
Thousand Hectares Baseline 7932.50 8025.87
7965.77 8030.50 8046.13 8045.15 8032.56 8039.02 8046.40 8059.57
8064.59 8026.19
Percentage Change Scenario 1 0.00 1.30 1.13 1.20 1.23 1.16 1.17 1.16 1.18 1.03 1.10 1.06
Scenario 2 0.00 1.75 1.47 1.43 1.99 2.35 2.51 2.69 2.59 2.62 2.75 2.01Scenario 3 0.00 2.46 2.20 2.21 2.22 2.47 2.58 3.28 3.62 3.29 3.46 2.53
116
Table 5.16: Effects of MFA Quota Elimination on Indian Cotton Yield
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 AverageNorthern Region Metric Tons Per Hectare
Baseline 0.33
0.34 0.34 0.35 0.35 0.35 0.35 0.35 0.36 0.36
0.36 0.35Percentage Change
Scenario 1 0.00 -0.62 -1.22 -1.78 -2.25 -2.57 -2.78 -2.91 -2.99 -2.97 -2.95 -2.09Scenario 2 0.00 -0.84 -1.63 -2.28 -3.06 -3.90 -4.67 -5.36 -5.85 -6.19 -6.46 -3.66Scenario 3
0.00 -1.18 -2.35 -3.38 -4.20 -4.93 -5.53 -6.30 -7.11 -7.63 -8.03 -4.60
Central Region
Metric Tons Per Hectare Baseline 0.35 0.35 0.36 0.36 0.37 0.38 0.38 0.39 0.39 0.40 0.40 0.38
Percentage Change0.39 0.39 Scenario 1
Scenario 20.00 0.68 0.44 0.46 0.44 0.38 0.38 0.31 0.35 0.380.00 0.92 0.56 0.53 0.79 0.89 0.90 0.94 0.85 0.85 0.88 0.74
Scenario 3
0.00 1.30 0.86 0.83 0.80 0.89 0.89 1.20 1.26 1.04 1.11 0.93
Southern Region
Metric Tons Per Hectare Baseline 0.58 0.60 0.62 0.64 0.66 0.68 0.70 0.71 0.73 0.74 0.76 0.68
Percentage Change Scenario 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Scenario 2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Scenario 3
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Average Yield
Metric Tons Per Hectare Baseline 0.40 0.41 0.42 0.42 0.43 0.43 0.44 0.45 0.45 0.46
0.47 0.44
Percentage Change Scenario 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Scenario 2 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00Scenario 3 0.00 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
117
Table 5.17: Effects of MFA Quota Elimination on Indian Fiber Supply 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Average Cotton Production Thousand Metric Tons Baseline 3180.48 3284.97
3310.70 3394.68 3450.53 3499.42 3543.77 3601.48 3659.62 3721.51
3777.90
3493.19Percentage Change
Scenario 1 0.00 1.69 1.23 1.25 1.19 1.04 1.03 1.01 1.02 0.83 0.95 1.02Scenario 2 0.00 2.28 1.59 1.43 2.08 2.39 2.45 2.57 2.34 2.35 2.48 2.00Scenario 3
0.00 3.20 2.41 2.25 2.11 2.34 2.38 3.23 3.50 2.91 3.12 2.50
MMF Production
Thousand Metric Tons Baseline 1640.02 1747.94
1847.88 1963.02 2093.28 2174.96 2296.19 2361.13 2423.02 2481.92
2537.98
2142.49Percentage Change
Scenario 1 2.56 2.25 1.93 1.57 1.16 0.83 0.50 1.49 0.12 0.43 0.65 1.23Scenario 2 2.56 2.25 2.34 2.66 2.16 1.77 1.38 2.35 2.08 1.84 2.90 2.21Scenario 3 3.80 3.75 4.40 4.60 3.53 4.98 4.33 4.16 4.17 4.28 4.44 4.22 118
Notes: Scenario 1 indicates a 10 percent increase in textile exports, scenario 2 indicates a 20 percent increase in textile exports, and scenario 3 indicates a 30 percent increase in textile exports.
Table 5.18: Effects of MFA Quota Elimination on Fiber Trade and World Prices 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Average
Cotton Net Imports Thousand Metric Tons Baseline 155.62
167.88 169.11 169.00 168.56 167.95 167.65 187.32 187.24 187.15
197.06 174.96Percentage Change
Scenario 1 3.20 3.81 4.78 4.71 4.30 3.96 3.78 3.23 2.95 3.11 3.17 3.73Scenario 2 3.96 4.24 5.02 5.98 6.19 5.98 6.06 4.98 5.05 5.28 5.20 5.27Scenario 3
7.73 6.41 6.09 6.60 7.40 6.56 7.72 7.08 6.32 6.75 7.13 6.89
MMF Net Exports
Thousand Metric Tons Baseline 291.88 236.71 212.19 203.38 229.31 222.22 219.84 212.21 212.54 222.62
221.26 225.83
Percentage Change Scenario 1 -24.98 -10.39 -20.85 -25.63 -27.96 -35.37 -40.71 -32.71 -48.97 -47.31 -43.84 -32.61
Scenario 2 -65.69 -55.01 -71.97 -76.34 -63.61 -66.27 -72.08 -64.49 -72.57 -72.87 -60.40 -67.39Scenario 3
-97.87 -82.66 -101.03 -108.97 -109.28 -97.90 -106.47 -100.61 -98.19 -103.51 -100.30 -100.62
A-Index Price
Cents per pound Baseline 64.40 63.09 61.94 61.86 62.44 63.22 64.51 65.91 66.94 67.46
67.46 64.47
Percentage Change Scenario 1 0.28 0.05 0.04 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.05
Scenario 2 0.52 0.08 0.07 0.07 0.06 0.05 0.04 0.04 0.04 0.04 0.03 0.09Scenario 3 0.58 0.08 0.08 0.07 0.07 0.06 0.05 0.04 0.04 0.04 0.04 0.10
119
Notes: Scenario 1 indicates a 10 percent increase in textile exports, scenario 2 indicates a 20 percent increase in textile exports, and scenario 3 indicates a 30 percent increase in textile exports.
over their respective bases in scenario 1(10 percent increase in textile exports). In the
case of a 30 percent increase in textile exports (scenario 3), cotton and man-made fiber
mill use are projected to rise each year by an average of 2.8 and 17 percent, respectively,
relative to the baseline levels. Expansion in the mill demand for fibers should increase
fiber prices, with cotton and polyester prices rising as much as 22 and 43 percent,
respectively. In the case of scenario 1, cotton and man-fiber prices are projected to be
higher each year by an average of 5.8 and 13.1 percent, respectively, over their respective
bases. The effects on fiber prices are much higher (an average of 14.7 percent for cotton
and 30.8 percent for man-made fibers each year relative to their baseline levels) for
scenario 3, where textile exports increase by 30 percent.
An increase in fiber prices should induce higher production levels of cotton and
man-made fibers. As shown in Table 5.17, cotton production is projected to increase
each year by an average of 1, 2, and 2.5 percent above the baseline level in scenario 1, 2,
and 3, respectively. Almost all the production growth is projected to come from the
cotton acreage expansion (Table 5.15) in response to higher prices, with little to no
change in yield (Table 5.16). The northern region accounts for most of the increase in
cotton acreage followed by the southern and central regions. The cotton area in the
northern region is projected to increase each year by an average of 2.2 to 5.1 percent over
their respective bases depending on the different levels of textile exports. Some acreage
expansion is also projected in the southern region, with an average increase of 1.6 to 3.96
percent each year compared to the baseline level.
120
In the case of man-made fiber, production is projected to be higher each year
between 1 and 4 percent (Table 5.17), depending on the level of textile exports. In
scenario 1, the average annual production increase each year is estimated to be 1.2
percent as compared to 4.2 percent over their respective bases in scenario 3. Most of the
increases in man-made fiber production in the initial years come from a higher utilization
of existing capacity rather than building additional capacity. However, capacity is
projected to rise towards the second half of the projection period, contributing to higher
man-made fiber production.
Since the expansion in domestic cotton production is not enough to meet the
rising mill demand, imports are projected to be higher than the baseline level. Cotton
imports in scenario 1 are estimated to be approximately 3 to 5 percent higher than the
baseline level with an average increase of 3.7 percent each year over its respective base
(Table 5.18). Similarly, higher textile exports are likely to result in higher cotton
imports. In scenario 3, cotton imports are projected to rise around 6 to 8 percent above
the baseline level. Unlike cotton, India is a net exporter of man-made fibers and with
rising mill use, its exports are projected to be lower in the scenarios (Table 5.18). In
scenario 1, man-made fiber net exports are projected to decline by as much as 50 percent,
with an average decline of 33 percent each year relative to the baseline level. Similarly,
in scenario two (20 percent increase in textile exports), man-made fiber net exports are
projected to decline each year by an average of 67 percent compared to its baseline level
during the period 2004/05 to 2014/15. As shown in Figure 5.4, India is projected to
121
-50
0
50
100
150
200
250
300
2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15
Baseline Scenario 1 Scenario 3
000 MT
122
Figure 5.4. Indian Man-made Fiber Net Trade Projections (Baseline vs. Scenario)
transform from a net exporter to a small net importer of man-made fibers, with the 30
percent increase in textile exports.
The world cotton price is estimated to increase by one-half of a percent in the first
year of simulation for scenario 2 (Table 5.18). However, adjustment by the competitors,
who boost production, takes away most of the price increase after the initial year. For the
remaining period, the world price is projected to increase by less than 0.05 percent. The
effects on world prices are similar for scenarios one and three.
Overall, the simulation results suggest that elimination of MFA quotas are likely
to lead to higher cotton imports by India. In addition, man-made fiber exports from India
are projected to decline significantly with the opening of textile markets in the developed
countries. Higher domestic cotton prices encourage acreage expansion in cotton in all
three regions but not enough to meet rising mill demand under the scenarios of higher
textile exports. Rise in cotton imports from India appears to have little effect on world
cotton prices. Most of the increase in world price is expected in the years immediately
after quota elimination. However, the effects die out as the other countries increase their
cotton production.
From the discussion of the simulation results at various levels of textile exports, it
becomes evident that India is and will continue to be a growing importer of cotton in the
future. However, it is not known if the U.S. is well positioned to capture the additional
market share when it arises. In the following section, the competitiveness of U.S. cotton
in the Indian market was examined by estimating own-, cross-price and expenditure
elasticities of imported cotton by country of origin.
123
5.6. Competitiveness of U.S. cotton
The LA/AIDS model was used to estimate the import demand elasticities of
Indian cotton in order to examine the competitiveness of U.S. cotton in the Indian market.
First, the theoretical restrictions of homogeneity and symmetry were tested
simultaneously. The unrestricted model with adding up constraint was estimated first,
and then the restricted model with homogeneity and symmetry was estimated. The
calculated Wald Chi-Square statistic of the unrestricted LA/AIDS model with adding-up
constraints, as shown in Table 5.19, has a p-value of 0.39492 and that of the restricted
model with homogeneity and symmetry restrictions has a p-value of 0.13492, which
imply that homogeneity and symmetry restrictions are not rejected even at a 10 percent
level. Therefore, all these restrictions, either individually or jointly, are in conformity
with the theory.
The demand system was estimated using Seemingly Unrelated Regression (SUR)
with symmetry and homogeneity imposed. After estimating the demand system, the
coefficients of the deleted equation were retrieved using the adding up constraint. Table
5.20 presents the estimated coefficients and standard errors associated with them.
Estimated coefficients were converted to their respective price and expenditure
elasticities using the average value from 1990 to 2000. The estimated uncompensated
(Marshallian) price and expenditure elasticities are reported in Table 5.21. The
expenditure elasticities of cotton from the United States, Australia, and the rest of the
world are 1.19, 0.93, and 0.98 respectively. This suggests that if total expenditure on the
124
Table 5.19: Wald Chi-Square Statistic Test for the Results of Unrestricted and Restricted Models Models No. of parameters Calculated χ2 P-Value
Unrestricted 10 0.723745184 0.39492
Restricted 8 2.2349402 0.13492
125
Table 5.20: Estimated Coefficients of the Restricted AIDS Model Market Share of cotton
Intercept U.S. Price of cotton
Australia Price of cotton
ROW Price of cotton
Expenditures
U.S. -0.318 (0.235)
0.020 (0.108)
-0.434 (0.104)
0.414 0.025 (0.013)
Australia 0.260 (0.213)
-0.102 (0.109)
-0.179 (0.091)
0.282 -0.008 (0.012)
ROW 1.058 0.082
0.614 -0.696 -0.018 (0.012)
Notes: Standard errors are reported in the parentheses below the coefficients.
126
Table 5.21: Estimated Uncompensated Elasticities of the Restricted Model Source Import Demand Elasticities with respect to the Cotton
Price of
Expenditure Elasticities
US Australia
ROW
US -0.876 (0.056)
-3.212 (0.051)
0.463
1.186 (0.012)
Australia -0.875
(0.024) -2.545 (0.019)
5.360
0.932 (0.009)
Rest of the world
0.113
0.823
-1.947
0.976 (0.009)
Notes: Standard errors are reported in the parentheses below the coefficients.
127
cotton imports in India increases by one percent, ceteris paribus, the quantity demanded
of U.S., Australian, and the rest of the world cotton will increase by 1.19 percent, 0.93
percent, and 0.98 percent, respectively. Based on Chang and Nguyen (2002), who found
that income elasticities associated with best or preferred grade are higher than the smaller
or lower grades, U.S. cotton may be classified as the preferred/premium cotton in the
Indian market.
The compensated or Hicksian own-price elasticities for the U.S., Australia and the
rest of the world cotton in the Indian market are -0.72, -2.44 and -1.22, respectively
(Table 5.22). This suggests that a one percent increase in U.S. cotton price will result in
a 0.72 percent decrease in U.S. cotton export to India, whereas the same increase in the
Australian cotton price will trigger a 2.44 percent decrease in Australian cotton exports to
India. Similarly, a one percent increase in the rest of the world’s cotton price will result
in a 1.22 percent decrease in cotton imports from the rest of the world. The inelastic
demand of U.S. cotton in the Indian market may be due to the quality differential
between cotton imported from the United States and Australia/rest of the world. India
primarily imports the extra long staple (ELS) cotton from the United States, which is a
premium cotton and is preferred for apparel, whereas cotton imported from Australia and
the rest of the world are medium staple and can be substituted with domestic cotton. In
addition, U.S. cotton has been preferred by the Indian mills because of its higher quality
in terms of less trash, uniform lots, and higher ginning out turn compared to cotton from
other origins.
128
Table 5.22: Estimated Compensated Elasticities of the Restricted Model Source Import Demand Elasticities with respect to the Cotton Price of
US
Australia
ROW US -0.715
-3.075
1.351
Australia -0.748
-2.437
6.058
ROW 0.246
0.936
-1.217
129
The positive Hicksian cross price elasticities of the U.S. and Australian cotton
with respect to the rest of the world’s price suggest that an increase in the price of cotton
from the rest of the world will raise the demand for U.S. and Australian cotton in the
Indian market, and vice-versa. However, U.S. cross-price elasticity with respect to the
rest of the world cotton price is found to be much smaller than Australian cross price
elasticity (1.35 vs 6.06). The unequal response of U.S. and Australian cotton in response
to change in rest of the world cotton price indicates a quality differential between cotton
from these two countries. In addition, the cross price elasticity of U.S. cotton with
respect to the price of rest of the world cotton (1.35) is found to be much higher than the
cross price elasticity of rest of the world cotton with respect to the U.S. price (0.25). This
suggests that a one percent increase in the rest of the world cotton price would increase
U.S. cotton imports by 1.35 percent, whereas a similar increase in the U.S. cotton price
will increase rest of the world cotton imports by only 0.25 percent. Similar cross price
responses are found between Australian and rest of the world cotton. These results
confirm that rest of the world cotton is least preferred in the Indian market as compared
to the cotton from the United States and Australia.
Interestingly, U.S. cotton and Australian cotton are found to have complementary
relationship in the Indian market, as suggested by negative Hicksian cross price
elasticities between them. In other words, this suggests that an increase in the price of
one will reduce the demand for the other and vice-versa. This is plausible under the
assumption that textile mills in India are blending high quality U.S. cotton with the
medium quality Australian cotton as a way to cut costs.
130
Overall, the results suggest that the demand for U.S. cotton in the Indian market is
price inelastic and that is likely due to higher quality and consistency of the U.S. cotton.
Based on the estimated expenditure elasticities, it may be inferred that the U.S. market
share in the Indian market is likely to go up in the future as the import demand for cotton
increases. However, the U.S. has to develop a differentiated market for its own cotton to
counter the freight advantages and shorter delivery periods enjoyed by Egypt, Australia,
Uzbekistan, and West Africa due to their geographical proximity to India. Recent trade
servicing missions by Cotton International have been helpful in developing better
appreciation for U.S. cotton by Indian mills. In the future, efforts should be directed to
raising awareness of U.S. cotton among the billion plus Indian consumers.
131
CHAPTER VI
SUMMARY AND CONCLUSIONS
6.1. Summary of the Results
India has the largest cotton-producing area in the world, accounting for 25 percent
of the world acreage. However, India’s contribution to the world production is estimated
to be around 14 percent, primarily because of low yield. In the last decade, cotton
production in India has increased only 8 percent, whereas consumption has risen by more
than 35 percent. The increase in cotton consumption is primarily driven by strong textile
consumption and exports. The textile exports during this period increased on an average
of 19 percent per annum. The disparity between the cotton production and consumption
turned India from a net exporter into a net importer of cotton. Since 1999, India has
accounted for 6 percent of the world cotton imports.
India’s reemergence as a major cotton importer has occurred under the constraint
of textile export quotas imposed by developed countries as part of the Multi-Fiber
Arrangement. These bilateral quotas have restricted Indian exports of cotton textile
products in which it has a strong comparative advantage. Bilateral textile quotas were
eliminated in the beginning of 2005, with India and other developing textile exporters
having access to major markets including the United States, the European Union, and
Canada. Researchers are more or less in agreement that quota elimination will increase
textile exports from developing to developed countries, with China, India, and Pakistan
as the major beneficiaries. In addition to an increase in Indian textile exports due to
132
greater market access, domestic textile consumption has been rising since the early 90s
and is expected to grow in the future due to strong economic growth.
In the face of a potential increase in rising textile exports due to MFA quota
elimination, it is critical to develop a better understanding of the effects of MFA quota
elimination on Indian and world fiber markets. The main objective of this study was to
measure the effects of MFA quota eliminations on the fiber markets by developing a
partial equilibrium structural econometric model of Indian fiber markets and to link it to a
world fiber model. Furthermore, the study provides an empirical assessment of the
competitiveness of the U.S. cotton in the Indian market.
The partial equilibrium Indian fiber model was developed using a theoretically
consistent framework and incorporated regional supply response, substitutability between
cotton and man-made fibers, and appropriate linkage between cotton and textile sectors.
The regional supply estimation was employed to avoid aggregation bias by allowing
different crop mix for each regions arising out of heterogeneity in growing conditions. A
two-step estimation of fiber demand was chosen to provide appropriate linkages between
cotton and textile sectors.
The major components of the Indian fiber model included a supply sector, a
demand sector, and price linkage equations for both cotton and man-made fibers. All of
these structural equations were econometrically estimated using historical data. On the
supply side, a detailed regional model of the annual cotton production was estimated. A
regional modeling framework, consisting of three cotton regions, was chosen because of
climatic differences and regional heterogeneity in availability of water and other natural
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resources that influence the mix of crops in various parts of the country. The fiber
demand was estimated in two stages. In the first stage, total textile consumption was
estimated and then allocated among various fibers such as cotton, wool, and man-made
fibers based on relative prices. Finally, the model was closed with ending stocks and
trade equations, and domestic fiber prices were endogenously solved in the model.
The estimated models were validated using Theil’s inequality coefficient and
decomposition of mean squared error. Theil’s inequality coefficient statistic showed that
the model was performing well. The estimated econometric model was used to develop
baseline projections for supply, demand, and prices of cotton, man-made fibers, and
textiles under a set of exogenous assumptions. The projections for macroeconomic
variables were borrowed mostly from Food and Agricultural Policy Research Institute
(FAPRI). After the baseline projections were developed, three scenarios, 10, 20, and 30
percent increases in textile trade, were hypothesized to assess the impact of MFA quota
elimination on Indian fiber sectors.
The estimated regional own-price acreage elasticities were found to be positive
and ranged from 0.15 to 0.36, with the highest for the southern region and the lowest for
the central region. Further, the cross-price supply elasticities ranged between -0.205 and
-0.347. The results for both own- and cross-price elasticities revealed the inelastic nature
of the crop supply response in India. The own-price elasticities indicated that southern
region cotton acreage is more sensitive to its own price than the other two regions.
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Man-made fiber production was calculated as the product of production capacity
and utilization rate. The man-made fiber production capacity was specified as the
function of the ratio of three to six year lagged prices of polyester and crude oil, and of
production capacity lagged one year. Similarly, utilization rate was explained by the
ratio of the prices of polyester and crude oil, and by the utilization rate lagged one year.
All the estimated coefficients were statistically significant, implying that utilization rate
is determined by the relative prices of input (crude oil) and output (polyester). This
means that an increase in polyester price or a decrease in crude oil price is associated
with an increase in man-made fiber capacity utilization rate. The statistically significant
coefficients of lagged dependent variables in both man-made fiber production capacity
and utilization rate suggest high degree of asset fixity in man-made fiber production
process.
In the first stage of fiber demand estimation, income was found to be a major
determinant of textile consumption, implying that future growth in income may
significantly influence the level of textile consumption. At the same time, estimated
coefficient of the ratio of food price to textile price indices was found to be statistically
significant and directly related with per capita textile consumption, suggesting that textile
consumption rises with the increase in food price index and decreases with an increase in
textile price index. The statistically significant and positive coefficient for the trend
variable indicates that part of the increase in textile consumption can be attributed to
factors other than income and price, such as availability of greater choices and brand
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names of textile products following textile and apparel trade liberalizations; representing
a shift in consumer preferences in India.
The second stage of fiber demand estimation included a demand system to
allocate textile consumption among the raw fibers. The demand system was estimated
using the non-linear SUR, with symmetry and homogeneity imposed. Own-price
elasticities of raw fibers were found to be negative and ranging from -0.04 to -0.63, with
the lowest for wool and the highest for man-made fiber. Demand for all the fibers were
found to be price inelastic; a comparison of these elasticities show that man-made fiber is
more sensitive to price changes than cotton and wool. The negative values of
compensated cross-price elasticities between cotton and wool exhibit a complementary
relationship among cotton and wool at the mill level. This means an increase in the price
of wool is associated with the decrease in the demand of cotton and vice-versa. On the
other hand, the Hicksian cross-price elasticities showed that cotton and man-made fibers
and man-made fibers and wool are net substitutes at the mill level. This suggests that
increase in the price of a fiber results in an increase in the demand of the other fibers. For
example, if the price of cotton rises, then the textile mill will substitute cotton with the
man-made fibers, which become relatively cheaper.
The baseline projections, assuming status quo in MFA quota system, show that
the textile consumption would increase by 30 percent in India during the baseline period.
Fuelled by textile demand, cotton and man-made fiber demand at the mill level is likely
to go up by 13 and 72 percent, respectively. Furthermore, prices of cotton and polyester
are projected to rise by 23 and 13 percent, respectively. Cotton production, on the other
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hand, is expected to increase by 19 percent. Overall, the baseline suggests increase in
cotton imports by the Indian textile mills in the next ten years.
The effects of MFA textile quota eliminations were introduced into the model
through higher textile exports. Since the exact impacts on textile exports are not known,
three scenarios were hypothesized- - an increase in textile exports by 10, 20, and 30
percent from the baseline level-- to provide a range of possible impacts.
Overall, the results suggest that elimination of MFA quotas are likely to result in
even higher cotton imports by India. On average, cotton imports are projected to rise by
4 to 8 percent annually. In addition, the man-made fiber exports from India are projected
to decline significantly from more than 30 percent to 100 percent annually with the
opening of textile markets in the developed countries. The results suggest higher
domestic cotton prices ( as much as 22 percent) and acreage expansion of cotton (from
around 1 percent to 5 percent) in all the three regions, but the supply increase does not
appear to be enough to meet rising mill demand under the scenarios of higher textile
exports. The rise in cotton imports from India appears to have little effect on world
cotton prices. Most of the increase in world price is expected in the years immediately
(2004/05 -2007/08) after quota eliminations. However, the effects should die out as other
countries increase their production.
Finally, the competitiveness of U.S. cotton in the Indian market was examined by
estimating own-, cross-price and expenditure elasticities of cotton by country of origin.
The estimated expenditure elasticity suggests that U.S. market share in the Indian market
is likely to rise in the future as the import demand for cotton in India increases. Further,
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the results suggest that demand for U.S. cotton in Indian market is relatively more price
inelastic than cotton from other major cotton importing countries.
6.2. Conclusions
It is evident from this analysis that even under the existing bilateral quota
restrictions, India is now in a position to become an increasingly important player in the
international cotton market. Both the cotton and man-made fiber consumption and
production are expected to increase significantly during the next ten years. Most of the
cotton production growth in India is expected to result from yield improvements rather
than area expansion. Total cotton acreage is expected to remain almost constant in the
next decade (2004/05-2014/15), though shift in the cotton acreage among the three
regions is likely based on relative profitability among crops. Increase in cotton acreage
in the northern region is projected to be offset by decline in acreage in the central and
southern regions. However, the cotton supply response being price-inelastic in India and
given that yield is expected to increase only marginally during the next decade, increase
in cotton production is not expected to be sufficient to meet the rising demand. Thus, it is
likely that India would continue to rely heavily on cotton imported from other countries.
Results of the study also suggest that man-made fiber production in India would be
increasing over the next decade. Currently, India is a net exporter of man-made fibers.
Strong domestic fiber demand in the future, fueled by rising textile exports, is likely to
make India a declining net exporter of man-made fibers.
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From Indian perspective, the results provide important insight into course of
action that could potentially prevent India from becoming a net importer of cotton. In
India, production of cotton needs to grow at a much faster pace than the historical rate to
be able to meet domestic demand in the future. Since production increase through area
expansion is limited, policies should be directed to improving productivity and quality.
In order to achieve this, management practices encouraging widespread adoption of high
yielding varieties of cotton and new technologies must be promoted. In addition,
improvement in fiber quality is also essential to meet the demand of high quality cotton
by export-oriented textile mills.
The results of the simulation performed at the different levels of textile exports
suggest that MFA quota elimination would further increase cotton imports by India. In
addition, man-made fiber exports from India are projected to decline significantly with
the opening of textile markets in the developed countries. Higher domestic cotton prices
should further stimulate acreage expansion in cotton in all three regions, but not enough
to meet rising mill demand under the scenarios of higher textile exports.
Most of the increases in man-made fiber production in the initial years should
come from a higher utilization of existing capacity rather than building of additional
capacity. However, capacity is projected to rise towards the second half of the projection
period, contributing to higher man-made fiber production. Rise in cotton imports from
India, however, appears to have little effect on world cotton prices. Most of the increase
in world price is expected in the years immediately after quota elimination, which should
die out as the other countries increase their cotton production.
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Results of the study clearly suggest that the U.S. market share of cotton in the
Indian market is likely to increase with the elimination of MFA quota. The higher quality
and consistency of cotton imported from the U.S. make it more desirable by the Indian
textile mills. However, there are reasons to believe that the U.S. exporters need to
develop strategies to counter the freight advantages and shorter delivery periods enjoyed
by Egypt, Australia, Uzbekistan, and West Africa due to their geographical proximity to
India.
6.3. Limitations of the Study
The Indian fiber model developed in this study can be improved in many ways.
The original model structure had to be simplified due to non-availability of data for many
of the relevant variables. For example, use of the regional net returns for cotton and
competing crops would have been more accurate in the acreage model, but farm gate
prices had to be used instead because of difficulty in finding the regional cost of
production data for a continuous period of time. Similarly, a net trade equation for man-
made fiber had to be estimated instead of separate exports and imports equations because
of data problems.
Another shortcoming of this study is the use of a static AIDS model in analyzing
import behavior of Indian mills. Static demand specifications are unlikely to capture the
behavior of textile mills because it is difficult to adjust fully to any changes in the market
condition, including price changes, in one season. Several factors account for this
incomplete adjustment on the part of textile mills. Habit formation can generate delayed
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responses (Pollak and Wales, 1969). This is particularly true for cotton because the
mill’s preference for a specific type of cotton depends on its end use. For example, ELS
cotton is preferred for apparel manufacturing, whereas medium/short staple cotton is
preferred for denim manufacturing. Thus, a textile mill that manufactures apparel will
still demand ELS cotton even if the ELS price increases relative to the other types of
cotton. But in a longer time period, demand might shift because of changes in
consumption patterns or because technological development might enable millers to
blend cheaper cotton to obtain the preferred characteristics. This study can be further
strengthened by incorporating dynamic AIDS model that can capture the short-run and
long-run behavior of textile mills in India.
In addition, it is recognized that this study does not fully capture the domestic and
international agricultural and trade policy environments concerning fiber and textile
sectors. Thus, attempts to infer implication of the elimination of MFA quota should be
done with caution.
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APPENDIX Table 1. List of Variables and their Unit of Measurement Variables Name Variable Definition Unit of MeasurementAC Regional Cotton Acreage Thousand Hectare AC1 Northern India Cotton Acreage Thousand Hectare AC2 Central India Cotton Acreage Thousand Hectare AC3 Southern India Cotton Acreage Thousand Hectare CES Cotton Ending Stocks Metric Ton CEX Cotton Exports Metric Ton CIM Cotton Imports Metric Ton CPR Total Cotton Production Metric Ton CSU Total Cotton Supply Metric Ton CTWH1 Food Price Index/Textile Price Index D01 Dummy Variable for 2001 D02 Dummy Variable for 2002 D0302 Dummy 2003+Dummy 2002 D86 Dummy Variable for 1986 D95 Dummy Variable for 1995 D9901 Dummy 1999+Dummy 2001 DC Total Mill Demand of Cotton Metric Ton DMF Total Mill Demand of Man-Made Fiber 1000 Metric Ton DMF Man-made Fiber Consumption 1000 Metric Ton DPC Deflated Domestic Price of Cotton Rupees per Kilogram DPP Deflated Domestic Polyester Price Rupees per Kilogram DUSPP Deflated US 1.5 Denier Polyester Price Cents per Pound DW Total Mill Demand of Wool Metric Ton EPC Expected Price of Cotton Rupees per Kilogram EPCM Expected Price of Competing Crops Rupees per Kilogram EPCM1 Expected Price of Rice Rupees per Kilogram EPCM2 Expected Price of Groundnut Rupees per Kilogram ET Export Tax on Cotton Percentage Expend1 Total Expenditure (Using Indian cotton,
Polyester Price, and U.S. Wool Price) Rupees
FA Fertilizer Application Kilogram per Hectare
GDP Gross Domestic Product Billion Rupees GDPIND Per Capita GDP Rupees I Per Capita Income Rupees
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Table 1.(Contd.) Variable Name Variable Definition Unit of MeasurementIT Import Tariff Rate in India Percentage LAC1 Northern India Cotton Acreage Lagged One
Year Thousand Hectare
LCES Cotton Endings Stocks Lagged One Year Metric Ton LDEFF Log of Polyester Price Lagged 3 to 6Years/
Log of Crude Oil Price Lagged 3 to 6 Years
LLMMPC Log of Man-made Fiber Production Capacity Lagged One Year
Thousand Metric Ton
LMMCUZ Man-made Fiber Capacity Utilization Lagged One Year
Percentage
LMMTR Manmade Fiber Trade Lagged One Year 1000 Metric Ton LRTC Long-Run Total Costs LYC1 Northern India Cotton Yield Lagged One
Year Metric Ton /Hectare
LYC2 Central India Cotton Yield Lagged One Year Metric Ton/ Hectare LYC3 Southern India Cotton Yield Lagged One
Year Metric Ton /Hectare
MMCUZ Man-made Fiber Capacity Utilization Percentage MMPC Man-made Fiber Production Capacity 1000 Metric Ton MMPR Man-made Fiber Production 1000 Metric Ton MMTR Man-made Fiber Net Trade 1000 Metric Ton PA A-Index Price of Cotton Cents per Pound PAUS Unit Import Values of Cotton from Australia Dollar per Kilogram PC Market Price of Cotton Rupees per Kilogram PERCVIRG Percentage Area Covered Under Irrigation Percentage PFD Food Price Index PMF Market Price of Man-made Fiber Rupees per Kilogram PO Price of Petroleum Crude Oil Dollar per barrel POP Population in India 1000 Million PP Polyester Price Rupees per Kilogram PRCTAI Deflated Domestic Price of Cotton/
Deflated and Adjusted A-Index Price for Tariff and Exchange Rate
PROW Unit Import Values of Cotton from ROW Dollar per Kilogram PRPET Polyester Price/ Crude Oil Price PTX Textile Price Index
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Table 1. (Contd.) Variable Name Variable Definition Unit of Measurement PUSA Unit Import Values of Cotton from U.S. Dollar per Kilogram PW Market Price of Wool Cents per Pound RF Rainfall Millimeter SC Share of Cotton in Textile SMF Share of Man-made Fiber in Textile SW Share of Wool in Textile T Time Trend Year TXPC Per Capita Textile Demand Kilogram WAUS Value of Market Shares of Australia WROW Value of Market Shares of rest of world WUSA Value of Market Shares of the U.S. WW Deflated Domestic Price of Cotton/
Deflated and Adjusted A-Index Price
XR Exchange Rate Rupees per Dollar Y Total Fiber Expenditure Rupees YC Cotton Yield Metric Ton /Hectare YC1 Northern India Cotton Yield Metric Ton /Hectare YC2 Central India Cotton Yield Metric Ton /Hectare YC3 Southern India Cotton Yield Metric Ton /Hectare YV Value of Total Cotton Imports in India Dollar
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