THE FUTURE OF INDIAN COTTON SUPPLY AND DEMAND

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 LIST OF TABLES LIST OF FIGURES I. INTRODUCTION 1.1. General Problem 1.2. Specific Problem 1.3. General Objective 1.4. Specific Objectives II. LITERATURE REVIEW 2.1. Partial Equilibrium Cotton Models 2.2. Studies Related to Cotton Supply Estimation 2.3. Studies Related to Cotton Demand Estimation 2.4. Studies Examining Competitiveness of Cotton 2.5. Summary III. CONCEPTUAL FRAMEWORK 3.1. Supply Response 3.1.1. Cotton Acreage and Yield Response 3.1.2. Man-made Fiber Production Response 3.2. Demand Specifications 3.2.1. Demand for Textile Products 3.2.2. Cotton Demand 3.3. Competitiveness of U.S. Cotton 3.3.1. Cotton Import Demand 3.4. Summary IV. METHODS AND PROCEDURES 4.1. Model Specification 4.1.1. Fiber Supply Estimation 4.1.1.1. Cotton Supply Model 4.1.1.2. Man-made Fiber Supply Model 4.1.2. Fiber Demand Estimation iii ii v vii 1 6 8 9 9 11 11 20 23 30 32 34 38 41 44 46 49 50 57 59 59 61 64 64 64 66 67 4.1.3. Cotton Ending Stocks and Trade Equations 4.1.4. Market Clearing Condition 4.2. Policy Simulations 4.3. Competitiveness of U.S. Cotton 4.4. Model Validation 4.5. Data Requirements 4.6. Summary V. RESULTS AND DISCUSSIONS 5.1. Fiber Supply Model 5.1.1. Cotton Acreage Model 5.1.2. Cotton Yield Model 5.1.3. Man-made Fiber Supply Model 5.2. Fiber Demand Models 5.2.1. Per Capita Textile Consumption 5.2.2. Fiber Demand 5.3. Fiber Trade and Cotton Ending Stocks Equations 5.4. Model Validation 5.5. Policy Simulation 5.5.1. Baseline Projections 5.5.2. Simulation Results 5.6. Competitiveness of U.S. Cotton VI. SUMMARY AND CONCLUSIONS 6.1. Summary of the Results 6.2. Conclusions 6.3. Limitations of the Study REFERENCES APPENDIX: List of Variables and their Unit of Measurement 69 70 72 73 74 78 80 82 82 82 87 90 92 92 94 99 105 107 109 113 124 132 132 138 140 142 145 iv LIST OF TABLES 5.1. Regression Results of Indian Regional Cotton Acreage Models 5.2. Elasticities of Indian Regional Cotton Acreage Model at mean level 5.3: Regression Results of Indian Regional Cotton Yield Models 5.4. Regression Results of Manmade Fiber Capacity and Utilization 5.5. Regression Results of Per Capita Textile Consumption 5.6. Regression Results of Fiber Demand System 5.7. Estimated Uncompensated Fiber Price and Income Elasticities 5.8. Estimated Compensated Fibers Price Elasticities 5.9. Regression Results of Cotton Trade Equations 5.10. Regression Results of Man-made Fiber Net Trade 5.11. Regression Results of Cotton Ending Stocks 5.12. Model Validation Statistics 5.13. Summary of Baseline Projections for Fiber Demand, Cotton Price, Polyester Price, Fiber Production, and Fiber Trade in India, 2004/05-2014/15. 5.14. Effects of MFA Quota Elimination on Indian Fiber Consumption and Domestic Fiber Prices 5.15. Effects of MFA Quota Elimination on Indian Cotton Area 5.16. Effects of MFA Quota Elimination on Indian Cotton Yield 5.17. Effects of MFA Quota Elimination on Indian Fiber Supply 5.18. Effects of MFA Quota Elimination on Fiber Trade and World Price 5.19. Wald Chi-Square Statistic test for the Results of Unrestricted and Restricted Models v 83 86 88 91 93 96 97 98 100 103 104 106 112 115 116 117 118 119 125 5.20. Estimated Coefficients of the Restricted AIDS model 5.21. Estimated Uncompensated Elasticities of the Restricted Model 5.22. Estimated Compensated Elasticities of the Restricted Model 126 127 129 vi LIST OF FIGURES 1.1. Market Shares of the United States in the Indian Cotton Market 3.1. Impacts of MFA Quota Elimination on World Cotton and Indian Textile Markets 4.1. Schematic Representation of the Indian Fiber Model 5.1. Baseline Projections for Textile Consumption in India 5.2. Baseline Projections of the Domestic Fiber Prices 5.3. Baseline Fiber Net Trade Projections 5.4. Indian Man-made Fiber Net Trade Projections (Baseline vs. Scenario) 2 35 62 110 111 114 122 vii 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 Percent 40 35 30 25 20 15 2 10 5 0 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 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. 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 1 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 nonstochastic 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 twostep 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 16 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 Rsquared 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 manmade 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 HongKong 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 price PT H A G C D S EI B BI F E Quantity a. Textile Market in India 35 price I P PIN I PW PW I IN S IN price price S RW D RW J ES ED I K X IN X IIN I YIN YIN K I ED X W X IW Quantity PRW I I YRW YRW X RW X RW Quantity Quantity b. Cotton Market in India c. World Cotton Market d. Rest-of-the-World Cotton Market 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 I to the increase in demand, the domestic cotton price in India rises to PIN (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 I PW to PW , and an increase in the volume of world cotton trade to X IW . Price rise in rest of the world results in declining cotton consumption, increasing cotton production, and I therefore, widening exports to ( X IRW - YRW ), (panel d). Higher prices results in an expansion of cotton production in India from X IN to X IIN ; however, due to increase in textile consumption cotton demand increases more than its production, thus, cotton I imports expands to ( YIN - XIIN ), (panel b). 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: F(q1......q s , x1...x n ) = 0 , (3.1) where qi ( q1...q s ), and xi ( x1...x n ) 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. 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: π= ∑ pi q i - ∑ w j x j , i =1 j =1 s n (3.2) 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: L= ∑ piqi - ∑ w jx j - λF(q1...qs , x1...x n ) , i =1 j =1 s n (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. δL = pi + λFi = 0 , δq i (3.4) δL = w j + λFj = 0 , δx j δL = F(q1 ,...,q s ,x1 ,...,x n ) = 0 , δλ (3.5) (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. q* = f i (p1...ps , w 1...w n ) , i (3.7) (3.8) x* = f j (p1...ps , w1...w n ) , j 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. λF11 λF21 F1 λF12 λF22 F2 F1 F2 > 0 0,...(-1) n+s λF11 λFn+s1 F1 λF1n+s λFn+s, n+s Fn+s F1 Fn+s 0 > 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: q* = A i* .Yi* , i (3.10) where A i* is the optimum area and Yi* represents the optimum yield. 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: A* = α 0 + α1Pte + U t , t (3.11) where A* is the desired cultivated area, and Pte is the expected price, in general it can be t said to be a vector of relative prices including the cotton price and prices of competing crops, U t 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 α1 is the coefficient associated with the expected price. 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 Pte - Pt-1 = γ( Pt-1 - Pt-1 ), 0 ≤ γ ≤1, (3.12) e where Pte is the price expected this year, Pt-1 is the price expected last year, and Pt-1 is the actual price last year. 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: A t - A t-1 = δ(A* - A t-1 ), t 0 ≤ δ ≤ 1, (3.13) where A t - A t-1 is actual change in acreage, A * - A t-1 is desired change in acreage and δ t 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. Substituting equation (3.11) into equation (3.13) and rearrangement gives the reduced form: A t = b0 + b1Pt-1 + b 2 A t-1 + Vt , where b 0 = α 0 δ b1 = α1δ b 2 = (1-δ) Vt = δU t Cotton yield and cotton acreage both are derived from the same cotton supply response function. Therefore, cotton yield, like cotton acreage, depends upon expected (3.14) 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: c (W,y) = WX (W,y) , (3.15) 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: SRTC = c (W, y, X f ) , LRTC = c (W, y) , (3.16) (3.17) 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. δ c (W, y, X f ) δ c (W, y) = , δy δy yc = yc (W, X) , (3.18) 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: Lπ = py + λ(C-WX) (3.19) Second, the capacity utilization rate (CU) was specified as the ratio of optimal output and capacity. 45 CU = y* , yc (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: u = u (x1 ,..., x n ) , (3.21) where x1 ,..., x n 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 ∑p x i i =1 n i =y, (3.22) where pi is the price of ith 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: L(x, λ) = u (x1 ,..., x n ) - λ( ∑p x i i =1 n i - y) , (3.23) where λ 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, xi and λ , and solving uniquely for x1,…, xn and λ in terms of prices and income yield the system: x i = x i (pi ,..., p n , y) , λ = λ(p1 ,..., pn , y) , 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 (3.24) (3.25) cost can be reformulated as one of selecting goods to minimize the budget necessary to reach utility level u: min y = ∑p x i i =1 n i , (3.26) s.t. u = u(x 1 ,..., x n ) 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: x i = fi (y, p) = h i (u, p) , (3.27) 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, u = g(x) = g(f(y, p)) = ψ(y, p) , (3.28) (3.29) y = ∑ pi x i = ∑ pi h i (u, p) = c(u, p) , The function ψ (y, p) is known as the indirect utility function and defined as the maximum attainable utility given prices p and outlay y. The next function c(u, p) is the cost function defined as the minimum cost of attaining u at prices 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: max U = f (X F , X NF ) subject to PF ×X F + PNF ×X NF = I , (3.30) where U = total utility X F = the fiber equivalent available to the individual X NF = other products equivalents available to the same individual PF = price of fiber equivalent PNF = 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 φ = U (X F , X NF ) + λ (I - ∑ pi q i ) , i =1 2 (3. 31) solving for the per capita fiber equivalent demand ( q F ): q F = f (P F , P N F , I) , Q F = q F * POP , where QF = total fiber equivalent demand POP = population Since it is assumed that textile products in India operate in a perfectly competitive (3.32) (3.33) 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 Max π = PF Q F - ∑ pi q i i =1 n , i = 1,..., n (3.34) s.t. Q F = f(q1 ,.., q n ) where pi = price of ith fiber q i = quantity demanded of the ith 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: q* = q* (PF , pi , p j ) i i 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: c (W, X) = c (X, c F (W F , X), c NF (W NF , X)) , (3.36) 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: δcF (W F , X) = q cot (W F , X) , δWcot (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, log a (p) = a 0 + ∑ α logp k k k k + 1 ∑∑ γ*kjlogpk logp j , 2 k j (3.39) (3.40) log b(p) = log a (p) + β 0 Π pβk , k and, thus AIDS cost function is written as 52 log c (u, p) = α 0 + ∑ α k logp k + k 1 ∑∑ γ*kjlogpk logp j + uβ0 Π pβkk , (3.41) k 2 k j * where α i , βi and γ kj 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. pi , we get c(u,p) δc(u,p) = q i . Multiplying both sides by δp i δ logc(u,p) pq = i i = wi , δ logp i c(u,p) (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: w i = αi + ∑ γ logp ij j j + β i uβ 0 Π pβk , k k (3.43) where γ ij = 1 * (γ ij + γ* ) , ji 2 (3.44) Combining these gives Hicksian demand (demand in terms of utility rather than the income), qi = c(u,p) ⎛ β ⎞ ⎜ αi + ∑ γijlogp j + βi uβ 0 Π p kk ⎟ , k pi ⎝ j ⎠ (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 log y = α 0 + ∑ α logp k k k + 1 ∑∑ γ*ijlogp k logp j + uβ0βi Π pβkk , k 2 k j (3.46) u= logy β 0 Π pβ k k k α 0 +∑ α k logp k + k 1 ∑∑ γ*ijlogpk logp j 2 k j , β 0 Π pβ k k k (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. ⎛ α0 + ⎜ logy βk w i = αi + ∑ γ ijlogp j + β 0β i Π p k ⎜ β k ⎜ β 0 Π p kk j ⎜ k ⎝ ∑ α logp k k k 1 ⎞ ∑∑ γ*ijlogpk logp j ⎟ 2 k j ⎟ , β 0 Π pβ k ⎟ k k ⎟ ⎠ + (3.48) AIDS demand function in the budget share form can thus be written as w i = αi + ∑ γ logp ij j j + β i log{y/p} , (3.49) where p is a price index defined by log p = α 0 + ∑ α logp k k k + 1 ∑∑ γ*kjlogpk logp j , 2 j k (3.50) If p is approximated by the Stone geometric price index p*, log p* = ∑ w logp k k k , (3.51) 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: ∑ αi = 1 i =1 n ∑ γij = 0 i =1 n ∑β i =1 n i = 0, (3.52) (3.53) (3.54) Homogeneity: ∑γ j ij = 0, Symmetry: γ ij = γ ji , 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: pi q i = αi + y ∑ γ logp ij j j + β i log ( y/P ) , Own-price elasticities: Rearranging the above equation, keeping only qi on the left side and then taking partial derivative with respect to pi: yw j ⎞ ∂q i y⎛ y = - 2 ⎜ αi + ∑ γijlogp j + βi log ( y/P ) ⎟ + γ ii 2 - βi 2 , and ∂pi pi ⎝ pi pi j ⎠ 55 ηii = -1 + γ ii - βi , wi (3.55) Cross-price elasticities: ∂q i y 1 y ⎛ wj ⎞ = γ ii - βi ⎜ ⎟ ∂pj pi p j pi ⎜ p j ⎟ ⎝ ⎠ ηij = γ ij wi - βi w j wi , (3.56) Expenditure elasticities: ⎞ βi ∂q i 1⎛ 1 ⎛pq ⎞ β = = ⎜ i i ⎟+ i , ⎜ αi + ∑ γ ijlogp j + βi log ( y/P ) ⎟ + ∂y pi ⎝ pi ⎝ y ⎠ pi j ⎠ pi ηi = 1 + βi , wi (3.57) Hicksian elasticities: Own-price elasticities: η* = ηii +w i ηi , ii (3.58) Cross-price elasticities: η* = ηij + w jηi , ij A good can be categorized according to the signs and magnitudes of the (3.59) 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 u = v (x1 , x 2 , x 3 ) = f [vC (x1 ), v M (x 2 ), v W (x 3 )] , (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 subutility 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 u = v (x1 , x 2 , x 3 ) = f [vC (x1 ), v M (x 2 ), v W (x 3 )] subject to ∑p x i i i =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 vi (x i ) Subject to ∑p x i i i = yi , 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: x i = x i (pi , yi ) , (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 Cotton Yield Cotton Production Beginning Stocks Cotton Imports Apparel Demand Industrial Demand Textile Trade Domestic Cotton Price Total Fiber Consumption Polyester Price Man-made Fiber Mill Use A-Index Cotton Mill Use Market Equilibrium Wool Mill Use 62 Domestic Cotton Supply Exogenous Variable Endogenous Variable Man-made Fiber Utilization Cotton Consumption Cotton Exports Cotton Ending Stocks Market Equilibrium Man-made Fiber Capacity Man-made Fiber Production A-Index Man Made Fiber Net Trade 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: AC i t = f(EPC i, t , EPCM i, t , AC i, t-1 ) , (4.1) where i=north, central, south, and the others regions in India. ACi t is the cotton acreage in ith region in time t; EPCi, t represents expected cotton price in ith region in time t; EPCM i, t are the expected prices of competing crops in the ith 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. YCi t = f (EPCi, t , YCi, t-1 , RFt , FA t , Tt ) , (4.2) where YCi t represents cotton yield in the ith region; YCi, t-1 is cotton yield lagged one year in the ith region; RFt represents rainfall in millimeter; and FA t is the fertilizer application in Kilogram/Hectare. Cotton is very sensitive to rainfall. Heavy or scanty rainfall at 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: CPR t = ∑ AC i=1 4 it *YCi t , (4.3) where CPR t represents total cotton production in India in time period t. 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. MMPC t = f (PPt - k , PO t - k , MMPC t-1 ) , (4.4) where MMPC t represents the man-made fiber productive capacity at time t; PPt - k is the lagged price of polyester; PO t - k is the lagged price of petroleum crude oil; MMPC t-1 represents the man-made fiber productive capacity at time t-1, and k = 3, …, 6. Man-made fiber capacity utilization is specified as the function of polyester and crude oil prices, and utilization rate lagged one year: 66 MMCUZt = f (PPt , PO t , MMCUZt-1 , T) , where MMCUZt is the man-made fiber capacity utilization in period t. (4.5) 4.1.2. Fiber Demand Estimation: As discussed in the conceptual framework, fiber demand is derived using a twostep 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: TXPC t = f (PTX t , PFD t , I t , T) , (4.6) where TXPCt is the per capita textile demand in fiber equivalent in time t; PTX t is the textile price index; PFD t is the food price index; I t 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. 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: ⎛Y⎞ SC t =α1 + γ11log(PC t ) + γ12 log(PMFt ) + γ13log(PWt ) + β1log ⎜ ⎟ + u1t , ⎝ P* ⎠ (4.7) ⎛Y⎞ SMFt = α 2 + γ 21log(PC t ) + γ 22 log(PMFt ) + γ 23log(PWt ) +β 2 log ⎜ ⎟ + u 2 t , (4.8) ⎝ P* ⎠ ⎛Y⎞ SWt = α 3 + γ 31log(PC t ) + γ 32 log(PMFt ) + γ 33log(PWt ) + β 3log ⎜ ⎟ + u 3 t , (4.9) ⎝ P* ⎠ where PC t is the price of cotton; PMFt is the price of man-made fiber; PWt is the price of wool; Y is the total fiber expenditure; P* is the stone price index; SC t is the share of cotton fiber in the textile; SMFt is the share of manmade fiber, and SWt represents the share of wool. Total Mill demand of the individual fiber in India can be estimated by multiplying respective fiber shares with total textile consumption and population as DC t = TXPC t *SC t *POPt , DMF t = TXPC t *SMF t *POPt , DW t = TXPC t *SW t *POPt , (4.10) (4.11) (4.12) where DC t , DMF t , DW t are the total mill demand of cotton, man-made fiber, and wool, respectively, and POPt 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. CESt = f(PC t , CESt-1 , CPR t ) , (4.13) where CESt 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. 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: CEX t = f (PC t , PA t * (1 - ETt )*XR t , CSU t ) , (4.14) where CEX t is the export of cotton; PAt is the international cotton price represented by A-Index c.i.f. North Europe; CSU t is the cotton supply; ETt the export tax on cotton in percentage, and XR t 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. 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. CIM t = f (PC t , PA t *(1+ITt )*XR t , I t ) , where CIMt is the import of cotton at time t; ITt is the import tariff rate in India in percentage, and gross domestic product represents the proxy for income ( I t ) in India. It is assumed that as I t 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. (4.15) 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: CPR t + CESt-1 + CIM t = DC t + CESt + CEX t . (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. MMPR t = DMFt + MMTR t , (4.17) 70 where MMPR t represents man-made fiber production, which is obtained through multiplying man-made fiber capacity utilization with its capacity; DMFt is the man-made fiber consumption, and MMTR t is the man-made fiber net trade (exports - imports). 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): ⎛Y ⎞ WUSA = µ1 + δ11log(PUSA ) + δ12 log(PAUS ) + δ13log(PROW ) + λ1log ⎜ v ⎟ + u1t (4.18) ⎝ Pv * ⎠ ⎛Y ⎞ WAUS = µ 2 + δ 21log(PUSA ) + δ22 log(PAUS ) + δ 23log(PROW ) + λ 2 log ⎜ v ⎟ + u 2t (4.19) ⎝ Pv * ⎠ ⎛Y ⎞ WROW = µ 3 + δ31log(PUSA ) + δ32 log(PAUS ) + δ33log(PROW ) + λ3log ⎜ v ⎟ + u 3t (4.20) ⎝ Pv * ⎠ 73 where WUSA , WAUS , WROW are, respectively, the value of market share of the United States, Australia, and the rest of the world, while PUSA , PAUS , PROW are the unit import values of cotton from the United States, Australia, and the Rest of the World, respectively. Pv * is the Stone price index, Yv is the value of total cotton imports in India, and u1t , u 2 t , u 3t are the error terms, associated with the share equations for the USA, Australia, and the rest of the world, respectively. The error terms, u1t , u 2t , u 3t ~N(µ, σ 2 ) are assumed to be normally distributed with constant means and variances, and may be contemporaneously correlated. 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): 1 T ∑ Yts -Yta T i=1 ( ) 2 U= 1 T ∑ Yts T i=1 ( ) 2 1 T + ∑ Yta T i=1 ( ) , 2 (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 Yts = Yta 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. The MSE measures the mean of the squared deviation between simulated and actual variables, and is expressed as: MSE = 1 T ∑ (Ys t -Y a t )2 , T t=1 (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 1 2 ∑ ( Yts -Yta ) = ( Ys -Y a ) + ( σs -σa ) + 2 (1-ρ ) σs σa , T (4.23) where Ys , σs are the mean and standard deviation of the series Yts ; and Y a , σ a are the mean and standard deviation of the series Yta ; and ρ is the correlation coefficient for the two series. Rearranging, equation 4.23 can be written as, ( Y -Y ) 1= 1 ∑ ( Y -Y ) T s a 2 s t a 2 t + ( σs -σa ) 2 2 1 ∑ ( Yts -Yta ) T + 2 (1-ρ ) σs σ a 2 1 ∑ ( Yts -Yta ) T (4.24) where UM = ( Y -Y ) 1 ∑ ( Y -Y ) T s a 2 s t a 2 t , (4.25) US = ( σs -σa ) 2 2 1 ∑ ( Yts -Yta ) T , (4.26) UC = 2 (1-ρ ) σs σ a 2 1 ∑ ( Yts -Yta ) T , (4.27) where UM , US , UC are the bias, variance and covariance proportions of U, respectively; and the sum of these three is equal to one. The UM represents the systematic error and it is expected to be equal to zero. A larger value of UM (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 76 fluctuation than the simulated series or vice versa, and thus it becomes necessary to revise the model. Finally, the covariance proportion, UC , measures the unsystematic error and is less worrisome than the other two. 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 1 2 ∑ ( Yts -Yta ) = ( Ys -Y a ) + ( σs -ρσa ) + (1-ρ2 ) σa2 T (4.28) Equation 4.28, after rearrangement, can be written as: 1= ( Y -Y ) 1 ∑ ( Y -Y ) T s a 2 s t s a 2 t + ( σs -ρσa ) 2 1 ∑ ( Yts -Yta T (1-ρ ) σ + 1 ) T ∑ ( Y -Y ) 2 2 a 2 s t a 2 t (4.29) where UM = ( Y -Y ) 1 ∑ ( Y -Y ) T a 2 s t a 2 t (4.30) UR = ( σs -ρσa ) 2 2 1 ∑ ( Yts -Yta ) T (4.31) UD = (1-ρ ) σ 1 ∑ ( Y -Y ) T 2 2 a s t a 2 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 online 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 manmade 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 EPC EPCM1 EPCM2 T LAC1 LYC2 D02 D86 DW Statistic DH Statistic Adj. R2 F-Stat 1.92 0.73 17.44*** 0.62 10.22*** 0.49 8.97*** 1.70 -309.94** (133.11) -604.96*** (166.81) 2.15 0.64*** (0.14) 4102.86** (1911.69) 587.06* (289.98) 0.88* (0.46) -6.97* (3.91) -19.71*** (5.83) 27.34* (15.38) 4115.38*** (593.03) 2.04** (0.92) 1431.15*** (185.73) 1.71*** (0.53) -7.94** (3.77) 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 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 ( ε ) that are derived from the application of mathematical formulations. In this case, β ’s are the estimated parameters for the price variables, and ε ’s are the ownand cross-price elasticities. Mathematically, it can be expressed as: ∧ ⎛X⎞ Variance( ε )= ⎜ ⎟ Variance(β ) ⎝Y⎠ ∧ 2 2 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 Northern Central Southern 0.20 (0.10) 0.15 (0.07) 0.36 (0.11) Rice -0.21 (0.11) -0.35 (0.10) -0.22 (0.10) Groundnut 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 87 Table 5.3: Regression Results of Indian Regional Cotton Yield Models Northern Central Intercept EPC PERCVIRG LAC1 T LYC1 LYC3 D01 DW Statistic DH Statistic Adj. R2 F-Stat -2.345 0.371 7.68*** 0.723 22.85*** 0.062* (0.031) 1.383 0.649*** (0.159) 0.006* (0.003) -0.0001* (0.0000) 0.006*** (0.001) 0.098 (0.058) -0.083 (0.048) 0.0002* (0.0001) Southern 0.019* (0.105) 0.004*** (0.001) 0.493*** (0.138) -1.28 0.911 200.76*** 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 89 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 LDEFF LLMMPC PRPET LMMCUZ T D0302 DH Statistic Adj. R2 F-Stat -0.359* (0.187) -0.304 0.937 94.85*** 2.915*** (0.938) -0.942** (0.433) 0.616*** (0.127) 0.857*** (0.277) 0.619** (0.286) 0.356* (0.185) -0.008** (0.004) -0.630 0.443 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 twostep 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 19702003. 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 CTWH1 GDPIND T DW Statistic Adj. R2 F-Statistic 0.846*** (0.276) 5.238*** (1.263) 0.215*** (0.069) 0.077** (0.030) 1.286 0.959 197.48*** 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 94 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 95 Table 5.6: Regression Results of Fiber Demand System Share Intercept Cotton Price Man-made Fibers Wool Price Expenditures ( αi ) ( γ i1 ) Price ( γ i2 ) ( γ i3 ) ( βi ) Cotton ( SC t ) Manmade Fibers ( SMFt ) Wool ( SWt ) 2.307*** (0.299) -1.44*** (0.30) 0.133 0.157*** (0.0148) -0.144*** (0.0148) 0.133 -0.144*** (0.0148) 0.147*** (0.015) -0.013 -0.013 -0.118*** (0.021) 0.127*** (0.022) -0.009 -0.003 -0.12 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 Wool Fibers Cotton -0.63 -0.18 -0.02 0.82 Man-made Fibers Wool -0.73 -0.36 -0.63 -0.08 -0.02 -0.04 1.39 0.47 97 Table 5.8: Estimated Compensated Fibers Price Elasticities Demand Elasticities with respect to the Price of Cotton Cotton Man-made Fibers Wool -0.09 0.18 -0.05 Man-made Fibers 0.09 -0.18 0.08 Wool -0.01 0.01 -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 99 Table 5.9: Regression Results of Cotton Trade Equations Cotton Exports Intercept CSU WW PRCTAI D9901 DW Statistic Adj. R2 F-Stat 1.826 0.113 2.970* 174188** (76321) 0.011 (0.014) -145900** (67850) Cotton Imports 52489 (44954) 27854 (25953) 96421*** (16840) 1.834 0.590 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 101 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 102 Table 5.10: Regression Results of Manmade Fiber Net Trade Manmade Fiber Net Trade Intercept DUSPP DPP LMMTR DH Statistic Adj. R2 F-Stat -166.386 (160.293) 457.850** (208.362) -13.646 (65.765) 0.916*** (0.158) 1.222 0.882 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 DPC LCES D95 DW Statistic Adj. R2 F-Stat 636077*** (208293) -511311 (308920) 0.399** (0.153) 505539** (176136) 1.795 0.396 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 Reg Dist (UM) (UR) (UD) AC1 0.00 0.05 0.95 AC2 AC3 YC1 YC2 YC3 TXPC CES CIM MMPC MMCUZ PC PP DC DMF MMTR AC CPR EXPEND1 0.01 0.11 0.12 0.01 0.44 0.04 0.66 0.41 0.84 0.87 0.68 0.03 0.00 0.65 0.39 0.06 0.13 0.02 0.07 0.01 0.00 0.02 0.12 0.17 0.12 0.22 0.01 0.00 0.22 0.16 0.68 0.16 0.02 0.00 0.38 0.68 0.93 0.88 0.88 0.98 0.44 0.79 0.23 0.36 0.16 0.12 0.10 0.82 0.32 0.19 0.59 0.94 0.49 0.30 Var (US) 0.35 0.05 0.14 0.44 0.39 0.32 0.05 0.00 0.49 0.05 0.11 0.04 0.08 0.22 0.18 0.53 0.16 0.04 0.36 Covar (UC) 0.65 0.94 0.75 0.44 0.61 0.23 0.91 0.34 0.10 0.12 0.01 0.28 0.89 0.78 0.17 0.08 0.78 0.83 0.62 U 0.05 0.02 0.05 0.09 0.08 0.13 0.02 0.12 0.53 0.13 0.07 0.21 0.17 0.05 0.03 0.16 0.02 0.05 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 UStatistic 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 tenyear 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 000 MT 6500 6000 5500 5000 4500 4000 110 3500 3000 2500 2000 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Figure 5.1. Baseline Projections for Textile Consumption in India Rupees per Kilogram 100 90 80 70 60 111 50 40 30 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 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 3397.16 3575.55 3849.48 (000 Metric Ton) (5%) (13%) Man-made Fiber Consumption (000 Metric Ton) Price of Cotton (Rupees/Kilogram) Price of Polyester (Rupees/Kilogram) Cotton Production (000 Metric Ton) Man-made Fiber Production (000 Metric Ton) Cotton Acreage (000 Hectare) Cotton Yield (Metric Ton/Hectare) Net Import of Cotton (000 Metric Ton) Net Export of Man-made Fiber (000 Metric Ton) 1348.15 52.50 77.78 3180.48 1640.02 7932.50 0.40 155.62 291.88 1952.73 (45%) 60.59 (15%) 77.34 (-0.6%) 3499.42 (10%) 2174.96 (33%) 8045.15 (1%) 0.43 (8%) 167.95 (8%) 222.22 (-24%) 2316.72 (72%) 64.45 (23%) 88.25 (13%) 3777.90 (19%) 2537.98 (55%) 8064.59 (2%) 0.47 (18%) 197.06 (27%) 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 000 MT 300 250 200 150 100 50 0 -50 -100 -150 -200 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 114 Cotton Man-made Fiber 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 Cotton Consumption Baseline Scenario 1 Scenario 2 Scenario 3 MMF Consumption Baseline Scenario 1 Scenario 2 Scenario 3 Indian Cotton Price Baseline Scenario 1 Scenario 2 Scenario 3 Indian Polyester Price Baseline Scenario 1 Scenario 2 Scenario 3 3397.16 0.15 0.19 0.36 2005/06 3459.06 1.79 2.37 3.35 2006/07 3453.83 1.41 1.78 2.62 2007/08 3506.99 1.43 1.67 2.49 2008/09 2009/10 2010/11 2011/12 Thousand Metric Tons 3544.42 3575.55 3609.59 3677.39 Percentage Change 1.36 1.20 1.18 1.15 2.32 2.62 2.69 2.77 2.41 2.60 2.70 3.53 Thousand Metric Tons 1952.73 2076.35 2148.92 Percentage Change 4.74 4.95 4.86 4.87 10.25 9.51 9.16 8.95 17.41 16.69 16.06 14.50 Rupees per Kilogram 60.59 61.75 Percentage Change 5.44 5.53 12.53 13.63 12.49 17.41 Rupees Per Kilogram 77.34 77.20 Percentage Change 13.74 13.95 29.72 31.25 30.51 34.63 2012/13 3729.88 1.15 2.55 3.76 2013/14 3786.31 0.97 2.57 3.20 2014/15 3849.48 1.09 2.70 3.43 Average 3599.06 1.17 2.20 2.77 1348.15 8.53 17.34 25.81 1511.23 4.23 11.22 17.29 1635.70 4.89 11.98 18.08 1759.64 4.71 11.79 17.73 1863.98 2210.48 4.84 9.26 14.01 2259.29 5.13 9.20 14.90 2316.72 4.90 8.94 14.45 1916.65 5.15 10.69 16.99 115 52.50 7.99 10.77 15.13 56.58 5.07 6.58 10.04 59.26 5.52 6.33 9.93 60.07 5.55 9.84 9.95 60.54 5.17 11.68 11.74 62.75 5.76 12.82 19.13 63.96 4.81 13.31 16.30 64.55 5.71 14.52 18.32 64.65 6.85 15.59 21.70 60.65 5.76 11.60 14.74 77.78 14.63 18.98 25.35 80.34 12.34 16.53 25.70 84.48 8.43 11.96 20.82 83.89 8.58 17.28 22.07 81.41 10.02 21.74 25.91 81.13 14.55 30.36 35.86 85.46 15.73 24.57 36.53 87.93 15.82 22.64 39.34 88.25 16.01 23.11 42.50 82.29 13.07 22.56 30.84 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 Northern Region Baseline Scenario 1 Scenario 2 Scenario 3 Central Region Baseline Scenario 1 Scenario 2 Scenario 3 Southern Region Baseline Scenario 1 Scenario 2 Scenario 3 Total Acreage Baseline Scenario 1 Scenario 2 Scenario 3 1303.59 0.00 0.00 0.00 2005/06 1328.22 1.45 1.96 2.75 2006/07 1350.50 1.90 2.51 3.68 2007/08 1365.07 2.28 2.83 4.27 2008/09 1366.76 2.51 3.64 4.63 2009/10 2010/11 Thousand Hectares 1358.87 1343.57 Percentage Change 2.59 2.67 4.46 5.11 5.15 5.58 Thousand Hectares 5182.99 5205.51 Percentage Change 0.67 0.65 1.45 1.50 1.46 1.50 Thousand Hectares 1422.29 1402.49 Percentage Change 1.66 1.69 3.74 3.89 3.76 3.88 Thousand Hectares 8045.15 8032.56 Percentage Change 1.16 1.17 2.35 2.51 2.47 2.58 2011/12 1327.76 2.73 5.71 6.64 2012/13 1311.43 2.79 5.96 7.60 2013/14 1295.89 2.67 6.19 7.79 2014/15 1279.18 2.72 6.51 8.19 Average 1330.08 2.21 4.08 5.12 5021.77 0.00 0.00 0.00 5069.67 0.91 1.23 1.73 5076.31 0.77 1.01 1.51 5127.70 0.75 0.88 1.37 5158.05 0.74 1.23 1.32 5238.57 0.65 1.58 1.95 5273.41 0.65 1.47 2.14 5310.98 0.55 1.46 1.85 5345.00 0.59 1.52 1.91 5182.72 0.63 1.21 1.52 116 1526.13 0.00 0.00 0.00 1546.98 2.49 3.35 4.71 1457.96 1.71 2.22 3.39 1456.73 1.86 2.13 3.34 1440.32 1.83 3.24 3.28 1391.69 1.69 4.16 5.32 1380.55 1.73 3.84 5.73 1371.69 1.42 3.93 4.81 1359.42 1.65 4.19 5.29 1432.39 1.61 3.15 3.96 7932.50 0.00 0.00 0.00 8025.87 1.30 1.75 2.46 7965.77 1.13 1.47 2.20 8030.50 1.20 1.43 2.21 8046.13 1.23 1.99 2.22 8039.02 1.16 2.69 3.28 8046.40 1.18 2.59 3.62 8059.57 1.03 2.62 3.29 8064.59 1.10 2.75 3.46 8026.19 1.06 2.01 2.53 Table 5.16: Effects of MFA Quota Elimination on Indian Cotton Yield 2004/05 Northern Region Baseline Scenario 1 Scenario 2 Scenario 3 Central Region Baseline Scenario 1 Scenario 2 Scenario 3 Southern Region Baseline Scenario 1 Scenario 2 Scenario 3 Average Yield Baseline Scenario 1 Scenario 2 Scenario 3 0.33 0.00 0.00 0.00 2005/06 0.34 -0.62 -0.84 -1.18 2006/07 0.34 -1.22 -1.63 -2.35 2007/08 0.35 -1.78 -2.28 -3.38 2008/09 2009/10 2010/11 2011/12 Metric Tons Per Hectare 0.35 0.35 0.35 0.35 Percentage Change -2.25 -2.57 -2.78 -2.91 -3.06 -3.90 -4.67 -5.36 -4.20 -4.93 -5.53 -6.30 Metric Tons Per Hectare 0.38 0.38 Percentage Change 0.44 0.39 0.39 0.79 0.89 0.90 0.80 0.89 0.89 Metric Tons Per Hectare 0.68 0.70 Percentage Change 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Metric Tons Per Hectare 0.43 0.44 Percentage Change 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2012/13 0.36 -2.99 -5.85 -7.11 2013/14 0.36 -2.97 -6.19 -7.63 2014/15 0.36 -2.95 -6.46 -8.03 Average 0.35 -2.09 -3.66 -4.60 0.35 0.00 0.00 0.00 0.35 0.68 0.92 1.30 0.36 0.44 0.56 0.86 0.36 0.46 0.53 0.83 0.37 0.39 0.38 0.94 1.20 0.39 0.38 0.85 1.26 0.40 0.31 0.85 1.04 0.40 0.35 0.88 1.11 0.38 0.38 0.74 0.93 117 0.58 0.00 0.00 0.00 0.60 0.00 0.00 0.00 0.62 0.00 0.00 0.00 0.64 0.00 0.00 0.00 0.66 0.71 0.00 0.00 0.00 0.73 0.00 0.00 0.00 0.74 0.00 0.00 0.00 0.76 0.00 0.00 0.00 0.68 0.00 0.00 0.00 0.40 0.00 0.00 0.00 0.41 0.00 0.01 0.01 0.42 0.00 0.00 0.00 0.42 0.00 0.00 0.00 0.43 0.45 0.00 0.00 0.00 0.45 0.00 0.00 0.00 0.46 0.00 0.00 0.00 0.47 0.00 0.00 0.00 0.44 0.00 0.00 0.00 Table 5.17: Effects of MFA Quota Elimination on Indian Fiber Supply 2004/05 Cotton Production Baseline Scenario 1 Scenario 2 Scenario 3 MMF Production Baseline Scenario 1 Scenario 2 Scenario 3 3180.48 0.00 0.00 0.00 2005/06 3284.97 1.69 2.28 3.20 2006/07 3310.70 1.23 1.59 2.41 2007/08 3394.68 1.25 1.43 2.25 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Average Thousand Metric Tons 3450.53 3499.42 3543.77 3601.48 3659.62 3721.51 3777.90 3493.19 Percentage Change 1.19 1.04 1.03 1.01 1.02 0.83 0.95 1.02 2.08 2.39 2.45 2.57 2.34 2.35 2.48 2.00 2.11 2.34 2.38 3.23 3.50 2.91 3.12 2.50 Thousand Metric Tons 2093.28 2174.96 2296.19 2361.13 Percentage Change 1.16 0.83 0.50 1.49 2.16 1.77 1.38 2.35 3.53 4.98 4.33 4.16 1640.02 2.56 2.56 3.80 1747.94 2.25 2.25 3.75 1847.88 1.93 2.34 4.40 1963.02 1.57 2.66 4.60 2423.02 0.12 2.08 4.17 2481.92 0.43 1.84 4.28 2537.98 0.65 2.90 4.44 2142.49 1.23 2.21 4.22 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. 118 Table 5.18: Effects of MFA Quota Elimination on Fiber Trade and World Prices 2004/05 Cotton Net Imports Baseline Scenario 1 Scenario 2 Scenario 3 MMF Net Exports Baseline Scenario 1 Scenario 2 Scenario 3 A-Index Price Baseline Scenario 1 Scenario 2 Scenario 3 155.62 3.20 3.96 7.73 2005/06 167.88 3.81 4.24 6.41 2006/07 169.11 4.78 5.02 6.09 2007/08 169.00 4.71 5.98 6.60 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14 2014/15 Thousand Metric Tons 168.56 167.95 167.65 187.32 187.24 187.15 197.06 Percentage Change 4.30 3.96 3.78 3.23 2.95 3.11 3.17 6.19 5.98 6.06 4.98 5.05 5.28 5.20 7.40 6.56 7.72 7.08 6.32 6.75 7.13 Average 174.96 3.73 5.27 6.89 291.88 -24.98 -65.69 -97.87 236.71 -10.39 -55.01 -82.66 212.19 -20.85 -71.97 -101.03 203.38 -25.63 -76.34 -108.97 Thousand Metric Tons 222.22 219.84 Percentage Change -27.96 -35.37 -40.71 -63.61 -66.27 -72.08 -109.28 -97.90 -106.47 229.31 Cents per pound 63.22 64.51 Percentage Change 0.03 0.03 0.05 0.04 0.06 0.05 212.21 -32.71 -64.49 -100.61 212.54 -48.97 -72.57 -98.19 222.62 -47.31 -72.87 -103.51 221.26 -43.84 -60.40 -100.30 225.83 -32.61 -67.39 -100.62 119 64.40 0.28 0.52 0.58 63.09 0.05 0.08 0.08 61.94 0.04 0.07 0.08 61.86 0.04 0.07 0.07 62.44 0.03 0.06 0.07 65.91 0.02 0.04 0.04 66.94 0.02 0.04 0.04 67.46 0.02 0.04 0.04 67.46 0.02 0.03 0.04 64.47 0.05 0.09 0.10 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 000 MT 300 250 200 122 150 100 50 0 -50 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 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 Restricted 10 8 0.723745184 2.2349402 0.39492 0.13492 125 Table 5.20: Estimated Coefficients of the Restricted AIDS Model ROW Price Intercept U.S. Price of Australia Market of cotton cotton Price of Share of cotton cotton U.S. -0.318 0.020 -0.434 0.414 (0.235) (0.108) (0.104) Australia 0.260 -0.102 -0.179 0.282 (0.213) (0.109) (0.091) ROW 1.058 0.082 0.614 -0.696 Expenditures 0.025 (0.013) -0.008 (0.012) -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 US US Australia Rest of the world -0.876 (0.056) -0.875 (0.024) 0.113 Australia -3.212 (0.051) -2.545 (0.019) 0.823 ROW 0.463 5.360 -1.947 Expenditure Elasticities 1.186 (0.012) 0.932 (0.009) 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 -0.715 -0.748 0.246 Australia -3.075 -2.437 0.936 ROW 1.351 6.058 -1.217 US Australia ROW 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 133 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. 134 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 135 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 136 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, 137 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. 138 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. 139 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 manmade 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 140 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. 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List of Variables and their Unit of Measurement Variables Name Variable Definition AC Regional Cotton Acreage AC1 Northern India Cotton Acreage AC2 Central India Cotton Acreage AC3 Southern India Cotton Acreage CES Cotton Ending Stocks CEX Cotton Exports CIM Cotton Imports CPR Total Cotton Production CSU Total Cotton Supply 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 DMF Total Mill Demand of Man-Made Fiber DMF Man-made Fiber Consumption DPC Deflated Domestic Price of Cotton DPP Deflated Domestic Polyester Price DUSPP Deflated US 1.5 Denier Polyester Price DW Total Mill Demand of Wool EPC Expected Price of Cotton EPCM Expected Price of Competing Crops EPCM1 Expected Price of Rice EPCM2 Expected Price of Groundnut ET Export Tax on Cotton Expend1 Total Expenditure (Using Indian cotton, Polyester Price, and U.S. Wool Price) FA Fertilizer Application GDP GDPIND I Gross Domestic Product Per Capita GDP Per Capita Income Unit of Measurement Thousand Hectare Thousand Hectare Thousand Hectare Thousand Hectare Metric Ton Metric Ton Metric Ton Metric Ton Metric Ton Metric Ton 1000 Metric Ton 1000 Metric Ton Rupees per Kilogram Rupees per Kilogram Cents per Pound Metric Ton Rupees per Kilogram Rupees per Kilogram Rupees per Kilogram Rupees per Kilogram Percentage Rupees Kilogram per Hectare Billion Rupees Rupees Rupees 145 Table 1.(Contd.) Variable Name Variable Definition IT Import Tariff Rate in India LAC1 Northern India Cotton Acreage Lagged One Year LCES Cotton Endings Stocks Lagged One Year 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 LMMCUZ Man-made Fiber Capacity Utilization Lagged One Year LMMTR Manmade Fiber Trade Lagged One Year LRTC Long-Run Total Costs LYC1 Northern India Cotton Yield Lagged One Year LYC2 Central India Cotton Yield Lagged One Year LYC3 Southern India Cotton Yield Lagged One Year MMCUZ Man-made Fiber Capacity Utilization MMPC Man-made Fiber Production Capacity MMPR Man-made Fiber Production MMTR Man-made Fiber Net Trade PA A-Index Price of Cotton PAUS Unit Import Values of Cotton from Australia PC Market Price of Cotton PERCVIRG Percentage Area Covered Under Irrigation PFD Food Price Index PMF Market Price of Man-made Fiber PO Price of Petroleum Crude Oil POP Population in India PP Polyester Price PRCTAI Deflated Domestic Price of Cotton/ Deflated and Adjusted A-Index Price for Tariff and Exchange Rate Unit Import Values of Cotton from ROW PROW PRPET Polyester Price/ Crude Oil Price PTX Textile Price Index Unit of Measurement Percentage Thousand Hectare Metric Ton Thousand Metric Ton Percentage 1000 Metric Ton Metric Ton /Hectare Metric Ton/ Hectare Metric Ton /Hectare Percentage 1000 Metric Ton 1000 Metric Ton 1000 Metric Ton Cents per Pound Dollar per Kilogram Rupees per Kilogram Percentage Rupees per Kilogram Dollar per barrel 1000 Million Rupees per Kilogram Dollar per Kilogram 146 Table 1. (Contd.) Variable Name PUSA PW RF SC SMF SW T TXPC WAUS WROW WUSA WW XR Y YC YC1 YC2 YC3 YV Variable Definition Unit Import Values of Cotton from U.S. Market Price of Wool Rainfall Share of Cotton in Textile Share of Man-made Fiber in Textile Share of Wool in Textile Time Trend Per Capita Textile Demand Value of Market Shares of Australia Value of Market Shares of rest of world Value of Market Shares of the U.S. Deflated Domestic Price of Cotton/ Deflated and Adjusted A-Index Price Exchange Rate Total Fiber Expenditure Cotton Yield Northern India Cotton Yield Central India Cotton Yield Southern India Cotton Yield Value of Total Cotton Imports in India Unit of Measurement Dollar per Kilogram Cents per Pound Millimeter Year Kilogram Rupees per Dollar Rupees Metric Ton /Hectare Metric Ton /Hectare Metric Ton /Hectare Metric Ton /Hectare Dollar 147