Percentage changes in world price due to India's trade in rice .... India's exports since independence were largely confined to primary products ... Basmati rice has got good export demand and fetches good export price in international ... continuous decline in the share of agriculture sector in India's gross domestic product to ...
EXPORT OF RICE FROM INDIA: PERFORMANCE AND DETERMINANTS
Thesis
Submitted to the Punjab Agricultural University in partial fulfillment of the requirements for the degree of
MASTER OF SCIENCE in AGRICULTURAL ECONOMICS (Minor Subject: Statistics)
By Anup Adhikari (L-2012-BS-245-M)
Department of Economics and Sociology College of Basic Sciences and Humanities PUNJAB AGRICULTURAL UNIVERSITY LUDHIANA-141004 2014
CERTIFICATE I This is to certify that the thesis entitled, “Export of Rice from India: Performance and Determinants” submitted for the degree of M.Sc. in the subject of Agricultural Economics (Minor subject: Statistics) of the Punjab Agricultural University, Ludhiana, is a bona fide research work carried out by Anup Adhikari (L-2012-BS-245-M) under my supervision and that no part of this thesis has been submitted for any other degree. The assistance and help received during the course of investigation have been fully acknowledged.
____________________________ Major Advisor (Dr. M. K. Sekhon) Senior Economist (Marketing) Department of Economics and Sociology College of Basic Sciences and Humanities Punjab Agricultural University Ludhiana-141004, India
CERTIFICATE II This is to certify that the thesis entitled, “Export of Rice from India: Performance and Determinants” submitted by Anup Adhikari (L-2012-BS-245-M) to the Punjab Agricultural University, Ludhiana, in partial fulfillment of the requirements for the degree of M.Sc. in the subject of Agricultural Economics (Minor subject: Statistics) has been approved by the Student‟s Advisory Committee along with Head of the Department after an oral examination on the same.
___________________ (Dr. M. K. Sekhon) Major Advisor
___________________ (Dr. Sukhpal Singh) Head of the Department
_____________________ (Dr. Gursharan Singh) Dean Postgraduate Studies
______________________________ (Dr. P. K. Dhindsa) External Examiner Professor, Punjab School of Economics Guru Nanak Dev University, Amritsar-143005
ACKNOWLEDGEMENT I have been able to bring this study in the present shape only because of heartily cooperation of number of heads and hands. There are some who have blessed, some who assisted and some who have supplemented. Firstly, I wish to acknowledge and thank Indian Embassy of Nepal, GOI for providing the financial assistance in the form of Nepal Aid Fund Scholarship Scheme to pursue M. Sc. Agricultural Economics at Punjab Agricultural University. I feel great pleasure to place on record my deep sense of appreciation and heartfelt thanks to my major advisor Dr. (Mrs) M. K. Sekhon, Senior Economist (Marketing), Department of Economics and Sociology, for her constant supervision, valuable guidance, kindness, encouragement and support. I am also heavily indebted to the members of my Advisory Committee, Dr. (Mrs.) Manjeet Kaur, Senior Economist, Department of Economics and Sociology, Dr. (Mrs) A. K. Mahal, Associate Professor of Statistics, Department of Maths., Statistics and Physics and Dr. M. S. Toor Professor of Economics, Department of Economics and Sociology (Dean PG Nominee) for their valuable comments, suggestions and support during the course of research work. I am grateful to Dr. Sukhpal Singh (Head, Department of Economics and Sociology), for providing necessary facilities for successful completion of this work. His positive attitude and regular enquiry throughout the period is notable. I feel proud and privileged to express my sincere thanks to Dr. M.S. Sidhu, Professor, Department of Economics and Sociology, for his valuable guidance and support during my master’s degree programme. I would like to pay my sincere gratitude to Dr. J. S. Sidhu , Professor of Economics, Department of Economics and Sociology, and Dr. Kamal Vatta, Director of Centers for International Projects Trust, for their inspiration, sustained help and co-operation. I would also like to thank all Faculty members of Economics and Sociology for their instantaneous help and valuable suggestions during my tenure of study. I am also thankful to the non-teaching staff of Department of Economics and Sociology especially to Mr. Tarwinder Singh and Jay Singh for their constant help and facilitation. I would like to extend my gratitude to my Seniors especially to Priyabrata sir, Juniors and Friends, without whom life would be bleak. I wish to express my deepest gratitude to parents, who nursed me with great affection and my brothers, sister and all relatives. Without their help, I would not have brought this study to fruition. A positive and loving feedback received from Nepali brothers Saroj dai, Deepak dai, Sagar, Esab, Chandru, Rajeev, Suvash, Suraj and Ritesh is something inexpressible. Everybody may not have been mentioned, but none is forgotten.
______________ (Anup Adhikari)
Title of the thesis
:
“Export of rice from India: Performance and determinants”
Name of the Student and Admission No.
:
Anup Adhikari (L-2012-BS-245-M)
Major subject
:
Agricultural Economics
Minor subject
:
Statistics
Name and Designation of Major Advisor
:
Dr. (Mrs.) M. K. Sekhon Senior Economist (Marketing)
Degree to be awarded
:
M.Sc. (Agricultural Economics)
Year of award of degree
:
2014
Total Pages in Thesis
:
85+Appendices+Vita
Name of University
:
Punjab Agricultural University, Ludhiana-141001 Punjab, India Abstract
Being highly dynamic with volatile nature of international market and increased demand of Indian rice globally, present study was carried out to examine the changes in composition of rice export, assessing export competitiveness and to analyze the factors affecting growth of rice export. The study was based on secondary data, however primary data were also used, of rice export in terms of basmati rice and non basmati rice collected from APEDA, various published journals, reports and websites for the period of 33 years (1980-81 to 2012-13). Tabular analysis, compound growth rate, instability index, Markov chain analysis, nominal protection coefficient, multiple linear regression and augmented Dickey Fuller test were employed to arrive at meaningful results. It was observed that the growth in value of basmati rice export (15.87%) was higher than quantity of export (7.55%). The growth rate of unit value of basmati rice export was higher in period I (13.48%) than period II (5.06%). The growth rates in export of non-basmati rice in terms of quantity, export earnings and unit value were 23.03 per cent, 30.91 per cent and 6.83 per cent respectively during the study period. The instability index was found highest for quantity (43.37 %) in case of basmati rice and value (106.25 %) in case of non basmati rice during entire period. The UAE was found to be highly preferred market for Indian basmati rice and Nigeria for Indian non basmati rice, as indicated by the probability of retention of their previous shares. It was estimated that during 2013-14 major markets for Indian basmati rice would be Iran, Saudi Arabia and others where as for Indian non basmati rice major markets would be Nigeria, South Africa and others. The study indicated that India is highly competitive in export of basmati rice due to higher price in international market and non competitive in export of non basmati rice due to lower price in global market. The results revealed that export price, international price, domestic consumption, lagged production and exchange rate were the major factors affecting the growth of rice export from India. The study suggested to develop and produce exportable quality of rice, improve post harvest technology, sanitary measures in order to gain access over global markets. Therefore suitable export promotion strategies have to be executed by the government and policy makers in order to boost rice exports at particular and overall exports in general. Key Words: Rice export, Performance, Markov chain analysis, Competitiveness, Determinants and India
______________________ Signature of Major Advisor
____________________ Signature of the Student
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CONTENTS CHAPTER
TOPIC
PAGE NO.
I
INTRODUCTION
1-4
II
REVIEW OF LITERATURE
5-16
III
MATERIALS AND METHODS
17-25
IV
RESULTS AND DISCUSSION
26-73
V
SUMMARY
74-79
REFERENCES
80-85
APPENDICES
i-x
VITA
7
LIST OF TABLES Table No.
Title
Page No.
3.1
Estimation of nominal protection coefficient (Exportable hypothesis)
22
4.1.
World rice production
26
4.2
India's rice production in global context during last decades
27
4.3
State wise production of rice in India
28
4.4
World rice trade
29
4.5
International and domestic wholesale price of rice
30
4.6
Percentage changes in world price due to India's trade in rice
31
4.7
Percentage shares of basmati rice export in total rice export and total agricultural exports
33
4.8
Percentage shares of non-basmati rice export in total rice export and total agricultural exports
36
4.9
Share of Indian rice export to total agricultural exports
38
4.10
Compound growth rates of quantity, value and unit value of basmati rice, non basmati and total rice export from India,1980-81 to 2012-13
42
4.11
Growth in export of basmati rice to major importing countries,1987-88 to 2012-13
44
4.12
Instability index of basmati rice export from India,1980-81 to 2012-13
45
4.13
Instability index of non- basmati rice export from India, 1980-81 to 2012-13
47
4.14
Instability index of rice export from India, 1980-81 to 2012-13
48
4.15
Transitional probability matrix of Indian basmati rice export, 2000-01 to 2012-13
52
4.16
Actual and estimated shares of basmati export from India, 2000-01 to 2012-13
54
4.17
Projected export of Indian basmati rice to major importing countries
55
4.18
Transitional probability matrix of non-basmati rice export from India, 2000-01 to 2012-13
60
4.19
Actual and estimated share of non basmati rice export from India, 200001 to 2012-13
62
4.20
Projected share of non-basmati rice to major importing countries, 2013-14 to 2017-18
65
4.21
Nominal protection coefficient of basmati rice export, 2012-13
66
4.22
Nominal protection of non basmati rice export, 2012-13
66
4.23
Stationary (unit root) test for variables
67
4.24
Estimates of determinants of rice export from India
68
4.25
Estimates of determinants of rice export from India by model I
68
4.26
Estimates of determinants of rice export from India by model II
70
4.27
Econometric test for Model I and Model II
71
9
LIST OF FIGURES
Figure No.
Title
Page No.
4.1
Export of basmati rice from India, 1980-81 to 2012-13
31
4.2
Export of non-basmati rice from India, 1980-81 to 2012-13
34
4.3
Export of rice from India, 1980-81 to 2012-13
37
4.4
Share of basmati rice exported from India to major importing countries during 1987-88 in value term
50
4.5
Share of basmati rice exported from India to major importing countries during 2000-01in value term
50
4.6
Share of basmati rice exported from India to major importing countries during 2008-09 in value term
51
4.7
Share of basmati rice exported from India to major importing countries during 2012-13 in value term
51
4.8
Actual and estimated proportion of basmati rice export to Saudi Arabia
56
4.9
Actual and estimated proportion of basmati rice export to Kuwait
56
4.10
Actual and estimated proportion of basmati rice export to UK
57
4.11
Actual and estimated proportion of basmati rice export to UAE
57
4.12
Actual and estimated proportion of basmati rice export to Iran
58
4.13
Actual and estimated proportion of basmati rice export to Iraq
58
4.14
Actual and estimated proportion of basmati rice export to other countries
59
4.15
Share of non basmati rice exported from India to major importing countries during 2001-02 in value term
59
4.16
Share of non basmati rice exported from India to major importing countries during 2012-13 in value term
60
4.17
Actual and estimated proportion of non basmati rice export to Nigeria
63
4.18
Actual and estimated proportion of non basmati rice export to Senegal
63
4.19
Actual and estimated proportion of non basmati rice export to Benin
63
4.20
Actual and estimated proportion of non basmati rice export to Cote D Ivoire
63
4.21
Actual and estimated porportion of non-basmati rice export to South Africa
64
4.22
Actual and estimated porportion of non-basmati rice export to UAE
64
4.23
Actual and estimated proportion of non-basmati rice export to other countries
64
CHAPTER I INTRODUCTION International trade is a branch of international economics which deals with the trade of goods and services of the nation to rest of the world. International trade is an engine of growth of any country. In the words of Haberler, " International trade has made a tremendous contribution to the development of less developed countries in the 19th and 20th centuries and can be expected to make an equally big contribution in future" (Rana and Verma, 2010). International trade mitigates the disadvantage of disproportionate geographical distribution of productive resources (Ohlin, 1952). International trade decidedly increases the exchangeable value of possessions, means of enjoyment and wealth of the countries concerned (Singla and Brar, 2008). That‟s why, international trade plays an important role in economic development and growth of a country (Krueger, 1980). International trade can be carried out through exports and imports of goods and services. Export is an important activity to accelerate the pace of economic development of any country (Sujatha et al 2003). Exports are vital to domestic economy as it is a major source of foreign exchange earnings. In the arena of international trade, exports have remained the main plank to be emphasized in the Indian economy. Policy changes were introduced in India in June, 1991 with a view to liberalizing the economy and integrating it with the world economy (Bhalla, 1994) in respect of modernization and up gradation of technology. In this regime, unlike past, export performance plays critical role of the engine of growth (Chow and Kellman, 1993). India's exports since independence were largely confined to primary products especially of the agricultural sectors. Indian trade policy of agricultural items is guided by the twin objectives; Ensuring food security and building export markets for enhancing the income of farmers depending on domestic availability (GOI, 2012a). The stable policy impetus provided by the government during the recent years to agricultural export and time to time change in trade policy at international level, and adjustment of national policy accordingly, helped India to improve its position in agricultural and food export to 10 th position globally, with 2.07 per cent share of world export in agricultural and food products during 2011 (GOI, 2013a). Agricultural exports have been remained as back bone of India's export and continued to be the backbone for future export growth, although, the share of agricultural export to total national exports has been decreased from 18.49 per cent in 1990-91 to 12.80 per cent in 201112(GOI, 2013b). Agricultural production in India has been increased several folds in last decades due to bumper production of cereals, pulses, fruits and vegetables, milk, fish and fish products mainly because of improved technology, increased area under cultivation, increase
in productivity and favorable governmental policies, ultimately resulted in to high marketable surplus. Rice is one of the important cereal crops in the world. Rice has shaped the culture, diets, and economics of thousand of millions of people. Rice is an important staple food crop for more than half of the world population. In context of India, rice is one of the most important food crops of India in terms of area, production and consumer preference. The green revolution, started in the late 1960s has significantly contributed to attain food self sufficiency with in a decades, consequently, India became net exporter of food grains. The speedy increase in rice production during green revolution, and later period boosted rice export rapidly. Rice is among the most important exportable agricultural commodity from India, which contributes substantially to the national income. Rice exports constituted 12.85 per cent (in value) of total agricultural export during 2011-12 (GOI, 2013b). India ranks second in the production of rice after China, accounting 22.13 per cent of global rice production in 2012-13(Anonymous, 2013a). The major rice producing states of India are West Bengal, Uttar Pradesh, Andhra Pradesh and Punjab. Global trade of rice is very low in comparison to its production. In export front, Thailand was the major player of rice export till 2011. But, when the Indian government uplifted ban on export of non-basmati rice in September,2011 India was emerged as a leading exporter of rice with market share( in terms of quantity) of 26.24 per cent followed by Vietnam(19.76 % ) and Thailand (17.78 % ) in 2012( Anonymous, 2014). Rice is exported from India in terms of basmati rice (aromatic) and non-basmati rice. The basmati rice exports are in three categories: White, brown, and parboiled. Basmati rice constitutes the major share of rice exports from India. Basmati rice captures higher returns as it is priced significantly higher over non-basmati rice in the international as well as in the domestic markets. Traditionally, India used to export only basmati rice, starting in 1978-79 when India exported 67000 tonne of basmati rice. Of the total rice exported from India during 2012-13, basmati rice shared 34.09 per cent in terms of quantity, whereas its share was 57.33 per cent in terms of total exchange earnings. India is the world's largest producer and leading exporter of basmati rice. India produces about 70 per cent of the total world basmati rice production and the rest is produced by Pakistan( Sidhu et al, 2014). Extra-long slender grains that elongates at least twice of their original size with a characteristic soft and fluffy texture upon cooking, superior aroma and distinct flavor make basmati rice as a unique among other aromatic long grain rice varieties. Basmati rice has got good export demand and fetches good export price in international markets due to such uniqueness. The higher price of basmati rice in international market made basmati rice as export competitive product. The major markets for Indian basmati rice during the year 2012-13 were Iran, Saudi Arabia, United Arab Emirates, Iraq and Kuwait. Similarly 2
major destinations for Indian's non-basmati rice exports are Nigeria, Senegal, Benin, Cote D Ivoire, South Africa and United Arab Emirate. The export of rice is strongly related with an excess buffer stock of rice held by the government. Because of declining per capita consumption of rice and comfortable buffer stock with government agencies due to bumper production, India becomes a major exporter of rice since two years to the world market. There is a strong consumer demand for rice in the international market due to the high population growth. In the view of increasing consumer demand for rice and India's strength for production of basmati as well as non-basmati rice, liberal export policy, weak currency, and large public stock, there is ample scope for rice export. During recent time, African countries shifted to Indian non-basmati rice because of price competitiveness (Chandrashekhar, 2013), which also suggests potentiality of rice export. In order to grab export potential, domestic production of rice should be encouraged. Expansion of irrigated areas, use of high yielding varieties, and proper utilization of cheap labour should consider to achieve higher production at domestic front. Performance of Indian economy is dependent upon the growth of agriculture sector. Growth in agriculture and allied sectors sets an important priority of Indian economy despite a continuous decline in the share of agriculture sector in India's gross domestic product to 14.1 per cent in 2011-12 and further to 13.68 in 2012-13 from 51.45 per cent in 1950-51 at constant price 2004-05.The agriculture sector shares about 58.2 per cent in total employment according to census 2001 and is a major source of food and nutrition security of the country. The higher agricultural growth is vital for employment, income and food security. Agriculture has also been earning substantial foreign exchange to the country through trade of agricultural products. Since rice is the leading agricultural export product from India, its growth rate over the period of time is crucial to determine future performance and opportunities of rice export. Also, quantitative assessment of the contribution of rice export to total agricultural export is very important to keep the pace of sustainable agricultural export of the country. The export performance of the two varieties of rice, namely basmati and nonbasmati has differed widely over the years. The exports of basmati rice have been increasing over the years while that of non-basmati variety have been fluctuating. The variability in rice export are primarily based on the analysis of export instability. Basically, export instability of rice is influenced by the increasing domestic demand as well as international demand, volatility of world prices, diversification of products, policy changes and so on. Therefore, the magnitude and causes of instability of rice exports is utmost importance to sustain rice exports. The new multilateral trade regime coupled with the policy changes adopted by the large number of nations aimed towards globalization and provided new opportunities and posed several challenges for expanding trade in agricultural exports. The global markets for 3
Indian rice are highly dynamics and barrier to trade are gradually being lowered all around the world (Singh, 2001). In this scenario, it is important to identify the major markets for Indian's rice, assess them in terms of their stability and estimates the future share of their market. Trade competitiveness is a dynamic phenomenon which would vary depending upon the changes in international and domestic price consequent upon demand and supply of commodities and market condition (GOI, 2012b). International trade increases international competition and exposure to volatility in international prices. India has competitive advantages in several agricultural commodities for agricultural exports because of self sufficiency of inputs, relatively lower labor cost and diverse agro-climatic condition. Indian rice is export competitive product because of the higher price in international markets governed by strong international demand. An assessment of export competitiveness of Indian rice is needed in order to know how far Indian domestic price of rice aligned to global price of rice. Prospects of rice exports from India depend on global demand for Indian rice at remunerative prices on the one hand, and the availability of exportable surplus from India, on the other (Sekhar, 2003). Instead, domestic price as well as global price of rice play vital role to determine the export of rice. In the present context, the production of rice has been increased, there by giving comfortable stock with central pool and stable policies govern the higher export of rice from India. Reliable estimates of determinants of export are essential for policy decisions. Under the new trade agreement known as World Trade Organization (WTO), there could be easy access to the world market leading to reduction of trade barrier and tariffs. India also had adjusted her policies according to WTO, to integrate domestic economy with world economy. However, India's agri-trade policy has been relatively restrictive and unstable with frequent export bans and irrational import duties. To enable the sector to realize its full potential an open, stable, neutral and rational agri-trade policy with moderate duties is the need of present. In this context of new opportunities and several challenges of international trade in agriculture sector, an empirical study is needed to understand the changes in composition, export competitiveness and factor affecting of rice export from India. The present study was carried out in this regard with the following specific objectives:1.
To examine the changes in composition of rice export;
2.
To assess the export competitiveness of rice; and
3.
To analyze the factors affecting the growth of rice export
4
CHAPTER II REVIEW OF LITERATURE A review of past research helps in developing a proper understanding of the research problem and identifying the conceptual and methodological issues relevant to the present study. This chapter deals with a brief review of the relevant research literature that has accumulated on the area of present study. Consistent with the objectives of the present study, the reviews of literature are presented under the following sub headings. 2.1
Growth performance and instability
2.2
Direction of trade
2.3
Export competitiveness
2.4
Determinants of rice export
2.1 GROWTH PERFORMANCE AND INSTABILITY Sharath (1993) measured the growth in exports of cardamom from India using exponential function of the form Y = a bt. A comparative performance was attempted splitting the time period into two periods; First period from 1970-71 to 1979-80 and the second from 1980- 81 to 1989-90. In the first period, the quantity of Indian cardamom exports registered a growth rate of 4.63 per cent, while the value of exports grew at the rate of 27.9 per cent. These were mainly attributed to 23 per cent increase in unit value realization in contrast to 17.05 per cent decline during the second period. Gangwar and Rai (1995) examined the performance of agricultural trade in India from the period 1971 to 1992 by computing compound growth rates .The annual compound growth rates of selected agricultural exports during the periods 1971 to 1981, 1982 to 1992 and 1971 to 1992 were calculated for the various agricultural products. Among the various agricultural export, the growth in terms of volume for the period 1971 to 1992 was highest for grapes (71.76%) followed by rice (28.24%), orange (24.91%) and Potato (10.36). For the same period, the growth rates were positive and significant for banana and oilseed cake where as growth rates were negative and significant for groundnut and sugar. During the period 1971 to 1981 export of rice, sugar, oilseed cake, potato, onion, orange and grapes grew at the compound growth rates of 16.08 per cent, 9.64 per cent, 6.47 per cent,10.0 per cent,46.07 per cent and 91.59 per cent per annum respectively. They observed that banana grew at compound growth rate of -0.39 per cent per annum. Except for cotton, groundnut, sugar, oilseed cake and banana, in general export of other commodities studied, observed positive growth rates for rice, potato, grapes and apple. Their findings also revealed better future for agricultural commodities in the international trade. Vani and Krishnaiah (1998) computed the compound growth rate of chilli exports during the period from 1987-88 to 1996-97 by using exponential function. The results showed
that the quantity and value realization from the export of chilli registered positive and significant growth rate of 20.16 per cent and 38.40 per cent per annum respectively during the study period. They also concluded that the increase in chilli export was found to be due to the increase in area under chilli and its production. Shende et al (1998) worked out the compound growth rates of rice from the period 1970-71 to 1993-94 .The export growth rate of rice was 18.11 per cent in quantity terms and 21.74 per cent in value terms, which were considerably higher than that of the world growth rates. They concluded that the production of scanted fine quality rice varieties like basmati should be encouraged in the country because these varieties of rice have more demand in the international markets. Wakraj (1999) examined the instability in export earnings for a sample of six countries between 1961 and 1971. The least instability index was recorded for Kenya, where the instability index of the total agricultural export earnings was 24.10 per cent. However, in terms of export value, fruits and vegetables were more stable as export crops with their magnitudes ranging between 12.30 and 26.34 per cent, respectively. The unstable commodity in export earnings both in terms of value and quantity was coffee with an instability index of 55.05 per cent. Shende et al (1999) analyzed export performance of India in tea, coffee and tobacco from 1970 to 1993 by fitting exponential function. Their study revealed that the growth rate of quantity exported for tea and tobacco were negative i.e. -0.28 per cent per annum and -1.09 per cent per annum respectively, which showed drastic fall in export from India, While coffee registered positive growth rate of 4.29 per cent per annum. They found that tea appeared as typical commodity in which growth rate of physical quantity of export was negative but in value realization tea had turned positive and significant growth rate of 4.09 per cent per annum. Coffee noted very high growth rate of 6.08 per cent per annum for India's value of export, and tobacco registered 2.18 percent per annum. Finally, they concluded that coffee and tea showed significant growth in India's value of export as compared to tobacco. The study firmly viewed strong export potential of tea and coffee in future Rajesh et al (2002) studied the trend in export of major spices in India for the period 1970-71 to 1999-2000 and found that black pepper registered a positive annual growth rate of 2.38 per cent in quantity and 12.78 per cent in value. While large cardamom registered 12.76 per cent of export quantity and 21.4 per cent export value, ginger registered 4.05 per cent growth in quantity and 10.15 per cent in value. Turmeric export registered 4.14 per cent in quantity and 13.08 per cent in volume during the period under study. Jyothi et al ( 2003) analyzed the performance of onion and potato exports from India with respect to quantity, value and unit value of exports for the period from 1970-71 to 19992000 (includes threes sub periods also) using an exponential growth model. During the 6
overall period, the quantity of onion and potato exports from India exhibited a positive and significant growth rate of 6.27 per cent and 4.38 per cent per annum. Similarly, export earning and unit value realization exhibited significant positive growth rates of 16.70 per cent, 12.28 per cent and 9.74 per cent 7.45 per cent for onion and potato respectively during the same period. They had also calculated the instability index by using formula suggested by Cuddy and Della for onion and potato exports during the same period . The result showed that the instability index during the whole period for quantity of onion and potato exports were 29.86 per cent and 102.13 per cent respectively where as for export earning and unit value of exports, the same were 45.95 per cent, 124.22 per cent , 28.52 per cent, 38.74 per cent respectively. The results indicated that in both onion and potato the unit value of exports were found to be stable when compared to the quantity and export earnings. Sujatha et al (2003) estimated the compound growth rates of mangoes exports from India during the period from 1989-90 to 2001-2002.The total period was divided into two parts; Pre WTO (1989-90 to 1994-95) and Post WTO (1995-96 to 2001-02).Their result showed that growth rate in the quantity and value of export (7.92% and 12.26% ) was more during the post WTO period in case of fresh mangoes compared to pre WTO period(6.81% and 11.62% respectively).At the overall level, India's mango export increased by 7.71 per cent and 9.35 per cent respectively per annum with respect to quantity and export earnings. The overall per annum growth rates of quantity and value of mango pulp export were 12.89 per cent and 20.53 per cent. They states that India's export of mangoes is not encouraging due to higher taxation, poor quality, lack of infrastructure, export promotional activities and research and development. Umadevi and Eswaraprasad (2006) worked out compound growth rates for quantity and value of total shrimp exports for the period 1978-79 to 2001-02 and cultured shrimp exports for the period 1988-89 to 2001-02 by fitting exponential function. The results showed that quantity and value of total shrimp exports registered a positive and significant growth in their exports with compound growth rates of 4.44 per cent and 16.66 per cent respectively during the foresaid period. Similarly, the quantity and value of cultured shrimp exports showed an increasing trend with compound growth rates of 10.79 per cent and 24.55 per cent respectively during the study period. Reddy (2008) analysed the growth in export of soy meal( in quantity term) from India during the period of 1969-70 to 2007-08 and a very high significant growth rate of 14.71 per cent was obtained. He concluded that the high significant growth rate may be attributed to the increasing demand of soy meal from India in the present years. Gireesh (2009) employed the exponential function to arrive at the growth rate in quantity, value and unit value of export of cashew kernel during the period 1978-79 to 200708 by dividing whole period in to two periods; Pre liberalization (1978-79 to 1990-91) and 7
post liberalization ( 1991-92 to 2007-08). The result showed that during pre liberalization period the quantity, value and unit value of cashew nut kernel recorded a compound growth rate of 2.89 per cent, 12.13 per cent and -8.98 per cent per annum respectively. Similarly, during the post liberalization period compound growth rate of quantity, value and unit value of cashew nut export were 5.18 per cent, 8.18 per cent and 2.85 per cent per annum respectively. Likewise, during overall period the quantity, value and unit value of cashew nut kernel export registered compound growth rate of 5.71 per cent, 14.62 per cent and 8.43 per cent per annum respectively. Meipporual and Bhanumathy (2010) calculated the annual compound growth rate of Coir products exports from India during the period of 1982-83 to 2006-07 by using exponential function. A positive and significant annual compound growth rate of 7.29 per cent per annum was achieved in quantity of export of coir products. Singh and Mathur (2011) analysed the trend and instability in marine exports from a period of 1992-93 to 2005-06, splitting whole period in to two periods; Pre WTO( 1992-93 to 1997-98) and post WTO ( 1998-99 to 2005-06). Their findings indicated that the overall growth in exports was 5 per cent per annum and growth in post WTO (4 %) was lower than pre WTO (11%). Similarly, the overall instability (17 %) of exports was higher than instability during pre WTO (10 %) and post WTO (7 %). Thumar et al (2012) computed the compound growth rates of major seed spices exported from India for the period 1960-61 to 2007-08 . The results showed quantity of export of coriander, cumin, fennel and fenugreek recorded positive growth rates of 8.49 per cent, 6.94 per cent, 4.32 per cent and 6.02 per cent per annum. Similarly, the value of export for coriander, cumin, fennel and fenugreek registered positive and significant growth rates of 17.52 per cent, 16.42 per cent, 13.51 per cent and 14.96 per cent per annum respectively. Goel and Walia (2012) computed the compound growth rates of agriculture export, agriculture imports and net agriculture exports during post reform period (1991-92 to 201011).Their results showed that both agriculture exports and imports had increased after the liberalization but the compound growth rate of agriculture imports( 18 per cent ) was greater than agriculture exports (13.4 per cent). Similarly, the net agriculture exports had grew at the compound rate of 10.9 per cent per annum. They concluded that lesser growth of agriculture exports may be due to hard competition of quality products and strict legislation relating to health and safety standards of the importing countries. Rajur and Patil (2013) analysed export performance of chilli during the period from 1984-85 to 2003-04 by splitting whole periods in to two periods: Period-I ( 1984-85 to 199394) and Period-II ( 1994-95 to 2003-04). Their results indicated that the growth rates both in quantity and value of export in chilli increased significantly over a period from 1984-85 to 2003-04. The growth in value of chilli exports (27.57%) was higher than quantity of exports 8
(19.37%). They concluded that the increasing pattern of growth in export of chilli may be attributed to the increase in the production of chilli. 2.2 DIRECTION OF TRADE Atkin and Blandford (1982) studied the structural changes in import of apples in UK. The changes in composition of UK apple imports during the period from 1963 to 1974 were analyzed using a first order Markov model. The study indicated that changes in market share had been systematic, stable and of long duration. The estimated transition probabilities could explain the nature of change by indicating the relative competitive strength of different exporters of apple to UK. The results showed that EC membership increased French market share in the UK market by more than 26 per cent and decreased the share of Australia and South Africa by 18 and 10 per cent, respectively. Srivastava and Ahmed (1986) analyzed the direction of exports from India for the period from 1960-61 to 1983-84. India‟s exports to the five countries viz., USA, former USSR, UK, Japan and erstwhile West Germany were studied. The export trade to the above said five countries declined during the study period. The UK remained no more as the principal destination of Indian trade as it was in the pre-independence period. In 1983-84 USA emerged as one of the major trading partners of India. Gemtessa (1991) analyzed the direction of coffee trade using Markov chain model. The share of Ethiopian coffee exports to USA drastically declined during 1979 to 1989. However, the results showed that West German market would be the potential market for Ethiopian coffee. The market share of Ethiopian coffee to USA, France, USSR and other countries were diverted to West Germany's market. It was also projected that the market share of Ethiopian coffee export to West Germany would increase to 32 per cent by 2000 mainly because of West Germany's preference for Ethiopian mild. Lakshiminarayana (1993) made an attempt to study the direction of trade of Indian silk exports by employing first order Markov process. The major importing countries considered for the analysis were USA, West Germany, UK, France, Italy and Japan. He found that exports to USA were very stable and would remain highly prefer to Indian silk. In addition the results indicated that the export of silk from India to UK, West Germany and Japan would switch over to USA over a period of time. Mandanna et al (1998) analyzed the structural change in India's tobacco exports for the period from 1980-81 to 1994-95 using Markov chain analysis. The study revealed that the' USSR, the largest market for un-manufactured Indian tobacco showed a high degree of preference. The markets of Western Europe, Asia, and the Middle East had taken the place of the USSR. However, in case of manufactured product, only cigarettes had a dominant presence in the export basket. The diversification of export markets was clearly evident, necessitating efforts in the direction of brand building for Indian tobacco. 9
Sreenivasamurthy and Subramaniyam (1999) analyzed the direction of Indian onion trade by using Markov chain model during the years 1980-81 to 1994-1995. The major gainer among importers of Indian onion was Malaysia which was having a transition probability value of 0.6459 from Saudi Arabia and 0.3488 from UAE. Sri Lanka in addition to having high probability of retention of its own share was also likely to gain from Saudi Arabia with a moderate gain of 0.3488. On the other hand, Saudi Arabia which was having zero probability of retention of its own share of exports of onion from India was likely to gain to some extent from Bangladesh and 'other countries'. Siju (2001) studied the direction of trade of cashew kernels from India using Markov chain analysis for the export data from 1985-86 to 1999-2000, and found that USA was the most stable market with a high probability of 0.5639 in retaining cashew kernel export from India. Next to USA, Netherlands had a probability of 0.1289 in sustaining cashew kernel export. Hugar (2002) studied the changes in the share of exports of onion from India to different countries. Markov model with first order finite Markov chain property was used to analyze the export shares by countries and forecast the export of onion, which follows stochastic process. Using one step transition probability, shares of major importers of onion from India were compared with observed export shares. One step and five step transition probabilities were also found to predict the export shares of countries for one year and five years after the base year. His results indicated that Malaysia and UAE were stable markets of onion export from India. Sadavatti (2006) analyzed the export of basmati rice and it's stability by using Markov chain analysis during the period of 1980-81 to 2000-01 at all India level. Results revealed that there were five major countries importing Indian basmati rice namely Saudi Arabia, Kuwait, UK, USA and UAE, which accounted for 80 to 90 per cent of total Indian basmati rice in 2000-01.Saudi Arabia alone accounted for nearly 56 per cent of total basmati rice during the same year. The result of Markov chain analysis revealed that the export would likely to be concentrated in Saudi Arabia and Kuwait in the future. The result of transitional probability matrix showed that Saudi Arabia was one of the stable major importer of Indian basmati rice as reflected in high probability of retention at 0.90 i.e. the probability that Saudi Arabia retained its export share from one period to another period was about 90 per cent. Similar interpretation could be made from USA with probability of retention of 0.57.On the contrary Kuwait, UK and UAE were having a probability of retention of zero indicating that they were the unstable importer of Indian basmati rice. Kumar et al (2007) examined the structural changes in the share of Indian mango exports during the period of 1980 to 2002 through a first order Markov chain model. They had selected five major mango importing countries which were Bangladesh, UAE, Saudi 10
Arabia, UK and USA. The major Indian mango export market were categories as stable market(USA,UAE and Bangladesh) and unstable market(UK, Saudi Arabia ) based on the magnitude of transitional probabilities. The value of probability of retention for USA, UAE, Bangladesh, and UK were 1.00, 0.39, 0.40, and 17.90 respectively. Reddy (2008) studied the changing direction of Indian soy meal export during the period of 2000 to 2007. The Markov chain analysis was used to estimate the transitional probability matrix in order to find out probability of retention. The major importing countries considered for the analysis were Indonesia, Korea, Thailand, China, Japan and Vietnam. It was found that Indonesia is a stable market followed by Vietnam with the probability of retention 0.7449 and 0.2946 respectively. Singh and Mathur (2011) analyzed the shift in share of existing importing countries in marine exports of India by using first order Markov Chain Model during the period of 199293 to 2005-06. They had divided the whole period in two sub periods; Pre WTO (1992-93 to 1997-98) and Post WTO (1998-99 to 2005-06). The major importing countries assumed here were USA, Japan, China, Spain, Belgium, UK and UAE. The results revealed that Japan continued to rank first with 84 per cent probability of retention followed by Belgium (50 %), Spain (35 %), USA ( 32 %) and UAE(28%) during the post WTO period. They also reported that impact of WTO policy had changed the entire scenario of the export of the Indian marine products in various dimensions such as India could not retain its share in traditional markets, possibly due to tough competition in these markets and explore new markets. Rajur and Patil (2013) analyzed the dynamics of change in the export trade of Indian chilli through the estimation of Markov transitional matrix during the period from 1998-99 to 2003-04. Their results showed that Sri Lanka found highly loyal market for export of Indian chilli with probability of retention of 25.10 per cent followed by USA (19.40 %) and others (34.70%). Bangladesh, Malaysia, Uganda and Indonesia were unable to retain their previous market share. At last, they concluded that the export of Indian chilli does not have any strong preference in any of the export market, except Sri Lanka. 2.3 EXPORT COMPETITIVENESS Gulati et al (1994) concluded that the commodities like rice, banana, grapes, sapota, leeches, onion, tomato and mushroom were highly competitive with NPC less than 0.75, while wheat, mango, potato and tomato paste were moderately competitive with NPC ranging between 0.75 to 1.00. Mamatha (1996) calculated the Nominal Protection Coefficient‟s (NPC) for Indian coffee by taking United States coffee price as the reference price. The NPC of coffee types namely plantation, Arabica and Robusta under the exportable hypothesis were 1.3, 1.3, and 1.85 respectively in1995, indicating that Indian coffee exports were not competitive and it was not efficient exportable commodity. 11
Desai (2001) examined the export potentialities of mango from India by using nominal protection coefficients for the period 1990-1998, which is the ratio of domestic price to the border price. The findings of the study indicated that on an average, the nominal protection coefficients value in fresh mango (0.89), and mango slices (0.45) were lower than one indicating their competitiveness in international market. Jayesh (2001) used the nominal protection coefficient technique for the export competitiveness of Indian pepper. Under the exportable hypothesis, the nominal protection coefficient value were found to be lesser than unity (0.849) in Calicut and (0.817) in Sirsi markets, indicating that the Indian pepper is competitive in the international market and which is an efficient export oriented commodity. Kumar et al (2001) computed the nominal protection coefficient under exportable hypothesis in order to measure the export competitiveness of selected livestock products in the global market from 1974 to 1998. Their results showed that butter has not been competitive internationally after T.E. 1982. India also lacks international competitiveness in poultry products, but India exhibited international price advantage in beef, pork and mutton. They concluded that beef was highly export competitive in all reference year and its NPCs varied from 0.162 in TE 1994 to 0.414 in TE 1985. Thumar et al (2012) analysed the export competitiveness of selected seed spices grown in India by estimating nominal protection coefficient ( NPC) for the year 2007-08 under exportable hypothesis. Their finding showed that export of coriander was found moderately competitive to Canada (0.57) and less competitive to South Africa (1.00). The export of cumin was moderately competitive to Japan (0.57), less competitive to Netherland ( 0.93) and non competitive to Bangladesh ( 1.46). The export of fennel found non competitive indicating that fennel exported from India was not profitable. The export competitiveness for the fenugreek indicated that the NPC values were between 0.75 and 1.00 thereby concluding less profitable export of fenugreek. GOI (2012b) calculated NPC of rice under exportable hypothesis during the year 2011 for two states; Punjab and Andhra Pradesh and found just competitive. The result showed that NPC for Andhra Pradesh (0.96) was lower than Punjab (1.00) indicating that Andhra Pradesh enjoyed price advantage than Punjab. 2.4 DETERMINANTS OF RICE EXPORT Das (1991) found the major supply factors affecting the quantum of coffee export during the period of 1972-73 to 1985-86.The results showed that other things remaining constant, the domestic production of coffee had a positive and significance influence on the export of coffee. One percent increase in the index of coffee production would result in the increase of index of coffee export by 1.70 per cent. An increase in the real capital national income would increase the demand for coffee with in the country and thereby reduce the 12
exportable surplus. Thus reduce the quantum of coffee export. The real export price had also a negative influence on the quantum of coffee export which implies that export of country can be increased only at lower level of real prices of coffee because of competitiveness in the international market. Lukonga (1994) had tried to review the performance of non-oil exports of Nigeria during the period 1970-90. Nigeria‟s exports supply was taken with respect to three commodities cocoa, rubber and palm kernel and depends upon ratio of exports price to domestic price index, productive capacity and domestic demand. Ordinary least square (OLS) method was used for estimations of export supply equations for these three commodities. Exports supply depends positively on price elasticity for cocoa and rubber while negatively for palm kernel which was insignificant. Productive capacity index was negative for cocoa & rubber while positive for palm kernel but only significant for cocoa. Domestic demand was negative for all three commodities. Dummy was positively significant for cocoa and rubber denoting a change in intercept and slope. Gangwar and Rai (1995) fitted log-linear export turn over function in order to quantify the determinants of exports for wheat, rice, potatoes, onion and banana by using time series data pertaining to 23 years (1971 to 1992) .The regression estimates indicated that the selected specification for all the commodities studied was good and explain more than 54 per cent of the variation. The lowest explanatory power of the model in case of wheat may be attributed to the higher fluctuating scenario observed in its exports. Net national product variable appeared to be more important in case of rice, potato and banana. The regression coefficient of lagged domestic production were found to be positive and significant for rice, onion and banana indicating increase in export earning with increases in production. The effect of relative export prices was not significant for any of the commodities examined. They concluded that domestic production and world trade have a significant promoting affect on rice, potato, onion and banana. Shende et al (1999) fitted Cobb-Douglas type of demand function in order to identify the factor affecting export of Tea, Coffee and Tobacco using time series data for the period from 1970 to 1993. They had taken India's export of tea, coffee and tobacco as a dependent variable and export price( Rs./mt), India's share in world production (%) , total world import(mt), ratio of domestic consumption to production, exchange rate(Rs./US dollar) and ratio of domestic price to world export price as independent variable. It had seen that vary high values of coefficient of multiple determination were noticed in all three commodities. Tea export was observed to be influenced by three factors i.e. export price, ratio of domestic consumption to production and exchange rate. Similarly in case of coffee ratio of domestic consumption to production and exchange rate found highly significant. In tobacco three
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factors were highly significant to export i.e. export price, world total import and ratio of domestic price to world price. Haleem et al (2005) had tried to estimate an export supply function for citrus fruit in Pakistan. Annual time series data from 1975-2004 was used for the analysis. Quantity of citrus exported depends upon export unit value index, domestic production, and domestic price index, GDP of Pakistan, and exchange rate. Tabulation method was used to determine export performance. Co-integration was used to estimate elasticity of price for citrus. Dickey Fuller test was used to check unit root. All series were stationary at first difference except domestic production which was stationary at level. Johansen co-integration method was used. Each variable had correct sign except citrus production. Domestic price index was negatively significant. Export price, exchange rate and GDP were positively affecting citrus exports. All variables were significant. Hema and Kumar (2007) analyzed the factors affecting exports of spices particularly black pepper and small cardamom by regression analysis for the year 1980 to 2002.Two types of function: Linear and Double log were employed and result indicated that in case of black pepper lagged domestic production has got a significant influence on its export from India. Domestic price negatively influenced the pepper export while lagged international price influenced positively. The dummy variable included to capture the effect of liberalization was not found to be significant. In case of cardamom, the results showed that none of the variables had significant effect on its export in the linear function. In the double log function, lagged production and lagged domestic price had positively and marginally significant impact on cardamom export. Kumar et al (2008) tried to find out empirically the performance, competitiveness and determinants of gherkin and cucumber exports. Time series data was used. Comparative advantage was examined through export performance ratio. Log linear model was used for determinants of exports. Exports depend upon total international trade in specific commodity, export price, exchange rate and world market size. Indian exports of gherkin and cucumber depend positively on their international trade volume, exchange rate, export prices but export price was insignificant. In findings India was highly competitive in exports of both these commodities and exchange rate was significant determinant than prices. Abolagba et al (2010) tried to determine the factors that can influence the agricultural exports of Nigeria with reference to cocoa and rubber. Time series data from 1970-2005 had been used for this purpose. OLS method was applied. Export of specific commodity was taken as dependent whereas domestic output, domestic consumption, exchange rate, average producer price, average world market price, interest rate and average total rainfall were independent. For Rubber Semi-log and for cocoa linear function were used. Domestic production and average producer prices were positively while exchange rate and domestic 14
consumption were negatively significant. Interest rate and world market prices were positive for rubber and negative for cocoa. In findings output, domestic consumption, average producer price and exchange rate play key role in exports. Sengupta and Roy (2011) analyzed the export behavior of eleven horticultural products by fitting non linear regression model for the period 1961 to 2005(45 years). They assumed that export behavior of horticultural products depend on production, export price, world unit price, world demand and producer price. They had incorporated dummy variable in the model to indicate the structural break in the export behavior. The result showed that exports of chilli and pepper have responded significantly to producer prices, relative export price and world demand in long run. But, exports of most of other horticultural commodities are found to be responsive to relative export price. Like chilli and pepper, exports of banana and walnut are inversely related to producer prices in the long run. The results also showed that most of the horticultural exports were not significantly determined by world demand in long run except in case of chilli and pepper, mango and walnut. The production is not also significant for long run behavior of most of the horticultural exports except banana, coffee and spices. Goel and Walia (2012) examined the relationship between agriculture and economic growth in India using simple linear regression model for the period 1991-92 to 2010-11 where GDP growth rate is regressed on agriculture growth rate (AGR).The result of the regression model stated that regression coefficient was significant which implied AGR was a significant variable influencing GDP. Bilal and Rizvi (2013) analysed the determinants of rice exports from Pakistan for the period 1980 to 2010. All variables have been used in log form. For stationarity of data Augmented Dickey-Fuller test has been used. All the variables are stationary at their first difference. Johansen co-integration method has been used to check for long run relationship. Rice production, domestic consumption as a proxy for domestic demand, world‟s total rice exports as a proxy for international demand, rice yield, domestic price and export price have been used as rice exports determinants. Results suggest that production, yield and international demand are positively significant while export price and domestic price are negatively significant. Domestic demand is insignificant. Vector error correction model (VECM) was used to check long run to short run equilibrium adjustment of the model. VECM shows that model is converging 0.56 per cent annually. Finally, they recommended that government should take necessary steps to improve the yield per hectare and also production of rice in order to increase its exports because these are found to be the most effective determinants.
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It has become evident that the existing literature is replete with the studies, which have attempted to examine the growth performance, trade direction, competitiveness and determinants of different agricultural commodities at all India level. The literatures revealed that there have been increase in export of different agricultural commodities in terms of quantity and value over a period of time. The analysis of direction of trade showed that markets for Indian agricultural products have been shifted as indicted by transitional probability matrix. India had got export competitiveness in most of the agricultural products like basmati rice, mangoes, coffee, pepper, beef, pork, coriander, fenugreek etc. In order to find out determinants of various agricultural commodities different function were fitted at different time period. Generally, export of agricultural commodities influenced by its production, export price, domestic price and exchange rate.
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CHAPTER III MATERIALS AND METHODS A good systematic design of study makes the right platform to the researcher to mapping out the research work in relevance to make solid plan. This chapter deals with the description of the study area, nature and sources of data and various tools and techniques employed for analyzing the data. The detail of the section is as follow: 3.1
Description of the study area
3.2
Nature and sources of data
3.3
Analytical tools and techniques employed
3.4
Definition of terms and concepts used
3.1 DESCRIPTION OF THE STUDY AREA The study was carried out at all India level. However, nominal protection coefficient of basmati and non-basmati rice were estimated for Amritsar market of Punjab, because Punjab is the leading producer of basmati rice in India. Also, Punjab contributes around 35-45 per cent of rice to the national pool every year. 3.2 NATURE AND SOURCES OF DATA In the view of increasing consumer demand of rice in the international market due to high population growth and possibility of Indian rice exports, rice is purposively selected for the study in order to analyze performance, competitiveness, and determinants of rice exports from India. The nature of data for the study is mainly based on secondary sources. However, primary data were also collected to fulfill the objectives. The time series data on export quantity, value and unit value were compiled from various published journals, periodicals, reports, websites, and books. The time series data were obtained for 33 years from (1980-81 to 2012-13) which was further divided in to two periods for analysis. Period-I: Pre WTO Period (1980-81 to 1994-95); and Period-II: Post-WTO Period (1995-96 to 2012-13). With the success of Green revolution, India attained food self sufficiency during 1980s. During 1980s India produced enough food grains not to feed its own population, but also for some export. That's why stating period 1980-81 was chosen for the analysis. The division of periods was based on the assumption that impact of liberalization as well as formation of WTO helped to boost up rice export. The data on export quantity and value were collected from agricultural statistical campadium, and APEDA. Similarly, production, import, stock, international price of rice were collected from indiastat.com, hand book of Indian economy, pink sheet of World Bank etc. Data on global exporters and importers were collected from various issue of Rice Year Book, USDA websites.
3.3 ANALYTICAL TOOLS AND TECHNIQUES The details on the methods and tools employed for the analysis of the data are as under: 1.
Tabular analysis
2.
Compound growth rate analysis
3.
Instability index
4.
Markov chain analysis
5.
Nominal protection coefficient
6.
Multiple linear regression analysis
7.
Augmented Dickey Fuller test
3.3.1 Tabular analysis For the meaningful interpretation of the data, appropriate percentages and averages were worked out and presented in the form of Tables. 3.3.2 Compound growth rate analysis Growth of any economic variable signifies its past performance. The analysis of growth is usually used in economic studies to find out the trend of a particular variable over a period of time. It clearly indicates the performance of the variable under consideration and hence it can be very well used for making interpretations and to evolve policy decisions. The growth in the export of rice was estimated using the exponential growth function of the form: Y = a bt Where, Y = Dependent variable i.e. Quantity, Value and Unit value a = Intercept b = Regression coefficient t = Time variable The compound growth rate was obtained for the logarithmic form of the equation as below: Ln Y= Ln a + t Ln b Where Ln Y is natural logarithm of Y, Ln a and Ln b are similarly defined. The compound growth rate was computed by using the relationship CGR ={ (b) – 1} × 100 The significance of the regression coefficient was tested using the students„t‟ test The compound growth rates were also calculated for major markets of basmati rice. But, compound growth rates for major export markets of non-basmati rice have not computed due to high fluctuation of data. The export markets for non-basmati rice are highly unstable and there is a year to year fluctuation in export markets.
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3.3.3 Instability index Instability index is a simple analytical technique to find out the fluctuation or instability in any time series data (Ramasamy et al., 2005; Gupta and Sharma, 2010). The formula suggested by Cuddy-Della Valle was used to measure instability, which is used as measure of instability in time series data ( Singh and Byerlee,1990; Singh,2001). This method corrects the coefficient of variation, if data are scattered around the negative or positive trend line. The Cuddy-Della Valle Index is given follows. I = CV *(1-R2)0.5 Where, CV is coefficient of variation defined as the ratio of sample standard deviation to its mean and R2 is the corrected coefficient of determination of the log linear trend function that fits the time series. If the F-test is significant at 5 per cent level of significance, then the Index is calculated by using R2. When test statistics is not significant or R2< 0, then CV is chosen to measure instability index. 3.3.4 Markov chain analysis The trade directions of export were analyzed using the first order Markov chain approach. Central to Markov chain analysis is the estimation of the transitional probability matrix Pij. The elements Pij of the matrix P indicates the probability that export will switch from ith country to jth country with the passage of time (Dent, 1967; Lee et al 1970; Gillet,1976). The diagonal elements of the matrix measure the probability that the export share of a country will be retained. Hence, an examination of the diagonal elements indicates the preference of an importing country to a particular country‟s exports. In the context of the current application, structural changes were treated as a random process with selected importing countries. The average exports to a particular country was considered to be a random variable which depends only on the past exports to that country, which can be denoted algebraically as r
Ejt =
Eit-1 x Pij + ejt
i=1
Where, Ejt = Exports from India to jth country during the year t. Eit-1 = Exports from India to ith country during the period t-1. Pij = Probability that the exports will shift from ith country to jth country. ejt = The error term which is statistically independent of Eit-1. t = Number of years considered for the analysis r = Number of importing countries The transitional probabilities Pij which can be arranged in a (c x r) matrix have the following properties. 19
O Pij 1 r
Pij = 1 for all i j=1
Thus, the expected export shares of each country during period„t‟ were obtained by multiplying the export to these countries in the previous period (t-1) with the transitional probability matrix. 3.3.4.1 Estimation of the Pij In the present study, Minimum Absolute Deviations (MAD) estimation procedure was employed to estimate the transitional probability, which minimizes the sum of absolute deviations (Fisher, 1967; Wagner, 1959). The conventional linear programming technique was used, as this satisfies the properties of transitional probabilities of non-negativity restrictions and row sum constraints in estimation. The linear programming formulation is stated as Min OP* + Ie Subject to, XP* + V = Y GP* = 1 P*e 0 Where, 0
= vector of zeroes.
P*
= vector in which probability Pij are arranged.
I
= appropriate dimensioned column vector of units.
e
= vector of absolute error (|U|).
Y
= vector of export to each country.
X
= block diagonal matrix of lagged values of Y
V
= vector of errors
G
= grouping matrix to add the row elements of P as arranged in P* to unity.
After calculating the transitional probability matrix, the expected shares of export were calculated by Yjt =
it-1
x Pij ( j=1,2,3…r)
Where, Yjt = Predicted proportions of jth country's share at time 't'. Yt-1=Observed proportion of ith country share at time 't-1'. Pij = Estimated transitional probability matrix.
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The values in the transitional probabilities matrix will have different interpretations. The value of diagonal elements indicates the probability of retention of the previous year values, while values in columns reveals probability of gain of a particular country from other countries, values in rows reveals probability that a country might lose to their countries in respect of a specific commodity exports.
3.3.5 Nominal protection coefficient (NPC) Nominal protection coefficient is a direct measure of competitiveness of a country towards a commodity in the context of free trade. The nominal protection coefficient (NPC) is defined as the ratio of the domestic price to the world reference price of the commodity under consideration. Although NPC measures only the deviation of domestic prices relative to world prices, the conclusion drawn regarding the policy environment facing agricultural production activity are essentially the same as those drawn from more robust calculation ( Scandizzo and Bruce,1980;Gotsch and Brown, 1980). Symbolically, NPC = Pd/ Pe Where, NPC
=
Nominal protection coefficient
Pd
=
Domestic price of rice adjusted for handling/marketing charges and transportation cost
Pe
=
Export price or FOB price
A decision criterion is if NPC is less than one, then the commodity is export competitive (under exportable hypothesis, and it is worth exporting). If NPC is greater than one, the commodity is not export competitive (not a good export product). The domestic price is normally the wholesale market price of commodity in the selected market adjusted for handling/marketing charges and transfer cost. The export price or FOB value is calculated by dividing value by its quantity. The interpretation of the coefficient is as follows: Under exportable hypothesis, NPC < 1 an efficient export product In the present study, nominal protection coefficient (NPC) was estimated under the exportable hypothesis for the year 2012-13.
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Table 3.1: Estimation of nominal protection coefficient (Exportable hypothesis) S. N. 1. 2. 3. 4. 5. 6.
Particulars Wholesale price Handling/marketing charges @5% of domestic price Plus transport cost to Adjusted domestic price (1+2+3) FOB price NPC (Row 4/Row5)
Place Domestic Market
Unit Rs./ Qtls.
Value
Rs./ Qtls. Port
Rs./ Qtls. Rs./ Qtls. Rs./Qtls.
3.3.6 Multiple linear regression analysis In order to identify the factors affecting the export of rice from India, multiple regression analysis was carried out, using ordinary least square (OLS) estimation procedure. On the basis of best fit log linear form was selected. 3.3.6.1Theoritical framework The study involves quantitative analysis of the variables, adopting the method of ordinary least square (OLS) econometric technique. The econometric model to be used to examine this study is export of rice (QT) as dependent variable whereas lagged production (LGP), domestic demand (DC), export price (FOB)(EXP), international price; Thai5% (INT), and exchange rate(EXC) as independent variables. Rice Exports= f(average export price, international price, lagged production, domestic consumption and exchange rate with dollars) 3.3.6.2 Estimation Procedure The model estimation will be done through the use of the ordinary least square (OLS) method of estimation using statistical software E-Views. 3.3.6.3 Model Specification The factors affecting the export of rice were identified using log linear type of demand function, as used by Shende et al (1999) and Kumar (2004) { Equation 3.3.6.3} QT=b0 (EXP)b1(INT)b2 (LGP)b3 (DMC)b4 (EXC)b5 Ui…………3.3.6.3 Taking Ln on both sides LnQT=b0 +b1 LnEXP+b2 LnINT +b3 LnLGP+ b4 LnDMC+b5LnEXC + µ Where, QT: Total Rice Export from India ('000'mt) EXP: Export Prices (Rs. /mt) INT: International Prices (Thai 5% $/mt) LGP: Lagged production of rice (million mt) DMC: Domestic consumption of rice ('000'mt) EXC: Exchange rate with Dollar (Rs./$) µ: Error term b1……b5 are the regression coefficient; b0 is constant and µ is the error term. 22
Export prices and international prices for rice have been represented by their respective unit values. International price of rice is generally represented by the Thai 5% broken rice, fob Bangkok, and unit value is quoted in US dollar. The monthly unit value of international price of rice was derived from international price pink sheet world bank and converted according to fiscal year. The total production of rice, stock of rice and exchange rate data were obtained from the website of Reserve Bank of India, Government of India. The regression analysis was carried out for a time span of 33 years (1980-81 to 2012-13), using the ordinary least square (OLS) method. Export of rice from India largely depends upon the governmental policies. The unavailability of secondary data on impact of governmental policies on rice export forced to leave this qualitative variable while determining factors affecting rice export. 3.3.6.4 A priori Expectation and Justification of the Variables in the Models 3.3.6.4.1 Lagged production Previous year production is more influencing on current year export. It is a main determinant that can increase exports. Therefore, a positive relation is expected between lagged production and exports. In empirical literature, it was observed positive and significant influence of lagged production not only on export of rice (Gangwar and Rai 1995, Sekhar 2003;Kumar et al 2007) but also on onion, banana and black pepper( Hema and Kumar, 2007) indicating increase in export earning with increase in production. 3.3.6.4.2 Domestic consumption Domestic consumption is used as a proxy for domestic demand of the rice. Increase in domestic demand causes domestic prices to increase as well. This increase in domestic demand will cause shift in supply curve is not favors of export. So this leads towards a negative relationship between domestic consumption and exports. Abolagba et al (2010), and Bilal and Rizvi (2013) noted negative relationship between domestic consumption and export of rice. Time series data on domestic consumption of rice has not available. So domestic consumption was computed as: Domestic consumption=Production + Import + Stock change - Export 3.3.6.4.3 Export Price Whenever export prices increase, export becomes costly to the importers. As a result, importers may decrease their imports. Increase in export prices may also result in a decrease in the nation‟s competitiveness with respect to other exporting nations. So, a negative impact of export prices is expected on rice exports. In empirical literature Abolagba et al (2010), Bilal and Rizvi(2013) and Shende et al (1999) have proved this relationship. However, Kumar et al (2008) found positive relationship between Indian export price on export of cucumber and gherkin products from India, but the result was non-significant indicating export price has not played any significant role on its export. 23
3.3.6.4.4 International price International price of rice is generally represented by the Thai 5% broken rice, FOB Bangkok, and unit value is quoted in US dollar. In international market, Thailand is considered as major competitor of India. When the price of Thai 5%
increases, importer
may attract to Indian rice because of competitive advantages. So, a positive impact of international prices is expected on rice exports from India. 3.3.6.4.5 Exchange rate When exchange rate of Indian Rupee with Dollar increase, then exports of rice also increases. Devaluation makes the exports cheaper than earlier. Thus, a positive impact of exchange rate is assumed on rice exports from India. Haleem et al (2005), Kumar et al (2008) and Shende et al (1999) proved the positive and significant effect of exchange rate. 3.3.7 Augmented Dickey Fuller (Unit Root Test) When we deal with a time series, the first and foremost step is to check whether the underlying time series is stationary or not. If we want to apply the appropriate technique on the underlying time series, then we must be aware of the order of integration of underlying time series. Stationarity is also important in the context that if we apply OLS to a non- stationary time series, it may result in spurious regression. A time series will be stationary if it fulfills following three characteristics. In a time series, the set of possible values at a particular time point 't' is denoted by Yt, and a time series is denoted by {Y(t), t∈ T}. For stationarity, Yt must fulfill the following three characteristics. i.
E(Yt) = µ
( i.e. Mean is constant)
ii.
Var(Yt)= E(Yt- µ) = ζ
iii.
ρk =E[(Yt - µ) (Yt+k - µ]
2
2
(i.e. Variance is constant) (i.e. Covariance is constant)
In short, for a stationary time series, its mean, variance and covariance remain the same and do not vary with time. If a time series does not fulfill all these characteristics then it is called as non-stationary time series. To check the unit root in the data, augmented Dickey-Fuller (ADF) test is used. ADF is an extended form of Dickey-Fuller (DF) test. In DF test, we assume that error terms are uncorrelated but if error terms are correlated then ADF is best because it also allows for Serial Correlation to be checked. ADF test has the following regression equation m
ΔYt = β1 + β2t + δYt-1 +
ai ΔYt-1 + εt i 1
Where εt is white noise error, ΔYt-1 = (Yt-1
– Yt-2) where Δ represents first
difference, m represents number of lagged difference, These lags are included to make 24
error term white noise in above equation. β1 is intercept and t represents time trend. ADF has a null hypothesis same as Dickey Fuller. H0: δ=0; There is unit root, H1: δ
