A Study of the Impact of Crude Oil Prices on Indian Economy

A STUDY OF THE IMPACT OF CRUDE OIL PRICES ON INDIAN ECONOMY

Thesis Submitted to the Padmashree Dr. D. Y. Patil University, Department of Business Management in partial fulfillment of the requirements for the award of the Degree of

DOCTOR OF PHILOSOPHY In BUSINESS MANAGEMENT

Submitted by PANKAJ BHATTACHARJEE (Enrollment No. DYP-PhD 076100001)

Research Guide Prof. (Dr.) R. K. SRIVASTAVA

PADMASHREE DR. D.Y. PATIL UNIVERSITY, DEPARTMENT OF BUSINESS MANAGEMENT, Sector 4, Plot No. 10, CBD Belapur, Navi Mumbai – 400 614. India. June 2013

A STUDY OF THE IMPACT OF CRUDE OIL PRICES ON INDIAN ECONOMY

DECLARATION I hereby declare that the thesis entitled “A STUDY OF THE IMPACT OF CRUDE OIL PRICES ON INDIAN ECONOMY” submitted for the award of Doctor of Philosophy in Business Management at the Padmashree Dr. D.Y.Patil University, Department of Business Management is my original work and the thesis has not formed the basis for the award of any degree, associateship, fellowship or any other similar titles.

Place: Navi Mumbai, India. Date:

Dr. R. Gopal. Dr. R. K. Srivastava Pankaj Bhattacharjee (Head of the Department) (Research Guide) (Research Scholar)

CERTIFICATE This is to certify that the thesis entitled “A STUDY OF THE IMPACT OF CRUDE OIL PRICES ON INDIAN ECONOMY ” has submitted by Pankaj Bhattacharjee is a bonafide research work for the award of the Doctor of Philosophy in Business Management at the Padmashree Dr. D.Y.Patil University, Department of Business Management in partial fulfillment of the requirements for the award of the Degree of Doctor of Philosophy in Business Management and that the thesis has not formed the basis for the award previously of any degree, diploma, associateship, fellowship or any other similar title of any University or Institution. Also certified that, the thesis represents an independent work on the part of the candidate.

Place: Navi Mumbai, Date:

Dr. R. Gopal Dr. R. K. Srivastava (Head of the Department) (Research Guide)

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ACKNOWLEDGEMENT I am greatly indebted to the Padmashree Dr. D.Y. Patil University, Department of Business Management which has accepted me for the Doctoral Program and I also thank Dr. R.Gopal, Head of the Department and Director for providing me with an excellent opportunity and support to carry out the present research work.

I am grateful to my guide, mentor, philosopher Dr. R. K. Srivastava for having guided me throughout the research span of time and for providing his constructive criticism which made me bring my best. I would also like to thank sir for being there at any point of time without considering his own precious time.

I would also like to thank all my senior ONGCians whose varied ideas and valuable suggestions have helped immensely in completion of my project and Dr.Sachin Deshmukh for having supported me throughout the study.

I sincerely thank my mother, mother in-law and my wife for providing me the necessary motivation for completing this dream project work. I hereby take this unique opportunity to thank my son Priyanshu for his moral support. I also wish to place on record my sincere thanks to my revered deity, my late father and late father in-law who have provided me with the strength and ability to carry this research out of the best of my ability.

Lastly I also wish to thank all my near and dear ones who have been directly and indirectly instrumental in the completion of my dissertation.

Place: Navi Mumbai Date: (Pankaj Bhattacharjee)

CONTENTS CHAPTER PAGE NO. TITLE NO. List of Tables List of Figures List of Abbreviations EXECUTIVE SUMMARY 1.0 Introduction 1 1.1 Hydrocarbons 1 1.2 Global Primary Energy Consumption 2 1.3 Properties of Fossil fuels 7 1.4 Character of the deposits of fossil fuels 8 2.0. Literature Review 10-16 2.1. Gap Analysis 16-17 3.0. Statement of the Problem 18-19 3.1. Objectives of the Study 20 3.2. Hypotheses 20 3.3. Defining Variable for the Study 21 3.4 Operational Definition of variables 22 1. Crude Oil Price 22 2. Inflation 23 3. GDP growth 23-24 4.0. Research Methodology 25 4.1. Conceptual Framework 25 4.2. Research Design 26 4.3. Sources of Data 26 4.4. Sample Size and Justification 27 4.5. Econometrics Modeling for the Hypotheses. 28-32 5.0 Global Oil Scenario 33

CONTENTS CHAPTER TITLE PAGE NO. NO. 5.1 Classification of Crudes 36 5.2 Structure of Industry and Global Oil Production 39 5.3 Global Oil Consumption 43 5.4. Indian Scenario 48 1. Pre Independence period (1886-1947) 48 2. Post-Independence period (1947-1960) 48 3. Mixed Economy Period (1961-1991) 50 4. Economic Liberalization Period 1991 51 5. Post Liberalization Period 51 5.4.1 Crude Oil and Natural Gas Production in India 52 5.4.2. Refining Capacity and Production 54 5.4.3 Production and Consumption of Petroleum Products 55 5.5. Oil Pricing 58 5.5.1. Historical Aspects 60 5.5.2. History of Oil Price 61 5.5.3. The Seven Sister (1928-1947) 62 5.5.4. The Seven Sister (1947-1971) 63 5.5.5. OPEC set Prices(1971-1986) 64 5.5.6. Development of Market Structure and Type of 65 transactions. 5.5.7. Supply Side –Issues of Peak Oil. 68 5.5.8. Comparative Study and Analysis of Global Oil 69 Reserves 1. Amount of Oil 69 2. Quality of Oil 72 3. Geographical Distribution 72

CONTENTS CHAPTER TITLE PAGE NO. NO. 4. Field-by-Field Analysis 73 5.5.8(a). Demand Analysis; Changing Composition of Global 74 Demand. 5.5.8(b). Consumption Analysis; Changing Pattern of Global 75 Consumption 5.6. Global Oil Demand Projections 78 5.6.1. Production: Non OPEC Supply. 78 5.6.2. Growing Dependence on OPEC 80 5.6.3. Implications of Dependence on OPEC. 81 5.6.4 Supply Issue: Need to offset Production Decline. 82 5.6.5 Impact of Price Elasticity 82 5.6.6 Implication for Oil Prices. 83 5.7. Oil Sector and Energy Development in India. 85 5.7.1. Imports and Prices of Crude Oil. 87 5.7.2. Imports and Exports of Petroleum Products 89 5.7.3. Crude Oil Demand Projection for India. 91 5.8 Role of Crude Oil Prices on Indian Economy. 93 5.8.1 Rise in cost of Imports 93 5.8.2 Widening of Trade Deficit 93 5.8.3 Increase in Oil Under Recoveries 94 5.8.4 Mounting Fuel Subsidy Burden 94 5.8.5 Worsening Fiscal Deficit 94 5.8.6 Reduced Amount For Infrastructure Investment. 94 6.0. Policy Framework for Oil Sector in India. 95 6.1. Institutional Framework. 95 6.2. Upstream Sector. 96 6.3. Intensifying Exploration 98

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CONTENTS CHAPTER PAGE NO. TITLE NO. 6.3.1. History of Pre-NELP Licensing Rounds 99 6.3.2. First Round of Exploration(1980) 100 6.3.3. Second Round of Exploration(1982) 102 6.3.4. Third Round of Exploration(1986) 102 6.3.5. Fourth Round of Exploration(1991) 103 6.3.6. First Development Round(1992) 103 6.3.7. Fifth & Sixth Round of Exploration/ Second 104 Development Round/ First Speculative Survey Round(1993) 6.3.8. Seventh & Eighth Round of Exploration/ Second 105 Speculative Survey Round(1994) 6.3.9(a) Exploration Rounds 106 6.3.9(b) Analysis of Foreign Investments in Exploration 109 Rounds 6.3.9(c) Speculative Survey Rounds 112 6.3.9(d) Analysis of Foreign Investments in Speculative 113 Survey Rounds 6.3.9(e) Development Round 114 6.3.9(f) Analysis of Foreign Investment in Development 115 Rounds 6.4. New Exploration License Policy (NELP). 117 6.5. NELP Bidding Round 124 6.5.1 NELP-I 124 6.5.1.1. Analysis of Foreign Investment under NELP-I 125 6.5.2 NELP-II 125 6.5.2.1 Analysis of Foreign Investment under NELP-II 126 6.5.3. NELP-III 127

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CONTENTS CHAPTER PAGE NO. TITLE NO. 6.5.3.1 Analysis of Foreign Investment under NELP-III 127 6.5.4 NELP-IV 128 6.5.4.1. Analysis of Foreign Investment under NELP-IV 129 6.5.5. NELP-V 130 6.5.5.1 Analysis of Foreign Investment under NELP-V 131 6.5.6 NELP-VI 131 6.5.7 NELP-VII 132 6.5.8 NELP-VIII 132 6.5.9 NELP-IX 132 6.6. Downstream Sector (Refineries in India) 133 6.7. Policy Framework 136 6.7.1. Product Imbalance 139 6.7.2. The Regulated Era 141 6.7.3. Changing face of Industry: The reform process. 143 6.7.4. Rangarajan Committee Recommendations 148 6.7.5. Chaturvedi Committee Recommendations 149 7.0. Crude Oil Price and Commodity Market 152 7.1. Crude Oil & Petroleum Products 153 7.2. Benchmark Crude 154 7.3. & 7.3(a) Crude transactions , Barter deal 157 7.4. Cargo transactions 157 7.5.; 7.6 Long term Contract ; Price formula 158 7.7. Netback Pricing 159 7.8. Refining Margins 160 7.9. Spot & Future Markets 162 7.9.1; 7.9.2. Spot Market, Forward Market 164-166

CONTENTS CHAPTER TITLE PAGE NO. NO. 7.9.3. Futures Market & Option Market 167 7.9.4. Analysis of International Crude Oil Prices. 167 8.0 Data Analysis, Interpretations and Model 170 Estimations 8.1 Karl Pearson’s correlation coefficient 170 8.2 Model 1, ( WPI and Crude oil price) 177 8.2.1 The test of Significance of estimated parameters 181 8.2.2 The test of Goodness of Fit, the coefficient of 187 Determination 8.2.3 Analysis of Variance 192 8.3. Model 2, (GDP growth and Inflation) 201 8.3.1 Two variable regression analysis 202 8.3.2 Calculation of standard Error of coefficient 204

8.3.3 ; 8.3.4 “t-test” and Confidence Interval for b1. 205-206 8.3.5 F- test. 207 8.3.6 Calculation of coefficient of Determination 208 8.4 Multivariable regression analysis 212 8.4.1 Test of Multicollinearity 214 8.5 Durbin Watson statistics 219 8.6 Stationarity in time series data 221-236 8.7 Model 3, (Ganger’s causality test) 236-237 8.8 Model 4 (Ganger’s causality test) 237-238 8.9 Model 5 239-242 9.0. Results and Discussion. 243 9.1 Hypothesis 1 243 9.2. Hypothesis 2 245

CONTENTS CHAPTER TITLE PAGE NO. NO. 9.2.1. Multivariable Linear Regression Model. 247 9.2.2. Run Test. 248 9.2.3. Test of Multicollinearity 249 9.3. Hypothesis 3 250 9.4. Hypothesis 4 253-254 9.5. Hypothesis 5 255-256 10.0. Summary of Hypotheses, Econometrics and 257-259 Statistical Tools Used and Results. 11.0. Conclusion 260-262 12.0 Managerial Implications 263-267 13.0 Acquisition Dynamics and Vertical Integration 268 13.1 Framework for an acquisition 268 13.2 Policy Environment of India 269 13.3 Target Evaluation 270 13.4 Target Valuation 271 13.5 Due Diligence 272 13.5.1 Conducting due diligence 272 13.5.2 Buyer’s due diligence 272 13.5.3 Limiting due diligence 272 13.5.4 Seller’s due diligence 273 13.5.5 Importance of due diligence report 273-274 13.6 Vertical Integration 275-276 14.0 Limitation of the Study and Future Scope of 277 Research. References 278-283 Appendix- I (Plots and Diagrams) 284-297

LIST OF TABLES

TABLE NO TITLE OF PAGE NO. TABLE 1.0. Primary Energy Consumption by Fuel 4-6 5.0. Distribution and Growth in world proved oil reserves 33-35 5.2. Global Oil Production 40-42 5.3. Global Oil Consumption 43-45 5.3(a) World Crude oil Import and Export data 46-47 5.4.1. Crude Oil and Natural Gas Production in India. 53 5.4.2. Refining Capacity and Production 55 5.4.3. Production and Consumption (indigenous sales) of 56 petroleum products 5.5.8. Estimates of Oil Reserves ( Trillions of Barrels) 71 5.5.8(a). Global Oil Consumption by Region ( Million Barrels 76-77 per day) 5.5.8(b). Changes in demand by Region.( Million Barrels per 77 day) 5.6.1. Non-OPEC production ( Million Barrels per day) 79 5.6.2. Growing dependence on OPEC (Million Barrels per 80 day) 5.6.6. Estimated needs for New Oil Production Capacity. 84 (Million Barrels per day). 5.7. Comparative data of crude oil demand, domestic 87 production and Crude Import. 5.7.1. Imports of crude oil and Average Crude Oil Prices 88 5.7.2. Imports and Exports of Petroleum Products 90 5.7.3. A summary of the projections of Crude Oil Demand 92 for India by various agencies 5.8.2. Widening of Trade Deficit 93

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LIST OF TABLES

TABLE NO TITLE OF PAGE NO. TABLE 6.3.9(a) Block offered under Pre NELP Exploration Rounds 109 6.3.9(f) Analysis of Foreign Investment in Development 116 rounds 6.4(a). Royalty payment on ad-valorem basis under NELP. 120 6.4(b) Major differences between Earlier Rounds of 121-122 bidding for Exploration blocks and NELP. 6.6. Refineries in India 133-136 7.9.3 Characteristics of Spot/ Forward / Future/ Option 167 Deals. 8.1 Data for Karl Pearson’s correlation coefficient 171-174 8.2 Two variable regression data (WPI &Crude oil price) 178-181 8.2.1 Calculation of Standard error of coefficient 183-186 8.2.2 Calculation of coefficient of Determination 188-191 8.2.4 Interpretation of Regression Model Summary 194 8.2.5 ANOVA table 194 8.2.6 Regression coefficient table 195 8.2.7 Log natural transformation data(WPI & Crude price) 196-199 8.3 Two variable data for correlation(GDP growth and 201 inflation ) 8.3.1 Two variable regression analysis 202-203 8.3.2 Calculation of Standard error of coefficient 204 8.3.6 Calculation of coefficient of Determination 208-209 8.3.7 Model Summary 209 8.3.8 ANOVA table 209 8.3.9 Coefficient table 210 8.3.10 Log natural transformation data (GDP growth & 210-211 Inflation)

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LIST OF TABLES

TABLE NO TITLE OF PAGE NO. TABLE 8.4.1 Test of Multicollinearity 214 8.4.2 Multivariable regression analysis 215-217 8.4.3 Probability output data 219 8.5.1 Residual data for D W statistics calculation 220 8.6.1 Logarithmic transformation GDP growth data 223 8.6.2 ACF and PACF 224 8.6.3 Variance and Covariance for inflation 226 8.6.3.1 ACF & PACF for Inflation 227 8.6.4 ACF& PACF for first order difference Inflation series 228 8.6.5. Variance & Covariance for Crude oil price change 230 8.6.5.1 ACF and PACF for Crude oil price change 231 8.6.6 ; 8.6.7 ANOVA for Unit root tests time series data 233-234 8.6.8 ANOVA for ADF test for Inflation 234 8.6.9 ANOVA for Unit root test Crude oil price change 235 8.9 Industries Data for regression 240-241 8.9.1 Log natural converted data 241-242 9.1. Results of Regression Analysis, Hypothesis1. 243 9.2. Results of Regression Analysis, Hypothesis2. 245 9.2.1 Results of Multivariable (Three Variable) Linear 247 Regression Analysis 9.2.3 Test of Multicollinearity; auxiliary regression results 249 9.3.0 Results of Stationarity Test of Time Series data. 252 9.3.1 Results of Granger’s Causality Test. 253 9.4.0 Results of Granger’s Causality Test. 254 9.5.0 Results of Regression 255 10.0. Summary of Hypotheses, Econometrics and 257-259 Statistical Tools Used with Result

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LIST OF FIGURES

FIGURE TITLE PAGE NO. NO. 1.0. Global Primary Energy Consumption 3 5.4.1. Percentage Growth in Crude oil and Natural Gas 54 Production 5.4.3. Percentage Growth in Production and Consumption 57 of Petroleum Products. 5.7.1. Percentage Growth in Imports of Crude oil and 89 average International Crude oil prices. 5.7.2. Percentage Growth in Imports and Exports of 91 Petroleum Products. 7.9.4 Plot of International Crude Oil Prices 169 8.2 Scatter Plot between WPI and Crude oil price 175 8.2.1 Fitting of Regression line (WPI and Crude oil price) 176 8.3 Scatter plot ( Quarterly GDP growth & Inflation with 202 Fitting line) 8.4 Plot of Inflation and Crude oil price change rate 212 8.6.2.1 ACF plot for time series GDP growth 224 8.6.2.2 PACF plot for time series GDP growth 225 8.6.3.2 ACF plot for time series Inflation 227 8.6.3.4 PACF plot for time series Inflation 228 8.6.4.1 ACF and PACF plots for first difference time series 229 Inflation 8.6.5.1 ACF plot for time series Crude oil price change rate 231 8.6.5.2 PACF plot for time series Crude oil price change 232 rate ACF plot for time series Crude oil price change rate A.I - 1.0 Bar diagram of Crude Oil Prices ( Indian Basket) 284 A.I - 2.0 Plot of Indian Crude Basket Price and WPI 285

LIST OF FIGURES

FIGURE TITLE PAGE NO. NO. A.I. – 3.0 Plot of GDP Growth, WPI and Crude Price 286 A.I. – 4.0 Plot of Prices of different grades, API crudes 287 A.I. – 5.0 Plot of Crude oil Consumption and Production of 288 India A.I. – 6.0 Plot of GDP Growth, Inflation and Crude Price 289 A.I. – 7.0 Scatter Plot of GDP Growth, WPI and Crude oil 290 price change in percent A.I. – 8.0 Line diagram of GDP growth, WPI & Crude oil price 291 change rate A.I. – 9.0 A plot of Quarterly GDP Growth and Quarterly 292 Inflation rate A.I –10.0 A plot of Quarterly Inflation rate and Quarterly 293 Crude oil price rate change A.I. – 11.0 Plot of GDP growth , inflation & crude oil price 294 change rate A.I. – 12.0 Oil production by Region at the end of 2011 (Mtoe) 295 A.I. – 13.0 Oil Production Outlook 2030 by Region (Mtoe) 296 A.I. – 14.0 Future Crude Oil Consumption in Million tonnes by 297 Regions

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LIST OF ABBREVIATIONS

ABV Accreditation in Business Valuation ACF Auto correlation function ADF Augmented Dickey-Fuller AIC Akaike Information Criteria AOC Assam Oil Company API American Petroleum Institute APM Administered Price control Mechanism ARTC Assam Railway and Trading Company BAU Business as Usual. BCS Best Case Scenario BEE Bureau of Energy Efficiency BOC Burma Oil Company BP British Petroleum BPE Bureau of Public Enterprises BPCL Bharat Petroleum Corporation Limited CCEA Cabinet Committee on Economic Affairs CCGT Combined Cycle Gas Turbine CO2 Carbon di-oxide CSO Central Statistical Organization C&AG Comptroller and Auditor General CVC Central Vigilance Commission CS Conditional Subsequent DD Due diligence DGH Director General of Hydrocarbons EIA Energy Information Administration FCC Fluid Catalytic Cracking FOB Free On Board or Freight On Board. FOREX Foreign Exchange

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LIST OF ABBREVIATIONS

GHG Green House Gases GJ/t Gigajoules per tons GJ/m3 Gigajoules per cubic meter GOI Government of India HOG High Output Growth HPCL Hindustan Petroleum Corporation Limited IEA International Energy Agency IHV India Hydrocarbon Vision IOCL Indian Oil Corporation Ltd IMF International Monetary Fund IPE International Petroleum Exchange IRAC Imported Refiner Acquisition Cost IRADe Integrated Research and Action for Development JVEP Joint Venture Exploration Program JVSSR Joint Venture Speculative Survey Round LNG Liquefied Natural Gas MBD or mbd Million Barrels per day (MB/d) MJ/m3 Mega joules per cubic meter MOP&NG Ministry of Petroleum and Natural Gas. Mt Million tons Mtoe Million tons of oil equivalent MRPL Mangalore Refinery and Petrochemicals Limited NELP New Exploration Licensing Policy NOC National Oil Company NYMEX New York Mercantile Exchange OECD Organization for Economic Co-operation and Development OIL Oil India Limited ONGD Oil and Natural Gas Directorate

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LIST OF ABBREVIATIONS

ONGC Oil and Natural Gas Corporation Ltd OPEC Organization of the Petroleum Exporting Countries ( Originally 13 members countries they are Algeria, Angola, Ecuador, Gabon, Iran, Iraq, Kuwait, Libya, Nigeria, Qatar, Saudi Arabia, United Arab Emirates, Venezuela. Gabon terminated its membership in 1995. OTC Over The Counter PACF Partial Auto correlation function PAD Project Appraisal Department PIB Public Investment Board PEL Petroleum Exploration License PPAC Petroleum Planning and Analysis Cell. PSE Public Sector Enterprise PwC Price Waterhouse Cooper R & D Research and Development RFO Residual Fuel Oil. SEBI Securities and Exchange Board of India SIC Schwarz Information Criteria Seven Anglo-Persian Oil Company( now BP); Gulf Oil; Standard Oil of Sisters California(SoCal);Texaco(now Chevron);Royal Dutch Shell; Standard Oil Company of New Jersey(Esso);Standard Oil Company of New York(Socony) now(Exxon Mobil) UNCITRAL United Nations Commission on International Law URT Unit Root Test USGS United States Geological Survey VIF Variance Inflation Factor WB World Bank WTI West Texas Intermediate

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EXECUTIVE SUMMARY

Energy is the prime mover of economic growth and is vital to the sustenance of a modern economy. Future economic growth crucially depends on the long-term availability of energy from sources that are affordable, accessible and environmentally friendly.

Efficient, reliable and competitively priced energy supplies are prerequisites for accelerating economic growth. For any developing country, the strategy to obtain and meet the energy requirements and energy developments are the integral part of the overall economic strategy. Efficient use of resources and long-term sustainability in its utilization is of prime importance for economic development. Sustainability would take into account not only available natural resources but also to take care of the related ecological and social aspects to meet the priority needs of the economy. Simultaneous and concurrent action is, therefore, necessary to ensure that the short-term concerns do not detract the economy away from the long-term goals.

Realisation of high economic growth aspirations by the country in the coming decades, calls for rapid development of the energy market. The energy resources available indigenously are limited and may not be sufficient in the long run to sustain the process of economic development translating into increased energy import dependence. The base of the country’s energy supply system is tilted towards fossil fuels, which are finite.

India meets nearly 35 per cent of its total energy requirements through imports. With the increase in share of hydrocarbons in the energy supply/use, this share of imported energy is expected to increase. The challenge, therefore, is to secure adequate energy supplies at the least possible cost. Although growth of the energy sector is moderate and has, to some extent, served the country’s social needs, it has put tremendous pressure on the Government’s budget. Energy is essential for living and vital for development. Affordable energy directly contributes to reducing poverty, increasing productivity and improving quality of

xx life. In UK, households that spend less than 10% of their income on heating their homes are officially stated to suffer from fuel poverty. In case of India, there is no such identification; as a result, some poor do not have access to minimum energy resources and its utilization for the quality life. Likewise lack of access to reliable energy is a severe impediment to sustainable social development and economic growth.

There are major disparities in the levels of consumption of energy across the world with some countries using large quantities per capita and others being deprived of any sources of modern energy forms. Energy supply has become a subject of major universal concern. Volatile oil prices threats to stable energy supply and energy security.

World primary energy consumption is 12274.6 Mtoe (Million tonnes of oil equivalent) in 2011, the primary energy consumption varies with availability and specific utilities of different types of fuels with the various pie, oil: 33.06%; natural gas: 23.67%; coal: 30.34 %: nuclear energy: 4.88%; hydroelectricity: 6.45%; renewable: 1.59%. China leads the order of absolute primary energy consumption with 21.29%, followed by US 18.49%, Russian Federation comes third with 5.59%, then comes India in Fourth with 4.55%.

Global Oil Scenario:-Crude oil is not distributed uniformly around the globe. Some regions and countries are well endowed, while others are not. Most of the proven reserves of conventional oil are to be found in Middle East Countries, namely, Iran, Iraq, Kuwait, Saudi Arabia and the United Arab Emirates (UAE), similarly, conventional gas is located primarily in Russia and other Former Soviet Union (FSU) countries, Iran, Qatar and Saudi Arabia.

The most important aspects of oil business are the locations of production and the refineries. Oil produced close to major market for refining will require less transportation and therefore will be more attractive and command a premium over oil produced further from the market and which has to incur large

xxi transportation costs to get to the market, but the analysts have focused on two key qualities of crude oil, namely, the API gravity and sulphur content to explain inter crude price differentials. Crude oil is considered light, if it has low density or heavy if it has high density and it may be referred to as sweet if it contains relatively little sulphur or sour if it contains substantial amounts of sulphur.

The global proven oil reserve is estimated to 1652.6 billion barrels by the end of 2011 as per BP. Almost 48.1% of the proven oil reserves are in Middle East. The Saudi Arabia has the second largest share of the reserves with 16.1%, whereas Venezuela ranks first in terms share of reserves with 17.9% and S & Cent America’s proven reserve of 19.7%.

Global oil production by region varies due to heterogeneous distribution of crude oil reserves. Global oil production is 3995.6 Mtoe in 2011, i.e. 83.57MB/day basis. The highest crude producing region is Middle East 32.6%, then Europe and Eurosia 21%, followed by North America with16.8% , Africa, Asia Pacific and South and Central America are 10.4%, 9.7% and 9.5% respectively; Country wise, Saudi Arabia in the highest producer 13.2%, then Russia Federation ranks second with 12.8%, US ranks third 8.8% followed by Iran 5.2%, China 5.1%, Canada 4.3%, UAE 3.8%, Mexico 3.6%, Venezuela 3.5% and Kuwait 3.5% respectively.

Global oil consumption varies from region to region and country to country, depending upon population, income and total spread of the economy. On region wise, Asia pacific region is the highest consumers of oil with 32.4% of total share, then North America 25.3%, followed by Europe and Eurasia 22.1%, Middle East 9.1%; and Africa with 3.9%.

Global oil consumption is 4059.1 Mtoe in 2011 i.e 88.03 MB/day basis. Among the countries US ranks first in terms of share of crude oil consumption and it is

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20.5% of global consumption followed by China 11.4%, Japan 5.0%, India 4.0%, Russia Federation 3.4%, Saudi Arabia 3.1%, Brazil 3.0% and Iran 2.1%.

Global oil consumption grew by a below average 0.6million bbls per day or 0.7% to reach 88.03 million bbls per day. Projected global oil consumption is expected to register a below average growth over the present levels. Oil is expected to be the slowest-growing fuel over the next 20 years. Recently published BP energy reports project incremental demand of liquid fuel about 16 million barrels per day (Mb/d) exceeding 103 Mb/d in 2030. Growth comes exclusively from rapidly- growing non-OECD economies. China (+8 Mb/d), India (+3.5 Mb/d) and the Middle East (+4 Mb/d) together account for nearly all of the net global increase, Non-OPEC (Organisation of Petroleum Exporting Countries) production, though showing an upward trend, will not be sufficient to service this incremental demand emphasising, once again, the continued dependence of the world on OPEC oil for its energy requirements.

Global oil demand is projected to increase from 86MB/day in 2010 to an estimated 93.3 MB/day in 2015 and an estimated 107.9 MB/day in 2030 based on the projection made by US department of energy (2011). Again, as per British Petroleum(BP) estimate June 2012, the estimated projection of demand of crude oil at the end of 2030 is 103MB/day, with a view that US and the developed economy is reducing the consumption of oil and switching over to efficient, renewable and green energy.

The fuel mix changes slowly, due to long gestation periods and asset lifetimes. Gas and non-fossil fuels gain share at the expense of coal and oil. The fastest growing fuels are renewables (including biofuels) which are expected to grow at 8.2% p.a. 2010-30, among fossil fuels, gas grows the fastest (2.1% p.a.), oil the slowest (0.7% p.a.), as per BP statistical Review, June 2012.OECD total energy consumption is virtually flat, but there are significant shifts in the fuel mix. Renewables displace oil in transport and coal in power generation; gas gains at

xxiii the expense of coal in power. These shifts are driven by a combination of relative fuel prices, technological innovation and policy interventions. The economic development of non-OECD countries creates an appetite for energy that can only be met by expanding all fuels. For many developing countries the imperative remains securing affordable energy to underpin economic development.

The growth of global energy consumption is increasingly being met by non-fossil fuels. Renewables, nuclear and hydro together account for 34% of the growth; this aggregate non-fossil contribution is, for the first time, larger than the contribution of any single fossil fuel. Renewables on their own contribute more to world energy growth than oil. The largest single fuel contribution comes from gas, which meets 31% of the projected growth in global energy.

India ranks fourth in the world in total energy consumption and needs to accelerate the development of the sector to meet its growth aspirations. The country, though rich in coal and abundantly endowed with renewable energy in the form of solar, wind, hydro and bio-energy has very small hydrocarbon reserves (1.0% of the world’s reserve). India, like many other developing countries, is a net importer of energy; more than 76 percent of crude oil is being met through imports. The rising oil import bill ( i.e.140 billion US dollar in 2011- 12) has been the focus of serious concerns due to the pressure it has placed on scarce foreign exchange resources and is also largely responsible for energy supply shortages. The sub-optimal consumption of commercial energy adversely affects the productive sectors, which in turn hampers economic growth.

India’s primary energy mix at the end of 2011 is containing 43.49% of oil, 23.03% of natural gas, 29.67% coal, 1.30% nuclear energy, 2.35% hydroelectricity and 1.44% renewables. The total primary energy consumption is 559.1 Mtoe (million tonnes of oil equivalent). India’s burgeoning economy and population of over 1.2 billion has created exceptionally high demand of primary energy. Among the primary fuels, the fossil fuels are particularly oil and gas dominating and total

xxiv accounts 66.52% of primary fuels. Currently, transportation fuel accounts for around 50% of domestic oil consumption, with other major users including agriculture, industry and power generation. Diesel run electricity generators have also become increasingly common due to the nations unreliable power supply. As a result, demand for oil has rapidly increased over the last decades. India is now the world’s fourth largest oil consumer and the world’s fifth largest oil importer, as per the BP Statistics Review of World Energy.

Currently India import around 76% to 80% of its oil demand, with this percentage showing little sign of cooling. It is predicted that the nation’s oil needs would rise 40% by 2020 and the need has only become more acute in recent years. It is vital for the nation’s energy security that India needs to increase domestic production and also increase energy efficiency. Increased domestic oil production would not only make the country more energy secure, it would also offer significant macroeconomic benefits too.

India is potentially sitting on significant hydrocarbon resources, but reduced exploration activities means their size and scope has yet to be properly determined. This is compounded by a lack of encouragement at national level for a more proactive approach to exploration, which has undermined India’s E&P sector.

In 2011, India’s proven balance of recoverable reserves was reported at 9.04 billion barrels of oil equivalent, placing the country in an unimpressive 19th position worldwide. However, according to a 2012 report by India’s Directorate General of Hydrocarbons – the regulatory body charged with the promotion and management of India’s oil and gas resources – in the financial year 2010-2011, 12% of India’s sedimentary basins remain unexplored, with a further 22% classed as “ poorly explored”. In the near future, production from India’s underdeveloped onshore and offshore fields is set to increase, as these are yet to realise their full potential.

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High oil prices have prompted increased investments in the Exploration and Production (E&P) sector posing new challenges for the sector in the form of increased cost of operations due to high service costs, exposure to logistically difficult terrain and shortage of technical manpower. Global refining scenario indicates very little to negligible addition in capacities in major developed consuming markets like the USA and the European countries. Developing countries like the Middle East, China and India are fast emerging as refining hubs. Needless to say that capacity augmentation in these regions would also result into possible integration of both the refining and petrochemicals business.

Energy imports are also currently hindering the nation’s economy. The Government of India currently spends billions of dollars on non-targeted subsidies, which the IEA claims the nation can ill afford. During the financial year 2011-2012, 54% of India’s trade deficit – which stood at US$189.9 billion –, was due to oil imports. Because of such a high deficit, the rupee weakened, inflation soared and there was a drawdown of almost US$13 billion in India’s foreign exchange reserves. According to PwC report, these events could have been avoided had India produced an additional 17 million tonnes of oil domestically. (Source: White paper by Price water house Coopers (PwC), titled “It’s our turn now –E&P partnerships for India’s Energy Security”). This increase in India’s domestic production would arrest currency depreciation, contain Inflation and reduce import bill – resulting higher GDP.

The past decade has seen an unprecedented rise in crude oil prices on the world oil market. The prices of Brent & WTI (West Texas Intermediate) – the leading benchmark types of crude crossed $30 per bbl in the early 2004. From then, crude oil prices increased continuously and touched at $145.29 per bbl on beginning of July 2008 and then settling at lower level below $40 per bbl by December 2008, thereafter oil prices oscillating and making the crude oil prices volatile. The price volatility of crude ranges from $40 to $80 per bbl, during 2009, $78 to $90 per bbl and $90 to $111.78 are during 2010 and 2011 respectively and making the world oil market more and more volatile. This volatility and the

xxvi rising trend of crude oil prices in international markets are sending shockwaves across the world.

It is indeed difficult to predict what will happen to oil prices over a five year period but current assessments indicate that oil prices will remain high. This will exert downward pressure on the economy. India meets nearly 76 percent of its total crude oil requirements through imports. With the increase in share of hydrocarbons in the energy supply/use, this share of imported energy is expected to increase. The challenge, therefore, is to secure adequate energy supplies at the least possible cost, to meet the country’s social needs; otherwise high crude oil price will put tremendous pressure on the Government’s budget.

The existing scenario of oil consumption and production of world indicates that there is more demand for than supply of crude oil in general, except a few developed economy, for the developing country like India in particular reveals that the country is able to meet 24% of crude oil requirement and the rests 76% are to be meet through import. Therefore, there are sometimes demand driven price rise otherwise price rise due to supply disruption. Under such circumstances it is very much essential to study the impact of crude oil price on the inflation and economic growth of our country.

Several researchers have studied the various aspects of Indian economy at different period of time due to change in crude oil prices. The review of literature does not reflect the exact impact of crude oil prices on Indian Economy after the second phase of economic liberalization. Therefore, it is required to understand the dynamics of petroleum economics and management of crude oil and petroleum business for the growth of Indian economy.

The objectives of study were:

1. To study and formulate the impact of crude oil prices on the whole sale price index of Indian economy.

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2. To study the waves of inflation rate (consumer price index) due to change in crude oil prices on the GDP growth of Indian economy

3. To examine and understand the direction of causality and to search and ascertain the causal relation and linkage between differential change rate of crude oil prices and Inflation , also between inflation vis-à-vis GDP growth of Indian economy.

4. To study the impact of energy price relative to the productivity of capital and labor of Indian industries based on the past data.

Methodology adopted:

The research has econometric and analytical areas of research. The econometric and analytical study of the research has uses secondary data. The source of the data has been taken from primary reports and publications of varies bodies of the Ministry of Petroleum and Natural Gas and Government of India; public sector undertaking annual reports. Economic Data has been collected from the economic survey (2010-11); Reserve Bank of India data and CSO data. The data of inflation rate (consumer price index) is collected from the trading economics data and the Indian basket Price of Crude oil data has been collected from Petroleum Planning and Analysis Cell (PPAC). Data of world oil has been taken from British Petroleum (BP) statistics in addition to Journals and periodicals reliable constitutional and company publications. Historical perspective of Indian petroleum industry from 1857 has been chronologically brought out to follow the growth and development of the oil industry especially after 1956 under the public sector in various phases both in exploration & production and refining by the Government of India both pre and post liberalization period, also after the abolition of administered price control mechanism and the resultant data emanating from the evolution has been considered as a source of this research.

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Findings of the Study:

1. Our study showed that crude oil price plays a significant role in rising the Whole sale price index (WPI); crude oil prices have positive impact on Whole sale price index (WPI), our double log regression model shows that the crude oil price elasticity of inflation 0.27 and Karl Pearson Coefficient is positively correlated.

2. Our study showed that “The role of inflation is significant in declining GDP growth of Indian economy”, Karl Pearson Co-efficient between Inflation and GDP growth is negatively correlated, the inflation elasticity of GDP growth in the double log regression model is –0.245, which indicates that increase of Inflation retards GDP growth.

3. Our multiple regression analysis (GDP growth, Inflation rate and Crude oil price change rate) estimates that the quarterly crude oil price change elasticity of GDP growth and quarterly inflation elasticity of GDP growth are 0.01 and -0.21 respectively.

4. On Grangers causality test, our findings are –

(i) “Crude oil price rate change Granger causes Inflation rate” again “Inflation Granger Causes the crude oil price rate change”. It is bidirectional causality. (ii) “Inflation does not Granger cause GDP growth”, it is uni-directional causality. But, GDP growth Granger causes Inflation.

5. On application of “Cobb Douglas Model”, our finding is –

“A rise in the price of energy relative to output leads to decline in the productivity of existing capital and labor.” That is with the increase of energy

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or fuel price relative the derived output leads to diminish the productivity of existing capital and labour.”

Recommendations

1. Increasing domestic production by attracting investments, both private and public in the upstream sector through investor friendly E&P investment regime. To increase foreign investment, the Indian government must first introduce reforms that encourage foreign and private investor for its trusted international partners to leverage their skills, expertise and technological capabilities.

2. Taking all steps to increase the production from NOC’s (National Oil Companies) assets including their maturing field.

3. Equipping domestic refining industry both existing and planned to successfully meet the quality of producing fuels complying with prescribed environment friendly specifications for export hub of petroleum products to earn foreign exchange.

4. Encouraging energy conservation through campaigns aimed at sensitizing the people about the significance of efficient use of energy.

5. Towards Energy Security: As India’s economy continues to grow, so will its need for energy security. By adopting various strategies for nonconventional and green energy and also alternative forms of energy in the form of renewable energy and nuclear power as a diversification to ensure energy security

Challenges in the nation’s hydrocarbon sector do exist. Yet, they are not insurmountable. Increased domestic production and investment in nation’s E&P sector will not only ease worries about India’s future energy security, but also reduce the nation’s trade deficit and rejuvenate the rupee on international foreign exchange market.

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Chapter-1 Introduction Oil is a magic word that always makes news. There is hardly a nation that does not seek this indispensable natural resource. A country that already possesses crude oil wants more. They struggle to explore it at almost any cost. The common man does not know much about this strange „mineral oil‟ although in almost every country he bears the burden of the cost of exploration of oil or its import.

Oil or Petroleum is defined in a variety of ways by geologists, chemists, refiners, engineers and lawyers. There is, therefore, no uniformity or full agreement. Since, it is a natural product forming a part of rocks, geological definition finds more general acceptance.

The word petroleum is derived from two Latin words petra means rock and oleum means oil. Petroleum is loosely called „rock oil‟ or „crude oil‟. It is a generic term covering a wide range of substances comprising hydrocarbons, which are naturally occurring molecules of carbon and hydrogen.

1.1. Hydrocarbons

Crude oil is a complex mixture of a large number of organic compounds that vary in appearance and composition from one oil field to another. Crude oil is classified as paraffinic, naphthenic, aromatic or asphaltic based on the predominant proportion of hydrocarbon series molecules which dictate their physical and chemical properties. Theories vary regarding the origin of crude oil though the general consensus is that most of the deposits have resulted from the burial and transformation of biomass over geological periods during the last 200 million years or so. In terms of quantities, therefore, the total amount of oil and gas residing in the earth‟s subsurface is certainly finite. While it is recognized that some of these resources have yet to be found, there is considerable uncertainty about the magnitude of the “undiscovered resources”. The most

widely used estimates of total amounts of crude oil to be found in the earth‟s subsurface are those of the US Geological Survey 2000, 2007, 2009 and IEA, 2004 which deals primarily with conventional Oil and Gas. There is no universally agreed definition of what is meant by conventional oil or gas, as opposed to nonconventional. Roughly speaking, any source of hydrocarbons that requires production technologies significantly different from mainstream in currently exploited reservoirs is described as non-conventional.

1.2. Global Primary Energy Consumption

The global primary energy consumption at the end of 2011 is equivalent to 12274.6 Million tonnes oil equivalent. The share of oil is the largest at 4059.1 Million tones oil equivalent; i.e. oil : 33.06%; followed by coal: 30.34 %, natural gas: 23.67%; hydroelectricity: 6.45%; nuclear energy: 4.88%; renewable: 1.59% respectively. The demand for natural gas in future will increase as industrialized countries take strong action to cut CO2 emissions.

World primary energy consumption is projected to grow by 1.6% p.a. over the period 2010 to 2030, adding 39% to global consumption by 2030. The growth rate has declined from 2.5% p.a. over the past decade, to 2.0% p.a. over the next decade, and 1.3% p.a. from 2020 to 2030. Almost all (96%) of the growth is in non-OECD countries. By 2030 non-OECD energy consumption is 69% above the 2010 level, with growth averaging 2.7% p.a. (or 1.6% p.a. per capita), and it accounts for 65% of world consumption (compared to 54% in 2010). OECD energy consumption in 2030 is just 4% higher than in 2010, with growth averaging 0.2% p.a. to 2030. OECD energy consumption per capita is on a declining trend (-0.2% p.a. 2010-30).

The International Energy Agency (IEA) defines energy security primarily in terms of stable supplies of oil and natural gas. A broader definition of energy resource portfolio and supply of energy services for the desired level of services that will

sustain economic growth and poverty reduction. Energy security covers many concerns linking energy, economic growth, environment and geopolitics.

Figure 1.0.Global Primary Energy Consumption

Hydro- Renewables electricity 1.59% 6.45%

Nuclear 4.88%

Oil 33.06% Oil Gas Coal Nuclear Coal 30.34% Hydro-electricity Renewables

Gas 23.67%

Source: BP Statistical Review of World Energy June-2012.

The above figure shows the breakup of various constituents of primary energy consumption (Million tonnes of oil equivalent, Mtoe) worldwide.

The primary energy consumptions of various countries and regions of the world are shown in table 1.0. It may be seen that India‟s absolute primary energy consumption is only 4.55% of the world, 21.29% China‟s, 18.49% USA‟s, 3.89% Japan‟s consumption.

Table 1.0. Primary Energy Consumption by fuel

Consumption by fuel* Million tonnes oil Natural Nuclear Hydro Renew- 2011 equivalent Oil gas Coal energy electricity ables Total

US 833.6 626.0 501.9 188.2 74.3 45.3 2269.3 Canada 103.1 94.3 21.8 21.4 85.2 4.4 330.3 Mexico 89.7 62.0 9.9 2.3 8.1 1.8 173.7 Total North America 1026.4 782.4 533.7 211.9 167.6 51.4 2773.3

Argentina 28.1 41.9 1.1 1.4 9.0 0.4 81.9 Brazil 120.7 24.0 13.9 3.5 97.2 7.5 266.9 Chile 15.2 4.7 5.3 - 4.7 1.0 30.9 Colombia 11.7 8.1 4.3 - 10.9 0.2 35.1 Ecuador 10.5 0.4 - - 2.2 0.1 13.2 Peru 9.2 5.6 0.8 - 4.9 0.1 20.7 Trinidad & Tobago 1.7 19.8 - - - ^ 21.5 Venezuela 38.3 29.8 2.0 - 18.9 - 89.1 Other S. & Cent. America 53.7 4.7 2.4 - 20.4 2.0 83.3 Total S. & Cent. America 289.1 139.1 29.8 4.9 168.2 11.3 642.5

Austria 12.5 8.5 2.5 - 6.9 1.6 32.0 Azerbaijan 3.6 7.3 ^ - 0.6 ^ 11.5 Belarus 9.0 16.5 ^ - ^ ^ 25.5 Belgium 33.7 14.4 2.1 10.9 ^ 2.1 63.3 Bulgaria 3.5 2.6 8.4 3.7 0.6 0.3 19.2 Czech Republic 9.1 7.6 19.2 6.4 0.6 1.1 44.0 Denmark 8.3 3.8 3.2 - ^ 3.4 18.7 Finland 10.5 3.2 3.3 5.3 2.8 2.6 27.7 France 82.9 36.3 9.0 100.0 10.3 4.3 242.9 Germany 111.5 65.3 77.6 24.4 4.4 23.2 306.4 Greece 17.2 4.1 7.3 - 1.0 0.9 30.5 Hungary 6.5 9.1 2.7 3.5 0.1 0.7 22.6 Republic of Ireland 6.8 4.2 1.3 - 0.2 1.1 13.6 Italy 71.1 64.2 15.4 - 10.1 7.7 168.5 Kazakhstan 10.2 8.3 30.2 - 1.8 - 50.5 Lithuania 2.7 3.1 0.2 - 0.2 0.1 6.4 Netherlands 50.1 34.3 7.8 0.9 ^ 2.7 95.8

Norway 11.1 3.6 0.6 - 27.6 0.4 43.4 Poland 26.3 13.8 59.8 - 0.6 2.2 102.8 Portugal 11.6 4.6 2.6 - 2.8 2.8 24.4 Romania 9.0 12.5 7.1 2.7 3.4 0.2 34.8 Russian Federation 136.0 382.1 90.9 39.2 37.3 0.1 685.6 Slovakia 3.7 5.6 3.3 3.4 0.9 0.1 17.1 Spain 69.5 28.9 14.9 13.0 6.9 12.7 145.9 Sweden 14.5 1.1 2.0 13.8 15.0 4.1 50.5 Switzerland 11.0 2.6 0.1 6.1 7.4 0.3 27.6 Turkey 32.0 41.2 32.4 - 11.8 1.3 118.8 Turkmenistan 4.9 22.5 - - ^ - 27.4 Ukraine 12.9 48.3 42.4 20.4 2.4 ^ 126.4 United Kingdom 71.6 72.2 30.8 15.6 1.3 6.6 198.2 Uzbekistan 4.4 44.2 1.3 - 2.3 - 52.2 Other Europe & Eurasia 30.3 14.9 20.8 2.0 19.7 1.4 89.1 Total Europe & Eurasia 898.2 991.0 499.2 271.5 179.1 84.3 2923.4

Iran 87.0 138.0 0.8 ^ 2.7 0.1 228.6 Israel 11.1 4.5 7.9 - - ^ 23.5 Kuwait 19.0 14.6 - - - - 33.6 Qatar 8.0 21.4 - - - - 29.4 Saudi Arabia 127.8 89.3 - - - - 217.1 United Arab Emirates 30.5 56.6 - - - ^ 87.2 Other Middle East 87.5 38.4 - - 2.3 ^ 128.1 Total Middle East 371.0 362.8 8.7 ^ 5.0 0.1 747.5

Algeria 15.6 25.2 - - 0.1 - 40.9 Egypt 33.7 44.7 0.9 - 3.1 0.3 82.6 South Africa 26.2 3.8 92.9 2.9 0.4 0.1 126.3 Other Africa 82.9 25.1 6.0 - 19.8 0.9 134.7 Total Africa 158.3 98.8 99.8 2.9 23.5 1.3 384.5

Australia 45.9 23.0 49.8 - 2.4 2.2 123.3 Bangladesh 5.0 17.9 1.0 - 0.3 ^ 24.3 China 461.8 117.6 1839.4 19.5 157.0 17.7 2613.2 China Hong Kong SAR 18.1 2.7 7.7 - - ^ 28.6 India 162.3 55.0 295.6 7.3 29.8 9.2 559.1 Indonesia 64.4 34.1 44.0 - 3.5 2.1 148.2

Japan 201.4 95.0 117.7 36.9 19.2 7.4 477.6 Malaysia 26.9 25.7 15.0 - 1.7 ^ 69.2 New Zealand 6.9 3.5 1.4 - 5.7 2.0 19.4 Pakistan 20.4 35.2 4.2 0.8 6.9 ^ 67.6 Philippines 11.8 3.2 8.3 - 2.1 2.3 27.7 Singapore 62.5 7.9 - - - ^ 70.4 South Korea 106.0 41.9 79.4 34.0 1.2 0.6 263.0 Taiwan 42.8 14.0 41.6 9.5 0.9 1.2 109.9 Thailand 46.8 41.9 13.9 - 1.8 1.6 106.0 Vietnam 16.5 7.7 15.0 - 6.7 ^ 45.9 Other Asia Pacific 16.7 5.2 19.1 - 8.8 0.1 49.9 Total Asia Pacific 1316.1 531.5 2553.2 108.0 248.1 46.4 4803.3

Total World 4059.1 2905.6 3724.3 599.3 791.5 194.8 12274.6 of which: OECD 2092.0 1386.1 1098.6 487.8 315.1 148.0 5527.7

Non-OECD 1967.0 1519.5 2625.7 111.5 476.4 46.8 6746.9

European Union 645.9 403.1 285.9 205.3 69.6 80.9 1690.7

Former Soviet Union 190.6 539.6 169.8 60.2 54.6 0.4 1015.1

* In this Review, primary energy comprises commercially traded fuels including modern renewables used to generate electricity. ^ Less than 0.05. Notes: Oil consumption is measured in million tonnes; other fuels in million tonnes of oil equivalent. Source: BP Statistical Review of World Energy June-2012.

1.3. Properties of fossil fuels Oil has the highest energy density of all fossil fuels, about 40-45 GJ/t or 35- 40GJ/m3, with some variation due to gravity and sulphur content.

Coal, by contrast, has only about 20-30 GJ/t, varying largely depending on the ash content, which for hard coal can be as high as 40% and even higher for lignite.

Gas, which has methane as its main component, has only one thousandth of the energy density of oil under-atmospheric pressure, i.e., 35-45 MJ/m3, with a lower value depending on the share of components higher then methane, typically ethane, propane and butane.

It is possible to increase the energy density of natural gas by putting it under pressure, e.g., by a factor of 100 if pressurized to 100 bar, but this still leaves a differential in energy density in the order of 10 compared with oil. It is also possible to liquefy natural gas by cooling it down to minus 162 degrees Celsius. The energy density of liquefied natural gas (LNG) is about half that of oil, but the technology necessary to liquefy, ship and re-gasify LNG is much more costly than that for handling oil.

Noxious components like sulphur, which can occur in all three fossil fuels, need treatment to protect the environment. Handling the ash contained in the coal requires substantial additional equipment for the combustion process and depositing the ash is a costly operation.

The use of coal is so far confined to boilers alone or combined with steam turbines (except for transforming it into a manufactured gas by a process of hydration), while gas and oil are easy to handle and can also be used in internal combustion engines (cars) and in gas turbines.

Oil and coal can be transported and stored in vessels without entailing high specific costs, making it easier to establish marketplaces for oil and coal trading.

The high energy density of oil, combined with easy handling storage and transportation, make it suitable for small applications like cars.

This does not apply for coal and only to a lesser extent for gas. Due to its gaseous aggregate and low energy density, and unless it is transported as LNG, gas requires a fixed pipeline infrastructure for transportation and distribution establishing a physical trading infrastructure of gas is more difficult because of its high specific costs.

Gas has a substantial advantage on GHG emissions; the CO2 emission factor from burning fuel oil is about 35% higher and, for coal about 55% higher than for gas. In addition, gas and oil can be used in gas turbines and in CCGTs, where the exhaust heat of a gas turbine process is used to run a steam turbine with a substantially higher electric efficiency (more than 55%) of the combined process than a standard coal-driven steam turbine, which has a maximum electric efficiency of 45%.

Oil can always replace gas, at the price of a higher CO2 emission factor, while gas can replace oil but is not well suited to fuel individual cars. All three fossil fuels can be used for power generation, gas and gas oil performing similarly, while burning heavy (residual) fuel oil causes more handling problems and burning coal required a different treatment and substantial higher investment than for oil or gas.

1.4. Character of the deposits of fossil fuels:

Oil and gas fields are subject to hydraulic communication; production from one part of a structure leads to a pressure reduction for all of the structure with repercussion for overall recovery. It is, therefore, common practice to unitise deposits that stretch across the borders of several licenses and to have oil or gas fields under a uniform operating regime, even the very large ones. By contrast, large deposits of solid minerals like coal can be produced at several places in parallel, without interference with each other. However, there are usually

economies of scope and scale stemming from a coordinated development of large coal deposits.

At large onshore oil fields with good production characteristic the drilling of additional wells to add production capacity is often not very costly. In such cases spare capacity can be kept in reserve or created at relatively short notice. As access to extra oil-tanker capacity is usually possible, large oil producers can react quickly to fluctuations in demand. By contrast, spare production for gas capacity is not expensive for large onshore fields, but the spare infrastructure to bring it to the market is very costly because of the low energy density of gas. For coal, spare production capacity would be costly because of the substantial idle equipment and the need to have enough qualified workforce at hand, while extra shipping capacity may be available on the mass freight-ship market, subject to competition with other users.

The consequence is that oil storage downstream is minimized except for strategic stocks, while, for gas, storage close to the market is usual for seasonal storage to avoid unnecessary capacity in the pipeline, and for coal many power plants have a coal stock close to the plant.

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Chapter-2 Literature Review Oil prices matter to the health of an economy, despite a consistent fall in global oil intensity; crude oil remains an important commodity and events in the oil market and continues to play a significant role in shaping global economic and political development. Crude oil is the world economy‟s most important source of energy and is therefore, critical to economic growth.

The price of crude in global market is essentially driven by supply and demand. The performance of world economy in general and the world‟s largest economies such as US, Japan and recently China have a significant impact on the demand for crude oil and vice versa. The various method developed by IMF, World Bank(WB) and OECD have estimated that 10 dollar increase in crude oil prices would lead to a decline of world production of goods and services by 0.5%. The world economic growth and world oil demand are moving in tandem and there is high correlation between world economic growth and demand for oil. It is essentially the supply that drives the prices of crude oil.

Many researchers agree in opinion that no other economic event in post-World War II era generated as much attention as the series of oil price shocks, mainly produced by OPEC countries. No studies were necessary to see the clear relationship between oil prices and main economic indicators. Nevertheless, this issue was new and researchers posed such a question as the numerical impact of oil shocks and their correlation with the policy conducted by government in order to predict the best instrument to cope with the negative impacts caused by oil price increases. Since then a large number of studies have reported a correlation between increases in oil prices followed by economic downturns.

Hamilton (1983) investigated the impact on the US economy. His evidence suggests that crude oil prices have a strong relationship with the US business cycle and tends to highlight cost-push inflationary effects, while the research of

Berndt and Wood (1975,1979) as well as Wilcox‟s (1983) indicates the complementarily between energy prices and capital in the US economy is rather strong, both before and after 1973. Hence, oil price rise lead to shocks may have a stronger effect than generally believed. These results were later extended by Mork (1989) and Hooker (1999) who argued that asymmetric and nonlinear transformations of oil prices restore that relationship, and thus the economy responds asymmetrically and nonlinearly to oil price shocks.

Later Hamilton (2000) reported clear evidence of nonlinearity-oil price increases is much more important than oil price decreases. An alternative interpretation was proposed based on the estimation of a linear functional form using exogenous disruptions in petroleum supplies as an instrument. His study shows that oil shocks play a crucial role in determining macroeconomic behavior because they disrupt spending by consumers and firms.

Hamilton extended his research work (2003, 2005, and 2009) and has presented empirical evidence suggesting that oil price shocks have been one of the main causes of recessions in the United States. Others, including Barsky and Kilian (2004), argue that the effect is small and that oil shocks alone cannot explain the U.S. stagflation of the 1970s. Taking a more intermediate position, Bernanke et al. (1997) argue that an important part of the effect of oil price shocks on the U.S. economy results not from the change in oil prices per se, but from the resulting tightening of monetary policy. In the same line of research, Blanchard and Gali (2007) present evidence showing that the dynamic effect of oil shocks has decreased considerably over time, owing to a combination of improvements in monetary policy, more flexible labor markets, and a smaller share of oil in production. Their results indicate that a 10 percent increase in the price of oil would, prior to 1984, have reduced U.S. GDP by about 0.7 percent over a 2–3 year period, while after 1984 the loss would be only about 0.25 percent. In contrast to the extensive literature on the impact of oil prices on the U.S. economy, there has Outside the U.S., studies of the relationship between oil prices and the macro-economy have almost exclusively been confined to other

OECD members, with results suggesting that they tend to be affected in broadly the same way as the U.S. but less strongly.

Rasche and Tatom (1977 and 1981) explain that energy price shocks alter the incentives for time to employ energy resources and alter their optimal methods of production. Energy -using capital is rendered obsolete by any energy price increase and the optimal usage of the existing stock is altered and production switches to less energy- intensive technologies. The reduced capacity output of the economy is usually referred to as decline in potential or natural output.

The authors state that domestic aggregate demand is affected due to a change in net imports of oil. The direction and extent of effects depends on the country‟s net oil export status. Net oil exporting countries experience an increase (decrease) in aggregate demand when oil price rise (fall). The effect on net oil importing countries is exactly opposite. Net oil exporting countries like Canada and the UK receive a boost to aggregate demand and output/ employment from a spike in oil price.

The impacts on productivity tend to work in the same direction regardless of the oil trade status of the country. An increase in oil prices has a negative impact on productivity. The theories suggest that energy price shocks should affect the productivity of capital and labor resources.

Rati Ram and David Ramsey (1989) also took a production function approach (Cobb-Douglas specification) to estimating the elasticity. Their estimates for the United States are somewhat unique in that they distinguish between privately owned and publicly owned capital. A relative energy price variable is also incorporated and the estimation period is from 1948 to 1985. They obtained statistically significance energy price – GNP elasticity estimates that ranged between -0.074 and -0.069, depending on the disaggregation of public capital.

Micha Gisser and Thomas Goodwin (1986) estimated equations involving real GNP, general price level, unemployment rate and real investment. They regressed each of those variables independently on contemporaneous and four lags of M1money supply, the high employment federal expenditure measure of fiscal policy and the nominal price of crude oil.

BAIC Economic Review Autumn 2006 (The business and industry advisory committee to the OECD), it has shown that the world economy slows down based on the BAIC Member Survey and at that time it was anticipated that the OECD –wide real GDP growth to drop from 3.1 % to 2.6% in 2007 and risk for growth was associated to oil price.

Hyun Joon Chang of Korea Energy Economics Institute in his paper “The Impact of Oil Price Increase on the Global Economy” discussed the impact of an oil price increase of $5 per bbl on global economy (IMF -2000).

In the case of oil price increases, there will be a transfer of income from oil consumers to oil producers. On an international level, the transfer is from oil importing countries to oil exporters, and oil exporters tend to expand demand only gradually. It will affect income redistribution of the global economy.

Also, when oil prices increase and energy input prices rise, there will be a rise in the production costs in the economy, depending on degree of competition of the markets. As the oil intensity of production in developed countries has fallen over the past three decades, the cost side impact of increases in oil prices can be expected to be less than in past oil price increases. In developing countries, however, where the oil intensity of production has declined less, the impact may be closer to that in the earlier period.

There will be a demand side impact of oil price increases. When oil prices rise, consumers are likely to delay or postpone their purchasing durables such as automobiles. This demand side impact leads to relative increase in inventories to sales and then decline industrial production.

Finally, depending on expected duration of price increases, the change in relative prices creates incentives for suppliers of energy to increase production and investment, and for oil consumers to economize.

The impact on developing countries seems to be at least as large as for many of the industrial countries. Oil exporting countries suffered seriously from the oil price decline in 1997-98 are expected to benefit substantially with recent oil price increases. On the other hand, there is a negative impact on oil importing countries, especially as dependency on oil has not fallen to the same extents as in industrial countries. Oil price increases affect developing countries very differently. Oil exporting countries such as UAE have a large current account surplus while many oil importing countries are expected to be adversely affected. The oil price increase would add to its current account deficit by 1.25 percent. A number of countries also face additional pressures from weak non-oil commodity prices, and have limited access to capital markets, which will further increase the adverse impact on domestic absorption.

In major emerging market economies, the results vary widely by region, depending on the relative size of oil importing to exporting countries. Asia experiences the largest negative impact on growth. Latin America, emerging Europe and Africa are less adversely affected by the oil shock. Among the oil importing countries, the largest impact on GDP growth and the balance of payments is expected to be in India, Korea, Pakistan, Philippines, Thailand, and Turkey. The oil price increases will affect China‟s economic recovery, yet the direct impact of oil price hikes on China‟s economy should be much less than that on most Asia-Pacific countries as the ratio of net oil imports to domestic oil consumption is much lower than the Asian average. The ratio for China is 22 percent, but 100.2 percent for Japan and 61.4 percent for the rest of Asia Pacific. Also, oil occupies only 26.6 percent in China‟s primary energy consumption, much lower than other Asian countries, which are heavily dependent on oil.

“Analysis of the impact of high oil prices on the global economy” by Economic Analysis Division, International Energy Agency reports in “Energy Prices and

Taxes”, 2nd Quarter 2004, wherein it has shown that the vulnerability of oil importing countries to higher oil prices varies markedly depending on the degree to which they are net importers and oil intensity of their economies. According to the results of a quantitative exercise carried out by the IEA in collaboration with the OECD Economics Department and with the assistance of the International Monetary Fund Research Department, a sustained for10 per barrel increase in oil price from $25 to $35 would result in the OECD as a whole losing 0.4% of GDP in the first and second years of higher prices. Inflation would rise by half a percentage point and unemployment would also increase. The OECD imported more than half its oil needs in 2003 a cost of over $260 billion-20% more than 2001. Euro-zone countries, which are highly dependent on oil imports, would suffer most in short term, their GDP dropping by 0.5% and inflation rising by0.5% in 2004. The U.S would suffer the least, with GDP would fall 0.4%, with its relatively low oil intensity compensating to some extent for its almost total dependence on imported oil. In all OECD regions, these losses start to diminish in following three years as global trade in non-oil goods and services recovers. This analysis assumes constant exchange rates.

The adverse economic impact of higher oil prices on oil-importing developing countries is generally even more severe than for OECD countries. This is because their economies are more dependent on imported oil and more energy- intensive, and because energy is used less efficiently. On average, oil-importing developing countries use more than twice as much oil to produce a unit of economic output as do OECD countries. Developing countries are also less able to weather the financial turmoil wrought by higher oil import costs. India spent $15billion, equivalent to 3% of its GDP, on oil import in 2003. This is 16% higher than its 2001 oil import bill. It is estimated that the loss of GDP average 0.8% in Asia and 1.6% in very poor highly indebted countries in the year following a $10 oil-price increase. The loss of GDP in Sub-Saharan African countries would be more than 3%.

World GDP would be at least half of one percent lower – equivalent to $255 billion- in the year following a $10 oil price increase. This is because the economic stimulus provided by higher oil –export earnings in OPEC and other exporting countries would be more than outweighed by depressive effect of higher prices on economic activity in the importing countries. The transfer of income from oil importer to oil exporter in the year following the price increase would alone amount to roughly $150billion. A loss of business and consumer confidence, inappropriate policy responses and higher gas prices would amplify this economic effect. For as long as oil prices remain high and unstable, the economic prosperity of oil-importing countries-especially the poorest developing countries-will remain at risk.

World Economic Outlook April-2007 reports there is a global macroeconomic implications of a supply induced oil price hike and persistent productivity shocks with low oil capacity.

In fiscal 2010, the India‟s import bill for crude oil was $100.08billion, which of 7.12% higher in volume than fiscal 2009, crude oil import bill increased to around $ 20.527billion. That means there was a jump of 25.8% in crude oil import bill for fiscal 2010 from previous fiscal 2009 i.e. $79.553billion.

2.1. Gap Analysis

Crude oil prices played a critical role in substantially reducing economic growth in any economy whether it is developed or developing economy.

Worldwide demand for crude oil arises from demand for the refined products that are made from crude; and changes in crude oil prices are passed on to consumers in the prices of the final petroleum products.

When the prices of petroleum products increase, consumers use more of their income to pay for oil-derived products, and their spending on other goods and services declines. The extra amount spent on those products is basically go to foreign oil producers as India is net importer of oil.

Oil is a vital input for the production of a wide range of goods and services, because it is used for transportation in business of all types. Higher oil prices thus increase the cost of inputs; and final product price increases cause inflation, if the cost increases cannot be passed on to consumers, economic inputs such as labor and capital stock may be reallocated. Higher oil prices can cause worker layoffs and the idling of plants, reducing economic output in the short term.

In a net importer of oil economy like India, higher oil prices shrink foreign reserves of the economy, affect the purchasing power of the economy in terms of International trade. The increased price of imported oil forces the businesses to devote more of their production to exports, as opposed to satisfying domestic demand for goods and services, therefore cause inflation, even if there is no change in the quantity of foreign oil consumed.

Higher oil prices cause, to varying degrees, increases in other energy prices. Depending on the ability to substitute other energy sources for crude the price increases can be large and can cause macroeconomic effects similar to the effects of oil price increases.

Thus, though energy is the prime mover in an economy, the demand and supply gap of crude oil must be bridged through import to meet the country‟s requirement, hence, crude oil price is an important parameter in determining reserve position and trade balance and finally balance of payment.

Inflation is also an important area arising with the increase of crude oil prices, with the increase of inflation, capacity to purchase is reduced and expenditure increases, saving decreases, ultimately slows down the business and economic activities thus slows down GDP growth.

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Chapter-3 Statement of the Problem Crude oil price is an important parameter for refining industries, which has a bearing on economy, because it is vital input for productivity. There is a vast gap in demand and production of crude oil in India. National oil companies are able to produce 23-24% of India‟s total requirements of crude oil. The production of crude oil from public sector enterprises in India has been decreasing due to old and the maturity of the fields.

India is not self-reliance on crude oil production; therefore, it is necessary and inevitable to import the crude oil to bridge the gap between demand and supply. The increase in international crude oil prices will make import costly and raise the Indian crude basket price. Therefore, both international crude oil price rise and import dependency on crude oil are the problematic area that may damage the Indian economy.

It is estimated that the import dependence of India associated with crude oil is expected to 94% by the end of 2030. Therefore, the trouble water in Indian crude oil demand and supply management is the rise in international crude oil prices followed with the extent of the increase in crude oil requirement with respect to feasible higher GDP growth 8% to 9%. The import dependence of India associated with crude oil is from 76% in 2011-12 to 80% by the end of twelfth plan (2012-17). As crude oil prices are rising globally and imports will be expensive, it is necessary to understand the consequences of crude oil price rise on the economy.

Therefore, there is an urgent need to look holistic picture of whether the changes in Indian crude basket prices have any implication on Inflation and GDP growth, or is there any link between Indian crude oil basket price change and Inflation or Inflation is the cause of concern for slowdown of GDP growth, what should be

our strategy to meet the growing demand of crude oil for economic growth. It is against this backdrop that we attempt, in this study, to critically analyze the impact of the change in crude oil prices on Indian economy. Therefore, there is an urgent need to look at holistic picture of investment in Brown field and Green field projects in petroleum industry, use of new technologies in the area for Oil and Gas business.

The desire of the study is to understand, how the increase in Indian basket price of crude due to raise in international crude oil prices impact the economic indicators like inflation and GDP growth. The essence of the study is to garner the understanding of the causal relationship with the phenomenon of complexity of historic facts in crude oil prices and social reality of economic development and economic growth. The study is essential for both – knowledge and to help in solving problems of businesses arising out due to inflation, predicting the future price signal in relation to the business environment and economic growth.

No similar research initiative has been undertaken in India that has focused on causal study and the impact of Indian crude basket price on the economic indicators like the inflation and GDP growth of the economy.

The import requirement of crude oil is 73 – 76 % of total demand, which is equal to 141.9MT for the year ending 2011-12 and growing annually at the rate of 2.9%. To meet the demand, the crude oil is being imported from gulf countries through long term contract and international tie up is essential to avoid any supply shock. The Indian basket of crude comprising of the composition represents average of Oman & Dubai for sour grades and Brent (Dated) for sweet grade in the ratio of 67.6:32.4 from 1st April'2010.

3.1. Objectives of the study: Based on the secondary data, literature review and the gaps identified, the following objectives of study were framed. The objectives of study are as follows:- • To study and formulate the impact of crude oil prices on the whole sale price index of Indian economy. • To study the waves of inflation rate (consumer price index) due to change in crude oil prices on the GDP growth of Indian economy. • To examine and understand the direction of causality and to ascertain the causal relation and linkage between differential change rate of crude oil prices and Inflation , also between inflation vis-à-vis GDP growth of Indian economy. • To study the impact of energy price relative to the productivity of capital and labor of Indian industries based on the past data.

3.2. Hypotheses

Hypothesis: 1

H01 : Crude oil price plays an insignificant role in rising WPI of Indian economy.

H11 : Crude oil price plays a significant role in rising WPI of Indian economy.

Hypothesis: 2

H02 : The role of Inflation is insignificant for declining GDP growth of Indian economy.

H12 : The role of Inflation is significant for declining GDP growth of Indian economy.

Hypothesis: 3

H03 : Crude oil price rate change does not Granger cause inflation.

H13 : Crude oil price rate change Granger causes inflation.

Hypothesis: 4

H04 : Inflation does not Granger cause GDP growth.

H14 : Inflation Granger causes GDP Growth.

Hypothesis: 5

H05 : A rise in the price of energy relative to output does not lead to decline in productivity of existing capital and labor.

H15 : A rise in the price of energy relative to output leads to decline in productivity of existing capital and labor.

3.3. Defining Variable for study:

Independent Variable: Crude oil price Dependent Variable : Inflation, GDP growth.

3.4. Operational Definition of Variables

1. Crude oil price: Crude oil prices measure the spot price of various barrels of oil, most commonly either the West Texas Intermediate or the Brent Blend. The OPEC basket price and the NYMEX Futures price are also sometimes quoted. West Texas Intermediate (WTI) crude oil is of very high quality, because it is light-weight and has low sulphur content. For these reasons, it is often referred to as “light, sweet” crude oil. These properties make it excellent for making gasoline, which is why it is the major benchmark of crude oil in the America. WTI is generally priced at about a $5-6 per barrel premium to the OPEC Basket Price and about $1-2 per- barrel premium to Brent.

Brent Blend is a combination of crude oil from 15 different oil fields in the North Sea. It is less “light” and “sweet” than WTI, but still excellent for making gasoline. It is primarily refined in Northwest Europe, and is the major benchmark for other crude oils in Europe or Africa. For example, prices for other crude oils in these two continents are often priced as a differential to Brent, i.e., Brent minus $0.50. Brent blend is generally priced at about a $4 per barrel premium to the OPEC Basket price or about a $1-2 per barrel discount to WTI.

The OPEC Basket Price is an average of the prices of oil from Algeria, Indonesia, Nigeria, Saudi Arabia, Dubai, Venezuela, and Mexico. OPEC uses the price of this basket to monitor world oil market conditions. OPEC prices are lower because the oil from some of the countries has higher sulphur content, making them more “sour”, and therefore less useful for making gasoline. The Indian basket of crude comprising of the composition represents average of Oman & Dubai for sour grades and Brent (Dated) for sweet grade in the ratio of 67.6:32.4 from 1st April'2010.

2. Inflation In economics, inflation is a rise in the general level of price of goods and services in an economy over a period of time. When the general price level rises, each unit of currency buys fewer goods and services. Consequently, inflation also reflects erosion in the purchasing power of money – a loss of real value in the internal medium of exchange and unit of account in the economy. A chief measure of price inflation is the inflation rate, the annualized percentage change in a general price index (normally the consumer price index) over time.

Inflation's effects on an economy are various and can be simultaneously positive and negative. Negative effects of inflation include a decrease in the real value of money and other monetary items over time, uncertainty over future inflation which may discourage investment and savings, and if inflation is rapid enough, shortages of goods as consumers begin hoarding out of concern that prices will increase in the future. Positive effects include ensuring central banks can adjust nominal interest rate (intended to mitigate recession), and encouraging investment in non-monetary capital projects.

There are two type of inflation- namely Whole sale price index (WPI) and Consumer price Index (CPI). The WPI can be interpreted as an index of prices paid by producer for their inputs. CPI is the money outlays required to purchase a given basket of consumption goods and services.

3. Gross Domestic Product (GDP) growth or Economic Growth

The term GDP refers to the monetary value of the gross output produced by the nationals of a country in the domestic economy. The change in a nation's Gross Domestic Product (GDP) from one period of time (usually a year) to the next. The economic growth rate shows by how much GDP has grown or shrunk in raw dollar or rupee amounts or in the currency of that country. It is considered one of the most important measures of how well or poorly an economy is performing.

Thus,

Economic growth rate = {(GDPyear2– GDPyear1) / GDPyear1 } * 100 The GDP growth rate is the most important indicator of economic health. If it's growing, so will business, jobs and personal income. If it's slowing down, then businesses will hold off investing in new purchases and hiring new employees, waiting to see if the economy will improve. This, in turn, can easily further depress the economy and consumers have less money to spend on purchases. If the GDP growth rate actually turns negative, then the economy of the country is heading towards a recession.

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Chapter- 4 Research Methodology

4.1. Conceptual Framework: Crude oil is the fundamental building block among the primary energy which dictates the overall energy mix in terms of its utility as basic input for economic growth. Oil is often thought of first fall back energy resource. Its price is the basic unit for all economic activities like agriculture, manufacturing, project evaluation directly or indirectly, for calculating price of manufacturing articles, product prices, transportation cost, service industry etc., even in pricing other forms of energy. Therefore, crude oil price increase is viewed throughout the world as it has a bearing on all the prices of final goods and services of the economic activities of the world. No economies in the world, whether exporters or importers are de-coupled from the impact of the increase of crude oil price. Crude oil price increase can be treated as the source affecting the economy, it may be treated as the epicenter of earthquake in an economy, which has the potential to cause catastrophe to any economy and can damage the business activities, in worst condition, it can bring down to business and economic recession.

As oil price increase can influence the economy through two important routes. First, with the increase of oil price in world market, i.e. Brent, WTI, Nigerian Forcados, Dubai, Arabian light, Iranian light etc., the Indian crude basket price will also rise. This price hike pass on to domestic refining petroleum product price, ratcheting up domestic price levels, in turn WPI. In India‟s WPI, for instance, the weight of mineral oils (comprising POL price mainly) is 6.99% (base year1993-94) and it is increased to 9.36% (base year 2004-05). Second, Higher oil price would raise the variable cost of industry. So, industries would seek to raise their product price to protect their profit margin. Thus, overall product price level of all the commodities on all the sectors of the economy will increase; hence raise the consumer price index level i.e. the inflation rate. The Inflation rate has a bearing on GDP growth. Higher crude oil prices would also impact balance of

payments significantly. In sum, oil price changes affect several domestic variables, occasion „economy-wide‟ effects and are a politically sensitive ingredient for parliamentarians and policy makers.

This is a quantitative and analytical research. Data analysis is done mainly by statistical and econometrics methods (deductive process) to find out correlation between the dependent and independent variables, also empirical relationship of the variables based on the objective and hypotheses followed with Granger‟s causality tests.

4.2. Research Design: Research design is a blue print of the study conducted, which includes steps of data collection, sample selection, process of data and finally interpretation of the data. The period of study is important in collecting the secondary data.

4.3. Sources of Data: Secondary data sources have been used to collect information about the Indian crude basket prices, whole sale price index, Inflation rate and GDP growth. Information collected from Secondary data sources include Central Statistical Organization (CSO) data of Indian Economy, RBI reports, Indian Economic survey reports, Petroleum Planning and Analysis Cell (PPAC) data, reports and websites.

For deriving relationship between crude oil price and inflation (WPI) for our log- log natural base regression model, WPI and Crude oil price data pertains from 2000 to 2009 have been used. Data on WPI has been taken from CSO data and the crude oil prices have been taken from Petroleum Planning and Analysis Cell (PPAC) data. Gross Domestic Product (GDP) growth rate (base year 2004-05) has been used from Reserve Bank of India data source and publications. Data for inflation rate has been used from trading economics statistics.

4.4. Sample Size and Justification The fundamental purpose of study is to examine whether crude oil price affect inflation, if so, whether rate of change in crude oil price affect inflation rate and GDP growth and what is the extent of impact. And finally it relates to the issue of causality test. Our study period is from financial year 2000 -2009 for the hypothesis 1, data have been collected on monthly basis for both crude oil price and whole sale price index (base year 1993-94=100) with sample Size 124. This period is important because so many events have taken place around the world like soaring international crude oil price from $24 ( Jan-2000) and touched to $147.27 per barrel( 11thAug 2008). Disruption caused by Hurricane Ivan in 2004, US government had announced on September 24, 2004 that it was prepared to lend some stocks from Strategic Petroleum Reserve (SPR). Traders felt that the supply was too small and hence the price would tend to rise. As a result, the Crude basket price of India went up to $39.21 in 2004 from previous year price of $27.97, followed with bad monsoon in 2004. There was devastating hurricane Katrina in 2005 in the history of US and thereafter hurricane Rita was the fourth- most intense Atlantic hurricane ever recorded and the most intense tropical cyclone ever observed in the Gulf of Mexico. Rita caused $12 billion in damage on the U.S. Gulf Coast in September 2005, followed by OPEC supply shock, Subprime crisis and bankruptcy of Lehman brothers, fear of terrorist attack on oil installations in various oil producing countries had added a premium to oil prices etc. and most important event in Indian context is the second phase of reform and the dismantling of the Administered Price Mechanism (hence forth referred to as APM) from 1st April 2002, Oil companies have given the freedom to buy crude oil and sell their products at market determined prices.

Quarterly data were also collected for crude oil price change of India basket, inflation and GDP growth base year ( 2004-05) of new series from 2005-06 to 2009-10 for carrying out the study with sample size 20.

Further, Yearly data have collected from 1992-93 to 2008-09 (base year 1993- 94) to examine whether a rise in the price of energy relative to output leads to improve or decline in productivity of existing flow of capital and labor for the study with sample size 17.

4.5. Econometrics Modeling for the Hypotheses:

The principal statistical tools considered for data analysis are using the Karl Pearson’s Correlation Co-efficient, followed with econometrics modeling of regression and causation. Correlation means a statistical relationship between sets of variables none of which has been experimentally manipulated i.e. (crude oil price and WPI), (Inflation and GDP growth).Therefore, Correlation means a relationship between un-manipulated variables. It measures the strength of linear association between two variables. Karl Pearson’s Correlation Co-efficient is used to study correlation between two variables crude oil price and WPI, also Inflation and GDP growth. Often in practice, correlation is followed by regression. The tacit assumption being, if we have established that two variables are linearly or log linearly related then we may predict one based upon the knowledge of other. The purpose of regression is also to study the model relationship between variables, describing the relationship between the explanatory and response variable and has been addressed using the modeling framework and followed by Granger‟s causality tests.

Model-1

In an attempt to determine, the influence of crude oil price on the inflation of Indian economy. The following time series regression equation was fitted.

Yt= a + bX + et ------(1) Where

Yt denotes the WPI (base year 1993- 94) „a‟ denotes constant quantity, i.e. the intercept of the line on Y- axis.

„b‟ denotes the co-efficient of X. „X‟ denotes the crude oil price. (monthly Indian basket price).

„et‟ is residual term of the model. The data used for the variables were from April‟2000 to July‟2009.

For deriving the elasticity co-efficient, the double log regression model was used. The above equation was converted into natural log – linear form. One attractive feature of double log model or log – log model is that the slope co-efficient “b” measure elasticity of Y with respect to X, that is percent change of Y for a given (small) percent change in X. Thus Y represents quantity of WPI increased and X its unit price; „b‟ measures the elasticity (Gujarati, 1995). The double log regression model was estimated using excel software package.

Model-2

In an another attempt, to carry out the quantification of expected influence of inflation on GDP growth, we have the econometric model which is depicted as follows-

YGDP = a1 + b1 XInflation+ et ------(2) Where,

YGDP represents the GDP growth, XInflation represent the CPI Inflation rate, „a1‟ represent the intercept and „et‟ denotes the residual term. The data used for the variables are quarterly data for both Inflation rate and GDP growth with base year (2004-05). The equations (1) and (2) are converted into natural log-linear form or double log regression model for deriving the elasticity co-efficient of the dependent variables. Here the independent variable is crude oil price in equation (1) and WPI is the dependent variable and in equation (2) Inflation is the independent variable and GDP growth is the dependent variable respectively.

Model-3

In a separate attempt, all the variables are taken systematically i.e. GDP growth, Inflation rate and Rate of change of crude price, all variables are in percent, data are collected on quarterly basis with base year 2004-05 for prediction of GDP growth by linear regression to study the hypotheses 1 and 2 by linear model. This is a Multivariable Linear Regression Model - or – Three Variable Model.

YGDP = β1 + β2Xinfla. + β3Xrate of change of crude oil price. On a priori reasoning let us assume that the GDP growth is dependent on inflation rate and rate of change in crude oil price. The linear regression model is estimated using excel data analysis software package. The output residuals are analyzed, if there is the existence of auto correlation, also DW statistics and Sign test followed by Auto regression and stationary.

Model-4.

Granger (1969) proposed a time – series data based approach in order to determine causality. In the Granger-sense x is a cause of y if it is useful in forecasting y. In this framework “useful” means that x is able to increase the accuracy of prediction of y with respect to a forecast, considering only past values of y.

The Granger causality Test: The Granger causality test assumes that the information relevant to the prediction of the respective variables, GDP growth and inflation rate, inflation rate and rate of change in crude oil price are contained solely in the time series data on these variables. The test involves estimating the following pair of regressions.

n n (i) Yt (infla). = Σ i=1αi X t-i (rate of change in crude oil price) + Σ j=1 βjYt-j(infla) + u1t

n (ii) X t (rate of change in crude price). = Σ i=1λiX t-i.(rate of change in crude oil price) + n Σ j=1 δjYt-j(infla) + u2t‟ Similarly,

n n (i) Yt (GDP). = Σ i=1αi X t-i ( infla) + Σ j=1 βjYt-j(GDP) + u1t

n n (ii) X t (infla). = Σ i=1λiX t-i.(infla) + Σ j=1 δjYt-j(GDP) + u2t‟

Where, disturbance terms u1t, u2t are uncorrelated.

Based on the estimated OLS coefficients for the two sets of equation different hypotheses about the relationship between rate of change in crude oil price and inflation also the relationship between GDP growth rate and inflation can be formulated.

Model-5. The “Cobb-Douglas” production function is applied for estimating the output of Indian industries. The real output of industries depend upon the capital and labour as well as energy resources. The “Cobb-Douglas” production function may be written as y= A ert ha kb Ec ------equation (1)

Where y = is output, h = labor measured in man-days k = Capital input or capital employed in the business,

E = flow of energy.

A = a scaling factor; t = year; r = is the trend rate of growth of output due to technological change;

a,b,c = are the output elasticities of respective inputs.

Now, if enterprises maximize economic profits, they employ energy at a rate where the value of additional product obtained from employing more energy equals its price. The demand for energy from equation above can be written as

-1 E = c.Y. ( pe / pd ) ------equation (2)

Where, pe is the price of energy and pd is the price of output of the business enterprise. The (pe / pd) is the relative price of energy, the relative price of energy measured by the ratio of whole sale price index of fuel, related products like power, light and lubricants to the wholesale price index and expressed in percent with respect to base year.

On simplification of the equations (1) and (2) with energy demand, the model reduced to ln(y/k) = α + β ln(h/k) + γ ln(pe/pd) +δ.t ------equation(3) Where, α = (1/1-c)ln A*, and A*=A.(c)c ; which is the intercept of the regression equation, β = a/(1-c) ; γ = (-c/1-c); δ = (r/1-c) are the regression coefficients. The equation (3) is the reduced form of productivity model that is applied for Indian industries in relation to rise in energy price.

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Chapter-5 Global Oil Scenario

Crude oil is not distributed uniformly around the globe. Some regions and countries are well endowed, while others are not. Most of the proven reserves of conventional Oil are to be found in the Middle East Countries, namely, Iran, Iraq, Kuwait, Saudi Arabia and the United Arab Emirates (UAE). Similarly, conventional gas is located primarily in Russia and other Former Soviet Union (FSU) countries, Iran, Qatar and Saudi Arabia. Since these reserves are often not in the same regions as the markets they serve, considerations of security and diversity of supply are among the important factors to be placed in the balance in decisions over squeezing more crude oil from deposits in other regions closer to home or over developing non-conventional crude oil.

Table 5.0. Distribution of World proved Oil reserves

Oil: Proved reserves at end 2011

Thousand Thousand million million Share R/P tonnes barrels of total ratio US 3.7 30.9 1.9% 10.8 Canada 28.2 175.2 10.6% * Mexico 1.6 11.4 0.7% 10.6 Total North America 33.5 217.5 13.2% 41.7

Argentina 0.3 2.5 0.2% 11.4 Brazil 2.2 15.1 0.9% 18.8 Colombia 0.3 2.0 0.1% 5.9 Ecuador 0.9 6.2 0.4% 33.2 Peru 0.2 1.2 0.1% 22.2 Trinidad & Tobago 0.1 0.8 0.1% 16.7 Venezuela 46.3 296.5 17.9% * Other S. & Cent. America 0.2 1.1 0.1% 22.1 Total S. & Cent. America 50.5 325.4 19.7% *

Azerbaijan 1.0 7.0 0.4% 20.6 Denmark 0.1 0.8 w 10.0 Italy 0.2 1.4 0.1% 34.3 Kazakhstan 3.9 30.0 1.8% 44.7 Norway 0.8 6.9 0.4% 9.2 Romania 0.1 0.6 w 18.7 Russian Federation 12.1 88.2 5.3% 23.5 Turkmenistan 0.1 0.6 w 7.6 United Kingdom 0.4 2.8 0.2% 7.0 Uzbekistan 0.1 0.6 w 18.9 Other Europe & Eurasia 0.3 2.2 0.1% 15.2 Total Europe & Eurasia 19.0 141.1 8.5% 22.3

Iran 20.8 151.2 9.1% 95.8 Iraq 19.3 143.1 8.7% * Kuwait 14.0 101.5 6.1% 97.0 Oman 0.7 5.5 0.3% 16.9 Qatar 3.2 24.7 1.5% 39.3 Saudi Arabia 36.5 265.4 16.1% 65.2 Syria 0.3 2.5 0.2% 20.6 United Arab Emirates 13.0 97.8 5.9% 80.7 Yemen 0.3 2.7 0.2% 32.0 Other Middle East 0.1 0.7 w 37.1 Total Middle East 108.2 795.0 48.1% 78.7

Algeria 1.5 12.2 0.7% 19.3 Angola 1.8 13.5 0.8% 21.2 Chad 0.2 1.5 0.1% 36.1 Rep. of Congo (Brazzaville) 0.3 1.9 0.1% 18.0 Egypt 0.6 4.3 0.3% 16.0 Equatorial Guinea 0.2 1.7 0.1% 18.5 Gabon 0.5 3.7 0.2% 41.2 Libya 6.1 47.1 2.9% * Nigeria 5.0 37.2 2.3% 41.5 Sudan 0.9 6.7 0.4% 40.5 Tunisia 0.1 0.4 w 15.0 Other Africa 0.3 2.2 0.1% 27.0 Total Africa 17.6 132.4 8.0% 41.2

Australia 0.4 3.9 0.2% 21.9 Brunei 0.1 1.1 0.1% 18.2

China 2.0 14.7 0.9% 9.9 India 0.8 5.7 0.3% 18.2 Indonesia 0.6 4.0 0.2% 11.8 Malaysia 0.8 5.9 0.4% 28.0 Thailand 0.1 0.4 w 3.5 Vietnam 0.6 4.4 0.3% 36.7 Other Asia Pacific 0.1 1.1 0.1% 10.4 Total Asia Pacific 5.5 41.3 2.5% 14.0

Total world 234.3 1652.6 100% 54.2 of which: OECD 35.7 234.7 14.2% 34.7

Non-OECD 198.6 1417.9 85.8% 59.7

OPEC 168.4 1196.3 72.4% 91.5

Non-OPEC £ 48.7 329.4 19.9% 26.3

European Union 0.9 6.7 0.4% 10.8

Former Soviet Union 17.2 126.9 7.7% 25.8

Canadian oil sands: Total 27.5 169.2 of which: Under active development 4.2 25.9

Venezuela: Orinoco Belt 35.3 220.0

Source: BP Statistical Review of World Energy June-2012.

5.1. Classification of Crudes

Crude oil differs in two important respects, the quality of crude and the location of the production. The exact composition of the mixture will determine the mix of the products that can be obtained from crude oil by refining and the case at which it is refined. Hence, the crude oil, which yields a large proportion of more valuable products and which can be treated by a large number of the world‟s refineries, will command a premium over those which produce a larger proportion of lower value products or which can be processed by only a limited number of refineries. Similarly, on the aspects of location of production, Oil produced close to major markets for refining will require less transportation and therefore will be more attractive and command a premium over oil produced further from the market and which has to incur lager transportation costs to get to the market (World Bank, 2005). Historically analysts have focused on two key qualities of crude oil, namely, the API gravity and sulphur content to explain inter crude price differentials.

The petroleum industry generally classifies crude oil by the geographic location it is produced in (e.g. West Texas Intermediate, Brent or Oman), its API gravity (an oil industry measure of density), and its sulfur content. Crude oil may be considered light if it has low density or heavy if it has high density; and it may be referred to as sweet if it contains relatively little sulfur or sour if it contains substantial amounts of sulfur.

The geographic location is important because it affects transportation costs to the refinery. Light crude oil is more desirable than heavy oil since it produces a higher yield of petrol, while sweet oil commands a higher price than sour oil because it has fewer environmental problems and requires less refining to meet sulfur standards imposed on fuels in consuming countries. Each crude oil has unique molecular characteristics which are understood by the use of crude oil assay analysis in petroleum laboratories.

The type of crude oil that are traded on the international market have steadily increased over the past few years, partly as a response to the desire to diversify sources of supply and partly because increasing global demand has encouraged production in less well-known oil producing areas.

According to The International Crude Oil Market Handbook, 2004, published by the Energy Intelligence Group, there are about 161 different internationally traded crude oils. They vary in terms of characteristics, quality, and market penetration. Two crude oils which are either traded themselves or whose prices are reflected in other types of crude oil include West Texas Intermediate and Brent. Comparing these two crude oils with EIA‟s Imported Refiner Acquisition Cost (IRAC), the OPEC Basket, and NYMEX futures is important to understand the differences among the various types of crude oil that are often referred to in the press and by analysts. Generally, differences in the prices of these various crude oils are related to quality differences, but other factors can also influence the price relationships between each other.

West Texas Intermediate (WTI): WTI crude oil is of very high quality and is excellent for refining a larger portion of gasoline. Its API gravity is 39.6 degrees (making it a “light” crude oil), and it contains only about 0.24 percent of sulfur (making a “sweet” crude oil). This combination of characteristics, combined with its location, makes it an ideal crude oil to be refined in the United States, the largest gasoline consuming country in the world. Most WTI crude oil gets refined in the Midwest region of the country, with some more refined within the Gulf Coast region. Although the production of WTI crude oil is on the decline, it still is the major benchmark of crude oil in the Americas. WTI is generally priced at about a $5 to $6 per-barrel premium to the OPEC Basket price and about $1 to $2 per-barrel premium to Brent, although on a daily basis the pricing relationships between these can vary greatly.

Brent: Brent Blend is actually a combination of crude oil from 15 different oil fields in the Brent and Ninian systems located in the North Sea. Its API gravity is 38.3

degrees (making it a “light” crude oil, but not quite as “light” as WTI), while it contains about 0.37 percent of sulfur (making it a “sweet” crude oil, but again slightly less “sweet” than WTI). Brent blend is ideal for making gasoline and middle distillates, both of which are consumed in large quantities in Northwest Europe, where Brent blend crude oil is typically refined. However, if the arbitrage between Brent and other crude oils, including WTI, is favorable for export, Brent has been known to be refined in the United States (typically the East Coast or the Gulf Coast) or the Mediterranean region. Brent blend, like WTI, production is also on the decline, but it remains the major benchmark for other crude oils in Europe or Africa. For example, prices for other crude oils in these two continents are often priced as a differential to Brent, i.e., Brent minus $0.50. Brent blend is generally priced at about a $4 per-barrel premium to the OPEC Basket price or about a $1 to $2 per-barrel discount to WTI; although on a daily basis the pricing relationships can vary greatly.

NYMEX Futures:- The NYMEX futures price for crude oil, which is reported in almost every major newspaper in the United States, represents (on a per-barrel basis) the market-determined value of a futures contract to either buy or sell 1,000 barrels of WTI or some other light, sweet crude oil at a specified time. Relatively few NYMEX crude oil contracts are actually executed for physical delivery. The NYMEX market, however, provides important price information to buyers and sellers of crude oil in the United States (and around the world), making WTI the benchmark for many different crude oils, especially in the Americas. Typically, the NYMEX futures price tracks within pennies of the WTI spot price, although since the NYMEX futures contract for a given month expires 3 days before WTI spot trading for the same month ceases, there may be a few days in which the difference between the NYMEX futures price and the WTI spot price widens noticeably.

Many players are involved in the Oil and Gas production chain, from the owners of the subsurface resources to financing organizations and on to operators, drillers, equipment manufacturers, facility constructors, service providers and

engineering companies. The producing companies are generally classified into the following three main groups: the international majors, the independents and the major resources holders. The international majors are one of the largest integrated companies in the world, the independents are the ones that have a presence in just one segment of the industry, say for example, only in E&P segment or only marketing and finally major resource owners are the one with large reserves of crude oil.

5.2. Structure of the Industry and Global Oil Production

The oil industry is commonly viewed by the public as a monolithic entity. The global petroleum industry is made up of many different actors engaged in different segments of the business. Crude oil exploration & production (henceforth referred to as E&P), gathering (generally called upstream sector), refining or manufacturing of intermediate and final products such as petrol, diesel and ATF, chemical feedstock, lubricant, and waxes (generally called downstream sector), refined product distribution and storage facilities such as pipelines and terminals, marketing and retail operations such as petrol stations, among others, constitute the functional characteristics of the global petroleum industry. The petroleum industry (Chazeau and Kahn, 1959) is what it is very largely because of the peculiarities of its raw material. Petroleum is a fluid, both as captured from the hidden recesses of the earth and as it passes through processing into final uses. It is concealed in the earth and all advances in modern scientific techniques have been incapable of eliminating the high element of gamble in its quest. It is a raw material of almost infinite potentialities some of will be lost forever if care is not taken, some can be secured simply while others can be tapped only with costly special equipment. And finally oil is an exhaustible resource, the total quantity of which is not clearly known.

The global proven oil reserve was estimated to 1652.6 billion barrels by the end of 2011 as per BP. Almost 48.1% of the proven oil reserves are in Middle East. Saudi Arabia has the second largest share of the reserve with 16.1%, Whereas

Venezuela ranks first in terms share of reserve with 17.9% and S & Cent America‟s proven reserve of 19.7%.The oil industry is not a scientific pursuit but a commercial venture. It is largely profit oriented and hence is carried out by individuals, companies or countries for their own needs and for commerce. The vagaries of the industry are because of several factors which are beyond the control of the industry, even though some powerful cartels and syndicates can influence the trend of production or prices from time to time. The pattern of world oil production is given in table:-5.2.

Table 5.2. Global Oil Production

Change Oil: 2011 2011 Production * over share Million tonnes 2008 2009 2010 2011 2010 of total

US 304.9 328.6 339.9 352.3 3.6% 8.8% Canada 155.9 156.1 164.4 172.6 5.0% 4.3% Mexico 157.6 147.4 146.3 145.1 -0.8% 3.6% Total North America 618.5 632.1 650.6 670.0 3.0% 16.8%

Argentina 34.1 33.8 32.5 30.3 -7.0% 0.8% Brazil 99.2 106.0 111.7 114.6 2.5% 2.9% Colombia 32.0 35.8 41.9 48.7 16.3% 1.2% Ecuador 27.4 26.3 26.3 27.1 2.8% 0.7% Peru 5.5 6.6 7.2 7.0 -2.8% 0.2% Trinidad & Tobago 6.6 6.6 6.3 5.9 -6.5% 0.1% Venezuela 154.1 149.9 142.5 139.6 -2.0% 3.5% Other S. &Cent. America 7.0 6.7 6.6 6.7 1.4% 0.2% Total S. & Cent. America 366.0 371.9 375.2 379.9 1.3% 9.5%

Azerbaijan 44.7 50.6 50.8 45.6 -10.3% 1.1% Denmark 14.0 12.9 12.2 10.9 -10.1% 0.3% Italy 5.2 4.6 5.1 5.3 3.9% 0.1% Kazakhstan 72.0 78.2 81.6 82.4 0.9% 2.1% Norway 114.2 108.8 98.6 93.4 -5.2% 2.3% Romania 4.7 4.5 4.3 4.2 -1.5% 0.1% Russian Federation 488.5 494.2 505.1 511.4 1.2% 12.8%

Turkmenistan 10.3 10.4 10.7 10.7 - 0.3% United Kingdom 71.7 68.2 63.0 52.0 -17.4% 1.3% Uzbekistan 4.8 4.5 3.6 3.6 -1.8% 0.1% Other Europe & Eurasia 20.6 19.9 19.2 19.2 0.3% 0.5% Total Europe & Eurasia 850.8 856.8 854.2 838.8 -1.8% 21.0%

Iran 213.0 204.0 207.1 205.8 -0.6% 5.2% Iraq 119.5 120.0 121.4 136.9 12.8% 3.4% Kuwait 135.8 121.0 122.7 140.0 14.1% 3.5% Oman 35.9 38.7 41.0 42.1 2.8% 1.1% Qatar 60.8 57.9 65.7 71.1 8.2% 1.8% Saudi Arabia 513.5 462.7 466.6 525.8 12.7% 13.2% Syria 19.8 19.9 19.1 16.5 -13.7% 0.4% United Arab Emirates 142.9 126.3 131.4 150.1 14.2% 3.8% Yemen 14.9 14.4 14.2 10.8 -24.0% 0.3% Other Middle East 1.5 1.7 1.7 2.2 32.0% 0.1% Total Middle East 1257.6 1166.6 1190.9 1301.4 9.3% 32.6%

Algeria 85.6 77.8 75.5 74.3 -1.6% 1.9% Angola 93.5 89.1 92.0 85.2 -7.3% 2.1% Chad 6.7 6.2 6.4 6.0 -6.7% 0.1% Rep. of Congo (Brazzaville) 12.2 14.2 15.1 15.2 1.0% 0.4% Egypt 34.6 35.3 35.0 35.2 0.3% 0.9% Equatorial Guinea 17.2 15.2 13.6 12.5 -8.1% 0.3% Gabon 11.8 11.5 12.5 12.2 -2.0% 0.3% Libya 85.3 77.1 77.4 22.4 -71.0% 0.6% Nigeria 105.3 101.5 117.2 117.4 0.2% 2.9% Sudan 23.7 23.4 22.9 22.3 -2.6% 0.6% Tunisia 4.2 4.0 3.8 3.7 -2.5% 0.1% Other Africa 8.1 7.7 7.1 10.9 52.7% 0.3% Total Africa 488.3 463.0 478.5 417.4 -12.8% 10.4%

Australia 24.4 22.6 24.6 21.0 -14.5% 0.5% Brunei 8.5 8.2 8.4 8.1 -3.8% 0.2% China 190.4 189.5 203.0 203.6 0.3% 5.1% India 36.1 35.4 38.9 40.4 3.9% 1.0% Indonesia 49.0 47.9 48.3 45.6 -5.6% 1.1% Malaysia 32.1 30.6 29.8 26.6 -10.9% 0.7% Thailand 13.3 13.7 13.8 13.9 0.8% 0.3% Vietnam 15.4 16.9 15.5 15.9 2.1% 0.4%

Other Asia Pacific 14.7 14.2 13.6 13.0 -5.1% 0.3% Total Asia Pacific 383.8 379.0 396.1 388.1 -2.0% 9.7%

World Total 3965.0 3869.3 3945.4 3995.6 1.3% 100% of which: OECD 863.7 864.0 868.1 866.7 -0.2% 21.7% Non-OECD 3101.3 3005.3 3077.3 3128.9 1.7% 78.3%

OPEC 1736.6 1613.6 1645.9 1695.9 3.0% 42.4%

Non-OPEC £ 1601.3 1611.1 1641.3 1640.1 -0.1% 41.0%

European Union # 105.4 99.0 92.7 80.9 -12.7% 2.0%

Former Soviet Union 627.1 644.6 658.2 659.6 0.2% 16.5%

* Includes crude oil, shale oil, oil sands and NGLs (the liquid content of natural gas where this is recovered separately). Excludes liquid fuels from other sources such as biomass and coal derivatives. ^ Less than 0.05.

£ Excludes Former Soviet Union. # Excludes Estonia, Latvia and Lithuania prior to 1985 and Slovenia prior to 1991.

Source: BP Statistical Review of World Energy June-2012.

5.3. Global Oil Consumption

World crude oil consumption in the energy mix is the basic premises on which the demand estimates are made. The crude oil consumptions are driven by consumption of petroleum products; in turn crude oil consumption dictates the demand of crude oil in the mix. The crude oil consumption pattern is given in table-5.3 below.

Table 5.3 Global Oil Consumption

Oil: Consumption * Change 2011 2011 over share Million tonnes 2008 2009 2010 2011 2010 of total

US 875.8 833.2 849.9 833.6 -1.9% 20.5% Canada 102.5 97.1 102.7 103.1 0.4% 2.5% Mexico 91.6 88.5 88.5 89.7 1.3% 2.2% Total North America 1069.9 1018.7 1041.1 1026.4 -1.4% 25.3%

Argentina 24.7 23.7 25.9 28.1 8.2% 0.7% Brazil 107.9 108.0 118.0 120.7 2.3% 3.0% Chile 16.8 15.6 14.8 15.2 2.8% 0.4% Colombia 10.7 10.6 11.4 11.7 2.4% 0.3% Ecuador 8.7 8.9 10.3 10.5 2.6% 0.3% Peru 8.0 8.1 8.5 9.2 9.0% 0.2% Trinidad & Tobago 1.8 1.7 1.7 1.7 -3.5% w Venezuela 33.3 34.8 36.9 38.3 3.8% 0.9% Other S. & Cent. America 56.7 54.5 53.5 53.7 0.4% 1.3% Total S. & Cent. America 268.5 266.0 281.0 289.1 2.9% 7.1%

Austria 13.3 12.8 12.9 12.5 -3.6% 0.3% Azerbaijan 3.6 3.3 3.2 3.6 11.9% 0.1% Belarus 8.3 9.4 7.3 9.0 22.8% 0.2% Belgium & Luxembourg 36.8 32.2 33.5 33.7 0.7% 0.8% Bulgaria 4.6 4.2 3.8 3.5 -6.4% 0.1% Czech Republic 9.9 9.7 9.1 9.1 -0.5% 0.2% Denmark 9.5 8.5 8.4 8.3 -1.7% 0.2% Finland 10.5 9.9 10.4 10.5 0.9% 0.3% France 90.8 87.5 84.4 82.9 -1.7% 2.0% Germany 118.9 113.9 115.4 111.5 -3.3% 2.7% Greece 21.3 20.1 18.7 17.2 -7.9% 0.4% Hungary 7.5 7.1 6.7 6.5 -3.1% 0.2%

Republic of Ireland 9.0 8.0 7.6 6.8 -10.4% 0.2% Italy 80.4 75.1 73.1 71.1 -2.7% 1.8% Kazakhstan 11.1 9.0 9.5 10.2 7.6% 0.3% Lithuania 3.1 2.6 2.7 2.7 0.8% 0.1% Netherlands 51.1 49.4 49.9 50.1 0.3% 1.2% Norway 10.4 10.6 10.8 11.1 3.5% 0.3% Poland 25.3 25.3 26.7 26.3 -1.5% 0.6% Portugal 13.6 12.8 12.5 11.6 -7.3% 0.3% Romania 10.4 9.2 8.7 9.0 4.4% 0.2% Russian Federation 129.8 124.8 128.9 136.0 5.5% 3.4% Slovakia 3.9 3.7 3.9 3.7 -5.3% 0.1% Spain 78.0 73.6 72.1 69.5 -3.7% 1.7% Sweden 15.7 14.6 15.3 14.5 -5.3% 0.4% Switzerland 12.1 12.3 11.4 11.0 -3.0% 0.3% Turkey 31.9 31.6 30.2 32.0 5.8% 0.8% Turkmenistan 5.1 4.6 4.8 4.9 3.9% 0.1% Ukraine 14.9 13.4 13.0 12.9 -0.8% 0.3% United Kingdom 77.9 74.4 73.5 71.6 -2.6% 1.8% Uzbekistan 4.5 4.2 4.3 4.4 0.7% 0.1% Other Europe & Eurasia 32.3 30.5 30.4 30.3 -0.4% 0.7% Total Europe & Eurasia 955.5 908.5 903.1 898.2 -0.6% 22.1%

Iran 91.0 91.9 89.8 87.0 -3.1% 2.1% Israel 12.2 11.5 11.2 11.1 -0.8% 0.3% Kuwait 17.8 17.5 19.0 19.0 0.2% 0.5% Qatar 6.2 6.2 7.4 8.0 8.3% 0.2% Saudi Arabia 106.1 115.4 123.2 127.8 3.7% 3.1% United Arab Emirates 29.6 27.5 28.9 30.5 5.6% 0.8% Other Middle East 78.7 80.2 84.7 87.5 3.2% 2.2% Total Middle East 341.6 350.3 364.3 371.0 1.8% 9.1%

Algeria 14.0 14.9 14.8 15.6 5.3% 0.4% Egypt 32.6 34.4 36.3 33.7 -7.2% 0.8% South Africa 25.3 24.7 26.1 26.2 w 0.6% Other Africa 78.1 80.3 83.4 82.9 -0.6% 2.0% Total Africa 150.1 154.2 160.6 158.3 -1.4% 3.9%

Australia 42.5 42.2 43.4 45.9 5.7% 1.1% Bangladesh 4.6 4.8 4.9 5.0 2.2% 0.1% China 376.0 388.2 437.7 461.8 5.5% 11.4% China Hong Kong SAR 14.6 16.6 17.9 18.1 1.0% 0.4% India 144.1 153.7 156.2 162.3 3.9% 4.0% Indonesia 58.7 60.6 65.2 64.4 -1.1% 1.6% Japan 220.9 198.3 200.3 201.4 0.5% 5.0% Malaysia 27.1 26.5 26.7 26.9 0.7% 0.7% New Zealand 7.2 6.9 7.0 6.9 -1.5% 0.2%

Pakistan 19.3 20.6 20.5 20.4 -0.2% 0.5% Philippines 12.3 13.1 12.2 11.8 -3.6% 0.3% Singapore 52.0 56.1 60.5 62.5 3.3% 1.5% South Korea 103.1 103.7 106.0 106.0 -0.1% 2.6% Taiwan 45.1 44.3 46.3 42.8 -7.5% 1.1% Thailand 44.2 45.6 45.8 46.8 2.2% 1.2% Vietnam 14.1 14.1 15.1 16.5 8.9% 0.4% Other Asia Pacific 15.7 15.9 16.0 16.7 4.5% 0.4%

Total Asia Pacific 1201.6 1211.2 1281.7 1316.1 2.7% 32.4%

Total World 3987.3 3908.9 4031.9 4059.1 0.7% 100.0%

of which: OECD 2208.9 2097.8 2118.0 2092.0 -1.2% 51.5%

Non-OECD 1778.3 1811.1 1913.9 1967.0 2.8% 48.5%

European Union # 705.6 667.7 662.8 645.9 -2.6% 15.9%

Former Soviet Union 187.2 178.0 180.4 190.6 5.7% 4.7%

* Inland demand plus international aviation and marine bunkers and refinery fuel and loss. Consumption of fuel ethanol and biodiesel is also included.

^ Less than 0.05.

# Excludes Estonia, Latvia and Lithuania prior to 1985 and Slovenia prior to 1991.

Note: Differences between these world consumption figures and world production statistics are accounted for by stock changes, consumption of non- petroleum additives and substitute fuels, and unavoidable disparities in the definition, measurement or conversion of oil supply and demand data.

Source: BP Statistical Review of World Energy June-2012.

Saudi Arabia is the largest oil producer in the world (at the end 2011 as per BP). With almost one-sixth of world proven oil reserves, some of the lowest production costs, and an aggressive energy sector investment initiative, Saudi Arabia is likely to remain the world‟s largest net oil exporter. Russia is another major world oil producer, sometimes surpassing even Saudi Arabia production. Although the United States ranks third in terms of oil production, it only ranks eleventh in terms of proven oil reserves. Table 5.3(a) indicates the World Crude oil Import and Export to different countries.

Table 5.3(a). World Crude oil Import and Export data

Oil: Imports and exports 2011

Million Tonnes Thousand barrels daily Crude Product Crude Product Crude Product Crude Product Imports Imports Export Exports Imports Imports Export Exports s s US 445.0 114.8 1.0 122.1 8937 2400 21 2552 Canada 26.6 12.7 111.7 26.8 533 265 2243 561 Mexico - 32.7 67.5 6.2 - 684 1356 131 S. & Cent. 18.7 62.6 139.0 46.5 375 1308 2791 972 America Europe 464.2 132.2 12.9 86.4 9322 2764 259 1806 Former † 5.1 319.3 108.9 ‡ 107 6413 2276 Soviet Union Middle East 10.7 11.4 879.4 100.0 214 239 17660 2090 North Africa 21.0 20.6 72.3 22.9 423 430 1451 478 West Africa † 11.8 224.1 7.4 ‡ 246 4501 154 East & Southern 2.4 11.6 16.6 0.3 48 243 334 6 Africa Australasia 26.8 16.6 14.2 8.0 538 346 285 168 China 252.9 75.2 1.5 29.8 5080 1571 30 623 India 169.7 8.2 0.1 41.8 3407 171 1.5 873 Japan 177.3 44.5 † 13.9 3560 930 0.6 290 Singapore 55.1 97.6 0.7 87.1 1107 2040 14 1822 Other Asia 224.4 133.2 34.3 82.6 4505 2785 690 1727 Pacific Total World 1894.7 790.7 1894.7 790.7 38050 16530 38050 16530 Note: Bunkers are not included as exports. Intra-area movements (for example, between countries in Europe) are excluded

* Includes changes in the quantity of oil in transit, movements not otherwise shown, unidentified military use, etc. † Less than 0.05. ‡ Less than 0.5.

Source: BP Statistical Review of World Energy June-2012.

If the production continues at today‟s rate, many of the present top ranking producers such as U.S, Russia, Mexico, Norway, China and Brazil will have their oil field largely depleted, and so will have much smaller share in the oil market in less than 20 years. At that point of time, world will have to depend mostly on Middle East for oil.

The non-Middle East Countries‟ overall reserves-to-production ratios are much lower than the Middle East Countries (about 22 to 40 for non- Middle East and about 90 for the Middle East producers).

Apart from the above conventional oil reserves, an estimated 800 to 900 billion barrels of unconventional oil reserves comprising of oil sands (or tar sands) and heavy oil are located in Canada and Venezuela. The R/P ratio for unconventional oils is very large expected to be 300 by 2020.

***

5.4. Indian Scenario. 1. Pre Independence period (1866-1947): The exploration of hydrocarbon is commenced in 1866 when Mr.Goodenough of McKoillop Stewart Co. drilled a well near Jaypore in upper Assam and struck oil. Mr.Goodenough, however, failed to establish satisfactory production. By 1882 the Assam Railway and Trading company (ARTC), a company registered in London in 1881, with an objective to explore the rich natural resources of Upper Assam, acquired rights for exploration over about 30 sq. miles in the same area. Sub-surface oil exploration activities started in the dense jungles of Assam in North-East India. The first commercial discovery of crude oil in the country was, however, made in 1889 at Digboi. In 1893, rights were granted to the Assam Oil Syndicate which erected a small refinery at Margharita to refine the oil produced at Margharita. A new company known as Assam Oil Company (AOC) was formed in 1899 with a capital of £ 310,000 headquartered at Digboi to take over the petroleum interests, including the Makum and Digboi concessions and the rights from Assam Oil Syndicate. A 500 BPD refinery was set up in Digboi in 1901, supplanting the earlier refinery at Margharita.

In 1921, UK based Burmah Oil Company (BOC) which had a successful oil exploration record in Burma, bought all the shares from ARTC and was appointed commercial and technical managers of AOC. By 1931, crude oil production has gone up to about 250,000 tonnes per annum and exploration activities were spread all over the Assam-Arakan region. Meanwhile another field was discovered at Badarpur in the Surma Valley and because the discovering party lacked the capabilities to exploit the find, BOC provided technical know-how, financial backing and managerial support.

2. Post-Independence Period (1947-1960): After independence, the Government of India (GoI) realized the importance of oil and gas for rapid industrial development and its strategic role in defense. Consequently, while framing the industrial Policy Statement of 1948, the development of petroleum industry in the country was given top priority.

While BOC and AOC continued development of Digboi oil field and intensified exploration activities in the North-East region, the Indo-Stanvac Petroleum Project (a joint venture between GoI and Standard Vacuum Oil Company of USA) was engaged in exploration work in West Bengal. In the year 1953, the first oil discovery of independent India was made at Nahorkatiya near Digboi and then in Moran in 1956.

In 1955, GoI decided to develop the oil and natural gas resources in the various regions of the country as a part of development of the Public Sector. With this objective, and Oil and Natural Gas Directorate (ONGD) was set up towards the end of 1955, as a subordinate office under the then Ministry of Natural Resources and Scientific Research. The department was constituted with a nucleus of geoscientists from the Geological Survey of India (GSI).

In April 1956, the GoI adopted the Industrial Policy Resolution, which placed mineral oil industry among the schedule „A‟ industries, the future development of which as to be the sole and exclusive responsibility of state.

Soon, after the formation of ONGD, it became apparent that it would not be possible for the Directorate with its limited financial and administrative powers as subordinate office of the Government to function efficiently. So in August, 1956, the Directorate was raised to the status of a commission with enhanced powers, although it continued to be under the government. ONGC started it systematic geo-scientific surveys in areas considered prospective on the basis of global analogies. A thrust in exploration was concentrated during the early years in the Himalayan Foothills and adjoining Ganga plains, in the alluvial tracts of Gujarat, Upper Assam and Bengal Basin. Exploratory drilling was initiated in the Himalayan Foothills in 1957 with drilling of the first well Jawalamukhi-1 in Himachal Pradesh. The year also saw drilling activities being taken up for the first time in Cambay Basin which ultimately resulted in the discovery of oil and gas in 1958. Meanwhile, Oil India Private Ltd. was incorporated on February 18, 1959 for the purpose of development and production of the discovered prospects of Nahorkatiya and Moran and to increase the pace of exploration in the North-

East India. It was registered as a Rupee Company in which AOC/BOC owned two-thirds of the shares and the GoI, one-third. In October 1959, ONGC was converted into a statutory body by an act of the Indian Parliament, which enhanced powers of the commission further. The main functions of ONGC subject to the provision of the ACT, were „to plan, promote, organize and implement programmes for development of petroleum resources and the production and sale of petroleum and petroleum products produced by it, and to perform such other functions as the Central Government may, from time to time, assign to it‟.

3. Mixed Economy Period (1961-1991): On July 27th 1961, the Government of India and BOC transformed OIL into a Joint Venture Company (JVC) with equal partnership. ONGC‟s Geo-Scientific surveys and exploratory drilling activities were also spread out to UP (1962), Bihar (1963), Tamil Nadu (1964), Rajasthan (1964), J&K (1970),Kutch (1972), and Andhra Pradesh (1978). In spite of limited success in these areas, ONGC pursued its exploratory efforts and was successful in identifying hydrocarbons in Cauvery basin and Krishna Godavari basins in the mid 1980‟s. Offshore exploration was initiated in 1962 through experimental seismic surveys in the Gulf of Cambay. Detailed seismic surveys carried out in the western offshore in 1972-73 resulted in the identification of a large structure in Bombay Offshore which was taken up for drilling in 1974 leading to India‟s biggest commercial discovery, thereby establishing a new hydrocarbon province. Encouraged by the success at Bombay Offshore, exploratory efforts were expended systematically in the entire Western Offshore including Kerala Konkan basin and Eastern Offshore areas leading again to large discoveries in the Western Offshore (Bassein and Neelam) and substantial accumulations in the Eastern Offshore (Ravva, PY-3 etc.). ONGC went offshore in the early 1970‟s and discovered a giant oil field in the form of Bombay High, now known as Mumbai High. This discovery, along with subsequent discoveries of huge oil and gas fields in Western offshore changed the oil scenario of the country. Subsequently over 5 billion tonnes of hydrocarbons were discovered. The most important contribution of ONGC, however, is its self-reliance and

development of core competence in E&P activates at a globally competitive level. ONGC went offshore in the early 1970‟s and discovered a giant oil field in the form of Bombay High, now known as Mumbai High. This discovery, along with subsequent discoveries of huge oil and gas fields in Western offshore changed the oil scenario of the country. Subsequently over 5 billion tonnes of hydrocarbons were discovered. The most important contribution of ONGC, however, is its self-reliance and development of core competence in E&P activates at a globally competitive level. On October, 14th, 1961, OIL became a wholly-owned GoI enterprise by taking over BOC‟s 50 per cent equity and the management of Digbol oilfields changed hands from the erstwhile AOC to OIL. For the time PEL‟s outside the North-East, were granted to OIL in Offshore Orissa (Mahanadi) in 1978, in Mahanadi Onshore (1981), North-East Coast Offshore (1983), Rajasthan (1983), Saurastra Offshore (1989) and Ganga Valley areas in UP in 1990.

4. Economic Liberalization 1991: The liberalized economic policy, adopted by the Government of India in July, 1991 sought to deregulate and de-license the core sectors (including petroleum sector) with partial disinvestments of government equity in Public Sector Undertaking and other measures. Following this, ONGC was re-organized in February 1994 as a limited company under the companies Act.

5. Post Liberalization: Several committees were set up to examine various proposals for restructuring and devising strategies to meet the challenge of the new economic environment. Among the most prominent report was the Sundarajan Committee Report in February 1995 which favoured de-regulation of the petroleum industry at one stroke. However, the strategic Planning group on Restructuring of the Indian Oil Industry, the „R‟ Group, headed by the then Petroleum Secretary. Dr. Vijay Kelkar, felt the Switchover should be in a phased manner. Commercial hydrocarbon discoveries were reported by OIL during 1990- 91 in Assam and Rajasthan. During 1993 -1994 ONGC‟s production from western offshore reached a low of 15.37 MMT, prompting ONGC to enter into

Joint Ventures for developing Ravva, Mid & South Tapti, Mukta and Panna fields. The JV initiative was fulfilled in as much as it increased the production from these declining fields by 5 MMT in 1994-95; during the same period 5 important discoveries were made in the Bombay, Krishna – Godavari and Cauvery Basins. A committee was constituted in 1992 under the chairmanship of P.K. Kaul former cabinet Secretary, to examine the need for restructuring of ONGC. This Committee recommended setting up of a body, with the name and style of the Director General of Hydrocarbons (DGH), for discharging the regulatory functions of leasing and licensing, safety and environment as also development, conservation and reservoir management of Hydrocarbon resources. Accordingly, DGH was set up by a Government Resolution in April, 1993 through which certain advisory regulatory roles were entrusted but no development role was assigned.

OIL also went overseas and acquired a 20 per cent participating interest in the production sharing contract for the Block 4 in Oman through a farm in agreement with TOTAL – FINA of France. It also involved in the exploration service contract for the Farsi Block in Iran along with OVL and Indian Oil Corporation Limited.

In 1997 the GoI in order to accelerate pace of exploration efforts in the country approved the New Exploration Licensing Policy (NELP) by providing a number of attractive fiscal and contractual terms. The 9th rounds of NELP have been concluded, out of the 254 blocks awarded under NELP 1-9 rounds, 54 blocks have been relinquished till date and balance of 181 blocks are active.

5.4.1. Crude Oil and Natural Gas Production in India. The trend in production of crude oil and natural gas during the period 2003-04 to 2010-11 is in Table-5.4.1 and Figure-5.4.1. The crude oil production has remained in the range of 33 to 34 MMT during the period 2002-03 to 2009-10. However, during 2010-11 the production of crude oil increased from33.69 MMT during 2009-10 to 37.712 MMT due to production from Rajasthan oil fields. Natural gas production increased substantially from 31.389 BCM in 2002-03 to 52.222 BCM in 2010-11, with some major discoveries by Pvt. /JVC‟s in Krishna

Godawari deep water; there was an increase by 11.94% over the year 2009-10 in production of crude oil in 2010-11.

The Government of India launched the ninth bid round of New Exploration Licensing Policy (NELP-IX) and fourth round of Coal Bed Methane Policy (CBM- IV) during October, 2010 to enhance the country‟s energy security. In addition, overseas oil and gas production in 2011-12 is likely to be about 7 MMT and 2 BCM per annum respectively.

Table 5.4.1. Crude Oil and Natural Gas Production in India

Year Crude Oil % Natural % Production Growth Production Growth (MMT) (BCM)

2002-03 33.044 - 31.389 - 2003-04 33.373 1.0 31.962 1.83 2004-05 33.981 1.82 31.763 -0.62 2005-06 32.190 -5.27 32.202 1.38 2006-07 33.988 5.59 21.747 -1.41 2007-08 34.118 0.38 32.417 2.11 2008-09 33.508 -1.79 32.845 1.32 2009-10 33.691 0.55 47.496 44.6 2010-11 37.712 11.94 52.222 9.95

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

Figure -5.4.1. Percentage Growth in Crude Oil & Natural Gas Production

50 44.6

20 9.95 10 1.83 1.38 2.11 1.32 -0.62 -1.41 0 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 -10

% Growth of Crude Oil Production - % Growth of Natural Gas Production -

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

5.4.2. Refining Capacity & Production

There has been a considerable increase in refining capacity over the years as may be seen in Table-5.4.2. Domestic refining capacity has increased by over 2.48% to reach 183.386 Metric Million Tonne Per Annum (MMTPA) in 2010-11 as compared to 177.968 MMTPA in 2009-10. The refining capacity has touched in 2011-12 at 213.06 MMTPA. It is expected that refinery capacity by the end of 2012-13 would reach 215.06 MMTPA (including private refiners).

The Refinery production (crude throughput) was 206.154 MMT during 2010-11 which marks net increase of 83.15.% over that produced during 2002-03 (112.56 MMT) and increase of 6.94%over 2009-10 (192.768 MMT) as depicted in Table- 5.4.2.

Table-5.4.2. Refining Capacity & Production

Refining % Growth (Crude % Growth Capacity Refinery Throughput) @ Productio (MMTPA) Year (MMTPA) n 2002-03 114.67 112.559 2003-04 116.968 2.00 121.84 8.25 2004-05 127.368 8.89 127.416 4.58 2005-06 127.368 0.00 130.109 2.11 2006-07 132.468 4.00 146.551 12.64 2007-08 148.968 12.46 156.103 6.52 2008-09 148.968 0.00 160.772 2.99 2009-10 177.968 19.47 192.768* 19.9 2010-11 183.386 3.04 206.154* 6.94 @ = As on 1st April of the Year.

*Includes other inputs by RIL Refineries.

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

5.4.3. Production and Consumption of Petroleum Products There was an increase of 5.78% in production of petroleum products, including fractioners, during 2010-11 compared to the year 2009-10. The indigenous consumption of petroleum products increased by 2.88% during 2010-11 compared to the previous year. During the year 2010-11, net consumption of petroleum products was 141.785 MMT against total production of 192.532 MMT. It may be mentioned that the consumption data does not include data in respect of RIL SEZ Refinery as it is presumed that all products have been exported and not consumed domestically. Year-wise production and consumption of petroleum products during 2003-04 to 2011-12 are depicted in Table-5.4.3. and Figure- 5.4.3. below. It is evident from Table-1.2.8, that production and consumption of petroleum products are substantially higher than 2003-04.

Table-5.4.3.

Production and Consumption (indigenous sales) of Petroleum Products

Year Production % Growth Consumption of % Growth of of Petro Production Petro Product** Consumption Product * of Petro (MMT) of Petro (MMT) Product Product

2002-03 106.51 104.126 2003-04 115.783 8.71 107.751 3.48 2004-05 120.819 4.35 111.634 3.6 2005-06 121.935 0.92 113.213 1.41 2006-07 137.353 12.64 120.749 6.66 2007-08 146.99 7.02 128.946 6.79 2008-09 152.678 3.87 133.599 3.61 2009-10 182.012 19.21 137.808 3.15 2010-11 192.532 5.78 141.786 2.88 *= include LPG production from Natural Gas. **=excludes refinery fuels includes import also.

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

Figure-5.4.3:

Percentage Growth in Production & Consumption of Petroleum Products

25.00

20.00 19.21

15.00 12.64

10.00 8.71 7.02 5.78 4.35 6.66 6.79 5.00 3.87

3.48 3.6 3.61 0.92 3.15 2.88 0.00 1.41 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11

% Growth Production of Petro Product % Growth of Consumption of Petro Product

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

5.5. Oil Pricing To begin with, it is necessary to distinguish between pricing mechanisms and the underlying forces which determine prices, or, in other words, to distinguish between how prices are determined and what determines prices. The first is about the organization of trade, exchange and marketplaces, including access, and the ways prices are negotiated, communicated and made public. This does not necessarily give an insight into what influences decision-making by buyers and sellers, nor about the resulting market balance and price level.

The price mechanism for a commodity can lead to a transparent and liquid market (as for crude oil) without any pressure for lower prices. However, the underlying structure of oil and gas trade will have an influence on pricing mechanism: a prominent question is the role of long-term contracts compared to liquid markets. As oil and gas are special commodities, it is useful to look at the range of economic paradigms, as well as the historical development of oil and gas markets, in order to find ways to interpret the developments of oil and gas pricing.

Price signals are visible to both producers and consumers and both sides follow them with their decisions on production (output) and consumption to optimize their profit or overall benefit. This not only presumes clear and visible signals but also the capacity and the willingness to transform these signals into action. This is put into question once demand reaches a certain inelasticity because consumers may have little choice for a given time horizon and it may then depend on the incentive on the producers side to compete with each other for a larger share in the market. Those incentives may be distorted in the case of high enough market concentration, but also as a function of risk perception or simply by the investment time-lag needed to adjust the production level, or eventually by regulatory or technical bottlenecks.

Price is signal from the market. It represents scarcity of the commodity in the market. When the price rises, demand is reduced to a level where supply

matches demand (and vice versa). It also indicates a foresight of supply and demand, as expectations are factored in both supply and demand curves.

Price is also a key signal for an efficient allocation of capital. A higher price relative to cost signals the need for new investment in production capacity, as the price signals a potential reward to investors. On the other hand, a low price discourages investment. It is worth noting that the oil and gas sector competes for capital with investment opportunities in other sectors. Therefore, a certain level of returns is needed to attract capital.

Oil and gas have many characteristic that distinguishes them from other commodities, such as:

i. The high uncertainty linked to resource development and the high specificity of investment all along the energy chain from production to consumption,

ii. The character of a natural resource,

iii. The finiteness of the resource exacerbated by the high concentration of reserves in about a dozen countries,

iv. The involvement of two decision makers on the production side: producing company and resource owner.

v. The often highly inelastic demand for energy and its interaction with concentration and capacity restriction on the supply side, and

vi. Market imperfections such as unavoidable externalities.

By market imperfection, it is meant that when market mechanisms alone do not allocate resources correctly, the occurrence is called „market imperfection‟ or „market failure‟. The word „failure‟ does not mean an economic collapse or a

breakdown in the market. The term is normally applied to situations where the inefficiency is particularly dramatic. Market imperfections typically occur due to:

(i) Imperfect competition arising from monopoly,

(ii) Price distortion caused by lack of information,

(iii) The existence of externalities (e.g., environment ,climate, health ) and

(iv) Non-rivalry and non-excludability of public goods (e.g., national security, fire-fighting) in which non- market institutions are more efficient than private companies.

Energy markets are often characterized by

(i) Imperfect Competition (ii) The existence of externalities and (iii) The presence of public goods. Price distortion caused by lack of information is increasingly excluded by the development of liquid markets and transparency initiatives by governments. By the laws of physics, energy cannot be recycled (contrary to mineral resources)and the burning of fossil fuels inevitably produces CO2, with negative externalities as a greenhouse gas. Security of supply of energy-especially for electricity but also for oil and gas –has the character of a public good. Internalizing of externalities is addressed by Pigou taxes i.e. pollution tax (which try to assess the negative externalities and charge them as a tax on the player causing it).

5.5.1. Historical Aspects

Current emerge energy markets development of their resource base is a fundamental feature. Oil production has a limited life span; its production over

time can be illustrated by the so-called Hubbert‟s curve, a bell-shaped distribution, initially proposed by M. King Hubbert in 1949 in relation to US oil production based on statistical methods.

Many seek to utilize Hubbert‟s curve in order to predict the end of the current oil era (peak oil debate / theory). But, as on a global basis the peak of the curve has moved up and to the right because exploration activities and new technologies have expanded the resource base (and proven reserves).

5.5.2. History of Oil Price

Until the beginning of the 1970‟s energy and Oil market development was described by the ascending branch with accelerated growth of Hubbert‟s curve. Production growth was based on the discovery of major new low-cost oil fields primarily in the Middle East. The international market was closed to any outsiders; first split between the Seven Sisters under the 1928 Achnacarry Agreement and by the end of the 1960‟s increasingly dominated by OPEC, especially after re-nationalization of their resources in the mid-1970‟s following the end of colonialism in the 1960‟s. However, the embargo in 1973 / 1974 and the oil price increases in 1973 /1974 and 1979 / 1980 triggered investment in oil outside of OPEC, the development of new technologies, oil substitution by other energies especially in power generation, more efficient energy use, and substitution of energy by other productive resources, firstly by capital. This finally led to the decrease of absolute volumes of world oil consumption in the early 1980‟s and to the oil price collapse in 1985 / 1986, to more competitive structures and finally to a liquid oil market.

The development of the oil market, its contractual structure and pricing mechanisms can be divided in four major time-periods from a historical perspective. Different forms of oligopolistic pricing dominated during the first three periods: prior to the 1970‟s, at the first two stages it was the oligopoly of international oil companies (with the strong back-up of their home states), at the third stage-it was the oligopoly of 13 major producer states (OPEC). It was only

after the oil price collapse in 1986 that pricing set by an oligopoly was substituted by exchange – based pricing.

5.5.3. The Seven Sisters (1928-1947)

Prior to the 1970‟s the vertical value chain for internationally traded oil was almost under the full control of the Seven Sisters. They received their oil mostly through long-term concession agreements with host (mostly developing) countries and exported it under long-term contracts (the trade arm of concession agreements) either to affiliates in their home countries (up to 70% of total oil export) or to independent non-integrated downstream companies. Transfer pricing dominated during this period. Posted prices (de facto the transfer prices of international oil companies) were established by the majors as a basis to calculate the royalties to be paid to host states and thus were understated since the international oil companies had their centers of profit in their respective home states. This helped to expand oil consumption, especially in competition with other energies, like coal, for electricity production. Competition happened in the end-user markets, but for crude oil itself a free market played only a very limited role (3-5% of world oil trade), used to fine-tune the volume balance of supply and demand, based on the posted prices set by the Seven Sisters.

The Achnacarry agreement of 1928 assigned to each company a specific quota of oil sales in the segments of the market outside the US. Its central element was the so-called „one-based pricing formula‟ known as „Gulf referring to Mexican Gulf which dominated the oil market until 1947. It increased the profitability of oil operations of the Seven Sisters by establishing a single price formula for all oil buyers outside the US, calculated as oil price FOB US Mexican Gulf coast, plus freight rates in force from this coast to the delivery point, independent of the origin of factual deliveries. According to the agreement each company was to physically deliver within its quota to markets outside the US, and usually the companies provide these deliveries from the nearest production area of that company. Under this system any buyer would pay the same price in the given location independent of the factual origin of the purchased oil; the savings on

freight for deliveries from areas closer to the buyer than the Mexican Gulf, as well as the difference between the posted price at the factual origin of the purchased oil and the price FOB Mexican Gulf, was extra profit for the International Oil Companies.

The Achnacarry agreement was not applicable within the domestic US market as it would have violated the US anti-trust law. But in accordance with the US Webb-Pomerene law of 1918, American companies were allowed to act abroad by means that would have been illegal under the anti-trust law in the domestic US market.

The Achnacarry agreement allowed oil majors to fix oil prices based on the high domestic US oil price level and thus provided extra profits due to the exploitation of the uniquely cheap oil reserves in the Middle East. In the domestic US market, a great number of small non-integrated American oil producing companies operated with high marginal costs. In order to keep up a high number of companies in the domestic market, the US government protect small producers by regulating domestic prices at the „marginal cost-plus‟ level, thus providing them with acceptable profitability. That is why the Achnacarry formula, based on Mexican Gulf FOB oil price, protected both the interests of American majors and of small and medium-sized American oil companies.

5.5.4. The Seven Sisters (1947-1971)

When, during World War II, the American and British Navies bunkered their ships from the local refinery in Abadan, in the Persian Gulf, they were to pay the price equal to the residual fuel oil (RFO) price FOB Mexican Gulf, plus fictive freight from the Mexican Gulf to Abadan. American and British administrative investigations after World War II forced the Seven Sisters to change the „one base‟ oil pricing formula. In 1947 the international oil companies accepted Persian Gulf as a second base for price calculations. As a modification of the initial Achnacarry agreement, the „two base‟ oil pricing formula was introduced, under which freight rates were calculated either from the Mexican Gulf or from

the Persian Gulf, but in all cases the oil price used for the calculation was the oil price FOB Mexican Gulf. Under this new formula the extra profits of the International Oil Companies were diminished by the deletion of virtual transportation costs, but the difference between the marginally low production costs in the Persian Gulf area and marginally high costs in the US (price FOB Mexican Gulf) remained. Through the transfer pricing mechanism of posted prices, the companies escaped taxation of their extra profits in the host states and transferred them to their profit centers in their home states. This formula is known as „two Gulfs plus Freight‟ (but should more accurately be labeled as „Mexican Gulf plus two Freights‟). That is why WTI was the marker crude during both of the two first pricing stages of oil market development.

5.5.5. OPEC set prices (1971 -1986)

In the 1970‟s control over domestic oil economies in the producer countries (upstream part of the energy value chain-resources, production, sales and selling prices) was acquired by the OPEC states. The upstream assets of the international oil companies in the major host (OPEC) countries were nationalized and formed the basis on which the new National Oil Companies (NOC‟s) were created. Almost all oil supplied to the world market at this time was no longer purchased on the basis of intra-or inter-company transactions (barter deals), but by commercial transactions between independent players at the official selling prices of the OPEC member-states. These prices began to play the role of world oil prices. These conditions triggered a disintegration of the previous structure as more companies entered oil trade operations downstream and upstream. While during the periods of the Seven Sisters , the only point of competition had been at the customers downstream, with OPEC dominance, competition also developed for crude oil supplies.

This stimulated the appearance of new contractual forms in the oil trade and an increased variety of trade operations. As the share of volumes traded under long-term contracts diminished, their prices began to be established on the basis of spot deals. By contrast, volumes traded on the spot market increased

significantly. The spot market began to balance supply and demand and began to be used as a reference point for price levels both for exporters and importers. It was during the first oil crisis of 1973-74 that the spot market first played its price-defining role as a reference point for OPEC to set official selling prices. Spot market volumes developed strongly during the period 1971 -1986: from 5- 8% of the international oil trade at the beginning of the 1970‟s and 10-15% in the middle of the 1970‟s – to not less than 40-50% in the mid-to-late 1980‟s.

After the introduction of OPEC official selling prices, oil pricing was converted to the „Persian Gulf plus freight‟ formula. The marker crude for official selling prices at this time was usually Light Arabian Crude FOB Ras-Tanura, geared (by regular updating by the OPEC states) to the development of spot market prices.

Sharp fluctuations in spot oil prices stimulated the introduction of risk management techniques into oil operations. Demand to standardize oil trade operations (as one of the risk-management instruments) was among the driving forces for introducing contracts for oil and petroleum products at the existing commodities exchanges (NYMEX) and for the establishment of specialized oil exchanges (IPE). Managers from financial markets became involved in the oil markets, introducing the techniques of financial markets and specialized oil derivatives (oil futures and options). By the end of the 1980‟s, the current complex contractual structure of the oil market was in place. It is now the oil exchange where world oil prices are determined, though all other contractual forms, determining oil prices at earlier stages, are still present, albeit without their former dominant role.

5.5.6. Development of World Oil Market Structure and Types of Transactions:-

The size, scope and complexity of global crude trade are unique among physical commodities. Currently more than 86.03 million barrels of oil are produced and consumed every day. Beyond the scale of trade in oil, the strategic importance

oil and the crucial role that it plays in the economy make it a commodity like no other.

The pricing mechanisms in the oil sector, particularly into its commodity-type pricing mechanism, which has developed since the official selling price system within long-term oil contracts established by OPEC came to an end in the mid – 1980‟s. Commodity pricing in the oil sector is well established and spot markets for oil have developed the full range of commodity pricing instruments. Nonetheless, long term oil contracts still play a significant role, albeit with different pricing mechanisms compared to previous periods.

The current spot markets have been developed since the early 1970‟s. at the beginning they were aimed at fine-turning oil demand and supply and covered not more that 3-5% of international oil trade. In the 1980‟s, rising oil production from non-OPEC areas went into the spot markets. Key benchmark grades, West Texas Intermediate (WTI), Brent and Dubai / Oman emerged and served as the reference for crude of similar qualities and locations. Previously the role was played by Arabian Light under OPEC‟s official selling price system.

Spot transactions are mainly conducted by telephone or computer network between two parties. It is an over-the-counter (OTC) market as opposed to an exchange. Spot markets do not necessarily have trading floors. The term „spot market‟ applies to all spot transactions concluded in an area where strong trading activities in one or more trading products take place.

The main spot markets or trading centers for crude oil are Rotterdam of Europe, Singapore for Asia and New York for the United States. Their benchmarks are: Brent, Dubai and WTI.

At the same time, futures markets have also developed in Western Countries. These arose from a desire on the part of oil companies to reduce risk in light of high price volatility. Developments in information technology, developments in financial theory and a political climate favoring markets over government administrative guidance led to the creation of financial derivative markets,

including futures and options. The New York Mercantile Exchange (NYMEX) and the International Petroleum Exchange (IPE) are two major financial markets for oil. World oil prices are led by these markets.

Long term contracts are still widely used. OPEC countries in the Middle East sell their crude exclusively to refiners through long term contracts, which usually have contract duration of one year with renewal clauses. The pricing formulas in the long-term contracts are linked to benchmark grades. There are no long term fixed price contracts, which existed between the two oil crises in the 1970‟s and prior to that time.

Oil prices were hit hard by the Asian financial crisis in 1997 and 1998. They fell to below $ 10 at the end of 1998. In March 1999, OPEC countries agreed to cut production, joined by Russia, Norway and Mexico. With the Asian Economies recovering from the financial crisis, prices increased during 1999. In 2003 and 2004 oil prices rose strongly in view of the war in Iraq and the fear of terrorist attacks on oil facilities in Middle East. This was also result of under investment in the international oil industry. Strong demand increases from the US and large developing countries, which were not followed by a similar expansion of supply, resulted in further increases in crude oil prices. That attracted speculators, who moved from financial and currency markets into commodity markets (oil) and contributed to the rise in prices. International Crude prices reached as high as $ 78 per barrel in summer 2006, although they fell from this peak later in 2006. Again, it went up to $ 147.27 per barrel in July- 2008 and later in fourth quarter period.

Looking into the oil market, increases in oil consumption are closely linked to economic growth. Where economies are growing, oil demand growth is taking place in China, India, the Middle East and the US. Global oil demand is expanding at around 1 MBD every year.

On the supply side, there is an ongoing debate called „peak oil theory‟. One school claims that oil production will soon peak and that the consequences for

the world economy will be severe. Others consider that the peak oil production will still be a moving target for some time, as new reserves become recoverable due to exploration and improvements in technology. The United States Geological Survey (USGS) considers that there are enough remaining petroleum reserves to continue current production rates for another 50 to 100 years. OPEC‟s 11 member countries produced 42.4% of the world‟s production in 2011 but hold 72.4% of oil reserves. OPEC ministers meet every three months to discuss production levels and take the stock of situation of supply demand balance sheet.

5.5.7. Supply side: Issue of Peak Oil

Global economy does not want to run out of fossil fuel any time soon. The economist and analyst are concerned with the upward trend in oil production that has been evident over the past century and will reach a peak of production and then decline. Peak production is a source of debate among the participants in energy market.

Peak oil is the point, when a given oil field reaches its maximum production, after that starts decline in production, no matter how many new wells are drilled. The ideas underlying peak oil were developed by a shell geologist, M King Hubbert (Hubbert, 1956). Back in 1956, Hubbert reviewed the production history of a number of oil fields in the US. He predicted that US oil production would peak in the 1970s, and he called the top within a few months. Since then, crude oil production has been declining in the US, despite the large discovery of oil made on North Slope of Alaska in the late 1970s.

Applying same methods to global production, proponents of the Hubbert predict that global production should peak in the next few years (Campbell, 2003 and Deffeyes, 2002 and 2005).Hubbert‟s curve shows that oil production rises and falls as a direct function of remaining oil reserves. In other words, production can increase until the cumulative production uses up half the total reserves in the field and then production begins to decline. What is critical in the analysis is the

half-way point (Campbell,2003). Once half of the oil is used up we have reached a point of no return and production will decline no matter how much new technology is applied or additional drilling occurs.

5.5.8. Comparative Study and Analysis of Global Oil Reserves:

According to the International Energy Agency (IEA), the world economies have extracted and consumed approximately 1 trillion barrels of crude oil over the last 100 years. A reserve is said to be attained Hubert‟s peak when the field has reached its maximum production and then begins to decline and this is the level of oil reserve. Current production uses about 31 billion barrels per year. Now, how much oil is left in the reservoir? Proponent like Kutasovic R. Paul‟s views are essential to understand the oil left in global oil reserves. If we have extracted half of all the oil that has ever existed, we are, by most definitions of the peak oil, at or past the peak. Obviously, a larger reserve base implies a later peak date than a smaller one. Following are the points borne in mind regarding oil reserves:

1. Amount of oil left to produce 2. Quality of remaining reserves 3. Geographic distribution of remaining reserves 4. Field by field analysis.

1. Amount of Oil

No geologist or analyst knows exactly the quantities of oil that exists beneath the earth or how much can be extracted. Instead, all the numbers and figures reported are essentially estimations based on probabilities. In fact, reserve definitions vary by country to country, making comparisons between them essentially useless. Reserves in a given oil field are classified in a number of categories. The news media nearly always uses the proven reserve figures and omits other categories. By definition, proven reserves are those that can be recovered with reasonable certainty using current technology and current prices. These are often classified as p-90 reserves since there is a 90% probability they will be extracted over the life of the field.

The oil field will have additional quantities of probable and possible reserves; these are recoverable with a probability of over 50% and under 50%, respectively, from the estimated total volume of oil-in-place in the field. The probable and possible reserves are undeveloped since they are unprofitable to produce at current prices and technology.

Finally, there are unconventional reserves, which include heavy oils, tar sands and oil shale. Processing these reserves is expensive and requires different production methods. While some consider recoverable reserves to be fixed by geology, in reality, their accessibility as energy source is more dictated by technology and oil price changes. In other words, economics is as important as geology in coming up with reserve estimates since a proven reserve is one that can be economically developed.

Field delineation and development involves release of wells by geologists and these wells are drilled by drilling team, followed by production testing. Once testing of well is done and successful, the oil is flowed to process platform in offshore field otherwise in onshore field it is flowed to group gathering station. The processed oil is sent through pipe lines to the refinery for refining the crude oil.

As technology improves and prices increase, probable and possible reserves are reclassified as proven. This process often leads to a situation where the level of proven reserves in an oil field trends upwards over time in spite of the ongoing extraction of oil from the field. This will occur as the rate of extraction is offset by the conversion of probable and possible reserves to the category of proven. In addition, proven, probable and possible reserves represent only a portion of oil in place in a given field since it is impossible to recover all the oil and gas. The recovery factor (reserves to oil in place) may change over time in response to improved technology and higher prices. Table 5.5.8 provides an estimate of ultimately recoverable reserves (a category that includes proven and probable reserves from discovered fields) as estimated by various sources.

Table 5.5.8. Estimates of Oil Reserves (Trillions of barrels)

Sources Ultimately Recoverable Reserves British Petroleum 1.6 Campbell 1.0 Exxon 3.2 International Energy Agency 1.3 Nashawi Kuwait University 1.2 Oil and Gas Journal 1.3 United States Geological Service 2.3

Sources : British Petroleum(2011), C. Campbell (2003), Exxon 2010 Annual Report, International Energy Agency (2008 and 2009), Nashawi (2010), Oil and Gas Journal (2010), and United States Geological Survey(2000).

Thus, the world supply of oil is not only determined by geology, but also by an interplay of economics, technology and most critically important in today‟s environment, geopolitics. Given the above, the concern is not that world will soon run out of oil in a direct sense. The consensus among most geologists is that world still have about 7-9 trillion barrels of oil-in-place left. The question, is how many of those barrels can be recovered and what will be the cost?

Most advocates of peak oil believe about 1 trillion barrels of oil are left. If true, that will put us at or beyond the peak since about 1 trillion barrels have been already produced and production must, therefore, decline. Other geologists estimate ultimately recoverable conventional reserves as high as 3 trillion barrels with another 2 trillion barrels of unconventional oil. Of course, the higher reserve figures yield a much later oil peak, with the USGS numbers suggesting a peak around 2037. A recent study (Nashawi, 2010) by researchers at Kuwait University estimated that the world could ultimately produce 2,140 billion barrels of oil, with 1,161 billion barrel remaining to be produced at 2005 end. This suggests a peaking of production is 2014.

Reviewing the other reserve estimates suggests that the claim that oil production has already peaked seems premature. If the more optimistic assessments hold up, we should have at least another decade or two of rising production, especially if production from unconventional reserves increases as expected. But, even assuming that the peak occurs as late as 2040, a crisis is in the making and preparation must soon begin for the difficult adjustment process of finding reasonable options and alternative energy sources.

2. Quality of Oil

Quality of oil reserves is also critical due to its impact on the cost of extracting and refining oil. The highest quality, light sweet crude, is easy to find and cheapest to produce and refine. But, most geologists, according to IEA and US Geological survey, believe that most of the high quality crude oil has already been discovered and its production in existing oil fields is set to decline. Replacing it will be one of several lower, heavier grades of crude (often containing sulfur) that are more expensive to extract and refine. Compounding the problem, it is getting more expensive to discover such new deposits worldwide. For example, recent discoveries of large quantities of crude oil offshore in Brazil and in the Gulf of Mexico involve extremely costly deep water drilling in waters over 2 miles deep. Furthermore, unconventional energy sources such as oil sands in Canada and Venezuela are expensive to produce and refine and have significant environmental costs. All this suggests that oil prices cannot help but trend upwards in the years ahead as cost of production rises.

3. Geographic Distribution

Finally, most of the world‟s proven reserves are found in OPEC region. The Middle East accounts for over 48.1% of world‟s reserves based on data of British Petroleum (June 2012). The rest of OPEC has 34% of reserves with Venezuela, Nigeria and Libya containing 17.9% and 2.3% and 2.9% respectively. Most of the OPEC reserves are found in countries with high geopolitical risks. Non OPEC reserves accounts for 19.9% of the world total with proven reserves in the US

estimated at 1.9% of total. Exploration, development and production costs are much higher in non-OPEC region. Most of the fields in the non-OPEC region are mature and in decline.

4. Field-by-Field Analysis

The rate of change in output from maturing oil fields is critical in assessing the point of peak production. The IEA (2008 and 2009) has compiled a database containing production profiles on the world‟s 798 largest oil fields. This database includes all 54 of the super-giant fields (proven reserves greater than 5 billion barrels) and 263 of the 320 giant fields (proven reserves greater than 500 million barrels). The bulk of global oil production comes from a small number of super- giant and giant fields. The IEA (2008 and 2009) shows that the 20 largest fields in the world produce over 19 million barrels per day (mbd) or about a quarter of the oil produced in 2008. In addition, the percentage of global production from super-giant and giant fields has grown as a share of total production and accounted for about 60% of global production in 2008 (IEA, 2008 and 2009) compared to around 56% in 1985.

The IEA (2008 and 2009) in an intensive field-by-field study found that 580 of the 798 largest oil fields are at peak or past peak in production. Output in 2008 at 16 of the 20 largest oil fields was below their historic peak. Most of the world‟s largest fields have been in operation for many years and few large discoveries have been made in recent years except for those in high cost deep offshore waters.

The average annual rate of decline in these 580 fields is 5.1%. This is equal to 3.6 mbd, based on 2008 levels of global production. The rate of decline can be slowed through the deployment of new secondary and enhanced recovery techniques, but this is extremely capital intensive and significantly increases the cost of producing a barrel of oil. The problem is that once production exceeds its peak, it is difficult to slow the rate of decline even if large investments are made. In fact, peak oil analysis suggests that the rate of decline will accelerate once

oilfields exceed peak production. A key implication of the analysis is that future supply must not only meet rising demand, but also offset the loss of capacity from existing fields as they mature. In fact, loss of capacity will have a more important impact on future supply needs than the increase in demand.

In summary, what the oil reserve data suggests is that we are not running out of oil per se, but that we are running out of high quality low cost oil and large-scale investment in future energy supply is needed to offset large declines in global production capacity.

5.5.8. (a). Demand Analysis (Changing Composition of Global Demand)

Perhaps, the most important development on the demand side of the oil market is the rising importance of emerging market economies. Tables 5.5.8(a) and 5.5.8(b) provide historic consumption data for 1980 to 2010 and projections out to 2030.

The composition of global oil demand is rapidly changing. Mature economies in the US, Europe and Japan still account for over half of global consumption, but their share are declining. The share of oil composition in advanced countries has declined from 62.2% in 1980 to 49.9% in 2010. What is happening is that most of the growth in the demand for oil is coming from emerging/developing countries. Due to a combination of rapid economic growth and an expanding manufacturing and transport sector, emerging economies are quickly cornering a larger pie of global oil consumption. Growth in manufacturing and vehicle ownership is the most important driver of oil demand in developing countries.

It is not surprising that the booming emerging economies have posted robust oil demand. This is especially true of China and India, with the GDPs growth at an annual average rate of around 10% and 8% respectively, over the past 5 years, with no reasonable expectation of a slowdown. From the table 5.5.8(a) and 5.5.8(b), the historic oil consumption and demand data, the following observations on the changing pattern of oil consumption have been made.

5.5.8.(b). Consumption Analysis (Changing pattern of Global Consumption)

Oil prices rose significantly in the decade of 1970s. Oil consumption responded to these price hikes with a lag as there was virtually no growth in global oil demand between 1980 and 1990. Demand in mature economies declined by 400,000 barrels per day over this 10 year period.

Between 1990 and 2000, global oil demand rose by 9.6mbd as economic growth worldwide was relatively strong. Despite the robust increase in demand, both nominal and inflation-adjusted oil prices declined through most of the decade and bottomed out in late 1998.

What was striking in the oil market in the decade of the 1990s was the sharp contraction in oil consumption in the former Soviet Union from 8.1 mbd in 1990 to 4.3 mbd in 2000.

Global oil consumption grew at a rapid rate between 2000 and 2010 despite the deep 2007-09 recession. Between 2000 and 2010, demand for oil increased by 9.4 mbd from 76.6 mbd in 2000 to 86.0 mbd in 2010.

Most of growth in oil demand between 2000 and 2010 has been due to growth in consumption in emerging economies. Between 2000 and 2010, oil consumption in emerging economies rose by 11.2 mbd accounting for all of the incremental growth in global demand over this period. In contrast, consumption in advanced economies declined by 2 mbd.

China alone increased consumption by 4.4 mbd between 2000 and 2010. Demand in India and the rest of Asia rose by 2.6 mbd from 2000 -10.

The forecast of crude oil demand estimate remains for 2013 at 10 mbd, for Japan it dropped from 5.4mbd to 4.6mbd and for India 3.75 mbd.

Changes in the price of oil are largely determined by incremental growth in demand. Emerging economies, given their rapidly expanding consumption, will

increasingly account for most of the overall incremental demand growth for oil and thus become one of the primary determinants of oil price.

Table -5.5.8 (a). Global Oil Consumption by Region (Million Barrels Per Day)

Mature 1980 1990 2000 2010 2015 2030 Economies US 17.4 17 19.7 19 20.6 21.6 Europe 14.6 14.2 14.6 14.1 14.3 14.8 Japan 4.9 5.3 5.5 4.4 4.3 4.5 Other* 3.3 3.4 5.1 5.4 5.8 6.2 Total 40.3 39.9 44.9 42.9 45.0 47.1 Mature Former 9.5 8.1 4.3 4.5 5.0 5.5 Soviet Union Emerging Economies

China 2.0 2.3 4.7 9.1 11.1 16.6

India 0.7 1.2 2.3 3.3 3.7 5.1

Rest of Asia 4.0 4.1 6.4 8.0 9.1 10.9

Latin 5.0 5.6 6.8 7.8 8.4 9.6 America

Middle East 2.0 3.7 4.9 7.1 7.5 9.0

Africa 1.3 2.1 2.3 3.3 3.5 4.1

Total 15.0 19.0 27.4 38.6 43.3 55.3 Emerging

Total World 64.8 67.0 76.6 86.0 93.3 107.9

Sources: US Energy Information Administration (EIA 2011) and British Petroleum (2011) Note: Other consumption in table is oil demand in Canada, Korea and Australia & New Zeeland

Table 5.5.8(b). Changes in Demand by Region (Million Barrels Per Day)

Changes in Projected Changes Consumption in Consumption 2000-10(mbd) 2010-30(mbd) Advanced Economies US -0.7 2.6 Europe -0.5 0.7 Japan -1.1 0.1 Other 0.3 0.8 Total Mature -2.0 4.2 Former Soviet 0.2 1.0 Union(FSU) Emerging Economies China 4.4 7.5 India 1.0 1.8 Rest of Asia 1.6 2.9 Latin America 1.0 1.8 Middle East 2.2 1.9 Africa 1.0 0.9 Total Emerging 11.2 16.7 Total World 9.4 21.9

Sources: US Energy Information Administration (EIA 2011) and British Petroleum(2011) Note: Other consumption in table is oil demand in Canada, Korea and Australia & New Zeland

5.6. Global Oil Demand Projections

Global oil demand is projected to increase from 86.0 mbd in 2010 to an estimated 93.3 mbd in 2015 and an estimated 107.9 mbd in 2030 based on projection made by US Department of Energy (2011). The US Energy Information Administration (EIA, 2011) forecast is essentially a consensus forecast is consistent with projection made by the International Energy Administration (IEA) and most private analysts.

Oil consumption in emerging economies will increased by an estimated 16.7mbd between 2010 and 2030 and account for most of the growth in global oil consumption. Oil consumption is expected to increase by 4.2 mbd in advanced economies over this period. China alone will increase its consumption by 7.5 mbd between 2010 and 2030 accounting for over 34% of the global increase in oil demand.

Growing vehicle ownership will play a key role in oil demand growth. Of the projected increase in oil use over 2010-30, 62% occurs in the transportation sector. Statistical studies by EIA(2010) and IEA (2008 and 2009) indicate that the demand for motor vehicles rises rapidly once per capita income exceeds $3,000. A growing portion of the population in China and India is now approaching this threshold level of per capita income and thus both countries will experience a significant surge in rates of motor vehicle ownership. In summary, the absolute size and importance of demand in emerging economies will have a major impact on price trends in the oil market.

5.6.1. Production: Non-OPEC Supply We now look at the non-OPEC supply side of oil market. Growth in non-OPEC oil supplies has played a significant role in the erosion of OPEC‟s market share over the past three decades. Production surge in Alaska, The North sea, South America and Mexico and recently in Africa. Many of these oil fields are now aging

and production is expected to decline. It is non-OPEC supply that will experience the impact of peak oil.

Table 5.6.1. – Non-OPEC Production (Million Barrels per day)

2000 2010 2015 2030 US/Canada 11.1 13.3 14.6 18.2 Mexico 3.5 2.9 2.3 1.5 Europe 5.8 4.5 3.5 3.1 Former Soviet 10.7 13.2 14.6 17.4 Union(FSU) Africa 2.5 2.6 3.0 3.5 Latin America 3.6 4.8 6.2 8.9 Rest of world 9.6 10.4 10.5 10.4 Total Non- 46.2 51.7 54.7 63.0 OPEC (Includes Unconventional) Unconventional 1.3 4.8 6.2 11.7 Production Total Non– 44.9 46.9 48.5 51.3 OPEC less Un conventional Total less 34.2 33.7 33.9 33.9 Unconventional and Former Soviet Union.

Source: US Energy Information Agency (EIA) Annual Energy Outlook (2011)

Table 5.6.1. provides supply projection for Non-OPEC countries to 2030 based on EIA estimates. The forecast assumes that conventional production (outside the Former Soviet Union) stays essentially flat through 2030 as efforts of peak oil become evident. Oil production increases due to growth in the former Soviet Union and gains in nonconventional production. The EIA assumes significant growth in nonconventional production with production increasing from 4.8 mbd in 2010 to 11.7 mbd by 2030. It is important to point out that unconventional production is capital intensive, expensive to produce and with large

environmental impacts. The extreme to which these unconventional resources will be utilized hinges on the price of crude and the cost of mitigating their impact on environment. Oil markets will be adversely impacted if either former Soviet Union or unconventional production falls below expectations.

5.6.2. Growing Dependence on OPEC Comparing the demand forecast provided in Table 5.5.8(a) with non-OPEC supply projections in Table 5.6.1, we can calculate the residual demand for OPEC oil. This is the amount of oil OPEC must produce to close the gap between global demand and non-OPEC supply. The results are provided in Table 5.6.2. Most long-term projections of oil supply and demand simply assume that OPEC production is a residual that will be available to meet market demand. Table 5.6.2. Growing Dependence on OPEC (Million Barrels Per Day)

2000 2010 2015 2030 Global 76.6 86.0 93.3 107.9 Demand Less Non 46.2 51.7 54.7 63.0 OPEC Supply Need for 30.4 34.3 38.6 44.9 OPEC Oil Source: US Energy Information Agency (EIA) Annual Energy Outlook (2011)

The need for OPEC oil will grow from 34.3 mbd in 2010 to 44.9 mbd in 2030. These amounts to a significant increase in OPEC output over a 20-year period. Most of the production increases will occur in the highly politically unstable Middle East. Essentially, the above analysis indicates that OPEC countries must find the equivalent production capacity of another Saudi Arabia over the next 20 years. Such a sizeable expansion in oil production capacity will prove to be a

daunting challenge for OPEC producers and will require a huge financial investment in both oil capacity and the infrastructure to transport it.

5.6.3. Implications of Dependence on OPEC

The growing projected reliance on OPEC production has the following implications:

1. Oil is a global market, therefore, once non OPEC production peaks and demand continues to grow, there will be strong upward pressure on oil prices. 2. Despite the two prices shocks in 1973-75 and 1979-1980, oil prices, after adjusting for inflation, have been essentially flat for the past 40 years with no clear trend. This is about to change. Over the next few decades, oil prices are expected to trend upwards and do so well above the inflation rate. 3. The world currently has little surplus oil capacity. According to EIA, spare global capacity is at its lowest in 30 years. Tight capacity is likely to be an ongoing characteristic of the oil market in the future, given the expected slowing in non-OPEC production. 4. With little spare capacity, oil prices will be highly volatile and will respond quickly to any sudden change in demand or supply. 5. There are major questions as to whether OPEC countries or countries in FSU will have the required financial wherewithal and technology to expand oil production to meet global market needs. This will create further uncertainty in the oil market. 6. Much of OPEC‟s production is in countries with high geopolitical risk. With a growing reliance on OPEC oil, a speculative risk premium will be a permanent feature in the oil market. 7. The threat is especially acute in Venezuela, where nationalistic policies could lead to a sharp drop in foreign investment and in output. At risk are foreign oil company‟s plans to finance the commercial development of an estimated 235 billion barrels of extra-heavy oil found in the Orinoco Belt.

5.6.4. Supply Issue: Need to Offset Production Declines

Global oil production must expand over the next two decades not only to meet the expected increase in demand, but also to offset declining production in existing oil fields. As we have noted earlier, many of the largest oil fields have been producing oil for decades and are likely to be close to a production peak and an eventual decline. The IEA (2008) estimated that oil production from existing oil production from existing oil fields is declining at an annual rate of 5.1%. Given this rate, the question is how much capacity must be added between 2010 and 2030 just to offset the production declines.

IEA(2010) data estimated global oil production capacity in 2010 as 85 mbd. With no addition to reserves, global production capacity will decline to 31.4 mbd by 2030 assuming an annual rate of decline of 5.1%. Thus, gross capacity of 53.6 mbd must be added by 2030 to compensate for declining production in existing fields. This estimate is probably conservative since the rate of decline is likely to accelerate over the next two decades.

5.6.5. Impact of Price Elasticity

The key unknown in the above projections is the eventual responsiveness of global demand and non-OPEC supply to higher prices. In economics jargon, we are referring to the concept of elasticity of demand for and supply of oil. Elasticity measures responsiveness of consumption and production of oil to changes in price. In other words, the coefficient of elasticity measures the extent to which consumption growth will slow and production will rise in response to higher prices. Estimates of demand and supply price elasticity for oil vary widely, but consensus shows that elasticity rises significantly with time as both businesses and consumers make adjustments in their spending habits and production decisions. Economic theory predicts that over longer periods, oil demand and supply should be highly responsive to price (a high level of elasticity). Historic

data completely supports this prediction. In fact, the decades of 1970s and 1980s provided a perfect test of this theory.

Considering with economic theory, the high price of oil in the 1970s was followed by a surge in non-OPEC investment and production in 1980s. At the same time following the 1979-1980 price shock, demand for oil stagnated for over 10 years. Surprising to many analysts, the global economy expanded at a healthy rate in the 1980s with essentially no growth in oil demand (energy-efficiency improved dramatically). What the data clearly shows is that in response to the higher oil price, there was a sharp slowdown in growth in demand for oil in 1980s while its supply rose. These results are just as predicted by economic theory and indicate a high value for long run elasticity of demand and supply.

Understanding the concept of elasticity has important implications for the future outlook of the oil market. Forecast in tables 5.5.8. (a) and 5.5.8(b) assumes that oil prices rise at a faster rate (not an unreasonable assumption), projection of future global demand will be considerably lower, and at the same time, higher production is likely from non-OPEC sources. The global demand and non-OPEC supply imbalance will be considerably less, as will be the need for OPEC production. The important point to understand is that higher the oil price, the more important is the elasticity effect. This means that demand will expand at a slower rate and supply will expand at a faster rate in response to the higher price of oil. This, in turn, will limit the extent to which oil prices rise (because of lower demand and higher supply).

5.6.6. Implications for Oil Prices The oil market is undergoing fundamental changes. On the supply side, global oil production is likely to peak in the next few decades. A careful analysis of global oil reserve data suggests it could occur as early as 2014 or as late as 2040. The impact on global oil supplies will be dramatic even if peak production occurs at a

later date since global production capacity is already falling due to aging of oil fields. On the demand side, growth in oil consumption will come entirely from emerging countries, with little growth in demand in advanced countries. Thus, pressure to add to oil production capacity is coming from both supply and demand sides of the oil market. Table-5.6.6. summarizes the amount of new oil production capacity that must be added globally by 2030 to meet growing global demand and to offset production declines. Table 5.6.6. Estimated Needs for New Oil Production Capacity ( Million Barrels Per Day).

Additional Oil Capacity need to: 2010-2030 Meet global demand 21.9 Replace loss of capacity due to aging 53.6 of fields Total 75.5

The results show that oil production capacity must increase by a staggering 75.5 mbd by 2030 to meet demand growth and replace depleted supply. This capacity increase is more than twice the level of current OPEC production. In fact, as shown in the above table, the loss of capacity will have more important impact on future supply needs than the increase in demand. What makes the situation even more challenging is that peak oil analysis indicates that the rate of decline will accelerate with the increase in the age of oil fields. If this prediction is correct and peak production occurs in the next few years, there will be an even greater need to discover more oil to offset the larger declines in production. Therefore, Investment in Exploration for New Discovery is essential and prime importance for getting new field for oil production. In addition, most new capacity coming on stream will be of lower quality, more difficult to refine, with higher production costs and located in countries with high

geopolitical risk. Given the need to replace a significant and growing amount of capacity and the growing demand from emerging economies, oil prices should rise considerably over the next two decades.

5.7. Oil Sector and Energy Development in India: Energy Security is essential for sustainable economic development. The modern trend of economic development in the world is characterized by country‟s Energy Security. In recent years India‟s economic growth has been achieved due to synchronization of primary energy consumption. Oil contributes about 29.028 percent and gas contributes 9.84 percent of total energy consumption of India, which is fourth largest energy consumer in the world. Energy is a vital input into production and this means that if India is to move to the higher growth rate of 9% that is now feasible, it must ensure reliable availability of energy at competitive prices.

India is both a major primary energy producer and a consumer. India‟s crude oil proved reserve at the end of 2011 as per BP statistics (June 2012) is 0.3% of world reserves. India‟s Crude oil production is 38.9 Million tonnes that is just 1% of World Crude oil production (i.e. 3913.7 Million tonnes). But, India is consuming 4.0 % of total world oil consumption as per BP annual statistical report (June 2012). However, the per capita energy consumption of India is one of the lowest in the world. India consumed 455 kgoe per person of primary energy in 2004, which is around 26% of world average of 1750 kgoe in that year. As compared to this, per capita energy consumption in China, Brazil was 1147kgoe and 1232kgoe respectively. In the year 2009, the per capita energy consumption of India is increased to 530Kgoe only.

India is not endowed with large primary energy reserves in keeping with her vast geographical area, growing population, and increasing final energy needs. The distribution of primary commercial energy resources in the country is quite skewed. Whereas coal is abundant and is mostly concentrated in the eastern

region, which accounts for nearly 70% of the total coal reserves, the western region has over 70% of the hydrocarbons reserves in the country. Similarly, more than 70% of the total hydro potential in the country is located in the northern and north eastern regions. The southern region, which has only 6% of coal reserves and 10% of the total hydro potential, has most of the lignite deposits occurring in the country.

The proven oil reserves of India as on 2011-12 is around 5.7 Thousand Million barrels or 0.8 Thousand Million tonnes, i.e 0.3% share of total world reserves. This can sustain the current level of production for the next 22 years. The current level of production barely caters to 24% of the petroleum products demand and the balance oil requirements are met by importing the crude. So, products prices are very sensitive.

Petroleum pricing is fundamental for the operation of efficient energy market. Petroleum product prices perform the important role of balancing consumer energy demand with producer supply. The basic objectives of energy pricing are economic efficiency, social equity and financial viability. Efficiency principle seeks to ensure regulation of prices in such a manner that the allocation of society‟s resources to the energy sector fully reflects their values in alternative uses. Equity principle relates to welfare and income distribution considerations. Financial principle suggests that energy supply system should be able to raise sufficient revenues to remain financially viable, so that continuity and quality of service is ensured and common people and community benefitted from the energy supply system for sustainable growth and development.

India‟s dependence on oil import is growing. Table-5.7 shows the comparative data of crude oil demand and domestic production, to sustain rate of 9% GDP growth, the requirement of crude oil during (2011-12) has been 145 Mt. The country is forced to resort to imports to bridge the gap between demand and supply.

Table 5.7. Comparative data of crude oil demand, domestic production and Crude Import.

Year Crude oil demand (XI) plan Crude oil Crude oil (MMT) production(MMT) import (MMT) Base case Upper case 2002-03 33.044 81.989 2003-04 33.373 90.434 2004-05 33.981 95.861 2005-06 32.190 99.409 2006-07 33.988 111.502 2007-08 116.4 117.6 34.118 121.672 2008-09 119.1 122.0 33.508 132.775 2009-10 122.0 127.8 33.691 159.259 2010-11 127.0 136.6 37.712 163.594 2011-12 131.8 141.8 Source: Petroleum Planning and Analysis Cell.

5.7.1. Imports and prices of Crude Oil: Imports of Crude Oil during 2010-11, in terms of quantity was 163.594 MMT valued at Rs.4,55,909 crores, this marked an increase by 2.72% in quantity terms w.r.t. 159.259 MMT during the year 2009-10 and an increase by 21.45% (w.r.t.Rs.3,75,378 crores) in value terms over the year of 2009-10. In terms of US$, the extent of increase in value of Crude imports was 25.73%. It may be noted that the imports of crude oil has doubled during this period when seen in relation to imports in 2002-03. During this period, the average price of International crude oil (Indian Basket) has increased from US$ 26.59/bbl in 2002-03toUS$ 85.09/bbl in 2010-11 i.e. an increase of about 220%. The trend in growth of crude oil imports and crude oil International (Indian Basket) prices are depicted inTable-5.7.1.and Figure-5.7.1.

Table 5.7.1. Imports of Crude oil and Average Crude Oil Prices

Import of % Growth Average Crude oil % Growth Crude Oil in Import Prices (US$/bbl.) in Crude (MMT) Crude Oil Prices

Year 2002-03 81.989 26.59 2003-04 90.434 10.30 27.98 5.23 2004-05 95.861 6.00 39.21 40.14 2005-06 99.409 3.70 55.72 42.11 2006-07 111.502 12.16 62.46 12.1 2007-08 121.672 9.12 79.25 26.88 2008-09 132.775 9.13 83.57 5.45 2009-10 159.259 19.95 69.76 -15.77 2010-11 163.594 2.72 85.09 21.97

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

Figure-5.7.1 Percentage Growth in Imports of Crude Oil & average International Crude Oil Prices.

50.00 42.11 40.14 40.00

30.00 26.88 21.97

20.00 % Growth in Import Crude 12.1 19.95 % Growth in Crude oil Prices 10.00 5.23 12.16 5.45 10.30 9.12 9.13 6.00 0.00 3.70 2.72

-10.00 -15.77

-20.00

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

5.7.2. Imports and Exports of Petroleum Products:

It may be seen that despite considerable variations in International prices of crude oil, imports have followed a steady growth primarily to meet domestic demand of a burgeoning economy, apart from re-exports of petroleum products. With substantial increase in refining capacity in India, as seen earlier, exports of petroleum products have picked since 2002-03 although declined shortly in 2008- 09 due to slowdown in global economy. Exports of petroleum products during 2010-11, in terms of quantity was 59.133MMT valued at Rs.1,96,112 crore, which marked an increase of 16.01% in quantity terms (w.r.t. 50.974 MMT during the year 2009-10), and an increase of 36.15% (w.r.t Rs1,44,037 crore) in value

terms in Indian rupees over the year of 2009-10.In terms of US$, the extent of increase of exports in value was 41.12%.The exports of petroleum products, it may be seen, has steeply increased by 475 % up to 2010-11. Imports of petroleum products are relatively limited with greater focus on imported crude oil to utilize domestic capacity as may be seen in Table-5.7.2 and Figure-5.7.2 below:

Table-5.7.2. Imports and Exports of Petroleum Products Import of % Growth in Export of % Growth in Petroleum Import of Petro- Export of Products Petro- Product( Petro- (MMT) Products MMT) Products Year 2002-03 7.228 10.289 2003-04 8.001 10.69 14.62 42.09 2004-05 8.828 10.34 18.211 24.56 2005-06 13.44 52.24 23.461 28.83 2006-07 17.66 31.40 33.624 43.32 2007-08 22.462 27.19 40.779 21.28 2008-09 18.524 -9.50 38.902 -4.6 2009-10 14.662 -20.85 50.974 32.15 2010-11 17.337 18.24 59.133 16.01

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

Figure-5.7.2: Percentage Growth in Imports & Exports of Petroleum Products

60.00

50.00 43.32 42.09 52.24 40.00 32.15 28.83 30.00 24.56 31.40 21.2827.19 % Growth in Import of 20.00 16.01 18.24 Petro- Products

10.00 10.69 10.34 % Growth in Export of Petro- Products 0.00 -4.6

-10.00 -9.50

-20.00 -20.85

-30.00

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

5.7.3. Crude Oil Demand Projection for India. A summary of the projections by various agencies is given in table 5.7.3. The projection by IEA and EIA are based on unrealistically low growth rates of GDP for India. It may be seen the demand for the year 2025 varies from 235 Mt for the best case scenario(BCS) of India Vision- 2020 to 368 Mt of India Hydrocarbon Vision-(IHV) 2025. The IRADe-PWC projections exclude Naphtha and their projection of 347Mt under high growth case (HOG) is comparable to 368 Mt of India Hydrocarbon Vision.

Table-5.7.3. A summary of the projections of Crude Oil Demand for India by various agencies

Year Projections by various Agencies EIA (2004) IEA IHV- India Working Power & IRADe& (2004) 2025 Vision- Group Energy PWC(2005) (2000) 2020(2002) Report Division‟s Reference High Low BAU BCS of 10th (Planning BAU HOG case case case plan Commission) (2001- Projections 02) (2003-04) Base 2001 (105 2001 2001 2000 1998- 1997(83 2001-02 2001-02 2003-04 ( year Mt) (105 (105 (102 99 Mt) (108 (108 Mt 109.7 Mt) Mt) Mt) Mt) (91 Mt) Mt)

2004- 119 122 115 122 132 121 112 119 124 125 127 05 2009- 139 149 129 145 175 153 135 139 147 162 176 10 2014- 157 194 154 171 226 193 162 164 174 191 212 15 2019- 219 254 189 201 288 245 195 195 207 212 259 20 2024- 264 324 204 230 368 309 235 232 240 260 347 25 2029- 271 276 281 320 465 30

EIA – Energy Information Administration, USA;IRADe – Integrated Research and Action for Development. IEA – International Energy Agency: BAU – Business as Usual; PWC – Price Waterhouse Cooper; IHV- India Hydrocarbon Vision 2025; BCS –Best Case Scenario HOG – High Output Growth . Source : Integrated Energy Policy (Report of the Expert Committee 2006)

5.8. Role of Crude Oil Prices on Indian Economy: Crude oil price is an important parameter for oil importing country like India; it has a bearing on economy, because crude oil is the raw material for refinery. The domestic production accounts for only 24 to 26% of total country‟s crude oil demand; rest is to be met by importing the crude. India‟s huge dependence on imported crude oil makes it vulnerable due to the shocks & disruptions in the Global Oil Market. Any sharp spike in oil prices in the global market results in an unfavorable economic situation. The reasons for the same are outlined below.

5.8.1. Rise in Cost of Imports: The first victim of rise in crude oil prices is the state exchequer. Every increase of $1 per barrel in Indian crude basket prices pushes up the annual oil import bill by $1.2 billion. The oil import bill of $140 billion is faced by India in 2011-12.( Source: World Oil, August 2012/ vol.233 No.8, p-25). It also leads to a faster depletion of India‟s Foreign Exchange (FOREX) Reserves.

5.8.2. Widening of Trade Deficit: There is a sharp increase in India‟s trade deficit. The steep increase in imports due to high oil prices leads to a further widening of the trade deficit. Table:-5.8.2. Year Export (Million $) Import (Million $) Trade balance (Million $) 2005-06 103090.5 149165.7 -46075.2 2006-07 126414.1 185735.2 -59321.2 2007-08 185295.0 303696.3 -88535.0 2008-09 185295.0 303696.3 -118401.3 2009-10 178751.4 288372.9 -109621.5 2010-11 251136.2 369769.1 -118632.9 2011-12 304623.5 489417.4 -184793.9 Source: RBI Handbook of Statistics of Indian Economy 2011-12, India‟s Foreign Trade.

5.8.3. Increase in Oil Under Recoveries: As the pricing of Diesel, LPG & Kerosene is still under government control; any rise in international oil prices is not reflected in the domestic market. The inability of OMCs to sell fuel at the market defined rate results in higher under recoveries. OMC have reported under recoveries totaling Rs. 1,385,410.00 million for the Financial Year 2012. ( ONGC, Annual Report 2011-12, P-96).

5.8.4. Mounting Fuel Subsidy Burden: Any hike in price of imported crude oil is absorbed by the OMCs along with the Upstream Oil Companies & the federal government. The fuel subsidy bill has witnessed a continuous rise for the past few years. Government‟s fuel subsidy bill amounts to US $ 9 billion during 2010- 11 (International Institute of Sustainable development, iisd, Fuel Subsidies in India, 14th Aug 2012).

5.8.5. Worsening Fiscal Deficit: India‟s Fiscal Deficit for 2009-10 stood at 6.6 % of Gross Domestic Product (GDP). Rise in crude oil prices worsens the situation as Government has to shell out more money in the form of fuel subsidy to OMCs. High subsidies are putting pressure on fiscal deficit which has touched 5.9 % of GDP in 2011-12 and Govt. has targeted to bring it down to 5.1% in 2012-13.

5.8.6. Reduced Amount for Infrastructure Investment: India aims to invest $1 Trillion in infrastructure development during the 12thFive Year Plan (2012-17). High prices of crude oil (leading to higher fuel subsidy & increase in fiscal deficit) have the potential to derail the government‟s plans as they eat into the amount of disbursal available with the government for infrastructure &social development schemes. A continuous rise in the subsidy bill & worsening fiscal deficit has forced the federal government to deregulate the petrol prices in the domestic market while in-principle approval has been given for deregulation of diesel prices. However, the Government reserves the right to intervene whenever the situation demands.

Chapter-6 Policy Framework for the Oil Sector in India Ministry of petroleum and Natural Gas (henceforth referred to as MOP&NG) is concerned with exploration and production of oil and natural gas (including import of crude oil, Liquefied Natural Gas), refining, distribution & marketing, import, export and conservation of petroleum products. Activities of the Ministry are carried through 17 (Seventeen) public sector undertaking, (03) private sector and (01) one Joint Venture of BPCL and Oman Oil Company. Thus companies under state dominate oil industry in the country today. These companies follow government policies and directions and are accountable to parliament. Besides, the Comptroller and Auditor General (C & AG) verify their books of accounts and Central Vigilance Commission (CVC) oversees their commercial transactions. The present pricing structure is influenced by Government policy. Even if one argues that state is operating a monopoly, it is a public monopoly with all the attendant control and the accountability in place (GOI,2006b). It is only recent past that the various activities within the petroleum sector are slowly ceding to private sector. Exploration and refining of petroleum has seen emergence of many private players apart from multinational firms; though in marketing of petrol and diesel very few private players have entered.

6.1. Institutional framework

The Indian petroleum sector can be divided into the following three sub-sectors:

1.Oil and Gas E&P;

2. Petroleum refining; and

3. Marketing

Petroleum Professional and Analyst in finding the business opportunities goes further and breaks down the petroleum industry into more segments, namely, exploration of potential oil basins, extraction of crude oil from the oil fields which

bear oil, storage, transportation, distribution, and marketing of crude oil; refining to end product stage; and transportation, storage, and marketing of end products.

The Ministry of Petroleum and Natural Gas (henceforth referred to as MoPNG) is entrusted with the responsibility of E&P of Oil and Natural Gas (including import of liquefied natural gas) their refining, distribution and marketing, import, export and conservation of petroleum products. The activities of the MoPNG are carried out through two Exploration and Production companies (hence forth referred to as E&P Companies) i.e. ONGC and OIL and Twenty one Refineries. The country is not only self-sufficient in refining capacity for its domestic consumption but also exports petroleum products substantially. The total refining capacity in the country as on 1.6.2011 stands at 193.386 MMTPA.

The Indian petroleum industry is still largely controlled by PSU‟s though; of late private players are making inroads into the industry. The Directorate General of Hydrocarbons, GOI, which acts as the upstream regulator, was established under the administrative control of MoPNG by a GOI Resolution in 1993 to promote sound management of the Indian petroleum and Natural gas resources, having balanced regard to the environment, safety, technological and economic aspects of petroleum activity, to review the exploration programmes of companies and to advise the GOI on the adequacy of these programmes. The downstream sector of the industry was regulated by the MoPNG, until the setting up of the Petroleum and Natural Gas Regulatory Board (henceforth referred to as PNGRB) to regulate the refining, processing, storage, transmission, distribution marketing and sale of petroleum, petroleum products and natural gas excluding production of crude oil and natural gas. The Board is also responsible for promoting competition in the industry.

6.2. Upstream Sector

Oil and natural gas also known as hydrocarbons are some of the most important fossil fuels to meet the energy requirements of the country and support economic growth. Currently, in India, the share of oil and gas in the primary energy is

about 38.8 percent. In spite of several advances in technology, exploration of oil and gas has been stated, till today, to be a probabilistic discipline with a high degree of risk of failure. The habitat for oil and natural gas having been determined by geological events of the remote past, any estimation of resources is based on various possible hypotheses and models constructed thereon. Exploration is undertaken based on the some hypotheses and geological models constructed on the basis of data acquired by exploration companies (GOI, 2005a). India has 26 sedimentary basins, comprising both onland and offshore areas. Only 19 of the 26 sedimentary basins in India have been taken up for exploration so far, with acquisition of seismic data and carrying out exploratory drilling. Of the total sedimentary basin area of India of 3.14 million sq. km., about 1.39 million sq. km area is in the onshore area and 0.39 million sq. km in the offshore area (up to 200 m isobaths water depth). The deep water area is about 1.35 million sq. km. Most of the basins are under various stages of active and /or reconnoiter exploration. The sedimentary basins of India have been classified into four categories as a function of geological knowledge of the basin, presence and or indication of hydrocarbons and current status of exploration.

In view of the great need to establish indigenous sources of oil, the nucleus of an organization for oil exploration was set up by GOI towards the end of the First Plan period. With limited equipment and technical personnel available, investigations were started in the Jaisalmer area of Rajasthan. The Second Plan provided for an intensification of the effort and enlargement of the organization, therefore, the Oil and Natural Gas Commission (now Corporation) was set up in 1956 by an Act of Parliament to explore and exploit hydrocarbon reserves in the Indian sedimentary basins. The Commission undertook geological surveys, geophysical investigations and exploratory drilling for oil in Punjab and later in the region of Cambay and in the Brahmaputra valley in Assam. In 1958, Burmah Oil also transferred bulk of its share to the GOI, and accordingly a joint venture company name OIL was formed.

In April 1956, the GOI adopted the Industrial Policy Resolution, which placed the mineral oil industry among schedule „A‟ industries, the future development of which was to be the sole and exclusive responsibility of the state. The Indian upstream sector, thus, historically, has been under the dominance of PSU‟s. ONGC and OIL, 33.3 million tonnes produced by the National Oil Companies (henceforth referred to as NOC‟s) and more recently private and joint venture companies are engaged in E&P of oil and natural gas in the country.

The domestic production of crude oil has remained between 33.3 and 37.712 million tonnes during 2003 – 2011. Not only has domestic production stagnated, known oil reserves have also remained in a narrow range with total oil reserves being of the order of 739 million tonnes in 1990 -91 and estimated to be 800 million tonnes in 2010 – 11. The proven reserve to production (R/P) ratio is found to be 22 as on 2010-11. We now import 76 per cent of our consumption and our import dependence is growing rapidly. There has been no significant increase in known crude oil reserves during the last decade in spite of large investments in exploration activities except Rajasthan MBA fields (i.e. Mangala, Bhagyam and Aishwariya) in onshore by Cairn India Limited and KG basin offshore Gas discovery by RIL. As a result, there is a vast gap between the demand and domestic availability of crude oil. The import dependence kept on rising. This raises serious concerns about India‟s energy security and our ability to obtain the oil we need and the impact of constrained supply on oil prices and on our economy. All projections show an increase in the gap between the domestic crude oil production and consumption. In fact, according to International Energy Agency, (henceforth referred to as IEA) India will be import dependent to the extent of 94 per cent by 2030.

6.3. Intensifying exploration

It is surprising that though exploration has been a top most priority in the GOI‟s agenda over the last four decades, the country is facing these dire consequences of increasing crude oil imports as far as performance of exploration is concerned,

the gap between import and domestic production is widening. The Third Plan talked about an intensified programme of mineral exploration and development. The Sixth Plan, similarly, called for a greatly intensified effort towards both exploration and development. Similarly, the Seventh Plan pointed out the need for intensifying exploration as well as for extending exploratory activities to inadequately explored and unexplored basin. Further, the Ninth Plan envisaged acceleration of exploration efforts, while the Tenth Plan pointed out that the thrust area is acceleration of exploration efforts especially in deep offshore and frontier areas. Both Eleventh and Twelfth plan also have given importance in intensifying exploration and development effort in both offshore and onshore. Therefore, it makes a great deal of economic sense to intensify exploration efforts in the Indian basins, especially in the frontier basins so that their hydrocarbon potential is fully assessed as early as possible. However, this intervention requires substantial investments. Fortunately, it has already dawned upon our policy makers that efforts of the two NOCs needed to be supplemented through additional investments in the E&P sector from multinationals and Indian companies.

It was against this background regarding the exploration scenario in the country that GOI started looking for private capital to complement capabilities of our NOC‟s. It must be pointed out that the importance of private capital has always been duly acknowledged earlier. The Third Plan, for instance, had clearly acknowledged the role of private capital to join the search of oil in India. GOI has been inviting private investment in exploration of oil and gas in the country since early 1980. However, initial efforts to attract private investment were limited to offshore areas only.

6.3.1. History of Pre – NELP Licensing Rounds The earliest effort at attracting foreign companies to invest was in the mid-1970 under the then Union Minister for Petroleum and Chemicals H.R. Gokhale. ONGC and OIL were the major players. Except for Carisberg of the US and Reading and Bates of Canada, the Government could not farm out any of the

areas to other parties. The two also eventually pulled out without finding any oil but made their documentation available to the Government.

Failure to meet reserve accretion targets prompted the Government to involve the private sector exploration bidding rounds started in 1979, but the early rounds were not successful. The first four rounds took 12 years to come (1979-91). The next five rounds came in two years (1994-1995) and succeeded in generating some interest in the international oil industry. An innovation was also introduced in the 9th round known as the JV round to reduce the risk for the private investors by associating ONGC/OIL as partners in these exploration ventures. However, the rigid decision – making structures of these National Oil Companies (NOC‟s) created problems of compatibility and reduced the attractiveness of this innovation.

To raise the interest of foreign companies in the E&P sector, the government decided to award some small and medium fields for development to the private and joint sectors, respectively, and came out with two rounds in 1992 and 1993. These rounds evinced tremendous response from foreign players. Also in order to upgrade the information on the hydrocarbon potential of India‟s unexplored sedimentary basins, the GoI offered blocks for geophysical surveys during 1993 to 1995.

The following discussion chronologically traces the various exploration rounds, Speculative Survey rounds and Development rounds announced by the GOI during the period from 1980 to 1995.

6.3.2. First Round of Exploration (1980) In 1980, GOI made a second offer to the international industry. This offer is now referred to as the First Round as it was the first such invitation in the 1980‟s. During this round 32 offshore blocks were placed offered to the international industry. The timing of the offer coincided with two significant international

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developments which adversely impacted the response of the industry. Firstly, the international market price of crude oil and products started showing signs of weakness. This was because of the combined impact of the decline in demand caused by the price rises of the 1970‟s and the addition to worldwide supply arising from the discovery of new hydrocarbon reserves in non- OPEC countries particularly Norway, UK, and Mexico. Falling oil prices narrowed the profit margins of the oil companies compelling them to be more selective in their choice of new international ventures.

Secondly, China threw open its off-shore acreage to international companies. The offshore areas of China had excited explorers ever since offshore exploration had become a technical reality. Its offshore sedimentary basin offered the possibility of large new discoveries. Thus, when China reversed its policy of isolation and adopted an open door attitude towards international exploration there was literally a scramble to take up acreage in the country. In the process funds which had been previously allocated to India and South Asia in general were diverted to exploration in China.

Four companies responded to the Government‟s offer for bidding for two blocks. After negotiations, the Government concluded an agreement in March, 1982 with Chevron Oil Company of USA. This was a production sharing contract that stipulated if Chevron had made a commercial discovery. ONGC would have had the option to take up to 50 per cent equity interest in the project. Chevron was obliged to sell its share of crude oil to India at the international market price. A 56.375 per cent corporate tax was to be levied on Chevron‟s profits. In addition a 15 per cent royalty was leviable on gross production. Chevron drilled three wells without success and relinquished its contract area in 1985.

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6.3.3. Second Round of Exploration (1982) The Second Round was announced in 1982. This time the Government placed on offer 50 onshore and offshore blocks. Unfortunately the market had weakened even further by then and no bids were received for the blocks on offer.

6.3.4. Third Round of Exploration (1986) In March, 1986, GOI announced its Third Round in which 27 offshore blocks were demarcated and placed on offer. The indicative terms of the production sharing contract were issued along with the announcement. The Government followed up the announcement with three presentations in Delhi, London and Houston during which a delegation from the Government, ONGC and OIL outlined the main provisions of the proposed contract and provided briefs on the geophysical and geological activities carried out by the two national oil companies in the blocks offered.

The framework of the contract offered in the Third Round was also of the production sharing kind. The detailed terms and conditions were however different from those offered in the earlier two rounds. Inter alia, the Royalty charge of 15 per cent was withdrawn and Corporate Tax was reduced from 56.375 per cent to 50 per cent.

Seven companies viz, Shell, Chevron-Texaco, Broken Hill Proprietary Britoil, Amoco, Albion and International Petroleum Corporation (IPC) made 12 bids for 9 blocks. The bids were evaluated in January‟1987 and the various companies were called for negotiations in February. In December 1987 four contracts were signed, three with the Chevron-Texaco group and one with IPC. All four contracts were for exploration in the offshore east coast.

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6.3.5. Fourth Round of Exploration (1991) In 1991, due to the Gulf crisis and disintegration of the Soviet Union, the Government further intensified its efforts and started announcing bidding rounds at regular intervals.

The Fourth round of exploration for oil and natural gas in India was announced in 1991. Gol invited bids from companies to explore for oil and natural gas in 72 blocks out of which 39 were offshore and 33 were onland. A number of foreign companies did participate in this round. These companies include Alboin India Inc., Coplex (India) Ltd., Vaalco Energy Inc., Rexwood-Oakland Joint Venture and Pan Energy Resources from USA. Nikko Resources Ltd., Canada, Shell India Production Development B.V., The Netherlands, and Sterling Resources N.L, Australia.

6.3.6. First Development Round (1992) GoI came out with the First Round of bidding for development of small and medium sized oil and gas fields in 1992. These fields were discovered either by ONGC or OIL but could not be developed on account of financial constraints of the companies.

A total of 31 small sized discovered fields were offered, out of which 10 were offshore and 21 onland. Of the above fields on offer, only 3 onland fields were discovered by OIL while the rest belonged to ONGC. The offshore basins in which the offered fields were located included the Andaman, Krishna-Godavari, Cauvery and Bombay basins. Onland blocks were in the Gujarat and Assam basins.

GoI offered 12 medium – sized fields 6 offshore while 6 onland to be developed by the companies in joint venture with ONGC/OIL, Offshore fields offered included the Ravva, Panna, Mukta, Mid and South Tapti and the R-Series. Onland fields included fields in Arunachal Pradesh, Assam and Rajasthan.

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6.3.7. Fifth & Sixth Round of Exploration / Second Development Round / First speculative Survey Round (1993) The Fifth Round of bidding for exploration of oil & natural gas in India was announced in 1993 in which a total of 45 blocks were offered 29 offshore and 16 onland. Rexwood – Oakaland JV, USA. Command Petroleum Holdings, Australia, Vaalco Energy, USA participated.

Again in the same year, as part of the continuous round the year bidding scheme for exploration acreages, GoI announced the Six Round of bidding for exploration of oil & gas in India. Twenty three blocks from those offed in the Fifth Round of bidding were offered again in this round. In addition, 23 other blocks were put on offer making a total of 46 blocks on offer, with 17 of them being offshore and 29 onshore. Among the foreign companies who showed interest under this round included Samson International Ltd., Amoco India Petroleum Ltd., and Enron Oil & Gas India Ltd from USA, BHP Petroleum (India) Ltd., Australia and Phonix Geophysics Ltd., Canada.

In order to upgrade the information available on the hydrocarbon potential of the unexplored sedimentary basins in the country. Gol announced the offer of blocks for carrying out speculative geophysical and other types of surveys. In the First Round, a total of 35 blocks 21 offshore and 14 onshore were put on offer.

Also in 1993, GoI invited offers from companies to participate in the development of medium sized and small sized oil & gas fields in India. Eight medium sized and 33 small sized fields were on offer. The medium sized fields were to be developed in joint venture between the companies and ONGC/OIL while the small sized fields were to be developed by companies on their own with no participation by ONGC/OIL. Of the 33 small size fields, 4 were offshore while the balance 29 was on land fields. Of the 8 medium sized fields 2 were offshore

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Ratna & R-Series and Basin Oil Rim while 6 were on land located in the Cambay and the Upper Assam basins

6.3.8. Seventh & Eighth Round of Exploration / Second Speculative Survey Round (1994) The Seventh Round of bidding for exploration of oil and natural gas blocks in India was announced by GoI in 1994. A total of 45 blocks were offered out of which 27 of them were on land and 17 were offshore and 1 on land block extended in offshore. Under this round companies like Rexwood – Oakland, Enpro India Ltd., Geo-Enpro Petroleum Ltd., Phoenix Overseas Ltd., and Enron Oil & gas India Ltd., participated among others.

In the same year the GoI announced the Eighth Round of bidding for the exploration acreages. A total of 34 blocks were offered out of which 19 of them were on land and 15 were offshore

GoI announced the Second Round of offer of blocks for carrying out speculative geophysical and other surveys. In this round a total of 12 blocks were on offer 11 on land and 1 offshore.

JV Exploration Programme / JV Speculative Survey (1995) The last of this series of rounds was the joint Venture Exploration Programme (JVEP) for the exploration of oil and natural gas in India announced by Gol in 1995. Under this programme the successful company / consortium was to form an unincorporated joint venture with ONGC/OIL. A total of 28 exploration blocks were placed on offer (23 of which were under licence to ONGC and 5 to OIL), with 10 of them being offshore and 18 onland.

The first two speculative survey rounds were unsuccessful, prompting GoI to announce a Joint Venture speculative Survey Round in 1995. Under this round the blocks were offered to carry out speculative geophysical and other type of

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surveys with the participation of its nominee, the Directorate General of Hydrocarbons (DGH). A total of 20 blocks were placed on offer with 16 being offshore and 4 being on land.

6.3.9(a) Exploration Rounds As discussed in the above section, during the pre-NELP period, a total of nine rounds of bidding for exploration took were held by Gol including the last Joint Venture (JV) Round. The first three rounds were announced between 1979 and 1986 while the fourth round was announced in 1991 when India opened its door for foreign investments in a number of industries. After the Fourth Round, Gol adopted a system of continuous round the year bidding with exploration blocks being offered every six months. Under this scheme, the Fifth to Eighth Rounds of bidding were held during January 1993 to July 1994. Bidding for JVEP was held in September, 1995.

The exploration blocks under the pre-NELP rounds were identified for offer in consultation with ONGC and OIL, who were the licensees. Notices were published in national and international dailies / journals inviting offers for the identified blocks. Companies were given about five to six months to submit their bids. The bids were invited under international competitive bidding system. Information docket / data packages were prepared by ONGC / OIL for each block on offer.

The main criteria for evaluating of bids were the technical and financial capability of the bidding company / consortium, work programme and the commercial terms offered to the government the bids were evaluated by ONGC / OIL / DGH. The evaluations were considered by the empowered Committee of Secretaries (ECS) comprising Petroleum Secretary, Finance Secretary and Law Secretary. The C&MDs of ONGC and OIL also assisted the committee as technical members. The recommendations of the ECS on the award of blocks were placed before the Cabinet Committee of Economic Affairs (CCEA) for consideration and approvals.

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The blocks were awarded to the successful bidders after obtaining CCEA approvals. Successful companies or consortium had to sign Production Sharing Contracts (PSC‟s) with GOI and ONGC or OIL.

The terms and conditions of the 9th Round of Exploration, which was the JV Round, were as follows:-

ONGC or OIL was to have a participating interest of 25-40 per cent in the joint venture, thus sharing exploration costs. In the case of crude oil and associated gas the contract was on a production-sharing basis for 25 years, from the date of commencement of the contract (with a possible extension of 5 years). For non- associated gas, the contract was for 35 years from the date of signing.

The exploration period was for a maximum 6 years divided into 1-3 commitment phases, with no single commitment phase exceeding 2 years. The company or consortium had the option to terminate the contract at the end each commitment phase.

Cost recovery of up to 100 per cent was allowed. The percentage of annual petroleum production expected to be allocated for recovering costs was required to be indicated.

Companies had to indicate the minimum exploration work they planned to carry out in each commitment phase.

The sharing of profit was to be based on a sliding scale tied to post – tax rates of return or multiples of investment recovered. Multiples of investment recovered was defined as the cumulative cash flow since the commencement of the project operations divided by the cumulative investment in the project.

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For Natural Gas, the joint venture had the freedom to make arrangements for marketing the gas. There were no production signature bonuses. All data gathered during the course of operation under the contract was the property of GoI.

If the joint venture opted to proceed to the second commitment phase, It has to relinquish 30 per cent of the original area of the blocks. Similarly, if it opted for the third commitment phase, the joint venture had to relinquish a further 40 per cent of the area. At the end of the last commitment phase, the joint venture had to relinquish all areas except those in which hydro carbons had been discovered or a development plan had been prepared. However, negotiations for certain blocks were allowed.

The joint venture was not required to pay royalty or cess and was exempt from customs duty on all operations under the contract.

Foreign companies were free to remit amounts due to them under the contract out of India. Soft loans were available for the exploration of blocks.

No private company in a consortium that had been awarded a block for exploration could unilaterally withdraw from the consortium. Further government approval was required for induction of any new player in the consortium.

The companies were required to adhere to the original schedule, and the government had the right to revoke the contract if the companies did not follow the schedule.

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Table-6.3.9(a)

Blocks Offered under Pre-NELP Exploration Rounds Year Round No of Blocks offered Bid Contracts Signed Received Offshore Onshore Total Offshore Onshore Total 1980 One 17 15 32 4 1 0 1 1982 Two 42 8 50 Nil 0 0 0 1986 Three 27 0 27 13 0 0 0 1991 Four 39 33 72 24 2 3 5 1993 Five 29 16 45 15 4 2 6 1993 Six 17 29 46 20 2 3 5 1994 Seven 17 28 45 12 2 3 5 1994 Eight 15 19 34 38 1 3 4 1995 Ninth 10 18 28 22 1 1 2 JV Round Given to Chevron in Saurashtra Offshore where 3 wells were drilled. Chevron exited from the block in 1993. Source: PetroFed, Paper on “Review of E&P Licensing Policy”, P-87.

6.3.9(b). Analysis of Foreign Investments in Exploration Rounds Before entering into a contract, an exploration and production (E&P) company has to balance the risk and reward of the venture and compare it with other ventures around the world that are competing for scarce resources. Companies will only bid on attractive acreage. The fiscal terms are generally of secondary importance. Attractive acreage has to be made available and be seen to be attractive. Success needs to be demonstrated.

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Despite favourable terms and conditions given to major countries world-wide the 9 bidding rounds conducted thus far have met with poor response. The reasons for such a performance have been discussed in the following paragraphs.

The stipulation that the prospective investors should participate in biddings seems to have put off many.

There was a perception that the blocks with high prospects were reserved for the NOC‟s and only high risk areas were offered to private investors. The NOC‟s continued to hold on to the blocks they were awarded on a nomination basis. They also played a decisive role in the delineation of the blocks. Though it was recommended that, where feasible, an exploration block should include a producing field or an area with oil / gas finds, there were instances where this was not done and the producing areas were deliberately left out of the blocks. There were also instances of a block being advertised and later withdrawn at the instance of a NOC. These factors reduced the commercial attractiveness of the blocks offered. If an acreage had been extensively explored (e.g. by the NOC‟s) without success it would not be considered attractive. Much of the acreage offered in rounds seemed to be on offer because it had already been unsuccessfully explored.

The incentive structure was designed after a study of practices followed by other countries such as China and Indonesia. The bidders, however, felt better incentives were necessary in India to make up for the higher perceived risks. Many improvements have indeed been made in the NELP.

A very important factor was the delay in making and implementing the exploration policies. The award of production sharing certificates (PSC‟s) required clearances from several ministries, leading to inordinate delays. Though there was an empowered committee of secretaries, it had limited success in cutting through bureaucratic red tape. While it should normally take a

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few months to award these contracts after the receipt of bids, in practice it took 2- 3 years. Further, the awards were followed by lengthy negotiations prior to signing of the contract. In all the procedural delays disappointed even the most determined bidders. Even after the contract was signed, many approvals, including the all-important exploration licence, were required. These also took years to be realized.

A disturbing point that has been made is that the agencies that were not associated with the negotiation of the PSC‟s but had to be approached subsequently for various approvals were not prepared to treat the PSC‟s as binding and sought to reopen matter that were negotiated and settled. Some state governments have been of the view that they have the right to select the awardees as the exploration licence has to be issued by them. These issues have not yet been fully resolved which could cause problems in awarding blocks in the future. The blocks already offered would not be affected by this problem.

The current PSC‟s allow ONGC to obtain up to 40 per cent equity risk free in a successful discovery. The remaining 60 per cent of the production is split between the government and the contractor, with the government receiving between 20-50 per cent. On the remaining portion, up to 48 per cent income tax is paid. The overall revenue received by the contractor is less than 18 per cent, over a period of 25 years.

Acreage is assessed by inspecting data. All data has to be freely available and of good quality. A reasonable time has to be allowed for the assessment of the data. Industry has to know what acreage is available. There was no comprehensive map showing all open acreages, or areas for which the NOC‟S have already put in an application to explore. The data dockets for the various blocks offered during the bidding rounds had insufficient data and were overpriced.

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The effective date was the date of signing the PSC. There are penalties if work does not commence within a specified time limit from this effective date. However, there was no corresponding penalty on the government if it did not provide approvals in time.

Exploration blocks were not of the size expected by international operators (the threshold size for exploration and developments considered to be of the order of 100-300 million barrels and 20-50 million barrels respectively).

The bidding process was handled by a group called the Exploration Contract Monitoring Group (EXCOM) which formed a part of ONGC. The bidders perceived this to be against their interest.

6.3.9(c). Speculative Survey Rounds In 1993, GoI offered blocks for geophysical and other surveys to upgrade the information on hydrocarbon potential of India‟s unexplored sedimentary basins. After completion of the work, GoI was to offer these blocks in the subsequent rounds of exploration. Until 1996, GoI announced three such rounds with the last round called the Joint Venture Speculative Survey Round (JVSSR), 1995, with a provision of risk participation by DGH of up to 50 per cent.

The companies could enter into a speculative survey contract by signing profit sharing contract with GoI through their nominee, DGH. The contract could be for any type of geophysical survey and companies were free to bid for any number of blocks, on their won or by forming a consortium. The participation in these rounds, however, was very low because of high perceived risk and the long-time taken to settle negotiations.

The terms and conditions of JVSSR were as follows;

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Provision for cost sharing by Gol / DGH up to 50 per cent. Data acquisition, processing and interpretation work to start within six months of obtaining the petroleum exploration licence. The work should be completed within 24 months from the date of signing the contract. The total period for sale of data is up to seven years from the announcement of subsequent exploration round in case the block is not awarded.

The acquired speculative survey data can be sold to any interested hydrocarbon exploration company in India. The original data acquired, as well as all the processed, re-processed and interpreted date is to be given free to the government. The price of data packages and any subsequent change have to be agreed upon by the government.

Companies have to indicate the minimum work programme and the expenditure that would be incurred to complete it. Further, the company has to indicate profit- sharing with the government, which has to be based on a sliding scale, after cost recovery.

In the case of taxes and duties, the Income Tax Act, 1961 is to apply. Companies are entitled to customs duty exemption on goods imported for us in petroleum operations under the contract. Foreign companies are fee to remit amounts out of India, which are due to the company under the contract.

6.3.9(d). Analysis of foreign Investments in Speculative Survey Rounds The first two speculative survey rounds failed to generate any response from companies and as a result GoI decided to go for a joint venture round in 1995. A contract was signed in 1997 when DGH formed a joint venture with Western Atlas, USA, to undertake 2D seismic survey in the deep-water areas of Bay of Bengal. This was the first joint venture to be formed under the joint venture round for speculative surveys announced in October, 1995 Western Atlas

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acquired more than 10,900 standard line km of data in the eastern offshore region.

6.3.9.(e). Development Rounds GoI offered the development of small and medium sized oilfield (having proven reserves and discovered by ONGC or OIL) to the private sector in August, 1992. This was done because of limited finances available with GoI its resultant predilection to develop and produce in medium and bigger fields with better oil recovery prospects.

Two JV rounds for the development of already-discovered fields were announced by GoI. Since fields were already discovered by the BOC‟s, the risk element as opposed to the exploration bid rounds was minimal and hence the response was much better than the exploration rounds.

These development rounds evoked much enthusiasm, especially for the medium sized fields. A total of 117 bids were received response to GoI‟s First Round of Development of medium and small sized oil and gas fields.

Companies or consortiums could participate in the development of medium and small sized fields offered under the various rounds. The terms and conditions of the last round are stated below:

The joint venture to be formed for development of a medium sized field could be an incorporated venture with equity participation of up to 51 per cent and the interest of ONGC or OIL being 40 per cent ONGC or OIL had no participating or carried interest in the case of small sized fields.

On signing of a PSC between the company or joint venture and the government the sharing of profit had to be indicated in the offer based on a sliding scale tied to post-tax rates of return or multiplies of investment recovered. Further the

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percentage of annual production of crude oil and gas expected to be allocated for cost recovery purposes was to be indicted.

As against the First Round of Development where the private players had to supply natural gas to GoI, the Second round allowed private players to market their natural gas. However, the domestic market was accorded the first priority to market the natural gas produced from any field. Arrangements for marketing the gas produced were negotiable between GoI and the company. The pricing formula for gas was based on internationally accepted principles.

A signature and production bonus was to be paid. Royalty, cess and other applicable levies were also to be paid. Companies were subject to a corporate income tax rate of 50 per cent of the taxable income. Ring fencing was allowed for development costs. No private company in a consortium that was awarded a field for additional development could unilaterally withdraw from the consortium. Further, government approval was required for the induction of any new player. Companies were required to adhere to the original schedule and the government had the right to revoke the contract if companies did not follow the schedule.

6.3.9.(f). Analysis of Foreign Investments in Development Rounds The offer of small sized and medium sized fields in India by the GoI received overwhelming response from the companies as can be seen from the number of bids submitted against the blocks on offer.

However, the operators of the medium sized fields which were awarded the fields for development in 1994-95 faced certain roadblocks. The PSC‟s provided that crude oil/gas sales agreements would be drawn up within 90 days. Adhoc arrangements were made to buy oil / gas from these fields as they came into production. The adhoc prices delayed the cost recovery by the operators and resulted in a lot of frustration amongst them. The teams engaged for negotiating

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the crude sales agreements were different from the teams negotiating the PSC‟s which created problems for the operators.

Table-6.3.9(f) Year Round Medium sized field Small Sized fields Bids No. of offered offered Received fields for Offshore Onshore Offshore Onshore which contracts awarded or signed Aug One 6 6 10 21 117 18 1992 Oct Two 2 6 4 29 54 12 1993 Source: PetroFed, Paper on “Review of E&P Licensing Policy”, P-92.

In spite of all the above sustained efforts aimed at increasing the indigenous production of oil and gas through the efforts of the private sector, and PSUs, it was felt that much more needed to be done. It was emphasized that despite these good efforts, they were not leading to a substantial increase in the domestic production of crude oil and natural gas. Moreover, constraints were also faced in the sense that companies were not ready to engage enough risk capital investments in the exploration. As a result, over two third of the Indian sedimentary basins remained unexplored or poorly explored. Out of the estimated total prognosticated hydrocarbons reserves of 29 billion tonnes, only less than one fourth had been established. The efforts of the NOC‟s also needed to be complemented and there was also a need to provide a level playing field as well as competition to the NOC‟s by giving similar fiscal and contract terms as applicable to private players.

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6.4. New Exploration Licensing Policy:-

Given the historically prevalent situation till about 1990‟s, the GOI reviewed the policy of inviting investment in exploration of oil and gas including the fiscal and contract terms. Given the concerns and clear objectives in mind, the GOI in February 1997 formulated “the New Exploration Licensing Policy” (henceforth referred to as NELP). The Union Cabinet announced NELP in the 1997-1998 Budget. But that was merely the first step; the follow-through has been extremely inexpedient. The course has had its ups and downs. NELP has not come through smoothly. In fact the dithering of the Union Government has proven to be quite expensive two successive governments took ages almost two fiscal years to finalize the tax incentives promised to prospective investors Meanwhile fate frowned. Crude prices tumbled to almost half.

NELP hug fire due to the lack of inter-Ministerial consensus on action necessary to operationalize the policy. These included the pros and cons of the new petroleum tax code the compilation of attractive fiscal incentives for investors. For instance, North Block shot down the Petroleum Ministry‟s proposal to exempt E&P companies from the minimum alternate tax (MAT). This was one of the six recommendations in the new petroleum tax code. The Revenue Department consented to accord „infrastructure statuses to petroleum companies and all tariff concessions that go with it, but not exemption from MAT. The Department also turned down the suggestion that multinationals be allowed to pay the same rate of corporate tax as NOC‟s, when developing new exploration blocks. NOC‟s paid 35 per cent tax and transnational‟s, roughly 10 per cent more.

There were ambiguities in the clauses pertaining to taxation in the model production –sharing contract drawn up for NELP. Also, a clause, which offset a loss in one exploration block with profits from another, needed to be amended before the policy was notified, as it could lead to a substantial revenue loss for

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the government. Then, the Ministry of Law raised objection to the bid evaluation criteria and the bidding format.

Numerous such snags delayed the NELP notification. The delay sent out signals that the government was not serious about opening up the hydrocarbons sector to private participation. It seemed to be doing very little to make NELP a success. Attracting foreign investment in the high-risk capital-intensive business of oil and gas exploration is difficult at the best of times but all the more so when oil companies were reeling under the impact of slackening global demand and the consequent fall in prices. With oil companies all over the world mostly reporting poor second or third quarter results in 1998, wide-ranging cost cuts were certain to offset pressure on margins. Hence many of them would take a second look at their exploration priorities and slash budgets for high risk new ventures. As it is the probability of striking hydrocarbons in a low potential country like India was seen to be extremely low.

The way the exploration business was conducting, indicated that there was a little interest in addressing the basic concerns of potential investors. First, it did little to remove misgivings that only unattractive acreage was being offered to them even under the new policy. The Government did not provide potential investors easy access to geological data of the blocks that it had, thereby denying them the opportunity of studying the blocks before bidding. Also the Government was oblivious to the frustrating delays experienced by investors in starting work on the exploration blocks already awarded. Further, a Government decision on the price of the crude oil or gas discovered and produced was interminably delayed.

The Oil fields Regulation and Development Act, 1948, was also awaiting amendment. The Royalty Amendment Bill to this Act would usher in a new and more rational royalty regime for new exploration blocks under NELP. Since the Bill could not be adopted in Parliament, an Ordinance was passed. This enabled the Government to fix different cost and risk factors attached. The new royalty rates for on land areas are 12.5 per cent for oil and 10 per cent for

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gas. Offshore the rate is 10 per cent for oil and gas except for deep-water discoveries (beyond 400 m bathymetry), which are at 5 per cent for the first seven years of production. The Lok Sabha finally passed the Oilfield Regulations Bill in December, 1998.

So, after the various enthusiastic go-aheads and the almost immediate half commands, NELP finally took shape at the beginning of 1999. New year, new hopes. But some critics felt that the response to the maiden international bidding proposed under NELP would be lukewarm, as there were mostly cosmetic changes in 44 of the blocks on offer.

This was countered by pointing out that a substantial difference between these and the earlier blocks was that they were financially much more attractive than earlier fiscal packages. The NELP, however, does not change the perceived geological prospects of discovering hydrocarbons in the oil blocks until and unless data packages for the offered blocks are substantially upgraded through fresh exploration efforts by NOC‟s.

The main Features of NELP are:

1. Fiscal stability provision in the contract. 2. Finalizations of contract on the basis of Model Production sharing Contract (MPSC). 3. Petroleum tax guide to facilitate investors. 4. Possibility of seismic option in the first phase of the exploration period. 5. NOC‟s to compete for acreages. 6. No payment of signature, discovery or production bonus. 7. No customs duty on imports required for petroleum operations. 8. No minimum expenditure commitment during the exploration period. 9. No mandatory state participation carried interest by NOC‟S.

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10. Freedom to sell crude oil and natural gas in domestic market at market related prices. 11. Biddable cost recovery limit up to 100 per cent. 12. Sharing of profit petroleum based on pre-tax investment multiple achieved and is biddable. 13. No cess on crude oil production. 14. Royalty payment for crude oil and natural gas on ad-valorem basis.

Table 6.4.(a) Royalty Payment on ad-valorem basis under NELP

Onland Blocks Offshore Deep Water #

Crude Oil 12.5 Per Cent 10 Per cent 5 Per cent *

Natural Gas 10 Per cent 1 Per cent 5 Per cent * *For first years of commercial production # Beyond 400m bathymetry

15. Option to amortize exploration and drilling expenditures over a period of 10 years from first commercial production. 16. Contribution to site restoration fund fully deductible in same year for income tax. 17. Liberal depreciation provisions making companies eligible for further tax adjustments. 18. 7 years tax holiday from the commencement of production. 19. Conciliation and Arbitration Act, 1996, which is based on UNCITRAL model, shall be applicable.

Under NELP companies are required to bid for:

a) Work programme commitment

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b) Profit petroleum share expected by the contractor at various level of pre-tax multiple of investments. c) Percentage of annual production sought to be allocated towards cost recovery.

An objective Bid Evaluation Criteria (BEC) is in place wherein the following main parameters will be considered while evaluating the bids.

1. Technical capability of the bidding company consortium. 2. Operatorship experience 3. Financial capability of the bidding company consortium. 4. Work Programme. 5. Profit sharing offered by the bidder / along with proposed cast recovery limit.

DGH provides the companies with seismic data on the Indian sedimentary basins. Companies are free to purchase and inspect this data. Successful Winning bidders enter into a Production Sharing Contract, based on the MPSC.

The major differences between earlier rounds of bidding for exploration blocks and NELP are:-

Table 6.4.(b)

Terms Earlier Rounds NELP Companies exempt from payment royalty. No Royalty / Cess. Cess. ONGC/OIL to bear these Companies to bear on private companies behalf, as per fiscal package approved by government

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0 percent 40 percent at their option except for the Participating interest by Joint Venture Exploration No participation by NOC‟S Programme (JVEP), NOC‟s as government where they had to take nominees. 25 per cent 40 per cent interest from the beginning. NOC‟s had 30 per cent carried interest Carried interest of NOC‟s exercisable on No carried interest by commercial discovery, NOC‟s except in JVEP where they have working interest from the start. NOC‟s carried the burden NOC‟s get international on behalf of private price on their production Level playing field for companies and for their of oil and gas and NOC‟s own operations they did exemption from payment not get the same terms of customs duty and available to private cess. investors.

NOC‟s to compete for NOC‟s got acreage on NOC‟s to compete for acreage preferential basis. acreage Only half royalty for Incentive for exploration No special incentive deep-water to be paid in the initial seven years

NELP terms beneficial to NOC‟s

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1. NOC‟s are exempted form payment of cess (a concession of almost US $3.0/bbl.). 2. The maximum royalty rate is 12.5 per cent of international price as against 20 per cent of the administered price in non NELP areas. 3. Incentive for deep water exploration with only half of the royalty payable in the initial seven years from commencement of commercial production 4. Exemption from customs duty 5. NOC‟s to get international prices on their production of oil and gas 6. Seven-year tax holiday from the date of commencement of commercial production. 7. Liberal depreciation provisions will make companies eligible for further tax adjustments 8. Contribution made to the Site Restoration Fund Scheme is deductible in the year of Contribution and not in the year of Site Restoration as per earlier provision of the income Tax Act.

NELP terms beneficial to private investors-other than all the above benefits that are applicable to private investors as well, the following benefits also apply.

(a) Carried interest of NOC‟s at 30 per cent has been abolished. (b) Companies are free to have 100 percent participating interest as earlier up to 40 per cent participating interest was to be held by NOC‟s this will also provide operational flexibility to the companies in selecting partners of their choice. (c) A level playing field as no blocks is reserved for NOC‟s.

Progress under NELP

According to a DGH press release the progress of NELP in terms of exploratory wells drilled and discoveries made can be summed up as follows:

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i. From the year 2000 onwards, so far 71 wells have been drilled under NELP PSC‟s. Out of these 37 wells have been successful in terms of striking hydrocarbons. Thus the success ratio of exploratory wells drilled under NELP is 50 per cent which is very encouraging. ii. As many as 23 discoveries have been notified by companies like Cairn Energy, Niko Resources, Gujarat State Petroleum Corporation and Reliance Industries. Out of these discoveries, two discoveries by Niko in the block CB – ONN – 2002/2 have already been brought to production. One discovery, namely Dhirubhai – 2, by Reliance has been declared commercial. iii. Development plans for two deep-water discoveries of Reliance Dhirubhai – I & Dhirubhai -3 have been approved and materialized.

6.5. NELP Bidding Rounds

6.5.1. NELP-I:- The first round of New Exploration Licensing Policy (NELP) was announced on January, 8, 1999 by Gol. A total of 48 blocks were put on offer for exploration of oil and natural gas. Of these, 12 blocks were deep water (beyond 400 m isobaths), 26 shallow offshore and 10 were onland blocks. The bid closing date was August, 18, 1999. The companies could bid for one or more blocks, singly or in association with other companies and the successful company / consortium was free to form an unincorporated or incorporated venture.

For the first time in India, blocks categorized under the nomenclature of Deep water blocks were put on offer under NELP I under the pre-NELP rounds there were only two categories of blocks, either on land or offshore. Companies were provided with only the Basin Information Docket for the deep water blocks as there was no separate Data Package available for each block. However, seismic and gravity-magnetic data was made available for each of the blocks along with Satellite Gravity Data.

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By the bid closing date of August, 18th1999, GoI received 45 bid for the 27 blocks on offer. Ten foreign, 6 Indian private companies and 5 public sector enterprises submitted their bids. The PSC‟s were signed for 24 exploration blocks comprising 7 deep water, 16 shallow offshore and 1 on land.

A total of 16 discoveries have been made in two KG deep water blocks and one shallow offshore block in Mahanadi- NEC. These discoveries include the world class gas discovery made by the RIL-Niko Resources consortium in 2002 in the Krishna Godavari (KG) basin deep water block KG-DWN-98/3. The other two discoveries include the gas discovery made by Scottish company Cairn Energy in 2001 in the deep water block KG – DWN 98/2 and gas discovery by RIL in block NEC-OSN-97/2 in the Mahanadi NEC shallow offshore area.

6.5.1.1. Analysis of foreign investment under NELP-I

The foreign companies which bid under NELP I, either on their own or in consortium with an Indian Company, include Enron Corporation-USA, PETRONAS Carigali-Malaysia, OAO Gazprom – Russia, Energy Equity India petroleum Pty Ltd.-Australia, Cairn Energy-Australia, Niko Resources Ltd.,- Canada, Geopetrol International Inc,-Panama, Mosbacher India LLC-USA, Grynberg Petroleum Co (RSM Production Corporation, USA) and south Asia Oil & Gas Plc-Australia.

Out of the 10 foreign companies who submitted their bids under NELP I only 5 were successful in bagging a block. These foreign companies were Cairn Energy (1 block), Niko Resources (12 blocks in consortium with RIL). OAO Gazpron (1 block) and 1 block by Mosbacher India Ltd, and Energy Equity India Pvt. Ltd.

6.5.2. NELP II (2000)

GoI invited bids under NELP II on December 15th, 2000 for 25 blocks for exploration of oil and natural gas. Of these, 8 blocks were deep water, 8 shallow offshore and 9 were onland blocks. For the first time blocks in the west coast

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were put on offer as at that time more than 50 per cent of the countries crude production came from ONGC‟s Mumbai High fields on the west coast. The bidders were given time duration of three and a half month to submit their bids and file their documents by March 31st, 2001.

After the NELP I round, comments were invited from 43 E&P companies and organizations on the MPSC and based on the comments received GoI approved some changes to the MPSC issued under NELP I. Also, to increase transparency in the bidding process and to make it more investor friendly the weightage of the broad parameters for bid evaluation were made public for the first time.

The PSC‟s for the 23 blocks were signed on July 17, 2001, three and half months from the closure of bids on March 31, 2001 as against just about seven and half months in the first round of NELP. The total investment committed in these 23 blocks was US$290 million (Rs. 1,300 crore) in phase –I and US$ 788 million( Rs 3700 Crore) in all three phases.

A total of 3 discoveries have been made in two blocks viz. CB-ONN-2000/1 & CB-ONN-2000/2 located in Cambay basin which were offered under NELP II GSPCL discovered oil in the CB-ONN-2000/1 block in August 2004. Niko Resources struck natural gas in the CB-ONN-2000/2 block in 2002. Subsequently significant quantities of Shallow gas (NSA field) have been discovered in the block.

6.5.2.1. Analysis of foreign Investment in NELP II

To woo private investors, both foreign and domestic, GoI appointed IHS Energy Group of USA as the marketing consultant for NELP II and road shows were held at Delhi, London, Houston, Singapore and Tokyo. Among the major oil firms that participated in these road shows included shell, British Petroleum, British Gas Premier Oil, Cairn Energy, Exxon Mobil, Marathon, Philips, Chevron. Texaco and Pertamina of Indonesia.

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The foreign companies who submitted their bid under NELP II round were Niko Resources, Canada, Cairn Energy, UK, Petrom, Romania, Heramec, UK, Hardy Exploration & Production India, UK, Joshi Technologies USA, Petrobas, Brazil, ExxoMobil, USA, Premier Oil Pan Canadian, Total Fina Elf France and BHP petroleum Australia.

6.5.3. NELP III (2002)

NELP III was announced on March 27th, 2002 and bids were invited by the GoI for 27 blocks for exploration of oil and natural gas. Of this 9 blocks were deep water, 7 shallow offshore and 11 were on land blocks. The bid closing date was August 28, 2002.

As in the previous rounds, the GoI undertook a comprehensive promotional exercise to promote the blocks though five road-shows at New Delhi, Singapore, London, Houston and Calgary and through an exclusive NELP III Indigo Pool website.

A total 45 bids were received for the 23 blocks on offer under the NELP III by the bid closing date. Out of the 27 blocks on offer, PSCs were signed for 8 on land blocks, 6 shallow water offshore blocks and 9 deep water blocks. No bids were received for 3 on land blocks and 1 shallow-water offshore block.

A further analysis of the NELP III response reveals that 18 blocks attracted multiple bids, whereas 5 blocks attracted single bids. Thus about 78 per cent of the blocks on offer attracted multiple bids under NELP III as compared to around 50 per cent blocks attracting multiple bids under NELP – I and NELP – II.

6.5.3.1. Analysis of foreign Investment in NELP III

The year 2002 was a mixed bag for the foreign E&P investors. On one hand efforts were being made by the GoI to attract foreign investments such as deregulation of the petroleum sector w.e.f. April, 1st 2002, and reduction in the income tax rate applicable to foreign companies from 48 per cent to 40 per cent while on the other hand apprehensions were being expressed by the investor

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community regarding the Indo-Pakistan border tension and travel advisories issued by certain countries.

By the time NELP III was announced in March 2002 hydrocarbon discoveries had been made under the first two rounds of NELP which included discoveries by Cairn Energy in block KG-DWN-98/2(NELP I) and Niko in block CB-ONN-2000/2 (NELP II). The Government, on the strength of these discoveries, was confident that the perception long held by the foreign E&P companies regarding the low to moderate hydrocarbon prospectivity of India, would change and that such discoveries would instill confidence among the foreign investors while investing in India.

However, the NELP III round received lackluster response from the foreign E&P companies. A total of 11 companies submitted their bids out of which 7 were domestic oil companies and 4 were foreign companies which included Cairn Energy, UK. Premier Oil, UK. Hardy Exploration and production, UK. and Geo Global Resources, Canada. Amongst the foreign companies Premier Oil and Geo Global Resources had bid for the first time under NELP.

Scottish explorer Cairn Energy and Premier Oil of UK had bid for one and three blocks respectively but drew a blank. However, Hardy Oil of UK in consortium with RIL was successful in bagging seven of the nine prime deep water blocks on offer, Geo Global Resources in consortium with Gujarat State Petroleum Corporation (GSPC) and Jubilant Enpro, was successful in bagging the KG- OSN-2001/3 in which a huge gas discovery of 20 tcf has been reported in June 2005 by GSPC.

6.5.4. NELP IV (2003)

Fourth round of NELP was announced by GoI on May 8th, 2003, under which it invited bids for 24 blocks for exploration of oil and natural gas. Of these, 12 blocks were deep water, 1 shallow offshore and 11 were onland blocks. The bid closing date was September 30th, 2003.

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As many as 48 companies reviewed the data packages for the 24 blocks on offer. A total of 19 companies submitted their bids. Out of these 12 were domestic (six Public Sector and six Indian private companies), and seven were foreign. Nine companies were first time bidders under NELP PSC‟s were signed for 20 exploration blocks comprising 10 deep water and 10 on land blocks. The two blocks in Manipur and Palar Offshore basin did not receive any response.

Some of the changes made by NELP IV include:

(a) Provision of fast-track arbitration.

(b) Higher weightage for technical and financial capability for deep water blocks. (c) Surcharge on income tax for foreign companies abolished. (d) Bank guarantee to be returned after minimum work programme completion.

6.5.4.1. Analysis of foreign investments in NELP IV

NELP IV was expected to generate a large participation from the foreign companies, especially in the aftermath of world class gas discovery reported by the RIL-Niko consortium in the KG-DWN-98/3 block in October 2002 in the KG basin. Although international oil majors like Total, ExxonMobil and Shell showed interest, especially in the deep water blocks, but decided not to participate in the NELP IV bidding process.

The seven foreign companies who participated in NELP IV round were Enpro Finance, Niko Resources, Canada, Canoro Resources, Canada, Cairn Energy, UK,; Geoglobal Resources, Zarubezneftgaz, Russia,; BG, UK; and Hardy Exploration & Production, UK.; Out of the above 7 foreign companies Zarubezneftgaz, BG and Canoro Resources had participated for the first time under NELP.

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6.5.5. NELP V (2005)

NELP V was launched on January 4th 2005 offering 20 blocks six deep water blocks, two shallow water blocks and 12 on land blocks. The launch was earlier scheduled for May 25th but was postponed due to the political uncertainty in the wake of the general elections. For the first time Maharashtra was included for exploration under NELP – V. The bid closing date was May 31st, 2005.

A total of 69 bids for 20 blocks (18 bids for six deep-water blocks, seven bids for two shallow water blocks and 44 bids for 12 onland blocks) were received. On July, 25th, 2005 the Cabinet Committee on Economic Affairs (CCEA) approved the award of 18 blocks under NELP V.

Some of the new features introduced under NELP V are:

1. All Geo-Scientific data was made available online through the internet to enable companies to view data at their own convenience and location. Work stations were provided at Data Centers at London, Houston, Calgary and Dubai to facilitate companies to review and analyze data and to provide on the spot clarifications. 2. Details of all operational blocks from earlier rounds such as work programme, fiscal terms, etc., were made available at Data Centers to enable companies to assess existing work programme as well as other bidding parameters while formulating their own bids and also help them in forming strategic alliances. 3. In order to provide marketing stability to the companies. The Government decided to exercise its option to take its profit share of natural gas in cash or in kind for a block of 5 years instead of such option being made every year as in the previous rounds. 4. In order to encourage small and medium sized investors, companies having a net worth of US$ 500 Million or more were not required to give a bank guarantee towards MWP commitment in

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respect of onland and shallow water blocks. This threshold value in the previous round was US$ 1,000 Million. 5. In order to provide transparency to the bidding process, weightage for all bid evaluation criteria including weightage for sub-criteria were made public under NELP V for the first time.

6.5.5.1. Analysis of foreign investments in NELP V

A total of 26 foreign companies and 21 Indian companies (eight Public Sector Undertaking and 13 Private Sector Undertakings) submitted their bids.

Out of the 26 foreign companies 17 companies submitted their bids for the first time. These companies are British Petroleum (UK), Petrobras (Brazil), ENI SPA (Italy), Hunt Oil (UK), Beach Petroleum (US), KUFPEC (Kuwait), Norwest Energy (US), Suntera Resources Limited (Russia), Zakros Holdings Ltd. (Cyprus), Foresight (UK), Providence Resources (UK), Birkbeck Investment Limited (Mauritius), Exspan Exploration and Production International (Indonesia), Istech Resources Asia (Indonesia), Jubilant Energy India (V) Ltd., (Cyprus), and Welwyn Resources Limited (Cananda).

Energy majors like shell (US),Total (France), BHP Billion (Australia), Statoil (Norway) , showed interest after initially purchasing the data packages for various blocks but stayed away from submitting bids at the last moment.

The handsome response generated by NELP V as compared to the previous NELP rounds can be attributed to the professional and extensive promotional exercise undertaken by the Government in promoting NELP V acreages and also the huge gas and oil discoveries made by Reliance Industries Ltd., Niko Resources consortium and Cairn Energy.

6.5.6. NELP VI

A total fifty five block (55) were offered during NELP VI round for exploration of oil and natural gas in 16 prospective sedimentary basins consists of 25 onland, 6 shallow water and 24 Deep water blocks. 165 bids from 68 E&P companies (36

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foreign and 32 Indian) had participated in the bidding process as consortium / individually. The PSC‟s were signed for 52exploration blocks comprising 21 deep water, 6 shallow water and 25 onland. The exploration activities are going on in 52 awarded blocks.

6.5.7. NELP-VII

A total of fifty seven blocks (57) were offered during the NELP VII round for exploration of oil and natural gas in 18 prospective sedimentary basins consists of 29 Onland, 9 shallow water and 19 deep water blocks. On 22 December 2008 Contracts were signed for 41 blocks out of which 11 blocks in deep water, 7 blocks in shallow water and 23 onland blocks.

6.5.8. NELP VIII

Under the eighth round of New Exploration Licensing Policy (NELP-VIII), Government has offered 31 production sharing contracts on 30 June 2010. There are 8 deep water blocks, 11 shallow water blocks and 12 onland blocks which are in state of Assam(2), Gujrat(8), Madhya Pradesh(1) and Manipur(1).

6.5.9. NELP IX

A total of 33 exploration blocks were offered during bidding process. State owned Oil and Natural Gas Corporation Ltd(ONGC) bagged 10 of the 33 oil and gas exploration blocks. Oil India Ltd(OIL) bid for as many 29 blocks and managed to get 10. Reliance Industries bid for two deep sea blocks in Andaman Basin in the Bay of Bengal and four onshore blocks in Rajasthan and Gujrat.

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6.6. Downstream sector (Refineries in India):

To meet the growing demand of petroleum products, the refining capacity in the country has gradually increased over the years by setting up of new refineries in the country as well as by expanding the refining capacity of the existing refineries. As of June, 2011 there are a total of 21 refineries in the country comprising 17 (seventeen) in the Public Sector, 3 (three) in the Private Sector and 1 (one) as a joint venture of BPCL & Oman Oil Company. The country is not only self-sufficient in refining capacity for its domestic consumption but also exports petroleum products substantially. The total refining capacity in the country as on 1.6.2011 stands at 193.386 MMTPA. The company-wise location and capacity of the refineries as on 1.6.2011 is given in Table 6.6:

Table 6.6. Refineries in India.

Name of Location of Capacity,MMTP S.no Company Refinery A

Indian Oil Guwahati, 1. Corporation 1.00 Limited (IOCL) Assam

Indian Oil

2. Corporation Barauni, Bihar 6.00 Limited (IOCL)

Indian Oil Koyali, 3. Corporation Vadodara, 13.70 Limited (IOCL) Gujarat

Indian Oil Haldia, West 4. Corporation 7.50 Limited (IOCL) Bengal

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Indian Oil Mathura, 5. Corporation 8.00 Uttar Pradesh Limited (IOCL)

Indian Oil

6. Corporation Digboi, Assam 0.65 Limited (IOCL)

Indian Oil Panipat, 7. Corporation 15.00 Limited (IOCL) Haryana

Indian Oil Bongaigaon, 8. Corporation 2.35 Limited (IOCL) Assam

Hindustan

Petroleum Mumbai, 9. 6.50 Corporation Maharashtra Limited (HPCL)

Hindustan Petroleum

Corporation Visakhapatnam, 10. 8.30 Limited Andhra Pradesh (HPCL)HPCL, Visakh

Bharat

Petroleum Mumbai, 11. 12.00 Corporation Maharashtra Limited (BPCL)

12. Bharat Kochi, Kerala 9.50

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Petroleum Corporation Limited (BPCL)

Chennai

Petroleum Manali, Tamil 13. 10.50 Corporation Nadu Limited

Chennai

Petroleum Nagapattnam, 14. 1.00 Corporation Tamil Nadu Limited (CPCL)

Numaligarh Numaligarh, 15. Refinery 3.00 Ltd.(NRL) Assam,

Mangalore

Refinery & Mangalore, 16. 11.82 Petrochemicals Karnataka Ltd. (MRPL)

Tatipaka Tatipaka, 17. Refinery 0.066 (ONGC) Andhra Pradesh

Bharat Petroleum Corporation Bina, Madhya 18. 6.00 Limited & Oman Pradesh Oil Company, joint venture,

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Bina

Reliance

Industries Ltd. Jamnagar, 19. 33.00 (RIL); Private Gujarat Sector

Reliance

Petroleum Jamnagar, 20. 27.00 Limited (SEZ); Gujarat Private Sector

Essar Oil Jamnagar, 21. Limited (EOL); 10.50 Private Sector Gujarat

TOTAL 193.386

Source: Indian Petroleum and Natural Gas Statistics, 2010-11, Govt. of India Ministry of Petroleum and Natural Gas, Economic Division, New Delhi.

6.7. Policy Framework

The objective of the GOI, for the refining sector was clear; move towards increased self-sufficiency and an uninterrupted supply of the country‟s basic requirements of petroleum products particularly petrol, kerosene, and diesel. This thinking is reflected across most of the Five Year Plans. Interestingly, this was also one of the main objectives when the multinational giants in the country built the first three refineries, in early 1950‟s. Until the early 1950‟s, India was almost entirely dependent upon imports for requirements of all kinds of petroleum products. The only source of domestic production was a small refinery established by the Assam Oil Company, a subsidiary of the larger Burmah Oil Company, at Digboi in the north-east of Assam. By 1951, the Indian requirement

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of petroleum products was about 4 million tonnes per year, and increasing at the rate of some 10 per cent a year (Khera, 1979).

Post-Independence, the first major development took place in the early 1950s with the establishment of three privately owned refineries by international oil companies, under the terms of formal agreements made with the GOI. All these were coastal refineries, two of them at Bombay (now Mumbai) owned by Burmah Shell and Esso respectively, and the third owned by Caltex in Vishakhapatnam (Henderson, 1975). The combined capacity of the three refineries was just less than 4 million tonnes of crude a year, just sufficient to supply the existing demand, but without the provision for an expanding market (Khera, 1979). These were important developments as far as the setting up of refineries was concerned. In the 1948 Industrial Policy Resolution, the GOI placed future development of the Oil industry, along with six other basic and strategic industries, under the ownership of the GOI.

It was not until mid-fifties that the idea of public ownership of refineries was translated into action (Dasgupta, 1971). The GOI then in 1959 set up an agency the Indian Oil Company, to undertake the distribution and marketing of oil products. The company had the responsibility of handling the distribution of the output of two PSU‟s refineries, which were under construction and later of the third refinery projected in Gujarat (GOI, 1961). Two more refineries, both in PSU, were constructed at Guwahati in Assam and Barauni in Bihar, to refine crude supplies from the eastern fields, and another refinery constructed at Koyali in Gujarat. It is important to point out that even at this early stage of the development of the Indian refining industry, questions were raised on the public ownership of refineries.

The expansion in the refining capacity was necessitated, given the growth in the demand for petroleum products. The period from mid-1950‟s to till about late 1970‟s was the period of rapid phase in demand for petroleum products, given that the country was seeing growth industrialization and demand for

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transportation. The consumption of petroleum products, which was 5.2 million tonnes in 1956, registered a more than fourfold increase by 1973 when it touched 23.7 million tonnes. After a brief respite, due to price increase, when the demand remained static at around 23 million tonnes, the growth in consumption has been quite perceptible being 25.4 million tonnes in 1976-77, 27 million tonnes in 1977- 78, 28 million tonnes in 1978-79 and 29.65 million tonnes in 1979-80. Such was the growth in demand for petroleum products that even with the additions of refining capacity we were expected to face shortage, leading to import of crude oil, and so also petroleum products. For instance, the Sixth Plan warned that even after completion of various schemes in refining sector, the availability of petroleum products will increase to only about 41 million tonnes against a demand of 49 million tonnes. Thus, it would be necessary to install additional refining capacity to the extent of 9 million tonnes by 1985-86 to keep the need for import of products at a manageable level (GOI, 1980).

Apart from the ever increasing problem of meeting the demand for petroleum products through the addition to the refining capacity, during the decade of 1960‟s and 1970‟s there were also problems in the optimum utilization of the existing Indian refineries. For instance (Hederson, 1975) rate of utilization was higher in the public and joint sectors than in private sector, which was mainly due to the disagreements between the GOI and the private companies, relating to both the amount of capacity which the companies were properly authorized to create and to sources, prices, and amounts of crude oil to be processed. By 1976, however, negotiations between the GOI and the foreign oil companies had resulted in mutually satisfactory agreements whereby the GOI took over the refineries and their running and paid mutually acceptable compensation to the companies (Khera, 1979).

During the 1950‟s and 1960‟s, the problem of product imbalance dominated the discussions of the oil sector in India. The refineries in India produced a surplus of light distillates, which accompanied an acute shortage of middle distillates, kerosene and diesel. The problem was that it gave rise to the hard task of

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finding markets for surplus products in a competitive world, while continuing to import, in exchange for foreign exchange, the deficit products (Dasgupta, 1971). Given the characteristics of Indian refineries, on one hand, lack of adequate secondary processing facilities and, on other hand, high demand for middle distillates, the tendency sometimes had been to think of other products as potentially surplus. Thus, conscious attempts were made during the 1960‟s to develop indigenous fertilizer and petrochemical plants based on naphtha as a feedstock, because surplus of light distillates was otherwise anticipated (Henderson, 1975). Analysts have time and again called for a design of refineries which more closely matched the pattern of product demand. The point was to develop secondary processing facilities of the Indian refineries.

6.7.1. Product Imbalance

One of the major distinguishing characteristic of Indian refining sector has been the proportionally high share of the middle distillates over the years. For instance, Henderson (1975) pointed out that during the decade from 1951 to 1961 the share of middle distillates gradually rose well to over 50 per cent, and that of other products also raised, both at the expense of the share of light distillates. Even, if we analysis the product wise consumption of petroleum products of PSU‟s, we see that at the end of 2001 to 2010, the consumption of light distillates is 20.4% , middle distillate 47.4% and heavy ends 11.0% respectively.

Compared to other countries, the share of middle distillates is unusually high even before and after deregulated era. This reflected the continuous usage of kerosene for domestic purposes, as well as the growth of consumption of diesel. Analysts have critiqued this particular development in the Indian fuels market over the years. Bhatia (1985) has pointed out that mainly on account of differential in excise duties there were significant differences in the market prices of petrol and diesel, which has provided the incentives for consumers to shift away from petrol using vehicles to diesel using vehicles, thereby exacerbating

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the growth in diesel consumption and hence pressure on the Indian refining industry to meet the demand for this fuel. Dasgupta (1968) pointed out that the discount rate imposed by the GOI in late 1950‟s and 1960‟s based on the pricing Committees‟ had aggravated the problem of product imbalances, by making the production of petrol more attractive than that of middle distillates from the point of view of the refineries.

Clearly, a conscious policy decision had created an unusual situation in the Indian fuels market. Interestingly enough, this anomaly still persists in the fuels market. That is, the historical practice still continues. A look at the consumption pattern highlights this fact due to differential taxation structure for both fuels. GOI too was equally conscious about the skewed pattern of demand for petroleum products. For instance, the Seventh Plan pointed out that such skewed growth is not compatible with refining capabilities. While with a hydrocracker, it may be possible to obtain from suitable imported crude over 60 per cent as middle distillates, the maximum yield from Fluid Catalytic Cracking (henceforth referred to as FCC) secondary processing facility which is presently installed in most of our refineries was about 52 per cent (GOI, 1985).

During the late 1980‟s and early 1990‟s major increases in the demand of middle distillates were foreseen and technology options were accordingly selected. It was realized that the hydrocracking option offered a technically more acceptable solution to maximize the production of middle distillates of very high quality and to offer the flexibility of upgrading existing refinery streams to the desired product quality by blending. Use of this process technology also made possible the processing of relatively low API and high sulphur crude as well. Accordingly, during this period a number of project arose where in hydrocracking was the primary secondary processing facility in grass-root units and a number of existing refineries. Hydrocracking units were installed in parallel or upstream of FCC units with the objective of improving product slate and providing additional operating flexibility (Singh & Babbar, 2005).

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6.7.2. The Regulated Era

Oil industry in India was operating as a free market till the mid-1970‟s and many of the multinational oil companies like Shell, Caltex and Esso had a significant presence in the market. Nationalization of the industry during mid1970‟s resulted in the private players being bought out by the GOI. Such was the phase of nationalization that from 3 per cent in 1962, the share of PSU refineries increased to about 93 per cent in 1976. This was in sharp contrast to the situation prevailing till about 1960 when foreign oil companies had the monopoly of refining and marketing of petroleum products in India. All the time units comprising oil industry were expected to be very soon under the GOI ownership during those times. Since then the state owned PSU‟s played a dominant role in this sector (Sudararajan, 2000).

The move towards nationalization also coincided with the implementation of recommendations of the Oil Prices Committee 1976. The concept of administered price mechanism (henceforth referred to as APM) in the oil industry was introduced by GOI (1976). A system of retention prices for each product and for each refinery was introduced with effect from 16th December, 1977. GOI (1976) suggested various norms and averages for industry performance. To adjust any variations from norms and averages, GOI (1976) suggested various pool accounts. Given the emergence of so many pool accounts; a need was felt for some organization to administer the pool accounts, among other things.

Thus, in the mid 1970‟s based on GOI recommendations, GOI(1976) set up a Committee named as Oil Coordination Committee (hence referred to as OCC) to provide technical and operational support to the MoPNG in making policy decisions and their implementation . In a sense the OCC acted as a regulator. MoPNG regulated the import of crude oil and refined petroleum products through the Empowered Standing Committee (ESC) set up by the Cabinet. Each refinery‟s crude Oil production and allocation were, to the extent feasible,

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controlled in line with the demand in its supply envelope movement of petroleum products to various demand centers was governed by the industry supply plan. This plan was formalized through the Industry Coordination Meeting and the Supply Plan Meeting, which were held on a monthly basis, which endeavoured to ensure availability of products to all the oil companies from various sources. Also it had to be ensured that there should not be any unwanted movement of goods. This was taken care by the hospitality arrangement among different oil companies. The amount charged for use of the owner company‟s facilities by other companies was regulated by the GOI and was based on the “costs plus a reasonable return on investment” principle (GOI, 1996).

The enforcement of distribution discipline and equalization of prices at the refinery gate were achieved though the various oil pool accounts by the OCC. The OCC administered the oil pool accounts under the APM and controlled the inflows and outflows to the pool account. Development of retail marketing network was decided by the GOI through annual marketing plans. Selection of dealers/distributors was accomplished with the help of a Committee appointed by the GOI, the oil selection boards. The enrolment of LPG customers, ceilings on distributors refill sales, kerosene quotas and commissions for dealers / distributors were also controlled by the GOI.

The pool accounts were maintained to provide uniform and stable prices within the country. They were supposed to be self-balancing. The inflow to the pool account was from the collection of surcharges on sale of petroleum products while the outflow was for meeting the variation in the elements of standards cost. The difference between the inflows and outflows represented the surplus/deficit position of the pool accounts. Though the number of pool accounts was more than 50, there were few key accounts for the major inflows and outflow. The OCC did play a significant role in almost all the policy decision-making processes in the downstream sector of the Indian oil industry. In the heydays of control in the oil industry, the OCC played a significant role. It acted in many ways as a downstream corporate planning – cum – supply – planning department.

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Thus from mid-1970‟s to about early 1990‟s the Indian Oil Industry evolved in a controlled environment, largely determined by GOI, 1976 and obviously by the then ruling GOI policies. The functioning of the Oil Industry was totally regulated, as was succinctly shown in the above analysis. The first wave of reforms in the sector started under the overall economic reform process that was initiated across the Indian economy from early 1990‟s.

6.7.3. Changing face of the industry: the reform process

After attaining political independence, our planners preferred to adopt the socialistic pattern of the society to attain economic self-reliance. The Second Five Year Plan stated the adoption of socialistic pattern of society as the national objective, as well as the need for planned and rapid development requires that all industries of basic and strategic importance, or in the nature of public utility services, should be in public sector. Other industries, which are essential and require investment on a scale, which only the state in the present circumstances, could provide, have also to be in the public sector. The state had, therefore, to assume direct responsibility for the future development of industries over a wide area. However, this perception started changing when after four decades of socialism. It was realized that PSU‟s seemed to perform well only when protected through GOI created monopolies, entry reservations, high tariffs, quotas etc. (GOI, 2002).

The major deviation from the then followed policy of socialism started in 1991 when the GOI started deregulating the areas of its operation and subsequently the disinvestment in PSU was announced. The Industrial policy of the 1991 started the process of de-licensing. The Industrial Policy Statement of July, 24th 1991 stated that the GOI would disinvest part of its holdings in selected PSU‟s but did not place any cap on the extent of disinvestment. Nor did it restrict disinvestment in favour of any particular class of investors. It was acknowledged that deregulation and dismantling of the bureaucratic controls along with the liberalization of trade, technology and capital inflows were some of the far –

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reaching changes initiated by the GOI under the structural reforms introduced from July 1991. The focus thus shifted to ensuring fair business practices, safeguarding consumer interest, and minimizing adverse effects of industrializations on the environment. No wonder in accordance with these overall objectives of the GOI, the Oil Industry also needed to be deregulated. Deregulation not only encourages domestic enterprises but it is also considered as an essential ingredient for improving the climate for foreign investment (GOI, 1995).

Beginning with the early 1990‟s a number of policy initiatives were taken by the GOI. For instance, in 1992, lubricating oil were first to be decontrolled when the import of base oil for blending of lubricants was allowed. International majors like Shell, Mobil Exxon and Caltex took advantage of this and started marketing their lubricants in the county. Again in February, 1993, the private sector was allowed to import LPG and kerosene under their own arrangements and sell it at market related prices. No controls on distribution or pricing were exercised. In addition to the products covered under the parallel marketing scheme, imports were allowed for products like the ATF, furnace Oil (henceforth referred to as FO), Benzene, Toluene, and Bitumen against “Special Import Licenses” (Sundararajan, 2000). The situation during this early phase of reform was very critical as far as the oil industry was concerned. On one hand, the share of oil and gas in commercial energy consumption was increasing, and on the other hand, domestic resources to meet this shift in consumption were dwindling .

During the last two decades leading to the early 1990s, the value of net imports of crude oil and petroleum products was consistently increasing, in the later part of the Seventh Plan; the import bill on petroleum products was substantial. This was largely on account of stagnation in domestic crude oil production levels (GOI, 1992). No wonder one of the thrust areas of the Eighth Plan was to restrict oil imports to reasonable levels. The Eighth Plan clearly spelled out the concerns when it pointed out that the import bill of petroleum products continues to be substantial and in fact has increased in later part of the Seventh Plan, as the

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level of demand satisfaction from indigenously available crude oil has declined from 70 per cent in 1985-86 to 56 per cent in 1990-91. This was on account of the stagnation in domestic crude oil production levels. It was envisaged that any further increase in dependence on oil imports, due to an increase in demand, is likely to pass severe pressure on foreign exchange reserves and in view of the uncertainty of world oil prices, make the economy more vulnerable. It would, therefore, be necessary to examine the oil intensity and dependence on petroleum products in each sector of the economy and to find ways to contain, and where possible to compress, the demand for the oil products.

The Report of the Group on “Hydrocarbon Perspective 2010: Meeting the Challenges” (GOI, 1995) laid the stone for the deregulation of the Indian Hydrocarbon sector. GOI (1995) was expected to carry out an in-depth analysis and make suitable recommendations, which would be inputs for consideration by a Strategic Restructuring Group also called the „R‟ Group (GOI, 1996). GOI (1995) among many recommendations recommended that it was necessary to abolish the APM and introduce market determined pricing mechanism where in the prices of crude oil and petroleum products will be determined by market forces.

GOI (1996) observed that oil industry also need to be liberalized with easier entry for a range of actors that could contribute to its development in keeping with the national objectives. This will include the private sector in India as well as international companies that could do business in this country‟s hydrocarbon sector. One of the most important perspectives of GOI (1996) was the emphasis on the need to introduce competition, domestic and international, in the hydrocarbon sector, upstream, mid-stream and downstream. Competitive markets and consequential market determined prices would help to mobilize massive resources for investment required and also deploy them effectively.

APM had worked satisfactorily until recently and helped the PSU oil companies to grow under a protective environment. However, APM had become a serious

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handicap in securing oil supplies for future. In order to achieve the primary objective of securing oil supplies to meet the future growing demand, it would be absolutely necessary to move towards a market-driven price mechanism and to free the petroleum sector from APM. GOI (1996 recommended the gradual phasing out of APM and introduction of a free marketing mechanism. In 1996, the GOI also appointed an Expert Technical Group (henceforth referred to as ETG) to examine the impact on various sector at different levels of duty structure in the face of dismantling of APM. The ETG dealt with phased movement to market determined pricing mechanism and rationalization of custom tariffs, and exercise duty rates in respect of dismantling of APM along with its impact on various other sectors.

The recommendations of GOI (1996) and the ETG paved the way for a phased dismantling of the APM structure. On 1st April 2002, the Administered Pricing Mechanism (APM) for petroleum products was abolished as a part of the continuing reform of petroleum sector towards a sector based on market mechanism. In theory, India‟s public downstream oil companies would now be free to set retail prices of all petroleum products based on an international parity pricing formula under the supervision of a petroleum sector regulator. The Government would abstain from influencing petroleum product pricing. Until then, prices were controlled (or administered) for two transport fuels, petrol and high speed diesel, two cooking fuels, kerosene and LPG. Therefore, with the beginning of the new F.Y on 1st April 2002, the APM and with it oil pool account was abolished.

The subsidies for the two cooking fuels are considered an important social instrument to help poorer households shift from biomass to modern fuel (The World Energy Outlook 2006 includes a discussion on health hazards of and premature deaths resulting from cooking with biomass). Following the abolishment of the APM, the Government would thus provide subsidies for kerosene and LPG ex-ante in its annual budget. Subsidies would not exceed 15% of the LPG and 33% of kerosene import parity price respectively. Within 3 to maximum 5 years

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all budget subsidies on LPG and kerosene would be abolished and market prices would be in place for all petroleum product in India. Petrol, diesel , LPG and kerosene account for 60 % of India‟s total petroleum product consumption. Diesel is India‟s single most important fuel as most of its vehicles, commercial and private, have diesel engine. Over 75% of India‟s crude requirement is imported.

The practice of retail price setting was different from the theory right from the beginning of the post-APM period .The so-called Public downstream “Oil Marketing Companies” (OMC) implemented regular retail price adjustment for petrol and diesel during first two financial years following the abolishment of APM. Despite these regular price increase the OMC incurred minor shortfalls for the sale of petroleum and diesel. However, these shortfalls were mitigated through the refining margins which now benefited from the import-parity pricing formula.

As of 1 April 2004 the intervals between price revisions grew larger and the OMCs started to incur substantial under recoveries for these two products in line with the drastic increase in international crude prices. This was the case despite a new semi-monthly automatic price adjustment formula put in place by Government on 1 August 2004, The formula gave the public the impression that prices were indeed set by the market while in reality OMCs were still required to seek approval from MoPNG for each price adjustment.

According to this formula the OMCs could increase prices on the basis of a rolling average CIF price of the last three months with a +/- 10% band. However when international prices continued to climb the formula was quietly abandoned as more often than not the OMCs were requested by the GOI to keep prices constant for social (and political) reasons. This resulted in mounting losses on account of sales of petrol and diesel to OMCs, the similar case for cooking fuels, thus OMCs suffer most. Therefore, GOI realized that the financial burden imposed on the OMCs was getting critical and was potentially undermining their long term financial health. Since the most obvious action, a sufficient increase in

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retail prices, was not considered politically feasible, the GOI came up with an innovative solution, let the upstream oil companies ONGC, OIL and Downstream natural gas company GAIL share the burden of under recovery. GOI also started issue government bonds to OMCs covering also one third expected under recoveries. Finally, “Rangarajan Committee” was formed under the Chairmanship of Dr. C. Rangarajan and the committee presented report in February 2006.

6.7.4. Rangarajan Committee Report.

The recommendations of the committee were based on the following principles: a) taxation should be rationalized to improved efficiency, b) petroleum product price should be aligned to the international prices, c)subsidy should be targeted to help BPL(Below Poverty Line) families and it should be transparently accounted for in the union budget, d)custom duties should be rationalized to ensure that domestic refineries are not at a disadvantage, e)excise duties should be rationalized to shield consumers from price volatility. The specific recommendations of the committee were as follows:

1. The price of motor spirit and diesel to refineries should be weighted average of import parity and export parity prices in the ration 80:20. This was based on the data that about 20% of refinery products are exported. 2. The customs duty on motor spirit and diesel should be reduced to 7.5 %, thereby reducing the protection to refineries. 3. The government should allow OMCs to fix retail prices of motor spirit and diesel, subject, if necessary to ceilings. This would encourage competition. 4. The principle of freight equalization should be discontinued. The government may consider some other manner of mitigating the impact of this measure on remote areas. 5. The ad valorem levies should be replaced with specific levies at the rate of Rs 5.00 per litre and Rs 14.75 per litre of motor spirit. 6. Subsidized Kerosene should be available only to BPL families.

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7. The price of domestic LPG should be raised by Rs 75 per cylinder(14.5 kg) and thereafter the price should be adjusted gradually to eliminate subsidy altogether. 8. The subsidy sharing by upstream companies(ONGC, GAIL and OIL) should be discontinued and instead the OIDB cess collected from them should be increased to Rs 4800 per tonne ( from the present Rs 1800 per tonne) 9. The share of the subsidy to be borne by the government should be met through budget provision. The thrust of the recommendations is clearly to bring in a regime where the prices of the petroleum products are benchmarked to international prices, the taxes are rationalized to remove distortions, the industry is encouraged to become more efficient, the responds to changes in the international prices of the products, the subsidies are capped and targeted properly and the burden of the subsidy is recognized today rather than being transferred to the future. The recommendations could not be accepted by the government for variety of reasons.

Nevertheless, even the Rangarajan Committee did not suggest a complete and clean break from the past to cast taxes purely on value added basis, and subsidies as direct subsidies, which alone would have removed the core distortions on account of adulteration , diversion, distortionary effect on value chain , competition and exports.

6.7.5. Chaturvedi Committee Report.

The Prime Minister constituted in 2008, the High Powered Committee on Financial Position of Oil Companies under Chairmanship of Mr. B.K.Chaturvedi to assess the implication of the severe negative impact of the petroleum products pricing policies of the government on the financial position of the oil marketing companies between 2004 -05 to 2008 and recommend measure to deal with the situation.

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Acknowledging the need to take steps urgently to improve the cash flow situation of OMCs so that they are in a position to undertake the investments required to sustain long term growth and maintained efficiency of operations and product quality, the Committee recommended the following measures:

I. The refinery gate price should be the FOB export prices ( to be revised every month on the basis of average prices for the month). II. The distribution and marketing expenses and the applicable Union taxes and duties should be added to the prices charged by the refineries to arrive at the retail selling prices. III. The refineries should be allowed to recover specific state taxes such as entry tax, octroi and CST from the OMCs in turn should be permitted to recover the same from the consumer of that state. IV. The import duty on motor spirit and diesel should be eliminated (as in case of kerosene, LPG and crude). The excise duties on these products should be simultaneously reduced and by March 2009, the domestic prices should reflect the prevailing international prices. V. Industrial consumers of diesel should be charged the full price of diesel with immediate effect. VI. The subsidy on diesel to Railways and State Road Transport Corporations also be rapidly done away with. VII. A gradual monthly increase in price of motor spirit and diesel for retail consumers should be effected with immediate effect, till the market prices are reached. The proposed increase in price of MS should be Rs 2 per litre and the increase in price of diesel should be Rs 0.75 per litre. VIII. SKO should be made available at concessional rate only to BPL families in long run. This subsidy should be delivered through smart cards or cash transfer and through supply of kerosene at below fair market prices. IX. The subsidy of domestic LPG should also be available only to BPL families in long run. This subsidy too (as the case of kerosene) should be delivered through smart cards or cash transfer and not through supply at below fair market prices.

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X. Special Oil Tax should be levied on domestic producers of crude Oil (on pre NELP leases). The tax will kick in if crude prices exceed $75 per barrel, at the rate of 100% for ONGC and OIL and 40% for private producers. The tax is seen as temporary measure till the product prices adjust fully to international prices.

It is quite evident that the Chaturvedi Committee too has in effect recommended that the process of arriving at the domestic prices of petroleum products should at the earliest possible start reflecting the prevailing International prices. This would ensure that the domestic industry becomes efficient and cost competitive and the economy responds to changes in the prices of the products. The state should take care of the burden of high prices on BPL families through disbursement of subsidies directly to eligible families and not through distortionary controls on pricing of products.

The Chaturvedi Committee still operated under the framework of pricing on parity which is essentially a regulation of the sector. The point though is that true exit from any administration of prices and market determination would mean allowing the companies to freely price their products.

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Chapter- 7 Crude Oil Price and Commodity Market This Crude oil pricing mechanism is like the commodity type pricing mechanism. The oil market developed commodity pricing mechanism in the mid 1980‟s, replacing the system of official selling oil prices determined by OPEC. The commodity pricing mechanism in the oil sector has evolved technically from the spot trading to the future market and financial derivatives, which are typically found in all commodity market. Oil is the most important energy source, accounting for more than a third of the world primary energy mix. It is expected to continue to hold the largest share in the coming decades, although the share will decline marginally. In volume terms, oil production / consumption fell after the second oil crisis in 1979 and bottomed in 1983. Since then, however, the volume has been continuously increasing, despite variations in the price.

Crude oil is a global commodity. It has been traded internationally soon after the modern oil industry started in Pennsylvania, US, in the 1860‟s. oil trading has come a long way from the stable, controlled system of the Major‟s, which ended in the late 1960‟s through OPEC‟s quota system in the 1970‟s and the first half of the 1980‟s to the market mechanism since the mid – 1980‟s. Crude trading represents the key link between the two poles of the industry: upstream (Exploration and Production) and downstream (refining and marketing), and crude prices give signals to both upstream and downstream operations.

The size, scope and complexity of global crude trade are unique among physical commodities. As of 2011, more than 86 million barrels of oil are produced and consumed every day. Beyond the scale oil has played a significant role in world history in the 20th century. The strategic importance of oil and the crucial role it plays in the economy make oil a commodity like no other.

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The global crude oil market has been in a constant process of transformation. The impact of burning fossil fuels (including Oil) on the environment became a serious issue in the late 1980‟s. The rise in terrorism and political uncertainties in the Middle East have revived supply security concerns. Higher oil prices are encouraging the development of non-fossil fuels, such as nuclear, fuel cells and biofuels. These and other factors will affect future prices and pricing mechanisms.

7.1. Crude Oil and Petroleum Products There are over 161 crude grades around the world. However, crude oil itself has almost no direct end use (one exception is direct burning of light, sweet Southeast Asian Crude at Power Plants in Japan and China). Crude oil needs to be refined into petroleum products (gasoline / Petrol, heating oil and other) to be consumed. It is the total value of the products processed from crude (called gross product worth or GPW that determines the crude value. (This does not mean that product prices set crude prices). The two are interactive. From the refiner‟s view point, GPW defines the upper limit of crude price. Each stream of crude has its own property and each generates different combinations of products.

Crude oil that has a low sulphur content (less than 0.5%) is called „sweet‟ and one with a high sulphur content (more than 1.5%) „Sour‟. To measure crude gravity, the API (American Petroleum Institute) standard is often used. Heavy crude is under API 22, while light crude is above API 33. Medium grades are in between. Some crude streams contain metals. All of these factors affect crude prices.

FOB (Free on Board) is a price for crude or products at the loading port, while CIF (Cost, Insurance and Freight) is one at the destination. Buyers have to pay the additional costs of transport when buying crude or products at a FOB price, which CIF prices include costs of transportation. Furthermore, the timing of the

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pricing is different. FOB prices are taken on the loading date and CIF prices on the unloading date. Since tanker transportation normally takes between a few days and a few weeks, the difference is often appreciable. It is more common for crude to be traded at a FOB price and for products at a CIF price. This means that crude buyers normally hire tankers to pick up crude at the terminal of oil exporting countries and product sellers usually deliver products to buyers.

7.2. Benchmark Crude In the late 1970‟s and 1980‟s, new benchmark crude grades emerged. A benchmark crude grade serves as the reference for crude of similar qualities and locations. Arabian Light, with its 5 MBD production volume, was the benchmark crude under OPEC‟s official selling price system. However, in light of the development of spot and futures markets, the role of Arabian Light was taken over by West Texas Intermediate (WTI) and Brent.

North Sea Brent possesses all of the vital criteria for a bench marker: security of supply, diversity of sellers and broad acceptance by refineries and consumers. Although Brent was not the largest field in the North Sea and had faced production problems in the past, its satellite fields provided enough production volumes for market trading liquidity. An important factor is that production is shared by several participants and is not concentrated in a single producer. This was the main reason why Forties, whose production was dominated by BP, did not become the North Sea benchmark, despite it being the first major North Sea oil field to come on stream, and that its production was larger than that of Brent.

WTI was selected as the reference grade for crude oil futures contract at the New York Mercantile Exchange (NYMEX) in 1983. Its landlocked delivery system and the distance from international markets may not best suit the conditions for a benchmark grade. Nor does it have a large physical production. Nonetheless, trading at the NYMEX saw a huge success. With large trading volumes, WTI gained worldwide recognition.

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While the financial market oriented WTI reacts immediately to market perceptions, Brent‟s linkage to the physical markets provides a picture on the international supply demand relationship. Benchmark grades are critical in defining the prices of other related crude. They became the key price variables in many pricing formulas. In addition, since the two benchmarks are the reference for trade in the futures markets, they also became the basis for most hedging and risk management operations and attracted more trading interests in the markets.

As Saudi Arabia sold its oil only under long-term contracts, Dubai displaced Arabian Light as the Middle East benchmark. Dubai became a benchmark because there was the need for a Middle East reference and for a heavier, high sulphur international benchmark. The Dubai trading now faces declining physical production and liquidity problems. As a result, Oman plays an increasing role in supporting Dubai. Dubai in combination with Oman is linked to other Middle East Crude. The monthly average of Dubai / Oman is a basic ingredient in retroactive pricing formula for the sales by large OPEC Middle East producers, such as Saudi Arabia, Iran and Kuwait.

Crude from various fields in Russia and the former Soviet republics is mingled when transported by Transneft‟s pipeline system and becomes the Urals grade. Urals exports are currently around 6.4 MBD, the second largest physical trading grade after Arabian Light. There was also another grade called Siberian Light, which was transported by a separate line of Transneft to the Black sea port of Taupse. Its export volumes were several hundred thousand barrels per day. The problem Urals is facing is that its markets are limited. Urals is sold mainly to Eastern Europe via the Druzhba pipeline; North West Europe by tanker from the Baltic Sea ports and the Mediterranean by tanker from the Black Sea ports through the Turkish Straits. It is currently sold at a larger discount to Brent than the quality difference.

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Most market places for crude oil are linked to ports. However, markets can be developed even in inland areas. Various market places for crude oil on the North American Continent and the market for Russian Urals are good examples. There has been heavy trading of Russian Urals along the Druzhba pipeline between crude oil producers and buyers (mainly refineries in Germany, Poland, Hungary, Slovakia and the Czech Republic). This has created a spot market and prices are quoted by reporting agencies.

There are other regional benchmark grades, such as Tapis (Malaysia), Minas (Indonesia) and Bonny Light (Nigeria). The Tapis field off Malaysia is operated by Exxon, and Malaysia‟s state-owned PETRONAS is a regular seller of spot Tapis. Most trading activity takes the form of swaps between regional producers and refiners. Indonesian Minas is traded regularly in the spot market, although not as much as Tapis. Minas is middle grade in its quality, and production volumes are the largest in the region. Minas production is in the hands of Caltex and Indonesian state owned Pertamina.

OPEC Basket price is a reference price – made up of 11 grades: Sharan Blend (Algeria), Minas (Indonesia), Iran Heavy (Islamic Republic of Iran), Basra Light (Iraq), Kuwait Export (Kuwait ), Es sider (Libya), Bonny Light (Nigeria), Qatar Marine (Qatar), Arab Light (Saudi Arabia), Murban (UAE) and BCF 17 (Venezuela).

While the benchmarks play the key role in defining the absolute price levels, most other crude are traded in the form of spread trading. The preference for spread trading reflects a natural reaction to the volatility that is common in international oil markets. The differences between prices tend to be less volatile than absolute price tend to be less volatile than absolute price levels. Spread trading reflects a need for markets to constantly adjust inter-market relationships in price fluctuations.

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7.3. Crude Transactions 7.3. (a) Barter Deal

Barter deals remain important, and are said to account for around 10% of total trading volumes. These transactions typically involve trading of crude oil or petroleum products in exchange for goods, services or finances. Middle Eastern countries use barter deals to acquire industrial facilities (e.g., desalination plants) in exchange for oil. Other countries pay for petroleum products, e.g., with cargoes of sugar or cashew nuts. Financing agreements can be part of these deals. Typically under these agreements, hard currency loans are provided and the principal and interest are paid by crude cargo deliveries. Countries which have difficulties in accessing international financial markets can benefit from this technique.

Closely related to barter deals are crude-for-product swaps and processing arrangements. They are used by oil exporters to meet domestic needs for refined products beyond their refining capacity. Under crude for product swaps, a certain volume of crude is swapped for refined products. A processing deal usually involves refining an amount of crude at a plant in a third country in return for products at pre agreed product yields. Some products are taken back while the rest is sold to the refiners or on the spot market. In some cases, these arrangements look like netback sales.

7.4. Cargo Transaction Spot and forward contracts are based on cargo by cargo transactions. Forward transactions (i.e., sales at a fixed price for a fixed future delivery) cover purchase and sale of cargoes with delivery scheduled typically for one to three months ahead. Spot transactions mean those with schedules within 15 days to one month (oil trading for delivery on the same day is rare). Volumes of oil traded on a spot basis are thought to amount to about 30% of international oil trade.

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7.5. Long Term Contract After the integrated system of the Majors, OPEC developed long-term contracts in the early 1970‟s. Producing countries took control of the upstream sector and as a result, the oil industry was transformed. Upstream concessions were replaced by contractual relations and then expropriated. Contracts were typically FOB priced since tanker transportation remained with international oil companies (IOCs). New national oil companies were emerging. The Majors lost control of oil prices, and oil prices were set at OPEC meetings as official selling prices. This official selling price system lasted until the mid-1980. Against this background long term contracts offered some degree of supply security.

Long term contracts are widely used in international crude trading today. Although comprehensive data are scarce, it is thought that more than 50% of internationally traded crude is under long term contracts. OPEC countries in the Middle East sell their crude exclusively to refiners through long term contracts. The situation is similar for Russian crude oil, which is transported to refineries by crude oil export pipeline. The duration of the contracts is normally one year with renewals, in terms of the trading volumes. For producing countries, long term contracts guarantee market access for their crude refiners in the consuming country can enjoy stable supply volumes and crude qualities provided by long term contracts. On this basis, refiners can optimise their operation by buying residual volumes through spot trading.

7.6. Price Formula Prior to 1979-80, long-term contracts accounted for most international trade. In the 1970‟s crude was sold at official selling prices, which were set according to differentials to Arabian Light. The differentials were based on physical properties of the grades and distances to the markets. However, the official price system, which was the basis for most long-term contracts then, was no longer working in the mid-1980‟s, under the decreasing call for OPEC oil due to increased non- OPEC production and diminishing oil demand in the early 1980‟s. Saudi Arabia,

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which played the role of swing producer within the OPEC quota system, established the netback pricing system in late 1985 to defend its market share, and abandoned the official prices. The netback pricing system tied the value of crude oil to the spot market prices of refined products.

The netback pricing system was followed by a brief, unsuccessful return to the fixed official price system. In late 1987, however, geographically specified pricing formulas were introduced. This system is still in place today. It has a direct reference to the global crude markets. It also permits sellers to target specific areas and customers by modifying formulas and other aspects of the contracts to meet individual needs. These adjustments have resulted in highly individualised contracts and price formulas. Although the use of tailor-made formula reduces transparency of prices, pricing formula has proved to be an effective, durable and flexible tool.

If a price formula is only linked to benchmark crude, the particular characteristic and special market circumstances of the referred crude can have large effects. To avoid this, the use of crude baskets involving more than one benchmark is common. For instance, common formulas for crude sales of Arabian Light to the Asia-Pacific market (eastbound sales) are linked to the Dubai and Oman grades. Meanwhile, those for Europe and North America (westbound sales) refer to IPE Brent futures price (IPE BWAVE). Normally the eastbound sales prices are higher than the west bound sales prices (the difference is called the „Asian Premium‟).

7.7. Netback Pricing Although netback pricing was a brief episode in the history of crude oil pricing mechanisms, the concept is often used in pricing other fuels than oil, e.g., natural gas. The netback pricing in the oil sector was developed by Saudi Arabia in 1985. By 1984-85 the official selling price system, which was the basis for most long term contracts, had broken down. Buyers were finding the strict conditions

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and official prices unacceptable, in the face of a global supply glut. At the time, Saudi Arabia was acting as swing producer with the OPEC quota system, lowering its production volumes so that total OPEC production could be kept within the volume to support the prices set by OPEC. However, under this policy, the country‟s production had to be cut back from 10 MBD to 3.5 MBD coming to the lower limit Saudi Arabia had to produce in view of associated gas needs. In additions, Saudi Arabia‟s efforts were not necessarily shared by the other OPEC countries. Finally, in 1985 King Fahd decided to increase production and recover his country‟s market share. Netback pricing was introduced as the instrument to implement this production increase. It proved to be a very effective tool for Saudi Arabia to quickly regain market share.

The netback pricing formula was; Crude oil price (FOB) = GPW in the spot market – fixed refining margin – transportation costs (from the terminal in the oil-exporting country to the refinery in the oil-importing country). This netback pricing system introduced the concept of market prices for crude oil, although it was based on petroleum products.

Netback pricing was also attractive to the buyers (refiners), which otherwise were suffering from unstable, low margins. However, the success of netback pricing and the increase in Saudi Arabia‟s production led to a huge drop in oil prices in 1986, plunging below 10$/bbl. This is sometimes called „the counter oil crisis as opposed to the two previous oil crises. Netback pricing was blamed for the price crash. After a brief period of netback pricing dominance, the fixed official selling prices returned briefly in late 1987, producing countries stopped posting the prices in 1988.

7.8. Refining Margins Refining margins represent monetary gains or losses associated with crude oil processing operation. To make comparisons possible by crude grade, refinery operation or region, calculations normally assume standardised refinery

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configurations. The margin calculation takes into account wages, construction and other associated costs incurred in refinery operation, together with variable costs including buying and processing crude oil. Although margin calculations are more reflective of economics of processing a marginal barrel rather than returns from base load operation, refining margins can suggest indications of financial returns to a refinery.

Refining margin = GPW – Crude Costs – Transport Costs and Applicable fees and Duties – Financial Costs – Variable Costs – Fixed Costs.

There are four main types of refining operation; hydro skimming catalytic, cracking, hydrocracking and cocking. The hydro skimming refineries are the basic, standard ones in which crude components are separated at atmospheric pressure by heating, condensing and cooling. The hydro skimming refineries are equipped with atmospheric distillation, naphtha reforming and hydrodesulphurisation facilities. The catalytic cracking refineries have, in addition to the above, vacuum distillation, catalytic cracking and alkylation processes. The catalytic cracking process breaks down the larger, heavier and more complex hydrocarbon molecules into simpler and lighter molecules by heat and the presence of a catalyst, but without adding hydrogen. Hydrocracking is similar to catalytic cracking, but with hydrogen and higher pressure. The hydrocracking process can convert heavy oil (fuel oil components) to lighter and more valuable products (notably naphtha and middle distillate components). A cocking unit thermally de-composes residues under high temperature and pressure, and produces lighter products (gasoline (petrol), naphtha, gas oil).

There are several refining centres in the world, including Northwest Europe, Mediterranean, US, Gulf Coast, US West Coast and Singapore. To calculate regional refining margins, it is common to reflect regional characteristics into the background assumptions. Brent and Urals are normally assumed to be crude inputs in Northwest Europe, and Urals and Es Sider from Libya in the

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Mediterranean. Refineries in the US Gulf Coast are typically equipped with cracking and cocking process facilities. Refineries in the US West Coast are designed to process heavier crude. Singapore refining margin calculation is often based on the Dubai Crude and hydro skimming and hydrocracking refineries.

7.9. Spot and Futures Markets The current spot transactions have their origin in the first and second oil crises. The Organisation of Arab Petroleum-exporting Countries (OAPEC) oil embargo of 1973 and the Iranian revolution of 1979, sparked fears of a shortage in crude supply.

Crude buyers became nervous and wanted crude at any price. Spot prices rose to higher levels than the official selling prices and supply volumes under long – term contracts shifted to spot markets. At the same time, rising volumes of new oil production from the non –OPEC area went into the spot markets. Cargoes from the North Sea were sold in the 1980‟s exclusively on a spot basis. Until 1985, most oil-producing countries nevertheless continued to offer long term fixed price contracts. These contracts increasingly countered resistant from the buyers. Finally, in 1988 long term fixed price contracts ceased to exist after an episode of netback pricing.

Although spot market took over the control of oil prices from OPEC, the task remained in the late 1980‟s to organise spot markets, as there were as many spot markets as crude streams. Gradually Brent and WTI emerged as the two most influential benchmarks. Markets were re-organised in line with these crude grades and the other grades are indexed to them.

At the same time futures markets were being formed in Western countries. There was a desire on the part of oil companies to reduce risk in light of high volatility after 1973. Developments in information technology, development in

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financial theory and a political climate favouring markets over government administrative guidance led to the creating of financial derivative markets, Including futures and options.

Oil futures markets are not new. Price volatility in the early days of the US oil industry resulted in the first oil futures contracts in Pennsylvania in 1860‟s, which took the form of pipeline certificates. During the next 30 years, more than 10 exchanges in the US, Canada and Europe traded crude futures. However, when Rockefeller established monopoly control and, later, when the Majors controlled the market, prices became more stable, the need for market risk management disappeared, and the early futures trading disappeared as well.

In 1979 heating oil became the first new futures contract at the NYMEX, and the International Petroleum Exchange (in London followed in 1981. Gasoline (petrol) futures trading started on the NYMEX in 1981. WTI trading started in 1983 on the NYMEX and Brent in 1988 on the IPE. The NYMEX launched natural gas futures in 1990 and the IPE in 1997. The NYMEX still has an open trading floor, called outcry, but it began electronic trading after hours on NYMEX access in 1993. At IPE, the open outcry system was abolished in 2005, and now all contracts of the IPE are traded electronically on screen only.

The NYMEX WTI future is the most actively traded commodity in the world some 230 MBD is currently traded, almost three times as much as the physical oil production / consumption. The contract trades in units of 1000 barrels and is listed for up to 72 months. The delivery point is Cushing, Oklahoma. Trading volumes of IPE‟s Brent futures are around 100 MBD. Like WTI, Brent contracts are 1000 barrels per unit and listed for up to 72 months. The IPE has a delivery system called exchange of futures for physicals (EFP). Under this system Brent contract holders can cancel out a future contract with a physical spot contract. By doing so, the holders can have the same result as physical delivery of the commodity.

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7.9.1. Spot Market Spot transactions are mainly conducted by telephone or computer network between two parties. It is an over the counter (OTC) market as opposed to an exchange. Spot markets do not necessarily have trading floors. The term „spot market‟ applies to all spot transactions concluded in an area where strong trading activities take place. A key advantage of the OTC market is that the terms of a contract do not have to have the specifications required by an exchange. A disadvantage is that there is usually a lack of transparency in the market. Counter party risk also exists in an OTC trade, which is otherwise taken by the exchange.

The main spot markets for crude oil are Rotterdam for Europe and New York for the US. These markets have their own benchmarks: Brent and WTI. In particular, Brent was the centre of spot and forward trading in the 1980‟s. There are other grades which have strong spot trading activities. They are: Ekofisk, Forties, Oseberg from the North Sea; Russian Urals; Dubai (UAE); Oman; Minas (Indonesia); Tapis (Malaysia); Alaska North slope (ANS) and West Texas Sour (WTS) in the US; and Forcados and Bonny light from Nigeria. Although most OPEC grades are contracted on a long – term basis, some OPEC countries are known to use spot transactions to sell part of their production.

The main markets for petroleum products are located in Northwest Europe (ARA – Amsterdam, Rotterdam, Antwerp), the Mediterranean (Genoa, Lavera), the Gulf, Southeast Asia (Singapore), US Gulf of Mexico (including the Caribbean) and US East Coast (New York).

Spot market participants are refiners and producers where crude oil is concerned. For petroleum products, buyers are traders or large consumers, and sellers are refiners. Traders play an essential middleman role. They buy cargoes from sellers and re-sell them to end-users or other traders. Alongside traders are trading divisions of oil companies. There are also intermediaries and

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brokers, who help conclude transactions. Although they do not buy or sell cargoes themselves, they earn a commission.

Formation of a spot market requires large trade volumes and various market operators. The Rotterdam market, sometimes referred to as the ARA area ideally matches these conditions. It has both the European consumption centres and the North Sea production region nearby. The area itself is heavily industrialised, with many refinery plants. There are also large storage capacities available. The area is the largest port in Europe. It has access to the northern European market by sea. Also barges go to Germany Switzerland and France via the Rhine and other rivers and channels. Many financial institutions and oil brokerage houses (Eurol, Frisol, Transol, Vanol and Vito) are based in the area. Overall, the open Dutch and Belgian economies helped establish a large crude and product market place.

Spot transactions take place in a similar manner from one market to another, a buyer who seeks a cargo of crude available within one month contract different producers and traders working in the area. Negotiations take place normally by telephone. Telephone conversations are recorded in case of disputes. Payment is made thirty days after loading of the ship for crude oil (payment deadlines are normally shorter for petroleum products). Spread trading mechanism governs most crude spot sales, in which negotiation does not centre on the price in absolute terms but on the price differential between the crude traded and the benchmark. Prices of North Sea Crude (e.g., Ekofisk or Forties), for instance, are normally indexed to that of Brent.

In the OTC market, transaction prices are normally known only to the two contracting parties. This can become a major obstacle to active and fluid spot trading. Therefore, there are publications which list price records. They are called reporting agencies. Platts Oilgram (McGraw Hill) and Petroleum Argus are the two most famous. To track prices, Platts journalists contact sellers and

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buyers in the market and interview them on transaction prices during the day. Platts accordingly publishes the previous day‟s quotations. As this price reporting is an estimate based on the survey, there is a risk of price manipulation.

7.9.2. Forward Market Spot trading generated on additional risk of high price volatility. To hedge this risk, forward and futures markets were established. In Europe, however, crude futures exchange started trading only in 1988. Instead, forward markets were developed around Brent crude in the 1980s. Therefore, Brent has three price quotations. Spot markets handle cargoes within fifteen-day availability, called „dated Brent‟ while forward markets were developed for more distant future deliveries, named „fifteen – day Brent‟. Brent traded on the IPE futures markets is called „IPE Brent‟.

The forward fifteen – day Brent market has more standardised operation than the spot dated Brent market. The cargo size is fixed at 500,000 barrels ± 5%. The delivery takes place at the Sulom Voe terminal in the North Sea. In the fifteen day Brent trading, only the month of delivery can be designated (e.g., January, delivery Brent, February delivery Brent, March delivery Brent, etc.). The buyer specifies the month and the volume and the seller indicates the delivery date of the cargo at least fifteen days prior. The name came from this practice. When a fifteen – day Brent cargo is name and dated, it becomes a spot dated Brent transaction. In addition to the Brent crude, there are forward markets of gasoline (Petrol), Diesel, Kerosene, Naphtha and heavy Fuel Oil in Europe.

Forward contracts are in between spot and futures contracts (Table 7.9). In a hedging operation, a position is taken in the forward market in an opposite direction to a position in the physical market. However, speculation also takes place in the forward market, when an operator takes a position in order to gain profit from price fluctuation. A cargo of crude oil can be transferred from one trader to another many times between loading and delivery. Series of

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consequential transactions in the forward market are called „Daisy Chains. Most transactions are cancelled out by reversed transactions.

Participant in the fifteen – day Brent market are normally limited to oil companies and large traders, because of the high risk involved in trading. Forward contracts are traded in OTC markets, which are not as well organised as the exchanges. Many elements are in the hands of the two parties in the deal. There is less price transparency in the forward market than in the futures market, despite the fact that Platt‟s, Petroleum Argus and other news services survey and report daily prices. Furthermore, unlike in the futures market, there is no clearing house system. Therefore, there is the counter – party risk and all transaction records have to be kept track of individually.

7.9.3. Futures Market and Option market Futures and Option markets have grown considerably since the mid-1980s. Oil companies and traders as well as financial institutions use the futures and option markets for hedging against the risk of price fluctuations and risk management.

Table 7.9.3 Characteristics of Spot / Forward / Futures / Options Deals

Contract Spot Forward Futures Options

Trading OTC OTC Exchange OTC/Exchange

Derivatives No Yes Yes Yes

Delivery Yes Yes No No

7.9.4. Analysis of International Crude Oil Price.

Oil has become a global commodity and in this global market place, there have been fundamental changes which will have a large impact on the future price of

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oil. On the supply side, the main concern is the availability of crude oil at affordable price. On demand side, global composition of demand is shifting away from the advanced economies in Europe, Japan and North America towards developing economies, especially those in Asia. This means the impact in US in determining oil price is becoming less and less of a factor.

The critical role played by crude oil, events in the oil market has a major impact on overall economy. Between 1945 to 1972 oil prices, as measured by West Texas Intermediate (WTI), were essentially flat and ranged from $2 to $3 a barrel. Then, the world economy faced two major oil shocks in 1973-74 and 1979-80, both of which were largely due to cutbacks/supply disruption in OPEC production. In 1973-74, oil prices rose from $2-$3 a barrel to about $11-$12 a barrel and then in 1979-1980 they spiked up again to about $39 a barrel. During both oil shocks, the US and much of the global economy moved into recession and unemployment rate rose sharply. Oil prices peaked in April‟1980 at $39.50 a barrel and then steadily declined for almost 20 years, until they bottomed out in December 1998 at $11.28 a barrel. This 20-year period of fall in prices set the stage for the price surge over the past decade. Investments in the oil industry became unprofitable and there was no longer much of an incentive for consumers to conserve energy. As a result, oil companies cut back on their capital budgets and oil rig counts and drilling activity fell sharply. The relatively low price of oil at the pump encouraged consumers to buy less fuel-efficient vehicles and bigger homes. Crude prices starting edging up again at the end of 1990‟s, but the upward price spike did not become noticeably pronounced until late 2003, with oil prices rising sharply between 2003 and 2008 and reaching a peak of over $148 a barrel in July 2008.

Prices for WTI fell from over $148 a barrel in 2008 to a low of $31 in December 2008. Despite sluggish recovery in advanced countries and record levels of inventories, oil prices trended upwards since the recession ended in 2009 and touched over $100 a barrel by June of 2012. Oil prices are now at levels that are well above those experienced prior to the global recession. Oil prices (WTI)

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averaged around $56 a barrel in 2005 and $66 a barrel in 2006 at a time when the global economy was expanding at a rapid rate.

Figure: 7.9.4

Plot of International Crude Oil Prices

120.00

100.00

80.00 Dubai,$/bbl *

Brent, $/bbl † 60.00

Nigerian Forcados, $/bbl

40.00 West Texas Intermdiate, $/bbl ‡

20.00

0.00

1986 1982 1984 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1980

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Chapter-8 Data Analysis, Interpretations and Model Estimations The study reports data analysis elaborately and step by step with statistical methods followed by interpretations and estimation of econometrics models. 8.1. KARL PEARSON'S CORRELATION COEFFICIENT (r):---

The Karl Pearson correlation coefficient (r) is used to measure the correlation between variables X (Crude oil price) and Y (Wholesale price index or WPI). The Karl Pearson coefficient is designated by the letter "r" and is sometimes called "Pearson's r." Pearson's correlation reflects the degree of linear relationship between two variables. It ranges from +1 to -1. A correlation of +1 means that there is a perfect positive linear relationship between variables. A correlation of -1 means that there is a perfect negative linear relationship between variables. A correlation of 0 means there is no linear relationship between the two variables.

Mathematical Formula:--

The quantity r, called the linear correlation coefficient, measures the strength and the direction of a linear relationship between two variables. The linear correlation coefficient is sometimes referred to as the Pearson product moment correlation coefficient in honor of its developer Karl Pearson.

The mathematical formula for computing r is:

Where, N = numbers of the observations

∑XY = Sum of the products of paired variables.

∑X = Sum of X variables

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∑Y = Sum of Y variables

∑X² = Sum of squared X variables.

∑Y² = Sum of squared Y variables.

Table- 8.1

WPI Crude monthly Price $, (Y) (X) (XY) (X²) (Y²) April,2000- 01 151.7 22.51 3414.77 506.70 23012.89 May 151.8 26.60 4037.88 707.56 23043.24 June 152.7 28.49 4350.42 811.68 23317.29 July 153.1 27.26 4173.51 743.11 23439.61 August 153.4 28.46 4365.76 809.97 23531.56 September 154.7 31.34 4848.30 982.20 23932.09 October 157.9 30.50 4815.95 930.25 24932.41 November 158.2 30.92 4891.54 956.05 25027.24 December 158.5 23.25 3685.13 540.56 25122.25 January 158.6 24.02 3809.57 576.96 25153.96 February 158.6 25.92 4110.91 671.85 25153.96 March 159.1 23.82 3789.76 567.39 25312.81 April,2001- 02 159.9 24.82 3968.72 616.03 25568.01 May 160.3 26.95 4320.09 726.30 25696.09 June 160.8 26.63 4282.10 709.16 25856.64 July 161.1 23.99 3864.79 575.52 25953.21 August 161.7 25.01 4044.12 625.50 26146.89 September 161.7 24.79 4008.54 614.54 26146.89 October 162.5 20.05 3258.13 402.00 26406.25 November 162.3 18.24 2960.35 332.70 26341.29 December 161.8 18.24 2951.23 332.70 26179.24 January 161 18.92 3046.12 357.97 25921.00 February 160.8 19.55 3143.64 382.20 25856.64 March 161.9 23.31 3773.89 543.36 26211.61 April,2002- 03 162.3 25.03 4062.37 626.50 26341.29 May 162.8 25.00 4070.00 625.00 26503.84 June 164.7 24.05 3961.04 578.40 27126.09

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July 165.6 25.18 4169.81 634.03 27423.36 August 167.1 25.86 4321.21 668.74 27922.41 September 167.4 27.49 4601.83 755.70 28022.76 October 167.5 26.90 4505.75 723.61 28056.25 November 167.8 23.68 3973.50 560.74 28156.84 December 167.2 27.11 4532.79 734.95 27955.84 January 167.8 29.59 4965.20 875.57 28156.84 February 169.4 31.26 5295.44 977.19 28696.36 March 171.6 28.83 4947.23 831.17 29446.56 April,2003- 04 173.1 24.21 4190.75 586.12 29963.61 May 173.4 25.00 4335.00 625.00 30067.56 June 173.5 26.42 4583.87 698.02 30102.25 July 173.4 27.46 4761.56 754.05 30067.56 August 173.7 28.66 4978.24 821.40 30171.69 September 175.6 26.27 4613.01 690.11 30835.36 October 176.1 28.45 5010.05 809.40 31011.21 November 176.9 28.20 4988.58 795.24 31293.61 December 176.8 28.97 5121.90 839.26 31258.24 January 178.7 30.01 5362.79 900.60 31933.69 February 179.8 29.61 5323.88 876.75 32328.04 March 179.8 32.21 5791.36 1037.48 32328.04 April,2004- 05 180.9 32.36 5853.92 1047.17 32724.81 May 182.1 36.09 6571.99 1302.49 33160.41 June 185.2 34.22 6337.54 1171.01 34299.04 July 186.6 36.35 6782.91 1321.32 34819.56 August 188.4 40.53 7635.85 1642.68 35494.56 September 189.4 39.15 7415.01 1532.72 35872.36 October 188.9 43.37 8192.59 1880.96 35683.21 November 190.2 38.82 7383.56 1506.99 36176.04 December 188.8 36.85 6957.28 1357.92 35645.44 January 188.6 41.00 7732.60 1681.00 35569.96 February 188.8 42.58 8039.10 1813.06 35645.44 March 189.4 49.27 9331.74 2427.53 35872.36 April,2005- 06 191.6 49.43 9470.79 2443.32 36710.56 May 192.1 47.02 9032.54 2210.88 36902.41 June 193.2 52.72 10185.50 2779.40 37326.24 July 194.6 55.01 10704.95 3026.10 37869.16 August 195.3 60.03 11723.86 3603.60 38142.09 September 197.2 59.74 11780.73 3568.87 38887.84 October 197.8 56.28 11132.18 3167.44 39124.84

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November 198.2 53.31 10566.04 2841.96 39283.24 December 197.2 55.05 10855.86 3030.50 38887.84 January 196.3 60.61 11897.74 3673.57 38533.69 February 196.4 58.95 11577.78 3475.10 38572.96 March 196.8 60.01 11809.97 3601.20 38730.24 April,2006- 07 199 67.06 13344.94 4497.04 39601.00 May 201.3 67.33 13553.53 4533.33 40521.69 June 203.1 66.90 13587.39 4475.61 41249.61 July 204 71.29 14543.16 5082.26 41616.00 August 205.3 70.87 14549.61 5022.56 42148.09 September 207.8 60.94 12663.33 3713.68 43180.84 October 208.7 57.26 11950.16 3278.71 43555.69 November 209.1 57.80 12085.98 3340.84 43722.81 December 208.4 60.34 12574.86 3640.92 43430.56 January 208.8 52.62 10987.06 2768.86 43597.44 February 208.9 56.49 11800.76 3191.12 43639.21 March 209.8 60.26 12642.55 3631.27 44016.04 April,2007- 08 211.5 65.48 13849.02 4287.63 44732.25 May 212.3 65.76 13960.85 4324.38 45071.29 June 212.3 68.10 14457.63 4637.61 45071.29 July 213.6 72.58 15503.09 5267.86 45624.96 August 213.8 68.97 14745.79 4756.86 45710.44 September 215.1 74.78 16085.18 5592.05 46268.01 October 215.2 79.33 17071.82 6293.25 46311.04 November 215.9 89.15 19247.49 7947.72 46612.81 December 216.4 87.92 19025.89 7729.93 46828.96 January 218.1 89.52 19524.31 8013.83 47567.61 February 219.9 92.16 20265.98 8493.47 48356.01 March 225.5 99.76 22495.88 9952.06 50850.25 April,2007- 08 228.5 105.77 24168.45 11187.29 52212.25 May 231.1 120.91 27942.30 14619.23 53407.21 June 237.8 129.72 30847.42 16827.28 56548.84 July 240 132.47 31792.80 17548.30 57600.00 August 241.2 113.05 27267.66 12780.30 58177.44 September 241.5 96.81 23379.62 9372.18 58322.25 October 239 69.12 16519.68 4777.57 57121.00 November 234.2 50.91 11923.12 2591.83 54849.64 December 229.7 40.61 9328.12 1649.17 52762.09 January 228.9 43.99 10069.31 1935.12 52395.21 February 227.6 43.22 9836.87 1867.97 51801.76

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March 228.2 46.02 10501.76 2117.84 52075.24 April,2008- 09 231.5 50.14 11606.39 2513.58 53592.25 May 234.3 58.00 13590.19 3364.39 54896.49 June 235 69.12 16242.09 4776.92 55225.00 July 238.7 64.82 15473.63 4202.23 56977.69 August 240.8 71.98 17332.59 5181.00 57984.64 September 242.6 67.70 16424.42 4583.51 58854.76 October 242.5 73.06 17718.09 5338.39 58806.25 November 247.2 77.39 19131.02 5989.35 61107.84 December 248.3 75.02 18626.53 5627.43 61652.89 January 250.5 76.61 19190.51 5868.91 62750.25 February 250.5 73.69 18460.42 5430.85 62750.25 March 253.4 78.02 19769.84 6086.86 64211.56 April,2009- 10 257.5 84.08 21651.05 7069.74 66306.25 May 260.4 76.16 19832.44 5800.56 67808.16 June 259.8 74.33 19311.22 5525.11 67496.04 July 262.5 73.54 19305.05 5408.58 68906.25 ∑ 24337.1 6,226.73 1303113.23 393668.43 4894250.07 N = 124

Therefore, Karl Pearson‟s correlation coefficient „ r „ = 0.829812

8.2. Model-1 :- To determine the influence of crude oil price on inflation of the Indian economy. The following time series regression equation was fitted.

Yt= a + bX + et ------(1) Where

Yt denotes the WPI ( base year 1993- 94 ) „a‟ denotes constant quantity, i.e. the intercept of the line on Y- axis. „b‟ denotes the co-efficient of X. „X‟ denotes the crude oil price.( monthly Indian basket price).

„et‟ is residual term of the model.

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Graph- 8.2

300

250

200

150

WPI WPI monthly 100 WPI monthly 50

0 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 Crude oil prices Scatter Plot of X and Y

We shall now briefly discuss the mechanics of the Model - 1, two variable linear regression, the equation of the model is Y= a + bx + et , the scatter plot of X= crude oil price and Y= WPI monthly is shown in the graph 8.2.

The observed data are used to estimate the two parameters, „a‟ and „b‟ of the model and „et‟ is the stochastic term or noise. The actual numerical estimates of the intercept and the slope are written as „a^‟ and „b^‟ , where the “hats” indicate that the quantity is an estimate of a model parameter – an estimate that is computed from the observed data.

The above equation can be written as Y=a+bX, in absence of error term, i.e. et=0. In the equation, the parameter „a‟ is the intercept, it gives the quantity of wholesale price index (WPI) without the influence of crude price, i.e. when X=0, and Constant „b‟ is the co-efficient of Y in relation to X or the slope.

The slope, a summary of the relationship between X and Y, answers the equation, when X changes by one unit, by how many units does Y change? The answer is that Y changes by „b‟ units. In the research of the impact of crude oil prices (Indian basket) on WPI (wholesale price index), the fitted line with the observed data is shown in the graph.

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Graph-8.2.1.

300 y = 1.00027x + 146.0375 R² = 0.6886 250

200

150

WPI monthly WPI WPI monthly 100 Linear (WPI monthly)

0 0.00 20.00 40.00 60.00 80.00 100.00 120.00 140.00 Crude oil prices Fitting a Regression line

The equation, WPI = 146.0375 + 1.00027*Crude oil price, fits the relationship between the incremental increase in WPI on the incremental increase of crude oil price. The estimated slope, „b^‟, is 1.00027; that is,

Changes in Y Change in percent of WPI „b^‟ = ------= ------= 1.00027 Changes in X Change in percent of crude oil price

This means that a 1% change in crude oil price was typically accompanied by a change of 1.00027% of WPI. Thus an increase of only 1% in crude oil price would increase substantially in WPI. Of course, it works in other way, too; a drop of 1% of crude oil price is associated with decrease of 1.00027% of WPI. The estimate of slope measures what is call “swing ratio” – the swing or change in WPI for a given change in crude oil prices.

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It can be seen from graph above that total change in Y is not explained by a change in X. The regression line can explain the total change in Y in response to change in X only if the entire crude oil price & WPI points fall on the regression line. But, as is evident from the graph, all crude oil price & WPI combination points do not fall on the regression line. Some points are placed above and some points are placed below the regression line. This means that b, i.e. the slope of the regression line, does not explain the total change in Y in response to a change in X. The unexplained part of Y is called the error term, the residual or the disturbance. The purpose of regression technique is to find the average values of „a‟ and „b‟ which make the values of observed pairs of X and Y, i.e.(X1,Y1), (X2,Y2), etc., as close to the regression line as possible. The line so fitted is called the best fit regression line. This objective is achieved by th minimizing the error terms, i.e., the deviation of observed value of Yt (t value, t=

1, 2, 3, … n) from its estimated value Yt^ can then be defined as error term, ^. therefore, error term is, et = Yt – Yt

Regression technique minimizes the error term with a view to find the best fits the observed data. So the problem is how to minimize the error term. It can be seen from the graph of the fitting line that the error terms in some months are positive as the points are above the line and in some months they are negative. So, one way to minimize the error could be to find the sum of the error terms. In this method positive and negative errors would tend to cancel out. It would mean error does not exist or there are no deviations from the estimated line whereas, it can be seen in graph, the positive and negative error term may not cancel out. Therefore, the sum of the error terms cannot be used as a measure of deviation of the observed data from the estimated one. This problem is avoided by using the square of the error term. The technique that regression analysis uses to minimize the error term is called Ordinary Least Square (OLS) method. It is the sum square of the error terms that regression techniques seek to minimize and find the values of „a‟ and „b‟ that produce best fit line.

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Table :- 8.2. Two variable regression

Yt Xt Xt Yt Xt²

WPI monthly Crude Price $ April,2000 -01 151.7 22.51 3414.77 506.70 May 151.8 26.6 4037.88 707.56 June 152.7 28.49 4350.42 811.68 July 153.1 27.26 4173.51 743.11 August 153.4 28.46 4365.76 809.97 September 154.7 31.34 4848.3 982.20 October 157.9 30.5 4815.95 930.25 November 158.2 30.92 4891.54 956.05 December 158.5 23.25 3685.13 540.56 January 158.6 24.02 3809.57 576.96 February 158.6 25.92 4110.91 671.85 March 159.1 23.82 3789.76 567.39 April,2001-02 159.9 24.82 3968.72 616.03 May 160.3 26.95 4320.09 726.30 June 160.8 26.63 4282.1 709.16 July 161.1 23.99 3864.79 575.52 August 161.7 25.01 4044.12 625.50 September 161.7 24.79 4008.54 614.54 October 162.5 20.05 3258.13 402.00 November 162.3 18.24 2960.35 332.70 December 161.8 18.24 2951.23 332.70 January 161 18.92 3046.12 357.97 February 160.8 19.55 3143.64 382.20 March 161.9 23.31 3773.89 543.36 April,2002-03 162.3 25.03 4062.37 626.50 May 162.8 25 4070 625.00 June 164.7 24.05 3961.04 578.40 July 165.6 25.18 4169.81 634.03 August 167.1 25.86 4321.21 668.74 September 167.4 27.49 4601.83 755.70 October 167.5 26.9 4505.75 723.61 November 167.8 23.68 3973.5 560.74 December 167.2 27.11 4532.79 734.95 January 167.8 29.59 4965.2 875.57 February 169.4 31.26 5295.44 977.19 March 171.6 28.83 4947.23 831.17

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April,2003-04 173.1 24.21 4190.75 586.12 May 173.4 25 4335 625.00 June 173.5 26.42 4583.87 698.02 July 173.4 27.46 4761.56 754.05 August 173.7 28.66 4978.24 821.40 September 175.6 26.27 4613.01 690.11 October 176.1 28.45 5010.05 809.40 November 176.9 28.2 4988.58 795.24 December 176.8 28.97 5121.9 839.26 January 178.7 30.01 5362.79 900.60 February 179.8 29.61 5323.88 876.75 March 179.8 32.21 5791.36 1037.48 April,2004-05 180.9 32.36 5853.92 1047.17 May 182.1 36.09 6571.99 1302.49 June 185.2 34.22 6337.54 1171.01 July 186.6 36.35 6782.91 1321.32 August 188.4 40.53 7635.85 1642.68 September 189.4 39.15 7415.01 1532.72 October 188.9 43.37 8192.59 1880.96 November 190.2 38.82 7383.56 1506.99 December 188.8 36.85 6957.28 1357.92 January 188.6 41 7732.6 1681.00 February 188.8 42.58 8039.1 1813.06 March 189.4 49.27 9331.74 2427.53 April,2005-06 191.6 49.43 9470.79 2443.32 May 192.1 47.02 9032.54 2210.88 June 193.2 52.72 10185.5 2779.40 July 194.6 55.01 10704.9 3026.10 August 195.3 60.03 11723.9 3603.60 September 197.2 59.74 11780.7 3568.87 October 197.8 56.28 11132.2 3167.44 November 198.2 53.31 10566 2841.96 December 197.2 55.05 10855.9 3030.50 January 196.3 60.61 11897.7 3673.57 February 196.4 58.95 11577.8 3475.10 March 196.8 60.01 11810 3601.20 April,2006-07 199 67.06 13344.9 4497.04 May 201.3 67.33 13553.5 4533.33 June 203.1 66.9 13587.4 4475.61 July 204 71.29 14543.2 5082.26 August 205.3 70.87 14549.6 5022.56

179

September 207.8 60.94 12663.3 3713.68 October 208.7 57.26 11950.2 3278.71 November 209.1 57.8 12086 3340.84 December 208.4 60.34 12574.9 3640.92 January 208.8 52.62 10987.1 2768.86 February 208.9 56.49 11800.8 3191.12 March 209.8 60.26 12642.5 3631.27 April,2007-08 211.5 65.48 13849 4287.63 May 212.3 65.76 13960.8 4324.38 June 212.3 68.1 14457.6 4637.61 July 213.6 72.58 15503.1 5267.86 August 213.8 68.97 14745.8 4756.86 September 215.1 74.78 16085.2 5592.05 October 215.2 79.33 17071.8 6293.25 November 215.9 89.15 19247.5 7947.72 December 216.4 87.92 19025.9 7729.93 January 218.1 89.52 19524.3 8013.83 February 219.9 92.16 20266 8493.47 March 225.5 99.76 22495.9 9952.06 April,2007-08 228.5 105.77 24168.4 11187.29 May 231.1 120.91 27942.3 14619.23 June 237.8 129.72 30847.4 16827.28 July 240 132.47 31792.8 17548.30 August 241.2 113.05 27267.7 12780.30 September 241.5 96.81 23379.6 9372.18 October 239 69.12 16519.7 4777.57 November 234.2 50.91 11923.1 2591.83 December 229.7 40.61 9328.12 1649.17 January 228.9 43.99 10069.3 1935.12 February 227.6 43.22 9836.87 1867.97 March 228.2 46.02 10501.8 2117.84 April,2008-09 231.5 50.14 11606.4 2513.58 May 234.3 58.00 13590.2 3364.39 June 235 69.12 16242.1 4776.92 July 238.7 64.82 15473.6 4202.23 August 240.8 71.98 17332.6 5181.00 September 242.6 67.70 16424.4 4583.51 October 242.5 73.06 17718.1 5338.39 November 247.2 77.39 19131 5989.35 December 248.3 75.02 18626.5 5627.43 January 250.5 76.61 19190.5 5868.91

180

February 250.5 73.69 18460.4 5430.85 March 253.4 78.02 19769.8 6086.86 April,2009-10 257.5 84.08 21651 7069.74 May 260.4 76.16 19832.4 5800.56 June 259.8 74.33 19311.2 5525.11 July 262.5 73.54 19305.1 5408.58 ∑ 24337.1 6226.73 1303113 393668.43 N=124 Mean 196.27 50.22

2 2 2 a ={(∑Xt ) (∑Yt) - (∑Xt)(∑XtYt)} †{ N(∑Xt - (∑Xt) }

a=146.0376

2 2 b={N(∑XtYt) - (∑Xt)(∑Yt)} ÷ {N(∑Xt ) - (∑Xt) }

b=1.00027

8.2.1. The Test of Significance of Estimate Parameters

We have estimated the parameters „a’ and „b’ in regression equation of two variable regression equation and have also discussed the use of the estimated regression to estimate the value of Y (WPI) for a given amount of crude oil price (X). The question that now arises is how reliable is the estimated value of coefficient b or how well does the estimated regression line fit to the observed data? For example, since b = 1.00027, an increase of $1 in crude oil price will cause an increase in WPI of approximately 1.00027. How far is this conclusion reliable? The technique that is used to answer this question is called test of statistical significance.

The process of a testing of statistical significance begins with making a hypothesis that estimate b = 0. This is called „Null hypotheses. It means assuming that there is no relationship between Y and X. The task is now to accept or reject the hypothesis. If null hypothesis is accepted, it means that

181

there is no relationship between Y and X or, in other words, the variation in Y (WPI) is not explained by the variation in X. On the contrary, if the null hypothesis is rejected, it means that estimated b ≠ 0 and that b > 0 significantly. The task now is, therefore, to test the null hypothesis. In fact, the task is to find the probability of rejecting the null hypothesis. The probability of rejecting a hypothesis is known as finding the level of significance. The rule in this regard is that if the level of significance is 5 per cent or less, then the hypothesis is rejected. It means that if the level of significance is 5 per cent or less, then the estimated coefficient b is statistically significant. That is, if estimated coefficient b is statistically significant at 5 per cent level of significance, then it is concluded that X ( Crude oil price) is a significant determinant of Y (WPI).

How is the level of significance determined? The level of significance is determined on the basis of the standard error and t-ratio to t statistic. We will first describe the precise method of calculating the standard error and the t-ratio.

The „standard error‟ is the standard deviation of the estimated value from the sample values. This is the principle of least squares, which says

2 ^ 2 Minimize Σet , -that is, minimize Σ(Yt – Yt ) ,

Therefore, to test the hypothesis that there is a statistically significant relationship between Y (WPI) and X (Crude oil price); we need to calculate standard error of coefficient b, denoted as Sb. The estimates of error (and other Statistical tests) are provided by computer programs of software. However, it is useful to know how Sb is estimated and used in the test of statistical significance. The formula for estimating Sb is given below.

^ 2 2 2 2 Sb= ∑( Yt – Yt ) / (N-k)∑(Xt – ̅) } = ∑et / (N-k)∑(Xt – ̅) }

^ Where Xt and Yt are the observed values for month t, Yt is the estimated value of Y in month t, ̅ is the mean value of X, N is the number of ^ observations and et= ( Yt – Yt ) is the error term, and k is the number of estimated

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coefficients ( 2 in the case of a two variable regression equation, „a‟ and „b‟). In fact, (N-k) is the degree of freedom, i.e. 124-2 =122.

Table:8.2.1. Calculation of Standard Error of Coefficient

Xt - Yt (WPI (Crude Xt = (Xt – X 2 monthly) Price $) Yt^ et =Yt – Yt^ (Yt – Yt^)² ) April,2000-01 151.7 22.51 168.56 -16.86 284.13 767.60 May 151.8 26.60 172.65 -20.85 434.60 557.69 June 152.7 28.49 174.54 -21.84 476.88 472.00 July 153.1 27.26 173.31 -20.21 408.34 526.96 August 153.4 28.46 174.51 -21.11 445.53 473.30 September 154.7 31.34 177.39 -22.69 514.77 356.29 October 157.9 30.50 176.55 -18.65 347.76 388.70 November 158.2 30.92 176.97 -18.77 352.25 372.32 December 158.5 23.25 169.30 -10.80 116.56 727.14 January 158.6 24.02 170.07 -11.47 131.48 686.21 February 158.6 25.92 171.97 -13.37 178.68 590.27 March 159.1 23.82 169.87 -10.77 115.92 696.73 April,2001-02 159.9 24.82 170.87 -10.97 120.27 644.93 May 160.3 26.95 173.00 -12.70 161.22 541.29 June 160.8 26.63 172.68 -11.88 141.07 556.28 July 161.1 23.99 170.04 -8.94 79.86 687.78 August 161.7 25.01 171.06 -9.36 87.55 635.32 September 161.7 24.79 170.84 -9.14 83.48 646.46 October 162.5 20.05 166.10 -3.60 12.93 909.96 November 162.3 18.24 164.28 -1.98 3.94 1022.44 December 161.8 18.24 164.28 -2.48 6.17 1022.44 January 161 18.92 164.97 -3.97 15.72 979.41 February 160.8 19.55 165.60 -4.80 22.99 940.38 March 161.9 23.31 169.36 -7.46 55.60 723.91 April,2002-03 162.3 25.03 171.08 -8.78 77.03 634.31 May 162.8 25.00 171.05 -8.25 68.01 635.82 June 164.7 24.05 170.10 -5.40 29.12 684.64 July 165.6 25.18 171.23 -5.63 31.66 626.78 August 167.1 25.86 171.91 -4.81 23.11 593.19 September 167.4 27.49 173.54 -6.14 37.67 516.45 October 167.5 26.90 172.95 -5.45 29.67 543.62 November 167.8 23.68 169.73 -1.93 3.71 704.14

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December 167.2 27.11 173.16 -5.96 35.49 533.87 January 167.8 29.59 175.64 -7.84 61.43 425.41 February 169.4 31.26 177.31 -7.91 62.54 359.31 March 171.6 28.83 174.88 -3.28 10.74 457.34 April,2003-04 173.1 24.21 170.26 2.84 8.09 676.29 May 173.4 25.00 171.05 2.35 5.54 635.82 June 173.5 26.42 172.47 1.03 1.07 566.23 July 173.4 27.46 173.51 -0.11 0.01 517.82 August 173.7 28.66 174.71 -1.01 1.02 464.64 September 175.6 26.27 172.32 3.28 10.78 573.39 October 176.1 28.45 174.50 1.60 2.57 473.74 November 176.9 28.20 174.25 2.65 7.04 484.68 December 176.8 28.97 175.02 1.78 3.18 451.37 January 178.7 30.01 176.06 2.64 6.98 408.26 February 179.8 29.61 175.66 4.14 17.16 424.59 March 179.8 32.21 178.26 1.54 2.38 324.20 April,2004-05 180.9 32.36 178.41 2.49 6.21 318.82 May 182.1 36.09 182.14 -0.04 0.00 199.53 June 185.2 34.22 180.27 4.93 24.31 255.86 July 186.6 36.35 182.40 4.20 17.64 192.25 August 188.4 40.53 186.58 1.82 3.31 93.81 September 189.4 39.15 185.20 4.20 17.64 122.45 October 188.9 43.37 189.42 -0.52 0.27 46.86 November 190.2 38.82 184.87 5.33 28.40 129.86 December 188.8 36.85 182.90 5.90 34.81 178.64 January 188.6 41.00 187.05 1.55 2.40 84.93 February 188.8 42.58 188.63 0.17 0.03 58.30 March 189.4 49.27 195.32 -5.92 35.09 0.89 April,2005-06 191.6 49.43 195.48 -3.88 15.08 0.62 May 192.1 47.02 193.07 -0.97 0.95 10.21 June 193.2 52.72 198.77 -5.57 31.07 6.27 July 194.6 55.01 201.06 -6.46 41.79 22.99 August 195.3 60.03 206.09 -10.79 116.34 96.32 September 197.2 59.74 205.80 -8.60 73.89 90.71 October 197.8 56.28 202.34 -4.54 20.57 36.78 November 198.2 53.31 199.36 -1.16 1.36 9.58 December 197.2 55.05 201.10 -3.90 15.25 23.37 January 196.3 60.61 206.67 -10.37 107.46 108.04 February 196.4 58.95 205.01 -8.61 74.06 76.29 March 196.8 60.01 206.07 -9.27 85.86 95.93

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April,2006-07 199 67.06 213.12 -14.12 199.32 283.74 May 201.3 67.33 213.39 -12.09 146.12 292.90 June 203.1 66.90 212.96 -9.86 97.18 278.37 July 204 71.29 217.35 -13.35 178.20 444.13 August 205.3 70.87 216.93 -11.63 135.24 426.61 September 207.8 60.94 207.00 0.80 0.65 115.01 October 208.7 57.26 203.32 5.38 28.99 49.62 November 209.1 57.80 203.86 5.24 27.50 57.52 December 208.4 60.34 206.40 2.00 4.01 102.50 January 208.8 52.62 198.67 10.13 102.53 5.78 February 208.9 56.49 202.55 6.35 40.38 39.37 March 209.8 60.26 206.32 3.48 12.14 100.89 April,2007-08 211.5 65.48 211.54 -0.04 0.00 233.00 May 212.3 65.76 211.82 0.48 0.23 241.63 June 212.3 68.10 214.16 -1.86 3.45 319.85 July 213.6 72.58 218.64 -5.04 25.40 500.17 August 213.8 68.97 215.03 -1.23 1.51 351.73 September 215.1 74.78 220.84 -5.74 32.95 603.41 October 215.2 79.33 225.39 -10.19 103.87 847.65 November 215.9 89.15 235.21 -19.31 373.03 1515.89 December 216.4 87.92 233.98 -17.58 309.19 1421.62 January 218.1 89.52 235.58 -17.48 305.70 1544.84 February 219.9 92.16 238.22 -18.32 335.80 1759.34 March 225.5 99.76 245.83 -20.33 413.18 2454.65 April,2007-08 228.5 105.77 251.84 -23.34 544.69 3086.30 May 231.1 120.91 266.98 -35.88 1287.56 4997.70 June 237.8 129.72 275.80 -38.00 1443.62 6320.96 July 240 132.47 278.55 -38.55 1485.78 6765.79 August 241.2 113.05 259.12 -17.92 321.15 3948.17 September 241.5 96.81 242.88 -1.38 1.89 2171.04 October 239 69.12 215.18 23.82 567.46 357.38 November 234.2 50.91 196.96 37.24 1386.54 0.48 December 229.7 40.61 186.66 43.04 1852.36 92.27 January 228.9 43.99 190.04 38.86 1509.95 38.76 February 227.6 43.22 189.27 38.33 1469.06 48.94 March 228.2 46.02 192.07 36.13 1305.20 17.60 April,2008-09 231.5 50.14 196.19 35.31 1246.86 0.01 May 234.3 58.00 204.06 30.24 914.52 60.65 June 235 69.12 215.17 19.83 393.07 357.20 July 238.7 64.82 210.88 27.82 773.83 213.42

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August 240.8 71.98 218.04 22.76 518.08 473.66 September 242.6 67.70 213.76 28.84 831.75 305.76 October 242.5 73.06 219.12 23.38 546.44 522.06 November 247.2 77.39 223.45 23.75 563.98 738.50 December 248.3 75.02 221.08 27.22 741.12 615.07 January 250.5 76.61 222.67 27.83 774.54 696.60 February 250.5 73.69 219.75 30.75 945.31 551.25 March 253.4 78.02 224.08 29.32 859.70 772.99 April,2009-10 257.5 84.08 230.14 27.36 748.33 1146.92 May 260.4 76.16 222.22 38.18 1457.56 673.19 June 259.8 74.33 220.39 39.41 1553.06 581.56 July 262.5 73.54 219.60 42.90 1840.16 544.17 ∑ 24337.1 6226.73 -0.27 36647.60 80989.69 N=124 Mean 196.26694 50.21556

Sb = 0.060902

t=b/Sb 16.42426

Now that we have obtained the values of two test-standard error and t-ratio-we use them finally to test the null hypothesis, that is there is no relationship between Y (WPI) and X (Crude oil price). To test the hypothesis we need to perform statistical t test, i.e., to compare the computed t – ratio (16.42) with the critical t value with different degrees of freedom.

The degrees of freedom is equals n – k = 124 – 2 = 122. The critical t values for different degrees of freedom are given in the t – table. The t test is usually performed at 5 per cent level number 122 under the degrees of freedom. When we link 122 with 5 per cent level of confidence, under the column 0.05, we get critical t value as 1.96 for the so called „two – tailed test‟. The value of t that we have calculated in our regression analysis is 16.42. This value of t (i.e., 16.42) far exceeds the critical t value (i.e.. 1.96) at the 5 per cent level of significance. Therefore, the null hypothesis that „there is no relationship between Y (WPI) and X (Crude oil price)‟ is rejected. The rejection of null hypothesis at 5 per cent level

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of significance means that there is a statistically significant relationship between Y (WPI) and X (Crude oil price). More precisely, we arrive at the conclusion that we are 95 per cent confident that there is a statistically significant relationship between Y (WPI) and X (Crude oil price).

8.2.2. The Test of Goodness of Fit: The Coefficient of Determination

Apart from testing for the statistical significance of the relationship between X (Crude oil price) and Y (WPI) another test is performed to test the overall explanatory power of the estimated regression equation. This test is performed by calculating the coefficient of determination. The coefficient of determination, denoted usually by the symbol R2, gives the measure of the overall strength of the association between the dependent (Y) and the independent (X) variables. The coefficient of determination (R2) is defined as the proportion of the total variation in the dependent variable Y (about its mean), explained by the variations in the independent variable or what is also called the explanatory variable, X. Given the definition, the coefficient of determination (R2) is measured as follows:

Explained Variation in Y R2 = ------Total variation in Y

The explained variation is the sum of the squares of the deviation of measured value of Y in each year from the mean of Y. That is,

Explained variation in Y =∑ )²

The total variation in Y equals the sum of the squares of the deviation of each observed value of Y from the mean of observed Y. That is,

Total variation in Y =∑ )²

Thus, the coefficient of determination (R2) can be redefined in terms of the ratio of explained variation in Y and total variation in Y as

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∑ )² R2 = ------

∑ )²

From an explanation point of view, total variation in Y is constituted of two parts (i) explained variation, and (ii) unexplained variation. Explained variation has already been defined above. The unexplained variation in Y equals the sum of squares of the difference between the observed value of Y in each month and the estimated value of Y for each month. That is,

Unexplained variation = ∑ )²

Thus, the total variation in Y can be redefined in terms of explained and unexplained variation in y as,

Total variation = Explained Variation + unexplained variation

= ∑ )² + ∑ )²

Although computer programs provide R2, we will illustrate here the process of its calculation. The process of calculation of the coefficient of determination (R2 as defined above) is given below,

Table :- 8.2.2. Calculation of Coefficient of Determination

Yt(WPI yt = Yt - Yt^ - et =Yt – (Yt – - - monthly) Y¯ (yt - Y¯)2 Yt^ Y (Yt^ - Y )² Yt^ Yt^)² April,2000- 01 151.7 -44.57 1986.21 168.56 -27.71 767.89 -16.86 284.13 May 151.8 -44.47 1977.31 172.65 -23.62 557.89 -20.85 434.60 June 152.7 -43.57 1898.08 174.54 -21.73 472.16 -21.84 476.88 July 153.1 -43.17 1863.38 173.31 -22.96 527.14 -20.21 408.34 August 153.4 -42.87 1837.57 174.51 -21.76 473.47 -21.11 445.53 September 154.7 -41.57 1727.81 177.39 -18.88 356.40 -22.69 514.77 October 157.9 -38.37 1472.02 176.55 -19.72 388.83 -18.65 347.76 November 158.2 -38.07 1449.09 176.97 -19.30 372.44 -18.77 352.25 December 158.5 -37.77 1426.34 169.30 -26.97 727.42 -10.80 116.56 January 158.6 -37.67 1418.80 170.07 -26.20 686.46 -11.47 131.48 February 158.6 -37.67 1418.80 171.97 -24.30 590.49 -13.37 178.68

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March 159.1 -37.17 1381.38 169.87 -26.40 696.99 -10.77 115.92 April,2001- 02 159.9 -36.37 1322.55 170.87 -25.40 645.17 -10.97 120.27 May 160.3 -35.97 1293.62 173.00 -23.27 541.48 -12.70 161.22 June 160.8 -35.47 1257.90 172.68 -23.59 556.48 -11.88 141.07 July 161.1 -35.17 1236.71 170.04 -26.23 688.04 -8.94 79.86 August 161.7 -34.57 1194.87 171.06 -25.21 635.55 -9.36 87.55 September 161.7 -34.57 1194.87 170.84 -25.43 646.70 -9.14 83.48 October 162.5 -33.77 1140.21 166.10 -30.17 910.32 -3.60 12.93 November 162.3 -33.97 1153.75 164.28 -31.98 1022.85 -1.98 3.94 December 161.8 -34.47 1187.97 164.28 -31.98 1022.85 -2.48 6.17 January 161 -35.27 1243.76 164.97 -31.30 979.80 -3.97 15.72 February 160.8 -35.47 1257.90 165.60 -30.67 940.75 -4.80 22.99 March 161.9 -34.37 1181.09 169.36 -26.91 724.18 -7.46 55.60 April,2002- 03 162.3 -33.97 1153.75 171.08 -25.19 634.55 -8.78 77.03 May 162.8 -33.47 1120.04 171.05 -25.22 636.06 -8.25 68.01 June 164.7 -31.57 996.47 170.10 -26.17 684.89 -5.40 29.12 July 165.6 -30.67 940.46 171.23 -25.04 627.01 -5.63 31.66 August 167.1 -29.17 850.71 171.91 -24.36 593.41 -4.81 23.11 September 167.4 -28.87 833.30 173.54 -22.73 516.63 -6.14 37.67 October 167.5 -28.77 827.54 172.95 -23.32 543.81 -5.45 29.67 November 167.8 -28.47 810.37 169.73 -26.54 704.40 -1.93 3.71 December 167.2 -29.07 844.89 173.16 -23.11 534.05 -5.96 35.49 January 167.8 -28.47 810.37 175.64 -20.63 425.55 -7.84 61.43 February 169.4 -26.87 721.83 177.31 -18.96 359.42 -7.91 62.54 March 171.6 -24.67 608.46 174.88 -21.39 457.50 -3.28 10.74 April,2003- 04 173.1 -23.17 536.71 170.26 -26.01 676.54 2.84 8.09 May 173.4 -22.87 522.90 171.05 -25.22 636.06 2.35 5.54 June 173.5 -22.77 518.33 172.47 -23.80 566.43 1.03 1.07 July 173.4 -22.87 522.90 173.51 -22.76 518.00 -0.11 0.01 August 173.7 -22.57 509.27 174.71 -21.56 464.80 -1.01 1.02 September 175.6 -20.67 427.12 172.32 -23.95 573.59 3.28 10.78 October 176.1 -20.17 406.71 174.50 -21.77 473.90 1.60 2.57 November 176.9 -19.37 375.08 174.25 -22.02 484.85 2.65 7.04 December 176.8 -19.47 378.96 175.02 -21.25 451.52 1.78 3.18 January 178.7 -17.57 308.60 176.06 -20.21 408.40 2.64 6.98 February 179.8 -16.47 271.16 175.66 -20.61 424.73 4.14 17.16 March 179.8 -16.47 271.16 178.26 -18.01 324.30 1.54 2.38 April,2004- 05 180.9 -15.37 236.14 178.41 -17.86 318.92 2.49 6.21 May 182.1 -14.17 200.70 182.14 -14.13 199.58 -0.04 0.00

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June 185.2 -11.07 122.48 180.27 -16.00 255.93 4.93 24.31 July 186.6 -9.67 93.45 182.40 -13.87 192.30 4.20 17.64 August 188.4 -7.87 61.89 186.58 -9.69 93.82 1.82 3.31 September 189.4 -6.87 47.15 185.20 -11.07 122.46 4.20 17.64 October 188.9 -7.37 54.27 189.42 -6.85 46.86 -0.52 0.27 November 190.2 -6.07 36.81 184.87 -11.40 129.88 5.33 28.40 December 188.8 -7.47 55.76 182.90 -13.37 178.68 5.90 34.81 January 188.6 -7.67 58.78 187.05 -9.22 84.93 1.55 2.40 February 188.8 -7.47 55.76 188.63 -7.64 58.30 0.17 0.03 March 189.4 -6.87 47.15 195.32 -0.94 0.89 -5.92 35.09 April,2005- 06 191.6 -4.67 21.78 195.48 -0.78 0.61 -3.88 15.08 May 192.1 -4.17 17.36 193.07 -3.19 10.20 -0.97 0.95 June 193.2 -3.07 9.41 198.77 2.51 6.29 -5.57 31.07 July 194.6 -1.67 2.78 201.06 4.80 23.02 -6.46 41.79 August 195.3 -0.97 0.93 206.09 9.82 96.42 -10.79 116.34 September 197.2 0.93 0.87 205.80 9.53 90.81 -8.60 73.89 October 197.8 1.53 2.35 202.34 6.07 36.82 -4.54 20.57 November 198.2 1.93 3.74 199.36 3.10 9.59 -1.16 1.36 December 197.2 0.93 0.87 201.10 4.84 23.41 -3.90 15.25 January 196.3 0.03 0.00 206.67 10.40 108.15 -10.37 107.46 February 196.4 0.13 0.02 205.01 8.74 76.37 -8.61 74.06 March 196.8 0.53 0.28 206.07 9.80 96.03 -9.27 85.86 April,2006- 07 199 2.73 7.47 213.12 16.85 283.96 -14.12 199.32 May 201.3 5.03 25.33 213.39 17.12 293.14 -12.09 146.12 June 203.1 6.83 46.69 212.96 16.69 278.59 -9.86 97.18 July 204 7.73 59.80 217.35 21.08 444.46 -13.35 178.20 August 205.3 9.03 81.60 216.93 20.66 426.93 -11.63 135.24 September 207.8 11.53 133.01 207.00 10.73 115.12 0.80 0.65 October 208.7 12.43 154.58 203.32 7.05 49.68 5.38 28.99 November 209.1 12.83 164.69 203.86 7.59 57.59 5.24 27.50 December 208.4 12.13 147.21 206.40 10.13 102.60 2.00 4.01 January 208.8 12.53 157.08 198.67 2.41 5.79 10.13 102.53 February 208.9 12.63 159.59 202.55 6.28 39.42 6.35 40.38 March 209.8 13.53 183.14 206.32 10.05 100.99 3.48 12.14 April,2007- 08 211.5 15.23 232.05 211.54 15.27 233.20 -0.04 0.00 May 212.3 16.03 257.06 211.82 15.55 241.83 0.48 0.23 June 212.3 16.03 257.06 214.16 17.89 320.10 -1.86 3.45 July 213.6 17.33 300.44 218.64 22.37 500.54 -5.04 25.40 August 213.8 17.53 307.41 215.03 18.76 352.00 -1.23 1.51 September 215.1 18.83 354.68 220.84 24.57 603.84 -5.74 32.95

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October 215.2 18.93 358.46 225.39 29.12 848.24 -10.19 103.87 November 215.9 19.63 385.46 235.21 38.95 1516.88 -19.31 373.03 December 216.4 20.13 405.34 233.98 37.72 1422.56 -17.58 309.19 January 218.1 21.83 476.68 235.58 39.32 1545.84 -17.48 305.70 February 219.9 23.63 558.52 238.22 41.96 1760.47 -18.32 335.80 March 225.5 29.23 854.57 245.83 49.56 2456.19 -20.33 413.18 April,2007- 08 228.5 32.23 1038.97 251.84 55.57 3088.21 -23.34 544.69 May 231.1 34.83 1213.34 266.98 70.72 5000.71 -35.88 1287.56 June 237.8 41.53 1725.00 275.80 79.53 6324.72 -38.00 1443.62 July 240 43.73 1912.58 278.55 82.28 6769.81 -38.55 1485.78 August 241.2 44.93 2018.98 259.12 62.85 3950.57 -17.92 321.15 September 241.5 45.23 2046.03 242.88 46.61 2172.42 -1.38 1.89 October 239 42.73 1826.11 215.18 18.91 357.65 23.82 567.46 November 234.2 37.93 1438.92 196.96 0.70 0.49 37.24 1386.54 December 229.7 33.43 1117.77 186.66 -9.61 92.27 43.04 1852.36 January 228.9 32.63 1064.92 190.04 -6.23 38.75 38.86 1509.95 February 227.6 31.33 981.76 189.27 -7.00 48.93 38.33 1469.06 March 228.2 31.93 1019.72 192.07 -4.19 17.59 36.13 1305.20 April,2008- 09 231.5 35.23 1241.37 196.19 -0.08 0.01 35.31 1246.86 May 234.3 38.03 1446.51 204.06 7.79 60.72 30.24 914.52 June 235 38.73 1500.25 215.17 18.91 357.47 19.83 393.07 July 238.7 42.43 1800.56 210.88 14.62 213.60 27.82 773.83 August 240.8 44.53 1983.19 218.04 21.77 474.01 22.76 518.08 September 242.6 46.33 2146.75 213.76 17.49 306.00 28.84 831.75 October 242.5 46.23 2137.50 219.12 22.86 522.45 23.38 546.44 November 247.2 50.93 2594.18 223.45 27.18 739.01 23.75 563.98 December 248.3 52.03 2707.44 221.08 24.81 615.51 27.22 741.12 January 250.5 54.23 2941.23 222.67 26.40 697.10 27.83 774.54 February 250.5 54.23 2941.23 219.75 23.49 551.65 30.75 945.31 March 253.4 57.13 3264.19 224.08 27.81 773.53 29.32 859.70 April,2009- 10 257.5 61.23 3749.49 230.14 33.88 1147.68 27.36 748.33 May 260.4 64.13 4113.05 222.22 25.96 673.66 38.18 1457.56 June 259.8 63.53 4036.45 220.39 24.12 581.98 39.41 1553.06 July 262.5 66.23 4386.82 219.60 23.34 544.57 42.90 1840.16 - ∑ 24337.1 0 117682.03 196.27 81033.43 36647.60

R2= 0.6885794 r = 0.8298069

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Since we have computed the elements of the coefficient of determination, we can calculate R2. Column 4 of Table above shows the total variation and column 7 shows the explained variation. Given the values, we get,

∑ )² 81033.43 R2 = ------= ------= 0.6885794

∑ )² 117682.03

From above calculation, R2 = 0.68. It means that 68 per cent of the total variation in the dependent variable Y (WPI) is explained by the independent variable X i.e., the crude oil price. It means that the regression equation has a high explanatory power and that the regression line is a „good fit‟

And important aspect of the coefficient of determination (R2) is that its square root gives the coefficient of correlation, denoted by r. That is, r=√ R² .The coefficient of correlation measures the degree of association between the dependent and the independent variables. It also important to note here that while regression equation assumes that the variation in the dependent variable (Y) is caused by the variation in the independent variable (X), the coefficient of correlation gives the measure of only the degree of association or covariance between the dependent and the independent variables. We may apply this formula to find the correlation coefficient between WPI and Crude oil price.

r = SQRT (R2) = SQRT (0.6885794) = 0.8298

This means that there is a very strong association or correlation between WPI and Crude oil price.

8.2.3. Analysis of Variance

The analysis of variance is a technique to test the overall explanatory power of the regression equation. For this purpose, the analysis of variance uses the F- statistics or F- ratio. The formula for computing the value of F – statistic is given below.

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{(Explained variation) / (k – 1)} F = ------{(Unexplained variation)/ (N - k)}

Where k= number of estimated parameters, N = number of observations.

The F – statistics can also be calculated by the following formula,

R²/ (k-1) F = ------(1 - R²)/ (N-k)

The F- statistics is used to test the hypothesis that the variations in the independent variables (X) explain a significant proportion of variation in dependent variable (Y). The F – statistic so calculated is checked in F- distribution table with respect to degrees of freedom and critical values. The computerized results also provide the analysis of variance.

(81033.43)/ (2-1) F= ------= 269.76 (36647.60) / (124 – 2)

Similarly, the regression outputs of WPI on Crude oil price using excel software package has three components:

 Regression statistics table or Model Summary

 ANOVA table

 Regression coefficients table.

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Table:-8.2.4.INTERPRETATION OF REGRESSION MODEL SUMMARY Model R R square Adjusted R Std. Error Number of square of the observation estimate 1 0.8298 0.68858 0.68603 17.3317 124

The Regression Statistics Table or model summary gives the overall goodness- of-fit measures: R2 = 0.68. The Correlation between dependent variable Y and independent variable X is r =√ (R²) =0.8298. The standard error here refers to the estimated standard deviation of the error term et. It is sometimes called the standard error of the regression. It equals SQRT (SSE/ (n-k)). Table:-8.2.5 ANOVA Model Sum of df Mean F square square 1 Regression 81034.43 1 81034.43 269.79 Residual 36647.60 122 300.39 Total 117682.03 123

The ANOVA (analysis of variance) table splits the sum of squares into its components.

Total sums of squares = Residual (or error) sum of squares + Regression (or explained) sum of squares.

2 2 2 Thus Σ i (yi - ybar) = Σ i (yi - yhati) + Σ i (yhati - ybar) ; where yhati is the value of yi predicted from the regression line and ybar is the sample mean of y.

Therefore, R2 = 1 - Residual SS / Total SS (general formula for R2) = 1 – 36647.60/117682.03 (from data in the ANOVA table) = 0.68858798 (which equals R2 found in the regression Statistics table above).

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Table : 8.2.6. REGRESSION COEFFICIENTS TABLE

Coefficients Std. Error t-statistics p-value

Intercept or (Constant) 146.0375 3.43148 42.558 4.75E-75

Crude_Price 1.000276 0.060901 16.424 1.07E-32

The population regression model is: y = a + bx + et ; where, the error et is assumed to be distributed independently with mean 0 and constant variance. we focus on inference on b, using the row that begins with crude price. Similar interpretation is given for inference on „a‟, using the row that begins with intercept. The column "Coefficient" gives the least squares estimates of “a” and “b”.

The column "Standard error" gives the standard errors (i.e. the estimated standard deviation) of the least squares estimate of “a” and “b” .

The second row of the column "t Stat" gives the computed t-statistic for H01: b = 0 against H11: b ≠ 0. This is the coefficient divided by the standard error: here 1.00027 / 0.060901 = 16.42449. It is compared to a T distribution with (n-k) degrees of freedom where here n = 124 and k = 2.

The column "P-value" gives for crude prices are for H01: b = 0 against H11: b ≠ 0. This equals the Pr{|T| > t-Stat}where T is a T-distributed random variable with n-k degrees of freedom and t-Stat is the computed value of the t-statistic given is the previous column. This P-value is for a 2-sided test. For a 1-sided test divide this P-value by 2 (also checking the sign of the t-Stat).

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A simple summary of the above output is that

 The fitted line is Y = 146.0375+1.00027*X

 The slope coefficient has estimated standard error of 0.060901  The slope coefficient has t-statistic of 16.424.

 The slope coefficient has p-value of 1.07E-32.  The standard error of the regression is 17.33  Correlation between WPI and Crude oil prices = 0.8298. 2 2  R = 0.6885 ; Adjusted R = 0.6860  The regression model is

Y = 146.0375 + 1.00027X

There is a strong positive correlation between WPI and Crude oil prices.

For deriving elasticity co-efficient of dependent variable, double log natural regression model was used. One attractive feature of double natural log model is that the slope coefficient „b‟ measure elasticity Y with respect to X, that is percent change of Y for a given percent change in X.

Table 8.2.7. Log natural transformation data of WPI and Crude Price

WPI Ln( Crude monthly Crude Price $ Ln(WPI) price) April,2000-01 151.7 22.51 5.021905 3.11396 May 151.8 26.60 5.022564 3.280911 June 152.7 28.49 5.028475 3.349553 July 153.1 27.26 5.031091 3.30542 August 153.4 28.46 5.033049 3.3485 September 154.7 31.34 5.041488 3.444895 October 157.9 30.50 5.061962 3.417727 November 158.2 30.92 5.06386 3.431403 December 158.5 23.25 5.065755 3.146305 January 158.6 24.02 5.066385 3.178887 February 158.6 25.92 5.066385 3.255015 March 159.1 23.82 5.069533 3.170526 April,2001-02 159.9 24.82 5.074549 3.21165 May 160.3 26.95 5.077047 3.293983 June 160.8 26.63 5.080161 3.282038

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July 161.1 23.99 5.082025 3.177637 August 161.7 25.01 5.085743 3.219276 September 161.7 24.79 5.085743 3.21044 October 162.5 20.05 5.090678 2.998229 November 162.3 18.24 5.089446 2.903617 December 161.8 18.24 5.086361 2.903617 January 161 18.92 5.081404 2.94022 February 160.8 19.55 5.080161 2.972975 March 161.9 23.31 5.086979 3.148882 April,2002-03 162.3 25.03 5.089446 3.220075 May 162.8 25.00 5.092522 3.218876 June 164.7 24.05 5.104126 3.180135 July 165.6 25.18 5.109575 3.22605 August 167.1 25.86 5.118592 3.252697 September 167.4 27.49 5.120386 3.313822 October 167.5 26.90 5.120983 3.292126 November 167.8 23.68 5.122773 3.164631 December 167.2 27.11 5.119191 3.299903 January 167.8 29.59 5.122773 3.387436 February 169.4 31.26 5.132263 3.442339 March 171.6 28.83 5.145166 3.361417 April,2003-04 173.1 24.21 5.153869 3.186766 May 173.4 25.00 5.155601 3.218876 June 173.5 26.42 5.156178 3.274121 July 173.4 27.46 5.155601 3.31273 August 173.7 28.66 5.15733 3.355502 September 175.6 26.27 5.168209 3.268428 October 176.1 28.45 5.171052 3.348148 November 176.9 28.20 5.175585 3.339322 December 176.8 28.97 5.175019 3.366261 January 178.7 30.01 5.185708 3.401531 February 179.8 29.61 5.191845 3.388112 March 179.8 32.21 5.191845 3.472277 April,2004-05 180.9 32.36 5.197944 3.476923 May 182.1 36.09 5.204556 3.586016 June 185.2 34.22 5.221436 3.53281 July 186.6 36.35 5.228967 3.593194 August 188.4 40.53 5.238567 3.702042 September 189.4 39.15 5.243861 3.6674 October 188.9 43.37 5.241218 3.769768 November 190.2 38.82 5.248076 3.658936

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December 188.8 36.85 5.240688 3.606856 January 188.6 41.00 5.239628 3.713572 February 188.8 42.58 5.240688 3.751385 March 189.4 49.27 5.243861 3.897315 April,2005-06 191.6 49.43 5.25541 3.900558 May 192.1 47.02 5.258016 3.850573 June 193.2 52.72 5.263726 3.964995 July 194.6 55.01 5.270946 4.007515 August 195.3 60.03 5.274537 4.094844 September 197.2 59.74 5.284218 4.090002 October 197.8 56.28 5.287256 4.030339 November 198.2 53.31 5.289277 3.976124 December 197.2 55.05 5.284218 4.008242 January 196.3 60.61 5.279644 4.10446 February 196.4 58.95 5.280153 4.07669 March 196.8 60.01 5.282188 4.094511 April,2006-07 199 67.06 5.293305 4.205588 May 201.3 67.33 5.304796 4.209606 June 203.1 66.90 5.313698 4.203199 July 204 71.29 5.31812 4.266756 August 205.3 70.87 5.324472 4.260847 September 207.8 60.94 5.336576 4.10989 October 208.7 57.26 5.340898 4.047602 November 209.1 57.80 5.342813 4.056989 December 208.4 60.34 5.339459 4.099995 January 208.8 52.62 5.341377 3.963096 February 208.9 56.49 5.341856 4.034064 March 209.8 60.26 5.346155 4.098669 April,2007-08 211.5 65.48 5.354225 4.181745 May 212.3 65.76 5.358 4.186012 June 212.3 68.10 5.358 4.220977 July 213.6 72.58 5.364105 4.284689 August 213.8 68.97 5.365041 4.233672 September 215.1 74.78 5.371103 4.31455 October 215.2 79.33 5.371568 4.373616 November 215.9 89.15 5.374815 4.49032 December 216.4 87.92 5.377129 4.476427 January 218.1 89.52 5.384954 4.494462 February 219.9 92.16 5.393173 4.523526 March 225.5 99.76 5.41832 4.602767 April,2007-08 228.5 105.77 5.431536 4.661267

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May 231.1 120.91 5.442851 4.795046 June 237.8 129.72 5.47143 4.865378 July 240 132.47 5.480639 4.886356 August 241.2 113.05 5.485626 4.72783 September 241.5 96.81 5.486869 4.57275 October 239 69.12 5.476464 4.235844 November 234.2 50.91 5.456175 3.930059 December 229.7 40.61 5.436774 3.704014 January 228.9 43.99 5.433285 3.783962 February 227.6 43.22 5.42759 3.766303 March 228.2 46.02 5.430222 3.829076 April,2008-09 231.5 50.14 5.44458 3.914731 May 234.3 58.00 5.456602 4.060501 June 235 69.12 5.459586 4.235776 July 238.7 64.82 5.475208 4.171685 August 240.8 71.98 5.483967 4.276377 September 242.6 67.70 5.491414 4.215111 October 242.5 73.06 5.491002 4.29134 November 247.2 77.39 5.510198 4.348869 December 248.3 75.02 5.514638 4.317704 January 250.5 76.61 5.523459 4.338712 February 250.5 73.69 5.523459 4.299925 March 253.4 78.02 5.534969 4.356943 April,2009-10 257.5 84.08 5.55102 4.431789 May 260.4 76.16 5.562219 4.332855 June 259.8 74.33 5.559912 4.308529 July 262.5 73.54 5.570251 4.297871

The regression is carried out using excel software package has three components:

 Regression statistics table or Model Summary

 ANOVA table  Regression coefficients table.

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SUMMARY OF REGRESSION OUTPUT

Regression Statistics Multiple R 0.886158 R Square 0.785276 Adjusted R Square 0.783516 Standard Error 0.072439 Observations 124

ANOVA Significance df SS MS F F Regression 1 2.34123705 2.341237 446.1723 1.42584E-42 Residual 122 0.64018069 0.005247 Total 123 2.98141774

Standard Coefficients Error t Stat P-value Intercept 4.230286 0.04952673 85.4142 1.1E-110 Ln( Crude price) 0.273585 0.0129521 21.12279 1.43E-42

Therefore, the double log regression model shows that the crude oil price elasticity of WPI is 0.27 and it is positively correlated. Thus, our natural log –log regression model is,

Ln(Y) = 4.230286 + 0.273585 Ln(X).

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8.3. Model 2. The Karl Pearson correlation coefficient (r) between the sets of variables Y (GDP growth rate) and X(Inflation) is calculated. Pearson's correlation reflects and measures the strength of linear association between two variables.

Table 8.3 Two variable data for correlation

Quarterly Quarterly India GDP India growth Inflation ² ² (Y) rate (X) (XY) (X ) (Y ) 2005-06 Q1 9.25 3.97 36.73 15.76 85.62 Q2 8.91 3.27 29.14 10.69 79.40 Q3 9.69 4.12 39.90 16.97 93.81 Q4 9.99 5 49.95 25.00 99.81 2006-07 Q1 9.81 5.57 54.66 31.02 96.29 Q2 10.13 6.92 70.09 47.89 102.59 Q3 9.38 7.06 66.22 49.84 87.96 Q4 9.59 7 67.12 49.00 91.93 2007-08 Q1 9.34 6.67 62.33 44.49 87.33 Q2 9.39 6.47 60.76 41.86 88.18 Q3 9.73 5.81 56.55 33.76 94.74 Q4 8.49 5.47 46.42 29.92 72.03 2008-09 Q1 8.04 7.81 62.79 61.00 64.65 Q2 7.81 8.52 66.54 72.59 61.00 Q3 5.59 10.22 57.16 104.45 31.28 Q4 5.76 9.93 57.23 98.60 33.21 2009-10 Q1 6.32 8.45 53.40 71.40 39.93 Q2 8.64 10.97 94.77 120.34 74.64 Q3 7.33 12.21 89.50 149.08 53.73 Q4 8.57 15.35 131.48 235.62 73.36 ∑ 171.76 150.79 1252.74 1309.30 1511.48 N= 20

r = - 0.536

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In order to measure the expected influence of inflation on GDP growth, the econometric model, YGDP = a1 + b1 XInflation+ et, which is shown in scatter plot and fitting regression line in the graph below. Graph-8.3

12.00 ) y = -0.2452x + 10.437 10.00 R² = 0.2851

8.00

6.00

4.00

2.00

0.00

Quarterly Quarterly IndiaGDP growth(Y 0 2 4 6 8 10 12 14 16 18 Quarterly India Inflation rate (X)

Scatter Plot of X and Y with Fitting of Regression line

^ ^ The ordinary least square method is used to develop values of a1 and b1 the estimates of model parameters a1 and b1 respectively. The resulted estimated ^ ^ ^ regression equation is Y GDP = a1 + b1 Xinflation .

8.3.1.Two variable regression analysis

yt = (YGDP Xinf xt = Xinf - YGDP - 2 2 (XInflation) growth) YGDP Xinf Xinf¯ YGDP¯ xt xtyt 2005-06, Q1 3.97 9.25 36.73 15.76 -3.57 0.66 12.74 -2.37 Q2 3.27 8.91 29.14 10.69 -4.27 0.32 18.23 -1.38 Q3 4.12 9.69 39.90 16.97 -3.42 1.10 11.69 -3.75 Q4 5 9.99 49.95 25.00 -2.54 1.40 6.45 -3.56 2006-07, Q1 5.57 9.81 54.66 31.02 -1.97 1.22 3.88 -2.41 Q2 6.92 10.13 70.09 47.89 -0.62 1.54 0.38 -0.95 Q3 7.06 9.38 66.22 49.84 -0.48 0.79 0.23 -0.38 Q4 7 9.59 67.12 49.00 -0.54 1.00 0.29 -0.54 2007-08, Q1 6.67 9.34 62.33 44.49 -0.87 0.76 0.76 -0.66

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Q2 6.47 9.39 60.76 41.86 -1.07 0.80 1.14 -0.86 Q3 5.81 9.73 56.55 33.76 -1.73 1.15 2.99 -1.98 Q4 5.47 8.49 46.42 29.92 -2.07 -0.10 4.28 0.21 2008-09, Q1 7.81 8.04 62.79 61.00 0.27 -0.55 0.07 -0.15 Q2 8.52 7.81 66.54 72.59 0.98 -0.78 0.96 -0.76 Q3 10.22 5.59 57.16 104.45 2.68 -3.00 7.19 -8.03 Q4 9.93 5.76 57.23 98.60 2.39 -2.83 5.71 -6.75 2009-10, Q1 8.45 6.32 53.40 71.40 0.91 -2.27 0.83 -2.07 Q2 10.97 8.64 94.77 120.34 3.43 0.05 11.77 0.18 Q3 12.21 7.33 89.50 149.08 4.67 -1.26 21.81 -5.88 Q4 15.35 8.57 131.48 235.62 7.81 -0.02 61.00 -0.18 ∑ 150.79 171.76 1252.74 1309.30 0.00 0 172.42 -42.28 N=20 Mean 7.54 8.59

Thus, from the above we can estimate a1 and b1.

^ b1 = (Σxtyt)/ Σx2 = -0.24522

- - a1^ =Y -b X = 10.439

It can be seen from graph above that total change in Y is not explained by a change in X. The regression line can explain the total change in Y in response to change in X only if all the inflation – GDP growth points fall on the regression line. But, as is evident from the graph, all inflation – GDP growth combination points do not fall on the regression line. Some points are placed above and some points are placed below the regression line. This means that estimated b1^, i.e. the slope of the regression line, does not explain the total change in Y in response to a change in X. The unexplained part of Y is called the error term, the residual or the disturbance. The purpose of regression technique is to find the ^ ^ average values of „a1 ‟ and „b1 ‟ which make the values of observed pairs of X and Y, i.e.(X1,Y1), (X2,Y2), etc, as close to the regression line as possible. The line so fitted is called the best fit regression line. This objective is achieved by minimizing the error terms, i.e. et .

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8.3.2. Calculation of Standard Error of Coefficient

(YGDP et = (YGDPt - ^ 2 2 (XInflation) growth) Y GDP Y^GDP) et xt = (Xt - X-)2 2005-06, Q1 3.97 9.25 9.47 -0.88 0.77 12.74 Q2 3.27 8.91 9.64 -1.05 1.10 18.23 Q3 4.12 9.69 9.43 -0.82 0.67 11.69 Q4 5 9.99 9.21 -0.62 0.39 6.45 2006-07, Q1 5.57 9.81 9.07 -0.46 0.21 3.88 Q2 6.92 10.13 8.74 -0.15 0.02 0.38 Q3 7.06 9.38 8.71 -0.12 0.01 0.23 Q4 7 9.59 8.72 -0.13 0.02 0.29 2007-08, Q1 6.67 9.34 8.80 -0.22 0.05 0.76 Q2 6.47 9.39 8.85 -0.26 0.07 1.14 Q3 5.81 9.73 9.01 -0.43 0.18 2.99 Q4 5.47 8.49 9.10 -0.51 0.26 4.28 2008-09, Q1 7.81 8.04 8.52 0.06 0.00 0.07 Q2 8.52 7.81 8.35 0.24 0.06 0.96 Q3 10.22 5.59 7.93 0.66 0.43 7.19 Q4 9.93 5.76 8.00 0.58 0.34 5.71 2009-10, Q1 8.45 6.32 8.37 0.22 0.05 0.83 Q2 10.97 8.64 7.75 0.84 0.70 11.77 Q3 12.21 7.33 7.44 1.14 1.31 21.81 Q4 15.35 8.57 6.67 1.91 3.66 61.00 ∑ 150.79 171.76 0.00 10.32 172.42 N=20 Mean 7.54 8.59

From the model and its assumption we can conclude that σ2 the variance of e, also represents the variance of Y values about the regression line. The deviation of the Y values about the estimated regression line is called residuals. Thus, SSE, the sum square residuals is a measure of the variability of the actual observations about the estimated regression line. The mean square error (MSE) provides the estimate of σ2, it is SSE divided by its degrees of freedom. MSE

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provides an unbiased estimator of σ2. Because the value of MSE provides an estimate of σ2, the notation S2 is also used.

SSE 10.32 MSE (Estimate of σ2) = S2 = ------= ------= 0.5733 N-2 20-2

To estimate σ, we take the square root of S2, the resulting value; S is referred to as the standard error of estimate. Therefore, S = = =0.7572.

8.3.3: t-Test.

The simple linear regression model is YGDP = a1 + b1 X inflation+ et. If x and y are linearly related we must have b1 ≠ 0. The purpose of the t test is to see whether we can conclude that b1 ≠ 0.

To test the parameter b1, following hypotheses are to be tested,

H0 : b1 = 0.

Ha : b1 ≠ 0

If H0 is rejected, we will conclude that b1 ≠ 0 and that a statistically significant relationship exists between the two variables. However, if H0 cannot be rejected we will have insufficient evidence to conclude that a significant relationship exists. The properties of the sampling distribution of b1, the least square estimator of b1, provides the basis for hypothesis test.

S 0.7572 0.7572 Sb1 = ------= ------= ------= 0.0576 - 2 SQRT {Σ(Xinfla – X ) } 13.1308

as the estimated standard deviation of b1.

The t test for a significant relationship is based on the fact that the test statistic

(b1^ - b1)/ Sb1 follows a t distribution with N-2 degrees of freedom. If the null ^ hypothesis is true, then b1 = 0 and t = b1 / Sb1 = - 0.245 / 0.0576 = - 4.2534.

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The t distribution table shows that with N-2 = 20-2 = 18 degree of freedom, t= 1.734 provides an area of 0.05 in the upper tail. Thus, the area in the upper tail of t distribution corresponding to the test statistic t= 4.2534 must be less than 0.05. because this test is a two tailed test, we double this value to conclude that p- value associated with t=4.253 must be less than 2(0.05) = 0.1, excel show the p- value =0.015, we reject the H0 and conclude that b1 is not equal to zero. This evidence is sufficient to conclude that a significant relationship exists between quarterly GDP growth YGDP(growth) and quarterly inflation Xinflation .

8.3.4: Confidence Interval for b1

The form of a confidence interval for b1 is as follows

b1± t α/2 Sb1

The point estimator is b1 and the margin of error is t α/2Sb1. The confidence coefficient associated with the interval is 1-α, and t α/2 is the t value providing an area of α/2 in the upper tail of a t distribution with N-2 degrees of freedom.

Suppose that we want to develop a 95% confidence interval estimate of b1. From t distribution table we find that the value of t corresponding to α=0.05 and

N-2 = 20-2=18 degrees of freedom is t0.05 = 1.734, thus 95% confidence interval estimate of b1 is

b1± t α/2 Sb1 = -0.245 ± 1.734( 0.0576) = -0.245 ± 0.0998 or -0.3448 to -0.1452. In using the t test for significance, the hypotheses tested were

H0 : b1 = 0.

Ha : b1 ≠ 0 At the α = 0.05 level of significance, we can use the 95% confidence interval as an alternative for drawing the hypothesis testing. Because 0, the hypothesized value of b1, is not included in the confidence interval (-0.3448 to -0.1452), we can reject H0 and conclude that a significant statistical relationship exists between the quarterly GDP growth and quarterly inflation.

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8.3.5: F test An F test, based on the F probability distribution, can also be used to test for significance in regression. With only one independent variable, the F test will provide the same conclusion as t test, i.e. if the t test indicates b1 ≠ 0 and hence a significant relationship, the F test will also indicate a significant relationship. But with more than one independent variable, only the F test can be used to test for an overall significant relationship. The logic behind the use of F test for determining whether the regression relationship is statistically significant is based on the development of two 2 independent estimates of σ , if the null hypothesis, H0 : b1 = 0 is true, the sum of squares due to regression, SSR, divided by its degrees of freedom provides another independent estimate of σ2, this estimation is called the mean square due to regression, or simply the mean square regression, and is denoted by MSR. In general, SSR MSR = ------Regression degrees of freedom For the model we consider the regression degree of freedom is always equal to the number of independent variables in the model.

SSR MSR = ------Number of independent variables Because we consider only regression model with one independent variable we have MSR = SSR/1 = SSR, hence MSR =SSR =10.365.

If the null hypothesis (H0: b1 = 0) is true, MSR and MSE are independent estimates of σ2 and the distribution MSR/MSE follows an F distribution with numerator degrees of freedom one and denominator degrees of freedom N-2.

Therefore when b1 = 0, the value of MSR/MSE =1. But if the null hypothesis is 2 fails, then b1≠ 0, MSR will over estimate σ and the value of MSR/MSE will be

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inflated, thus large values of MSR/MSE lead to the rejection of H0 and the conclusion that the relationship between x and y is statistically significant. 10.365 F= ------= 7.181 1.443 The f distribution table shows that at 1 degree of freedom in the numerator and N-2=20-2=18 degrees of freedom in denominator, F= 4.41 provides an area of 0.05 in the upper tail. Thus, the area in the upper tail of F distribution corresponding to test statistic F= 7.181 must be less than 0.05. Thus, we conclude that the p-value must be less than0.05. Excel show the p-value= 0.015

8.3.6 Calculation of Coefficient of Determination

Yt = et = (YGDP - (YGDP - Y^ - (YGDP (YGDP - 2 (XInflation) YGDP Y¯) Y¯) Y^ Y- (Y^ - Y-)² - Y^) Y^)² 2005- 06, Q1 3.97 9.25 0.66 0.4420 9.47 0.88 0.77 -0.21 0.045 Q2 3.27 8.91 0.32 0.1041 9.64 1.05 1.10 -0.72 0.518 Q3 4.12 9.69 1.10 1.2037 9.43 0.83 0.69 0.26 0.066 Q4 5 9.99 1.40 1.9664 9.21 0.62 0.39 0.78 0.605 2006- 07, Q1 5.57 9.81 1.22 1.4991 9.07 0.46 0.21 0.74 0.547 Q2 6.92 10.13 1.54 2.3726 8.74 0.15 0.02 1.39 1.922 Q3 7.06 9.38 0.79 0.6253 8.71 0.12 0.01 0.67 0.451 Q4 7 9.59 1.00 0.9999 8.72 0.13 0.02 0.87 0.749 2007- 08, Q1 6.67 9.34 0.76 0.5728 8.80 0.22 0.05 0.54 0.293 Q2 6.47 9.39 0.80 0.6435 8.85 0.26 0.07 0.54 0.289 Q3 5.81 9.73 1.15 1.3122 9.01 0.43 0.18 0.72 0.518 Q4 5.47 8.49 -0.10 0.0103 9.10 0.51 0.26 -0.61 0.373 2008- 09, Q1 7.81 8.04 -0.55 0.3002 8.52 -0.06 0.00 -0.45 0.203 Q2 8.52 7.81 -0.78 0.6050 8.35 -0.24 0.06 -0.54 0.291 Q3 10.22 5.59 -3.00 8.9727 7.93 -0.66 0.43 -2.34 5.476 Q4 9.93 5.76 -2.83 7.9822 8.00 -0.58 0.34 -2.24 5.022 2009- 10, Q1 8.45 6.32 -2.27 5.1476 8.37 -0.22 0.05 -2.05 4.192 Q2 10.97 8.64 0.05 0.0026 7.75 -0.84 0.70 0.89 0.793 Q3 12.21 7.33 -1.26 1.5835 7.44 -1.14 1.31 -0.11 0.013 Q4 15.35 8.57 -0.02 0.0005 6.67 -1.91 3.66 1.89 3.574

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∑ 150.79 171.76 0.00 36.3459 0.00 10.33 0.00 25.940 N=20 Mean 7.54 8.5882

∑ )² 10.33 R2 = ------= ------= 0.284

∑ )² 36.3459

Table :8.3.7 Model Summary Std. Error of the Number of Model R R Square Adjusted R Square Estimate observations d i m e n 2 0.534a 0.2851 0.2454 1.201 20 s i o n 0 a. Predictors: (Constant), Inflation

Table : 8.3.8 ANOVAb TABLE Sum of Model df Mean Square F Sig. Squares

Regression 10.36556025 1 10.36556025 7.18158 0.01529173

Residual 25.98036549 18 1.443353638

Total 36.34592574 19 a. Predictors: (Constant), Inflation b. Dependent Variable: GDP growth

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Table 8.3.9 Coefficientsa

Coefficients Std. Error t-statistics p-value

Intercept or 10.4368021 0.740285064 14.09836 3.62E-11 (Constant)

Inflation -0.245191 0.091494387 -2.67985 0.015292

Residuals or sum of error term in output regression is zero.

Therefore, The regression model is

YGDP growth = 10.436 – 0.245Xinflation

There is a strong negative correlation between GDP growth and Inflation.

For deriving elasticity co-efficient of dependent variable, double log natural regression model was used. One attractive feature of double natural log model is that the slope coefficient „b1‟ measure elasticity Y with respect to X, that is percent change of Y for a given percent change in X.

Table : 8.3.10. Log natural transformation data (GDP growth & Inflation)

Quarterly India Ln(Quarterly Ln(Quarterly Quarterly India Inflation India GDP India Inflation GDP growth rate growth) rate) 9.253026216 3.97 2.224951 1.378766 8.910811668 3.27 2.187265 1.18479 9.685300842 4.12 2.270609 1.415853 9.990474822 5 2.301632 1.609438 9.812564453 5.57 2.283664 1.717395 10.12849792 6.92 2.315353 1.934416 9.378929362 7.06 2.238466 1.954445 9.588109882 7 2.260524 1.94591 9.344993483 6.67 2.234841 1.89762 9.390370435 6.47 2.239685 1.867176 9.733684021 5.81 2.275592 1.759581

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8.486877024 5.47 2.138521 1.699279 8.040235203 7.81 2.084458 2.055405 7.810385895 8.52 2.055454 2.142416 5.592739847 10.22 1.721469 2.324347 5.762913079 9.93 1.751443 2.29556 6.319358159 8.45 1.843618 2.134166 8.639328802 10.97 2.156325 2.395164 7.329826066 12.21 1.991952 2.502255 8.565269211 15.35 2.147716 2.731115

SUMMARY OUTPUT

Regression Statistics Multiple R 0.538234 R Square 0.289695 Adjusted R Square 0.250234 Standard Error 0.15537 Observations 20

ANOVA Significance df SS MS F F Regression 1 0.177217 0.177217 7.341241 0.014359732 Residual 18 0.434518 0.02414 Total 19 0.611735

Standard Coefficients Error t Stat P-value Intercept 2.614991 0.180101 14.51956 2.22E-11 Ln(Inflation) -0.24589 0.090753 -2.70947 0.01436

Therefore, the double log regression model shows that the inflation elasticity of GDP growth is - 0.24 and it is negatively correlated. Thus, our natural log –log regression model is,

Ln(Y) = 2.614 - 0.245 Ln(X).

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8.4. Multivariable Regression Analysis: Multivariable regression analysis is a statistical technique for estimating relationship among variables. We will now extend the work to multivariable regression analysis or what is also called multiple regressions. When a dependent variable (Y) is a function of more than one independent or exploratory variable, it is called multivariable regression. We have the variables (GDP growth, inflation rate and Crude oil price change rate and let us mark these by1, 2, 3). The correlation coefficients are given below. Correlation co-efficient

r12 -0.534

r13 0.454

r23 -0.207

In order to analyze and understand the impact of both crude oil price change rate and inflation rate on GDP growth, we have to calculate the partial correlation coefficients. As we have considered 1, 2, 3 are the three variables GDP growth, inflation rate and Crude oil price change rate then we denote by r12,3 the coefficient of partial correlation between GDP growth and inflation rate keeping crude price change rate constant, then

r12 - r13. r23

r12,3 = ------= - 0.505 2 2 sqrt{1-(r13) }. sqrt{1-(r23) }

Similarly, the coefficient of partial correlation between GDP growth and crude oil price change rate, keeping inflation rate constant. Which is denoted by r13,2.

r13 - r12. r23

r13,2 = ------= 0.415 2 2 sqrt{1-(r12) }. sqrt{1-(r23) }

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and in the same way, the coefficient of partial correlation between Inflation rate and crude oil price change rate, keeping GDP growth constant. This is denoted by

r23,1.

r23 - r12. r13

r23,1 = ------= 0.0469 2 2 sqrt{1-(r12) }. sqrt{1-(r13) }

Again, we have a basic assumption is that the independent variables are not interdependent. But there is often a chance that there exist interdependency between the independent variables, most independent variables in a multiple regression are correlated to some degree with one another, in multiple regression analysis this correlation among the independent variables is called multicollinearity. Statisticians have developed thumb rule, according to the rule of thumb test, multicollinearity is a potential problem if the absolute value of the sample correlation coefficient exceeds 0.7 for any two of the independent variables. (Source: David R Anderson, Dennis Sweeney, Thomas A. Williams, “Statistics for Business and Economics,” India Edition, 2008, Chapter 15, pp-655 ). Thus in our multivariable analysis, to find multicollinearity between the independent variables, we can treat inflation rate as dependent variable and crude oil price change rate as independent variable to determine correlation coefficient, rx1x2 = -0.207,

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Graph 8.4 (Plot of Inflation and crude oil price change rate)

5 y = -0.0153x + 7.851 0 R² = 0.0431 Inflation rate percent in r= - 0.207 -100 -50 0 50 100 Crude oil price change rate in percent …

Thus, we find the correlation coefficient is r = -0.207, which is less than 0.7; therefore, the multicollinearity problem can be neglected for proceeding multiple regression analysis. 8.4.1. Test of Multicollinearity: To test for multicollinearity, each explanatory variable is regressed against other explanatory variable and the auxiliary R2 is calculated. The variance inflation factor (VIF) is calculated for the auxiliary R2 . The VIF is a method of detecting how severe the multicollinearity is, it is calculated as;

2 VIF (βi) = 1/( 1- R ) and the general rule is that If VIF>5 indicate severe multicollinearity.

Table 8.4.1: Test of Multicollinearity; auxiliary regression results

Variable Auxiliary R2 VIF 1. Qrty rate change 0.043 1.044 in Crude oil price &Qrly. Inflation rate

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Explanation and discussion: As VIF calculated value is less than 5, therefore, there is no Multicollinearity between the explanatory variables.

In order to carry out the regression analysis among the variables, GDP growth as dependent variable, crude oil price change rate and inflation rate as independent variable. An analysis has been carried out on Multivariable linear regression model, In the present case, GDP growth rate is the dependent variable, inflation and rate of change of crude oil price are the independent variables and all are in percent, the regression is carried out in excel package.

Table 8.4.2 Multivariable Regression Analysis

Quarterly India Quarterly India Crude oil price GDP growth in Inflation rate in rate change in percent percent percent 2005-06, Q1 9.253026 3.97 45.29515 Q2 8.910812 3.27 50.65943 Q3 9.685301 4.12 38.30645 Q4 9.990475 5 35.18519 2006-07,Q1 9.812564 5.57 34.93564 Q2 10.1285 6.92 16.20323 Q3 9.378929 7.06 6.523324 Q4 9.58811 7 -5.69663 2007-08,Q1 9.344993 6.67 -0.96885 Q2 9.39037 6.47 6.514032 Q3 9.733684 5.81 46.18543 Q4 8.486877 5.47 66.18246 2008-09,Q1 8.040235 7.81 78.80795 Q2 7.810386 8.52 58.24435 Q3 5.59274 10.22 -37.3508 Q4 5.762913 9.93 -52.6596 2009-10,Q1 6.319358 8.45 -50.2694 Q2 8.639329 10.97 -40.2594 Q3 7.329826 12.21 40.36235 Q4 8.565269 15.35 71.37131

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SUMMARY OUTPUT

Regression Statistics Multiple R 0.638732 R Square 0.407979 Adjusted R Square 0.338329 Standard Error 1.12505 Observations 20

ANOVA df SS MS F Significance F Regression 2 14.82837 7.414185 5.857596 0.011610911 Residual 17 21.51756 1.265739 Total 19 36.34593

Standard Coefficients Error t Stat P-value Intercept 9.9326232 0.743423776 13.36065 1.91E-10 Quarterly India Inflation rate -0.211065 0.087586424 -2.40979 0.027575 Change in crude oil price 0.012115 0.006451942 1.877726 0.077685

RESIDUAL OUTPUT

Observation Predicted Y Residuals Standard Residuals 1 9.643444952 -0.390418736 -0.366868969 2 9.856178536 -0.945366868 -0.888343043 3 9.527117291 0.158183551 0.148642037 4 9.303566033 0.686908789 0.645474963 5 9.180235753 0.6323287 0.594187105 6 8.668355263 1.46014266 1.372067944 7 8.521534352 0.857395011 0.805677583 8 8.386153839 1.201956043 1.12945495 9 8.513082205 0.831911278 0.78173101 10 8.64595013 0.744420305 0.699517427 11 9.265871069 0.467812952 0.439594823 12 9.579896815 -1.093019791 -1.027089653 13 9.238962221 -1.198727018 -1.126420699

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14 8.839978549 -1.029592654 -0.967488393 15 7.323034883 -1.730295037 -1.62592493 16 7.198777669 -1.435864589 -1.349254308 17 7.540111922 -1.220753763 -1.147118807 18 7.129498626 1.509830177 1.418758346 19 7.844508694 -0.514682627 -0.483637355 20 7.557437592 1.007831619 0.947039967

Graph- 8.4.2.1 Residual plot

Inflation rate Residual Plot 2

0 0 5 10 15 20 Residuals -1

-2 X Variable inflation rate

Graph- 8.4.2.2 Inflation rate line fit plot

Inflation rate Line Fit Plot 12

Y 6 Y 4 Predicted Y 2

0 0 5 10 15 20 X Variable inflation rate

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Graph- 8.4.2.3 Residual plot

Crude oil price change rate Residual Plot 2

-60 -40 -20 0 20 40 60 80 100 Residuals -1

-2 X Crude oil price change rate

Graph- 8.4.2.4, Crude oil price change rate Line Fit Plot

Crude oil price change rate Line Fit Plot 12

Y 6 Y 4 Predicted Y 2

0 -100 -50 0 50 100 X Crude oil price change rate

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Table 8.4.3 Probability output data

PROBABILITY OUTPUT Normal Probability Plot

12 Percentile Y

2.5 5.59274 10 7.5 5.762913

12.5 6.319358 8 17.5 7.329826

22.5 7.810386 Y 6 27.5 8.040235 32.5 8.486877 4 37.5 8.565269 42.5 8.639329 2 47.5 8.910812 52.5 9.253026 0 0 20 40 60 80 100 120 57.5 9.344993 Sample Percentile 62.5 9.378929 67.5 9.39037 72.5 9.58811 77.5 9.685301 82.5 9.733684 87.5 9.812564 92.5 9.990475 97.5 10.1285

8.5 : Durbin Watson Statistics The Durbin – Watson statistics provides the test of existence of autocorrelation. A Durbin-Watson statistics around of 2 indicates the absence of autocorrelation. If Durbin Watson statistics is significantly greater or less than the value of 2, it shows the existence of autocorrelation. The formula for D-W statistics is

∑ D-W = ------

∑

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Table 8.5.1 Residual data for D W statics calculation

2 2 Residuals Rt-1 (Rt- Rt-1 ) Rt Rt.Rt-1 -0.39042 0.152427 -0.94537 -0.39042 0.3079674 0.152427 0.369089 0.158184 -0.94537 1.2178235 0.893719 -0.14954 0.686909 0.158184 0.2795504 0.025022 0.108658 0.632329 0.686909 0.002979 0.471844 0.434352 1.460143 0.632329 0.685276 0.39984 0.92329 0.857395 1.460143 0.3633047 2.132017 1.251919 1.201956 0.857395 0.1187223 0.735126 1.030551 0.831911 1.201956 0.1369331 1.444698 0.999921 0.74442 0.831911 0.0076547 0.692076 0.619292 0.467813 0.74442 0.0765116 0.554162 0.348249 -1.09302 0.467813 2.4361989 0.218849 -0.51133 -1.19873 -1.09302 0.011174 1.194692 1.310232 -1.02959 -1.19873 0.0286064 1.436946 1.234201 -1.7303 -1.02959 0.4909838 1.060061 1.781499 -1.43586 -1.7303 0.0866893 2.993921 2.484469 -1.22075 -1.43586 0.0462727 2.061707 1.752837 1.50983 -1.22075 7.4560887 1.49024 -1.84313 -0.51468 1.50983 4.0986521 2.279587 -0.77708 1.007832 -0.51468 2.3180496 0.264898 -0.51871 sum 20.169438 20.65426 10.84876

2 2 2 DW=∑{(Rt- Rt-1 ) } / ∑(Rt ), ρ = ∑( Rt.Rt-1) / ∑( Rt ), which is known as the coefficient of auto covariance also interpreted as the first order coefficient of autocorrelation

DW = 20.16944/20.65426 = 0.976527 Rho(ρ) =0.525255

d = 2(1-10.848/20.65426) = 0.949489

H0 : ρ = 0 versus H1 : ρ > 0. Reject H0 at α level if d < dU. From DW d statistic, at n=20, k‟=2,at 0.05 level of significance, du=1.537, that means d

Computation of ρ in excel is found as ρ = 0.517, and is a first order autoregressive scheme and is denoted as AR(1).

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8.6. Stationarity is time series data:- Loosely speaking, a time series is stationary if its characteristics ( e.g , mean , variance, and covariance) are time invariant; that is , they do not change over time. In the time series literature, weak stationarity or covariance stationary means that mean and the variance of a stochastic process do not depend on t (that is they are constant) and the auto- covariance between Yt and Yt+τ only can depend on the lag τ (τ is an integer, the quantities also need to be finite).

To explain weak stationarity, let Yt be a stochastic time series with this properties;

Mean: E(Yt) = μ

Variance : var(Yt) = E(Yt – μ)² = σ²

Covariance γk = E{( Yt – μ)( Yt+k – μ)}

Where γk, the covariance ( or auto-covariance) at lag k, is the covariance between the values of Yt and Yt+k that is between two Y values k periods apart. If k=0, we obtain γ0, which is simply the variance of Y (=σ²); if k=1, γ1 is the covariance between two adjacent values of Y. One simple test of stationarity is based on so called auto correlation function

(ACF). The ACF at lag k, denoted by ρk is defined as

γk ρk = ------γ0

covariance = ------Variance

Since variance and covariance measured in the same units of measurement, ρk is unit less or a pure number. It lies between -1 to +1 as any correlation coefficient does. If we plot ρk against k, the graph we obtain is known as the population correlogram. Since in practice we only have a realization ( i.e. sample) of a stochastic process, we can only compute sample autocorrelation function(SAFC) ρ^k .

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To compute this, we must first compute the sample covariance at lag k, γ^k and the sample variance γ^0, which is defined as

∑ {( Yt – ̅)( Yt+k – ̅)} γ^k = ------n

∑( Yt – ̅) γ^0 = ------n where, n is the sample size and ̅ is the sample mean. Therefore, the sample autocorrelation function at lag k is

γ^k ρ^k = ------γ^0

Which is simply the ratio of sample covariance( at lag k ) to sample variance. A plot of ρ^k against k is known as sample correlogram.

8.6.1 Box- Jenkins strategy: It is a common practice to suitably transform the original series, the logarithmic transformation has been done in our time series data, i.e. GDP growth rate, inflation rate and crude oil price change rate. This logarithmic transformation is given by,

Yt = log10 yt,

Where yt are the time series data of the variables. For which the logarithmic transformations are given in table 8.6.1 below.

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Table 8.6.1. Logarithmic transformations of GDP growth

Yt=log10yt(GDP variance co variance

growth), at lag k=1 ;

i.e.γ1 GDP growth 2005-06, Q1 9.253026 0.966284 0.001486 Q2 8.910812 0.949917 0.000492 0.000855 Q3 9.685301 0.986113 0.003409 0.001295 Q4 9.990475 0.999586 0.005163 0.004195 2006-07, Q1 9.812564 0.991783 0.004103 0.004603 Q2 10.1285 1.005545 0.006055 0.004984 Q3 9.378929 0.972153 0.001973 0.003457 Q4 9.58811 0.981733 0.002916 0.002399 2007-08, Q1 9.344993 0.970579 0.001836 0.002314 Q2 9.39037 0.972683 0.002021 0.001926 Q3 9.733684 0.988277 0.003666 0.002722 Q4 8.486877 0.928748 1.04E-06 6.16E-05 2008-09, Q1 8.040235 0.905269 0.000504 -2.3E-05 Q2 7.810386 0.892672 0.001229 0.000787 Q3 5.59274 0.747625 0.032438 0.006314 Q4 5.762913 0.760642 0.027918 0.030093 2009-10, Q1 6.319358 0.800673 0.016143 0.02123 Q2 8.639329 0.93648 7.66E-05 -0.00111 Q3 7.329826 0.865094 0.003923 -0.00055 Q4 8.565269 0.932741 2.51E-05 -0.00031

Mean = 0.92773, To compute ACF, one third of the sample is considered as the choice of lag length of the time series, since n=20, hence lags of 6 to 7 will do.

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For this series the lag variance and covariance also lag autocorrelation and partial auto correlations are given below in table. Table 8.6.2.ACF and PACF lag variance covariance ACF PACF 1 0.00577 0.00449 0.77747 0.77747 2 0.00577 0.00360 0.623917 0.04919208 3 0.00577 0.002186 0.378856 -0.7024688 4 0.00577 0.001189 0.206066 -0.2582486 5 0.00577 -0.00014 -0.024263 -0.497024 6 0.00577 -0.00086 -0.149047 -0.7095343 7 0.00577 -0.00119 -0.206239 -1.1297106 8 0.00577 -0.00198 -0.343154 -3.0287074

The above data of ACF and PACF are plotted is two different graphs against the lag lengths.

Graph-8.6.2.1 ACF plot

ACF 1

0.8

0.6

0.4

0.2

0 1 2 3 4 5 6 7 8 -0.2

-0.4

From the above ACF plot, it is seen that ACF declines sharply in the bar graph.

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The PACF plot indicates that PACF declines sharply after lag1 in the graph below, therefore the series is AR(1). Graph:8.6.2.2, PACF

PACF 1 0.5 0 1 2 3 4 5 6 7 8 -0.5 -1 PACF -1.5 -2 -2.5 -3 -3.5

AR(1) SERIES.

Similarly, the inflation and crude oil price change rate data are taken and logarithmic transformation done, and the mean, variance and covariance are computed, then by computing auto correlation function (ACF) and partial auto correlation function (PACF), the data are tabulated below

225

Table:- 8.6.3 , Variance, covariance table for inflation

Yt=log10yt(Inflation), variance covariance at lag k=1 ;

i.e.γ1 Inflation 2005-06, Q1 3.97 0.59879 0.06096 Q2 3.27 0.51455 0.10965 0.08175 Q3 4.12 0.61490 0.05326 0.07642 Q4 5.00 0.69897 0.02152 0.03386 2006-07, Q1 5.57 0.74586 0.00997 0.01465 Q2 6.92 0.84011 0.00003 0.00056 Q3 7.06 0.84880 0.00001 -0.00002 Q4 7.00 0.84510 0.00000 0.00000 2007-08, Q1 6.67 0.82413 0.00046 0.00001 Q2 6.47 0.81090 0.00121 0.00075 Q3 5.81 0.76418 0.00664 0.00283 Q4 5.47 0.73799 0.01160 0.00878 2008-09, Q1 7.81 0.89265 0.00221 -0.00506 Q2 8.52 0.93044 0.00718 0.00398 Q3 10.22 1.00945 0.02682 0.01388 Q4 9.93 0.99695 0.02288 0.02477 2009-10, Q1 8.45 0.92686 0.00659 0.01228 Q2 10.97 1.04021 0.03784 0.01579 Q3 12.21 1.08672 0.05810 0.04689 Q4 15.35 1.18611 0.11589 0.08205

Mean = 0.84568 To compute ACF, one third of the sample is considered as the choice of lag length of the time series, since n=20, hence by this rule lags of 6 to 7 will do.

For this series the lag variance and covariance also lag autocorrelation and partial auto correlations are given below in table.

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Table : 8.6.3.1 ACF , PACF for Inflation

lag variance covariance ACF PACF 1 0.02764 0.21798 7.886111 0.788611 2 0.02764 0.73005 26.41185 -3.8690346 3 0.02764 0.726 26.26533 1.2605498 4 0.02764 0.726 26.26533 1.39563292 5 0.02764 0.722 26.12062 1.92342389 6 0.02764 0.711 25.72266 -0.0731604

The above data of ACF and PACF are plotted is two different graphs against the lag lengths.

Graph 8.6.3.2 ACF plot for Inflation

ACF PLOT for INFLATION 30 25 20 15 10 5 0 1 2 3 4 5 6

PACF plot for inflation against lag length is shown below,

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Graph 8.6.3.4 PACF plot for Inflation

PACF plot for Inflation 3 2 1

-1 1 2 3 4 5 6 PACF -2 -3 -4 -5 lag

From the above two plots we found that ACF and PACF do not drop down fast and remain fairly large. This suggests that the series is nonstationary. Therefore, a first order differenced series is considered, delta Yt = Yt –Yt-1 is obtained and for this differenced series again ACF and PACF are computed,

Table 8.6.4 ACF, PACF for first order difference inflation series lag variance covariance ACF PACF

1 0.00433 -0.00004 -0.00878 -0.00088

2 0.00433 -0.00021 -0.04850 -0.04851

3 0.00433 -0.00089 -0.20554 -0.20839

4 0.00433 -0.00218 -0.50346 -0.57463

5 0.00433 -0.00064 -0.14781 -0.72472

6 0.00433 -0.00037 -0.08545 -0.94392

The above data of ACF and PACF are plotted is two different graphs against the lag lengths.

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Graph 8.6.4.1 ACF PLOT

ACF 0.00000 1 2 3 4 5 6 -0.10000

-0.20000

-0.30000 ACF

-0.40000

-0.50000

-0.60000

ACF plot indicates sharp decline after lag 2. The PACF plot indicates that PACF declines sharply after lag2 in the graph below, therefore the inflation time series is AR(2). The PACF plot is shown below Graph-PACF plot

PACF 0.00000 -0.10000 1 2 3 4 5 6 -0.20000 -0.30000 -0.40000 -0.50000 PACF -0.60000 -0.70000 -0.80000 -0.90000 -1.00000

AR(2) Series.

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Table 8.6.5 , variance covariance for crude oil price change rate

Yt=log10yt(Crude oil price variance co variance

change rate), at lag k=1 ;

Crude oil i.e.γ1 price change rate 2005-06, Q1 45.29 1.656 0.9004 Q2 50.66 1.7047 0.9951 0.9466 Q3 38.31 1.5833 0.7677 0.8741 Q4 35.19 1.5464 0.7044 0.7354 2006-07, Q1 34.94 1.5433 0.6993 0.7018 Q2 16.2 1.2095 0.2524 0.4201 Q3 6.52 0.8142 0.0115 0.0538 Q4 -5.69 -0.755 2.1381 -0.157 2007-08, Q1 -0.96 0.0177 0.4752 1.008 Q2 6.51 0.8136 0.0113 -0.073 Q3 46.19 1.6645 0.9167 0.1019 Q4 66.44 1.8224 1.2439 1.0679 2008-09, Q1 78.81 1.8966 1.4148 1.3266 Q2 58.24 1.7652 1.1196 1.2586 Q3 -37.35 -1.572 5.1957 -2.412 Q4 -52.65 -1.721 5.8976 5.5355 2009-10, Q1 -50.26 -1.701 5.8001 5.8486 Q2 -40.26 -1.605 5.3453 5.568 Q3 40.36 1.606 0.8079 -2.078 Q4 71.37 1.8535 1.3143 1.0304

Mean = 0.707107

For this series the lag variance and covariance also lag autocorrelation and partial auto correlations are given below in table.

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Table 8.6.5.1, ACF and PACF

lag variance covariance ACF PACF

1 1.80056 1.14513 0.63599 0.77747 2 1.80056 0.26300 0.14607 -0.68915 3 1.80056 -0.47500 -0.26381 -1.08480 4 1.80056 -1.10200 -0.61203 -4.03720 5 1.80056 -0.84800 -0.47096 1.68389 6 1.80056 -0.22990 -0.12768 1.83051 7 1.80056 0.41000 0.22771 1.68704 8 1.80056 0.44400 0.24659 1.49953

The above data of ACF and PACF are plotted is two different graphs against the lag lengths. Graph:- 8.6.5.2, ACF plot

ACF 0.80000

0.60000

0.40000

0.20000

0.00000 ACF 1 2 3 4 5 6 7 8 -0.20000

-0.40000

-0.60000

-0.80000

From the above ACF plot, it is seen that ACF declines sharply after lag1 and the ACF plot follows the sine curve fashion, the PACF plot indicates that PACF declines sharply after lag1 in the graph below, therefore the series is AR(1).

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Graph:- 8.6.5.2, PACF plot

PACF 3.00000

2.00000

1.00000

0.00000 1 2 3 4 5 6 7 8 -1.00000 PACF

-2.00000

-3.00000

-4.00000

-5.00000

The Series is stationary and Autoregressive at lag1, i.e. AR(1).

Further, the stationarity of the above time series data is tested by unit root test, and in case unit root test fails then augmented Dickey – Fuller (ADF) test is done.

We start with Yt = ρYt-1 + ut , -1≤ρ≤+1, where ut is a white noise error term. We know that if ρ = 1, that is in case of unit root, becomes a random walk model without drift, which we know is a nonstationary stochastic process. Therefore if we regress Yt on its lagged value Yt-1 and find out the estimated ρ is statistically equal to 1.then we say Yt is nonstationary. That is the general idea behind the unit root test of stationarity.

For theoretical reasons, we will subtract Yt-1 from the both side of the above equation to obtain:

Yt –Yt-1 = ρYt-1 - Yt-1 + ut

= ( ρ – 1) Yt-1 + ut Which can be alternatively written as

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Δ Yt = δYt-1 + ut ; Where δ = ( ρ – 1) and Δ, as usual, is the first difference operator, therefore, we will estimate and test the null hypothesis that δ = 0, if δ = 0, then ρ = 1, that is we have a unit root, meaning the time series under consideration is nonstationary. If δ = 0, then the equation become

Δ Yt = (Yt –Yt-1) = ut,

Since ut is a white noise error term, it is stationary, which means that the first differences of a random walk time series are stationary.

On estimation δ, the first difference GDP growth rate is regressed with the lagged values of growth rate, we found that

ANOVA table 8.6.6. df SS MS F Regression 1 2.118027 2.118027 2.232459 Residual 16 15.17987 0.948742 Total 17 17.29789

Standard Coefficients Error t Stat P-value Intercept 2.057256 1.408729 1.460363 0.163548 Yt-1 -0.24295 0.162603 -1.49414 0.154598

Thus we have δ=ρ-1, now δ is estimated slope coefficient in this regression, if δ is zero, we conclude that Yt is nonstationary, But if it negative we conclude that

Yt is stationary.

As δ = -0.24295, ρ = 1+δ =1- 0.24295 = 0.75705. i.e. ρ<1, the series is stationary

Again, in case of testing of unit root test for inflation rate stationary series, the estimation δ, the first difference inflation rate is regressed with the lagged values of inflation rate, we have found the ANOVA table 8.6.7

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Table:- 8.6.7 ANOVA TABLE ANOVA df SS MS F Regression 1 0.592 0.592 0.3857 Residual 16 24.56 1.535 Total 17 25.152

Coefficients Standard Error t Stat P-value Intercept 0.1025 0.9611 0.1066 0.9164 Yt-1 0.0778 0.1254 0.621 0.5433

Thus we have δ=ρ-1, now δ is estimated slope coefficient in this regression, if δ is zero, we conclude that Yt is nonstationary, But if it negative we conclude that

Yt is stationary. As δ = 0.0778, therefore the series is nonstationary, Further, ρ = 1+δ =1+ 0.0778 = 1.0778, Since, ρ>1, the series is nonstationary the unit root test failed. Therefore we will proceed for augmented Dickey – Fuller (ADF) test. We have estimated the slope coefficients for the inflation series using one lagged difference of inflation, the results are as follows ANOVA Table:8.6.8 df SS MS F Regression 4 25.067344 6.266835938 3.819E+32 Residual 11 1.805E-31 1.64098E-32 Total 15 25.067344

Standard Coefficients Error t Stat P-value Intercept 1.75E-16 1.395E-16 1.254999329 0.23548694 Yt-1 -1.03E-16 3.598E-17 -2.876551535 0.01506393 (Yt-1 - Yt-2) -1.37E-16 3.682E-17 -3.729114119 0.00332859 (Yt-2 - Yt-3) -1 5.016E-17 -1.9937E+16 6.349E-175 t 4.82E-17 1.55E-17 3.107927116 0.00996224

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The t (= τ ) value of the inflation(Yt-1) coefficient(=δ ) is -2.8765, but this value in absolute terms is much higher than critical value of t- statistics at df=11 at 5% to 10% is 1.796, suggesting the series is stationary. Therefore, ADF test holds true, the inflation series is stationary.

Again in case of testing the stationarity of crude oil price change rate, we have carried out the unit root test. On estimation of δ, the first difference crude oil price change rate is regressed with the lagged values of crude oil price change rate and we have found that Table 8.6.9

ANOVA df SS MS F Regression 1 2864.454761 2864.5 2.7891 Residual 17 17459.30372 1027 Total 18 20323.75848

Coefficients Standard Error t Stat P-value Intercept 6.9300379 8.070177182 0.8587 0.4024

Yt-1 -0.3140823 0.188066506 -1.67 0.1132

Thus we have δ=ρ-1, now δ is estimated slope coefficient in this regression, if δ is zero, we conclude that Yt is nonstationary, But if it negative we conclude that

Yt is stationary.

As δ = - 0.3140, ρ = 1+δ =1- 0.3140 = 0.6860. i.e. ρ<1, the series is stationary

Stationarity of the three time series ( GDP growth rate, inflation rate and crude oil price change rate) are tested and found stationary. For the number of lagged terms to be introduced in the causality tests , Akaike or Schwarz information criterion is used, AIC and SIC values are -0.06787 and 0.280635 at lag length 3 for inflation and 3 for crude oil price change rate respectively, similarly, AIC and

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SIC values are 0.736304 and 1.184383 at lag length 4 for inflation and 4 for crude oil price change rate.

8.7. Ganger causality test:- Model 3

To proceed with Ganger causality test, we have the null hypothesis that crude oil price rate change does not granger cause inflation. Time series inflation is regressed with lagged inflation without including any lagged terms of crude oil price change rate and this is the restricted regression, and the restricted residual sum square is obtained (RSSR = 19.56, at lag length 3 of inflation). Now, Inflation is regressed with 3 lagged inflation and with 3 lagged crude oil price changed rate , and this is the unrestricted regression, and the unrestricted residual sum square is obtained (RSSUR = 9.28).

The F-statistics is defined as

{( RSSR - RSSUR )/m} F =

{RSSUR/T-k}

Where, m = number of lagged crude oil rate change terms; n = number of lagged inflation; T = number of observations = 20 k = is the numbers of parameters estimated in unrestricted regression.( m+n+ 1)

( 19.56 – 9.28) / 3 F = ------= 4.79, ( 9.28/ 13) the estimated F value 4.79 is significant than the critical F value at 5% level 3.41, ( for 3 and 13df ) and therefore null hypothesis is rejected, the alternative hypothesis crude oil price change rate granger causes inflation.

Similarly,

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we have the null hypothesis inflation does not granger cause crude oil price rate change. Time series crude oil price change rate is regressed with lagged crude oil price changed rate without including any lagged terms of inflation and this is the restricted regression, and the restricted residual sum square is obtained

(RSSR = 6059.78, at lag length 3 of crude oil price change rate). Now, crude oil price change rate is regressed with 3 lagged of crude oil price change rate and with 3 lagged inflation , and this is the unrestricted regression, and the unrestricted residual sum square is obtained (RSSUR = 3265.73). and the F- value is

(6059.78 – 3265.73) / 3 F = ------= 3.71, (3265.73/ 13) the estimated F value 3.71 is significant than the critical F value at 5% level 3.41, ( for 3 and 13df ) and therefore null hypothesis is rejected, the alternative hypothesis inflation granger causes crude oil price change rate.

8.8. Model :4

Now we can proceed Ganger causality test with another null hypothesis that the inflation does not granger cause GDP growth rate. Time series GDP growth is regressed with lagged GDP growth rate without including any lagged terms of inflation and this is the restricted regression, and the restricted residual sum square is obtained (RSSR = 10.92, at lag length 3 of GDP growth rate). Now, GDP growth rate is regressed with 3 lagged GDP growth rate and with 3 lagged inflation , and this is the unrestricted regression, and the unrestricted residual sum square is obtained (RSSUR = 9.716 ).

(10.92 – 9.716) / 3 F = ------= 0.54, (9.716 / 13)

Similarly , four lagged terms

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(10.34 – 7.17) / 4 F = ------= 1.21, (7.17 / 11)

the estimated F values 0.54 and 1.21 are insignificant than the critical F value at 5% level 3.41, ( for 3 and 13df ) and 3.36 (for 4 and 11df) and therefore null hypothesis is accepted.

Similarly,

Ganger causality test is carried out with another null hypothesis that the GDP growth does not granger cause inflation. Time series inflation is regressed with lagged inflation without including any lagged terms of GDP growth and this is the restricted regression, and the restricted residual sum square is obtained (RSSR = 23.22, at lag length 3 of inflation). Now, inflation is regressed with 3 lagged inflation and with 3 lagged GDP growth, and this is the unrestricted regression, and the unrestricted residual sum square is obtained (RSSUR = 10.25 ).

(23.22 – 10.25) / 3 F = ------= 5.48 (10.25 /13)

Similarly, four lagged terms

(20.66 – 5.5) / 4 F = ------= 7.58 (5.5 / 11)

the estimated F values 5.48 and 7.58 are significant than the critical F values at 5% level 3.41, (for 3 and 13df ) and 3.36 (for 4 and 11df) and therefore null hypothesis is rejected. Therefore the alternative hypothesis holds true, thus GDP growth granger causes inflation.

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8.9. Model 5.

The “Cobb-Douglas” production function is applied for estimating the output of Indian industries. The real output of industries depend upon the capital and labor as well as energy resources. The “Cobb-Douglas” production function may be written as y= A ert ha kb Ec ------equation (1)

Where y = is output, h = labor measured in man-days k = capital input,

E = flow of energy.

A = a scaling factor; t = year; r = is the trend rate of growth of output due to technological change; a,b,c = are the output elasticities of respective inputs.

The estimated production function was restricted by requiring that the sum of exponents a,b,c equal to unity. The basic implications of such a “Cobb-Douglas” production function are constant return to scale and partial elasticities of substitution of unity.

Now if enterprise maximize economic profits, they employ energy at a rate where the value of additional product obtained from employing more energy equals its price. The demand for energy from equation above can be written as

-1 E = c.y.( pe / pd ) ------equation (2)

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light and lubricants to the wholesale price index and expressed in percent with respect to base year.

On simplification of the equations (1) and (2) with energy demand, the model reduced to ln(y/k) = α + β ln(h/k) + γ ln(pe/pd) +δ.t ------equation(3) Where, α = (1/1-c)lnA*, A*=A.(c)c ; β = a/(1-c) ; γ = (-c/1-c); δ = (r/1-c) Table:- 8.9. Data of Indian Industries

WPI of Capital Fuel, Output in input in power, whole sale Crores at crores at light and price index constant constant Labour in lubricants( , AC, (base price (y) price (k) („000man- base year year 1993- Year days) 1993-94) 94) 1992-93 371250 284966 4755575 86.55 99.29 1

1993-94 425744 320855 4772361 100.0 100.0 2

1994-95 460024 347065 5017841 108.9 112.6 3

1995-96 551410 417345 5590226 114.5 121.6 4

1996-97 583182 437826 5341039 126.4 127.2 5

1997-98 629771 480496 5102960 143.8 132.8 6

1998-99 557051 433578 4505809 148.5 140.7 7

1999-00 617989 488207 4386738 162.0 145.3 8

2000-01 595313 480766 4270813 208.1 155.7 9

2001-02 596688 483092 4133469 226.7 161.3 10

2002-03 677794 549272 4267805 239.2 166.8 11

2003-04 731894 591031 4209588 254.5 175.9 12

2004-05 892985 727678 4539818 280.2 187.3 13

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2005-06 976141 789595 4893916 306.7 195.5 14

2006-07 1168631 945351 5418029 323.9 206.1 15

2007-08 1285646 1029622 5622386 327.2 215.9 16

2008-09 1399230 1137873 6047169 351.3 233.9 17

Table 8.9., Ln converted data

y/k h/k (pe/pd) ln(y/k) ln(h/k) ln(pe/pd) t

1992-93 1.3028 16.688 0.8717 0.2645 2.8147 -0.137 1

1993-94 1.3269 14.874 1 0.2828 2.6996 0 2

1994-95 1.3255 14.458 0.9671 0.2818 2.6712 -0.033 3

1995-96 1.3212 13.395 0.9416 0.2786 2.5949 -0.06 4

1996-97 1.332 12.199 0.9937 0.2867 2.5014 -0.006 5

1997-98 1.3107 10.62 1.0828 0.2705 2.3628 0.0796 6

1998-99 1.2848 10.392 1.0554 0.2506 2.3411 0.054 7

1999-00 1.2658 8.9854 1.1149 0.2357 2.1956 0.1088 8

2000-01 1.2383 8.8834 1.3365 0.2137 2.1842 0.2901 9

2001-02 1.2351 8.5563 1.4055 0.2112 2.1467 0.3404 10

2002-03 1.234 7.7699 1.4341 0.2103 2.0503 0.3605 11

2003-04 1.2383 7.1225 1.4468 0.2138 1.9633 0.3694 12

2004-05 1.2272 6.2388 1.496 0.2047 1.8308 0.4028 13

2005-06 1.2363 6.198 1.5688 0.2121 1.8242 0.4503 14

2006-07 1.2362 5.7312 1.5716 0.212 1.7459 0.4521 15

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2007-08 1.2487 5.4606 1.5155 0.2221 1.6976 0.4158 16

2008-09 1.2297 5.3144 1.5019 0.2068 1.6704 0.4067 17

SUMMARY OUTPUT

Regression Statistics Multiple R 0.92485 R Square 0.85535 Adjusted R Square 0.82197 Standard Error 0.01343 Observations 17

ANOVA df SS MS F Regression 3 0.01389 0.00462 25.62 Residual 13 0.00234 0.00018 Total 16 0.01633

Coefficients Standard Error t Stat P-value Intercept 0.207 0.2675 0.7736 0.459 ln(h/k) 0.020 0.0943 0.2191 0.829 ln(pe/pd) -0.148 0.0536 -2.7602 0.010 t 0.002 0.0067 0.2762 0.780

The regression coefficient of log natural energy relative is negative, indicates that a rise in price of energy relative to output leads to decline in productivity of capital and labor. Thus, the regression equation is

ln(y/k) = 0.207 + 0.02ln(h/k) – 0.148ln (pe/pd) + 0.002 t (0.2675) (0.0943) (0.0536) (0.0067), ( )in the parenthesis is s.e. Therefore, output elasticities of the inputs are, a=0.017; b= 0.855; c = 0.128; r=0.0017 and A=1.6, and thus Cobb Douglas (C-D) production function for Indian industries for the period; 1992-2009: is Y = 1.6 e0.0017t h0.017 k0.855 (E)0.128. ********

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Chapter-9

Results and Discussion Based on the data and methodology discussed, we have applied statistical tool of Correlation and the regression models to test hypotheses and to obtain the results of the models.

9.1. Hypothesis: 1

H01 : Crude oil price plays an insignificant role in rising WPI of Indian economy.

H11 : Crude oil price plays a significant role in rising WPI of Indian economy. In the analysis of data for testing hypothesis 1, We have first calculated Karl Pearson‟s correlation co-efficient between crude oil price and WPI. It is found that there is a positive correlation exist between crude oil price and WPI and value of r =0.829. Then, we have run first model by considering entire data sets considering 124 observations comprising of WPI and crude oil price. Table- 9.1 presents the regression results.

Table-9.1. Results of regression analysis:-

Regression Statistics Multiple R 0.886158237

R Square 0.785276421

Adjusted R Square 0.783516392

Standard Error 0.07243882

Observations 124

N=124, Inflation (wpi) elasticity w.r.t crude oil price =0.27.

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Explanation:-Based on the Karl Pearson‟s correlation co-efficient and the regression analysis it is evident that there is significant positive correlation between crude oil price and inflation (WPI) (r = 0.829, R = 0.886, R2 = 0.7852, F =446.17, P = 1.42584E-42), 88% of variance on WPI is explained by crude oil price.

Discussion & comment:- F-Table value (95% confidence)at (dfn1 = 1, and dfn2

=122) i.e F0.95(1,122)= 3.89 i.e. tabled F value 5% significance level

Calculated F value= 446.17

FCALCULATED > F0.95(1,122),

Hence, H01 is rejected.

H11 is accepted.

Thus, The Hypothesis H11, “Crude oil price plays a significant role in rising WPI of Indian economy” is accepted.

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9.2. Hypothesis: 2

H02 : The role of Inflation is insignificant for declining GDP growth of Indian economy.

H12 : The role of Inflation is significant for declining GDP growth of Indian economy.

In testing the hypothesis 2, we have first calculated Karl Pearson‟s correlation coefficient between quarterly data GDP growth and Inflation. It is found that there is a negative correlation exist between GDP growth and Inflation and value of Pearson‟s co-efficient, r = - 0.536. Then, we have run second model by quarterly data sets of 20 observations comprising of GDP growth rate and Inflation. Table 9.2 presents the regression results.

Table-9.2. Results of regression analysis:-

Regression Statistics Multiple R 0.538233596

R Square 0.289695404

Adjusted R Square 0.250234037

Standard Error 0.155370176

Observations 20

N= 20, GDP growth elasticity w.r.t Inflation = - 0.245; Explanation:-Based on the Karl Pearson‟s correlation co-efficient and the regression analysis it is evident that there is significant negative relationship between GDP growth and inflation rate( r = -0.536, R = 0.538 , R2 = 0.2896 , F = 7.341 , P = 0.01 ), 53.8 % of variance on GDP growth retardation is explained by inflation, with sign negative of the coefficient of regressor.

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Discussion & Comment: Table value of F (95% confidence)at (dfn1 = 1, and dfn2 =18) i.e F0.95(1,18) = 4.41 i.e. tabled F value 5% significance level Calculated F value= 7.341

FCALCULATED > F0.95(1,18),

Hence, H02 is rejected.

H12 is accepted.

Thus, The Hypothesis H12, “The role of inflation is significant in declining GDP growth of Indian economy” is accepted.

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9.2.1. Multivariable Linear Regression Model

An analysis has been carried out on Multivariable linear regression model, which is with three variables, one dependent and two independent variables. In the present case, GDP growth rate is the dependent variable, inflation and rate of change of crude oil price are the independent variables and all are in percent.

Table-9.2.1.Results of Multivariable (three variable ) linear regression analysis:- t- R2 Durbin- Rho (ρ) d Variable Coefficient statistic Watson Intercept 9.9326 13.3606 Quarterly inflation -0.211 -2.4097 rate Quarterly 0.407 0.976 0.525 0.949 rate of change in 0.012 1.8777 crude oil price Residuals: AR(1),ρ=0.517,

H0 : ρ = 0 versus H1 : ρ > 0. Reject H0 at α level, if d

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9.2.2. Runs test: ( - - ) ( + + + + + + + + + ) ( ------) ( + ) ( - ) ( + ).

N= 20.

N1=11 ( + Runs)

N2= 9 (- Runs)

R = 6 (Runs)

Mean = (2N1N2/N) + 1

Variance =σ2 =2N1N2 (2N1N2-N)/ (N)2 (N-1)

Mean = 10.9

Variance = σ2 = 4.637

Therefore, σ = 2.153369

The 95% confidence interval for R in our test is thus ((10.9 - 1.96*2.153369); (10.9+1.96*2.153369))

= ( 6.674 ; 15.1206 )

Obviously this interval does not include 6. Hence, we can reject the hypothesis that the residuals in our GDP growth, inflation & crude price change regression are random with 95% confidence. In other words, the residuals exhibit autocorrelation. Swed and Eisenhart have developed special tables that give critical values of the runs expected in a random sequence of N observations if N1 or N2 is smaller than 20. Using these tables , in the present case 20 observations, we have N1=11 and N2 = 9, the critical values of runs at the 0.05 level of significance are 6 and 16 as shown by tables of critical values of runs in the run test, as in our application , we have found that the numbers of the runs 6 which is equal to the tabled value 6, we can reject( at the 0.05 level of significance ) the hypothesis that the observed sequence is random. Therefore,

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we find that the residuals in our regression are indeed nonrandom, actually they are positively correlated.

9.2.3. Test of Multicollinearity: To test for multicollinearity, each explanatory variable is regressed against other explanatory variable and the auxiliary R2 is calculated. The variance inflation factor (VIF) is calculated for the auxiliary R2 . The VIF is a method of detecting how severe the multicollinearity is, it is calculated as;

2 VIF(βi) = 1/( 1- R ) and the general rule is that If VIF>5 indicate severe multicollinearity.

Test of Multicollinearity; auxiliary regression results Table 9.2.3 Variable Auxiliary R2 VIF Qrty rate change in 0.043 1.044 Crude oil price &Qrly. Inflation rate

Explanation and discussion: As VIF calculated value is less than 5, therefore, there is no Multicollinearity between the explanatory variables.

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9.3. Hypothesis: 3

H03 : Crude oil price rate change does not Granger cause inflation.

H13 : Crude oil price rate change Granger causes inflation.

The totality of the data that we are going to analyze is quarterly frequency of rate of change of crude oil price and inflation respectively. The two series obtained in values are the rate of change of crude price of India basket and the inflation rates have been used.

The first step in this analysis concerns the stationarity of the series of rate of inflation and the rate of change of crude oil price. Granger causality requires that the series have to be covariance stationary, so as Unit root test or Augmented Dickey- Fuller test can be done and has been calculated. For all the series the null hypothesis H0 of non-stationary can be rejected at 5% confidence level.

Then, since the Granger causality test is very sensitive to the number of lags included in the regression, both Akaike Information Criteria(AIC) and Schwarz Information Criteria(SIC) have been used in order to find an appropriate number of lags.

After these requirements have been satisfied, Granger – causality tests are computed. Taking Granger equation (i), the two steps procedure in testing whether crude price change causes inflation is as follows

1. Inflation is regressed on the lagged inflation excluding lagged crude price rate change in the regressors. This is called the restricted regression, from which we obtained the restricted sum squared residuals. 2. Thus a second regression is computed including the lagged crude oil price rate change, this is called the unrestricted regression from which the unrestricted sum of squared residuals is obtained.

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3. The statistics is defined as

{( RSSR - RSSUR )/m} F =

{RSSUR/T-k}

Where, m = number of lagged crude oil rate change terms; n = number of lagged inflation; T = number of observations. k = is the numbers of parameters estimated in unrestricted regression.( m+n+ 1)

Results

The series are found covariance stationary, also the slope co-efficient of first difference operator , δ is found not equal to 0,Hence, the hypothesis that δ=0, i.e. non-stationary is rejected, the time series are stationary.

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Results of stationarity test: Table 9.3.0. Series Covariance Unit root test, ADF test stationary (δ) value Rate of stationary AR(1) -0.320 - change of crude oil price Inflation rate stationary AR(2) 0.0778 Critical value of t- statistics at df= 16-5=11 at 5% to 10% is 1.796, which is less than the calculated value of t- statistics(- 2.876) in absolute term. Hence by ADF test the series is stationary. GDP growth stationary AR(1) -0.2429 - rate

AIC = -0.06787, lag length 3 for inflation and 3 for crude oil price rate change.

SIC = 0.280635, lag length 3 for inflation and 3 for change in crude oil price

AIC = 0.736304 , lag length 4 for inflation and 4 for crude oil price rate change.

SIC = 1.184383, lag length 4 for inflation and 4 for change in crude oil price

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Results of Granger-Causality tests

Table 9.3.1 Direction of causality F value Decision Change in rate of 4.79, m=3,n=3,df=13 Reject the null CoPInflation hypothesis and accept the alternative hypothesis. Inflation Change in 3.71,m=3,n=3,df=13 Reject the null rate of CoP hypothesis and accept the alternative hypothesis

This is the bidirectional causality. CoP = Crude Oil Price.

Explanation :-These results suggest that the direction of causality is bidirectional, since the estimated F values are significant at 5% level, the critical F value at 5% level is 3.41, ( for 3 and 13df ) .

Discussion: The F-critical value is less than the F calculated vales, therefore reject the null hypothesis and accept the alternative hypothesis. Hence, Crude oil price rate change Granger causes inflation and vice versa.

9.4. Hypothesis: 4

H04 : Inflation does not (Granger) cause GDP Growth. H14 : Inflation (Granger) causes GDP Growth.

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Results of Granger Causality test Table 9.4.0 Direction of causality F value Decision Inflation  GDP growth 0.53, m=3,n=3,df=13 Don‟t reject (i.e. accept) 1.21, m=4,n=4,df=11 the null hypothesis and Reject the alternative hypothesis. GDP growth Inflation 5.48,m=3,n=3,df=13 Reject the null 7.58,m=4,n=4,df=11 hypothesis and accept the alternative hypothesis

Explanation: - The estimated F values are insignificant at 5% level for which the critical F values are 3.41 (for 3 and13 df) and 3.36 (for 4 and 11 df). Hence, the null hypothesis “Inflation does not Granger cause GDP growth” is accepted. Again, the estimated value of F for reverse hypothesis is significant at 5% level than the critical F value are 3.41,( for 3 and 13df ) and 3.36 (for 4 and 11df).Hence, the null hypothesis is rejected and the alternative hypothesis “GDP growth ganger causes inflation” is accepted.

Discussion and Comment: Comparing the F-critical value with F- calculated, it can be seen that, the direction of causalities are unidirectional, therefore accept null hypothesis and reject the alternative hypothesis. Therefore “Inflation does not ( Granger) cause GDP Growth” is accepted. Similarly, reversing the hypothesis and on testing causality with F values, it is found that the alternative hypothesis holds true for reverse hypothesis, hence “GDP growth (Granger) causes Inflation.

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9.5. Hypothesis: 5

H05 : A rise in the price of energy relative to output does not lead to decline in productivity of existing capital and labor.

H15 : A rise in the price of energy relative to output leads to decline in productivity of existing capital and labor.

Regression Equation: ln(y/k) = 0.207 + 0.02ln (h/k) – 0.148ln (pe/pd) + 0.002 t (0.2675) (0.0943) (0.0536) (0.0067), ( )in the parenthesis is s.e.

Results of regression :

Table 9.5.0 Number of Intercept Co- R2 t Stat F observation efficient value values

Intercept 17 0.207 0.85535 25.62 0.7736 ln(h/k) 0.02 0.2191 ln (p /p ) -0.148 e d -2.7602 t 0.002 0.2762

Explanation: Based on the regression analysis it is evident that there is significant negative relationship between the energy price relative to output productivity of capital and labor, ( R = 0.92485 , R2 = 0.85535 , F =25.62 , P = 0.010 ), 92.48% of variance on productivity decline is explained by energy price relative.

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Comment: Table value of F (95% confidence)at (dfn1 = 3, and dfn2 =13) i.e

F0.95(3,13) = 3.41 i.e. tabled F value 5% significance level

Calculated F value= 25.62

FCALCULATED > F0.95(3,13),

Hence, H05 is rejected .

H55 is accepted .

Therefore, the alternative hypothesis “A rise in the price of energy relative to output leads to decline in productivity of existing capital and labor” is accepted.

*****

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Chapter-10 Summary of the Hypotheses, Econometrics and Statistical Tools Used with Results

Table 10.0. Sr. Hypothesis Statistical / Results Comments No Econometric tools 1 Null Hypothesis Correlation ( r = 0.829, R = Therefore

H01: Crude oil price and 0.886 , R2 = “Crude oil plays an insignificant Regression 0.7852 , F price plays a role in rising WPI of Model-1 =446.17 , P = significant Indian economy. 1.42584E-42 ), role in rising Alternative Hypothesis 88% of WPI of Indian

H11: Crude oil price plays variance on WPI economy” is a significant role in rising is explained by accepted WPI of Indian economy. crude oil price . 2 Null Hypothesis Correlation r = -0.536, R = Therefore

H02: The role of inflation and 0.538, R2 = “The role of is insignificant in Regression 0.2896 , F = inflation is declining GDP growth Model-2 7.341 , P = 0.01 significant in of Indian economy. ), 53.8 % of declining Alternative Hypothesis variance on GDP growth

H12: The role of inflation GDP growth of Indian is significant in retardation is economy” is declining GDP growth explained by accepted. of Indian economy inflation regressor.

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Sr. Hypothesis Statistical / Results Comments No Econometric tools 3 Null Hypothesis Test of Test of Accept the

H03:Crude oil price rate covariance stationarity, Alternative change does not stationary, URT, ADF Hypothesis Granger cause Unit root test, AIC, SIC, “Crude oil inflation. ADF test, Causality test price rate AIC, SIC and valid change Alternative Hypothesis Granger‟s Granger

H13: Crude oil price rate Causality causes change Granger test. inflation.” causes inflation. Bidirection al causality holds true. 4 Null Hypothesis Test of co - Test of Accept the

H04: Inflation does not variance stationary, URT, Null Granger cause GDP stationary, ADF Hypothesis growth. Unit root test, AIC, SIC, “Inflation ADF test, Causality test does not Alternative Hypothesis AIC,SIC, valid Granger

H14: Inflation Granger Granger‟s Cause GDP causes GDP Growth. Causality test growth”.

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Sr. Hypothesis Statistical / Results Comments No Econometric tools

5 Null Hypothesis (H05): A Regression ( R = 0.92485 , Therefore, “A rise in the price of energy analysis R2 = 0.85535 , rise in the relative to output does (natural Log F = 25.62 , P = price of not lead to decline in linear form) 0.01 ), 92.48% energy productivity of existing of variance on relative to capital and labor. productivity output leads decline is to decline in Alternative Hypothesis explained by productivity

(H15): A rise in the price of energy price of existing energy relative to output relative. capital and leads to decline in labor” is productivity of existing accepted. capital and labor.

*******

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Chapter-11 Conclusion

There always remains uncertainty for the availability of crude oil at stable prices. Crude oil is the most important ingredient which controls the prices of other fuels in the energy mix. Crude oil prices remain an important economic variable inflicting inflation and cause substantial damage to GDP growth of the economy of oil importing country like India.

This study adds to the existing literature by bringing an awareness of the importance of the impact of crude oil prices on Indian economy. The objectives and the hypotheses of the study have brought about certain conclusions with respect to the study. The study confirms that crude oil prices have inflationary effect, which plays a significant role in rising whole sale price index of Indian economy. Crude oil prices have positive impact on Whole sale price index (WPI), Karl Pearson Correlation coefficient between crude oil price and inflation (WPI) is positively correlated and is equal to 0.829. Our double log regression model shows that the crude oil price elasticity of inflation 0.27. The analysis of variance indicates that F- statistic is 446.17 and p-value is 1.42584E-42 is highly significant against the critical value of F- distribution 3.89 for 5% level of significance. Therefore, the null hypothesis is rejected and the alternative hypothesis “The crude oil price plays a significant role in rising the inflation (WPI) of Indian economy” is accepted.

It is observed statistically that the role of inflation is significant in declining GDP growth of Indian economy. The study of Karl Pearson Correlation coefficient between inflation and GDP growth is -0.536, indicates that there is a negative linkage between inflation and GDP growth. The log-log model shows that the inflation elasticity of GDP growth is -0.24. The analysis of variance indicates that F- statistic is 7.34 and p-value is 0.01 is significant against the critical F-value of 4.41 for 5% level of significance. Based on statistical results null hypothesis is

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rejected and the alternative hypothesis is accepted. Therefore, it is inferred that the role of inflation is significant in declining GDP growth of Indian economy.

It is also revealed during multivariable regression analysis of GDP growth, crude oil price change rate and inflation rate, the coefficient of explanatory variables crude oil price change rate and inflation rate are 0.012 and -0.21 respectively; the DW statistic of the residual data is 0.9765, ρ=0.5252, d=0.9494, and by Run test of residuals, it is found that there is statistically significant positive auto correlation exist and it is first order autoregressive scheme and is denoted by AR(1). Further, it is statistically observed by partial correlation that the impact of inflation on GDP growth keeping the crude oil price change rate constant is

(r12,3)= -0.505, Similarly, the impact of crude oil price change rate on GDP growth keeping inflation rate constant is (r13,2)= 0.451 and the impact of crude oil price change rate on inflation rate keeping GDP growth constant is (r23,1) = 0.0469. The absolute value of correlation coefficient of the independent variables is 0.207 which is less than 0.7, therefore there is no multicolinearity between explanatory variables, which is further validated by variance inflation factor (VIF) which is found 1.044 and is less than 5 confirms the nonexistence of multicolinearity.

The study has a magnificent revelation that the time series data of the variables (GDP growth, inflation rate and the crude oil price change rate) are stationary with respect to unit root test for both GDP growth and Crude oil price change rate respectively, also with respect to augmented Dickey-Fuller (ADF) test for inflation rate. It is also observed econometrically and by ACF, PACF that the times series data of the variables are autoregressive, i.e. AR (1) for GDP growth; AR (2) for inflation rate and AR (1) for crude oil price change rate. These meet the fundamental requirements for the study of the Granger‟s causality test for hypotheses 3 and 4.

It is observed in testing Granger‟s causality test for hypothesis 3 that the alternative hypothesis “Crude oil price change rate causes inflation and vice

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versa” are accepted at lag length 3, by rejecting null hypotheses with F statistics 4.79 and 3.70 respectively at 5% level of significance. Similarly, for hypothesis 4, it is observed that the null hypothesis “Inflation does not granger cause GDP growth of Indian economy” is valid and accepted at leg length 3 and 4 by rejecting the alternative hypothesis with F statistics 0.54 and 1.21 respectively at 5% level of significance, but for the Granger‟s causality test for reverse hypothesis 4, it is observed that the alternative hypothesis “GDP growth ganger causes inflation in Indian economy “ is accepted at lag length 3 and 4 by rejecting the null hypothesis with F statistics 5.48 and 7.58 respectively at 5% level of significance.

The econometric fitting of “Cobb-Douglas” production function to the data for the period 1992-2009 for Indian industries, yielded the following results: ln(y/k) = 0.207 + 0.02ln (h/k) – 0.148ln (pe/pd) + 0.002 t (0.2675) (0.0943) (0.0536) (0.0067), ( )in the parenthesis is s.e. R2=0.8553. The Goodness of Fit is 0.8553, which indicates the model is fit and acceptable. The regression coefficient of log natural energy relative is negative, indicates that a rise in price of energy relative to output diminish the capital and labor productivity. Further, the F statistic is 25.62 which is highly significant than the critical F value 3.2 (at dfn1=3 and dfn2= 13 at 5% level of significance). Therefore, the null hypothesis is rejected and the alternative hypothesis “A rise in the price of energy relative to output leads to decline in the productivity of existing capital and labor” is accepted. Thus it is inferred that with the increase of energy or fuel price relative the derived output leads to diminish the productivity of existing capital and labour.” The output elasticities of the inputs are, a=0.017; b= 0.855; c = 0.128; r=0.0017 and A=1.6, and thus Cobb Douglas (C-D) production function for Indian industries for the period; 1992-2009: Y = 1.6 e0.0017t h0.017 k0.855 (E)0.128. ******

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Chapter-12

Managerial Implications There is always an uncertainty in sourcing crude oil at optimum price for importing country like India. To meet the requirement of crude various strategies are need to be adopted by both Explorers and Refiners. The key strategic points are (a) Enhancing availability of resources for sustainable development. (b) Ensuring accessibility of resources for growth. (c) Reforms in petroleum sectors for both upstream and downstream companies also making India as export hub for petroleum products to earn foreign exchange. (d) Initiatives for Diversification Strategy for nonconventional and green energy etc. (e) Initiative for Strategic Reserves. Therefore, there is a need of multilateral strategy for the oil companies to source the raw material through long term contracts and at the same time to sourcing the crude oil through acquisition of oil block in foreign countries, public as well as private investment is required to be intensified for the exploration block of the country through NELP (New Exploration license Policy) bid , increasing the oil pie in the primary energy of the country by exploration and production through PSC (Production Sharing Contract), expansion of refining capacities and creation of refining hub in India in the Asia Pacific Region is most important area of management for exporting petroleum products and earning foreign exchange to protect the foreign reserves also to offset high crude oil prices.

Diversification Strategy:- The need of the hour is to take the opportunity by oil companies through conglomeration in the area of Nuclear energy , Renewable energy like solar energy, wind energy, biomass energy, tidal energy through collaboration or alliance with domain expert for Green energy and Green positioning of the companies both explorers and refiners.

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Strategic Crude Oil Reserves:- This stockpile would take care of oil security concerns of the country and could be released to meet contingencies arising out of supply disruptions and cushion abnormal increase in prices.

India has begun the development of a strategic crude oil reserve sized at 37.4 million barrel i.e. 5.33 million tonnes enough for two weeks of consumption.

The construction of the proposed strategic storage facilities is being managed by Indian Strategic Petroleum Reserves Limited (ISPRL), a Special Purpose Vehicle, owned by Oil Industry Development Board (OIDB).

The construction strategic facilities expected to be completed by 2013 and further studies have been initiated to construct space to store additional 12.5 million tonnes of strategic reserves by 2017.

Petroleum Product Pricing:- Petroleum pricing is fundamental for the operation of efficient energy markets. Petroleum product prices perform the important role of balancing consumer energy demand with producer supply. The fundamentals of energy pricing are economic efficiency, social equity and financial viability. Efficiency principle seeks to ensure the regulation of prices in such a manner that the allocation of the society‟s resources to the energy sector fully reflects their values in alternative uses. Equity principle relates to welfare and income distribution considerations. This may result in differential pricing schemes on grounds of basic and essential needs or the establishment of uniform prices to specific user groups regardless of different costs of supply, justified on the basis of regional equity or similar concerns. Financial principle suggests that energy supply systems should be able to raise sufficient revenues to remain financially viable, so that continuity and quality of service is ensured and common people and community benefits from the energy supply system for sustainable growth and development.

Petroleum product pricing in India is frequently seen as a black hole of subsidies. Economists and oil companies complain about the impacts those subsidies have

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on public finances, financial performance of oil companies and demand-side management. The petroleum product pricing in India is more complex than the one-way flow of subsidies. It distorts product prices and encourages unhealthy substitution of subsidized products for other products which are more efficient. It dampens price signals and discourages energy conservation. It creates vast distortions and makes good governance almost impossible. It also threatens India‟s international competitiveness in long run. With the abolition of APM, the current market economy has tried to address the above short comings in product pricing and to deliver efficient pricing. Therefore, the product price should be free and fair enough for oil refining and marketing companies so that investments in refining and distribution are not distorted and efficiencies are rewarded at the same time some variant must be kept in pricing for the end consumer for the beneficiary of social sector particularly economically poor people of India.

Green House Gases (GHG):- Combustion of hydrocarbon based fuels in industrial activity generates by-product materials, many of which are considered to be air pollutants. The emissions are the greenhouse gases (GHG) and particulate matter which could cause impact on environment quality, global warming and climate change are to be reduced with regulations and control. India is faced with the challenge of sustaining its rapid economic growth while dealing with the global threat of climate change. This threat comes from accumulated man made greenhouse gas emission in the atmosphere generated through long term, intensive industrial growth and high consumption life style. India is very vulnerable to climate: floods, droughts, vector borne disease, cyclones, ocean storm surges etc.

National Action Plan on Climate Change (NAPCC) document released in 2008 and it identified measures to advance India‟s development without affecting climate change related adaptation and mitigation.

1. Protecting the poor and vulnerable section of the society through sustainable development strategy sensitive to climate change.

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2. Achieving national growth objectives, while enhancing ecological sustainability leading to mitigation of greenhouse gas emissions. 3. Devising efficient and cost-effective strategies for Demand Side Management. 4. Deploying appropriate technologies for both adaption and mitigation of greenhouse gases emissions extensively as well as rapidly. 5. Engineering new and innovative forms of market, regulatory and voluntary mechanisms to promote sustainable development. 6. Effecting implementation of programs and projects through local government institutions and public private partnership.

Energy conservation is another area to be strengthened; energy conservation refers to efforts made to reduce energy consumption. Energy conservation can be achieved through increased efficient energy use in conjunction with decreased energy consumption and/or reduced consumption from conventional energy sources. Energy Conservation and Energy Efficiency are separate, but related concepts. Energy conservation is achieved when growth of energy consumption is reduced in physical terms. Energy Conservation, therefore, is the result of several processes or developments, such as productivity increase or technological progress. On the other hand Energy efficiency is achieved when energy intensity in a specific product, process or area of production or consumption is reduced without affecting output, consumption or comfort levels. Promotion of energy efficiency will contribute to energy conservation and is therefore an integral part of energy conservation promotional policies.

The Government of India has enacted the Energy Conservation Act in 2001 to provide legal framework and institutional arrangements for enhancing energy efficiency. This act led to the creation of Bureau of Energy Efficiency (BEE) as the nodal agency at the center and State designated Agencies (SDA‟s) at the State level to implement the provisions of the Act. Under the Act, Central Government, State Government and Bureau of Energy Efficiency have major roles to play in implementation of the Act. The Mission of BEE is to develop

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policy and strategies based on self-regulation and market principles with the goal of reducing, energy intensity of the Indian economy. This will be achieved with active participation of all stakeholders, resulting in rapid and sustained adoption of energy efficiency in all sectors.

Energy conservation can result in increased financial capital, environmental quality, national security, personal security, and human comfort. Individuals and organizations that are direct consumers of energy choose to conserve energy to reduce energy costs and promote economic security. Industrial and commercial users can increase energy use efficiency to maximize profit.

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Chapter-13 Acquisition Dynamics and Vertical Integration With the Corporate restructuring followed with deregulation and opening up of petroleum sector by the Govt. of India after economic liberalization; the acquisition has become essential for shaping the complexity of energy business and for the energy security of the country.

Growth of a business can be organic or inorganic. In an organic growth environment there is an incremental growth of a Company‟s people, customers, infrastructure resources whereby it positively impacts the revenue and profits of the company. Inorganic growth on the other hand involves leapfrogging several stages in growth process. Acquisition form part of inorganic growth of any Company.

13.1. Framework for an acquisition

Competitive forces resulting from globalization and deregulation in many industries have forced many corporate to consolidate. To embark on an acquisition strategy there is a need to put in place a framework of considerations which inter alia includes the following.

1. Synergies through consolidation: Synergies can be realized through cheaper production bases or cost savings and pooling of resources in R&D, marketing and distribution. Research has also proved that the return on capital goes up when concentration index rises.

2. Vertical Integration To sustain growth a Company could merge to achieve increased market share, gaining access to additional customers and better access to distribution and marketing. This can be either backward-integration with suppliers and lateral or horizontal-integration with customers or forward / upward integration for product market.

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3. Technology Acquisitions / mergers take place to keep pace with technology and to graduate to a higher level of technology.

4. Tax Consolidations Reduction in sales tax in case of vertical mergers and taxation benefits in case of reverse mergers are instances. Legal provision are spelt under Sections 35A, 35AB, 35ABB, 35D, 47 and 72A of the Income tax Act, 1961 and the legislations in India on indirect taxation.

13.2. Policy Environment of India

The policy environment of India governing Mergers and Acquisitions is complex and straddles several areas of law and accounting which include:

1. The laws relating to acquisition of share of listed/quoted companies are governed by SEBI (Substantial Acquisition of Share and Take over) Regulations.

2. Under Indian Companies Act 1956,

i. Section 293 – If the acquisition value exceeds 60% of Indian company‟s net worth or 100% of its free reserves, then the Indian company is required to take prior approval from its shareholders for making investment in the target company. ii. For merger the detailed procedure prescribed under Section 391- 394 are to be followed. iii. Section 372A providing ceiling on investment has a decisive impact.

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3. Clauses 40A and 40B of the listing agreement govern the takeover of a listed company.

4. Provisions of Competition Act 2002 regulating mergers and acquisitions based on monetary limits with respect to assets and turnover.

5. The tax benefits under Income Tax Act, 1961, supra, are one of the prime motivators in Merger deals.

6. The accounting aspects are governed by Accounting Standard-14 issued by Institute of Chartered Accountant of India.

13.3. Target Evaluation

The steps undertaken to evaluate a target for acquisition would include the following steps:

1. Check whether the acquisition fits into the vision and strategy of all stakeholders.

2. Institute a reasonable thorough search for the right candidate(s) where in aim is to achieve optimal results.

3. Evaluate the candidates on the basis of some key criterion like contribution towards profitability, market share, image, core competency etc.

4. Settle down on right price.

5. The value to the acquirer and acquiree will also influence the eventual price. Capitalization, assets in the balance sheet of the acquiree Company

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etc. Afterwards, the right price shall be negotiated and the exchange is harmonized.

13.4. Target Valuation:

Perfect valuation of a target company is a challenge. There are various valuation methods /models are used for valuation purpose.

1. Equity Valuation Model 2. Dividend Discount Model 3. Constant Growth Model 4. Price- Earnings Ratio 5. Discounted Cash Flow Technique. 6. Economic Profit Model 7. Operational Value. In general checks are made whether the cash flow is sustainable in future; whether assets are legally held in the name of the company; whether feedstock supply is guaranteed for long run; are there any major liabilities that could wipe out future profitability and are there any contingent liabilities that do not appear in the accounts.

The methodologies adopted to value a company also suffer from various limitations. So the challenge includes selecting the appropriate techniques for valuation. Valuation is a scientific exercise which requires competence and experience of analyst conducting it. Two important elements in selecting a valuation professional are experienced and demonstrated ability in the industry in which the firm to be valued completes and considering certifications like ABV (Accreditation in Business Valuation).

“The value of the deal is not that important, vis-à-vis the success or failure of acquisition. What justifies the value is how well one integrates the entity with the existing business”.

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13.5. Due Diligence

Due Diligence is the process of examining all aspects of a Company including manufacturing, financial, legal, tax, IT system, labor issues, checking for regulatory issues, as well as understanding issues related to IPR, the environment and other factors. It is done to investigate and evaluate a potential Company for acquisition purposes.

13.5.1. Conducting due diligence

The parties are to any transaction always should conduct their own due diligence to obtain the most accurate assessment of risks and rewards. Though some degree of protection is achieved through a well-written contract; legal agreements should never be viewed as a substitute for conducting formal due diligence. Various aspects include:

13.5.2. Buyer due diligence

It is the process of validating assumptions underlying valuation. Primary objectives are to identify and confirm sources of value and to mitigate real or potential liability by looking for fatal flaws that reduce value. Due diligence involves three primary reviews;

1. Strategic / Operational / Marketing review conducted by senior operations and marketing management. 2. Financial review directed by financial and accounting personnel. 3. Legal review by legal counsel. Selecting due diligence team

Teams should include those with expertise in financial, environment, legal and technology issues.

13.5.3. Limiting Due Diligence process

Due Diligence is an expensive and exhausting process. The buyer will want as much time necessary while seller will try to limit the length and scope. It is highly

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intrusive and places demand on managers‟ time and attention. It rarely works to seller‟s advantage as long as detailed due diligence is likely to uncover items that buyer will use an excuse to lower price. Consequently, sellers may seek to terminate it before buyer feels is appropriate. Thus, it is in the interests of buyer to conduct a thorough due diligence in shortest possible time so as not to alienate the seller and disrupt business.

Sometimes buyer and seller may agree to abbreviate due diligence period. The theory is buyer can be protected in a well-written agreement of purchase and sale in agreement; seller is required to make certain representations and warrant that they are true. Such “representations and warranties” could include seller‟s acknowledgement that they own all assets listed in agreement free of any liens or attachments. If representation is breached the agreement will include a mechanism for compensating buyer for any material loss. What constitutes material loss is defined in contract, relying on “representations and warranties‟ is rarely a good idea. A data room is another method used by sellers to limit the due diligence. This amounts to the seller sequestering the acquirer‟s team in a room to complete due diligence.

13.5.4. Seller’s Due Diligence;

Though bulk of due diligence is done by buyer, seller should also perform it on buyer and themselves. By doing so, seller can determine if buyer has financial wherewithal to finance purchase price. In addition, seller as part of its own due diligence will require its managers to frequently sign documents stating that to the “best of their knowledge” what is being represented in the contract that pertains to their area of responsibility is true. By doing so, seller hopes to mitigate liability stemming from inaccuracies in seller‟s representations and warranties made agreement of purchase and sale.

13.5.5. Importance of Due Diligence (DD) Report

1. It factors all critical issues which impact the decision on valuation of the target.

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2. It becomes the basis for negotiating the valuation price. 3. It provides in the transaction documentation comprehensive representations and warranties. 4. Where issues that cannot be immediately resolved before closing the deal, they are put under what is called as „Conditional Subsequent‟ (CS). Normally, Industries and Corporate bodies are hiring the financial services from Merchant / Investment Bankers for providing services in relation to merger and acquisition. Merchant / Investment Bank will check all the documents of target company; prepare all reports like Financial Statement Analysis, Due diligence, SWOT Analysis, Cash Flow statement, Valuation etc. of target company and finally the feasibility report for merger/acquisition will place before the Corporation/ public sector enterprise (PSE). The corporate planning/ Investment wing of PSE goes for debate and deliberation in line of strategic planning of GoI, that is as per India‟s developmental five year plans and finally tabled the feasibility report in board meeting before the Board of Directors, on assent and duly signed by Board, the feasibility report sends to its administrative ministry.

The administrative ministry carries out a preliminary scrutiny of feasibility report and sends copies of the same to the various appraising agencies, namely, the planning commission, the department of Economic Affairs and the Plan Finance Division of the Finance Ministry and the Bureau of Public Enterprises (BPE) for their comments.

The Project Appraisal Department (PAD) of the Planning Commission carries out a detailed appraisal.

The Investment Planning Committee of Planning Commission discusses the appraisal note of the PAD and recommends to the PIB the view of the Planning Commission on whether the project should be accepted, rejected, deferred or redesigned.

The Public Investment Board (PIB) considers the:

a) Appraisal note of the PAD along with the view of the Planning Commission

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b) The comments of BPE. c) The comments of Plan Finance Division of the Ministry of Finance, and d) The note of Administrative Ministry. If the PIB clears the project, it sends to the Cabinet for its approval. The Cabinet generally accepts the recommendations of PIB and approves the Implementation.

13.6. Vertical Integration: - (Inbound Acquisition by ONGC)

In a significant development in 2002, ONGC was granted rights for marketing transportation fuels on the condition of assured sourcing of products. Indian oil officials and Industry experts felt that ONGC‟s new strategy was essential. They felt that there was a pricing cycle for crude, gas, refinery margin, marketing margin, petrochemical margin and that international prices operated on different cycles in each case. This meant that confining to one sector, where upstream or downstream or petrochemicals would make any organization vulnerable to the ups and downs of a particular cycle. The integration of these activities would ensure profitable operation across a number of cycles and financial stability.

To fulfill this, ONGC acquired 297 mn shares (i.e. 37.39 per cent equity stake) of MRPL from A V Birla (AVB) group, a leading business conglomerate in India, for Rs 2 per share in March 2003. It thus diversified into the downstream (refining and retailing) business. The Company pumped in Rs 6 bn by issuing fresh equity of MRPL, increasing its equity stake to 51 percent. Later on, ONGC purchased 356 mn shares from institutional investors and increased its stake in MRPL, to 71.5 percent. This deal was worth about Rs 3.9 bn. The total amount invested by ONGC in MRPL was about Rs 10.494 bn. In addition to equity, ONGC lent Rs 24 bn to MRPL at a rate of 6%, saving MRPL an estimated interest cost of Rs 820 mn per annum.

MRPL had a refining capacity of 9.69 mn metric tonnes per year. This company had been established when the APM was in practice in Indian Oil Industry. GoI‟s regulatory framework provided assured returns. However, after the refining

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sector was deregulated in 1998, MRPL lost the regulatory protection and became vulnerable to price fluctuations in the international market. This affect the company‟s operating profitability significantly and it posted continuous losses for four year in a row, and became sick eventually.

Despite this poor financial performance, ONGC acquired MRPL, for venturing into the retail business because it possessed advanced technology, including the capability to meet Euro II norms for transportation of fuel quality. The acquisition was considered good for ONGC in the long term, as setting up a similar state-of- the-art nine million tonnes refinery would cost four times the acquisition amount. Moreover, by taking over a loss- making company, ONGC was entitled to huge tax concessions.

The retail business also promised growing demand for petroleum products and consequent stability to ONGC‟s financial position, even if its core business was in trouble. Because of MRPL, ONGC could divert oil from Mumbai High to the refinery for captive consumption. The GoI permitted ONGC to set up 600 retail outlets for marketing products from MRPL refinery. MRPL was also a partner in the Mangalore- Hassan- Bangalore product pipeline, which helped mobilize products into remote areas.

Due to the injection of funds and operational and managerial support of ONGC, the operational performance and credit profile of MRPL, improved considerably. During 2002-03, it registered an operating profit of Rs 3.48 bn, in spite of net loss of Rs 4.12 bn. Due to the access to Mumbai High Crude, for the year 2002-03, MRPL processed 7.25 mn tonnes of crude against 5.5 mn tonnes in 2001-02. Grant of marketing rights and acquisition of MRPL were the major steps in transforming ONGC into an integrated oil and gas corporate.

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Chapter-14 Limitation of the Study and Future Scope of Research The limitations of the study are as follows – (i) The data for the study of the impact of crude oil prices was confined to average Indian Basket Prices of crude oil on Indian economy. Data from International Crude oil prices for different types and API grades of crude would have enabled a comparative analysis. (ii) The study was confined to the economic impact of Indian Basket Prices (Crude), and it has not covered the areas of taxation, duties, Government revenues derived from crude oil and petroleum products. (iii) There are ample scope of future research in the field of Petroleum products distribution and marketing, infrastructure investment and oil field development, petroleum products transportation through pipelines both national and transnational, taxation of volatile oil prices with policy recommendation for ensuring minimum level of consumption and conservation.

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283

Appendix-I (Plots and Diagrams)

Bar diagram A.I. – 1.0

140

2000-01 120 2001-02 100 2002-03 2003-04 80 2004-05 60 2005-06 2006-07 40 2007-08 20 2008-09

0 2009-10 2010-11

Crude Oil Price in $ (Indian Basket)

284

Line diagram, A.I. - 2.0

300

250

200

150 WPI monthly 100 Crude Price $ 50

October October October October October October October October October October

April,2003-04 April,2001-02 April,2002-03 April,2004-05 April,2005-06 April,2006-07 April,2007-08 April,2007-08 April,2008-09 April,2009-10 April,2000-01

Plot of Indian Crude Basket Price (Average) in $ and WPI

285

Line diagram, A.I. - 3.0

300

250

200 gdp growth 150 wpi

100 crude price

0 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11

Plot of GDP growth, WPI and Crude Price

286

Line diagram A.I. - 4.0

120.00

100.00

80.00 Dubai,$/bbl *

Brent, $/bbl † 60.00

Nigerian Forcados, $/bbl

40.00 West Texas Intermdiate, $/bbl ‡

20.00

0.00

1986 1982 1984 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 1980

Plot of Prices of different grades, API crudes

287

Line diagram, A.I. – 5.0

180.0

160.0

140.0

120.0

100.0 Consumption 80.0 Production

60.0

40.0

20.0

0.0

1989 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 1965

A plot of Crude oil Consumption and Production of India.

288

Line diagram, A.I. - 6.0

140

120

100

80 gdp growth inflation 60 crude oil price 40

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

A Plot of GDP growth, Inflation, Crude oil price

289

Scatter plot diagram, A.I. - 7.0.

300

250

200

150 gdp growth 100 wpi crude oil price change 50

0 0 5 10 15 20 25 -50

-100

Scatter plot of GDP Growth, WPI and Crude Oil Price Change.

290

Line diagram A.I. - 8.0

300

250

200

150 gdp growth 100 wpi crude oil price change 50

Q2 Q3 Q2 Q3 Q4 Q3 Q4 Q2 Q4 Q2 Q3 Q4 Q2 Q3 Q4 -50

-100

2005-06,Q1 2007-08,Q1 2008-09,Q1 2009-10,Q1 2006-07,Q1

Line diagram of GDP Growth, WPI and Crude Oil Price Change

291

Line diagram A.I. - 9.0

10 Quarterly India GDP growth

8 Quarterly India Inflation 6 rate

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

A Plot of Quarterly GDP growth and Inflation rate

292

Line diagram A.I. - 10.0.

100

40 Quarterly India Inflation rate 20 Change in crude oil price 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -20

-40

-60

A Plot of Quarterly Inflation rate and crude oil price rate change

293

Line diagram, A.I. - 11.0

100

40 gdp growth 20 inflation Crude oil price change rate 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 -20

-40

-60

Plot of GDP growth, Inflation and Crude oil price change rate .

294

Pie diagram, A.I. - 12.0.

Oil Production by Region at the end of 2011 (Mtoe)

399.4, 10% 648.2, 17% North America 478.2, 12% S & C America 350.0, 9% Europe & Eurasia Middle East Africa 1184.6, 30% 853.3, 22% Asia Pacific

295

Pie diagram A.I. - 13.0

Oil Production Outlook 2030 by Region (Mtoe)

311.3, 7% 788.3, 17% 497.6, 11% North America S & C America

477.7, 11% Europe & Eurasia Middle East

1646.4, 36% Africa 790.5, 18% Asia Pacific

296

Bar diagram A.I. – 14.0

2000.0

1800.0

1600.0

1400.0

1200.0

1000.0 2010 800.0 2030

600.0

400.0

200.0

0.0 North S & C Europe & Middle East Africa Asia Pacific America America Eurasia

Future Crude oil consumption in Million tonnes by Region

297