Effect Of Crude Oil Prices On Indian Economy Economics Essay

This paper analyzes the effect of crude oil prices on the macro economic variables of the Indian economy. The oil prices have started rising significantly since the initiation of the twenty first century; one can analyze the impact of an oil price shock. As the oil prices changes there is a huge impact on the GDP, inflation, unemployment rate and industrial growth production .In short, oil price fluctuation has adverse effects on the economy .The paper seeks to find out the trends, causes of oil price hike in recent times and its impact on the macroeconomic variables of India using multiple regression as a methodology using SPSS software.

Acknowledgment

I would like to express my sincere thanks and gratitude to my mentor for Research Project -2 in Finance, Ms charu banga for her extremely valuable guidance and suggestions throughout the making of this report.

I also thank the dean Dr. Sunil Rai for providing me with an excellent opportunity to learn and present my studies in the form of this project report.

Lastly, I thank my parents for their continuous moral support and encouragement.

I have great satisfaction and immense pride in being a part of this educational institute: SVKM’s NMIMS’s Mukesh Patel School of Technology Management & Engineering

Introduction

Basic intro

Fossil fuels are expected to continue to supply a large amount of the energy world-wide regardless of fears of peaking oil. Oil remains a dominant energy source as its importance in the transportation and industrial sectors is increasing day by day.

India is highly dependent on imported oil products and the drastic increase in the prices of crude oil to as high as $148/bbl, this jump has become a greater concern as a risk factor in a fragile Indian economy. But for the steep fall in crude Price, it would have most likely disrupted the growth process of our economy. Crude oil is one of the most demanded commodities and India is importing more 100 million tons of crude oil and petroleum products and is spending huge amount of foreign exchange. India imports over 70% of crude oil and the figure may reach 85% by the end of the decade. Thus impact of increasing crude oil prices on the Indian economy is, a matter of grave concern. Slightest change in prices has both direct and indirect influence on India. (International journal of social sciences & interdisciplinary research )

The increase in oil prices has the Indian economy quite significantly and the country to produce about one trillion worth of GDP to fulfill the needs of its huge population. In order to produce this trillion dollar worth of output, India needs 2.5 million of oil per day this is 6.5 percent of total world demand for oil.

Motivation for study

Easy accessibility of energy has been a key driver of development and industrialization in the last century. Large amount of this energy has been generated from non-renewable fossil fuels. The current economy is dependent on these fuels , most remarkably oil. Fast logistics is the key for achievement of recent manufacturing industry. People stay far away from their work and thus depend on vehicles for commuting. Oil prices have been very volatile in recent years. Volatility in oil prices does harm in numerous ways. Both developing and developed countries are getting affected. Spiking high prices impact the indigent more directly because fuel costs are important in food and transportation expenses, which are necessarily to spend. High oil costs also hit economies on a macro-level and have been leading to factors in economic cycles. Short-term low prices on the other hand delay necessary energy investments in current and alternative sources, which are required for securing supply in the future. Changing prices also make it difficult for customers to understand new consumption styles and look for alternatives. Adding to supply and cost issues are ecological concerns, in which oil also has a main role.

Changes in oil prices shift political balances around the world. Oil exporters gain power with high prices, but face severe difficulties when prices drop. Control over oil sources has historically driven many countries into war.

Research Objective

The present scenario of high international crude oil prices have posed some serious challenges in the Indian perspective because of their implications to vital needs of domestic heating and cooking , transportation and energy .To insulate the domestic economy from volatile oil prices in the international markets attempts are made to sustain only for short periods of time. My objective in this paper is simply to understand what factors determine oil prices. Also as to analyze why the prices touched record highs in the past years and why did they drop and the impact of the hike in crude prices on the macro economic variables such as GDP, inflation rate, unemployment rate and industrial growth production.

Research problem

To study the factors influencing the crude oil prices and its impact on Indian macroeconomic variables such as GDP, inflation rate, unemployment rate and industrial growth production.

Contribution

My contribution is I have analysed the trends and the factors affecting the hike in crude oil prices .i have gathered the past 13 years data and found out the impact of crude oil prices on the various macro economic variables such as GDP, inflation rate, unemployment rate and industrial growth production using multiple regression through SPSS software. It presents the market and its underlying basics or fundamentals to a reader who has no knowledge or experience on the market or in advanced economics.

Background of study

About crude oil

Crude oil is a naturally-occurring substance found in certain rock formations in the earth and this is mixture of mud & by organic material is rich in hydrogen & carbon. Over millions of years this layer of organic rich mud becomes buried thousands of feet deep in the earth and temperature of the earth becomes hotter as you go deeper in to the earth. The combination of increasing temperature & pressure on the organic mixture causes change in to crude oil. ( international journal of social sciences & interdisciplinary research )

Severe fluctuations of oil prices:

Prices of oil have started to increase since the early days 2002. However, oil prices showed significant changes since the US invasion in Iraq in 2003 as Iraq has a huge oil reserve. The clash occurred almost simultaneously with an increase in global demand for petroleum, but it also brought down the current production of oil in Iraq. This has been partially blamed for oil price increases. With the reduction in production capacity in Iraq the crude oil price increased drastically to a new height in 2004-2005. During the period of 2004-05, the oil price became as soaring as US$70 per barrel after the attacks of the hurricanes. In U.S., the average level of West Texas Intermediate (WTI) oil price became US$57 in 2005. The rise is still ongoing and expected to continue in future also.

During the year of 1987, there occurred a shift in the process of setting oil prices. The system of setting oil price had experienced a shift from standard oil price system to a new method of fluctuating price system which has a close link to the market price. The late 1980s and early 1990s witnessed only a momentary hike in prices during the period of Gulf crisis. Until 1995, the prices of oil were more or less stable with the prices hovering between US$14 and US$20 per barrel. However, prices of oil started to fluctuate wildly outside this range ever since the beginning of the year of 1996, and the price of oil did not reduce below the level of U.S$30 even in 2000 when OPEC increased production of oil significantly. Even though the process continued to increase in 2000 this increasing trend halted as the Iraq was started in 2003. At the start of the Iraq war, prices of petroleum declined and therefore generated a huge expectation among people that this decline would continue in future also. However, this expectation did not happen in reality. In fact, at the end of the War, price started to rise again and that too at a rapid pace. In 2004, the prices of oil exceeded the level of US$ 35 and continued to increase further. In October, 2004, prices even surpassed the abnormal level of US$ 50. Towards the end of 2004, there occurred a very temporary decline in the price level, but it did not take much time to resume its growth and followed a upward rising trend during 2005 also. At the end of 2005, the oil prices exceeded the level of US$60. The price continued to increase in the succeeding years also. Until recently, the prices have been following the same rising trend and once rose to an abnormally high level of U.S.$140. Though, the present trend of the oil prices is found to be somewhat declining causing significant recovery of the FTSE100 companies. The current price of crude oil is US$108 (TermPaperWriter.org)

Factors influencing hike in the prices of oil:

Increase in demand of crude oil is one of the most essential causes of rise in price of oil. It is seen that the demand for crude oil around the world since 1994 till 2006 grew at an average rate of 1.76% per annum, reaching a height of 3.4% in 2003-2004. The demand in the developing countries is irresistible due to their economic development increasingly depending on mechanization. The increase in demand for crude oil has been already predicted and the developing economies including China and India may be the greatest contributors to demands owing to their progressively more urbanized lifestyle and increasing urbanization . With the rapidly increasingly economy, the sector which is considered to be the highest consumer of oil is the transportation sector in the form of new demand for vehicles of personal use. These vehicles are powered by internal combustion engines running on petrol/diesel. Growth in population also causes an increasing demand of oil.

Reduction or deduction of the state fuel subsidies in order to reduce the government’s cost of subsidization also can be treated as a reason behind rising and falling oil price as the state subsidies were responsible to protect consumers from price rises in many countries.

Also an increasingly short supply of oil in the world is the major cause for rises in prices. According to statistics the world has been demanding and consuming more oil than can be produced. Presently, production of oil in most countries will soon be reducing and has already gone down – leaving less of a surplus to use – but at the same time, demand also keeps increasing. The supply remains tight and prices keep rising despite OPEC’s decision to increase crude oil production by 500,000 barrels per day. With little price elasticity from both demand and supply, any trivial event will send prices skyrocketing.

Since oil is being traded in US dollars, the changes in values of US dollars are also said to have impact on the oil prices.. According to studies, when the dollar devalues by 1 percent, it causes an oil price hike of the same degree. In addition, technical, meteorological and political elements also affect prices.

Loose monetary policies may also be blamed for the increase in oil price and devaluation of dollar. Labor strikes, threats from hurricane to oil platforms, threats or challenges faced by risks of fires and terrorist at the refineries and similar other factors are also considered as the causes of short term price rise but these have no significance to long term increase in the price of oil.

Methodology

Our objective is to investigate if there is any direct influence of the explanatory variable which is the oil price on the macro economic variables that are GDP, inflation, unemployment rate and industrial production growth rate. Multiple regression analysis is a statistical tool for understanding the relationship between two or more variables. Multiple regression involves a variable to be explained-called the dependent variable-and additional explanatory variables that are thought to produce or be associated with changes in the dependent variable. Multiple Regression method is used to analyze if any correlation exists between them and for statistical analysis SPSS software is used to study the combined effect of all the factors bearing on oil prices.

DATA COLLECTION

Data of oil prices, GDP growth rate ,inflation rate, unemployment rate, industrial production growth rate from 2000 to 2012 is taken from International Monetary Fund (IMF) – World Economic Outlook April 2012

year

oil prices

GDP

Inflation

unemployment rate

industrial production growth rate

 

$

%

%

%

%

2000

36.54

5.83

4.02

7.32

7.5

2001

28.8

3.9

5.4

8.1

6.8

2002

30.56

4.6

5.4

8.8

6

2003

34.94

6.9

3.8

9.5

6.5

2004

44.05

7.6

4.2

9.2

7.4

2005

58.04

9.033

4.2

8.9

7.9

2006

67.92

9.53

5.3

7.8

7.5

2007

75.12

9.99

6.4

7.2

8.5

2008

99.71

6.2

8.3

6.8

4.8

2009

63.79

6.8

0.109

10.7

9.3

2010

80.66

10.1

0.117

10.8

9.7

2011

105.8

7.2

0.089

9.8

4.8

2012

101.08

6.9

0.082

3.8

8.2

Regression

This first table gives the mean. Standard deviation and sample space of the dependent and independent variable.

Descriptive Statistics

Mean

Std. Deviation

N

Oil Prices

63.6162

27.69087

13

GDP

7.2756

1.95652

13

Inflation

3.6475

2.72686

13

Unemployment Rate

8.3631

1.87147

13

Industrial Production Growth

7.3000

1.51493

13

Dependent Variable: Oil prices

Independent Variable: GDP, Inflation, unemployment rate, industrial Production growth

MEAN: The Mean or Average is the central tendency of a collection of numbers taken as the sum of the numbers divided by the size of the collection.

STANDARD DEVIATION: In statistics, standard deviation (σ) shows how much variation or dispersion exists from the mean. A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data points are spread out over a large range of values.

This table gives the details of the correlation between each pair of variables.Correlations

 

 

Oil prices

GDP

Inflation

Unemployment Rate

Industrial production growth

Pearson Correlation

Oil prices

1

0.419

-0.315

-0.273

-0.066

GDP

0.419

1

-0.165

0.151

0.516

Inflation

-0.315

-0.165

1

-0.266

-0.417

Unemployment Rate

-0.273

0.151

-0.266

1

0.12

Industrial production growth

-0.066

0.516

-0.417

0.12

1

Sig. (1-tailed)

Oil prices

.

0.077

0.148

0.184

0.415

GDP

0.077

.

0.296

0.311

0.036

Inflation

0.148

0.296

.

0.19

0.078

Unemployment Rate

0.184

0.311

0.19

.

0.348

Industrial production growth

0.415

0.036

0.078

0.348

.

N

Oil prices

13

13

13

13

13

GDP

13

13

13

13

13

Inflation

13

13

13

13

13

Unemployment Rate

13

13

13

13

13

Industrial production growth

13

13

13

13

13

Pearson’s Correlation: It is the correlation between two variables which reflects the degree to which the variables are related to each other. But we cannot conclude that just because two measurements vary together that one has caused the other, there may be some other external factor affecting that may be the cause of their relation. The most common measure of correlation is the Pearson Product Moment Correlation (called Pearson’s correlation for short). Pearson’s correlation reflects the degree of linear relationship between two variables. It ranges from +1 to -1.

The possible values of r and their interpretation are given below:

A value of 1 implies that a linear equation describes the relationship between X and Y which are both oil prices in the 1st case with all data points lying on a line for which Y increases as X increases.

A value of −1 implies that all data points lie on a line for which inflation, unemployment rate and Industrial production growth decreases as X increases.

A value of 0 implies there is no linear correlation between the variables.

If we have a series of n measurements of X and Y written as xi and yi where i = 1, 2, …, n, then the sample correlation coefficient can be used to estimate the population Pearson correlation r between X and Y. The sample correlation coefficient is written

r_{xy}=frac{sumlimits_{i=1}^n (x_i-bar{x})(y_i-bar{y})}{(n-1) s_x s_y}

=frac{sumlimits_{i=1}^n (x_i-bar{x})(y_i-bar{y})}

{sqrt{sumlimits_{i=1}^n (x_i-bar{x})^2 sumlimits_{i=1}^n (y_i-bar{y})^2}},

where x and y are the sample means of X and Y, and sx and sy are the sample standard deviations of X and Y.

This table gives us the value of R, R Square and adjusted R square which helps in determining the relation between the dependent and independent variables.Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.805a

.648

.472

20.12243

a. Predictors: (Constant), Industrial production growth, Unemployment Rate, Inflation, GDP

R, R Square, Adjusted R Square

R is a measure of the correlation between the observed value and the predicted value of the dependent variable.

R Square (R2) is the square of this measure of correlation and indicates the proportion of the variance in the dependent variable which is accounted for by the model. In essence, this is a measure of how good a prediction of the dependent variable we can make by knowing the independent variables. This is an overall measure of the strength of association and does not reflect the extent to which any particular independent variable is associated with the dependent variable.

However, R square tends to somewhat over-estimate the success of the model when applied to the real world, so an Adjusted R Square value is calculated which takes into account the number of variables in the model and the number of observations (participants) our model is based on. This Adjusted R Square value gives the most useful measure of the success of our model. So in our case, for example we have an Adjusted R Square value of 0.472 we can say that our model has accounted for 47% of the variance in the dependent variable.

Standard Error of the Estimate (SEE) is a measure of the accuracy of the regression predictions. It estimates the variation of the dependent variable values around the regression line. It should get smaller as we add more independent variables, if they predict well.

http://cs.gmu.edu/cne/modules/dau/stat/regression/multregsn/see.gif

Analyzing the above Model Summary box we can conclude the following:

The correlation between the observed and predicted value of the dependent variable is 80.5% because value of R=0.805.

To measure the overall strength of the model and to see how well the independent variables are associated with the dependent variable we see the value of R2 which is 0.648. Therefore, we can say combined effect of the independent variables on dependent variable is 64.8%.

Adjusted R square takes into consideration the number of independent variables and sample space so from the above adjusted R square value we can conclude that our model has accounted for 47.2% of the variance in the dependent variable.

ANOVAb

Model

Sum of Squares

Df

Mean Square

F

Sig.

1

Regression

5962.112

4

1490.528

3.681

.055a

Residual

3239.297

8

404.912

Total

9201.409

12

a. Predictors: (Constant), Industrial Production Growth, Unemployment Rate, Inflation, GDP

b. Dependent Variable: Oil prices

This table helps us in determining the overall significance of the model. It doesn’t give much information about the success of the model helps in deciding whether to accept or reject the null hypothesis. This table reports an ANOVA, which assesses the overall significance of our model. As p < 0.05 our model is significant

Model

 

Unstandardized Coefficients

Standardized Coefficients

 

 

 

 

B

Std. Error

Beta

t

Sig.

1

(Constant)

151.268

45.924

 

 

 

GDP

10.102

3.5

0.714

2.886

0.02

Inflation

-5.872

2.424

-0.578

-2.422

0.042

Unemployment Rate

-6.807

3.245

-0.46

-2.098

0.069

Industrial production growth

-11.343

4.874

-0.621

-2.327

0.048

The Standardized Beta Coefficients give a measure of the contribution of each variable to the model. A large value indicates that a unit change in this independent variable has a large effect on the dependent variable. The t and Sig (p) values give a rough indication of the impact of

each independent variable – a big absolute t value and small p value suggests that a predictor variable is having a large impact on the criterion variable.

Beta (standardized regression coefficients)

The beta value is a measure of how strongly each independent variable influences the dependent variable. The beta is measured in units of standard deviation. For example, a beta value of 2.5 indicates that a change of one standard deviation in the independent variable will result in a change of 2.5 standard deviations in the dependent variable. Thus, the higher the beta value the greater the impact of the independent variable on the dependent variable. When you have only one independent variable in your model, then beta is equivalent to the correlation coefficient between the independent and the dependent variable. This equivalence makes sense, as this situation is a correlation between two variables. When you have more than one independent variable, you cannot compare the contribution of each independent variable by simply comparing the correlation coefficients. The beta regression coefficient is computed to allow you to make such comparisons and to assess the strength of the relationship between each independent variable to the dependent variable.

These are the standardized coefficients. These are the coefficients that you would obtain if you standardized all of the variables in the regression, including the dependent and all of the independent variables, and ran the regression. By standardizing the variables before running the regression, you have put all of the variables on the same scale, and you can compare the magnitude of the coefficients to see which one has more of an effect. You will also notice that the larger betas are associated with the larger t-values and lower p-values.

This table helps in finding the regression equation or the coefficients of independent variables.

Constant is the intercept of the equation.

Therefore, the equation with unstandardised coefficients is:

Oil prices= 151.268+10.102*GDP-5.872*inflation-6.807*unemployment rate-11.343*industrial production growth

Equation with standardized equation is as follows:

Oil prices=0.714*GDP-0578*inflation-0.46*unemployment rate-0.621*industrial production growth

Conclusion and analysis