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(Editor in Chief)
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April 2015

 

Determination of Crude Oil Consumption in India

 

Dr. N.K.Dashora

Guest Faculty

Rajeev Gandhi Tribal University Udaipur

 

Sunil Kumar

Research scholar

Pacific University Udaipur

Email: narandrad@gmail.com

 

 

Abstract

Recent upheaval in the crude oil price in international market has created renewed interest in the data analysis. But even before this, the energy reports generated internationally have squarely yelled about growing crude oil consumption in India and China. India’s share of global demand rises to 8% in 2035, accounting for the second largest share of the BRIC countries with China at 26%, Russia 5%, and Brazil 3%.The object of this paper is to find out whether the price changes and income changes have the same impact on the elasticity of consumption as shown in the theory of elasticity of demand. The yearly data used are from 1985 to 2013.The log value of consumption, income and the adjusted inflation price gives the best results. The coefficient values have been estimated for price and income elasticity.

 

Keywords: Crude Oil, Consumption, India.

Introduction:

While the crude oil consumption has always been a matter of concern internationally, it has direct implication for self sufficiency, overall prices and for the balance of payments. Recent upheaval in the crude oil price in international market has created renewed interest in the data analysis. But even before this, the energy reports generated internationally have squarely yelled about growing crude oil consumption in India and China. The rising population and higher growth trajectory has put this demand on international map. India was the fourth-largest consumer of crude oil and petroleum products in the world in 2013, after the United States, China, and Japan. The country depends heavily on imported crude oil, mostly from the Middle East.

The three startling remarks about projection of India ‘s demand for future in coming twenty years are as following :

(i)  India’s share of global demand rises to 8% in 2035, accounting for the second largest share of the BRIC countries with China at 26%, Russia 5%, and Brazil 3%.

(ii)   India’s demand growth of 128% outpaces each of the BRIC countries as Russia (+14%), China (+60%) and Brazil (+72%) all expand more slowly. India’s growth is almost double the non-OECD aggregate of 63%.

 (iii)  India’s energy production as a share of consumption declines from 59% today to 56% by 2035; imports rise by 143%. (BP Energy Outlook 2035).

Similar concerns have been echoed by International Energy Association and US energy Information and other global reports.

The object of this paper is to find out the association between growth in income and the energy price .The research question is to estimate the validity of the statement that price elasticity of crude oil consumption is negative and the income elasticity is positive.

 

Research Hypothesis:

 

H0  1. The price elasticity of demand is negative and significant

H1.  1. The price elasticity of demand is positive and significant

H o. 2 The income elasticity of demand is positive and significant

 H 2. The price elasticity of demand is negative and significant

 

Review of Literature:

 

Several studies on India use the ordinary least square (OLS) method (Goldar and

Mukhopadhyay 1990; Rao & Parikh 1996; Parikh et al., 2007), but most variables involved are actually non-stationary. Other studies that used co-integration

techniques focused on petroleum derivatives (Ramanathan 1999; Ghosh 2010; Chemin

2012) or on demand for imported oil only (Ghosh 2009). Thus, none of these studies

estimates and forecasts the total crude oil demand for India. The studies that estimate

imported crude oil demand (Ghosh 2009) used, with data until 2005–06. Pradeep Agrawal (2012) empirically estimated demand relations for crude oil, diesel, and petrol for India using the ARDL co-integration procedure and data from 1970 to 2011. These estimations show the income elasticity of about 1 for crude oil and diesel and 1.39 for petrol. The price elasticity of the petroleum products was found to be negative and statistically significant in all the models. The values of price elasticity estimates were found to be -0.41, -0.56 and -0.85 for crude oil, diesel, and petrol respectively, While the absolute value is less than one that inelastic the sign shows the inverse relationship between price rise and demand.

Data

For uniformity the data used are from Energy Statistics 2014 and its prior editions. In case of adjusted inflation price of crude oil the data are from Index Mundi. It may be acknowledged that international crude oil price data do not fully reflect the price behavior for the simple reason that several adjustments are made in fixing the price.

 

Summary Statistics, using the observations 1985 – 2013

 

Variable

Mean

Median

Minimum

Maximum

Reserves

5.36413

5.60635

3.50000

7.99710

Production

665.568

661.420

534.000

782.340

consumption

2064.53

2031.25

894.900

3509.00

Nominalprice

36.6762

23.0000

11.9100

91.4800

InflationAdjusyedPrice

47.2155

35.5500

17.2600

100.010

PCINNP

22177.7

20079.0

12095.0

39904.0

Variable

Std. Dev.

C.V.

Skewness

Ex. kurtosis

Reserves

1.01209

0.188677

0.398414

0.527339

Production

58.4482

0.0878170

0.115065

0.180980

consumption

830.729

0.402382

0.180061

-1.25816

Nominalprice($)

26.5740

0.724557

1.05374

-0.405856

InflationAdjusyedPrice($)

24.2479

0.513558

0.882616

-0.570873

PCINNP

8838.14

0.398516

0.722700

-0.771556

 

 

The summary statistics indicate that production and consumption have normal distribution but Reserves and prices and per capita income are skewed. Also there is Excess Kurtosis (> 3 ) in each of these variables. We examine the crude oil consumption as dependent variable and per capita income and nominal price as repressors. Both the sign are statistically significant.

 

 

 

 

 

 

 

 

Model 1: OLS, using observations 1985-2013 (T = 29)

Dependent variable: consumption

 

 

Coefficient

Std. Error

t-ratio

p-value

 

const

-232.105

115.004

-2.0182

0.05400

*

PCINNP

0.121084

0.00942236

12.8507

<0.00001

***

Nominal_price

-10.5987

3.13375

-3.3821

0.00229

***

 

Mean dependent var

 2064.527

 

S.D. dependent var

 830.7289

Sum squared resid

 700213.8

 

S.E. of regression

 164.1076

R-squared

 0.963763

 

Adjusted R-squared

 0.960975

F(2, 26)

 345.7479

 

P-value(F)

 1.86e-19

Log-likelihood

-187.4810

 

Akaike criterion

 380.9619

Schwarz criterion

 385.0638

 

Hannan-Quinn

 382.2466

rho

 0.660907

 

Durbin-Watson

 0.681402

 

 

 

From the model one it is obvious that the per capita income has positive and price has negative sign.R-Square is sufficiently high.Though DW statistic is low.

 

Model 2: OLS, using observations 1985-2013 (T = 29)

Dependent variable: consumption

 

 

Coefficient

Std. Error

t-ratio

p-value

 

const

4.27255

82.1937

0.0520

0.95894

 

PCINNP

0.110539

0.00627453

17.6171

<0.00001

***

Inflation Adjusted Price

−8.28634

2.28701

−3.6232

0.00124

***

 

Mean dependent var

 2064.527

 

S.D. dependent var

 830.7289

Sum squared residual

 669989.9

 

S.E. of regression

 160.5268

R-squared

 0.965327

 

Adjusted R-squared

 0.962660

F(2, 26)

 361.9313

 

P-value(F)

 1.05e-19

Log-likelihood

−186.8412

 

Akaike criterion

 379.6824

Schwarz criterion

 383.7843

 

Hannan-Quinn

 380.9670

rho

 0.695536

 

Durbin-Watson

 0.617639

 

 

Model 2 denotes inflation adjusted price. The model is slightly improved as far as Akaike and other criterion are concerned. However the predictive ability is hardly improved in this model as compared to model 1 above.

 

Model 3: OLS, using observations 1985-2013 (T = 29)

Dependent variable: l_consumption

 

 

Coefficient

Std. Error

t-ratio

p-value

 

const

−6.02301

0.692683

−8.6952

<0.00001

***

l_PCINNP

1.44244

0.0841815

17.1348

<0.00001

***

l_Nominalprice

−0.224841

0.0492504

−4.5653

0.00011

***

 

Mean dependent var

 7.546872

 

S.D. dependent var

 0.432760

Sum squared resid

 0.158264

 

S.E. of regression

 0.078020

R-squared

 0.969819

 

Adjusted R-squared

 0.967498

F(2, 26)

 417.7375

 

P-value(F)

 1.72e-20

Log-likelihood

 34.40719

 

Akaike criterion

−62.81438

Schwarz criterion

−58.71250

 

Hannan-Quinn

−61.52972

rho

 0.634979

 

Durbin-Watson

 0.741173

 

Model  3 is Double log model, with the same set of variables. From this the price elasticity and the income elasticity of consumption can be directly read out. The Akaike and other criterion  have improved greatly. The DW statistic has slightly improved.

 

Model 4: OLS, using observations 1985-2013 (T = 29)

Dependent variable: l_consumption

 

 

Coefficient

Std. Error

t-ratio

p-value

 

const

−4.60602

0.408795

−11.2673

<0.00001

***

l_PCINNP

1.30804

0.0508049

25.7463

<0.00001

***

l_InflationAdjusyedPrice

−0.225224

0.0401918

−5.6037

<0.00001

***

 

Mean dependent var

 7.546872

 

S.D. dependent var

 0.432760

Sum squared resid

 0.129148

 

S.E. of regression

 0.070479

R-squared

 0.975372

 

Adjusted R-squared

 0.973477

F(2, 26)

 514.8442

 

P-value(F)

 1.23e-21

Log-likelihood

 37.35508

 

Akaike criterion

−68.71015

Schwarz criterion

−64.60826

 

Hannan-Quinn

−67.42549

rho

 0.574011

 

Durbin-Watson

 0.852549

 

In model 4 the variable choosen are the same as in model 2 that is inflationary adjustement price. There is again an improvement in the model. This model stands the best as far as predictive ability is concerned. The DW statistic too has improved.While the sign and value of the price change remain almost the same , ther is decline in income elasticity of demand . This might be the result of common trend in the inflation and income variables.Since these are yearly data much conversion of income and price takes place within a year therefore lagged data have not been used .

 

 

References :

 BP Energy Outlook 2035, http://www.bp.com/en/global/corporate/about-bp/energy-economics/energy-outlook/country Insight India

Chemin, Elodie Sentenac (2012), “Is the price effect of fuel consumption symmetric? Some evidence from an empirical study”, Energy Policy, 41, 59-65.

Ghosh, Sajal (2006), “Future demand of petroleum products in India”, Energy Policy, 34, 2032-2037.

Ghosh, Sajal (2009), “Import demand of crude oil and economic growth: Evidence from India”, Energy Policy, 37, 699-702.

Ghosh, Sajal (2010), “High speed diesel consumption and economic growth in India”, Energy, 35, 1794-1798.

Goldar, Bishwanath. And Mukhopadhyay, Hiranya., (1990), “India’s Petroleum Imports: An Econometric Analysis”, Economic Political Weekly, 25 (42/43), 2373-2377

Parikh, Jyoti., Purohit, Pallav. And Maitra, Pallavi., (2007), “Demand projections of petroleum products and natural gas in India”, Energy, 32, 1827-1837.

Pradeep Agrawal  (2012)   Empirical Estimations and Projections for the Futurewww  IEG Working Paper No. 319 , 2012,    Institute of Economic Growth Dehli : , http://www.iegindia.org.

Ramanathan, R. (1999), “Short- and long-run elasticities of gasoline demand in India: An empirical analysis using cointegration techniques”, Energy Economics, 21, 321-330.

Rao, Raghavendra D and Parikh, Jyoti K. (1996), “Forecast and analysis of demand for petroleum products in India”, Energy policy, 24(6), 583-592.

 

 
 

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