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
fourthlargest 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 nonOECD 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:
H_{0}
1. The price elasticity of demand is negative and significant
H_{1}.
1. The price elasticity of demand is positive and significant
H
_{o}. 2 The income elasticity of demand is positive and significant
H
_{1 }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 nonstationary. Other studies that used cointegration
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 cointegration 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
19852013 (T = 29)
Dependent variable: consumption

Coefficient

Std. Error

tratio

pvalue


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

Rsquared

0.963763


Adjusted
Rsquared

0.960975

F(2,
26)

345.7479


Pvalue(F)

1.86e19

Loglikelihood

187.4810


Akaike
criterion

380.9619

Schwarz
criterion

385.0638


HannanQuinn

382.2466

rho

0.660907


DurbinWatson

0.681402

From
the model one it is obvious that the per capita income has positive and price
has negative sign.RSquare is sufficiently high.Though DW statistic is low.
Model 2: OLS, using observations
19852013 (T = 29)
Dependent variable: consumption

Coefficient

Std. Error

tratio

pvalue


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

Rsquared

0.965327


Adjusted
Rsquared

0.962660

F(2,
26)

361.9313


Pvalue(F)

1.05e19

Loglikelihood

−186.8412


Akaike
criterion

379.6824

Schwarz
criterion

383.7843


HannanQuinn

380.9670

rho

0.695536


DurbinWatson

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
19852013 (T = 29)
Dependent variable: l_consumption

Coefficient

Std. Error

tratio

pvalue


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

Rsquared

0.969819


Adjusted
Rsquared

0.967498

F(2,
26)

417.7375


Pvalue(F)

1.72e20

Loglikelihood

34.40719


Akaike
criterion

−62.81438

Schwarz
criterion

−58.71250


HannanQuinn

−61.52972

rho

0.634979


DurbinWatson

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
19852013 (T = 29)
Dependent variable: l_consumption

Coefficient

Std. Error

tratio

pvalue


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

Rsquared

0.975372


Adjusted
Rsquared

0.973477

F(2,
26)

514.8442


Pvalue(F)

1.23e21

Loglikelihood

37.35508


Akaike
criterion

−68.71015

Schwarz
criterion

−64.60826


HannanQuinn

−67.42549

rho

0.574011


DurbinWatson

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 .
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