Journal of Research in International Business Management (ISSN: 2251-0028) Vol. 2(4) pp. 77-83, April 2012.
Available online @http://www.interesjournals.org/JRIBM
Copyright ©2012 International Research Journals
Full Length Research Paper
A Comparison analysis of the energy price effects on
consumptions of the different forms of energy
Ali Emami Meibodi1 , Hesam Mardan Tabar 2and Robab Aalami3
1
Associate Professor, Department of Economics, Allameh Tabatabaei University, Tehran, Iran
2
MA of Economics, Sharif University, Tehran, Iran
3
MA. Student, Department of economic Development and Planning,), AllamehTabatabaei University, Tehran, Iran
Accepted 19 March, 2012
In recent years, economic development has been associated with the usage of different kind of energy
by applying energy diversification. The energy crisises in the seventies were accompanied with the
recession in the major economies countries. It has been led the energy topics to be in a special place in
the economic literature. Industrialized countries in compare to DCs allocate the mass of energy
consumption to themselves. Even though, they have generally high energy efficiency and low energy
intensity. This paper attempts to investigate the effect of energy price on the different forms of energy
consumptions in Iran and OECD countries (2000-2009) by using modern econometric techniques,
including panel co-integration and panel unit roots. Then, it has been analysed symmetry and
asymmetry relationship between them. The results show, negative effect of energy price on energy
consumption in all periods in OECD countries and Iran. The effect of GDP on energy consumption is
positive in long- run. In addition, there is an co integration relationship between energy consumption,
energy price and GDP in long-run. It is also different between the OECD countries and Iran, because of
different structures of countries. According to these results, we offer some policy implications.
Keywords: Energy Consumption, Energy Prices, Panel Data Model, JEL classification: C01, C23, C33, Q43,
Q48.
INTRODUCTION
Energy has been considered as the input in the
production functions during the two past decades. It has
a more important role in social and economical conditions
along with other factors such as the labor, capital and the
row materials due to its political effect. Therefore, the
importance of energy and its issue is doubled among
owners of energy in developing countries. Energy
identification and related issues, such as energy effects
on production and economic growth of developed
countries can pave the path for solving some of
developing
country
problems.
Consequently,
investigation of energy systems and fluctuation in energy
*Corresponding Author Email: r_alami2010@yahoo.com
consumption and energy price are critical subjects for
every country.
Rumi Masih (1998) presented the economic structures
of the countries are different among DCs. Energy
consumption is high in developing countries. The most
portion of energy consumption is spent on non productive
sections. In Iran as a developing country, because of
wide sources of oil and gas, an easy accessibility to this
resource and low energy price, there are not much
incentive for saving and rational usage of them. Energy
price index for economic activities is constantly low in
comparison with price index of the other inputs. In the
meantime,the Iranian economy has been depended on
energy revenues.
As the result, fluctuations of internal and international
oil prices have always confronted the Iranian economy
78 J. Res. Int. Bus. Manag.
with many problems. Consequently, fulfillment of long
term programs and annual budgets of the country
involves more accurate in predicting energy consumption.
During the recent years, investigating the trend of energy
consumption among the countries of OECD , shows the
amount of consumption energy has been reduced. The
main reasons are high technology and low energy
intensity that interpreted in high energy efficiency and low
energy consumption.
Regarding to importance of energy in production
process, the relations between the energy consumption,
energy price and GDP should be studied before
reforming the pricing mechanism for energy. Also, if
energy consumption positively affects on production,
executing any policy related to energy price, must be
measured with utmost precaution .
In this paper the effect of energy price on different
forms of energy consumption In Iran and OECD countries
are researched. Furthermore, This paper explores
whether or not there is a long-run relation between
energy price and energy Carrier consumption.
The paper is organized as follows: The Theoretical
backgrounds and Literature Review are discussed in
Section 2. Section 3, devoted to Model Specification.
Economic Analysis are discussed in Section 4. Finally,
Section 5 concludes this study.
The Theoretical backgrounds and Literature Review
Price, consumption and demand of energy have affected
on GDP and economic growth. Therefore, the study of
the relationship between them has a great
importance.Consequently,
explanation
of
these
relationship significantly helps to clarify the policies of
energy section in the country. Accordingly, revenue
components
and price components
as well as
international shadow prices separately affect behavioral
functions of consumption of different forms of energy. An
example, the energy prices are partly or completely
controlled by the government in Iran. Because of the
government behavior, many costs are not included in the
energy prices. In Iran, energy prices are lower than in the
international market. In essence, it is a kind of energy
price subsidy.
According to the government final consumption
expenditure in the future, continuation of this trend would
not be possible. The main government performance may
be insufficient because of governmental expenses.
Energy as an important input in macroeconomic level has
significant effect in economic growth and development.
Therefore production will be a function of labor, capital,
energy, and the row materials. That function is as
follows:
Q=f(K,L,E,M)
(1)
K= Represent capital factor, L= Represent labor factor,
E= Represent energy factor and M= Represent the row
materials.
When, the index of total energy price rises ,energy
consumption will decrease.
The higher energy price leads an increase in
production costs and decrease in production in short run.
In long-run, it will also depend on the possibility of
substitution among the different forms of energy.
During the last years numerous studies have been
devoted to energy price and consumption. Therefore, we
would try to review international papers comprehensively.
One of the researches inspired by Chaoqing Yuan
and et.al (2010). They researched the relationship
among energy prices and energy consumption in China
by cointegration equations, impulse response functions,
granger causality and variance decomposition. The result
of their investigation indicated the cointegration relation
between energy price and household energy.
Also, Paul Edelstein and LutzKilian (2009) and John
Asafu-Adjaye(2000)
tried to study about energy
consumption, Energy price and economic growth.
The present study has some advantages which it is
separated from other studies:
Firstly, most of the extant studies are related to
energy consumption and economic growth and they just
refer to one country, while in this study, the effect of
energy price and GDP on energy consumption in Iran
and OECD countries are investigated. Also, we use
modern econometric techniques, including panel cointegration and panel unit root.
Model Specification
The econometric approach used in this study consists of
several main parts. First, to identify the degree of the
cointegration for time series data by using panel unit root
tests. If the degree of the cointegration is similar for two
series; the panel cointegration technique that developed
by Pedroni (2004) are used to examine the existence of
long-run relationships between the variables in question.
At the end, are explored the long term relationship
between the variables by using pooled least square test.
The Panel datasets has four main practical
advantages: First, increase the sample size considerably,
allowing for higher degrees of freedom and more
accurate and reliable statistical tests. Second, they also
reduce multicollinearity between regressors. Another
advantage of using panel cointegration is that it allows for
heterogeneity between countries. Furthermore, the
number of observations available when testing the
stationarity of the residual series in a level regression is
greatly increased in a panel framework and this can
substantially increase the power of the cointegration tests
especially when annual data are considered.
Meibodi et al. 79
Unit root tests for panel data
According to the last years numerous studies by Belke,
Dobnik (2011) that have been devoted to econometric
analysis, Unit root tests for panel data have higher
statistical power than unit root tests based on individual
time series and could also be preferable because of their
weak restrictions. Thus in this study, we have considered
several alternative unit root tests such as LLC , IPS and
ADF to examine the stationary for the variables. LLC and
IPS seem to be the most popular tests, where LLC
assumes homogeneity in the dynamics of the
autoregressive coefficients for all panel members,
whereas IPS allows for heterogeneity in these dynamics.
As can be seen in table1 and table 2, it was assumed
that all the time series of EC , GDP and P are stationary
after one difference
In this study cointegration relationship among P, ECP
and GDP is studied by determination of the following
relationship:
〖LEC〗_it=α_it+δ_i t +β_1i LP_it+β_2i LGDP_it+ε_(it )
(4)
Based on calculation results of cointegration test, all of
variables are cointegration from degree one. The null
hypothesis based on, no cointegration in heterogeneous
panels. As, table 3, shows the results of cointegration
tests.
Panel Data Econometric Analysis
In this section, we discuss the results of the empirical
analysis, which is based on the estimation of the metaanalytical models discussed in Section 3.
Panel cointegration
Data Analysis
The panel cointegration technique developed by
significantly improves the conventional cointegration
analysis applied on single country series allowing for
cross-sectional interdependence with different individual
effects in the intercepts and slopes of the cointegrating
equation.
After stationary tests, we figure out the esixtance of
long-run relation among the variables in question.
Because of heterogeneity
in dynamics and error
correction term of variance, The panel cointegration
technique of Pedroni (2004) has been used.
The panel cointegration technique of Pedroni uses
residuals of the regression that have been explored from
long-run regression.The main model as follows:
y_it=α_i+δ_i t+β_1i x_1it+β_2i x_2it+⋯+β_mi x_mit+ε_it
(2)
Where i=1,2,3,…,N denote the country in the model
and t=1,2,3,…,T refers to period of time and m points
out to some explanatory variables. Variables and allow
to study the special fixed effects of countries and also the
determined trends.
is residuals of regression of the
long-run relationships. In order to recognition of long-run
relationships between Pedroni variables, statistical
significance
is investigable through the following
relationship:
ε ̂_it=γ_i ε _
̂ (it-1)+µ_(it)
(3)
suggests two types of residual-based tests for the test of
the null of no cointegration in heterogeneous panels. As
for the first type, four tests are based on pooling the
residuals of the regression along the within-dimension of
the panel (panel tests); as for the second type, three tests
are based on pooling the residuals of the regression
along the between-dimension of the panel (group tests).
In both cases, the hypothesized cointegrating relationship
is estimated separately for each panel member and the
resulting residuals are then pooled in order to conduct the
panel tests .
For this study, annual data covering the period (2000 2009) are used for 29 OECD countries (Australia, Austria,
Belgium and Luxembourg, Canada, Chile, Czech
Republic, Denmark, Finland, France, Germany, Korea,
Mexico, Netherlands, New Zealand, Norway, Portugal,
Slovak Republic, Spain, Sweden, Switzerland, Turkey,
United Kingdom, United States, Japan, Greece, Hungary,
Iceland, Ireland and Italy) and Iran for the sample. The
data of real gross domestic product (GDP), energy
consumption and real prices of energy are collected
which are derived respectively from International Energy
Agency edition2011(www.IEA.com) and Data on real
GDP (1990millions of $US) is taken from World
Development Indicators (www.worldbank.com).
Note that, Energy consumption (EC) is represented by
energy use in kg of oil equivalent and P, is represented
as real prices of energy (US dollars per crude oil barrel).
Also, we use Eviews6 for the calculation and data
analysis process.
Empirical results
This paper will study the relationship among energy
consumption and energy prices in Iran and OECD
country by panel. The variables will be tested for
stationarity by LLC, IPS and ADF tests. Before that, all
the variables should be logarithmic transformed.
Considering that stationary and cointigration tests in
panel datasets. Table 3 presents the empirical results of
cointigration tests. Both in levels and after one difference,
the results of all of the tests do not establish stationarity
for the levels of any of the series.
Also, most of the test statistics (at least 5 statistics in
each case) strongly reject the hypothesis based on non
existence of cointegration vector in the level of 10 percent
for both countries.
80 J. Res. Int. Bus. Manag.
Table1. Panel unit root tests in Iran (2000-2009)
1stdifference
Level
Trend
and
Intercept
Tests
EC
P
GDP
Trend
and
Intercept
Intercept
Intercept
ADF
21.6305**
(0.0000)*
16.7931
(0.0002)
IPS
-5.24821
(0.0000)
-4.21369
(0.0000)
LLC
-4.22211
(0.0000)
- 3.55054
(0.0002)
ADF
25.6725
(0.0000)
20.8389
(0.0000)
IPS
-5.7169
(0.0000)
-4.7219
(0.0000)
LLC
-5.0505
(0.0000)
-4.3470
(0.0000)
ADF
9.37127
(0.0092)
3.33628
(0.1886)
IPS
-2.4965
(0.0063)
-0.8058
(0.2102)
LLC
-3.03242
( 0.0012)
-1.23371
( 0.1087)
ADF
0.48883
(0.7832)
5.90177
(0.0523)
IPS
0.70901
(0.7608)
-1.5583
(0.0596)
LLC
-0.7217
(0.2352)
-1.6166
(0.0530)
8.02538
(0.0181)
-2.16588
(0.0152)
-2.17122
(0.0150)
11.4175
(0.0033)
-2.8232
(0.0024)
-2.7640
(0.0029)
1.83985
(0.3985)
-0.2049
(0.4188)
0.42669
(0.6652)
0.13702
(0.9338)
1.49662
(0.9328)
0.20826
(0.5825)
Table 2. Panel unit root tests in OECD country (2000-2009)
1stdifference
Level
Trend
and
Intercept
Trend
and
Intercept
Intercept
Intercept
ADF
IPS
LLC
ADF
IPS
LLC
ADF
IPS
LLC
ADF
IPS
LLC
EC
25.1876**
(0.0000)*
-5.9216
(0.0000)
-4.6539
(0.0000)
28.7055
(0.0000)
-6.393
(0.0000)
-5.5898
(0.0000)
18.4251
(0.0001)
-4.5515
(0.0000)
-3.9821
(0.0000)
21.5614
(0.0000)
-4.83864
( 0.0000)
-4.490
(0.0000)
P
16.7931
(0.0002)
-4.2136
(0.0000)
-3.5505
(0.0002)
20.8389
(0.0000)
-4.7219
(0.0000)
3.33628
(0.1886)
-0.8058
(0.2102)
-1.2337
(0.1087)
5.90177
(0.0523)
- 1.5583
(0.0596)
-1.6166
(0.0530)
GDP
17.2388
(0.0002)
-4.3128
(0.0000)
-3.2808
(0.0005)
20.1018
(0.0000)
-4.5748
(0.0000)
-4.3470
(0.0000)
3.93205
(0.0000)
1.4364
(0.4876)
-0.0004
(0.4998)
- 0.8981
(0.1846)
1.11143
(0.5737)
0.13858
( 0.5551)
-1.2260
(0.1101)
Tests
Table3. Cointegration tests in Iran and OECD countries (2000-2009)
countries
OECD
Trend and Intercept
Iran
Intercept
within-dimension
Trend and Intercept
Intercept
Panel v-Statistic
1.81458(0/0348)*
1.960987(0/0249)*
11.11515 (0/00)*
2.645169*** (0/0041)*
Panel rho-Statistic
-1.728078(0/042)*
-3.78909(0/0001)*
-0.829844(0/2033)
-1.622644 (0/0523)*
Panel PP-Statistic
-3.88662(0/0001)*
-3.727154(0/0001)*
-1.355429(0/0876)*
-2.546198 (0/0054)*
Panel ADF-Statistic
-4.581977(0/00)*
-3.068135(0/0011)*
between-dimension
-0.707754(0/02395)*
-2.447313 (0/0072)*
Group rho-Statistic
-1.118516(0/1317)
-3.329387(0/0004)*
-0.235071(0/4071)
-1.037805 (0/1497)
Group PP-Statistic
Group ADF-Statistic
-3.908391(0/00)*
_4.683719(0/00)*
-4.158509(0/00)*
_3.335428(0/0004)*
-1.086092(0/01387)*
_0.363929(0/0358)*
-2.683556 (0/0036)*
_2.560054(0/0052)*
Meibodi et al. 81
Table4. Model estimation by using fixed effect model in Iran and OECD countries
variables
countries
Dependent
Variable (EC)
c
Specification model
Fixed effect model
Fixed effect model
OECD
Iran
Coefficient (t: stat)
Coefficient (t:stat)
*
*
2.619054 (37.07048)
*
-0.288531 (11.48491)
1.4909 (21.2793)
GDP
0.084332* (6.622394)
0.381604* (48.9467)
R-squared
0/812
0.920225
P
-0.060213 * ( -2.76588)
*** Statistics. ** variables prob . * critical values is 10%
source: researcher calculation
The results accept the hypothesis of 1 cointegration
equations at the 10% significance level. It indicates that
there exist one cointegration equations among energy
consumption, energy price and economic output in longrun. As, equation (5) reflects the long-run relationship
between LP, LEC and LGDP that we should estimate it:
〖LEC〗_it=〖Lα〗_it+β_1i LP_it+β_2i LGDP_it+ε_(it )
(5)
Before estimating the equation (5), we should
investigate whether the fixed effects panel model or
random effects model are usefulness or not, by using
Hausman test . According to the results of Hausman test
fixed effects model used for estimation of the model.
Based on the result of Hausman test, the null hypothesis
of nonexistence of correlation between the error terms
and regressors are rejected at the 95% significance level
that means the results of model will be biasedness if they
are estimated by this method. Consequently, we used
fixed effects method for estimation. As , the results of this
are presented in table 4 .
Regarding to the findings of model estimation, all the
coefficients of variables are significant at critical level of
10 percent (confidence level of 90 percent), (t-statistics of
all the coefficients are higher than 2 and thereby are
significant). The results show, negative effect of energy
price on energy consumption in all periods in OECD
countries and Iran. Whereas, empirical results based on
parallelism of GDP with energy consumption can be
Hausman test is used for decision making on
selection of two methods of Random effects and fixed
effects and its statistics is as follows:
bˆ = the estimated coefficients in model considering
the fixed effect 1
βˆ= the estimated coefficients in model considering the
random effects 1
Σ= matrix of covariance of estimated coefficients
considering the random effects
If the calculated X2 is compared with X2k which is the
number of explanatory parameters, and calculated X2 is
more than X2 from the table then the random effects
model must be used. But if calculated X2 is less than X2
from the table, there is not any difference between using
fixed effects or random effects models.
Seen. In other words, The results indicate higher GDP
will increase energy consumption both in Iran and OECD
countries.
There is an cointegration relationship between energy
consumption, energy price and GDP in long-run. It is also
different between the OECD countries and Iran, because
of different structures of countries. Thereby, different
vectors are obtained. Based on results, can be showed
the existence of a relationship between energy price and
energy consumption in selected countries. In other
words, hypothesis of price effects of different forms of
energy on energy consumption in selected countries can't
be rejected. During the period of study, energy
consumption averagely has been less than the
economical growth of these countries and as a result
energy intensity has decreased in these countries.
In sum up, it can be explained that function of energy
consumption (oil and gas) in Iran is a descending function
with the sharp slope. In fact the reaction and sensitivity
of energy consumption in comparison with energy price in
Iran is less than OECD countries. As it was mentioned,
the reason of this problem is wide discrepancy of internal
prices with international prices, great resources of oil and
gas, lack of appropriate substitute energies, and so on.
And it has led the energy price not to have a significant
effect on energy consumption. So that energy
consumption in 2009 in comparison with 2000 has had an
increase of 70 percent. So, reflection of this issue can be
82 J. Res. Int. Bus. Manag.
seen in annual average increase of 7 percent of energy
consumption. Whereas the amount of energy
consumption among the OECD countries has been
adjusted with increase of energy price, because of
improvement in production technologies and increase in
efficiency.
Also, R2 statistics illustrate that in OECD countries and
Iran respectively 81 and 92 percent of changes of
depended variable are explained in equation by
explanatory variables that show the strong power of
explanation of equation.
CONCLUSIONS
This paper has explored the effect of energy price on
energy consumption (Oil and Gas) in Iran and OECD
countries (2000-2009) by using modern econometric
techniques, including panel co-integration and panel
unit root. Then symmetry and asymmetry relationship
energy price on energy consumption have been
analysed. The results explains negative effect of energy
price on energy consumption in all periods both in OECD
countries and Iran.
In Iran, energy prices are lower than in the international
market. In essence, it is a kind of energy price subsidy.
Thus, pricing mechanism for energy should be gradually
reformed, and at last be in line with the international level.
With the rise of the energy prices, energy consumption
will correspondingly decrease.
while, the results indicate higher GDP will increase
energy consumption countries in long- term.
According to this result, some policy implications are
proposed as below to help the policy-makers in economic
planning:
First, the pricing mechanism for energy should be
further reformed in countries under study, because of the
undeniable role of energy (oil and gas) in creating the
value added of countries, elevating the energy efficiency
and optimization of energy consumption. In fact, the
dependence of production on this factor must be reduced
by reforming the pricing mechanism for energy.
Second, it is inevitable that energy prices will grow
higher and higher. The energy consumption is mainly
determined by economic outputs. With the economic
growth, energy consumption will increase rapidly. And
more and more energy consumption will make the energy
prices higher and higher according to the relation
between energy consumption and energy prices. Then
higher energy price can significantly reduce energy
consumption. So higher energy prices are inevitable and
necessary to confine
energy consumption to a
sustainable level.
Third, regarding to security of energy supply, the
environment economic, elevation of energy efficiency,
usage of new energies, development of nuclear energy
and encouraging and punishing policies in energy section
are among the important strategies of energy
management.
ACKNOWLEDGEMENT
Thanks to three anonymous reviewers for their very
helpful comments.
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