315 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
The Effect of Natural Gas Price liberalization on Natural Gas
Consumption in Residential sector in Iran
Mohammad Babazade1, Vahid Ghorbani Pashakolaei
2 and
Khalil Ghadimi
Dizaj3
1. Introduction
High natural gas consumption in residential sector in Iran is an important issue that
Price liberalization and omitting natural gas subsidy is one of the solutions to reduce
natural gas consumption in residential sector.
Natural gas price in Iranian residential sector is 100 rials per cubic meters while cost
Price is 3000 rials per cubic meters in 2010. In this study we applied CPI index to
calculate real price. The investigation of nominal and real price of natural gas for the
period 1975-2010 shows that in spite of increasing nominal price of natural gas
between these years, the real price decreased. Also, results showed that the average
growth of natural gas consumption in residential sector was 10%. Because of some
facts such as low price of natural gas in Iran, none existence of a suitable substitution,
high consumption and its effects on macroeconomic variables it would be necessary to
investigate the main factors which affects the natural gas consumption in residential
1 Assistant Professor of Economics, Islamic Azad University Branch Firozkooh, Iran,
[email protected] 2 PhD student of petroleum economics,Allame Tabatabaei University, Tehran, Iran,
[email protected] 3 Master of pricing, National Iranian Gas Company (NIGC),
JGE
316 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
sector. Therefore this study aimed to estimate long and short-run natural gas
consumption function in residential sector in Iran.
Our Objectives in this study is:
1) Estimation of long and short- run natural gas consumption functions in residential
sector in Iran;
2) Determination of long-run and short-run price and income elasticities;
3) The impacts of cutting subsidies on residential consumption of natural gas are
studied in two scenarios (in first scenario the liberalization process is considered in 3
years (2011-13) and in second scenario is considered in 5 years (2011-15)).
The present paper is organized as follows: the next (second) section presents literature
review; the third section provides the econometric specification of the model for
residential demand for natural gas and discusses the ARDL cointegration technique.
The forth section presents the data and evaluates the results of the econometric
analysis and examines the withdrawal of subsidies on natural gas. Finally there are
concluding remarks and some recommendations regarding to the results of the study.
2. Literature review Here a few recent researches in this context are reviewed (See Table 1).
Table 1 - Selected empirical results
Results method Country Sources
Income and Price elasticities less than one AIDS Iran Moshiri &
Shahmoradi (2005)
Long run price elasticity = -0.13 Long run Price elasticity = 0.17
STSM Iran Keshavarz &
Mirbagherijam
(2007)
Income and Price elasticities less than one AR Iran Lotfalipor &
Bagheri (2003)
Price elasticity less than one And income elasticity more than one
ECM United
state
Winston T. Lin,
Yueh H. Chen and
Robert
Chatov(1987)
Price elasticity less than one And income elasticity more than one
AIDS Australia
Akmal,
Mohammad, and
David Stern(2001)
317 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
Short-run & Long run price elasticities less than one
and Total Response to
10% Price increase lead to decrease consumption
between 1.9% and 3.3% in different regions
Dynamic
Model U.S
Joutz and
Trust(2007)
existence of long-run relationships between the
energy
prices before and after the liberalisation, implying
the possibility of substitution among the different
forms of energy
VAR-
cointegration UK
Ferreira, P. Soares,
I. Araujo, M (2004)
that there is a significant relationship between
the fuel demand and fuel subsidy factors
This study recommends a gradually
controlled withdrawal of fuel subsidy at the level it
will be minimally harmful to the economy
Fuel subsidy Nigeria Nwachukwu,
Chike (2011)
Subsidies for electricity, natural gas and coal are
even more pervasive. In virtually all of the countries
studies, the prices of these fuels do not reflect
marginal costs and withdrawing this subsidies is
necessary.
analyses the
Commercial
energy
subsidies
developing
countries Kosmo(2003)
3. Methods
The method in this study is based on this process:
1) Demand Model: Determination of natural gas demand structure in residential
sector
2) ARDL Model: this step we explained research methodology and cointegration
concept
3) Short-run Model: this step we show short-run equation estimation method
4) Stability test: In order to ensure the stability of the long-run parameters of our
econometric specification, we applied the CUSUM and the CUSUMQ tests for the
residuals of the error-correction.
5) Winter’s Method: for determination of amount of dependent variable (natural gas
consumption), we need GDP and price level, price level determined in two scenarios
and GDP forecast with winter’s Method.
3-1. Demand Model
318 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
A modified residential natural gas demand model in logarithmic form is adopted based
on:
ln𝐶𝑜𝑡 = 𝛼0 + 𝛼1 ln𝑌𝑡 + 𝛼2 ln𝑃𝑡 + 𝛼3 ln𝑃𝑒 +𝜀𝑡 (1)
Where Cot is the residential natural gas consumption (cm3) , Yt is the real gross
domestic product, Pt is the real residential natural gas price (rials/cm3) , Pe is the real
residential electricity price (rials/kwh)
ln is the natural logarithm transformation.
As for the expected signs in Eq. (1), one expects that α1>0 because higher added value
in residential sector should result in greater economic activity and accelerated
purchases of natural gas technology. The coefficient of price level is expected to be
less than zero for usual economic reasons, therefore, α2<0 and α3>0 because the
natural gas and electricity in residential sector is complementary goods.
3-2. ARDL Model
A recent single cointegration approach, known as autoregressive distributed lag
(ARDL).
An ARDL representation of Eq. (1) is formulated as follows pesaran et al. (2001):
(2) lnlnlnlnlnlnln 1161514
0
3
0
2
1
10 itCOtCOtCO
m
i
itCO
m
i
itCO
m
i
itCOCOt PYCOPYCOCO
(3) lnlnlnlnlnlnln 1161514
0
3
1
2
0
10 itPEtPEtPE
m
i
itPE
m
i
itPE
m
i
itPEPEt YCOPYCOPP
(4) lnlnlnlnlnlnln 1161514
0
3
1
2
0
10 itYtYtY
m
i
itY
m
i
itY
m
i
itYYt PCOYPCOYY
It has to be mentioned that the F-statistic obtained by performing the Wald test has a
non-standard distribution, whose asymptotic critical values are provided by Pesaran et
al. (2001).
In Eq.(2), where tCOln is the dependent variable, the null hypothesis of no
cointegration amongst the variables is )0 : ( 6CO5CO4CO0 H against the alternative
hypothesis )0 : ( 6CO5CO4CO1 H . This is denoted as ),( PYCOFCO. In Eq.(4), where
319 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
tYln is the dependent variable, the null hypothesis is )0 : ( 6Y5Y4Y0 H against
the alternative )0 : ( 6Y5Y4Y1 H . This is denoted as ),( PCOYFY. In Eq.(3), where
tPEln is the dependent variable, the null hypothesis for cointegration is
)0 : ( 6PE5PE4PE0 H against the alternative )0 : ( 6PE5PE4PE1 H . This is
denoted as ),( COYPFPE.
Having identified the existence of a cointegration, general specification for the ARDL
(p1, q2, q3,) model is presented below:
(5) lnlnlnln211
0
3
0
2
1
10
q
i
iti
q
i
iti
P
i
itit PbYbCObbCO
3-3. Short-run Model
We estimated an error correction model associated with the long-run estimates. This is
specified as follows:
(6) EC lnlnlnln 1-t
1
3
1
2
1
10
311
t
q
i
iti
q
i
iti
p
i
itit PYCOCO
Where is the parameter of adjustment speed and 1-tEC denotes the residuals
obtained from the estimated cointegration model of Eq. (1).
3-4. stability test
To test for parameter stability, we follow Pesaran and Pesaran (1997) and estimate the
following error correction model:
∆𝑙𝑛𝐶𝑜𝑡 =∝0+ ∝1𝑖 ∆𝑙𝑛𝐶𝑜𝑡−𝑖 (7)
𝑚
𝑖=1
+ ∝2𝑖 ∆𝑙𝑛𝑌𝑡−𝑖 + ∝3𝑖 ∆𝑙𝑛𝑃𝑡−𝑖 + 𝛾𝐸𝐶𝑀𝑡−1 + 𝜀𝑡
𝑚
𝑖=0
𝑚
𝑖=0
All variables are as previously defined and the error correction term is calculated from
the long-run relationship. After estimating the model the cumulative sum of recursive
320 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
residuals (CUSUM) and the CUSUM of squares (CUSUMSQ) tests were applied to
test for parameter constancy.
3-4. winter's Method
After evaluation of demand function, we could determine the value of dependence
variable (natural gas consumption). For this purpose we must firstly determine the
value of independence variable and forecast them (GDP and price).
In this section we explain the seasonal GDP forecast process. There is different
forecast method for seasonal data. The most famous seasonal method is moving
average, Single Exponential Smoothing (SES), Double Exponential Smoothing (DES)
and winters' Method. In this paper we used the winter's Method; this method is based
on eq. (8) to (11): Black (1997) and Rubin et al (1989)
Et =∝ Xt
St−L + (1−∝)(Et−1 + Tt−1) (8)
Tt = β Et − Et−1 + 1 − β Tt−L (9)
St = γ Xt − Et + 1 − γ St−L (10)
Ft+k = (Et + KTt)St−L+k (11)
That Et is the smoothing value, Tt related to trend, St seasonal fluctuations and Ft+k
indicated the value of forecast for t+k period. Coefficient indicated the weight of
variables and between 0 and 1, best value for this coefficient determine when the error
of forecast is minimized. Mean Square Deviation (MSD) index is used for estimation
accuracy that definition in eq.12:
MSD = et
2
n (12)
That et equal to Xt-Ft. We suppose that alpha, beta and gamma values are 0.2 and
MSD is 0.004.
321 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
4. Data and empirical results
The data used in this paper are annual time series spanning the period 1975-2010. The
sources of our data on price of natural gas were obtained from the Iranian national gas
company (INGC) (annual publication). Residential natural gas consumption and GDP
were obtained from the Iranian central bank.
We performed the Augmented Dickey – Fuller (ADF) test to verify the exact order of
integration of the variables. Table 2 below displays the results of ADF tests. Results
show that all variables used in our study are an I(1) in 5% level of critical values
Table 2-ADF tests
Level
p- value
1 st Differences p-
value
Order of
Integration Variable ADF
stat. Variable ADF stat.
LNconsum -2.99 0.28 LNconsum -6.01 0.00 I(1)
LNpriceGas -0.7 0.92 LNpriceGas -5.98 0.00 I(1)
LNpriceEle -0.31 0.18
LNpriceEle -6.22 0.00 I(1)
LNgdp -0.81 0.22 LNgdp -6.03 0.00 I(1)
Note: ADF stands for the Augmented Dickey – Fuller test. All level variables are in logs. ∆ is
the first difference operator.
The F test proposed by Pesaran et al. (2001) can be used to determine whether a long-
run relationship exists. It has to be mentioned that the F-statistic obtained by
performing the Wald test has a non-standard distribution, whose asymptotic critical
values are provided by Pesaran et al. (2001). The calculated F-statistics, together with
the critical values, are reported in Table 3.
While GDP (Y) is the dependent variable no long-run relationship is observed. There
are long-run relationships among variables when natural gas consumption and natural
gas price are the dependent variables at 1% and 10% level, respectively. Overall, the
results of the bounds F test in Table 3 imply that at 10% level, the null hypothesis that
there is no cointegration among the variables in Eq. (2) and Eq. (3) cannot be
accepted, while for Eq. (4), where the calculated F-statistics are less than the critical
values, the null hypothesis is accepted suggesting a long-run relationship between
322 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
natural gas consumption and price and GDP when natural gas consumption is the
dependent variable.
Taking for granted the existence of a long-run equilibrium, we do estimation by
setting two the maximum lag-length and using the Schwarz Bayesian Criterion (SBC)
to select the lag order of the model. The specification finally selected is an ARDL (1,
0, 0). The derived long-run elasticities and diagnostic tests for the ARDL model are
shown in Table 4. The estimated elasticities display the expected signs which are
negative for the price of natural gas and positive for the GDP, respectively.
Table 3 - Bounds testing for cointegration
Panel
A
Fco(Co/y,p)
=17.25a Fp(P/y,Co)=6.58 Fy(y/Co,P)=1.57
Panel
B
1% 5% 10%
I(0) I(1) I(0) I(1) I(0) I(1)
6.78 8.21 5.45 5.74 4.57 5.6
a. denote statistical significant at 10% level.
Critical values are obtained from Narayan (2005) for 30 observations. K=2 and it is the number of
regressors.
The results indicate that long-run price elasticity is -0.36 meaning that if residential
natural gas price increases by ten percent, the residential natural gas consumption
decreases by 3.6%; also the long-run income elasticity is 0.88. Finally, diagnostic tests
for the underlying ARDL model verify that the residuals are non-serially correlated,
correct functional form, normal, and non-heteroscedastic .
323 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
Table 4 - Long-run coefficients for the ARDL (1, 0, 0) model
Variable C LnP LnGdp
Coefficient -0.5 -0.36 0.88
t-statistic 1.05 -1.36 1.97**
p-value 0.44 0.51 0.04
Diagnostic tests :
Variable t-statistic p-value
Serial Correlation 0.8 0.32
Functional Form 0.002 0.93
Normality 0.95 0.51
Heteroscedasticity 2.16 0.09
The maximum lag length was set 2.
** indicate 5% levels of significance.
The results of the short-run model (Eq. (6)) and diagnostic statistics for the short-run
ARDL model are presented in Table 5. As expected, all short-run elasticities are lower
in absolute value than those in the long-run model. The short-run price and income
elasticity are -0.12 and 0.62, respectively. The lagged error correction term is
statistically significant with the expected negative sign.
Figs. 1 and 2 below, display the results of CUSUM and CUSUMQ tests, respectively.
In both figures the dotted lines represent the critical upper and lower bounds at the
0.05 level of significance. The visual inspection of Figs. 1 and 2 reveals that there is
no evidence of parameter instability, since the cumulative sum of the residuals and the
cumulative sum of the squared residuals move within the critical bounds.
We used the dynamic ARDL model. For investigating the liberalization process in
first scenario (2011-13): 𝐿𝑁𝑐𝑜𝑛 = −0.051 + 0.15 𝐿𝑁𝐶𝑜𝑛 −1 − 0.12𝐿𝑁𝑝𝑟𝐺𝑎𝑠
+ 0.627𝐿𝑁𝑔𝑑𝑝 (13)
Table 5 - Error-correction representation
a results. lnCO , is the dependent variable
324 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
Variable C ∆LnP ∆LnGdp 1-tEC
Coefficient -0.05 -0.12 0.627 -0.95
t-statistic 0.356 -0.365 1.95** -5.83***
p-value 0.724 0.71 0.00 0.00
Diagnostic statistics :
R2-adjusted 0.55 Schwarz criterion 39.3049
F-statistic 9.01 Akaike criterion 51.4
DW-statisticb 1.61 RSS 0.02
*** and ** indicate 1% and 5% levels of significance, respectively.
b. DW is the Durbin-Watson statistic and RSS is the residual sum of squares
Fig. 1. CUSUM test
325 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
Fig. 2. CUSUMQ test
For the liberalization process we investigated monotonous decrease of the natural gas
subsidy (monotonous increase of natural gas price) in three years that indicated in
table5. With use of this prices and GDP value that forecasted with winters' Method for
the first scenario, we could calculate the natural gas consumption for this years. We
also forecast the consumption if subsidy do not withdrawal to compare the
consumption in two different situation. The findings indicate that the annual growth of
natural gas consumption in residential sector has been 9.9 % in the period of 1975-
2010 while this rate will be decreases to 2.7% if subsidy withdrawal during the period
2011 to 2013. These changes indicated in table 6 and Fig3
Table 6- natural gas consumption during the process of subsidy withdrawal in first scenario
Years
Price
(Rials/cubic
meter)
Consumption forecast
if subsidy withdrawal(with
price liberalization)
(million cubic meter)
Consumption forecast
if subsidy does not
withdrawal(without price
liberalization)
(million cubic meter)
2011 834.37 36258.56 44881.61
326 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
2012 1556.25 36075.51 46321.73
2013 2278.12 37352.13 47676.5
Fig. 3. Residential natural gas Consumption in first scenario (2011-13)
The liberalization process in first and second scenario (2011-15) is the same. We used
the dynamic ARDL model and GDP value. Monotonous increase of natural gas price
indicate that price during 2011 to 2015 will be 593.75, 1075, 1556.25, 2037.5 and
2518.75 rials/ cubic meters respectively. The results indicated that consumption during
2011-15 with consider subsidy withdrawal process will be 37965.73, 38003.13,
39414.1, 41454 and 43931.57 million cubic meter respectively. We also forecast
natural gas consumption without subsidy withdrawal with auto regressive (1) and
results show that consumption will be 44881.61, 46321.7, 47676.5, 48950.9, 50149.9
million cubic meters. The rate of consumption growth will be decreases to 4.2% if
subsidy withdrawal during the period 2011 to 2015 (see table 7 and Fig.4). Therefore,
this policy could reduce the annual growth of natural gas consumption in first and
second scenario by 7.2 % and 5.7% respectively.
0
10000
20000
30000
40000
50000
60000 with price liberalization
without price liberalization
327 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
Table 7- natural gas consumption during the process of subsidy withdrawal in second scenario
Years
Price
(Rials/cubic
meter)
Consumption forecast
if subsidy withdrawal(with
price liberalization)
(million cubic meter)
Consumption forecast
if subsidy does not
withdrawal(without price
liberalization)
(million cubic meter)
2011 593.75 37965.73 44881.61
2012 1075 38003.13 46321.73
2013 1556.25 39414.1 47676.5
2014 2037.5 41454 48950.9
2015 2518.75 43931.57 50149.96
Fig. 4. Residential natural gas Consumption in second scenario (2011-15)
5. Conclusions
0
10000
20000
30000
40000
50000
60000with price liberalization
without price liberalization
328 Journal of Global Economy (ISSN Print-0975-3931, Online -2278-1277),
Volume 8 No 4, October-December, 2012
This paper examines the effect of natural gas price liberalization on natural gas
consumption in residential sector in Iranian economy. The econometric specification
assumes that the residential demand for natural gas depends on the price of natural gas
and GDP in Iran. To estimate the model, we used ARDL cointegration technique. The
error correction model is consistent with the expectations about the signs of the short-
run parameters and their magnitude which are found lower than their long-run
counterparts.
The results indicates that short-run price and income elasticities are -0.12 and 0.62,
and long-run price and income elasticities are -0.36 and 0.88, respectively. Low price
elasticity leads to low efficiency of price policy because when the prices are too low
the responsiveness of demand with respect to price could be low as well. Hence it is
possible that in much higher range of prices the consumer response to price changes
could increase too, withdrawing subsidies on natural gas is one of the solutions to both
increasing the efficiency of price policy and reducing natural gas consumption.
Withdrawing subsidies on natural gas leads to reduction of annual growth of natural
gas consumption in first scenario (2011-13) and second scenario (2011-15) by 7.2 %
and 5.7% respectively. Also this sudden increase in natural gas price probability could
have significant negative side effects such as welfare loss or fuel poverty which
should be considered.
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