Causality relationship between growth and energy use, a case study of vietnam

Policies and Sustainable Economic
Development | 129

Causality Relationship between Growth and
Energy Use:

A Case Study of Vietnam
NGUYEN THI TAM HIEN
The University of Danang, Campus in Kon Tum

NGUYEN THI PHUONG THAO
The University of Danang, Campus in Kon Tum

VU THI THUONG
The University of Danang, Campus in Kon Tum

Abstract
Recently, Vietnam is becoming an energy-dependent country. In spite of the
important contribution of energy to Vietnam economic growth via import and
industrial production, the increase in energy consumption also raises the
concerns of resource scarcity, the overwhelming dependence on energy, and
sustainable growth. This study investigates the causal relationship between
economy growth and energy consumption in the case of Vietnam from 1986 to
2013. Through testing different types of Granger Causality based on the Vector
Error Correction Model (VECM), the main finding is the unidirectional Granger
causal connection from energy consumption to economic growth, which is
different from previous research in Vietnam. In addition, the paper indicates the
negative effect of energy consumption on economic growth.

Keywords: energy consumption; Granger causality; Vector Error Correction
Model (VECM)

130 | Policies and Sustainable Economic Development

1. Introduction
Over past two decades, the dramatical increase in the demand for
energy to meet the rapid economic development in the Asian countries
along with inefficient energy use has caused energy scarcity. Besides, the
high volatility of energy prices along with the rising greenhouse gas
emissions in recent years also has become a challenge to the sustainable
development of these countries. The building of energy conservation
policy intended to ensure energy security as well as promote sustainable
growth has attracted many scholars’ concerns. It is important in design
this policy is that policy makers must understand clearly the causal
relationship between energy consumption and economic growth. In other
words, policymakers have to answer the question whether economic
growth boosts energy consumption or whether energy consumption
causes economic growth. However, up to now, there has a lack of
consensus among economists due to the mixed findings from previous
studies.
In term of energy economics literature, there has a massive body of
academic research on the linkage between energy consumption and
economic growth. These studies have conducted in various countries with
different periods and using different econometric methodologies (Ozturk,
2010). However, the directions of this causality are still mixed and
controversial among empirical pieces of research. Azlina (2012) classifies
the causal relationship between energy consumption and economic
development into four views. The first view suggests that economic
development is considered as the main driver for energy demand
economy grows. It means that economy growth to lead to the energy

demand of the economy also increases. On the contrary to the first view,
the second view points out the important roles of energy in the economic
development process. In addition to capital, labor, and materials, energy is
considered as an input to production. The third view shows that there has
a two-way causal relationship between energy consumption and economic
development. The fourth view argues that both energy consumption and
economic development are neutral with respect to each other.
Over more past two decades, Vietnam has witnessed impressive economic
growth in the Southeast Asia. However, its consumption of energy also
increased tremendously accompanied by high economic growth. In particular,
before the impact of 2008 World Economic Crisis, the economic growth rates
in Vietnam on average reached over 7 percent for the period from 1990 to
2007. At the same time, the energy consumption per capita in Vietnam also
1
increased by 9.3 percent per year . CIEM (2012) also show that in two last
decades, the economic growth in Vietnam has relied heavily on draining a lot
of its natural resources and energy intensity levels has been higher than other
countries in the region. In particular, to generate $ 1,000 of GDP, Vietnam
must consume about 600 kg of oil equivalent, 1.5 times higher than in
Thailand and more than 2 times compared to the average level of the world.
(Hong Quang, 2015). Do and Sharma (2011) also give a forecast that the total


1

Toan, P. K., Bao, N. M., & Dieu, N. H. (2011)


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energy consumption in Vietnam is projected to rise from 55.6 Mtoe in 2007
to 146 Mtoe in 2025. Vietnam is facing the risk of dependence on imported
energy. This impressive performance has placed an interesting question to
economists and policy makers of whether energy consumption is the cause
or effect of economic growth in Vietnam.
To the best of our knowledge, there have been some studies examining
the energy-growth nexus, such as Chontanawat et al. (2008), Phung
(2011), Le (2011), and Nguyen (2012). However, this causal relationship
between energy consumption and economic growth has not been reached
2
to a consensus among economists .
Awareness of the importance of understanding the causal relationship
between energy consumption and economic growth in policy implications
leads us to continue this issue. Performing Granger causality test and Vector
Error Correction Model (VECM) using the energy consumption per capita and
income per capita from the year 1986 to 2013, this paper aim to answer the
following questions: (1) Does there exist causality between economic growth
and energy consumption in short-term and in long-term? (2) If yes, what is the
sign and magnitude of such effects?

The remainder of this paper is organized as follows: Section 2 briefly
reviews the literature on the causal relationships between energy
consumption and economic growth. Section 3 outlines the data and the
econometric methodology. Then, the econometric results are discussed in
section 4. Conclusions follow in Section 5.
2. Literature review
The causal linkage between energy consumption and growth has been a
widely studied topic in the literature for a long time. Since the pioneering
work of Kraft and Kraft (1978), which concluded a unidirectional causality

from income to energy consumption in the United States for the 1947-1974
period, many economists have joined the debate with either supporting or
conflicting views during the next two decades. For example, Abosedra and
Baghestani (1989) confirms the Kraft and Kraft’s result by applying the
standard test of Granger causality. Cheng and Lai (1997) with Taiwanese data
from 1955 to 1993 and Cheng (1999) with two time series of India also
support the causality running from GDP to energy consumption without
feedback. Conversely, Akarca and Long (1980) detect the issue of temporal
sample instability in Kraft and Kraft’s study. Therefore, by replacing the time
period, the relationship turned out to be no statistically significant. The same
neutrality property is proved by Yu and Hwang (1984), Yu and Jin (1992) with
updated data to 1979 and 1990, respectively. Using standard Granger
technique, Yu and Choi (1985) also have the same conclusion for the case of
US, UK, and Poland. However, the causal relationship runs from GNP to total
energy consumption for South Korea and vice versa for the Philippines. Masih
& Masih (1996) also found different results when checking the growth-energy
consumption nexus for 6 Asian countries. The Johansen's


2

Tang, C. F., Tan, B. W., & Ozturk, I. (2016)


132 | Policies and Sustainable Economic Development

multivariate cointegration tests and dynamic vector error correction model
(VECM) show mutual effects between development and energy use in
Korea, Taiwan, and Pakistan. In sum, most studies in this period used
bivariate models and Granger causality approach. Hence, major reasons

for these contradicted findings may be the usage of different tests and lag
terms for time series, the diversification in data of various countries and
various time scales.
In the recent years, the debate on the growth-energy nexus is even
more extensive and diversified with various research directions and
economic techniques. Both country-specific and multi-country provide a
broad context of the research issue with the aim of drawing a definite
conclusion on the relationship and its direction between economic growth
and energy consumption. This ambitious purpose has still not been
achieved due to a consensus on the subject matter so far. Table 2.1
provides a summary of controversial arguments in the last 15 years.
From the review of recent literature, some noticeable points are
summarised as follows:
The previous two decades experience a vigorous debate of a large
number of researches worldwide. Some studies focus on a specific nation
while others assess a group with certain common properties such as
developing countries, industrialized countries, G-7 group, oil-exporting
countries, same region or same continent countries.
- Except for the study of Stern and Enflo (2013) which uses 150-year
time series for Sweden, the examined period often ranges from 3 to 5
decades, assuring the reliability of findings. However, the most recent year
is 2011 that is relatively out of date. In the context of increasingly urgent
environmental issues, research with more updated data should be carried
out to draw conclusions that are more suitable.
- In terms of methodology and techniques, most of researches
implement 3 main steps:
First, test the stationarity of the series or their order of integration in all
variables, using Augmented Dickey-Fuller (ADF) test and Phillips-Perron
(PP) test. Some authors apply more tests such as Kwiatkowski-PhillipsSchmidt-Shin (KPSS) test (Ang, 2008), Zivot and Andrews test (Altinaya &
Karagol, 2004) to verify their findings.

Second, examine the presence of a long-run relationship, utilizing the
popular approaches of Johansen (1991) or Pedroni (1997, 2004). A few
studies are distinguished by some newly developed cointegration tests, for
example, test of Pesaran et al. (2001) and a modified version of the
Granger causality test due to Toda and Yamamoto (1995) in the studies of
Wolde-Rafael (2006), Fatai et al. (2004).
Third, identify the direction by employing Granger causality tests (VAR
model) or vector error correction model (VECM). Some other techniques


are also applied depending on data’s characteristics like Hsiao’s version of
Granger causality tests (Aqeel & Butt 2001).


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- There are two types of models: (1) bivariate model in which two main
variables are energy and growth (total or per capita) and electricity
consumption seems to be the most popular measure energy use. Other
energy sources such as oil, gas are also mentioned in several studies. (2) A
multivariate model which usually adds energy price, labor, capital as
exogenous factors.
Although the results are mixed and contradicting, there are three main
strands: (1) unidirectional causality which can be energy consumption –
growth or growth – energy consumption directions; (2) bi-directional causality;
(3) no relationship. Since most of researches lead to policy suggestions
accordingly to their findings, evidence in either direction is useful and
important. To be specific, if there is unidirectional causality running from
energy consumption to economic growth, conducting and promoting energy

preservation programs could harm the economy. Similarly, with bi-directional
causality, economic growth demands more energy at the same time energy
levels also affect economic growth. Energy-saving strategy, therefore, may
slow down the momentum of development. On the other hand, if the growth energy consumption direction is found, policies aiming at reducing energy use
may be implemented with little or no negative influence on economic growth.
The same suggestions will be made in the case of no causality.

Several studies on the growth-energy consumption relationship in
Vietnam have only been carried out recently, including Phung (2011), Le
(2011) and Nguyen (2012). Three authors used the bivariate model to
study the relationship between energy consumption/ electricity
consumption and economic growth in periods 1976 – 2010, 1975 – 2010,
and 1986 – 2006 respectively. Despite the different time scales, they came
up with a unidirectional relation from growth to energy consumption.
These results could not explain the energy-led economy in Vietnam. This
makes difficult to understand the energy as a determinant of economic
growth and giving implications to policymakers in energy policies or
sustainable growth policies.


134 | Policies and Sustainable Economic Development

Table 1
An overview and evaluation of existing literature
Study

Countries

Period

Paul &
Bhattacharya

India

1950–1996

(2004)
Ghosh (2006)

1970-2002

Soni, Singh &
Banwet (2013)

1981-2011

Ang (2008)

Malaysia

1971–1999

Loganathan,
Nanthakumar
&
Subramaniam
(2010)

1971-2008

Azlina (2012)

1960-2009

Altinaya &
Karagol
(2004)

Turkey

1950-2000


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Lise & Van
Montfor
(2005)

1970-2003

Jobert &
Karanfil
(2007)

1960-2003

Stern & Enflo

(2013)

Sweden

1850-2000

Glasure
(2002)

Korea

1961-1990

Oh & Lee
(2004)

1970-1999

Mozumder &
Marathe
(2007)

Bangladesh

1971-1999

Aqeel & Butt
(2001)

Pakistan

1955-1996


136 | Policies and Sustainable Economic Development

Yang (2000)

Taiwan

1954–1997

Lolos &
Papapetrou
(2002)

Greece

1960-1996

Shiu & Lam
(2004)

China

1971-2000

Fatai, Oxley &
Scrimgeour

NZ

1960-1999

Jumbe
(2004)

Malawi

1970-1999

Morimo &
Hope

Sri Lanka

1960-1998

Soytas & Sari
(2003)

G-7
countries
and 16
emerging
markets

1950-1994

Narayan &

Smyth (2008)

G7countries

1972–2002

(2004)

(2001)


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Yoo (2006)

Asian
countries

1971–2002

Mehrara
(2007)

11 exporting
oil countries

1971–2002

Lee (2005)

18
developing
countries

1975-2001

Joyeux &
Ripple
(2007)

7 East
Indian
Ocean
countries

1971-2001

Wolde - Rafael
(2006)

17 African
countries

1971–2001

Asafu-Adjaye
(2000)

India,

Indonesia,
Philippines
Thailand

1973–1995

Source: Authors’ analysis and synthesis


138 | Policies and Sustainable Economic Development

3. Data and econometric methodology
3.1. Data
To carry out the analysis of Granger causal relationship between energy
consumption and economic growth, this study uses the energy consumption
in kg of oil equivalent per capita (denoted

E) and GDP per capita in current $US (denoted Y). These secondary data is
collected from World Development Indicators (WDI, 2016). Both data have
the time horizons from 1986 to 2013. The logarithm form is applied to
both variables to reduce heteroscedasticity.
3.2. Econometric methodology
To analyze the causal relationship between the energy consumption and
economy growth, we apply the process as follows
3.2.1. Stationary testing
This study investigates the stationary of logY and logE using augmented
Dickey and Fuller approach – ADF (Dickey and Fuller, 1979) and Phillips –
Perron test – PP (Phillips and Perron, 1988). The purpose of the stationary test
is to avoid the spurious regression and then determine the order of
integration of each variable. The fitted regression model both logE and logY is

expressed as:
where:

∆ = 0 + 1 + 3 −1
∆ is the first difference of the log of variables

+∑

=1

∆

−

+

t is the time or trend variables (if available)
is the pure white noise error term
is the maximum length of the lagged depentent variable
Under the null hypothesis of stationary testing, the time series contain a unit root or - 0: 3 = 0.The alternative hypothesis is 0:
test, Granger causality test, and, VECM, logY and logE are expected to be non-stationary at level and stationary at the first difference.

3

< 0. For next cointegration

3.2.2. Testing for cointegration
The regression among nonstationary variables can lead to the spurious
result that is not meaningful to decision making. However, in the context
that the time series in the study are under cointegration, the spurious

regression does not occur. In this interesting event, the cointegrated
variables show the short- term deviation from their association that must
converge to equilibrium in long-term.
Granger (1986) and Engle and Granger (1987) introduced the EngleGranger test for cointegration between two variables, basing on the
residual of a linear combination of variables.


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Relying on this procedure, we conduct the cointegration test between logY and logE.
Loosely speaking, logY and logE are cointegrated if they are ~(1), and the residual of the
linear combination of variables is an I(0) process. Therefore, in nature, the Engle-Granger
test or augmented Engle – Granger tests are ADF test.

The Engle – Granger test process is as follows. First, we estimate
following equations using ordinary least square – OLS method, and find the
corresponding residuals.
=1+2

where

1

,

2

+1

=1+2
are the uncorrelated error terms with zero mean and constant variance

The residuals are calculated as:
1
2

=

=

+2

− 1−

− 1−

2
2

Then, we use ADF test mentioned above to test if the residuals are
stationary. The null hypothesis states that two variables are not
cointegrated or the residuals obtained is not I(0) process. The alternative
hypothesis states the contrast.
3.2.3. Granger causality test and VECM
The test for causality is first introduced by Granger (1969). The core idea of
the causality relation is “the correlation of current value of one variable and
the past value of others” (Brooks, 2008). To address this issue, a simple linear
regression between the variable and the lagged values of both itself and other
variables is conducted. In this study, the regression models are exhibited as

follows:

where

1

,

2

are the uncorrelated error terms with zero mean and constant variance

In the presence of causality, the coefficients of past logE in the first
equation and/or the coefficients of past logY in the second equation are
jointly equal to zero.
In particular, to investigate if logE causes logY, the joint hypothesis is that the logE does not Granger cause logY or α21 = α22 = ⋯ = α2j = 0
Similarly, to invest if logY causes logE, the null hypothesis is that the logY does not Granger cause logE or β21 = β22 = ⋯ = β2j = 0


140 | Policies and Sustainable Economic Development

There are four possible outcomes for the test above. They are logE
Granger causes logY, logY Granger cause loge; there is feedback between
two variables and two variables are independent. Nevertheless, the
Granger Causality test just indicates the direction, but nothing of the sign
and the magnitudes of the relationship between two variables. Running
VECM is necessary to find those missing answers.
The VECMs for logY and logE in this study are:

∆logYt = φ0 + ∑

1

∆logYt−i

=1

= φ0 + ∑

1

∆logYt−i

+∑

2

∆logYt−j + δ(logYt−1 − a1 − a2logEt−1)

+ ϑ1t

∆logEt = θ0 + ∑

1

∆logEt−i

= θ0 + ∑

+

1

∆logEt−i
ϑ

2t

=1

+∑

2

∆logYt−j + γ(logYt−1 − a1 − a2logEt−1)
=1

The ECT1t−1 and ECT2t−1 respectively show the size of deviation of logY and logE from their
corresponding long-term equilibrium. and indicate how much disequilibrium of logY and logE in
previous year is corrected next period. The cointegrating vector [1 –a 1 –a2] represent the
coefficients of long-term relationship between logY and logE, in which a 2 measures the energy
consumption elasticity.

We will test three type of Granger Causality, including weak Granger
causality, long-run Granger causality, and strong Granger causality. In
particular, the null hypotheses for each type are expressed as:
Weak Granger causality
0
0

:∑
:∑

=1 2
=1 2

= 0 : No Granger causality relationship
= 0: No Granger causality relationship

Long-run Granger causality
0:
0:

= 0 : no Granger causal connection
= 0 : no Granger causal connection

Strong Granger causality
0:

=∑

=1 2

= 0: no Granger causal connection


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0:

=∑

4.

=1 2

= 0: no Granger causal connection

Results and analysis

4.1. Unit root test
The Augmented Dickey-Fuller tests are performed for both variables,
logY and logE at the level and first difference and without trend and
intercept. The results are displayed in Table 2. As can be seen from this
table, logY and logE are individually non-stationary at level because the
corresponding p-values are greater than any conventional level of
significance. However, they are stationary at the first difference at a
significance level of 1%, or they follow the I(1) process.
Table 2
The Augmented Dickey-Fuller and Phillips – Perron Unit root test

logY
logE
Note: Table 2 presents the test statistics of unit root test using Augmented Dickey Fuller
approach and Phillips-Perron approach. The asterisk indicate the level of significance,
including *** (1%), ** (5%), * (10%)

4.2. Testing for cointegration
Naturally, the selection of lag length is as a pre-estimation of VAR to
obtain the information criteria by which compare to other criteria to

determine the best-fitted model.
As can be seen from Table 3, all criteria confirm that four lags are the
most suitable for the intended VECM model.
Table 3
The selection of lag length
Lag
0
1
2
3
4
Note: Table 3 reports the results of four information criteria to select the lag lengths for
VECM as well as the likelihood ratio test (LR). Those four information criteria include final
prediction error (FPE), Akaike’s information criterion (AIC), Schwarz’s Bayesian
information criterion (SBIC) and the Hannan and Quinn information criterion (HQIC). The
asterisk indicates the optimal lag at 10%

LR


142 | Policies and Sustainable Economic Development

Following the lag order selection is the test of cointegration which is
presented in Table 4. Since there are two variables in considered model, at
most two cointegrated equations exist. As a result, the trace statistic of r
= 1 (in which r is the number of the cointegrating equation) is of statistical
significance. This shows the rejection of the null hypothesis that there is
one or no cointegrated relation. This result determines only one equation
of cointegration.
Table 4

Test of cointegration
Trace test
Null hypothesis
=0

=1

Note: Table 4 reports the number of the cointegrating equation. R is the number of the
cointegrating equation. The asterisk * indicates the number of r chosen by Johansen’s
multiple trace test process at 10%.

4.3. The Granger causality test and VECM
Table 5 presents the result of Granger causality test between energy
consumption and economic growth. The statistically significant Wald F –
test indicates the aid of logE on the prediction of logY. This means the
energy consumption is a strongly exogenous regressor in determining the
economic growth. However, the similar F-test refuses the reversal effect
from income per capital to energy consumption per capita.
Table 5
Granger causality test – residual-based approach

While demonstrating the unidirectional Granger causality relation from
energy consumption to economic growth, it is still important to run the
VECM whose results will give more information on the good or bad effect
of energy consumption on the economy in both long-term and short-term.
Table 6 displays the results of VECM. Meanwhile, Table 7 presents several
Granger Causality test based on VECM results.
It is clear from Table 6 that energy consumption has negative effects on economic
growth in short-term. A 1% growth in energy consumption can cause a 1.1020%
decrease in the economic growth next year. The presence of such negative effect

also maintains in long-term. The cointegration equation is: − 2.2956×
+ 7.3861.
The energy consumption elasticity is of – 2.2956 and very significant, indicating the
greater magnitude of negative effect and the long-run causality. This evidence may
support the energy conservation policies as well as other environmental protection
policies.


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The statistically significant coefficient of error correction term (ECT)
points out the imbalance of economic growth in relation to energy
consumption. This coefficient equaling to – 0.4536 shows that on average,
the economic growth will converge to its equilibrium point about 45,4%
next year. Such response is relatively considerable.
As can be seen from Table 7, the small p-values indicate the validity of
three type of Granger causality relationship, including weak Granger
Causality, Granger causality relation in long-term and strong Granger
causality. In contrast, the great p-values of test relied on the equation of
logE shows the acceptance of null hypotheses or there is not enough
evidence to Granger causality running from economic growth (logY) to
energy consumption (logE). This results contrast to Phung (2011), Le
(2011) and Nguyen (2012) who indicated the opposite unidirectional
connection from economic growth to energy consumption.
Table 6
VECM
∆

−1

.36233***
∆

(6.21)

-.0291
∆

(-0.77)

Notes: Table 6 displays the results of VECM. The figure in parentheses () is the t-statistics.
The asterisk indicate the level of significance, including *** (1%), ** (5%), * (10%)

Table 7
Granger Causality results based on VECM
Dependent
variables

Type

Weak
Granger
Causality

Chi- squared
Statistics

9.62
(0.0221)

Excluded
Reject/accept
Null

∆

Reject


Hypothesis
Notes: Table 7 displays the Chi-squared statistics of three types of Granger Causality
based on VECM. The figure in parentheses () is the p-value.


144 | Policies and Sustainable Economic Development

5. Conclusion
This paper studies the causality relationship between the energy
consumption and economic growth in case of Vietnam from the year 1986
to 2013. This study finds the unidirectional Granger causality from energy
consumption to economic growth. Performing VECM shows that consuming
more energy can reduce the Vietnam rate of growth in both short – run
and long – run. More importantly, this result may suggest the government
to review the state of energy consumption, the dependence of the
economy on energy as well as reassess the trade-off between the
contribution of energy consumed industries to the whole economy and
erosion of sustainable growth. Therefore, the government should consider
the policies of conserve energy, the use of renewable resources, and
various green growth policies.

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