DSpace at VNU: Energy Cosumption and Economic Development: Granger Causality Analysis for Vietnam

ENERGY CONSUMPTION AND ECONOMIC
DEVELOPMENT: GRANGER CAUSALITY
ANALYSIS FOR VIETNAM
Loi, Nguyen Duy
Abstract
In the 1980s, after two oil crises, the studies on this relationship mainly
focused on the effects o f energy prices, particularly oil prices, on economic
activities. In recent years, the relationship between energy consumption and
economic growth was examined. Because energy is not only considered as an input
for the process o f productions in enterprises and the consumption o f households but
also reckoned as an indirect source o f many serious environmental problems,
particularly air pollution.
Since the adaptation o f reform policy, domestic and international trade were
liberalized, tariff and non- tariff barriers were also reduced and then alleviated
gradually, exports were promoted

by the government through many economic

policies and measures such as tax preferences, export- processing zones and
industrial zones, etc. As a result together with FD1, trade has been become one of
the sources to speed up the hieh rate o f economic growth during the period of
reform. The paper aims at investigating the causal relationship between energy
consumption, GDP and trade in Vietnam for the period o f reform (Doi moi),19862006. We apply the method o f Granger causality test to exam ine this relationship in
order to answer some questions such as: Is high economic growth due to the energy
- led growth or export - led growth? Does energy saving harm economic growth?
Does the rapid trade growth intensify the level o f energy consumption - a source to
cause environmental pollution? On the basis o f this empirical study, some policy
implications will be proposed for Vietnam ’s economic sustainable policy.
Keywords: Granger causality, energy consumption, GDP, trade
JEL: F14, F18, o i l

* The author is s e n i o r researcher, vice director, Institute o f W o rld E c o n o m ic and Politics. The
views expressed in this p aper are those o f the author an d d o no t necessarily reflect the views
o f IWEP. A n y errors b elo n g to the author.

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ENERGY CONSUMPTION AND ECONOMIC DEVELOPMENT.

1.1 Literature review
The literature has recently concentrated in the Environmental Kuznets curve
(EKC). which represents the relationship between environmental degradation and
income by an inverted U-shape. Many environmental degradation indicators, which
energy consumption is one of, have been investigated. Many studies on the
relationship between economic growth and energy consumption are dominated by
panel and time series analyses, employing Granger causality test for the relationship
between economic growth and energy consumption.
The finding o f the first study by Kraft and Kraft (1978) was the Granger
causality running from income to energy consumption in the u s, with the data of
the period o f 1947-1974. He drew policy implications that energy conservation
policy may not be affected negatively on the economic side. Later, empirical studies
have been investigated to include many developing countries with the aim of
seeking a relevant energy policy. Masih and Masih (1996), Glasure and Lee (1997)
and Asafu- Adjaye (2000) examined the causal relationship between energy
consumption and income for developing countries, with the use o f cointegration and
vector error correction (VEC). The results are mixed. Soytas and Sari (2003)
investigated the causal relationship for emerging market for the period o f 19501992. The result was also mixed, bidirectional causality for some countries, and no
cointegration for others. Oh and Lee (2004) estimated the causal links between
energy and in come in South Korea for the period o f 1970- 1999. The result shows
that the lonR run bidirectional causal relationship between energy and GDP and

short- run unidirectional causality running from energy to GDP exist.
Data on time series have been tested for investigation o f the causal
relationship between energy and economic development. Chieng- Chiang Lee and
Chun- Ping Chang (2005), who examined the causal relationship between energy
consumption and economic growth for the period o f 1954- 2003 in Taiwan, found
that energy acts as an engine of economic growth in the long run, unstable
cointegration relation between energy consumption and GDP, and their policy
implications implies that energy conservation policy may harm economic growth.
Mehrzad Zamini (2007) studied the causal relationship between GDP and value
added in Industry and Agriculture in the period o f 1967- 2003 in Iran, and found
that there is a long- run unidirectional relation from GDP to energy. Jia- Hai Yuan,
Jian- Gang Kang, Chang- Hong Zhao and Zhao- Guang Hu (2008) investigated this
relationship in China and discovered a short- run Granger causality running from
GDP to energy. They proposed enhancing energy efficiency, diversifying energy
resources and exploiting renewable energy. Wietze Lise and Kees Van Montfort
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(2007) examined the Granger causality link between energy consumption and GDP
in the period o f 1970- 2003 and figured out that there is a unidirectional causality
running from GDP to energy consumption, and the saving o f energy would harm
economic growth.
Ugur Soytas and Ramazan Sari (2007) used the cross- sector data to research
the causal links between energy and productions in Turkish manufacturing industry,
with the use o f multivariate framework and vector error correction, their finding
indicates the unidirectional causality from electricity consumption to manufacturing
value added; and their policy implications are to enhance energy saving
technologies and to increase energy efficiency. Many studies exploited cross­

country data to investigate the Granger causality relationship between energy and
economic development. Chieng- Chane Lee (2005) examined the c a u s a l
relationship between energy consumption and GDP for 18 developing countries in
the period o f 1975- 2001, and their findings indicate the long- and short- run
causality running from energy consumption to GDP, but not vice versa, and their
drawing lessons imply that energy conservation may harm economic growth in
developing countries. Other authors such as Stem (2000), Lee and Chang (2005),
Altinay and Karagol (2005), Richmond and Kaufmann (2006), also investigated this
causal relationship and the results are also mixed.
Brian M.

Francis,

Leo

Moseley,

and

Suanday

Osaretin lyare (2007)

investigated the causal relationship between energy consumption and projected
growth in some Caribbean countries and found the short- run bidirectional Granger
causality running from energy consumption to per capita GDP. They emphasized on
the increase o f efficiency in energy use, production and distribution o f energy, and
the application o f new technologies.
Theodoroi Zachariadis (2007) exploited cross- country data to investigate the
causal relationship between energy use and economic growth for G7 countries, and

the results are mixed, some countries having unidirectional Grander causality from
energy to economic growth, the other finding bidirectional causality. Nicholas
Apergis and James E. Payne (2009) studied the causal relationship between energy
consumption and economic growth in Central America for the period o f 19802004, application the method o f multivariate framework, panel cointegration and
vector error correction. They found both short- and long-run Granger causality from
energy consumption to economic growth, their policy implications are increasing
energy efficiency, reducing the long-run consequences o f the dependence on
imported energy. Yemane Wolde-Rufael (2009) investigate the causal link between
energy consumption and economic growth for African countries, and their fidings

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are conflicting because energy is no more than a contributing factor to output
growth, but not as important as capital and labor and energy consumption play a
minor role in economic growth in Africa.
Many studies, with the use o f time series or panel data, have been investigated
the causal relationship between energy consumption and economic development.
They took some similar steps as such nonstationary test, cointegration test and then
Granger causality test between enersy and economic series. The findings o f the
causal relationship are mixed, some finding unidirectional causality running from
energy to economic growth or vice versa, others finding bidirectional causality, and
the other finding the neutrality hypothesis. The results o f these studies are largely
depending on country and groups o f country considered, and time considered.
This paper aims at providing an estimation o f the Grander causality
relationship between energy consumption and economic development, consisting o f
per capita GDP and trade in Vietnam, which would contribute confidential
evidences to enriching the discussion on the causal relationship between energy

consumption and economic development. This paper has some sections as follows.
Section 1 shows the introduction and literature review. Section 2 presents data and
methodology. Section 3 discusses estimation results and section 4 concludes.

1.2. Overview of trade development in Vietnam

B efore reform or “D oi m oi”, V ietnam w as in long econom ic hardship. Since
the launching o f 4iDoi moi” in 1986, Vietnam has successfully transformed from a
centrally-planned economy to a market one, becoming one o f the most successful
transitional economies in the World; Inflation had been reduced successfully, from
three digit level (780% in 1986) to two dieit level (12% in 1995). Vietnam which is
regarded as an Asian tiger achieved high economic growth, with an average over
7% per annual from 1986 to 2006. Per capita Gross Domestic Product (GDP)
increased almost 10 times during the period o f 1986- 2006, from about 80 u s
dollars to 830 u s dollars. The percentage o f population living in poverty has
impressively reduced by half in one decade, from 58% in 1993 to 29% in 2002
(World Bank, various years). The overall adult literacy rate is very high, much
higher than the economies having the same level o f development, with a similar rate
between males and females, 95% and 91%, respectively. The reform, which
conducted the open up o f the Vietnam’s economy to the World, liberalization
foreign trade, attraction foreign direct investment (FDI) and integration to the
region and the World, contributed significantly to this prominent economic
performances. Figure 1 shows per capita GDP which has increased rapidly since the
economic reform in 1986.
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F igu re 1: Per capita GDP growth, 1986- 2006

G D P

Energy which is regarded as an engine for an economy plays a crucial role in
economic development. Energy is not only considered as an input for the process o f
productions in enterprises and the consumption o f households but also reckoned as
a source o f environmental abatement that may cause many serious environmental
problems. Energy supplies for economic activities, households and government and
vice versa, these actors demand for energy consumption. The development o f the
energy sector targets at meeting the demands for socio-economic development and
ensuring national energy security.
The energy sector in Vietnam has expanded drastically for the post- reform
period. In 2005, Vietnam produced 52.28 billion KWh o f electricity, 35 million ions
o f coal, 18.6 million tons o f crude oil, and 6.6 billion nr o f gas; the V ietnam ’s
export o f coal which achieved ỉ Í million tons in 2004 ranked as the first exporter o f
coal in the World, etc, (Ministry o f Industry, 2006). Vietnam also released national
policy for energy development in 2005.
Industry, transportation and households sectors consumed energy the most in
Vietnam. The industry sector consumed 1.5 million TOE (tons o f oil equivalent) in
1990 and 6.17 million TOE in 2003, with an average increase o f 11.4% per annual.
The sector o f transportation increased energy consumption from 1.64 million TOE
in 1990 to 5.63 million TOE in 2003, with an an average growth o f 10% per year;
the data for household sector was 0.46 million TOE in 1990 and 2.3 million TO E in
2003, with an average increase o f 13.2% per year. The data for the sector o f trade
and services was 0.35, 1.3 and 10.6 respectively; the data for agricultural sector was
0.26, 0.8 and 9.0% respectively. The trading energy consumption per capita
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ENERGY CONSUMPTION AND ECONOMIC DEVELOPMENT

increased from 63 kgOE (oil equivalent) in 1990 to 315 kgOE in 2004; the trading
electricity consumption per capita raised from 93 KWh in 1990 to 541 KWh in
2003 (Ministry o f industry). The energy consumption per capita is equal to one third
of the average level in the World. The Vietnam’s structure o f energy consumption
mainly concentrated in coal, oil and electricity where the percentage in total energy
consumption was 25.3% for coal, 54.8% for oil and 19.9% for electricity (Ministry
of Industry). Figure 2 shows the graph o f per capita energy consumption for
Vietnam.
F igure 2: Per capita energy consumption, 1986-2006

ENERGY

Since the adaptation o f reform policy, domestic and international trade were
liberalized, tariff and non- tariff barriers were also reduced and then alleviated
gradually, exports were promoted

by the government through many economic

policies and measures such as tax preferences, export- processing zones and
industrial zones, etc. As a result, trade has been increasing rapidly during the period
of 1986- 2006, except the period o f the Asian financial crisis. Trade per capita

us$ in 1986 to 1008 us$ in 2006; trade rose
almost 3 times, from 29.5 billion us$ in 1986 to 88 billion us$ in 2006.
increased more than 20 time, from 49

Consequently, the percentage o f trade in GDP rose from 21% in 1986 to 160% in
2006. Along with foreign direct investment (FDI), trade, particularly exports, has
been the main source for high economic growth during this period. The rapid

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growth o f trade and the high level o f openness, however, may result in dependence
in external markets and could be sensitive to any economic shocks from the outside.
F igure 3: Trade per capita, 1986- 2006

TRADE

2. Data and Methodology
2.1 D ata

The data, which was compiled from the World Development Indicators
(WDI), the World Bank, cover the time series o f per capita GDP (Gross Domestic
Product), per capita! energy consumption and per capita trade for the period 19862006. In order to reduce fluctuations o f the trade time series, we transform trade's
data into trade per capita by using the equation below. Variables are total primary
energy consumption per capita measured in kg o f oil equivalent; GDP per capita in
thousand real 2000 u s dollars from the WDI. Trade per capita in current u s dollars
obtaining from the WDI is estimated as follows:
Trade. = (IMt+EXt)/Pt
Where IM is imports, EX- export, P- numbers o f population at time t, and t is
time trend
The structure o f the total primary consumption consists o f consumptions of
petroleum, natural gas, coal, hydroelectric power, nuclear power and renewable
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ENERGY CONSUMPTION AND ECONOMIC DEVELOPMENT.

electric power (geothermal, solar, wind, wood and waste). We aim at examining the
Granger causality links between energy consumption and economic development
as a whole, energy consumption and trade, and GDP and trade, therefore we don’t
calculate the percentage o f coal, petroleum and gas, and hydroelectric, nuclear
and renewable electric power in total energy consumption. All variables are
logarithmic for the purpose o f avoiding fluctuations and smoothing in the time
series variables.

2.2 Methodology
In the paper, we try to examine the Granger causality links between energy,
GDP and trade in both bivariate and multivariate framework for the sake o f
avoidance spurious results. Firstly, we test whether each variable is nonstationary or
having unit root or not. Secondly, if the time series variables are nonstationary and
same order integration series, then we will test cointegration relations. Thirdly, if
cointegration relations exist, then we will test Granger causality among these time
series variables.
2.2.1 Unit root test
We take the test o f unit root in order to judge the stationarity o f time series.
There are several kinds o f methods1 for testing, however we just take two methods
out o f them as follows; Augmented Dickey- Fuller (ADF) and Phillips-Perron (PP)
tests. The test critical values (p- value) o f these methods are approximate for
different sample o f small size. In 1996, MacKinnon used annual data to estimate the
critical values for 20 observations2. In this sample, we have 21 observations for the
period o f 1986- 2006, more than the number o f observations used by MacKinnon3.
This is why we use A D F unit root tests. We calculate the following equation for the
ADF test
The equation for ADF test can be calculated with three different types:
equation with constant, equation with constant and deterministic trend, and equation

1. They are Dickey-Fuller (DF) test, ADF test, KPSS test, ERN test, pp test and NP test, of
which DF and ADF tests are the most common uses.
2. 20 observations are enough for test p-values available in the econometric software of Eview
5.0 and 6.0.
3. MacKinnon (1996) figured out the advantage to use annual data over quarterly or monthly
data under error terms. Annual data has been examined by us because of non- available
monthly or quarterly data for energy consumption and GDP.
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without constant. In this paper, we choose to run the test with constant and
deterministic trend. The ADF test, which bases on the construction a parametric
correction for higher-order correlation, may be incoưect if the series having a unit
root and a structural break. For solving these problems, we take the pp test which
produces a more robust estimation.
2.2.2 Cointegration test
Cointesxation links between variables are necessary for Granger causality test.
If two series o f nonstationary same order integration, which have a stationary linear
combination, calls a cointegration equation. In the paper, we explore the Johansen
(1988) cointegraton test within a vector autoregressive (VAR) framework for
examining the presence o f cointegration links between the variables. The Trace and
maximum-eigenvalue tests in the VAR model and vector error coưection (VEC)
show the level series o f energy, trade and GDP and the first-difference series
denergy, dtrade and dGDP respectively. For mitigating the spuriousness of the
regression and investigating the long-term relation, we apply a vector error correction
model (VEC)
2.2.3 Granger causality test
The presence o f the cointegration relation is necessary for Grander causality

test. We need to test whether a long- term balance relation between variables can
indicate Granger causality or not. We examine the causal relationships between the
three series variables in both bivariate and multivariate framework. Using the VEC
model to test Granger causalitv with the t- statistic test includes the first difference
series o f the three variables so that spuriousness may be avoided. We explore the
bivariate tests for the series variables with the F- statistic for investigating the short
run Granger causality between the variables. Multivariables o f denergy, dtrade and
dgdp in the VAR model estimate the interactions amone; their p-lag variables to test
the Granger causality relations. The VAR (p) model is as below:

Y t = n + A ,y t.i + A2yt-2 +■. ■+ Apyt.p + fit

(j)

Where yt is a (3x1) column vector o f the endogenous variables: denergy,
dtrade and dgdp, (.1 is a (3x1) constant vector, p is the order o f lags, each of
Ai A 2,„ Ap is a (3x3) coefficient matrix, each o f yt.|, yt_2,

yt-p is a (3x1) vector of

the lag endogenous variables, and £t is a (3x1) vector o f the random eư or term. The
lag length p in level series VAR is chosen by the minimum AIC with maximum lag
equals to 3.
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ENERGY CONSUMPTION AND ECONOMIC DEVELOPMENT.

3. Etimation results

3.1 Unit root test
We first take the ADF and p p tests o f level series for each variable o f energy,
trade and gdp. Table 1 shows the test’ results that energy, trade and gdp are
nonstationary because the test statistics do not exceed the critical value. Table 2
presents the ADF and p p tests o f first difference that the series variables of first
difference have first order integration. Therefore, cointegration relations exist among
the three variables o f energy, trade and gdp.

Table 1: A DF and p p unit root tests: level series
ADF

pp

Lags

Test statistic

Prob.

Test statistic

Prob.

Energy

0

-1.0305

0.9162

-1.0305

0.9162

T rade

1

-2.7623

0.2246

-1.8839

0.6269

GDP

1

-3.6336

0.0511

-4.0880

0.0213

Table 2: ADF and pp unit root tests: first difference

ADF

pp

Lags

Test
statistic

Prob.

Test statistic

Prob.

Energy

0

-4.8183

0.0053

-4.8190

0.0053

Trade

0

-3.1660

0.1178

-3.0623

0.1402

GDP

3

-2.9436

0.1699

-2.2459

0.4424

linear

combination

3.2 Cointegration test
If the

cointegration

relations

exist within

the

of

nonstationary series, they must have Granger causality. Tables from 3 to 6 show the
results o f Johansen cointegration test1. For the bivariate cointegration test, the trace
and maximum-eigenvalue tests for three pairs o f variables: energy-gdp, energy1. Johansen 1991, Greene 2003
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trade and trade-gdp indicate that there is only one cointegration equation in the pairs
o f GDP- trade at the 5% level (table 5). Table 5 shows that one cointegration
equation exists for the pair o f trade-gdp because the test statistic is higher than the
critical value, so we reject the null hypothesis.

Table 3: Johansen cointegration test for a pair o f Energy-G DP
Eigenvalue

Trace statistic

5% critical
value

Prob.

r=0*

0.5837

24.8572

25.8721

0.0665

r
0.3067

7.3266

12.5179

0.3117

Max-Eigen
statistic

5% critical
value

Prob

r=0*

17.5305

19.3870

0.0912

r
7.3266

12.5179

0.3117

Energy-GDP
Cointegration
rank (r)

1. The cointegration equation includes linear deterministic trend
2. Trace and Max-Eigen statistic tests indicate no cointegration equation at the
5% level
* Denotes rejection o f the hypothesis at the 5% level

Table 4: Johansen cointegration test for a pair o f Energy-trade
Einergy- trade

Eigenvalue

Cointegration rank

T race

0,05

Statistic

Critical Value

Prob.**

(r)
r=0

0.383043

17.47835

25.87211

0.3801

r
0.323594

7.819233

12.51798

0.2668

Max- Eigen
Statistic

Critical Value

Prob.**

r=0

9.659113

19.38704

0.6553

r
7.819233

12.51798

0.2668

Max-eigenvalue test indicates no cointegration at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
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ENERGY CONSUMPTION AND ECONOMIC DEVELOPMENT.

Table 5: Johansen cointegration test for a pair of GDP-trade
Trace

0.05

Statistic

Critical Value

*
o
II

0.655281

33.64360

25.87211

0.0044

r
0.415531

11.27809

12.51798

0.0798

Max-Eigen

0.05

Statistic

Critical Value

Prob.**

r=0*

22.36551

19.38704

0.0179

r
11.27809

12.51798

0.0798

GDP- trade

Eigenvalue

Cointegration rank
(r)

Prob.**

M ax-eigenvalue and Trace tests indicate 1 cointegrating equation at the
0.05 level
* denotes rejection o f the hypothesis at the 0.05 level

Table 6: Johansen cointegration test: m utilvariate model
Prob.**

Trace

0.05

Statistic

Critical Value

0.759599

49.55804

42.91525

0.0095

r
0.460088

21.04909

25.87211

0.1774

r<2

0.353450

8.722090

12.51798

0.1982

Max-Eigen

0.05

Statistic

Critical Value

Prob.**

r=0*

28.50895

25.82321

0.0216

r
12.32700

19.38704

0.3853

r<2

8.722090

12.51798

0.1982

Cointegration rank
(r)

Eigenvalue

r=0*

M ax-eigenvalue and Trace tests indicate 1 cointegrating equation at the
0.05 level
* denotes rejection o f the hypothesis at the 0.05 level

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For the multivariate cointeeration test, table 6 shows the results o f the tests
that the Trace statistic test indicates one cointegration equations at the 5% level;
however, M ax-Eisen statistic test also indicates one cointegration at the 5% level,
on the one hand. The test shows that cointearation is not stable and may be affected
by some economic events.

3.3 The VEC model and Granger causality test
According to the VAR (p) equation (j), we first estimate the optimal lag length
in the level series VAR. Table 7 shows the optimal las length by different criteria.
The optimum las, is 4 for AIC, and we don’t have to add an extra lag in a model
with limited number o f observations. We based on the equations (d), (e), and (f) for
calculating the optimum lag length.

Table 7: VAR lag order selection criteria
Lag

LR

FPE

AIC

SC

0

NA

3.07e-06

-4.180373

-4.031013

1

129.8889*

2.29e-09

-11.39843

-10.80099*

2

10.45922

2.72e-09

-11.30298

-10.25746

3->

16.14080

1.63e-09*

-12.01706

-10.52346

4

8.367582

1.91e-09

-12.31243*

-10.37075

* Indicates lag order selected by the criterion
LR: sequential modified LR test statistic (each test at 5% level)
FPE: Final prediction error
AIC; Akaike information criterion
SC: Schwarz information criterion

We apply the vector error correction model (VEC) to test the Granger
causality, with the aim o f avoiding the spuriousness in the series and investigating
the long- term relation between variables. The results in table 8 base on the firstdifference series. The optimal lag length for the three endogenous variables is
selected by the minimum AIC method. Table 9 shows the critical values for tests.
We found a strong long-term balanced bidirectional Granger causality
between GDP and trade as the t-statistic indicates the significant in long- term
causal effect. We also found a weak unidirectional Granger causality link from trade
to energy; and Granger causality link running from energy to GDP. The
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ENERGY CONSUMPTION AND ECONOMIC DEVELOPMENT...

bidirectional Granger causality between GDP and trade indicates that trade,
particularly export, is a driving force for rapid economic growth in Vietnam, and the
higher level of economic growth could increases trade volumes. This is consistent
with the export- led growth hypothesis which is prevailing in East Asia. The
unidirectional Granger causality running from trade to energy states that an increase
in trade may cause a rise in the level o f energy consumption. This is consistent with
the pollution haven hypothesis and industrial relocation hypothesis. The
unidirectional Granger causality running from energy to GDP implies that energy
leads economic growth in the lone run. However, these unidirectional Granger
causality links are weak. We then investigate the short- run causality relations
among series variables in the pair Grander causality test (table 10).

Table 8: Vector Error Correction M odel (VEC) G ranger Causality tests
E rro r Correction:
CointEql

D(ENERGY(-1))

D(ENERGY)

D(GDP)

D(TRADE)

-0.064885

0.006748

-0.221540

(0.02804)

(0.01638)

(0.10184)

[-2.31367]

[0.41186]

1-2.17542]

-0.433024

0.030129

-1.675716

(0.26207)

(0.15311)

(0.95167)

[-1.65231]

[0.19678]

[-1.76082]
Trade —^Energy

D(ENERGY(-2))

D(GDP(-1))

D(GDP(-2))

0.251991

0.170778

1.862625

(0.29914)

(0.17477)

(1.08629)

[ 0.84237]

[ 0.97717]

[ 1.71466]

0.893535

0.585243

2.165886

(0.40483)

(0.23651)

(1.47006)

[ 2.20720]

[ 2.47449]

[ 1.47333]

-0.143301

-0.036232

-0.562240

(0.10888)

(0.06361)

(0.39540)

[-1.31608]

[-0.56957]

[-1.42197]

Energy —>GDP
D(TRADE(-1))

Trade -+GDP

0.059032

-0.014127

0.458997

(0.06146)

(0.03591)

(0.22319)

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VIỆT NAM HỌC - KỶ YÉƯ HỘI THẢO QUÓC TÉ LẦN THÚ TƯ

D(TRADE(-2))

[ 0.96046]

[-0.39341]

[ 2.05654]

0.123850

-0.014612

0.003347

(0.08006)

(0.04677)

(0.29073)

[ 1.54695]

[-0.31240]

[ 0.01151]

GDP —>trade
c

-0 .0 2 5 3 8 7

0.024291

0.025133

(0.01740)

(0.01017)

(0.06319)

[-1.45902]

[2.38951]

[ 0.39777]

Standard errors in ( ) & t-statistics in [ ]
—>: mean Granger causality relation

Table 9: Granger causality W ald Tests

Wald test of coefficients
causality direction (1)
Wald test of coefficients

causality direction (2)

D(ENERGY)

D(GDP)

D(TRAl)E)

7.973391

8.553953

9.324276

[ 0.046565]

[ 0.035849]

[ 0.025276]

5.400254

1.867456

6.854937

[ 0.144728]

[ 0.600367]

[ 0.076668]

Numbers in [ ] are p-values
Table 10 indicates the results o f the pairwise Granger causality test implying
the short- run relations between variables. Three pairwises o f Granger causality
tests show that the tests statistics exceed ihe critical values, therefore we reject the
null hypothesis. On the basis o f the cointegration test, a strong unidirectional
Granger causality running from GDP to trade was found. This means that the high
level o f economic growth increases volumes in trade. This bidirectional causality
relation exists in the short-run as the F-statistic indicates. We also found a weak
unidirectional Granger causality running from GDP to Energy, and another weak
unidirectional causal relation running from trade to energy. The Granger causality
between energy and GDP is not clear in the short run in Vietnam.
The results o f the Granger causality test show that there is a Granger causality
running from GDP to trade; the Granger causality running from GDP to energy.
This Granger causality relation

is inconsistent with the export-led growth

hypothesis, the increase in GDP growth leads to an increase in trade or more
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ENERGY CONSUMPTION AND ECONOMIC DEVELOPMENT.

openness to trade, if we consider the trade as the openness index. The Granger
causality running from GDP to energy indicates that an increase in GDP leads to an
increase in the level o f energy consumption.

Table 10: Pairwise Granger Causality Tests

F-Statistic

Probability

GDP does not Granger Cause ENERGY

2.86535

0.08832

ENERGY does not Granger Cause GDP

0.46816

0.63500

TRADE does not Granger Cause ENERGY

2.08738

0.15857

ENERGY does not Granger Cause TRADE

0.09108

0.91344

TRADE does not Granger Cause GDP

0.14353

0.86739

GDP does not Granger Cause TRADE

7.17933

0.00595

Null Hypothesis:

As the economy rises, it demands for more energy consumption. Therefore,
the efficiency in enerey uses is paid attention to with the aim o f lower energy
consumption

for

a given

level

o f economic

growth.

Vietnam

may

have

environmental policies in general and energy- use policies in particular aiming at
decreasing energy intensity, increasing the efficiency o f energy consumption, and
developing a market for emission trading. The country also needs to invest in
research and development (R&D) for the creation o f new technologies that makes
the alternative energy

sources possible,

increases the efficiency o f energy

consumption, and thus reduces environmental pressures.

3.4 Variance decomposition o f variables
We decompose the variance for the sake o f separation the variation in an
endogenous variable into the component shocks to the VAR. Therefore, the
variance decomposition provides information about the relative importance o f each
random innovation in affecting the variables in the VAR. Table 11 shows separate
variance decompositions for each endogenous variable. The S.E column contains
the forecast error o f the variable at the given forecast horizon. The source o f this
forecast error is the variation in the current and future values o f the innovations to
each endogenous variable in the VAR. The other columns o f endogenous variables
eive the percentage o f the forecast variance due to each innovation, with each row
adding up to 100.
In this part, we just measure the variance decomposition o f endogenous
variable in the multivariate framework because we can find a similar trend in the
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VIỆT NAM HỌC - KỶ YẾU HỘI THẢO QUỐC TÉ LẦN THỨ T ư

bivariate framework. Table 11 shows the results o f variance decomposition of
variables. Firstly, we look at the variance decomposition o f energy variable. At the
period o f 10th for example, the percentage o f the forecast variance o f energy is 37%
by its own innovations or shocks, 55% by innovations of GDP and 8% by
innovations o f trade. Secondly, the variance decomposition o f GDP presents that at
the period o f 9th, the percentage o f the forecast variance o f GDP is almost 85%
because o f its own innovations or shocks, 15% by energy’s innovations and 0.25%
by trade's innovations. Lastly, for the variance decomposition o f trade, the forecast
variance for trade is 14% by its innovations or shocks, 8.6% by energy’s
innovations and 77% by G D P ’s innovation at the 8th period.
The variance decomposition indicates that the relative importance o f each
random innovation affects variables in the VAR. The large percentage o f variance
decomposition o f one variable is explained by the other two variable’s innovations.
This is consistent with the findings of the long- and short- term bidirectional and
unidirectional Granger causality relations between energy, GDP and trade.

Table 11: Variance decom position of variables in the m ultivariate fram ework
Variance Decomposition of ENERGY
Period

S.E.

ENERGY

GDP

TRADE

1

0.025438

100.0000

0.000000

0.000000

2

0.028735

98.29503

0.848681

0.856287

i

0.032087

91.73976

4.026435

4.233809

4

0.035542

82.48570

5.951244

11.56306

5

0.037755

75.83506

8.486757

15.67818

6

0.039489

69.63570

14.11880

16.24550

7

0.042098

61.36983

23.92528

14.70489

8

0.046146

51.89036

35.78685

12.32279

9

0.051388

43.44832

46.57940

9.972288

10

0.057400

37.08524

54.90037

8.014391

Variance Decomposition of GDP
Period

S.E.

ENERGY

GDP

TRADE

1

0.015905

0.464559

99.53544

0.000000

2

0.028308

1.142712

98.77462

0.082672

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E N E R G Y C O N S U M P T IO N AN D E C O N O M IC D E V E L O P M E N T .

1 .

3

0.039774

2.637153

97.03298

0.329864

4

0.050581

4.507999

94.96370

0.528300

5

0.061137

6.700169

92.79611

0.503718

6

0.071853

9.019900

90.59935

0.380745

7

0.082880

11.21684

88.49281

0.290348

8

0.094110

13.15543

86.58956

0.255014

9

0.105336

14.82755

84.91919

0.253261

10

0.116383

16.28160

83.44964

0.268761

Variance Decomposition of TRADE
Period

S.E.

ENERGY

GDP

TRADE

1

0.098413

0.004808

30.02834

69.96685

2

0.158120

2.035476

54.40499

43.55953

j->

0.193973

1.712695

69.34029

28.94701

4

0.218622

1.564356

73.32725

25.10839

5

0.237040

2.109511

74.68720

23.20329

6

0.255406

3.774076

75.91757

20.30836

7

0.278650

6.200470

76.73336

17.06617

8

0.306772

8.601558

77.22053

14.17792

9

0.337077

10.62111

77.54512

11.83377

10

0.367406

12.31897

77.66768

10.01335

3.5 Generalized Impulse response

In order to trace the effects o f a shock to one endogenous variable on to the
other variables in the VAR, we apply impulse response functions. A shock to a
variable not only directly affects this variable but is also transmitted to all o f the
other endogenous variables through the dynamic (lag) structure o f the VAR. An
impulse response function traces the effect o f a one-time shock to one o f the
innovations on current and

future values o f the endogenous variables.

A

decomposition method in the impulse response function is developed by Pesaran
and Shin (1998) and called generalized impulses. Pesaran and Shin construct an
orthogonal set o f innovations that does not depend on the VAR ordering. The
generalized impulse responses, therefore, measure a response from an innovation to
a variable.
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In figure 4, graphs 1, 4 and 7 indicate the response of each endogenous
Variable to a shock or an innovation in Energy. A shock in Energy may bring about
negative effects to trade and GDP. Graphs 2, 5 and 8 show what extent each
endogenous variable response to a shock in GDP. It seems that a change in GDP has
little effect on trade, and a positive effect on energy. Graphs 3, 6 and 9 indicate that
a change o f trade may cause a little effect in energy consumption and GDP.

Figure 4: The graphs of Impulse responses
Response to Generalized One S.D. Innovations ± 2 S.E.

Response of ENERGY to ENERGY

Response of ENERGY to GDP

Response of ENERGY to TRADE

Response of GDP to ENERGY

Response of GDP to GDP

Response of GDP to TRADE

Response of TRADE to ENERGY

Response

of

TRADE to GDP

Response of TRADE to TRADE

4. Conclusion and policy implications
This paper employs time series data o f Vietnam for the period o f 1986- 2006
to estimate the Granger causality relationship between energy consumption and
economic development. In many previous studies, data for developed countries is
available for a period o f sufficient long time to ensure a robust analysis o f times

458

E N E R G Y C O N S U M P T IO N AN D E C O N O M IC D E V E L O P M E N T .

series. However, data for developing country like Vietnam is not available for a
period of Ions time for test. We estimated the critical values for ADF and pp tesis
for the sample o f small size. Therefore, the critical values should be considered BS
the approximations.
In this study, we applied both bivariate and multivariate frameworks for the
cointegration test. The vector error correction model has been conducted to test longrun Grander causality. The results indicate the existence o f Granger causality running
from GDP to trade, and from GDP to energy. The GDP- trade Granger causality
indicates that the GDP growth lead to increase in and more openness to trade.
For the short run, we found a strong unidirectional Granger causality runnirg
from GDP to trade; the unidirectional Granger causality running from GDP 10
Energy, and another weak unidirectional causal relation running from trade Í0
energy. The Granger causality between energy and GDP, between trade and GDP
are not clear in the short run in Vietnam.
The results o f the studies on Granger causality between energy and e c o n o m ic
dev elopment vary, depending on countries and times o f studies. In this study, v/e
found weak evidence to support the important role o f energy for economic g ro w th .
Energy just acts as a factor o f input for economic development in Vietnam. Higher
levels of economic development may or may not induce more energy c o n s u m p tio n .
However, the long- run trend in energy consumption plays an important role
because it relates to environment protection and economic development.
On the basis o f this study, some policy implications could be drawn as such:
(i) the government should propose and implement a series o f comprehensive
policies to aim at increasing efficiency in consumption, distribution and p r o d u c tio n
o f energy, and developing research & development measures to adopt new
technologies; (ii) guarantee energy supply by executing corresponding measures to
enhancing energy efficiency to save energy, diversifying energy sources, and
developing alternative and renewable energy, and supplying adequate electricity;

(iii) Cope with rising oil prices and energy crisis, energy- related strategies should
be based on sound economic analysis.
As the eoal set by Kyoto Protocol to cut down emission for reducing global
warming, energy policies for many countries, especially a developing country like
Vietnam need to be changed in accordance with this Protocol. Therefore, in the
long- run, the country should transform development pattern for reducing the longrun environment consequences and ensuring sustainable development; cutting
reliance on resource- and energy- dependent industries./

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