Examining the relationship between electricity consumption, financial development and economic growth in Asean countries: Evidence from a Bayesian analysis
International Journal of Energy Economics and
Policy
ISSN: 2146-4553
International Journal of Energy Economics and Policy, 2021, 11(2), 49-56.
Examining the Relationship between Electricity Consumption,
Financial Development and Economic Growth in ASEAN
Countries: Evidence from a Bayesian Analysis
Canh Chi Hoang
Faculty of Business Administration, University of Finance - Marketing, Ho Chi Minh City, Vietnam.
*Email: [email protected]
Received: 21 September 2020
Accepted: 20 December 2020
ABSTRACT
The literature has suggested that financial development and electricity consumption are key determinants of economic growth. However, existing studies
usually was applied the frequentist inference, which is an outdated estimator. By applying the Bayesian approach via the Metropolis-Hasting and Gibbs
samplers as the MCMC methods, the study aims to re-examine the impact of financial development and electricity consumption on economic growth
in ASEAN+6 countries from 1980 to 2016. The obtained outcome shows that the impact of both financial development and electricity consumption
is strong and positive on economic growth. There is a uni-directional causality running from economic growth to energy consumption, supported the
Conversation hypothesis. Based on the empirical result, several policy implications are suggested for emerging countries,ASEAN+6 nations, in particular.
Keywords: Financial Development, Energy Consumption, Economic Growth, Bayesian, ASEAN Countries
JEL Classifications: F43, O13, O47, Q42, Q43
leads to a fixed interest rate, decreasing banking activities,
increasing the real exchange rate, reducing export, discourage
1. INTRODUCTION
the development of capital markets, and hurts economic growth.
Physical capital accumulation is a crucial factor contributing
to economic growth (Romer, 1990; Stiglitz, 2000). Following
the pioneering of Schumpeter (1912), majority of the economic
researcher is persuaded that financial development allows foreign
direct investment flows, encourages the investment of enterprises,
reduces costs of loans, boost household consumption, and
increase banking activities, less financial risks. The pressure of
improving income per capita leads to pumping more money into
the financial system by Government. The consequence of more
money is a high-inflation situation, and the financial crisis of 2008
provided practical evidence to re-examine the benefit of financial
development to growth. Now, the notion “more money, more
oversight” has been supported by many governments worldwide.
However, a financial reduction is not good for economic growth.
McKinnon (1974), Shaw (1974) argues that financial reduction
Understanding and quantifying the relationship between energy
consumption and economic growth is one of the hot topics for
economics researchers and administrators. Energy is used as
an input in the production, transportation, and consumption of
nearly all goods or services (Ha and Ngoc, 2020; Long et al.,
2018; Stern, 2000). The linkage between energy consumption and
economic growth has been well-studied by several researchers.
Nevertheless, the conclusion of existing studies has failed to
provide a consistent answer. For example, Tang (2009) investigates
the connection between electricity consumption, income, foreign
direct investment, and population in Malaysia from 1970 to
2005. The obtained results by the ARDL approach shows that
economic growth has a positive impact on electricity consumption,
This Journal is licensed under a Creative Commons Attribution 4.0 International License
49
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
Hoang: Examining the Relationship between Electricity Consumption, Financial Development and Economic Growth in Asean Countries: Evidence from a Bayesian Analysis
supported the Conversation hypothesis. However, Yoo (2005)
used the cointegration and vector error correction model to
analyzes the short- and long-run causality between electricity
consumption and economic growth in Korea from 1970 to 2002.
The empirical outcome reveals that there is bi-directional causality
between economic growth and electricity, supported the Feedback
hypothesis. Even using the panel data, the results of Chen et al.
(2007) are mixed in ten newly industrializing and developing
ASIAN countries. Accordingly, there is a uni-directional short-
run causality running from economic growth to electricity
consumption and a bi-directional long-run causality between
electricity consumption and economic growth if the panel data
procedure is implemented.
because it will reduce the return rate of financial assets. Besides,
it encourages people/enterprises to invest in non-financial assets
(e.g., gold, real estate) and generates back financial markets.
Indeed, Bretschger and Steger (2004) showed two channels that
financial development affects on economic growth, including (i)
the scale-effect channel; (ii) the factor-reallocation effect channel.
Accordingly, they confirmed that the efficiency banking system
is the vital factor for economic development due to its role in
mobilizing and allocating saving and the funding of economic
activity investment.
Regarding empirical studies, King and Levine (1993) found that
the development of the financial sector is robustly related to per
capita GDPgrowth, and it positively enhances the accumulation of
physical capital. Likewise, Ben Jedidia et al. (2014) used theARDL
approach to analyze the connection between financial development
and economic growth in Tunisia from 1973 to 2008. The obtained
result shows that domestic credit to the private sector positively
affects economic growth, and financial development is a driver
of long term economic growth. The positive impact of financial
development on economic growth is supported by the study of
Liang and Teng (2006), Komal andAbbas (2015), Salahuddin and
Gow (2016). Another study by Alsamara et al. (2018) examines
the impact of financial development and trade openness on the
real GDP per capita in Turkey during the period 1960-2014. The
empirical result from the ARDL approach with structural break
reveals that both the trade openness and financial development
have a positive impact on per capita real GDP. Accordingly, a 1%
increase in money supply to GDPratio leads to a 0.36% increase in
real GDPper capita. Using the non-linear framework, Masten et al.
(2008), Law and Singh (2014) found that the impact of financial
development was depended on the critical threshold, exceed this
critical threshold, the more money is not good for economic growth.
The above mentioned previous studies indicate that the linkage
between financial development, electricity consumption, and
economic growth is an interesting topic, which is still the subject
of an ongoing debate (Omri, 2014; Tiba and Omri, 2017). The main
aim of this work is to inspect the impact of financial development,
electricity consumption, and economic growth in ASEAN+6,
including Indonesia, Malaysia, Philippines, Singapore, Thailand,
Vietnam during the period 1980-2016. Our study is different from
several previous studies in multiple points, as follows: First, to
the best of our knowledge, the available studies analyzed in the
case of ASEAN countries have drawn little attention. Second,
most previous studies have been conducted in a linear framework
and used the frequentist inference. In the study, we employed the
Bayesian inference approach through the integrated Markov chain
Monte-Carlo sampler to provide probabilistic interpretations of
model uncertainty and varying effects of financial development
and electricity consumption on economic growth. The advantage of
Bayesian inference compared to frequentist inference is presented
in Section 3. To our knowledge, the obtained result could be
enrichment in existing economic literature and for theASEAN+6
countries in particular.
Goldsmith (1969) was the first to work a positive correlation
between economic growth and financial development in his 35
countries sample. Abid et al. (2016) used a multivariate vector
autoregressive model to inspect the linkage between financial
development (measured by the stock market return) and economic
growth in ten MENA(the Middle East and NorthAfrica) countries.
The result provides evidence that the GDP growth response to
Qatar GDPgrowth shock is statistically significant for all countries,
while the stock market response to Morocco stock market shock is
insignificant in Qatar, SaudiArabia, and UAE. The positive impact
of financial development on economic growth is confirmed by
the study of Ibrahim and Alagidede (2018). Applying the system
GMM method, Ibrahim andAlagidede (2018) found that financial
development supports economic growth. The extent of finance
helps growth depends crucially on the simultaneous growth of real
and financial sectors in 29 sub-SaharanAfrican countries over the
period 1980-2014. Greenwood and Jovanovic (1990) explained
that individuals or enterprises have many opportunities to invest
in several projects. The developed financial system, as mentioned
by Schumpeter (1912) must mobilize and allocate saving capital
flows into projects, which have high productivity or output. That
means stock market allocates these capital flows into priority
sectors, which have the highest return rate, and generates several
optimal stock lists. Greenwood and Jovanovic (1990) pointed out
The rest of the study is organized as follows: Section 2 focuses on
present the literature and the existing studies. Section 3 describes
the model, data, and methodology. The obtained outputs are
shown in section 4, while section 5 provides a conclusion and
policy implication.
2. LITERATURE REVIEW
2.1. Financial Development and Economic Growth
The role of the financial development and financial system is
a vital one for any economy. The pioneering of Schumpeter
(1912) found that a developed financial system boost investment
activities, increase transparency between lenders and borrowers,
reduces costs of credits, and leads to beneficial for economic
growth. Schumpeter (1912) stated that most of the enterprises need
credit in order to buy material, machinery, and paying salaries.
In simple capital markets, the bank becomes the producer of this
commodity. Thus, the banking system plays the most critical
channel, where intermediating financial activities are supported
and enhance growth. Consistent with this view, McKinnon (1974)
and Shaw (1974) devote to financial liberalization. They pointed
out that the Government should not strictly control interest rates
50
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
Hoang: Examining the Relationship between Electricity Consumption, Financial Development and Economic Growth in Asean Countries: Evidence from a Bayesian Analysis
that if individual or enterprises select an optimal stock list, which
leads to beneficial for economic growth.
uni-directional causality in the short-run, a bi-directional causality
between per capita electricity consumption and per capita GDP
in the long-run. Another study by Acaravci (2010) explores the
short- and long-run causality between electricity consumption
and economic growth in Turkey from 1968 to 2005. The VECM
Granger causality shows that there is a uni-directional causality
running from electricity consumption to economic growth.
However, some administrators and economics researchers are not
advocating for financial development. Edwards (2001), Okada
(2013) argues that the overload of financial development leads to
an increase in inflation, which harms economic growth in the long-
run. Emerging markets have a low financial institution, and should
be highly susceptible to the volatility of the global financial market,
which is especially severe for countries with an open capital account.
Demetriades and Hussein (1996) used the VAR model to examines
the influence of financial development on economic growth in 16
countries. The obtained result reveals that the money supply is the
danger of economic growth. Likewise, Rousseau and Vuthipadadorn
(2005) found that financial development has a dampening effect on
investment and growth in tenASIAN countries. Similarly, Ono (2017)
found that there is no causality from money supply to economic
growth in the case of Russia during the period of 2009-2014.
The conversation hypothesis was found by the pioneering study of
Kraft and Kraft (1978). They examine the impact of economic growth
on electricity consumption in the United States over the period 1947-
1974. The Granger causality provides that there is a uni-directional
causality running from economic growth to electricity consumption.
Likewise, Balcilar et al. (2019) used the Maki cointegration to inspect
the linkage between electricity consumption, real gross domestic
product, and carbon dioxide emissions in Pakistan.Auni-directional
causality running from economic growth to electricity consumption
was found by the Toda-Yamamoto causality test, which supported
the Conversation hypothesis.
2.2. Energy Consumption and Economic Growth
In developing countries, administrators and economic researchers
have advocated analysis of the linkage between energy
consumption and economic growth with the expectation that
energy production and energy consumption are key determinants
of economic growth. In fact, energy is a necessary input of
economic activities, such as transportation, production (Abosedra
et al., 2009; Chandran et al., 2010; Golam and Nazrul, 2011;
Ngoc, 2019; Zhang et al., 2017). The energy-growth nexus has
been well-studied in the energy economics literature. However,
the available studies have failed to provide a consistent answer
(Ha and Ngoc, 2020; Tiba and Omri, 2017), and it is still the
subject of an ongoing policy debate. There are four hypotheses
found by existing works about the relationship between energy
consumption and economic growth, including the “Conversation,”
the “Growth,” the “Feedback” and the “Neutrality” hypothesis.
Some studies found the neutrality hypothesis. Ghosh (2009) does
not found the interaction between electricity supply, employment,
and real GDPfor India. Similarly, Payne (2009) applied the Toda-
Yamamoto causality tests. The obtained result shows that the
absence of Granger-causality between renewable or non-renewable
energy consumption and real GDP in the case of the United States
from 1949 to 2006, which supports the neutrality hypothesis.
The impact of financial development and energy consumption on
economic growth has been studied by several previous works, such
as Kahouli (2017), Rafindadi and Ozturk (2016), Burakov Burakov
and Freidin (2017), and Mahi et al. (2019). However, the conclusion
of these studies is not consistent, even ambiguous. To explain the
different above-mentioned conclusion,Apergis and Payne (2010) point
out that the interaction between energy consumption and economic
growth nexus is depended on the level of national development. In
poor countries, economic activities based on natural extraction (e.g.,
planting, fishing). Thus, the demand for energy is low, and the energy
consumption does not enhance economic growth. However, it is not
true in developing or developed countries. The pressure improving
income per capita leads to many projects or policies were issued by
the Government, which requires more energy as a driving force of
production of goods or services. So, energy production and energy
consumption is an essential factor for development.
Supporting the Feedback hypothesis, based on the Cobb-Douglas
production function, Hamdi et al. (2014) inspect the linkages
between electricity consumption, foreign direct investment, capital,
and economic growth from 1980Q1 to 2010Q4 for the Kingdom
of Bahrain. The empirical result from the ARDL bounds testing
and VECM causality shows that there exist a positive impact
and bi-directional causality between electricity consumption
and economic growth. Likewise, Ibrahiem (2015) analyzes the
relationship between renewable electricity consumption, foreign
direct investment, and economic growth in Egypt from 1980 to
2011. The existence of cointegration among the examined variable
is found by theARDL bounds testing, and the Granger causal test
identifies the bi-directional causality between economic growth
and renewable electricity consumption. The positive influence of
electricity consumption on economic growth is confirmed by the
study of Tang (2009) for Malaysia, Long et al. (2018) for Vietnam,
or Zhang et al. (2017) for China’s economy.
Of course, the above-mentioned studies do not adequately
represent all previous studies on financial development-energy
consumption-growth nexus. Nevertheless, this review showed
that most of the available studies use frequentist inference. No
studies apply Bayesian inference. It is a methodology gap, which
this work want to address.
3. RESEARCH MODEL AND
METHODOLOGY
About the growth hypothesis, Golam and Nazrul (2011) discover
the connection between per capita electricity consumption and
per capita GDP in the case of Bangladesh from 1971 to 2008. The
obtained outcome reveals mixed results. Accordingly, there is a
The main aim of this study is to investigate the impact of financial
development and electricity consumption on economic growth
51
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
Hoang: Examining the Relationship between Electricity Consumption, Financial Development and Economic Growth in Asean Countries: Evidence from a Bayesian Analysis
in ASEAN+6 countries from 1980 to 2016, so the model is
preliminarily set as follows:
p(y M j ) p(M j )
p(M j y) =
(4)
p(y)
LnGDP ꢀ ꢂ0 ꢁꢃi ꢁ ꢂ1.LnECi,t ꢁ ꢂ2.LnFIi,t
i,t
Since it is challenging to calculate p(y), a popular practice is to
compare two models, for example, Mj and Mk via posterior odds
ratio:
ꢁꢂ3.Ln(EC.FI)i,t ꢁ ꢂ4.UBi,t ꢁ ei,t
(1)
where, i is the country (1,…, N: including Indonesia, Malaysia,
Philippines, Singapore, Thailand, Vietnam, respectively), t is
time (1,…, T: from 1980 to 2016). υi are random intercepts,
e(i,t) is an error. In Eq.1, LnFI variables is the logarithm of
financial development (measured by M2 money supply, units:
million U.S. dollar), LnEC variable is the logarithm of the
electricity consumption per capita, unit: kWh/year), Ln(EC.FI)
is the interaction variable (= LnEC*LnFI), and UB is the rate
of urbanization (unit: percentage), which plays as the control
variable in the model. Annual data is collected from the IMF and
the World Bank. The dependent variable is LnGDP per capita
(at the fixed price 2010, unit: U.S. dollar). This work used the
Bayesian inference, which has several advantages outperforms
the Frequentist inference, as follows:
p(y M j ) p(M j )
p(M j y)
POj,k
=
=
(5)
p(Mk y) p(y Mk ) p(Mk )
If all models are equally plausible, that is p(Mj)=1/r, the posterior
odds ratio is transformed into the Bayes factor, which is simply
ratios of marginal likelihoods (Jeffreys, 1962).
p(y M j )
BFj,k
=
(6)
p(y Mk )
The detailed process of estimation is acted through three steps,
as follows:
First, we use the fixed-effect model (FEM) and the random-effect
model (REM) to provide a general view of the influence of financial
development and electricity consumption on economic growth.
First, Bayesian analysis is based on the Bayes rule and the posterior
distribution results from updating the prior knowledge about model
parameters with evidence from the observed data. The Bayesian
analysis rests on Bayes’ theorem of probability theory:
Second, we apply the Bayesian approach via the Metropolis-
Hasting and Gibbs samplers as the MCMC methods to estimate
the impact of financial development and electricity consumption
on economic growth.
p(y ꢀ).p(ꢀ)
p(ꢀ y) ꢁ
(2)
p(y)
Finally, we use Dumitrescu and Hurlin (2012) test to check the
causality between energy consumption and economic growth.
where, θ stands for a set of unknown parameters, y represents a
marginal distribution of data, p(θ) denotes the prior distribution
of the parameters θ (pre-existing information such as expert
opinion, theory, or other external resources), p(y|θ) is a likelihood
distribution, p(y) is the marginal distribution of y, and p(y|θ) denotes
the posterior distribution, which is the probability of the parameters
θ conditional on the data x. Equation (2) may be expressed as:
4. EMPIRICAL RESULTS
4.1. Descriptive Statistic
In two past decades, the ASEAN+6 countries, including Indonesia,
Malaysia, Philippine, Singapore, Thailand, and Vietnam, have
changed rapidly in most socio-economic fields. Rapid growth leads
to a change in the structure of the economy. The industry sector is
focused on investing by the Government.Also, urbanization leads to
a great demand for energy.Acknowledge that financial development
and energy consumption are actively contributing to growth in these
countries. The descriptive statistic of all variables is shown inTable 1.
p(ꢀ y) ꢁ p(y ꢀ) p(ꢀ)
(3)
where, ∝ implies “proportional to.” The posterior is proportional
to the prior multiplied by the likelihood.
Second, the frequentist inference assumes that all parameters
are considered unknown but fixed quantities, while Bayesian
inference allows all parameters are random quantities and thus
can incorporate prior knowledge. Hence, Bayesian analysis yields
an entire probability distribution of a parameter, while frequentist
results are point estimates. Also, the Bayesian paradigm allows
for probability statements, such as a variable is likely or unlikely
to impact on another, or the true value of a parameter falls into
a certain interval with a pre-specified probability (Bernardo and
Smith, 1994; Thompson, 2012).
4.2. Model Comparison
This subsection compares five posterior regression models, where
the respective Gaussian prior distributions specified are N(0,1),
N(0,10), N(0,100), N(0,1000), and N(0,10000).
The results of the model comparison are presented inTables 2 and 3.
In general, the less the DIC value, the more the log(ML) and
log(BF) estimate, the better a model fits the data. P(My) shows
the posterior model probability. Consequently, model 1 is the best.
Because our data sample size is sufficiently large, noninformative
priors are enough for our model specification. For comparison
purposes, we also specify informative priors for the model
parameters. Accordingly, we conduct five posterior simulations.
A sensitivity analysis to prior choice will be performed through
a Bayes factor test and a model test. We assume to have models
Mj parameterized by vectors θj,j=1,2,…r. By applying Bayes’s
theorem, we calculate the posterior model probabilities:
Table 1: The descriptive statistic of all variables
Variables
LnGDP
LnEC
Mean
8.128
6.763
2.573
17.301
50.26
Maximum
10.885
9.088
Minimum
5.735
3.832
Std. error
1.234
1.358
0.725
4.734
LnFI
LnECFI
UB
4.275
31.028
100
-1.472
-11.528
19.25
25.56
52
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
Hoang: Examining the Relationship between Electricity Consumption, Financial Development and Economic Growth in Asean Countries: Evidence from a Bayesian Analysis
The model summary reports the rate of acceptance and algorithm
efficiency as initial indicators of MCMC convergence. The
acceptance rate is the number of proposals accepted in the total
proposals, whereas algorithm efficiency is the mixing properties of
MCMC sampling. Concerning the chosen model 2, the acceptance
rate of 0.84 is larger than the minimum level of 0.1, whereas
average efficiency is equivalent to 0.35, which is more than the
acceptable level of 0.01. This implies that the obtained results
from Bayesian multilevel regression are reliable.
4.3. MCMC Convergence Test
In the application of an MCMC method, a convergence check is
needed before proceeding to inference. Once chain convergence is
established, the model parameters have converged to equilibrium
values. To avoid pseudo convergence, in this study, we simulate
three MCMC chains and verify whether the results satisfy the
convergence rule. This is because pseudo convergence takes place
when the chains have seemingly converged, but indeed, they
explored only a portion of the domain of a posterior distribution.
As demonstrated in Table 4, the maximum Gelman-Rubin statistic
Rc of 1.0001 is close to 1.1, indicating MCMC convergence.
Additionally, it is useful to conduct a graphical inspection. For
this, CUSUM plots as an accessible tool are applied. As shown in
Figure 1, the CUSUM plots of the parameters corresponding to
three chains are jagged, not smooth, running across the X-axis. So
MCMC chains for the model parameters are well-mixed, which
is a sign of sequence convergence.
Table 2: Bayesian information criteria
Model Gaussian distribution
DIC
log(ML) log(BF)
1
2
3
4
5
N(0,1)
N(0,10)
N(0,100)
N(0,1000)
N(0,10000)
103.8528 −75.6626
104.0392 −79.5551 -3.8924
104.0978 −85.1067 -9.4441
104.1043 −90.8425 −15.1799
104.1050 −96.5969 −20.9342
4.4. FEM, REM and Bayesian Estimation
The estimation of the Eq.1 by frequentist and Bayesian inference
is presented in Table 5. The obtained outcome from FEM and
REM model shows that there is a positive impact of electricity
consumption on economic growth. Accordingly, a 1% increase
in electricity consumption leads to 0.747% increase in economic
growth. Besides, financial development is helpful to economic
growth (P_value = 0.000).A1% increase in financial development
leads to 0.629% increase in economic growth.
Table 3: Bayesian model tests
Model Gaussian distribution
log(ML)
−75.6626
−79.5551
−85.1067
−90.8425
−96.5969
P(M)
P(My)
1
2
3
4
5
N(0,1)
N(0,10)
N(0,100)
N(0,1000)
N(0,10000)
0.2000 0.9799
0.2000 0.0200
0.2000 0.0001
0.2000 0.0000
0.2000 0.0000
With the Bayesian inference, the result in the lower section of
Table 5 reveals that both the influence of electricity consumption
and financial development is beneficial for economic growth. With
a probability of mean between 0.7 and 1, electricity consumption
and financial development exert a powerfully positive effect
on economic growth. The 95% credible intervals also point to
similar results. Thus, we can confirm that the value of 0.7951 of
the coefficient for LnEC belongs to the interval [0.6587, 0.9315]
with a 95% probability. Similar interpretations can be made for
the remaining parameters of the model. With a probability of mean
Table 4: Gelman-rubin convergence diagnostic
Max gelman-rubin Rc=1.000142
<Convergence rule (=1.1)
Dependent variable: LnGDP
LnEC
LnFI
LnECFI
UB
Intercept
Rc value
1.000142
1.00009
1.000076
1.00008
1.00013
0.999963
var
Table 5: FEM, REM and Bayesian simulation results
Variables
Coefficient
P_value
Coefficient
P_value
FEM result
REM result
LnEC
LnFI
LnECFI
UB
Intercept
F-test
0.8324
0.5715
−0.0824
−0.009
2.916
0.000
0.000
0.000
0.000
0.000
0.7461
0.6298
-0.0891
0.0054
2.7009
0.000
0.000
0.000
0.021
0.000
F-statistic=254.24 (P_value = 0.000)
Hausman test
F-statistic=467.59 (P_value = 0.000)
Variables
Mean
Bayesian result
Std. Dev.
MCSE
Probability of mean>0
Equal-tailed (95% Cred. Interval)
Dependent variable: LnGDP
LnEC
LnFI
LnECFI
UB
0.7951
0.0691
0.1652
0.0230
0.0016
0.4849
0.0004
0.0009
0.0001
0.0000
0.0028
1
1
1*
1
(0.6587, 0.9315)
(0.6983, 1.3473)
(−0.1919, −0.1019)
(0.0221, 0.0280)
(0.4216, 2.3275)
1.0226
-0.1469
0.0251
1.3753
Intercept
0.99
* is probability of mean < 0
53
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
Hoang: Examining the Relationship between Electricity Consumption, Financial Development and Economic Growth in Asean Countries: Evidence from a Bayesian Analysis
Figure 1: CUSUM plots of model parameters
Table 6: Results of the causality test
Null hypothesis: No causality
6. CONCLUSION
W-bar Z-bar P-value
LnEC does not granger cause LnGDP 1.9879 1.711
LnGDP does not granger cause LnEC 4.6660 6.3549
0.087
0.000
The study applies the Bayesian approach via the Metropolis-
Hasting and Gibbs samplers as the MCMC methods to investigate
the impact of financial development and electricity consumption
on economic growth inASEAN+6 countries over the period 1980
to 2016. Five simulations are conducted with Gaussian prior
distributions ranging from (0,1) to (0,10000).As shown by model
comparison results via a Bayes factor and a model test, the model
with a noninformative, namely, N(0,1) prior fits the best.According
to the estimation results, we claim in view of the probability that
both electricity consumption and financial development strongly
and positively affects economic growth.
is one, we can state that urbanization is a good contribution to
economic growth in examined countries.
4.5. The Causality Test
Finally, the study used Dumitrescu and Hurlin (2012) test to
examine the causality relationship between energy consumption
and economic growth. Both the W-bar and Z-bar statistic test
presented in Table 6 provides evidence in favor of the rejection
of the null hypothesis (P_value < 0.05). This result implies that
there is a uni-directional causality running from economic growth
to energy consumption in examined countries, which supported
the Conversation hypothesis.
Based on the empirical results, some policy implications are
suggested, as detailed:
Firstly, electricity consumption is beneficial for growth, so the
Government should intend to expand energy supply through the
development of renewable or green energies, such as solar, wind,
biofuels, and geothermal power.
5. DISCUSSION
The empirical result shows that the impact of financial
development and electricity consumption on economic growth is
beneficial. These results are in line with the conclusion by Ben
Jedidia et al. (2014) for Tunisia, Sarkar et al. (2019) for Malaysia,
Glasure and Lee (1997) for South Korea and Singapore, or Long
et al. (2018); Ngoc (2019); Nguyen and Ngoc (2020) for Vietnam.
Physical capital accumulation and a developed financial system
will enhance economic growth through the process of mobilizing
and allocating the saving capital flows into projects, which have
high productivity or output. All six countries in our sample are
developing or developed countries, so the demand for production,
distribution, or household consumption is rapid growth.According
to the forecasting of the International Energy Agency, the energy
demand is growing by 1.4% per year until 2035. This is valid for
both emerging or developed countries.
Secondly, financial development will drive economic growth if
the country has a transparent and efficient financial system. Thus,
the rate of the money supply should be calculated corresponding
to the rate of growth. Adeficiency in the money supply will result
in a decrease in economic growth, negatively impacting other
economic activities.
REFERENCES
Abid, F., Bahloul, S., Mroua, M. (2016), Financial development and
economic growth in MENA countries. Journal of Policy Modeling,
38(6), 1099-1117.
Abosedra, S., Dah, A., Ghosh, S. (2009), Electricity consumption and
economic growth, the case of Lebanon. Applied Energy, 86(4),
429-432.
54
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
Hoang: Examining the Relationship between Electricity Consumption, Financial Development and Economic Growth in Asean Countries: Evidence from a Bayesian Analysis
Acaravci, A. (2010), Structural breaks, electricity consumption and
economic growth: Evidence from Turkey. Romanian Journal of
Economic Forecasting, 2, 140-154.
Ibrahim, M., Alagidede, P. (2018), Effect of financial development on
economic growth in sub-SaharanAfrica. Journal of Policy Modeling,
40(6), 1104-1125.
Alsamara, M., Mrabet, Z., Barkat, K., Elafif, M. (2018), The Impacts of
trade and financial developments on economic growth in Turkey:
ARDL approach with structural break. Emerging Markets Finance
and Trade, 55(8), 1671-1680.
Apergis, N., Payne, J.E. (2010), Energy consumption and growth in South
America: Evidence from a panel error correction model. Energy
Economics, 32(6), 1421-1426.
Balcilar, M., Bekun, F.V., Uzuner, G. (2019), Revisiting the economic
growth and electricity consumption nexus in Pakistan. Environmental
Science and Pollution Research, 26(12), 12158-12170.
Ben Jedidia, K., Boujelbène, T., Helali, K. (2014), Financial development
and economic growth: New evidence from Tunisia. Journal of Policy
Modeling, 36(5), 883-898.
Bernardo, J.M., Smith, A.F.M. (1994), Bayesian Theory. Hoboken: John
Wiley & Sons, Inc.
Bretschger, L., Steger, T.M. (2004), The dynamics of economic
integration: Theory and policy. International Economics and
Economic Policy, 1(2-3), 119-134.
Burakov, D., Freidin, M. (2017), Financial development, economic
growth and renewable energy consumption in Russia:Avector error
correction approach. International Journal of Energy Economics and
Policy, 7(6), 39-47.
Jeffreys, H. (1962), Theory of probability. Geophysical Journal
International, 6(4), 555-558.
Kahouli, B. (2017), The short and long run causality relationship among
economic growth, energy consumption and financial development:
Evidence from South Mediterranean countries (SMCs). Energy
Economics, 68, 19-30.
King, R.G., Levine, R. (1993), Finance, entrepreneurship and growth:
Theory and evidence. Journal of Monetary Economics, 32(3),
513-542.
Komal, R., Abbas, F. (2015), Linking financial development, economic
growth and energy consumption in Pakistan. Renewable and
Sustainable Energy Reviews, 44, 211-220.
Kraft, J., Kraft,A. (1978), On the Relationship between energy and GNP.
The Journal of Energy and Development, 3(2), 401-403.
Law, S.H., Singh, N. (2014), Does too much finance harm economic
growth? Journal of Banking and Finance, 41, 36-44.
Liang, Q., Teng, J.Z. (2006), Financial development and economic growth:
Evidence from China. China Economic Review, 17(4), 395-411.
Long, P.D., Ngoc, B.H., My, D.T.H. (2018), The relationship between
foreign direct investment, electricity consumption and economic
growth in Vietnam. International Journal of Energy Economics and
Policy, 8(3), 267-274.
Chandran, V.G.R., Sharma, S., Madhavan, K. (2010), Electricity
consumption-growth nexus: The case of Malaysia. Energy Policy,
38(1), 606-612.
Mahi, M., Phoong, S.W., Ismail, I., Isa, C.R. (2019), Energy-finance-
growth nexus in ASEAN-5 countries: An ARDL bounds test
approach. Sustainability, 12(1), 1-16.
Chen, S.T., Kuo, H.I., Chen, C.C. (2007), The relationship between GDP
and electricity consumption in 10 Asian countries. Energy Policy,
35(4), 2611-2621.
Masten, A.B., Coricelli, F., Masten, I. (2008), Non-linear growth effects
of financial development: Does financial integration matter? Journal
of International Money and Finance, 27(2), 295-313.
Demetriades, P.O., Hussein, K.A. (1996), Does financial development
cause economic growth? Time-series evidence from 16 countries.
Journal of Development Economics, 51(2), 387-411.
Dumitrescu, E.I., Hurlin, C. (2012), Testing for Granger non-causality
in heterogeneous panels. Economic Modelling, 29(4), 1450-1460.
Edwards, S. (2001), Capital Mobility and Economic Performance: Are
Emerging Economies Different? NBER Working Paper No. 8076.
Ghosh, S. (2009), Electricity supply, employment and real GDP in India:
Evidence from cointegration and Granger-causality tests. Energy
Policy, 37(8), 2926-2929.
Glasure, Y.U., Lee, A.R. (1997), Cointegration, error-correction, and the
relationship between GDP and energy: The case of South Korea and
Singapore. Resource and Energy Economics, 20, 17-25.
Golam, A.M., Nazrul, I.A.K. (2011), Electricity consumption and
economic growth nexus in Bangladesh: Revisited evidences. Energy
Policy, 39(10), 6145-6150.
McKinnon, R.I. (1974), Money and capital in economic development.
The Economic Journal, 84(334), 422-423.
Ngoc, B.H. (2019), Energy consumption and economic growth nexus in
Vietnam: An ARDL approach. In beyond traditional probabilistic
methods in economics. In: Kreinovich, V., Thach, N., Trung, N.,
van Thanh, D., editors. Beyond Traditional Probabilistic Methods in
Economics. ECONVN 2019. Studies in Computational Intelligence.
Vol. 809. Cham: Springer. p311-322.
Nguyen, H.M., Ngoc, B.H. (2020), Energy consumption-economic
growth nexus in Vietnam: An ARDL approach with a structural
break. The Journal of Asian Finance, Economics and Business,
7(1), 101-110.
Okada, K. (2013), The interaction effects of financial openness
and institutions on international capital flows. Journal of
Macroeconomics, 35, 31-143.
Omri, A. (2014), An international literature survey on energy-economic
growth nexus: Evidence from country-specific studies. Renewable
and Sustainable Energy Reviews, 38, 951-959.
Goldsmith, R.W. (1969), Financial Structure and Development. New
Haven: Yale University Press.
Greenwood, J., Jovanovic, B. (1990), Financial development, growth,
and the distribution of income. Journal of Political Economy, 98(5),
1076-1107.
Ono, S. (2017), Financial development and economic growth nexus in
Russia. Russian Journal of Economics, 3(3), 321-332.
Payne, J.E. (2009), On the dynamics of energy consumption and output
in the US. Applied Energy, 86(4), 575-577.
Ha, N.M., Ngoc, B.H. (2020), Revisiting the relationship between energy
consumption and economic growth nexus in Vietnam: New evidence
by asymmetric ARDL cointegration. Applied Economics Letters,
2020, 1789543.
Rafindadi, A.A., Ozturk, I. (2016), Effects of financial development,
economic growth and trade on electricity consumption: Evidence
from post-Fukushima Japan. Renewable and Sustainable Energy
Reviews, 54, 1073-1084.
Hamdi, H., Sbia, R., Shahbaz, M. (2014), The nexus between electricity
consumption and economic growth in Bahrain. Economic Modelling,
38, 227-237.
Ibrahiem, D.M. (2015), Renewable electricity consumption, foreign direct
investment and economic growth in Egypt: An ARDL approach.
Procedia Economics and Finance, 30, 313-323.
Romer, P.M. (1990), Endogenous technological change. Journal of
Political Economy, 95(5), 71-102.
Rousseau, P.L., Vuthipadadorn, D. (2005), Finance, investment, and
growth: Time series evidence from 10 Asian economies. Journal of
Macroeconomics, 27(1), 87-106.
Salahuddin, M., Gow, J. (2016), The effects of internet usage, financial
55
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
Hoang: Examining the Relationship between Electricity Consumption, Financial Development and Economic Growth in Asean Countries: Evidence from a Bayesian Analysis
development and trade openness on economic growth in South
Africa: A time series analysis. Telematics and Informatics, 33(4),
1141-1154.
instability. World Development, 28(6), 1075-1086.
Tang, C.F. (2009), Electricity consumption, income, foreign direct
investment, and population in Malaysia. Journal of Economic
Studies, 36(4), 371-382.
Sarkar, M.S.K., Al-Amin, A.Q., Mustapa, S.I., Ahsan, M.R. (2019),
Energy consumption, CO2 emission and economic growth: Empirical
evidence for Malaysia. International Journal of Environment and
Sustainable Development, 18(3), 318-334.
Schumpeter, J.A. (1912), The Theory of Economic Development: Harvard
Economic Studies.
Thompson, S.K. (2012), Sampling. 3rd ed. Hoboken, New Jersey: John
Wiley & Sons. Inc.
Tiba, S., Omri, A. (2017), Literature survey on the relationships between
energy, environment and economic growth. Renewable and
Sustainable Energy Reviews, 69, 1129-1146.
Shaw, E.S. (1974), Financial deepening in economic development. The
Journal of Finance, 29(4), 1345-1348.
Stern, D.I. (2000), A multivariate cointegration analysis of the role of
energy in the US macroeconomy. Energy Economics, 22, 267-283.
Stiglitz, J.E. (2000), Capital market liberalization, economic growth, and
Yoo, S.H. (2005), Electricity consumption and economic growth:
Evidence from Korea. Energy Policy, 33(12), 1627-1632.
Zhang, C., Zhou, K., Yang, S., Shao, Z. (2017), On electricity consumption
and economic growth in China. Renewable and Sustainable Energy
Reviews, 76, 353-368.
56
International Journal of Energy Economics and Policy | Vol 11 • Issue 2 • 2021
8 trang
10120
Free
Bạn đang xem tài liệu "Examining the relationship between electricity consumption, financial development and economic growth in Asean countries: Evidence from a Bayesian analysis", để tải tài liệu gốc về máy hãy click vào nút Download ở trên
File đính kèm:
- examining_the_relationship_between_electricity_consumption_f.pdf
