Causality between trade openness and energy consumption: what causes what in high, middle and low income countries.
Shahbaz, Muhammad ; Nasreen, Samia ; Ling, Chong Hui 等
ABSTRACT
This paper explores the relationship between trade openness and
energy consumption using data of 91 high, middle and low income
countries. The study covers the period of 1980-2010. We have applied
panel cointegration and causality approaches to investigate the long run
and causal relationship between the variables.
Our results confirm the presence of cointegration between the
variables. The relationship between trade openness and energy
consumption is inverted U-shaped in high income countries but U-shaped
in middle and low income countries. The homogenous and non-homogenous
causality analysis reveals the bidirectional causality between trade
openness and energy consumption.
Keywords: Trade, Energy, Causality
INTRODUCTION
Trade liberalisation has affected the flow of trade (goods and
services) between developed and developing countries. The
Heckscher-Ohlin trade theory reveals that under free trade, developing
countries would specialise in the production of those goods that are
produced by relatively abundant factors of production such as labour and
natural resources. Developed countries would specialise in the
production of those goods that are produced by human capital and
manufactured in capital-intensive activities. Trade openness entails
movement of goods produced in one country for either consumption or
further processing to other country. Production of those goods is not
possible without the effective use of energy. Trade openness affects
energy demand via scale effect, technique effect and composite effect.
Other things being same, trade openness increases economic activities,
thus stimulates domestic production and hence economic growth. A surge
in domestic production increases energy demand , which is commonly
referred as scale effect. Such scale effect is caused by trade openness.
Economic condition of the country and extent of relationship between
economic growth and trade openness determine the impact of trade
openness on energy consumption [Shahbaz, et al. (2013); Cole (2006)].
Trade openness enables developing economies to import advanced
technologies from developed economies. The adoption of advanced
technology lowers energy intensity. The use of advanced technologies
result in less energy consumption and more output that is usually
referred to as technique effect [Arrow (1962)]. Composite effect reveals
the shift of production structure from agriculture to industry with the
use of energy intensive production techniques. In initial stages of
economic development economy is based largely on agriculture sector,
thus the use of energy is relatively less. As economy starts shifting
from agriculture to industry, the energy consumption increases. Arrow
(1962) calls it positive composite effect. Finally, at the later stage
of economic development, economic structure shifts from industry to
services, there is less energy consumption, which implies that energy
intensity is lowered because of composite effect.
Energy affects trade openness via various channels. First, energy
is an important input of production because machinery and equipment in
the process of production require energy. Second, export or import of
manufactured goods or raw material require energy to fuel
transportation. Without adequate energy supply, trade openness will be
adversely affected. Consequently, energy is an important input in trade
expansion and adequate consumption of energy is essential to expand
trade via expanding exports and imports. The relationship between trade
openness and energy consumption is important. Since energy plays a key
role to promote exports or imports hence policies aiming at reduction of
energy consumption such as energy conservation policies will negatively
impact the flow of exports or imports and hence, reduce the benefit of
trade openness. The bidirectional causal relationship between trade
openness and energy consumption suggests that energy expansion policies
should be adopted because energy consumption stimulates trade openness
and trade openness affects energy consumption [Sadorsky (2011)]. The
energy conservation policies will not have an adverse effect on trade
openness if causality is running from trade openness to energy
consumption or if neutral relationship exists between trade openness and
energy consumption [Sadorsky (2011)].
Energy consumption in the world increases parallel to technological
development, increase in trade and population growth. The world average
energy consumption was 1454 Kg of oil equivalent per capita in 1980,
which increased to 1852 Kg of oil equivalent per capita in 2010 (see
Figure 1). According to American Energy Information Administration (EIA)
and the International Energy Agency (IEA), the worldwide energy
consumption will on average continue to increase by 2 percent per year.
[FIGURE 1 OMITTED]
Between 1980 and 2006, energy consumption has increased but fuel
consumption structure varies by region. Coal has the largest share in
fuel consumption of the world, accounting for 30.4 percent of total
increase; Asia and Oceania contributed 97.7 percent of total coal
increase between 1980 and 2006. During the same period, natural gas
ranks second in total energy consumption, accounting for 28.7 percent,
Asian and Oceania still contributed the largest part, 24 percent of
total gas increase, Eurasia, Europe and Middle East contributed about 17
percent and 20 percent respectively. Oil ranked as the third fuel in
total consumption, accounting for 21.5 percent. Asia and Oceania still
were the biggest contributors; accounting for about 67.9 percent of
increase in oil consumption. The nuclear power contributed about 10.7
percent to total increase, the increase was mainly contributed by
Europe, North America and, Asia and Oceania where more new nuclear
reactors have been started. Hydropower has developed in Asia and Oceania
and Central and, South America, because of their abundant hydro
resources. And these two regions contribute 80 percent to global
hydropower increase. However, global industry sector has reduced the use
of total energy from 33 percent in 1980 to 27 percent in 2006 because
most developed countries used less energy in industry by improvement in
energy efficiency, technology development and major production structure
changes.
Growth in world energy consumption reached 5.6 percent in 2010, the
highest growth rate since 1973. Energy consumption in OECD countries
grew by 3.5 percent while in non-OECD countries by 7.5 percent in 2010.
Chinese energy consumption grew by 11.2 percent and China surpassed the
United States as the world's largest energy consumer. Oil remained
the world's leading fuel in 2010, and accounted for 33.6 percent of
global energy consumption. World natural gas consumption grew by 7.4
percent in 2010, the most rapid increase since 1984. The United States
witnessed the world's largest increase in consumption, which rose
by about 5.6 percent in 2010. Asian countries also registered large
increase of about 10.7 percent, led by a 21.5 percent increase in India.
Coal consumption grew by 7.6 percent in 2010, the fastest global growth
since 2003. The share of coal in world energy consumption is 29.6
percent, more than 25.6 percent of ten years ago. China consumed 48.2
percent of world coal and accounted for nearly two-third of global coal
consumption. The use of modern renewable energy sources including wind,
solar, geothermal, marine, modern biomass and hydro continued to grow
rapidly and accounted for 1.8 percent of world energy consumption in
2010, up from 0.6 percent in 2000. Energy use in transport sector
increased very rapidly during the recent years due to rapid economic
development and population growth. Over the past 30 years, energy use in
transport sector has doubled. Transport sector accounts for 25 percent
of world energy consumption in 2010 [International Energy Agency
(2012)].The volume of merchandise trade among countries has been rapidly
increasing for last two decades due to globalisation. Global merchandise
trade (exports plus imports of goods) was US$ 3.8 trillion in 1980 but
it amounted to US$ 37 trillion in 2010 (see Figure 2).
[FIGURE 2 OMITTED]
In 2006, merchandise exports in volume terms increased among
regions. Exports from North America and Asia grew faster than imports.
The growth rate of Asian export was 13 percent while imports grew by 9
percent. Europe recorded balanced export and import growth of 7 percent.
For South and Central America, the Commonwealth of Independent States,
Africa and the Middle East, import growth was larger than exports. This
pattern is attributed to more favourable terms of trade due to increases
in commodity prices in the past few years. The global economies faced
negative trade shock in 2009. This negative trade shock was mainly due
to massive contraction of global demand that reduced commodity prices in
all regions of the world. The trade shock was strongest in transition
economies and the economies of Western Asia and Africa. However, the
similar situation does not exist in 2010. All WTO regions experienced
double-digit increase in the dollar value of both exports and imports in
2010 due to rise in prices of fuel and other commodities. The top
merchandise exporters in 2010 were China (US$ 1.58 trillion) followed by
United States (US$ 1.28 trillion), Germany (US$ 1.27 trillion), Japan
(US$ 770 billion) and Netherlands (US$ 572 billion). The leading
merchandise importers in 2010 were United States (US$ 1.97 trillion),
China (US$ 1.40 trillion), Germany (US$ 1.07 trillion), Japan (US$ 693
billion) and France (US$ 606 billion) (Source: World Trade Report,
2011).
There are a few studies that examined the relationship between
energy consumption and economic growth [Masih and Masih (1996); Yang
(2000); Narayan, et al. (2008)], energy consumption and exports [Narayan
and Smyth (2009); Lean and Smyth (2011); Halicioglu (2010); Shahbaz, et
al. (2013a)]. However, the relationship between trade openness and
energy consumption is still understudied. The objective of this study is
to fill this gap by investigating the relationship between trade
openness and energy consumption using global data of 91 high, middle and
low-income countries for the period 1980-2010. The pooled mean group and
mean group models are used to show non-linear relationship between trade
openness and energy consumption. Test for establishing the long-run
relationships between variables are carried out by using the panel
cointegration approach developed by Larsson et al. (2001) while test for
causality is conducted by using a modified version of Granger causality
test developed by Hurlin and Venet (2001).
The rest of the paper is organised as follows: Section 2 gives a
brief review of empirical studies, Section 3 presents the methodology
and data source, Section 4 presents the results and discussion and
Section 5 gives the conclusions and policy implications.
2. LITERATURE REVIEW
There is an extensive literature available on the relationship
between economic growth and energy consumption. Energy consumption is an
important factor of production like capital and labour and it affects
economic growth. After the end of 1970s energy crisis, many studies
[e.g. Kraft and Kraft (1978), Akarca and Long (1979 and 1980), Yu and
Choi (1985)] exposed that energy consumption is positively correlated
with economic growth. However, empirical evidence provided by Zahid
(2008), Amirat and Bouri (2010), Noor and Siddiqi (2010), Apergis and
Payne (2010) is conflicting about direction of causality. For instance,
Nondo and Kahsai (2009) investigated the long-run relationship between
total energy consumption and economic growth for a panel of 19 African
countries. They applied Levine, et al. (2005), Im, et al. (2003) and
Hadri (2005) panel unit root tests to test the integrating properties of
real GDP and total energy consumption. Their analysis indicated that
both the variables are cointegrated for long run relationship confirmed
by Pedroni (1999) panel cointegration approach. Moreover, they noted
that economic growth is cause of energy consumption in long run as well
as in short run. Noor and Siddiqi (2010) investigated the causal
relationship between per capita energy consumption and per capita GDP in
five South Asian countries namely Bangladesh, India, Nepal, Pakistan and
Sri Lanka. They applied panel unit root tests IPS, LLC and MW, and
Pedroni cointegration as well as Kao residual cointegration approaches.
They reported that energy consumption enhances economic growth. Their
causality analysis reveals that economic growth Granger causes energy
consumption in South Asian countries. (1)
There are a few studies investigating the relationship between
trade openness and energy consumption. For instance, Cole (2006)
examined the relationship between trade liberalisation and energy
consumption. Cole (2006) used data of 32 countries and found that trade
liberalisation promotes economic growth, which boosts energy demand.
Moreover, trade liberalisation stimulates use of capital intensive
techniques, which in turn affects energy consumption. Jena and Grote
(2008) investigated the impact of trade openness on energy consumption.
They noted that trade openness stimulates industrialisation via scale
effect, technique effect, composite effect and comparative advantages
effect, which affect energy consumption. Narayan and Smith (2009)
examined the causal relationship between energy consumption and economic
growth by incorporating exports as an indicator of trade openness in
production function for a panel of six Middle Eastern countries namely
Iran, Israel, Kuwait, Oman, Saudi Arabia and Syria. They applied panel
unit root test, panel cointegration and panel causality tests. Their
analysis confirmed the presence of cointegration relationship between
variables. Furthermore, they reported that that a short-run Granger
causality exists running from energy consumption to real GDP and from
economic growth to exports but neutral relationship is found between
exports and energy consumption.
Later on, Sadorsky (2011) examined the causal relationship between
total energy consumption and trade openness. The panel means group
cointegration and panel Granger causality approaches were used for the
panel of 8 Middle Eastern countries namely, Bahrain, Iran, Jordan, Oman,
Qatar, Saudi Arabia, Syria and UAE. The empirical evidence reported that
long run relationship exists between the variables. Sadorsky found that
that 1 percentage increase in real per capita GDP increases per capita
energy consumption by 0.62 percent. A 1 percent increase in real per
capita exports increases per capita energy consumption by 0.11 percent
while 1 percent increase in real per capita imports increases per capita
energy consumption by 0.04 percent. Panel Granger causality analysis
revealed that exports Granger cause energy consumption and the feedback
is found between imports and energy consumption in short run. Similarly,
the bidirectional causality exists between GDP and energy consumption in
short run. Sadorsky (2012) used production function to investigate the
relationship between trade openness and energy consumption in South
American countries namely Argentina, Brazil, Chile, Ecuador, Paraguay,
Peru, and Uruguay over the period of 1980-2007. The panel cointegration
developed by Pedroni (2004), fully modified ordinary least squares
(FMOLS) and the VECM Granger causality approaches were applied. The
empirical evidence confirmed the presence of cointegration for long run
relationship between the variables. The relationship between exports and
energy consumption is bidirectional and imports Granger cause energy
consumption in short run. Using data of 52 developed and developing
economies, Ghani (2012) explored relationship between trade
liberalisation and energy demand. The results indicated that trade
liberalisation has insignificant impact on energy consumption but after
a certain level of capital per labour, trade liberalisation affects
energy consumption.
Hossain (2012) examined the relationship between electricity
consumption and exports by adding foreign remittances and economic
growth as additional determinants in SAARC countries namely Pakistan,
India and Bangladesh. The author reported the no causality between
exports and electricity demand. Dedeoglu and Kaya (2013) investigated
the relationship between exports, imports and energy consumption by
incorporating economic growth as additional determinant of trade
openness and energy consumption using data of the OECD countries. They
applied the panel cointegration technique developed by Pedroni (2004)
and used the Granger causality developed by Canning and Pedroni (2008).
Their analysis showed the cointegration between the variables. They also
noted that economic growth, exports and imports have positive impact on
energy consumption. Their causality analysis revealed that the
relationship between exports (imports) and energy consumption is
bidirectional.
3. ESTIMATION STRATEGY
Panel Unit Roots
We apply Levine, et al. (2002) (LLC), Im, et al. (2003) (IPS),
Maddala and Wu (1999) (MW, ADF) and Maddala and Wu (1999) (MW, PP) panel
unit root tests to check the stationarity properties of the variables.
These tests apply to a balanced panel but the LLC can be considered a
pooled panel unit root test, IPS represents a heterogeneous panel test
and MW panel unit root test is non-parametric test.
3.1. LLC Unit Root Test
Levin, et al. (2002) developed a number of pooled panel unit root
tests with various specifications depending upon the treatment of the
individual specific intercepts and time trends. This test imposes
homogeneity on the autoregressive coefficient that indicates the
presence or absence of unit root problem while the intercept and the
trend can vary across individual series. LLC unit root test follows ADF
regression for the investigation of unit root hypothesis as given below
step by step:
(1) We use a separate ADF regression for each country:
[DELTA][y.sub.i,t] = [[alpha].sub.i] + [[rho].sub.i] [y.sub.it-1] +
[[p.sub.i].summation over (j=1)] [[alpha].sub.i,j] [DELTA][y.sub.i,t-j]
+ [[epsilon].sub.i,t] ... (1)
The lag order [p.sub.i] is allowable across individual countries.
The appropriate lag length is chosen by allowing the maximum lag order
and then using the t-statistics for ij b to determine if a smaller lag
order is preferred.
(2) We run two separate regressions and save the residuals
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
[DELTA][y.sub.i,t] = [[lambda].sub.i] + [[p.sub.i].summation over
(j=1)] [[gamma].sub.i,t-j] [DELTA][y.sub.i,t-j] + [[eta].sub.i,t] [??]
[[??].sub.it] ... (2)
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (3)
LLC procedure suggests to standardise the errors [MATHEMATICAL
EXPRESSION NOT REPRODUCIBLE IN ASCII] regressing the standard error
through the ADF equation provided above:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (4)
(3) Regression can be run to compute the panel test statistics
following Equation 5:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (5)
The null hypothesis is as follows: [H.sub.o] : [[rho].sub.1],.. =
... [[rho].sub.n] p = 0 and alternate hypothesis is: [H.sub.A] : [rho] =
... [[rho].sub.n] = [rho] < 0
3.2. IPS Unit Root Test
Im, Pesaran and Shin (IPS), (2003) introduced a panel unit root
test in the context of a heterogeneous panel. This test basically
applies the ADF test to individual series thus allowing each series to
have its own short-run dynamics. But the overall t-test statistic is
based on the arithmetic mean of all individual countries' ADF
statistic. Suppose a series ([TR.sub.ti,] [EC.sub.ti]) can be
represented by the ADF (without trend).
[DELTA][x.sub.i,t] [[bar.[omega]].sub.j] + [[bar.[omega]].sub.i]
[x.sub.i,t-1] + [[p.sub.i].summation over (j=1)] [[phi].sub.i,j]
[DELTA][x.sub.i,t-j] + [v.sub.i,t] ... (6)
After the ADF regression has different augmentation lags for each
country in finite samples, the term E([t.sub.T]) and var([t.sub.T]) are
replaced by the corresponding group averages of the tabulated values of
E([t.sub.T], [P.sub.i]) and var([t.sub.T],[P.sub.i]) respectively. The
IPS test allows for the heterogeneity in the value
[[bar.[omega]].sub.i], under the alternative hypothesis. This is more
efficient and powerful test than usual single time series test. The
estimable equation of IPS unit root test is modeled as follows:
[t.sub.NT] = I/N [N.summation over (i=1)] [t.sub.i,t]([P.sub.i])
... (7)
where [t.sub.i,t] is the ADF t-statistics for the unit root tests
of each country and [P.sub.i] is the lag order in the ADF regression and
test statistic can be calculated as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... (8)
As [t.sub.NT] is explained above and values for E[[t.sub.iT]
([P.sub.i], 0)] can be obtained from the results of Monte Carlo
simulation carried out by IPS. They have calculated and tabulated them
for various time periods and lags. When the ADF has different
augmentation lags ([P.sub.i]) the two terms E([t.sub.T]) and
var([t.sub.T]) in the equation above are replaced by corresponding group
averages of the tabulated values of E([t.sub.T], [P.sub.i]) and
var([t.sub.T], [P.sub.i]) respectively. (2)
3.3. MW Unit Root Test
The Fisher-type test was developed by Maddala and Wu (1999), which
pools the probability values obtained from unit root tests for every
cross-section i. This is a non-parametric test and has a chi-square
distribution with 2nd degree of freedom where n is number of countries
in a panel. The test statistic is given by:
[lambda] = -2 [n.summation over (i=1)] [log.sub.e] ([p.sub.i]) ~
[[chi].sup.2.sub.2n] (d. f.) ... (9)
Where [p.sub.i] is probability value from ADF unit root tests for
unit i. The MW unit root test is superior to IPS unit root test because
MW unit root test is sensitive to the lag length selection in individual
ADF regressions. Maddala and Wu (1999) performed Monte Caro simulations
to prove that their test is more advanced than the test developed by IPS
(2003).
3.4. The Likelihood-based Panel Cointegration Test
The panel LLL trace test statistics is actually derived from the
average of individual likelihood ratio cointegration rank trace test
statistics of the panel individuals. The multivariate cointegration
trace test of Johanson (1988, 1995) is applied to investigate each
individual cross-section system autonomously, in that way, allowing
heterogeneity in each cross-sectional unit root for said panel. The
process of data generation for each of the groups is characterised by
the following heterogeneous VAR ([p.sub.i]) model:
[Y.sub.i,t] = [[p.sub.i].summation over (j=1)] [[LAMBDA].sub.i,j]
[Y.sub.i,t-j] + [[epsilon].sub.i,t] ... (10)
Where i = 1, ..., N;t = 1, ... T
For each one, the value of [Y.sub.i,-j+1], ... [Y.sub.i,0] is
considered fixed and [[epsilon].sub.i,t] are independent and identically
distributed (normally distributed): [epsilon] ~ [N.sub.K] (0,
[[OMEGA].sub.i]), where [[OMEGA].sub.i] is the cross-correlation matrix
of the error terms: [[OMEGA].sub.i], = E([[epsilon].sub.i,t],
[[epsilon].sub.i,t]). The Equation 10 can be modified as vector error
correction model (VECM) as given below:
[DELTA][Y.sub.i,t] = [[PI].sub.i][Y.sub.i,t-1] +
[[p.sub.i]-1.summation over (j=1)] [[GAMMA].sub.i,j]
[DELTA][Y.sub.i,t-j] + [[epsilon].sub.i,j] ... (11)
Where [[PI].sub.i] = [[LAMBDA].sub.i,1] + ... + [[LAMBDA].sub.pi] -
1 and [[GAMMA].sub.i,j] = [[LAMBDA].sub.i,j] - [[GAMMA].sub.i,j-1],
[[PI].sub.i], is of order (k x k). If [[PI].sub.i], is of reduced rank:
rank ([[PI].sub.i]) = [r.sub.i], which can be de-composed into
[[PI].sub.i] = ab, where [[alpha].sub.i] and [[beta].sub.i], are of
order (k x [r.sub.i]) and of full column rank that represents the error
correction form. The null hypotheses of panel LLL (2001) rank test are:
[H.sub.o] = rank ([[PI].sub.i]) = [r.sub.i] [less than or equal to]
r for all i = 1, ..., N against
[H.sub.a] = rank ([[PI].sub.i]) = k for all i = 1, ..., N
The procedure is in sequences like individual trace test process
for cointegration rank determination. First, we test for
[H.sub.[omicron]] = rank([[PI].sub.i] = [r.sub.i] [less than or equal
to] r, r = 0, if null hypothesis of no cointegration is accepted, this
shows that there is no cointegration relationship (rank ([[PI].sub.i]) =
[r.sub.i] = 0) in all cross-sectional groups for said panel. If null
hypothesis is not accepted then null hypothesis r = 1 is tested. The
sequence of procedure is not disconnected and continued until null
hypothesis is accepted, r = k -1, or is rejected. Accepting the
hypothesis of cointegration r = 0 along with null hypothesis of rank
([PI].sub.i]) = r [less than or equal to] 0(0 < r < k) implies
that there is at least one cross-sectional unit in panel, which has rank
([[PI].sub.i]) = r > 0. The likelihood ratio trace test statistic for
group i is as following;
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] ... (12)
Where [[lambda]'.sub.l] is the lth largest eigen value in the
ith cross-section unit. The LR-bar statistic is calculated as the
average of individual trace statistics:
L [bar.R]iT [H(r)/H(k)] = 1/N [n.summation over (i=1)] [LR.sub.iT]
[H(r)/H(k)] ... (13)
Finally, modified version of above equation is defined as:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (14)
Where E([Z.sub.k]) and Var([Z.sub.k]) are mean and variance of the
asymptotic trace statistics, which can be obtained from simulation. The
LLL (2001) proves the central limit theorem for the standard LR-bar
statistic, according to which under the null hypothesis,
[[lambda].sub.LR] [??] N(0,1) as N and T [right arrow] [infinity] in
such a way that [[square root of NT].sup.-1] [right arrow] 0, under the
assumption that there is no cross-correlation in the error terms, that
is given below:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
LLL (2001) notes that T [right arrow] [infinity] is needed for each
of the individual test statistic to converge to its asymptotic
distribution, while N [right arrow] [infinity] is needed for the central
limit theorem.
3.5. Panel Causality Test
Hurlin and Venet (2001) extended the Granger (1969) causality test
for panel data models with fixed coefficients. The estimable equation
for empirical estimation is modeled as following:
[y.sub.i,t] = [P.summation over (K=1)] [[gamma].sup.(K)]
[y.sub.i,t-K] + [P.summation over (K=0)] [[beta].sup.(K).sub.i]
[x.sub.i,t-K] + [v.sub.i,t] ... (15)
With P [member of] [N.sup.*] and [v.sub.i,t] = [[partial
derivative].sub.i] + [[epsilon].sub.i,t], where [[epsilon].sub.i,t], are
i.i.d (O, [[sigma].sup.2][epsilon]). In contrast to Nair-Reichert and
Weinhold (2001), we assume that the autoregressive coefficients
[[gamma].sup.(k) and the regression coefficients slopes
[[beta].sub.i.sup.(k)] are constant [OMEGA]k[epsilon][1, p]. We also
assume that parameters [[gamma].sup.(k)] are identical for all
individuals, whereas the regression coefficients slopes
[[beta].sup.(K).sub.i] could have an individual dimension. Hurlin and
Venet (2001), consider four principal cases following Equation 15.
3.6. Homogenous Non-Causality Test
Initially the homogenous non-causality (HNC) hypothesis has been
discussed. Conditional to the specific error components of the model,
this hypothesis assumes no prevalence of any individual causality
association:
[for all]i [member of] [1, N] E
([y.sub.i,t]/[[bar.y].sub.i,t],[[alpha].sub.i]) = E
([y.sub.i,t]/[[bar.y].sub.i,t],[[bar.x].sub.i,t],[[alpha].sub.i]) ...
(16)
In Equation 15, the corresponding test (3) is defined by:
[H.sub.o]: [[beta].sup.(K).sub.i] = 0 [[for all].sub.i] [member of]
[1,N], [for all]k [member of] (1, p) ... (17)
[H.sub.a] : [there exists](i, k)/[[beta].sup.(K).sub.i] [not equal
to] 0
In order to test these [N.sub.p] linear restrictions Wald Statistic
is employed:
[F.sub.hnc] = ([RSS.sub.2] - [RSS.sub.1])/(Np)/[RSS.sub.1]/[NT -
N(1 + p) - p] ... (18)
Where [RSS.sub.2] indicates the restricted sum of squared
residuals. [RSS.sub.1] corresponds to the residual sum of squares of
equation-15. If the realisation of this statistic is not significant,
the homogeneous non-causality hypothesis is accepted. This result
implies that the variable X is not causing Y in finite sample set in all
countries. If the non-causality result is totally homogenous then
further empirical exercise is stopped.
3.7. Homogenous Causality Test
Secondly, homogenous causality (HC) hypothesis is proven, in which
there exist N causality relationships:
[for all]i [member of] [1, N] E
([y.sub.i,t]/[[bar.y].sub.i,t],[[alpha].sub.i]) [not equal to] E
([y.sub.i,t]/[[bar.y].sub.i,t],[[bar.x].sub.i,t], [[alpha].sub.i]) ...
(19)
In this case, suppose that the N individual predictors, obtained
conditional to the fact that [[bar.Y].sub.i,t], [[bar.X].sub.i,t] and
[[alpha].sub.i], are the same:
[for all] (i,j) [member of] [1, N] E
([y.sub.i,t]/[[bar.y].sub.i,t],[[bar.x].sub.i,t] [[alpha].sub.i]) = E
([y.sub.i,t]/[[bar.y].sub.j,t],[[bar.x].sub.j,t],[[alpha].sub.j]) ...
(20)
Two configurations could appear, if we reject hypothesis of
non-homogenous causality. The first one corresponds to the overall
causality hypothesis (homogenous causality hypothesis) and occurs if all
the coefficients [[beta].sup.K.sub.i] are identical for all k. The
second one is more plausible, which is that some coefficients
[[beta].sup.K.sub.i] are different for each individual. Thus, after the
rejection of the null hypothesis of non-homogenous causality, the second
step of the procedure consists of testing if the regression slope
coefficients associated [x.sub.i,t-k] are identical. This test
corresponds to a standard homogeneity test. Formally, the homogenous
causality hypothesis test is as following:
[H.sub.o]: [for all]k [member of] [1,p]/[[beta].sup.k.sub.i] =
[[beta].sup.k] [for all]i [member of] [1,N] ... (21)
[H.sub.a]: [their exists]k [member of] [1,p], [there exists] (i,j)
[member of] [1,N]/[[beta].sup.k.sub.i] [not equal to]
[[beta].sup.k.sub.j]
The homogenous causality hypothesis implies that the coefficients
of the lagged explanatory variables [x.sup.i,t-k] are identical for each
lag k and different from zero. Indeed, if we have rejected, in the
previous step, the non-homogenous causality hypothesis
[[beta].sup.K.sub.i] = 0 [for all](i,k), this standard specification
test allows testing the homogenous causality hypothesis. In order to
test the homogenous causality hypothesis, F-statistic is calculated by
applying the given mechanism:
[F.sub.hc] = ([RSS.sub.3] - [RSS.sub.1])/[p(N - 1)/[RSS.sub.1]/[NT
- N(1 + p) - p] ... (22)
where, [RSS.sub.3] corresponds to the realisation of the residual
sum of squares obtained in Equation 15 when one imposes the homogeneity
for each lag k of the coefficients associated to the variable
[x.sub.i,t-k]. If the [F.sub.hc] statistics with P(N -1) and NT - N(1 +
P) - P degrees of freedom is not significant, the homogenous causality
hypothesis is accepted. This result implies that the variable X is
causing Y in the N countries of the samples, and that the autoregressive
processes are completely homogenous.
3.8. Heterogeneous Causality Test
Third case is relevant to the heterogeneous causality hypothesis.
Under HEC hypothesis, it is assumed there exists at least one individual
causality relationship (and at the most N), and second that individual
predictors, obtained conditional to the fact that [[bar.y].sub.i,t],
[[bar.x].sub.i,t], [[bar.[lambda].sub.t] and, [[alpha].sub.i] are
heterogeneous.
[there exists]i [member of] [1,N] E([y.sub.i,t]/[[bar.y].sub.i,t],
[[alpha].sub.i]) [not equal to] E([y.sub.i,t]/[[bar.y].sub.i,t],
[[bar.x].sub.i,t], [[alpha].sub.i]) ... (23)
[there exists](i,j) [member of] [1,N]
E([y.sub.i,t]/[[bar.y].sub.i,t], [[bar.x].sub.i,t], [[alpha].sub.i])
[not equal to] E([y.sub.j,t]/[[bar.y].sub.j,t], [[bar.x].sub.j,t],
[[alpha].sub.j]) ... (24)
3.9. Heterogeneous Non-causality Test
Finally, heterogeneous non-causality hypothesis assumes that there
exists at least one and at the most N-1 equalities of the form:
[there exists]i [member of] [1,N] E([y.sub.i,t]/[[bar.y].sub.i,t],
[[alpha].sub.i]) = E([y.sub.i,t]/[[bar.y].sub.i,t], [[bar.x].sub.i,t],
[[alpha].sub.i]) ... (25)
The third step of the procedure consists of testing the
heterogeneous non-causality hypothesis (HENC). The following equation
explains this mechanism:
[H.sub.o]: [there exists]i [member of] [1,N]/[for all]k [member of]
[1, p] [[beta].sup.K.sub.i] = 0 ... (26)
[H.sub.a]: [for all]i [epsilon] [1,N]/[there exists]k [epsilon] [1,
N]/[[beta].sup.K.sub.i] [not equal to] 0
This test is proposed to test this last hypothesis with two nested
tests. The first test is an individual test realised for each
individual. For each individual i = 1 ... N, we test the nullity of all
the coefficients of the lagged explanatory variables [x.sub.i,t-k].
Then, for each i, we test the hypothesis [[beta].sup.K.sub.i] = 0, [for
all]k [member of] [1,p]. For that, we compute N statistics:
[F.sup.i.sub.hene] = ([RSS.sub.2,i] -
[RSS.sub.1])/p/[RSS.sub.1]/[NT - N(1 + 2p) + p] ... (27)
where, [RSS.sub.2,i] corresponds to the realisation of the residual
sum of squares obtained in model (15), when one imposes the nullity of
the k coefficients associated to the variable [x.sub.i,t-k] only for the
individual i. A second test of the procedure consists of testing the
joint hypothesis that there is no causality relationship for a sub-group
of individuals. Let us respectively denote [I.sub.c] and [I.sub.nc] as
the index sets corresponding to sub-groups for which there exists a
causal relationship and there does not exist a causal relationship. In
other words, we consider the following model [for all]t [member of] [1,
T]:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] (28)
with
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]
Let [n.sub.c] = dim([I.sub.c]) and [n.sub.nc]=dim ([I.sub.nc]).
Suppose that [n.sub.c]/[n.sub.nc] [right arrow] [right arrow]
[theta]< [infinity] as [n.sub.c] and [n.sub.nc] tend to infinity. One
solution to test the HENC hypothesis is to compute the Wald statistic.
[F.sub.henc] = ([RSS.sub.4] -
[RSS.sub.1])/([n.sub.nc]p)/[RSS.sub.1][NT - N(1 + p) - [n.sub.c]p] ...
(29)
where [RSS.sub.4] corresponds to realisation of the residual sum of
squares obtained from equation-15 when one imposes the nullity of the k
coefficients associated to the variable [x.sub.i,t-k] for the [n.sub.nc]
individuals of the [I.sub.nc] sub-group. If the HENC hypothesis is
accepted, it implies that there exists a sub-group of individuals for
which the variable x does not cause the variable y. The dimension of
this sub-group is then equal to [n.sub.nc]. On the contrary, if the HENC
hypothesis is rejected, it implies that there exists a causality between
x and y for all individuals of the panel.
3.10. Data and Data Sources
The 91 countries are selected for the estimation of causality
between energy consumption and trade openness on the basis of data
availability. (4) The study covers the period 1980-2010. All necessary
data for the sample period are obtained from World development
Indicators (CD- ROM, 2012). Energy consumption in kg of oil equivalent
per capita is used to measure energy consumption, real exports (US$)
plus real imports (US$) divided by population are used to measure trade
openness. Both variables are used in their natural logarithmic form.
4. EMPIRICAL RESULTS AND THEIR DISCUSSIONS
The results of ADF unit root test in the presence of intercept and,
intercept and trend reported in Table 1 suggest that all the series are
non-stationary at their level, but stationary at first difference. This
implies that real trade per capita ([TR.sub.t]) and energy consumption
per capita ([EC.sub.t]) are integrated at I(1) for each country in our
sample.
The unit root test results set the stage for Johansen cointegration
approach. The results are presented in Table 2. We find the acceptance
of null hypothesis i.e. no cointegration in case of Angola, Brazil,
Bulgaria, Cameroon, Congo Dem Rep, Congo Rep, Israel, Italy, Kenya,
South Korea, Kuwait, Nicaragua, Pakistan, Panama, Philippines, Sudan,
Tunisia, Turkey, Zambia and Zimbabwe. We find two cointegrating vectors
in case of Benin, Saudi Arabia, Cyprus, Denmark, Ecuador, Ghana,
Indonesia, Luxemburg and Paraguay and for the rest of countries, we find
one cointegrating vector. The existence of one or two cointegrating
vectors confirms the presence of cointegration between the variables.
This shows that trade openness and energy consumption have long run
relationship over selected period of time i.e., 1980-2010.
This ambiguity in the results based on single country study prompts
us to apply panel cointegration approach. (5) For this purpose, we apply
panel unit root tests to check for stationary properties of the series.
The results based on the LLC, IPS, MW (ADF) and MW (PP) unit root tests
with constant and, constant and trend are reported in Table 3. The tests
show that all variables are found to be non-stationary at level. At
first difference, all the series are integrated i.e. I(1). This unique
order of integration of the variables helps us to apply Johansen panel
cointegration approach to examine long run relationship between the
variables for selected panel.
The results are reported in Table 4. We find that maximum
likelihood ratio i.e. 5.9035 is greater than critical value at 1 percent
level of significance. This leads us to reject the null hypothesis of no
panel cointegration between the variables. We may conclude that the
panel cointegration exists between trade openness and energy consumption
in sampled countries. The Table 5 shows that trade openness affects
energy consumption in high, middle and low-income countries. In
high-income countries, we find that the relationship between trade
openness and energy consumption is inverted U-shaped. This implies that
initially trade openness is positively linked with energy consumption
and after a threshold level, it declines energy demand due to adoption
of energy efficient technology. This indicates that a 1 percent increase
in trade openness raises energy demand by 0.860 percent and negative
sign of nonlinear term of trade openness corroborates the delinking of
energy consumption as trade openness is at optimal level. In case of
middle and low income countries, relationship between trade openness and
energy consumption is U-shaped which reveals that trade openness
decreases energy consumption initially but energy consumption is
increased with continuous process of trade openness. In middle-income
countries, trade openness stimulates industrialisation, which raises
energy demand [Cole (2006)]. It is argued by Ghani (2006) that
low-income countries are unable to reap optimal fruits of trade
liberalisation because these economies are lacking in utilisation of
energy efficient technology to enhance domestic production.
The presence of cointegration between the series leads us to
investigate the direction of causality. In doing so, we have applied
homogeneous and non-homogenous panel causality and results are reported
in Table 6. The results of non-homogenous causality reveal the feedback
hypothesis between trade openness and energy consumption as
bidirectional causal relationship is confirmed between both the series.
We find that trade openness Granger causes energy consumption confirmed
by homogeneous causality (see Table 6).
Our results of non-homogenous causality validated the presence of
feedback effect, as trade openness and energy consumption are
interdependent. The unidirectional causality is found running from trade
openness to energy consumption. This validates the presence of
trade-led-energy hypothesis confirmed by homogenous causality approach.
This ambiguity in results would not be helpful in policy-making and
leads us to apply homogenous and non-homogenous causality approach using
data of low, middle and high-income countries. This will not only help
us in obtaining results region-wise but also enable us to design a
comprehensive trade and energy policy for sustained economic growth and
better living standards. In doing so, we have investigated the
homogenous and non-homogenous causal relationship separately for high,
middle and low-income countries. The results are reports in Table 7. In
high income countries, non-homogenous causality confirms the
unidirectional causality running from trade openness to energy
consumption but feedback effect is confirmed by homogenous causality
between both variables. The relationship between trade openness and
energy consumption is bidirectional for middle and low-income countries
confirmed by homogenous and non-homogenous causality approaches.
The results of heterogeneous causality reported in Table 7 suggest
the feedback relationship between trade openness and energy consumption
i.e. bidirectional causality exists in case of Albania, Cote
D'Ivoire, Cyprus, Egypt, Finland, Gabon, Honduras, Hong Kong,
Kuwait, Morocco, Norway, Panama, Saudi Arabia, Togo, Uruguay and Unites
States. Energy consumption Granger causes trade openness in case of
Bangladesh, Benin, China, Cuba, Ecuador, Ethiopia, France, Greece,
Guatemala, Indonesia, Mozambique, The Netherlands, Nicaragua, Paraguay,
Philippines, Sweden, Switzerland, Trinidad and Tobago, United Arab
Emirates, Venezuela and Zambia.
The unidirectional causality is found running from trade openness
to energy consumption. This validates the trade-led-energy hypothesis in
case of Algeria, Angola, Argentina, Australia, Botswana, Brazil, Chili,
Costa Rica, El Salvador, Ghana, India, Italy, Japan, Jordan, Mexico,
Nepal, Oman, Pakistan, Portugal, Sudan, Thailand and Turkey. The neutral
relationship between trade openness and energy consumption i.e. no
causality exists between both the variables for Austria, Belgium,
Bolivia, Brunei Darussalam, Bulgaria, Cameroon, Canada, Colombia, Congo
Democratic Republic, Congo Republic, Denmark, Dominican Republic,
Hungary, Iceland, Iran, Ireland, Israel, Kenya, South Korea, Luxemburg,
New Zealand, Nigeria, Senegal, South Africa, Spain, Syria, Tunisia,
United Kingdom, Vietnam and Zimbabwe.
5. CONCLUDING REMARKS AND FUTURE DIRECTIONS
This paper explores the relationship between trade openness and
energy consumption using data of 91 heterogeneous (high, middle and low
income) countries over the period of 1980-2010. In doing so, we have
applied time series as well as panel unit root tests to examine the
integrating properties of the variables. Similarly, to examine
cointegration between the variables, we have applied single country as
well as panel cointegration approaches. The homogenous and
non-homogenous causality approaches are applied to examine the direction
of causality between the variables in high, middle and low-income
countries. Heterogeneous causality approach has also been applied to
examine relationship between trade openness and energy consumption at
country level analysis.
Our results indicated that our variables are integrated at I(1)
confirmed by time series and panel unit root tests and same is inference
is drawn about cointegration between trade openness and energy
consumption. The pooled mean group estimation analysis reveals an
inverted U-shaped relationship in high income countries and vice versa
in middle and low income countries. The causality analysis confirms the
existence of feedback effect between trade openness and energy
consumption in middle and low income countries but bidirectional
causality is confirmed by homogenous causality approach in high income
countries, however non-homogenous causality approach indicates
unidirectional causality running form trade openness to energy
consumption. Heterogeneous causality exposes that in 18 percent of
sampled countries, the feedback effect exists while 24 percent show that
trade openness causes energy consumption and rest of sample countries
confirm the presence of neutral relationship between trade openness and
energy consumption.
Overall, our results demonstrate that the feedback effect exists
between trade openness and energy consumption, which suggests in
exploring new and alternative sources of energy to reap optimal fruits
of trade. Trade openness stimulates industrialisation that in turn
affects economic growth. This channel of trade affects energy demand via
economic growth. Similarly, insufficient energy supply impedes economic
growth, which affects exports as well as imports, and as a result energy
consumption decreases. Trade openness also is a source of transferring
advanced technologies i.e. energy efficient technology from developed
countries to developing economies. Our findings confirm that the
relationship between trade openness and energy consumption is U-shaped.
This suggests that middle and low-income countries should import energy
efficient technologies from developed economies to lower energy
intensity. This will not be possible if developed countries do not
promote those technologies and lower prices for countries, which do not
have access to required amounts of capital. Further, it will have a
positive impact on the world economy as it will save natural resources
for future generations and it will reduce environmental pollution.
This paper can be augmented for future research by incorporating
financial development, industrialisation, urbanisation in energy demand
function following Shahbaz and Lean (2012) in case of low, middle and
high-income countries. The semi-parametric panel approach proposed by
Baltagi and Lu (2002) could be applied to investigate the impact of
financial development, industrialisation, trade openness and
urbanisation on energy consumption using global level data. Using global
level data, trade openness, financial development, industrialisation,
urbanisation and CO2 emissions nexus could be investigated by applying
heterogamous panel under cross-sectional dependence framework.
Muhammad Shahbaz <shahbazmohd@live.com> is affiliated with
the COMSATS Institute of Information Technology, Lahore. Sarnia Nasreen
<sami_leu@yahoo.com> is affiliated with the Government College
University Faisalabad, Faisalabad Chong Hui Ling
<felicia.chonghl@gmail.com> is affiliated with the University of
Malaya Kuala Lumpur Malaysia. Rashid Sbia <Rashid.Sbia@ulb.ac.be>
is affiliated with the Department of Applied Economics, Free University
of Brussels, Brussels, Belgium.
APPENDIX A
List of World Countries
High Income Countries Middle Income Countries Low Income Countries
Angola Algeria Bangladesh
Australia Argentina Benin
Austria Bolivia Congo Dem Rep
Albania Botswana Ethiopia
Belgium Brazil Kenya
Brunei Darussalam Bulgaria Mozambique
Canada Cameroon Nepal
Cyprus Chile Togo
Denmark China Zimbabwe
Finland Colombia
France Congo Rep
Greece Costa Rica
Hong Kong Cote D'Ivoire
Hungary Cuba
Iceland Dominican Rep
Israel Ecuador
Italy Egypt
South Korea El Salvador
Kuwait Gabon
Luxemburg Ghana
The Netherlands Guatemala
New Zealand Honduras
Norway India
Oman Indonesia
Portugal Iran
Saudi Arabia Ireland
Spain Jamaica
Sweden Japan
Switzerland Jordan
Trinidad and Tobago Mexico
United Kingdom Morocco
United Arab Emirates Nicaragua
Unites States Nigeria
Pakistan
Panama
Paraguay
Peru
Philippines
Senegal
South Africa
Sudan
Syria
Thailand
Tunisia
Turkey
Uruguay
Venezuela
Vietnam
Zambia
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Comments
Paper gives a good comparison among the high, middle and low income
countries in terms of energy usage. Few comments which can improve the
paper are; inclusion of the role of mediating/moderating variable which
is production through which energy has causal relationship between trade
openness. Baron and Kenny (1986) (1) gives a good technique of using
moderating/mediating variable. Battery of tests is estimations are done
in the paper but authors are very miser to explain the results. Since
the panel data estimation is done to obtain the estimates therefore
there is no need for single country regression or if authors have
different objective in their mind then they did not explain it in the
text. The paper says that 25 percent of the sample countries have
positive association between energy and trade openness, what would
author infer from this result. Since the data is from 1980-2010, thus I
would recommend to apply a structural break test on the variables.
M. Ali Kemal
Pakistan Institute of Development Economics, Islamabad.
(1) Reuben M. Baron and David A. Kenny (1986) "The
Moderator-Mediator Variable Distinction in Social Psychological
Research: Conceptual, Strategic, and Statistical Considerations",
Journal of Personality and Social Psychology, Vol. 51, No. 6, 1173-1182.
(1) Payne (2010) and Ozturk (2010) presented comprehensive survey
studies on the relationship between economic growth and energy
consumption.
(2) Karlsson and Lothgren (2000) demonstrate the power of panel
unit root tests by Monte Carlo simulation. The null of all these tests
is that each series contains a unit root and thus is difference
stationary. However, the alternative hypothesis is not clearly
specified. In LLC the alternative hypothesis is that all individual
series in the panel are stationary. In IPS the alternative hypothesis is
that at least one of the individual series in the panel is stationary.
They conclude that the "presence or absence of power against the
alternative hypothesis where a subset of the series is stationary has a
serious implications for empirical work. If the tests have high power, a
rejection of the unit root null can be driven by few stationary series
and the whole panel may inaccurately be modelled as stationary. If, on
the other hand, the tests have low power it may incorrectly concluded
that the panel contains a common unit root even if a majority of the
series is stationary" (p. 254). The simulation results reveal that
the power of the tests (LLC, IPS) increases monotonically with: (1) an
increased number (N) of the series in the panel; (2) an increased time
series dimension (T) in each individual series; (3) increased proportion
of stationary series in the panel. Their Monte Carlo simulations for
N=13 and T=80 reveal the power of the test is 0.7 for LLC tests and
approaching unity for the IPS tests.
(3) Here, we do not consider instantaneous non-causality
hypothesis.
(4) The selection of countries is restricted to availability of
data. The names of countries are listed in Appendix-A.
(5) In some countries we could not find cointegration while in rest
of the countries we found the existence of cointegration between the
variables.
(6) Hausman test indicates that PMG model is preferred over PG
model.
(7) A graph is provided in Appendix for high income countries.
Table 1
ADF Unit Root Test
Level 1st Difference
Country/ Intercept Trend & Intercept Trend &
Variable Intercept Intercept
Algeria
[TR.sub.t] 0.4189 -0.8701 -3.8052 ** -5.1733 *
[EC.sub.t] -0.6407 -1.4528 -5.8948 * -5.2814 *
Argentina
[TR.sub.t] -1.0531 -3.0792 -5.2571 * -5.0271 *
[EC.sub.t] -0.8932 -2.8109 -3.6245 ** -3.6308 **
Austria
[TR.sub.t] -0.5524 -2.4505 -3.2985 ** -3.5066 ***
[EC.sub.t] -0.1863 -2.5139 -4.6619 * -4.4885 *
Bangladesh
[TR.sub.t] 0.6132 -3.0994 -3.9199 * -3.9065 **
[EC.sub.t] 1.0205 -2.3929 -4.6232 * -5.1651 *
Benin
[TR.sub.t] -0.3299 -2.3450 -4.9286 * -5.0471 *
[EC.sub.t] -1.9601 -2.6871 -3.5797 ** -3.5434 ***
Botswana
[TR.sub.t] -1.4420 -2.4192 -3.9853 * -4.0636 **
[EC.sub.t] -1.0734 -1.3623 -3.0628 ** -5.6302 *
Brunei Darussalam
[TR.sub.t] -0.3508 -1.4825 -3.6958 ** -5.7109 *
[EC.sub.t] -1.9429 -3.1187 -3.7129 ** -3.6122 ***
Canada
[TR.sub.t] -1.9408 -2.4400 -4.9088 * -5.2583 *
[EC.sub.t] -2.0028 -3.1663 -3.7820 * -3.7348 **
Chili
[TR.sub.t] -0.7908 -2.4845 -5.5118 * -5.3639 *
[EC.sub.t] 0.3533 -2.8041 -2.9216 *** -4.6043 *
Angola
[TR.sub.t] 1.5123 -0.5634 -3.5182 ** -4.5661 *
[EC.sub.t] -1.6214 -1.5625 -3.2417 ** -5.9735 *
Australia
[TR.sub.t] 0.3937 -2.6913 -4.3756 * -4.5020 *
[EC.sub.t] 0.1996 -2.7783 -4.1198 * -4.2963 **
Albania
[TR.sub.t] -0.7642 -1.6930 -4.4905 * -4.9971 *
[EC.sub.t] -1.5043 -1.2434 -3.0995 ** -3.2659 ***
Belgium
[TR.sub.t] -0.5282 -2.2922 -3.0316 ** -3.5863 ***
[EC.sub.t] -1.9601 -2.6871 -3.5797 ** -3.5434 ***
Bolivia
[TR.sub.t] 0.2859 -1.3079 -2.9710 *** -4.3259 **
[EC.sub.t] -1.4582 -2.1065 -3.5069 ** -3.4382 ***
Brazil
[TR.sub.t] 1.1870 -2.1045 -4.5757 * -4.8461 *
[EC.sub.t] -0.9027 -2.4494 -3.1364 ** -3.7495 **
Bulgaria
[TR.sub.t] -0.4585 -0.4585 -2.7263 *** -4.3906 **
[EC.sub.t] -1.3805 -2.2254 -3.3030 ** -3.9770 **
China
[TR.sub.t] 0.1074 -2.1102 -4.8452 * -4.8994 *
[EC.sub.t] 0.6452 -2.0721 -2.9494 ** -3.2235 ***
Congo Dem Rep
[TR.sub.t] -2.5579 -2.8169 -3.9579 * -3.8466 **
[EC.sub.t] -0.6483 -1.9564 -4.2579 * -4.1745 **
Colombia
[TR.sub.t] -0.0635 -2.6416 -3.1969 ** -1.5686 *
[EC.sub.t] -1.1615 -1.4324 -4.8072 * -4.8553 *
Congo Rep
[TR.sub.t] -1.5302 -2.7516 -3.9847 * -3.8813 **
[EC.sub.t] -1.2094 -0.5212 -3.2900 ** -3.4620 ***
Cote D'Ivoire
[TR.sub.t] 0.2225 -1.9929 -3.6169 ** -3.8302 **
[EC.sub.t] -0.9567 -1.7444 -3.9964 * -4.8263 *
Cuba
[TR.sub.t] -1.8938 -1.6057 -2.7562 *** -3.9406 **
[EC.sub.t] -1.4306 -2.8859 -2.9979 ** -2.9527 ***
Denmark
[TR.sub.t] -0.0910 -2.3117 -3.2089 ** -3.5203 ***
[EC.sub.t] -2.0518 -2.7916 -3.7190 ** -3.6570 **
Ecuador
[TR.sub.t] 0.7030 -2.0413 -3.4003 ** -3.9494 **
[EC.sub.t] -0.1665 -1.1361 -3.3996 ** -4.2587 **
El Salvador
[TR.sub.t] -0.0745 -2.2870 -3.4843 ** -3.3700 ***
[EC.sub.t] -0.0416 -1.7824 -2.8539 *** -3.7315 **
Finland
[TR.sub.t] -0.6923 -2.7347 -3.7078 ** -3.5774 ***
[EC.sub.t] -2.3395 -2.7686 -4.3644 * -4.1951 **
Gabon
[TR.sub.t] -0.9361 -2.7341 -3.9640 * -4.2463 **
[EC.sub.t] -2.2723 -1.0959 -3.5525 ** -4.5870 *
Greece
[TR.sub.t] 0.5889 -2.8057 -3.5020 ** -3.6567 **
[EC.sub.t] -1.8250 -2.0913 -4.5134 * -5.0303 *
Costa Rica
[TR.sub.t] -0.2737 -2.3264 -3.6127 ** -3.5250 ***
[EC.sub.t] -0.2865 -0.3390 -3.2568 ** -3.8902 **
Cameroon
[TR.sub.t] -1.5618 -2.9541 -2.7506 *** -5.6762 *
[EC.sub.t] -1.0496 -1.0088 -3.6118 ** -4.1561 **
Cyprus
[TR.sub.t] -0.4131 -1.6628 -3.3912 ** -3.3175 ***
[EC.sub.t] -1.5058 -0.5346 -3.3796 ** -3.8715 **
Dominican Rep
[TR.sub.t] -0.5985 -2.1949 -5.3140 * -5.2511 *
[EC.sub.t] -0.9124 -1.6794 -3.9453 * -3.8494 **
Egypt
[TR.sub.t] 0.5745 -2.7622 -2.7713 *** -3.6586 **
[EC.sub.t] -1.0024 -2.4033 -3.5517 ** -3.3564 ***
Ethiopia
[TR.sub.t] -0.0839 -1.2336 -4.3298 * -4.6814 *
[EC.sub.t] -1.4764 -1.9549 -3.2659 ** -3.8596 **
France
[TR.sub.t] -0.4312 -2.3780 -3.2569 ** -3.6901 **
[EC.sub.t] -1.3933 -1.7466 -4.2313 * -4.6509 *
Ghana
[TR.sub.t] -1.7857 -1.5640 -5.0802 * -5.4612 *
[EC.sub.t] -1.0468 -1.0777 -4.1390 * -4.2675 **
Guatemala
[TR.sub.t] 0.7712 -3.0441 -3.3703 ** -3.6195 **
[EC.sub.t] -1.3829 -2.0519 -3.3144 ** -3.4552 ***
Honduras
[TR.sub.t] -2.0091 -3.1213 -3.8804 * -4.4064 *
[EC.sub.t] -1.0752 -2.0968 -4.1316 * -4.7148 *
Hong Kong Sar China
[TR.sub.t] -1.1785 -1.3189 -2.6850 *** -3.8314 **
[EC.sub.t] -2.2905 -2.1313 -4.1514 * -4.6741 *
Iceland
[TR.sub.t] -0.0669 -2.9149 -3.9574 * -3.6995 **
[EC.sub.t] 1.3877 -1.0638 -2.6858 *** -4.4322 *
Indonesia
[TR.sub.t] 0.2339 -2.9163 -3.0756 ** -3.2696 ***
[EC.sub.t] -0.8880 -1.1027 -3.0141 ** -5.4069 *
Ireland
[TR.sub.t] -0.3663 -2.9986 -3.4761 * -4.3522 **
[EC.sub.t] -0.7152 -1.7686 -2.8905 *** -3.9752 **
Italy
[TR.sub.t] -0.4589 -2.1827 -3.0526 ** -3.6232 **
[EC.sub.t] -0.6640 -0.6640 -3.7542 * -3.5772 ***
Japan
[TR.sub.t] -0.5783 -1.5631 -3.7380 * -3.7787 **
[EC.sub.t] -1.5272 -0.7059 -2.9823 *** -3.4728 **
Kenya
[TR.sub.t] 0.9276 -2.3376 -3.6645 ** -4.5061 *
[EC.sub.t] -1.8363 -3.0614 -3.3529 ** -3.3313 ***
Kuwait
[TR.sub.t] -0.9690 -2.0366 -4.6979 * -5.2502 *
[EC.sub.t] -2.3481 0.4619 -4.8638 * -5.8653 *
Luxembourg
[TR.sub.t] -0.2836 -2.2064 -4.9548 * -4.8930 *
[EC.sub.t] -2.3473 -2.3293 -4.0122 * -5.6876 *
Mexico
[TR.sub.t] 0.2913 -2.4058 -3.8353 * -3.8029 **
[EC.sub.t] 0.2726 -1.6751 -4.5094 * -5.8401 *
Hungary
[TR.sub.t] 1.7100 -1.6508 -3.2192 ** -4.3836 **
[EC.sub.t] -1.5879 -1.6464 -4.2076 * -4.1344 **
India
[TR.sub.t] 1.8877 -0.6580 -3.0276 ** -3.8732 **
[EC.sub.t] -0.0584 -2.1698 -3.4824 ** -3.3593 ***
Iran
[TR.sub.t] -1.8514 -3.1574 -3.9574 * -3.8381 **
[EC.sub.t] -1.7349 -2.6435 -4.8904 * -4.8000 *
Israel
[TR.sub.t] 0.2725 -3.0813 -4.7457 * -4.6242 *
[EC.sub.t] -1.3830 -1.3627 -2.6706 *** -3.9254 **
Jamaica
[TR.sub.t] -0.9943 -1.0985 -3.0749 ** -3.3349 ***
[EC.sub.t] -0.5598 -2.9249 -2.9871 *** -3.9866 **
Jordan
[TR.sub.t] 1.6131 -1.0977 -3.5064 ** -4.1582 **
[EC.sub.t] -1.6982 -2.4034 -3.9477 * -3.7925 **
South Korea
[TR.sub.t] -0.4298 -2.3466 -3.7693 * -3.7279 **
[EC.sub.t] -1.1716 -1.7710 -3.3229 ** -3.2994 ***
Morocco
[TR.sub.t] -0.9696 -2.0819 -4.3410 * -4.1784 **
[EC.sub.t] -0.9635 -2.1519 -5.0387 * -5.2066 *
Nepal
[TR.sub.t] -2.3691 -1.8741 -3.7489 * -4.3319 *
[EC.sub.t] 0.4621 -1.3866 -3.7507 * -4.3404 *
Mozambique
[TR.sub.t] 0.3713 -0.5526 -3.1407 ** -3.3170 ***
[EC.sub.t] -2.2439 -1.5365 -3.5940 ** -3.7322 **
Netherland The
[TR.sub.t] -1.4168 -3.2000 -3.8649 * -3.9471 **
[EC.sub.t] -2.4361 -2.8255 -5.0101 * -4.9431 *
Nicaragua
[TR.sub.t] -0.4710 -1.1263 -3.3732 ** -3.3756 ***
[EC.sub.t] -1.5720 -1.9819 -4.6927 * -4.9537 *
Norway
[TR.sub.t] -1.1537 -2.6473 -4.9267 * -4.7619 *
[EC.sub.t] -1.4857 -2.6535 -3.7932 * -3.6945 **
Pakistan
[TR.sub.t] -0.8509 -1.5699 -3.6078 ** -3.7826 **
[EC.sub.t] -0.7991 -1.2641 -3.6304 ** -3.6256 **
Paraguay
[TR.sub.t] -1.0733 -1.8795 -3.3666 ** -3.2948 ***
[EC.sub.t] -1.9243 -1.5327 -3.4150 ** -3.5757 ***
Philippines
[TR.sub.t] 0.0850 -2.4948 -2.9139 *** -4.0941 **
[EC.sub.t] -1.0685 -0.8958 -2.7434 *** -5.7293 *
Senegal
[TR.sub.t] 0.3681 -1.9134 -3.9852 * -4.0835 **
[EC.sub.t] -2.0357 -1.7417 -3.7402 * -4.0870 **
Sweden
[TR.sub.t] -0.2027 -3.2173 -3.6094 ** -3.5278 ***
[EC.sub.t] -2.3509 -2.2029 -3.7852 * -4.1207 **
Spain
[TR.sub.t] -2.6228 -2.9807 -2.9065 *** -3.9750 **
[EC.sub.t] 0.3351 -2.5762 -3.3364 ** -3.6564 **
Sudan
[TR.sub.t] 0.9521 -0.2051 -2.6364 *** -3.7561 **
[EC.sub.t] 0.0171 -1.6685 -4.6910 * -5.0355 *
New Zealand
[TR.sub.t] -1.0605 -2.9833 -5.2135 * -5.1376 *
[EC.sub.t] -1.7181 -0.4779 -3.0886 ** -3.3346 ***
Nigeria 62
[TR.sub.t] -0.1775 -2.4375 -3.5531 ** -3.9467 **
[EC.sub.t] -1.7124 -2.4091 -4.8954 * -4.7717 *
Oman
[TR.sub.t] 0.5709 -1.9620 -4.7076 * -5.4118 *
[EC.sub.t] -1.6655 -1.1611 -3.2912 ** -3.8308 **
Panama
[TR.sub.t] -0.0274 -2.9196 -3.6502 ** -3.7050 **
[EC.sub.t] -1.4526 -2.1700 -3.5667 ** -3.5796 ***
Peru
[TR.sub.t] 0.9379 -1.2987 -4.1376 * -4.8637 *
[EC.sub.t] -2.4168 -1.6216 -3.0831 ** -3.8628 **
Portugal
[TR.sub.t] -0.9716 -1.9043 -3.1984 ** -3.7547 **
[EC.sub.t] -1.4205 -0.5693 -3.0971 ** -3.4068 ***
Saudi Arabia
[TR.sub.t] -1.1196 -3.0603 -2.9303 *** -3.8555 **
[EC.sub.t] -0.4166 -2.4292 -4.3369 * -4.4657 *
South Africa
[TR.sub.t] -0.1611 -2.2382 -3.3540 ** -3.5337 ***
[EC.sub.t] -2.4185 -2.7120 -3.9703 * -3.8643 **
Switzerland
[TR.sub.t] -0.5370 -2.1945 -3.0437 ** -3.6199 **
[EC.sub.t] -2.1958 -2.3868 -3.8958 * -4.1728 **
Thailand
[TR.sub.t] -0.6347 -1.8510 -2.9256 *** -3.8709 **
[EC.sub.t] -0.6523 -2.1115 -2.9460 *** -3.2717 ***
Syrian Arab Rep
[TR.sub.t] 0.7897 -2.2773 -3.2714 ** -3.7719 **
[EC.sub.t] -1.3196 -0.1094 -3.9862 * -4.2562 **
Togo
[TR.sub.t] -1.6974 -2.0971 -3.2771 ** -3.4455 ***
[EC.sub.t] -0.6940 -2.2815 -3.7204 ** -3.6245 **
Tunisia
[TR.sub.t] 0.2968 -2.9650 -2.6946 *** -3.8919 **
[EC.sub.t] -0.0885 -2.2401 -3.8989 * -3.6826 **
United Kingdom
[TR.sub.t] 0.2412 -3.2119 -2.7876 *** -3.2986 ***
[EC.sub.t] -1.7197 -0.5494 -3.4085 ** -4.1409 **
Uruguay
[TR.sub.t] -0.1814 -2.6080 -3.0855 ** -3.7887 **
[EC.sub.t] -2.3534 -3.0691 -4.1359 * -4.1451 **
Venezuela R.B.De
[TR.sub.t] 0.1327 -2.2907 -3.9118 * -4.8369 *
[EC.sub.t] -1.8629 -1.8146 -3.5727 ** -3.4811 ***
Zambia
[TR.sub.t] 0.7516 0.3288 -3.4925 ** -4.2436 **
[EC.sub.t] -1.5577 -0.5170 -3.8687 * -4.4820 *
Trinidad and Tobago
[TR.sub.t] 1.0311 -0.9596 -2.8083 ** -4.8930 *
[EC.sub.t] 1.4450 -0.9133 -3.1422 ** -3.4384 ***
Turkey
[TR.sub.t] -0.4813 -3.1314 -1.9825 * -4.7570 *
[EC.sub.t] -1.0464 -2.1727 -3.6186 ** -3.5759 ***
United Arab Emirates
[TR.sub.t] 1.1937 -2.0504 -2.7599 *** -3.7995 **
[EC.sub.t] -2.4012 -1.6495 -3.6501 ** -4.0875 **
United States
[TR.sub.t] -0.5591 -2.7876 -4.2063 * -3.9376 **
[EC.sub.t] -2.4541 -1.7094 -5.8708 * -5.6874 *
Vietnam
[TR.sub.t] -1.2282 -2.2356 -5.6683 * -5.7772 *
[EC.sub.t] 1.6287 -0.7176 -3.7120 ** -4.7837 *
Zimbabwe
[TR.sub.t] -1.6008 -1.6471 -3.1144 ** -3.4239 ***
[EC.sub.t] -1.1851 -2.0258 -4.1822 * -4.2352 **
Note: *, ** and *** denote significant at 1 percent, 5 percent and
10 percent levels respectively.
Table 2
Johansen Cointegration Test
Country Likelihood 5% critical P-value
Algeria Ratio Value
R = 0 348179 * 25.8721 0.0030
R [less than or equal to] 0 5.09129 12.5179 0.5833
Argentina
R = 0 27.1434 ** 25.8721 0.0346
R [less than or equal to] 0 6.42493 12.5179 0.4083
Austria
R = 0 27.04634 * 25.8721 0.0094
R [less than or equal to] 0 4.400725 12.5179 0.1968
Bangladesh
R = 0 28.7918 * 25.8721 0.0210
R [less than or equal to] 0 4.95061 12.5179 0.6035
Benin
R = 0 41.7722 * 25.8721 0.0003
R [less than or equal to] 0 15.0975 * 12.5179 0.0181
Botswana
R = 0 27.4591 ** 25.8721 0.0315
R [less than or equal to] 0 6.463937 12.5179 0.4038
Brunei Darrulsalm
R = 0 29.4351 ** 25.8721 0.0172
R [less than or equal to] 0 9.58154 12.5179 0.1474
Cameroon
R = 0 24.3665 25.8721 0.0761
R [less than or equal to] 0 9.47495 12.5179 0.1531
Chili
R = 0 31.5805 * 25.8721 0.0087
R [less than or equal to] 0 8.96315 12.5179 0.1826
Angola
R = 0 18.4636 25.8721 0.3136
R [less than or equal to] 0 7.45470 12.5179 0.2995
Australia
R = 0 29.8304 ** 25.8721 0.0152
R [less than or equal to] 0 8.00144 12.5179 0.2516
Albania
R = 0 33.7549 * 25.8721 0.0042
R [less than or equal to] 0 7.23212 12.5179 0.3209
Belgium
R = 0 26.6517 ** 25.8721 0.0400
R [less than or equal to] 0 7.11880 12.5179 0.3323
Bolivia
R = 0 66.8464 * 25.8721 0.0000
R [less than or equal to] 0 13.1493 12.5179 0.0392
Brazil
R = 0 13.7969 25.8721 0.6743
R [less than or equal to] 0 3.11117 12.5179 0.8631
Bulgaria
R = 0 21.5356 25.8721 0.1578
R [less than or equal to] 0 3.88762 12.5179 0.7583
Canada
R = 0 26.8541 ** 25.8721 0.0377
R [less than or equal to] 0 12.1440 12.5179 0.0577
China
R = 0 25.9354 ** 25.8721 0.0491
R [less than or equal to] 0 8.62820 12.5179 0.2045
Colombia
R = 0 26.9458 ** 25.8721 0.0367
R [less than or equal to] 0 7.87041 12.5179 0.2624
Congo Rep
R = 0 13.0347 25.8721 0.7355
R [less than or equal to] 0 2.38065 12.5179 0.9406
Costa Rica
R = 0 26.6582 ** 25.8721 0.0399
R [less than or equal to] 0 5.27551 12.5179 0.5573
Cuba
R = 0 35.5558 * 25.8721 0.0023
R [less than or equal to] 0 8.0965 12.5179 0.2439
Denmark
R = 0 36.5301 * 25.8721 0.0016
R [less than or equal to] 0 13.6372 ** 12.5179 0.0324
Ecuador
R = 0 49.3521 * 25.8721 0.0000
R [less than or equal to] 0 13.7689 ** 12.5179 0.0307
El Salvador
R = 0 35.1654 * 25.8721 0.0026
R [less than or equal to] 0 12.2436 12.5179 0.0555
Finland
R = 0 26.9650 ** 25.8721 0.0365
R [less than or equal to] 0 6.82323 12.5179 0.3633
Gabon
R = 0 30.0153 * 25.8721 0.0144
R [less than or equal to] 0 11.7234 12.5179 0.0676
Greece
R = 0 28.2878 ** 25.8721 0.0245
R [less than or equal to] 0 8.29920 12.5179 0.2282
Congo Dem Rep
R = 0 11.5926 25.8721 0.8392
R [less than or equal to] 0 3.06221 12.5179 0.8691
Saudi Arabia
R = 0 35.8987 * 25.8721 0.0020
R [less than or equal to] 0 17.0467 * 12.5179 0.0082
Cote D Ivories
R = 0 27.6100 ** 25.8721 0.0301
R [less than or equal to] 0 4.79881 12.5179 0.6254
Cyprus
R = 0 29.5951 ** 25.8721 0.0164
R [less than or equal to] 0 12.9237 ** 12.5179 0.0427
Dominican Rep
R = 0 41.7294 * 25.8721 0.0003
R [less than or equal to] 0 9.29973 12.5179 0.1627
Egypt
R = 0 35.8685 * 25.8721 0.0021
R [less than or equal to] 0 6.10382 12.5179 0.4472
Ethiopia
R = 0 30.3543 ** 25.8721 0.0129
R [less than or equal to] 0 5.16437 12.5179 0.5729
France
R =0 34.3356 * 25.8721 0.0035
R [less than or equal to] 0 6.76451 12.5179 0.3697
Ghana
R = 0 35.1224 * 25.8721 0.0027
R [less than or equal to] 0 14.1094 ** 12.5179 0.0268
Guatemala
R = 0 29.5195 ** 25.8721 0.0168
R [less than or equal to] 0 10.5420 12.5179 0.1046
Honduras
R = 0 26.0812 ** 25.8721 0.0471
R [less than or equal to] 0 10.9387 12.5179 0.0905
Hungary
R = 0 44.9969 * 25.8721 0.0001
R [less than or equal to] 0 8.98506 12.5179 0.1813
India
R = 0 26.1574 ** 25.8721 0.0461
R [less than or equal to] 0 4.72569 12.5179 0.6361
Iran
R = 0 37.4250 * 25.8721 0.0012
R [less than or equal to] 0 9.92483 12.5179 0.1306
Israel
R = 0 24.6479 25.8721 0.0704
R [less than or equal to] 0 4.03627 12.5179 0.7368
Jamaica
R = 0 29.4438 ** 25.8721 0.0172
R [less than or equal to] 0 7.55742 12.5179 0.2900
Jordan
R = 0 33.1366 * 25.8721 0.0052
R [less than or equal to] 0 3.17938 12.5179 0.8545
South Korea
R = 0 27.3817 ** 25.8721 0.0322
R [less than or equal to] 0 8.74030 12.5179 0.1970
Luxemburg
R = 0 40.8911 * 25.8721 0.0003
R [less than or equal to] 0 19.2744 * 12.5179 0.0032
Morocco
R = 0 29.1988 ** 25.8721 0.0186
R [less than or equal to] 0 6.63904 12.5179 0.3837
Hong Kong
R = 0 37.9506 * 25.8721 0.0010
R [less than or equal to] 0 7.72672 12.5179 0.2748
Iceland
R = 0 38.8020 * 25.8721 0.0007
R [less than or equal to] 0 5.81125 12.5179 0.4847
Indonesia
R = 0 31.2241 * 25.8721 0.0098
R [less than or equal to] 0 12.2892 ** 12.5179 0.0546
Ireland
R = 0 34.3030 * 25.8721 0.0035
R [less than or equal to] 0 7.14944 12.5179 0.3292
Italy
R = 0 17.09164 25.8721 0.4081
R [less than or equal to] 0 4.836427 12.5179 0.6200
Japan
R = 0 39.5565 * 25.8721 0.0006
R [less than or equal to] 0 10.5050 12.5179 0.1060
Kenya
R = 0 17.3930 25.8721 0.3862
R [less than or equal to] 0 6.66917 12.5179 0.3803
Kuwait
R = 0 28.2335 ** 25.8721 0.0250
R [less than or equal to] 0 9.24276 12.5179 0.1659
Mexico
R = 0 48.3444 * 25.8721 0.0000
R [less than or equal to] 0 6.1009 12.5179 0.4476
Mozambique
R = 0 31.0356 ** 25.8721 0.0104
R [less than or equal to] 0 10.8260 12.5179 0.0943
Nepal
R = 0 27.6112 ** 25.8721 0.0301
R [less than or equal to] 0 2.17146 12.5179 0.9572
New Zealand
R = 0 28.1404 ** 25.8721 0.0257
R [less than or equal to] 0 8.54960 12.5179 0.2100
Nigeria
R = 0 31.4737 * 25.8721 0.0090
R [less than or equal to] 0 8.19985 12.5179 0.2358
Oman
R = 0 26.4988 ** 25.8721 0.0418
R [less than or equal to] 0 8.58027 12.5179 0.2078
Panama
R = 0 21.1596 25.8721 0.1728
R [less than or equal to] 0 8.20377 12.5179 0.2355
Peru
R = 0 26.0875 ** 25.8721 0.0470
R [less than or equal to] 0 8.41322 12.5179 0.2198
Portugal
R = 0 12.4912 25.8721 0.7769
R [less than or equal to] 0 3.69726 12.5179 0.7854
Spain
R = 0 35.3192 * 25.8721 0.0025
R [less than or equal to] 0 10.2042 12.5179 0.1182
Sweden
R = 0 31.8140 * 25.8721 0.0081
R [less than or equal to] 0 6.4377 12.5179 0.4068
Syrian Arab Rep
R = 0 29.8728 ** 25.8721 0.0150
R [less than or equal to] 0 11.4533 12.5179 0.0748
Netherland The 62
R = 0 26.4791 ** 25.8721 0.0420
R [less than or equal to] 0 11.6056 12.5179 0.0707
Nicaragua
R = 0 11.8624 25.8721 0.8214
R [less than or equal to] 0 2.8651 12.5179 0.8922
Norway
R = 0 28.8942 ** 25.8721 0.0204
R [less than or equal to] 0 10.5826 12.5179 0.1031
Pakistan
R = 0 18.0948 25.8721 0.3376
R [less than or equal to] 0 3.5568 12.5179 0.8048
Paraguay
R = 0 35.5854 * 25.8721 0.0023
R [less than or equal to] 0 14.3679 * 12.5179 0.0242
Philippines
R = 0 10.9235 25.8721 0.8795
R [less than or equal to] 0 1.93863 12.5179 0.9723
South Africa
R = 0 31.1438 ** 25.8721 0.0100
R [less than or equal to] 0 4.3126 12.5179 0.6965
Sudan
R = 0 20.9619 25.8721 0.1811
R [less than or equal to] 0 7.2129 12.5179 0.3228
Switzerland
R = 0 27.5750 ** 25.8721 0.0304
R [less than or equal to] 0 7.2930 12.5179 0.3149
Thailand
R = 0 39.8339 * 25.8721 0.0005
R [less than or equal to] 0 6.4373 12.5179 0.4069
Togo
R = 0 48.6538 * 25.8721 0.0000
R [less than or equal to] 0 5.0368 12.5179 0.5911
Tunisia
R = 0 44.0057 * 25.8721 0.0001
R [less than or equal to] 0 16.1203 ** 12.5179 0.0120
United Kingdom
R = 0 44.3407 * 25.8721 0.0001
R [less than or equal to] 0 7.7262 12.5179 0.2748
Uruguay
R = 0 35.8733 * 25.8721 0.0020
R [less than or equal to] 0 5.38711 12.5179 0.5418
Venezuela R.B.De
R = 0 30.9671 ** 25.8721 0.0106
R [less than or equal to] 0 12.8779 ** 12.5179 0.0435
Zambia
R = 0 30.39876 ** 25.8721 0.0127
R [less than or equal to] 0 2.449747 12.5179 0.9345
Senegal
R = 0 31.1438 ** 25.8721 0.0100
R [less than or equal to] 0 4.3126 12.5179 0.6965
Trinidad and Tobago
R = 0 27.7872 ** 25.8721 0.0286
R [less than or equal to] 0 9.6121 12.5179 0.1459
Turkey
R = 0 30.0648 ** 25.8721 0.0141
R [less than or equal to] 0 6.6956 12.5179 0.3773
United Arab Emirates
R = 0 33.2987 * 25.8721 0.0049
R [less than or equal to] 0 6.3311 12.5179 0.4194
United States
R = 0 31.4441 * 25.8721 0.0091
R [less than or equal to] 0 1.6455 12.5179 0.9861
Vietnam
R = 0 26.1699 ** 25.8721 0.0459
R [less than or equal to] 0 8.0407 12.5179 0.2484
Zimbabwe
R = 0 24.9006 25.8721 0.0657
R [less than or equal to] 0 10.0065 12.5179 0.1269
Note: * and ** denote rejection of null hypothesis at 1 percent and
5 percent levels of significance respectively.
Table 3
Panel Unit Root Test
IPS TEST
Level 1st Difference
Trend and Trend and
Variables Intercept Intercept Intercept Intercept
[TR.sub.t] 10.5763 -1.1019 -19.8147 * -16.6784 *
[EC.sub.t] 2.5184 0.6182 -21.5562 * -17.8725 *
LLC TEST
Level 1st Difference
Trend and Trend and
Variables Intercept Intercept Intercept Intercept
[TR.sub.t] 5.6390 -0.4516 -19.1851 * -16.5538 *
[EC.sub.t] 1.7180 3.4397 -16.4287 * -13.5677 *
MW(ADF) TEST
Level 1st Difference
Trend and Trend and
Variables Intercept Intercept Intercept Intercept
[TR.sub.t] 30.9469 182.3521 366.570 * 296.0253 *
[EC.sub.t] 164.2160 200.3711 563.351 * 445.5541 *
MW(PP) TEST
Level 1st Difference
Trend and Trend and
Variables Intercept Intercept Intercept Intercept
[TR.sub.t] 32.2558 178.6561 1064.9488 * 895.8082
[EC.sub.t] 169.0261 196.1862 1471.0689 * 1282.0323 *
Note: * Denotes rejection of null hypothesis at 1 percent
significance level.
Table 4
Panel Cointegration Test
Hypotheses Likelihood Ratio 1% Critical Value
R = 0 5.9035 * 2.45
R [less than
or equal to] 0 0.9523
Note: * Denotes rejection of null hypothesis at 1 percent
significance level.
Table 5
Panel Cointegration Estimates
Pooled Mean Hausman
Variables Group (PMG) Mean Group(MG) Test (6)
High Income Panel (7)
[TR.sub.t] 0.860 * 1.315 ** 3.31
(0.000) (0.041) (0.191)
[TR.sup.2.sub.t] -0.015 * -1.688 **
(0.000) (0.054)
Middle Income Panel
[TR.sub.t] -0.023 ** -0.191 *** 1.45
(0.014) (0.063) (0.484)
[TR.sup.2.sub.t] 0.003 * 0.116 **
(0.000) (0.043)
Low Income Panel
[TR.sub.t] -1.493 * -2.827 ** 1.68
(0.000) (0.023) (0.321)
[TR.sup.2.sub.t] 0.0387 * 0.114 **
(0.000) (0.030)
Note: *, ** and *** show significance at 1 percent, 5 percent
and 10 percent levels respectively.
Table 6
Non-Homogenous and Homogenous Causality
Non-homogenous Causality
Dependent
Variables In [TR.sub.t] In [EC.sub.t]
In [TR.sub.t] -- Causality Exists *
In [EC.sub.t] Causality Exists * --
Homogenous Causality
Dependent
Variables [TR.sub.t] [EC.sub.t]
In [TR.sub.t] -- No Causality
In [EC.sub.t] Causality Exists * --
Note: * Represents significance at 1 percent level.
Table 7
Homogenous and Non-homogenous Causality
Homogenous Causality
Variables High Income Countries
In [TR.sub.t] In [EC.sub.t]
[TR.sub.t] -- Causality Exists *
[EC.sub.t] Causality Exists * --
Variables Middle Income Countries
In [TR.sub.t] In [EC.sub.t]
[TR.sub.t] -- Causality Exists *
[EC.sub.t] Causality Exists * --
Variables Low Income Countries
In [TR.sub.t] In [EC.sub.t]
[TR.sub.t] -- Causality Exists *
[EC.sub.t] Causality Exists *
Non-homogenous Causality
Variables High Income Countries
[TR.sub.t] [EC.sub.t]
[TR.sub.t] -- No Causality
[EC.sub.t] Causality Exists *
Variables Middle Income Countries
[TR.sub.t] [EC.sub.t]
[TR.sub.t] Causality Exists *
[EC.sub.t] Causality Exists *
Variables Low Income Countries
[TR.sub.t] [EC.sub.t]
[TR.sub.t] Causality Exists *
[EC.sub.t] Causality Exists *
Note: * Represents the significance at 1 percent level.
Table 8
Heterogeneous Causality
Country Variables [TR.sub.t] [EC.sub.t]
Algeria [TR.sub.t] ---- No Causality
[EC.sub.t] Causality exists * --
Angola [TR.sub.t] -- No Causality
[EC.sub.t] Causality exists * ----
Argentina [TR.sub.t] -- No Causality
[EC.sub.t] Causality exists * --
Australia [TR.sub.t] -- No Causality
[EC.sub.t] Causality exists * --
Austria [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Albania [TR.sub.t] -- Causality exists *
[EC.sub.t] Causality exists *** --
Bangladesh [TR.sub.t] -- Causality exist ***
[EC.sub.t] No Causality --
Belgium [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Benin [TR.sub.t] -- Causality exist **
[EC.sub.t] No Causality --
Bolivia [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Botswana [TR.sub.t] -- No Causality
[EC.sub.t] Causality exists * --
Brazil [TR.sub.t] -- No Causality
[EC.sub.t] Causality exists * --
Brunei [TR.sub.t] -- No Causality
Darussalam [EC.sub.t] No Causality --
Bulgaria [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Cameroon [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Canada [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Chile [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
China [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Colombia [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Congo Dem Rep [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Congo Rep [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Costa Rica [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Cote D'Ivoire [TR.sub.t] -- Causality exist ***
[EC.sub.t] Causality exist * --
Cuba [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Cyprus [TR.sub.t] -- Causality exist **
[EC.sub.t] Causality exist * --
Denmark [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Dominican Rep [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Ecuador [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Egypt [TR.sub.t] -- Causality exist ***
[EC.sub.t] Causality exist * --
El Salvador [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Ethiopia [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Finland [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
France [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Gabon [TR.sub.t] -- Causality exist ***
[EC.sub.t] Causality exist * --
Ghana [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Greece [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Guatemala [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Honduras [TR.sub.t] -- Causality exist **
[EC.sub.t] Causality exist * --
Hong Kong [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist *** --
Hungary [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Iceland [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
India [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Indonesia [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Iran [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Ireland [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Israel [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Italy [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Jamaica [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
Japan [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Jordan [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Kenya [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
South Korea [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Kuwait [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
Luxemburg [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Mexico [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Morocco [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
Mozambique [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Nepal [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist ** --
The [TR.sub.t] -- Causality exist *
Netherlands [EC.sub.t] No Causality --
New Zealand [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Nicaragua [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Nigeria [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Norway [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
Oman [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Pakistan [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Panama [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
Paraguay [TR.sub.t] -- Causality exist ***
[EC.sub.t] No Causality --
Peru [TR.sub.t] -- Causality exist ***
[EC.sub.t] No Causality --
Philippines [TR.sub.t] -- Causality exist ***
[EC.sub.t] No Causality --
Portugal [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist ** --
Saudi Arabia [TR.sub.t] -- Causality exist **
[EC.sub.t] Causality exist * --
Senegal [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
South Africa [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Spain [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Sudan [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Sweden [TR.sub.t] -- Causality exist ***
[EC.sub.t] No Causality --
Switzerland [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Syria [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Thailand [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
Togo [TR.sub.t] -- Causality exist ***
[EC.sub.t] Causality exist *** --
Trinidad [TR.sub.t] -- Causality exist *
and Tobago [EC.sub.t] No Causality --
Tunisia [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Turkey [TR.sub.t] -- No Causality
[EC.sub.t] Causality exist * --
United [TR.sub.t] -- No Causality
Kingdom [EC.sub.t] No Causality --
United Arab [TR.sub.t] -- Causality exist *
Emirates [EC.sub.t] No Causality --
Uruguay [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
Unites States [TR.sub.t] -- Causality exist *
[EC.sub.t] Causality exist * --
Venezuela [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Vietnam [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Zambia [TR.sub.t] -- Causality exist *
[EC.sub.t] No Causality --
Zimbabwe [TR.sub.t] -- No Causality
[EC.sub.t] No Causality --
Note: *, ** and *** represent significance at 1 percent, 5 percent and
10 percent levels respectively.