Financial Development, Energy Consumption and Trade ... - BZU

Pakistan Journal of Social Sciences (PJSS) Vol. 36, No. 1 (2016), pp. 505-518

Financial Development, Energy Consumption and Trade Openness Nexus: Empirical Evidence from Selected South Asian Countries Muhammad Hanif Akhtar, PhD Professor of Finance Department of Commerce Bahauddin Zakariya University Multan, Pakistan [email protected]

Muhammad Ramzan Sheikh, PhD Associate Professor of Economics School of Economics Bahauddin Zakariya University Multan, Pakistan [email protected]

Anam Altaf M. Phil Student School of Economics Bahauddin Zakariya University Multan, Pakistan [email protected]

Abstract This study examines the long run relationship and causality across financial development, energy consumption and trade openness for a panel of selected South Asian countries like Pakistan, India, Sri Lanka and Bangladesh. This study covers the period from 1974 to 2013 by using panel data estimation techniques. Pedroni panel Co-integration test has been applied for long run relationship where an evidence of long run relationship has been proved by Pedroni. A bi-directional causality is found between financial development and energy consumption and also for industrial and energy sectors. Unidirectional causality exists from growth to energy consumption and from energy to trade, financial development to industry, economic growth to industry and financial development and trade openness. Keywords:

Financial development, Trade openness, Energy consumption, Cointegration, Causality

1. Introduction The role of energy sector is crucial for an economy as the energy supply is essential for growth of economy being a crucial input in the production process. The industrial sector of an economy requires a reasonable energy supply to produce commodities on a large scale for domestic as well as external sectors. The extant literature reports positive association between energy use and trade openness which in turn leads to economic growth. An important contribution of this paper is to drive the attention towards energy conservation policies as if these tend to have a significant effect on economic activity. The direction of causality is of vital importance between energy consumption and economic growth. Thus, the developed countries need to evolve prudent

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energy policies to rationalize the energy consumption. If causality is found to be running from energy consumption to economic growth, the impact of energy conservation policies might be seen as negative on economy’s growth since it could reduce the output of goods and services. Financial development can be defined as policies, factors and institutions that lead to the efficient intermediation and effective financial markets. Financial development contributes to increase economic growth rate by increasing investment via level and efficiency effects. The level effect advocates that financial sector conduct resources from unskilled schemes to gainful schemes. This clearness in financial markets and reporting system appeals domestic and foreign investment by attracting investors’ confidence (Sadorsky, 2010). Financial development leads to boost economic growth by providing wider choices and by augmenting saving rate, saving investment ratio, marginal productivity of capital and making efficient distribution of resources. Energy consumption refers to the total amount of energy which is consumed in a process or system, by organization or society. We have different indicators of energy consumption such as electricity consumption, gas consumption, oil consumption and coal consumption. The demand of energy is increasing rapidly in both developed and developing countries. In many countries of the world, increased energy consumption has played a crucial role for economic development through increasing industrial output. Trade openness has also affected the demand for energy through scale effect, technique effect and composite effect. Scale effect indicates that all else remaining constant, trade openness heaves economic activity by stimulating domestic output and economic growth. The technique effect refers to improvements in technology that lowers the intensity of energy which, in turn, reduces the energy use to produce more quantity of output (Arrow, 1962). Composite effect discloses that the energy intensive production leads towards economic development by shifting from agriculture sector towards industry. Severe shortfalls of energy in emerging economies have proved energy to be a mandatory input for all the sectors of production. During the 19th century, coal has emerged as a substantial source of energy in stimulating economic growth in developed nations. Both developed and developing countries preferred the use of coal for electricity generation because of increasing costs of energy generated from the substitute sources of energy such as petroleum and natural gas. As petroleum and natural gas tend to be expensive, coal has become a reasonable source to promote economic enlargement and growth being a comparatively cheaper source of energy. The modern exertion to exchange goods, services, capital, labor, information, and ideas across the boundaries is referred to as trade openness. The trade openness has influenced stream of goods and services between developed and developing nations. The trade theory by Heckscher and Ohlin proposes the developing countries to specialize in production of those commodities that can be produced comparatively cheaper through factor abundance. Many of the developing countries like Pakistan are labor abundant and can produce labor abundant commodities relatively cheaper as compared to developed countries. However, through trade openness, the developing countries can import latest technologies from advanced nations which are being technology abundant.

Muhammad Hanif Akhtar, Muhammad Ramzan Sheikh, Anam Altaf

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The general objective of study is to empirically examine the causal relationship across energy consumption, trade openness and financial development in selected South Asian countries during the period 1974 to 2013. The specific objectives are as under:  To examine the relationship across energy consumption, trade openness and financial development in selected South Asian countries through an econometric model based on a balanced panel data series.  To test for the long run relationship across energy consumption, trade openness and financial development.  To explore the existence of causation across energy consumption, trade openness and financial development.

II. Review of Assorted Studies There exists a great amount of literature on the relationship across energy consumption, trade openness and financial development across the globe. However, some of the worth-mentioning studies are reviewed in Table-1 below. Table 1: Summary of Extant Research References

Period

Fakhr & Sheikhbahaie 1975-2005 (2008) Arouri et al. (2013).

1975-2011

Gries & Redlin 1970-2009 (2012)

Countries

Methodology

Model/ variables

Causality & results

East Asian Countries

Two equation Simultaneous Model

GDP=f(HDI, open, INV, Def, Pop Growth)

Open→Growth

Bangladesh

ARDL, granger causality test

GDP=f(FD, Trade openness)

FD & EG→Exports EG→Imports Trade↔EG

OECD Countries

Reza et al (2014)

1973-2011

Pakistan

Yucel (2009)

1989-2007

Turkey

Omotor (2008) 1970-2005

Nigeria

Yanikkaya (2003) Shahbaz & Lean (2012)

100 Developed 1970-1997 & Developing Countries 1971-2008

Tunisia

Aqeel and Butt 1947-1974 (2001)

Pakistan

Panel cointegration ECM & Openness & GDP Openness→GDP GMM Et=β0+β1yt+β2Ex EC↔EG ARDL & VECM ports+β3 Imports↔ Energy Imports+εt Demand Johansen coM2/GDP, integration & FD→Trade Exports+Imports/ Granger Causality Trade→EG GDP, Real GDP Test GDP, GNP, Hsiao s Granger Industrial causality production & EC→EG &Johansen coEnergy integration test consumption Panel OLS technique

Trade barriers have Yt=f(yt, K, Ht & u) adverse effects on Economic growth

ARDL & Granger EN=f( FD, GDP, causality test IND, URB) Hsiao’s Granger causality & 2SLS/OLS

Y=f(EN, Employment)

FD↔EC FD↔IND IND↔EC EG→EC(Petroleum) Electricity→EG

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Zeren & Koc (2014)

1971-2010

Nanthakumar & 1971-2008 Subramaniam (2010)

Asghar (2008) 1971-2003

Nasreen & 1980-2011 Anwar (2014)

Newly Industrialized

Malaysia

EC→FD Toda Yamamoto (Philippine) Three indicators causality test & EC↔FD( India, of FD EC & G0DP GMM Turkey & Thailand) ARDL/ ECM DOLS/ FMOLS

VAR Model 5 South Asian Toda & Yamamoto countries Granger noncausality test

South Asian countries

EC=f(GDP)

EG↔EC

EC=f(GDP)

Coal→GDP(Pak) GDP→Electricity (Srilanka) GDP→Electricity & Gas→GDP (Bangladesh) Petrolium→GDP (Nepal)

Pedroni, Larsson, EN=f(Yit, Tit, VECM Energy prices, εit)

EG↔EC Trade↔EC

Note: * EG denotes economic growth, EC refers to energy consumption and FD is used for financial development. ** α and β shows parameters.

III. Methodology A. Sample, Data Sources and Econometric Model Four of the South Asian countries have been chosen for the analysis of the econometric model on the basis of data availability and the strategy to use a balanced panel. The countries in the balanced panel comprise of Pakistan, India, Bangladesh and Sri Lanka. This study covers a period of 40 years between the years 1974 to 2013. The data on all of the variables used in the analysis was extracted from World Development Indicators by the World Bank. A detailed description of the variables is as under. Table 2: Description of Variables Variables Energy consumption GDP growth

Symbol

Trade openness

LT

Financial development

DC

Industrial output

IND

LEN LY

Unit of Measurement Log of Kilogram of oil equivalent per capita. Log of real GDP per capita in constant US dollars. Log of trade openness measured by exports (US$) plus imports (US$) divided by GDP. Domestic credit provided to private sector (households and businesses) as a percentage of GDP. Industry value added as percentage of GDP.

The model for causality across financial development, energy usage and openness to trade for the selected South Asian countries consists of the variables as financial development, energy consumption, economic growth, trade openness and industrialization. The relationship across energy consumption (EN), economic growth (y), openness to trade (T), financial sector development (DC) and industrialization (IND) is modelled as follows: ENit  f (Yit , Tit , DCit , INDit )

(1)

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LENit



 1iLYit   2iLTit   3iDCit   4iINDIT   it

(2)

In equation 2, the number of cross sections has been indicated by subscript i (i =1, 2,…, N) and time period is presented by the subscript t (t=1, 2,…, T) and u is the stochastic error term. B. Panel Unit Root Tests The co-integration analysis requires the co-integrating order of all the variables to be same. Thus, to reach at this end, the most common panel unit root tests, applied in this research is; Im, Pesaran and Shin (IPS) test. IPS Unit Root Test Im, Pesaran and Shin (1997) introduced a unit root test for heterogeneous panel. It is modified version of Levin et al. (1993) test as they allowed that coefficient of the lagged explained variables could also be heterogeneous and this test allows each series for its own short run dynamics. They recommended a test for the unit root that is established on the basis of arithmetic mean of distinct unit root test statistics. IPS test is on the contrast with LLC (Full name and not in references) unit root test in context as IPS undertakes individual unit root method while the latter assumes common unit root procedure. Assume that data series can be presented by the following equation without trend as follows. xj , t   k   jxj , t

j

1

   jk xj , t

 k



jt

k 1

Subsequently, the ADF regression has various augmentation lags for every country in the limited samples, the terms for expected value E  tT  and variance terms var  tT  are substituted by the corresponding group means of the table values of E  tT , pj  and var  tT , Pj 

correspondingly. The Im et al. test statistic permits that values of  j can be heterogeneous under the alternative hypothesis. This test has a more power and efficiency than a common single time series test. The equation of estimation for IPS unit root test can be presented as follows.

t

NT



1 N

N

t

j, t ( j)

k 1

Where ti, t indicate the Augmented Dickey-Fuller (ADF) test statistics for the i shows the order of lags used for ADF stationarity tests for each country and regression so test statistics can be estimated as follows based on the null hypothesis that the series are non-stationary with the alternative hypothesis of the series being stationary.



At  

N (T )( tT  E (tT )) var(tT )

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C. The Panel Cointegration Test To discover out the long run relationship across the variables, Pedroni panel test for co-integration has been used. Pedroni Panel Cointegration Test All the series are found to be non-stationary at levels but become stationary after first differencing reflecting the fact that series is co-integrated. To find out the long run relationship among the variables, it is necessary to perform a panel co-integration test. Latest co-integration tests developed for panel data have a greater power than the traditional ones. Some of the useful tests for long run relationship have been developed by Pedroni (1999, 2004). Pedroni (1999) introduced the following equation for the cointegration relationship.

xi,t= i+ it+ 1iZ1i.t+

  miZmi.t+ it

Wherever x and Z are supposed to be co-integrated. The αi is used as intercept term γ1i; γ2i; …; γmi are slope coefficient that may differ diagonally individual members of the panel. Pedroni (1999, 2004) recommended seven various test statistics for cointegrating association across heterogeneous panel. These tests avoid the biases resulting from possibly endogenous regressors. Pedroni (1999, 2004) introduced the common time dummies to be included for the elimination cross sectional dependence. The seven different test statistics (shown below) by Pedroni (1999, 2004) are categorized as interdimension and intra-dimensional ones. Panel v statistic: ˆ 2 1, jˆ 2 jt Zv  T 2 N 3/ 2 ( Nj 1 Tt 1 p

)1

1

Panel ρ statistics can be calculated from the following formula;

zp  T N ( Nj 1 Tt 1 pˆ 2 1, iˆ 2 it  1)1  Nj 1 Tt 1 pˆ 2 1, i(ît  1ît  î) Panel t-statistics, based on non-parametric equation is as below;

zt  ( 2  Nj 1 Tt 1 pˆ 2 1, jˆ 2it  1)1/2  Nj 1 Tt 1 pˆ 2 1, i(ˆ jt  1ˆ jt  ˆ j ) Panel t-statistics based on parametric is z t  ( s 

2

N, T

ˆ 2ˆ 2 jt  Nj 1 Tt 1 p

ˆ 2 1, jˆ 2 it )1/ 2  Nj 1 Tt 1 p

1

ˆ jt

1

Group ρ-statistics for the Pedroni panel co-integration is given below:

zp  TN 1/ 2  Nj 1 (Tt 1 ˆ 2 jt  1)1 Tt 1 (ˆ jt  1ˆ jt  î ) Group t-statistics based on non-parametric ˆ 2 T  ˆ 2 jt zt  N 1/ 2  Nj 1 ( t 1

)1/ 2 Tt 1 (ˆ jt

1

ˆ jt  ˆ j )

1

Group t-statistics based on parametric:

z t  N 1/ 2  Nj 1 (Tt 1 s  ˆ 2 jt  1)1/ 2 tN1 ˆ jt  1ˆ jt 2


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The first four test statistics presented above are based on inter-dimensions while the remaining is based on intra-dimension test statistics. Pedroni (1999) defines seven different test statistics where the first test statistics is based on simple panel cointegration statistics which is a kind of non-parametric variance ratio test. The second test is a panel sort of a nonparametric statistics that is related to the familiar Phillips–Perron rho-statistics. The third statistics is familiar to the Philip Perron and is non-parametric tstatistic. The fourth test is consistent with the ADF statistics which depend on common panel co-integration. (Pedroni 1999, p. 658). The remaining of the test statistics are established on the basis of a group mean methodology. The first among these statistics is similar to the Philips and Perron rho-statistics, and the last two are related to both the Phillips and Perron statistics and the augmented Dickey–Fuller statistics, correspondingly (Pedroni, 1999, p. 658). Pedroni (2004) observed the power properties of small samples for his seven test statistics. He originated that the alteration size is slight and the power is great in case when T=100. He further suggested that the best power properties that are followed by the panel ADF test; the panel variance test and group rho test cannot perform well in case of a small T. All of the seven test statistics tend to have a null hypothesis that there is no co-integration for all cross sections against alternative hypothesis of co-integration in panel for interdimensions and intra-dimensions. D. Estimation of Panel Co-integrating Regression If all the variables in analysis are integrated of order 1, then next phase is the estimation of long run parameters of inter-linked variables. If variables are found to be co-integrated, the OLS estimators would lead to biased and non-consistent outcomes. To overcome this problem, numerous techniques has been introduced. For example, Kao and Chiang (2000) introduced Dynamic Ordinary least Square (DOLS) estimation method for panel data (that groups the data for within dimension of the panel) gives better estimates when sample size is small for the co-integrating panels. Although, the panel DOLS developed by Kao and Chiang (2000) does not take into account the significance of heterogeneity for the cross sections developed in the alternative hypothesis. Pedroni (2000 and 2001) introduced the group mean fully modifies the OLS estimation method that overcome the endogeneity problem and serial correlation to attain consistent and unbiased estimates for the co-integrating vectors and perform well if sample size is small. The formula for fully modified OLS is as follows; ˆ  N 1  Nj 1  Tt 1 ( yjt  y )2 

1



T t 1

( yjt  y  z  jt  Tˆ j

Where: z  jt  ( zjt  z ) 

ˆ Lˆ 21 j ˆ 0  L 21 j ( 22 j   ˆ0 ) yjt ,ˆ j   21 j   21 j 22 j Lˆ 22 j Lˆ 22 j

L indicate the lower triangular for the decomposition of Ω. The value for tstatistics thus becomes;

t ˆ   N 1/2  Nj 1 t ˆ  , j

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E. Dumitrescu Hurlin Panel Causality Test When co-integration relationship exists among variables, the next step is to scrutinize the direction of causality across variables. Various approaches have been developed to examine the direction of causality but this study applies a simple version of Granger (1969) panel non-causality test developed by Dumitrescu and Hurlin (2012). The test is based on heterogeneous panel data model when coefficients are fixed. It also takes two dimensions of heterogeneity under consideration. We can mention causality test in form of linear model as:

yit   i  LL1  i ( L ) yi , t  L  LL1  ( L ) xi , t  L  it Where i  1, 2,......, N (i is used for cross sections and t stands for time period. The t  1, 2,......, T variables x and y in above equation are stationary detected for N cross section in T period while the  i And α represent individual effects which are fixed in time dimension, it is assumed that lag orders of L is same for all individual units of the panel. This test permits the autoregressive parameters iL regression coefficients  iL  to diverge across groups. The null hypothesis under Dumitrescu and Hurlin (DH) causality test is the existence of no causal relationship for any unit of panel, while the alternative hypothesis indicates the presence of causal relationship across variables. Null and alternative hypothesis can also be written as;

H 0   i  0 , i  1,......., N H1 :  i  0 , i  1,...., N1   0 , i  N1  1,...., N The acceptance of the null hypothesis indicates that no causality has existed across variables under consideration while the acceptance of the alternative hypothesis, means there exists a unidirectional causality.

IV. Empirical results and discussion The detailed results of each estimation techniques are discussed below. A. IPS Unit Root Tests Results The results of all unit root tests, mentioned in table-3 and table-4 below show the acceptance of null hypothesis at levels. All of the series become stationary after first differencing leading to the conclusion that all series are non-stationary at level but become stationary after the first differencing. Thus, the series are integrated to the order one (co-integrated) for the panel of selected South Asian countries. Tables 3 & 4 reflect the results of IPS panel unit root tests at both level and first difference as depicted below.

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Table 3: Unit Root Tests at Level Variables LEN LY DC LT IND

Without trend 8.69003 16.6099 3.00361 0.16224 0.93175

p-value 1.0000 1.0000 0.9987 0.4356 0.1750

With trend 3.39722 4.20796 0.80029 0.69348 1.19814

p-value 0.9999 1.0000 0.2118 0.2440 0.1154

Table 4: Unit Root Tests at First Difference Variables LEN LY DC LT IND

Without trend 7.06311 8.69825 6.44006 12.2145 11.7649

p-value 0.0000* 0.0000* 0.0000* 0.0000* 0.0000*

With trend 9.84631 17.3681 5.26992 11.3447 12.5149

p-value 0.0000* 0.0000* 0.0000* 0.0000* 0.0000*

*indicate that null hypothesis is not accepted

Table 5: Pedroni Panel Co-integration Outcomes Test Statistics P-value

Panel v Statistics 1.190778 0.1169

Panel ơ Statistics -1.003885 0.1577

Panel PP Statistics -5.102064 0.0000*

Panel ADF Statistics -3.558665 0.0002*

Group ơ Statistics -0.298664 0.3826

Group pp statistics -5.194765 0.0000*

Group ADF statistics -3.457474 0.0003*

*indicate the rejection of null hypothesis of no co-integration at 1%, 5% and 10% level of significance

The variables used in analysis are integrated to the order one so they are said to be co-integrated. The panel co-integration tests for examining the long run relationship as suggested by Pedroni (1999, 2004) are applied as shown in Table-3 above. Pedroni’s panel co-integration test is used to calculate the test statistics for panel (within dimension) and three group statistics to examine (between dimension) results for the panel of selected South Asian countries to see whether they are co-integrated or not. The expected values of test statistics for within dimension statistics are dependent on estimators that combined the autoregressive coefficients of different cross-sections on the behalf of the unit root test on the estimated errors. While on the other hand, the estimated values of test statistics for inter-dimensions group relay on the estimates that are independently valued coefficients used for each cross-section. The empirical evidence on intra-dimensions test statistics and inter-dimensions statistics indicate the rejection of null hypothesis as in the case of four statistics out of seven cases, the p-value is less than 5 percent. Thus, it can be concluded that the variables are said to be co-integrated which means that a long run relationship exists across variables under consideration. The empirical results are supported by Omotor (2008), as he found a long run relation between energy consumption and economic growth. Arouri et al. (2013) also discovered the long run association across financial development, openness and economic growth.

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Table 6: FMOLS Country Specific Results (LEN is dependent variable) Country BANG INDIA PAK LKA

Variables Coefficient P-value Coefficient P-value Coefficient P-value Coefficient P-value

DCit 0.006222 0.0116* -0.002137 0.0230* 0.013055 0.0059* 0.003769 0.0623

LYit LTit 0.546832 0.016906 0.0000* 0.6189 0.673961 -0.089892 0.0000* 0.0037* 0.740663 -0.150762 0.0000* 0.0699 0.327796 0.074639 0.0000* 0.3208

CONSTANT INDit 0.004576 1.430853 0.1255 0.0032* 0.007605 1.952777 0.0148* 0.0000* 0.013055 1.494486 0.0059* 0.0000* 0.014284 2.899730 0.0714 0.0000*

*display that results are significant at 5% and 10% significance level

Table 6 shows the results of Fully-modified ordinary least square (FMOLS) which are country- specific as they are calculated at individual level. The results reveal that the co-efficient for economic growth is positive for all the countries, implying that economic growth has a positive impact on energy consumption. The results in case of Bangladesh display the impact of financial development on energy consumption is positively significant. The coefficients on openness and industrialization are positive but have insignificant influence on energy consumption. The value for the constant is also positive and significant. The impact of domestic credit to private sector is negative and significant on energy consumption for India. The coefficient on industrialization is positive and highly significant and coefficient of trade liberalization is negatively significant. The coefficient on trade openness is negative but insignificant for Pakistan while the coefficients of other variables are positive and significant. The impact on financial sector, openness and industry is insignificant on energy consumption in Sri Lanka. Table 7: DOLS Country Specific Results (LEN is dependent variable) Country Bang INDIA PAK LKA

Variables Coefficient P-value Coefficient P-value Coefficient P-value Coefficient P-value

DCit 0.005323 0.2553 -0.001001 0.2032 0.002683 0.2748 0.001707 0.6440

LYit LTit 0.595144 -0.020576 0.0012* 0.7451 0.670052 -0.077206 0.0000* 0.0041* 0.741378 -0.269049 0.0000* 0.0018* 0.357092 0.189681 0.0000* 0.0703

INDit 0.007282 0.1889 0.001980 0.6549 0.022093 0.0000* 0.029801 0.0312*

CONSTANT 1.217019 0.1758 2.067664 0.0000* 1.703903 0.0000* 1.874838 0.0317*

*indicate the rejection of null hypothesis at 5% and 10% level of significance as p-value is less than 5%

Table 7 presents the result of DOLS which indicate that the coefficients of financial sector growth, openness and industrialization are positive but insignificant except for economic growth whose coefficient is positive and significant in Bangladesh. The effect of financial development in India is negative but not significant on energy and industrial output also have insignificance influence on the dependent variables. Both openness and economic growth have significant impact on energy use per capita but for openness, the coefficient is negative while in case of growth it is positive. In Pakistan, the impact of all explanatory variable on energy consumption is significant except for the financial sector development that has no effect on energy consumption. Trade openness coefficient stands as negative in the case of Pakistan. The impact of financial development, openness, growth and industrialization is positive on energy use in Sri

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Lanka but the co-efficient financial sector and trade openness are insignificant. Nanthakumar and Subramaniam (2010) also noticed positive and significant impacts of economic performance on energy consumption for Malaysia. Table-8: FMOLS and DOLS results for Panel (LEN: dependent variable) Variables DC LY LT IND

FMOLS Coefficient 0.002525 0.572313 -0.037278 0.009880

DOLS p-value 0.0056* 0.0000* 0.1782 0.0000*

Coefficient 0.005323 0.595144 -0.020576 0.007282

p-value 0.0209* 0.0000* 0.5119 0.0076*

*indicate that null hypothesis is rejected at 1%, 5% and 10% level of significance

Table 8 reflects combined results on FMOLS and DOLS for the panel of selected south Asian countries including Pakistan, India, Sri Lanka and Bangladesh. The results specify that the signs of coefficients are according to the theoretical literature. The results on financial development are significant according to both FMOLS and dynamic OLS models. It indicates that a 1% increase in financial development would surge the energy use by 0.002%. The economic growth and industrialization variables have been found to be positive and statistically significant according to both FMOLS and DOLS estimation techniques. However, the impact of trade openness on energy consumption is found to be statistically insignificant in both FMOLS and dynamic OLS models, and the sign on coefficient is negative. The panel results show that a 1% increase in economic growth rises the energy usage by approximately 0.57 per cent. Energy consumption rises by 0.09 percent while the industrialization increases by 10 per cent. Thus, according to these results the impact of trade openness is not important for economic growth while the role of economic growth and industrialization is positive towards energy consumption. The findings by Nasreen and Anwar (2014) and Raza et al. (2014) support the co-integrating relationship across variables by indicating the positive and significant impact of economic growth and trade on energy consumption. Table 9: Dumitrescu Hurlin Panel Causality Tests Null Hypothesis IND does not homogeneously cause LEN LEN does not homogeneously cause IND DC does not homogeneously cause LEN LEN does not homogeneously cause DC LY does not homogeneously cause LEN LEN does not homogeneously cause LY LT does not homogeneously cause LEN LEN does not homogeneously cause LT DC does not homogeneously cause IND IND does not homogeneously cause DC LY does not homogeneously cause IND IND does not homogeneously cause LY LT does not homogeneously cause IND IND does not homogeneously cause LT LY does not homogeneously cause DC

W-Stat. 5.00421 6.90476 4.52810 4.44748 4.53509 2.35889 1.09357 4.86229 5.61749 2.35409 6.09740 1.76020 3.27052 4.05328 7.04577

Z-Stat. 2.53195 4.20562 2.11268 2.04168 2.11883 0.20242 -0.91185 2.40697 3.07201 0.19819 3.49463 -0.32480 1.00522 1.69454 4.32979

Prob. 0.0113* 3.E-05* 0.0346* 0.0412* 0.0341* 0.8396 0.3618 0.0161* 0.0021* 0.8429 0.0005* 0.7453 0.3148 0.0902 1.E-05*

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DC does not homogeneously cause LY LT does not homogeneously cause DC DC does not homogeneously cause LT LT does not homogeneously cause LY LY does not homogeneously cause LT

1.08023 2.86798 2.55576 1.76806 6.59222

-0.92360 0.65073 0.37579 -0.31788 3.93038

0.3557 0.5152 0.7071 0.7506 8.E-05*

*shows that null hypothesis of non-causality is rejected at five percent level of significance, *shows that null hypothesis of non-causality is rejected at five percent level of significance.

B. Dumitrescu Hurlin Panel Causality Test Results Table 9 reveals a bi-directional causality across industrial output and energy consumption vis-à-vis, financial development and energy consumption. Uni-directional causality is found from economic growth to energy consumption and these results are in line with those by Aqeel and Butt (2001) and Asghar (2008). While Nanthakumar and Subramaniam (2010) found a bi-directional causality between economic growth and energy consumption. The results disclose one-way causality from energy consumption to trade openness. Raza et al. (2014) discovered feedback causal relation between these variables while Nasreen and Anwar (2014) found a uni-directional causality trade openness to energy consumption. Uni-directional causality is found from economic growth to energy consumption, energy consumption to trade openness, financial development to industrialization, and from economic growth towards industrialization. No causal relationship is found between openness and industrialization in either direction. There is presence of uni-directional causality from economic growth towards financial development and trade openness. The presence of uni-directional causality from economic growth towards financial development is also evaluated by Yucel (2009).

V. Conclusion and Policy Implications The major purpose of the study is to examine the significance of financial development, openness, economic growth and industrialization for the energy consumption. IPS panel unit root test is applied to test for the stationarity of variables. The results of panel unit root test show that data is non stationary at levels while the series become stationary after first differencing. All variables have been found to be integrated of order one and are said to be co-integrated. To explore the long run relation among variables, Pedroni panel co-integration techniques have been applied. To infer impacts of explanatory variables on the energy consumption that is dependent variables, fully modified OLS and dynamic OLS tests, developed by Pedroni, have been applied. The results of fully modified OLS indicate the impact of economic growth and industrial output as positive and significant on energy consumption while the effect of trade is found to be negative but insignificant on energy consumption. Results of the study expose a positive association between energy and growth which implies as GDP growth increases, it requires a greater use of energy. The GDP growth rate is essential for the development of economy as the causality runs from economic growth to energy consumption, trade openness, industrialization and financial development. This leads to a policy implication where policy makers need to develop energy growth policies without any harmful impacts on economic growth. Furthermore, measures ought to be taken towards augmenting economic growth rates as it is necessary to liberalize trade and financial markets by boosting the industrial output as well. The economic growth can be promoted by adopting policies that increase productivity through innovation, through human and physical capital formation and breakthrough on technological fronts. The


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emigration of skilled labor force needs to be stimulated by providing an attractive and secure atmosphere. Foreign direct investment from developed and developing countries could also be used as a strategy in this regard by providing a liberal and supportive investment climate to foreign investors. There is need to adopt energy-saving techniques of production which could amplify the output and growth levels in these economies.

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