Energy Consumption and Economic Growth: Evidence from 10 Asian Developing Countries

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1 J. Basic. Appl. Sci. Res., 2(2) , , TextRoad Publication ISSN Journal of Basic and Applied Scientific Research Energy Consumption and Economic Growth: Evidence from 10 Asian Developing Countries Nazar Dahmardeh 1, Majid Mahmoodi 2, Elahe Mahmoodi 3 1 Faculty of Economics, University of Sistan and Baluchestan, Zahedan, Iran 2 Young Researchers Club, Zahedan Branch, Islamic Azad University, Zahedan, Iran 3 Young Researchers Club, Zahedan Branch, Islamic Azad University, Zahedan, Iran ABSTRACT The purpose of this study is to analyze the causal relationship between energy consumption and economic growth for a panel of ten Asian developing countries during the 1980 to 2008 years. A Panel-VECM framework employed for investigate bi-variate model of energy consumption and real GDP. The results of panel cointegration test support the long-run relationship between variables, and panel causality test indicates bi-directional causality relationship between energy consumption and economic growth, which support the feedback hypothesis and imply important policy decision. KEY WORDS: Energy Consumption, Economic Growth, Panel Causality. JEL classifications: C33, Q43 1. INTRODUCTION Energy plays a substantial role in economic, and knowledge of the nature of relationship between energy and economic has an important policy implication. Hence investigate the relationship between energy consumption and economic growth has been one of the most important issues in the past three decade. Since the pioneering study of Kraft and Kraft (1978), many studies have attempted to investigate the relationship between energy consumption and economic growth. There are no common consensuses regarding this relationship among the different studies. Some studies found a unidirectional causality relationship; while some others found bidirectional causality, and few others cannot find any evidence of causality. The absence of common consensus can be due to the different time periods, different countries, different methodology, and of course, the structure and stages of economic development of countries. The results of some of these studies presented at follows. Lee and Chang (2007) analyzed the dynamic interactions between energy consumption and real GDP for 18 developing countries during the years and 22 developed countries over the periods by employing a panel VARs with GMM techniques. The results of the panel VARs indicate bidirectional causality in developed countries and unidirectional from economic growth to energy consumption in developing countries. Furthermore, impulse response functions show that all variables in the panel VARs have a positive effect on each other. Ozturk et al. (2010) studied the relationship and causality between energy consumption and economic growth for 51 countries (low income group, lower-middle income group and upper-middle income group) from 1971 to 2005 years. The results of panel causality test show that there is a long-run causality from economic growth to energy consumption for low income group and bidirectional causality for lower-middle income and upper-middle income group. In addition, the result of the panel FMOLS and DOLS estimator reveals that there is no strong relation between energy consumption and real GDP. Mehrara (2007) employed panel cointegration and panel causality test to investigate the causal relationship between energy consumption per capita and GDP per capita for a panel of oil-exporting countries over the 1971 to 2002 periods. The empirical results indicate a unidirectional strong causality from economic growth to energy consumption, which implies energy conservation is a feasible policy for these countries. The study of Chontanawat et al. (2008) attempted to examine the causality relationship between energy consumption and GDP by using time series data of 30 OECD countries and 78 non-oecd countries. Empirical finding indicated that causality from energy to economic growth are more prevalent in the OECD countries compared to the non- OECD countries. Akinlo (2008) examined the causality relationship between energy and economic growth for 11 Sub-Sahara African countries over the 1980 to 2003 years. The results of ARDL-bounds test indicate the existence of cointegration between energy and GDP for seven countries. Finally, the results of causality tests are mixed for different countries. *Corresponding Author: Majid Mahmoodi, Young Researchers Club, Zahedan Branch, Islamic Azad University, Zahedan, Iran. majid_mahmoodi63@yahoo.com 1385

2 Dahmardeh, 2012 Karanfil (2008) studied the long-run relationship between energy consumption and economic growth in turkey with considering to the unrecorded economy. The results indicate unidirectional causality from official GDP to energy in short-run and long-run. But when he considered unrecorded economy, empirical finding cannot show evidence of cointegration and also causality between energy consumption and true GDP. Soytas and Sari (2003) employed time series data of G-7 countries and 10 emerging countries to investigate the causality relationship between energy and GDP. The finding of this paper indicates unidirectional causality from energy consumption to economic growth for Turkey, France, Germany and Japan, causality from GDP to energy for Italy and Korea, and bidirectional causality for Argentina. Lee (2006) examined the causality relationship between energy consumption and economic growth for 11 major industrialized countries by employing Toda-Yamamoto (1995) approach. The results of empirical finding are mixed for different countries; there are no evidence of causality for United Kingdom, Germany and Sweden, bidirectional causality for United States and unidirectional causality from energy to GDP in Canada, Belgium, Netherlands and Switzerland, which implies that energy conservation policy may hinder economic growth in the latter five countries. In addition, the results indicate causality from GDP to energy for France, Italy and Japan. Esso (2010) employed Gregory and Hansen (1996a, b) threshold cointegration approach and Toda-Yamamoto (1995) causality test to analyze the relationship between energy consumption and economic growth for seven Sub-Saharan African countries over the 1970 to 2007 years. The results indicate that GDP has a significant positive effect in long-run on energy consumption in Cameroon, Cote d'ivoire, Ghana, Nigeria and South Africa before 1988 while this effect is negative after 1988 for Ghana and South Africa. Also, the results of causality test reveal bidirectional causality between energy consumption and economic growth in Cote d'ivoire, unidirectional causality from GDP to energy consumption for Congo and Ghana, and for other countries, there is no evidence of causality. However, the aim of this study is to extend the empirical literature on the relationship between energy consumption and economic growth with investigate it for 10 Asian developing countries. The rest of paper organized as follows: Section 2 discussed data and methodology. Section 3 present empirical results and finally conclusion presented in Section Data 2. DATA AND METHODOLOGY The use of panel data has several benefits in contrast with time series data: controlling for individual heterogeneity and give more informative data, more variability, less colinearity among the variables, and more efficiency (Baltagi. 2005). Therefore, this paper applies balanced panel data of energy consumption and real GDP of ten Asian developing countries include: Bangladesh, India, Indonesia, Iran, Jordan, Pakistan, Saudi Arabia, Sri Lanka, Syrian Arab Republic, and United Arab Emirates. Annual data of energy consumption and real GDP over the 1980 to 2008 years obtained from World Development Indicators (WDI). Energy consumption (EC) defined as energy use in kilotons of oil equivalent and real GDP measured in constant 2000 US dollars. The natural logarithms of variables are denoted as LEC and LGDP. 2.2 Methodology Panel Unit Root Test Several Panel unit root tests proposed to discover the stationary properties of panel data. This paper applied four tests proposed by Levin et al. (LLC, 2002), Im et al. (IPS, 2003), Breitung (2000) and Fisher-type test proposed by Maddala and Wu (1999) and Choi (2001) to test the null hypothesis of having unit root. Following Dickey and Fuller (1979, 1981), Levin and Lin (1993), and Levin, Lin and Chu (2002), consider a panel extension of the null hypothesis that each individual time series in the panel contains a unit root against the alternative hypothesis that all individual series are stationary.(hsiao, 2003). The adjusted t-statistic of LLC is: t NTS ˆ ˆ ˆ( ˆ ) 2 * * N mt t * (1) mt Where μ and σ are the mean and standard deviation adjustments provided by table 2 of LLC. Levin, Lin and Chu show that t is asymptotically distributed as N (0, 1). The test of Im, Pesaran and Shin (IPS, 2003) allow for a heterogeneous coefficient of y it-1 and propose an alternative testing procedure based on averaging individual unit root test statistics. IPS suggests an average of the ADF tests when u it is serially correlated with different serial correlation properties across cross-sectional units. The t-statistic of IPS given as follows: 1386

3 J. Basic. Appl. Sci. Res., 2(2) , N N ( t E[ t 0]) i 1 it i N t N (0,1) IPS (2) 1 N var[ t 0] i 1 it i N Values of E[t it ρ i = 0] and var[t it ρ i = 0] obtained from the results of Monte Carlo simulations carried out by IPS. As mentioned in Baltagi (2005), Breitung (2000) found that the LLC and IPS tests suffer from a dramatic loss of power if individual-specific trends are included. Breitung suggests a test statistic that does not employ a bias adjustment whose power is substantially higher than LLC or the IPS tests using Monte Carlo experiments. Maddala and Wu (1999) and Choi (2001) proposed a Fisher-type test of unit root, which combines the p-values from unit root tests for each cross-section i to test for unit root in panel data. The Fisher test is nonparametric and distributed as chi-square with two degrees of freedom: pλ = 2 log π (3) Panel Cointegration Test Several test presented to examine the existence of cointegration in panel data. This paper applied panel cointegration test of Kao (1999) and Pedroni (1999, 2004). Kao (1999) introduced parametric residual-based panel cointegration. He expanded four DF-types and one ADFtype tests for testing the null hypothesis of no cointegration. The tests are based on the spurious least squares dummy variable (LSDV) panel regression equation with one single regressor. Pedroni presented seven statistics for testing the null hypothesis of no cointegration in panel data. Four statistics called panel cointegration statistics and based on pooling along what is commonly referred to as the within-dimension. And other three statistics developed by Pedroni called group-mean panel cointegration statistics, are based on pooling along what is commonly referred to as the between-dimension Granger Causality Test The causality between two variables in panel data can be studied by using the following bi-variate vector autoregressive (VAR) model and employing Wald's test: y = λ + α y + β x + ε x = θ + γ y + δ x + υ Where i=1,, N; t=1,, T; k refers to the lag, and ε and υ denote white-noise error terms. 3. EMPIRICAL RESULTS 3.1. Panel Unit Root Test The results of Levin et al. (LLC, 2002), Im et al. (IPS, 2003), Breitung (2000) and Fisher-type panel unit root test reported in table 1. Table 1: Panel Unit Root Tests Variable LGDP Test Levels 1st differences Levels 1st differences LLC (2002) *** *** IPS (2003) *** *** Breitung (2000) *** *** ADF-Fisher *** *** PP-Fisher *** *** Note: *** denote statistical significance at the 1% levels. The results of several panel unit root tests cannot reject the null hypothesis of unit root in levels, which means that LGDP and LEC are non-stationary in levels. But the results of panel unit root tests in the first difference indicate that LGDP and LEC become stationary after first difference. (4) (5) LEC 1387

4 Dahmardeh, Panel Cointegration Test The results of Kao panel cointegration test presented in table 2. All statistics of Kao panel cointergartion test, exceptdf, support the existence of cointegration between energy consumption and economic growth. Test Statistics DF ρ DF t DF ρ DF t ADF Note: *** and ** denote statistical significance at the 1 and 5 % levels. Table 2: Kao Panel Cointegration Test Statistics *** *** *** ** Table 3: Pedroni Panel Cointegration Test Test Statistics Statistics Panel v-statistic *** Panel ρ-statistic ** Panel non-parametric (PP) t-statistic *** Panel parametric (ADF) t-statistic *** Group ρ-statistic Group non-parametric t-statistic * Group parametric t-statistic ** Note: ***, ** and * denote statistical significance at the 1, 5 and 10% levels, respectively. Table 3 presents the results of Pedroni panel cointegration tests. All statistics of Pedroni, except group ρ-statistic, reject the null hypothesis of no cointegration and indicate the existence of cointegration among energy and GDP. In overall results of Kao and Pedroni tests support the existence of cointegration between LEC and LGDP, which implies long-run relationship between energy consumption and economic growth Panel Causality Test As Granger (1969, 1988) points out, if there exists a cointegration between variables, there is causality among these variables at least in one direction. Therefore, to determine the direction of causality a panel-vecm causality which is based on Wald test applied in this paper. A bi-variate panel-vecm for examine the causality between energy consumption and economic growth can be written as follows: ΔLGDP = c + α ΔLGDP + β ΔLEC + γ ECT + ε (6) ΔLEC = c + α ΔLGDP + β ΔLEC + γ ECT + υ (7) Where is the first difference operator and ECT is lagged values of the error correction term. Short-run causality from energy consumption to economic growth is tested based on H 0 : β 1ik= 0 for all i and k in Eq. (6). Similarly, the null hypothesis for Eq. (7), is H 0 : α 2ik = 0 for all i and k, which test short-run causality from economic growth to energy consumption. The null hypothesis on no long-run causality in each Eq. (6)-(7), is tested by examining the significance of the coefficient of the respective error correction term. Before running causality, this is important to find the optimal lag; hence Akaike's information criterion (AIC) and Schwarz information criterion (SC) used for Lag-length selection and indicated 2 lags. Table 4: Panel-VECM Causality Test Dependent variable Source of causation (independent variables) Short-run Long-run LGDP LEC ECT LGDP *** *** LEC *** Note: ***, ** and * denote statistical significance at the 1, 5 and 10% levels, respectively. The results of panel causality indicate unidirectional causality from energy consumption to GDP in short-run, and bidirectional causality between energy consumption and economic growth in long-run. 1388

5 J. Basic. Appl. Sci. Res., 2(2) , Conclusion The aim of this study is to examine the causality between energy consumption and economic growth in short-run and long-run for 10 Asian developing countries over the 1988 to 2008 years. For this purpose, a panel-vecm causality framework employed. At first, panel unit root test performed and indicate that LGDP and LEC are integrated of order one. Then, results of Panel co integration test revel long-run relationship between LGDP and LEC. Finally, panel-vecm causality based on Wald test performed and indicates unidirectional causality from energy consumption to economic growth in short-run, and bidirectional causality between LEC and LGDP in long-run. In other words, feedback hypothesis is confirmed in these countries. This result implies that the level of economic activity and energy consumption mutually influence each other, for instance, any negative shock on GDP will have negative effects on energy and vice versa. REFERENCES 1. Akinlo, A.E., Energy consumption and economic growth: Evidence from 11 Sub-Sahara African countries. Energy Economics 30, Baltagi, B.H., Econometric Analaysis of Panel Data. John Wiley & Sons, Ltd Third Edition. 3. Breitung, J., The local power of some unit root tests for panel data. Advances in Econometrics 15, Choi, I., Unit root tests for panel data. Journal of International Money and Finance 20, Chontanawat, J., L.C.Hunt, and R.Pierse, Does energy consumption cause economic growth?: Evidence from a systematic study of over 100 countries. Journal of Policy Modeling 30, Dickey, D.A., and W.A.Fuller, Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association 74, Dickey, D.A., and W.A.Fuller, Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica 49, Esso, L.J., Threshold cointegration and causality relationship between energy use and growth in seven African countries. Energy Economics 32, Granger, C.W.J., Investigating causal relationships by econometric models and cross-spectral methods. Econometrica 37, Granger, C.W.J., Some recent development in a concept of causality. Journal of Econometrics 39, Gregory, A.W., and B.E.Hansen, 1996a. Residual-based tests for cointegration in models with regime shifts. Journal of Econometrics 70, Gregory, A.W., and B.E.Hansen, 1996b. Tests for cointegration in models with regime and trend shifts. Oxford Bulletin of Economics and Statistics 58, Hsiao, C., Analysis of Panel Data, Second Edition. Cambridge University Press, Cambridge. 14. Im, K.S., M.H.Pesaran, and Y.Shin, Testing for unit roots in heterogeneous panels. Journal of Econometrics 115, Kao, C., Spurious regression and residual-based tests for cointegration in panel data. Journal of Econometrics 90, Karanfil, F., Energy consumption and economic growth revisited: Does the size of unrecorded economy matter?. Energy Policy 36, Kraft, J., and A.Kraft, On the relationship between energy and GNP. Journal of Energy Development 3, Lee, C.C., The causality relationship between energy consumption and GDP in G-11 countries revisited. Energy Policy 34, Lee, C.C., and C.P.Chang, Energy consumption and GDP revisited: A panel analysis of developed and developing countries. Energy Economics 29, Levin, A., and C.F.Lin, Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties. Working paper, University of California, San Diego. 21. Levin, A., C.F.Lin, and C.S.J. Chu, Unit-root test in panel data: asymptotic and finite sample properties. Journal of Econometrics 108, Maddala, G.S., and S.Wu, A comparative study of unit root tests with panel data and a new simple test. Oxford Bulletin of Economics and Statistics 61, Mehrara, M., Energy consumption and economic growth: The case of oil exporting countries. Energy Policy 35, Ozturk, I., A.Aslan, and H.Kalyoncu, Energy consumption and economic growth relationship: evidence from panel data for low and middle income countries. Energy Policy 38, Pedroni, P., Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61,

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