Another Look at the Electricity Consumption-Growth Nexus in South America

This article was downloaded by: [University of Iowa Libraries] On: 05 October 2014, At: 01:24 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Energy Sources, Part B: Economics, Planning, and Policy Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uesb20 Another Look at the Electricity Consumption-Growth Nexus in South America N. Apergis a & J. E. Payne b a Department of Banking and Financial Management , University of Piraeus , Piraeus , Attiki , Greece b Department of Finance , College of Business, University of South Florida , Tampa , Florida , USA Published online: 10 Jan 2013. To cite this article: N. Apergis & J. E. 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Payne2 1Chair and Professor, Department of Banking and Financial Management, University of Piraeus, Piraeus, Attiki, Greece 2Department of Finance, College of Business, University of South Florida, Tampa, Florida, USA This study estimates a panel error correction model to determine the Granger-causal relationship be- tween renewable and non-renewable electricity consumption and economic growth for South America. The results show a long-run equilibrium relationship between real gross domestic product, renewable electricity consumption, non-renewable electricity consumption, real gross fixed capital formation, and the labor force with the respective long-run coefficient estimates positive and statistically significant. The Granger-causality results indicate bidirectional causality between renewable and non-renewable electricity consumption, respectively and economic growth in both the short-run and long-run. Keywords: electricity consumption, Granger–causality, growth, panel South America 1. INTRODUCTION The causal relationship between energy consumption and economic growth has undergone exten- sive investigation as documented in the surveys of Ozturk (2010) and Payne (2010a,b). Indeed, it is important to understand the extent to which energy consumption contributes to the economic growth process. Does energy consumption have a direct impact on economic growth? Is energy consumption driven by economic growth? Is there an interdependent relationship between energy consumption and economic growth or no relationship at all? Answers to these questions are vital to the design and implementation of energy conservation policies. For instance, if energy consumption and economic growth are positively correlated, the result that energy consumption has a direct impact on economic growth or an interdependent relationship would suggest that energy conservation policies that reduce energy consumption will have an adverse impact on economic growth. On the other hand, if economic growth actually drives energy consumption or there is no significant relationship between energy consumption and economic growth, energy conservation policies will have little or no impact on economic growth. For developing and emerging market economies, prudent use of energy resources is critical to the growth prospects of these countries. Such is the case in South America, a region rich in both Address correspondence to James E. Payne, Department of Finance, College of Business, University of South Florida, Tampa, FL 33620 USA. E-mail: [email protected] 171 D ow nl oa de d by [ U ni ve rs ity o f Io w a L ib ra ri es ] at 0 1: 24 0 5 O ct ob er 2 01 4 172 N. APERGIS AND J. E. PAYNE renewable and non-renewable energy resources and recently experiencing tremendous growth.1 The South American region is among the world’s leaders in oil, natural gas, hydroelectricity, and ethanol production. Venezuela, Brazil, Ecuador, and Argentina are emerging as major oil producers. Venezuela is also blessed with substantial natural gas reserves while Argentina is the region’s largest natural gas producer. In regards to renewable energy resources, Brazil is one of the largest ethanol producers in the world and along with Paraguay, a major producer of hydroelectricity. However, South America’s dependence on fossil fuel energy sources for both export and tax revenues has raised some concerns regarding a sustainable energy consumption mix that would ensure stable economic growth in the long-run. While some countries in South America, like Brazil, are perhaps at the forefront of developing their renewable energy sector, other countries in the region are not. Therefore, policymakers need to evaluate the relative impact of renewable and non-renewable energy consumption on economic growth in order to gauge the optimal energy consumption mix for the South American region. This study extends recent work on the energy consumption-growth nexus that distinguishes between renewable and non-renewable energy consumption and the causal relationship of each with respect to economic growth to the case of South America. Specifically, the causal relationship between both renewable and non-renewable electricity consumption and economic growth within a production model framework is examined for nine South American countries via a panel error correction model. The study utilizes the panel cointegration procedure of Larsson et al. (2001) which provides additional power by combining the cross-section and time series data while allowing for heterogeneity across countries. Section 2 surveys the energy consumption-growth literature specific to South American coun- tries. Section 3 presents the data, methodology, and results. Section 4 provides concluding remarks. 2. THE ENERGY CONSUMPTION-GROWTH LITERATURE FOR SOUTH AMERICA Previous studies of the energy consumption-growth causal relationship for South American coun- tries have yielded mixed results. Nachane et al. (1988) find unidirectional causality from commer- cial energy consumption per capita to real gross domestic product (GDP) per capita in the case of Argentina and Chile while finding bidirectional causality for Brazil, Colombia, and Venezuela. Murray and Nan (1996) show unidirectional causality from real GDP to electricity consumption for Colombia. Cheng (1997) reports unidirectional causality from energy consumption to real GDP for Brazil and the absence of causality in the case of Venezuela. Soytas and Sari (2003) reveal bidirectional causality between energy consumption and GDP per capita in the case of Argentina. Mahadevan and Asafu-Adjaye (2007) find bidirectional causality between energy consumption per capita and real GDP per capita for Argentina and Venezuela. Squalli (2007) shows unidirectional causality from electricity consumption per capita to real GDP per capita in the case of Venezuela. In an exhaustive study of over 100 countries, Chontanawat et al. (2008) report unidirectional causality from energy consumption per capita to real GDP per capita for Chile, Colombia, and Uruguay; unidirectional causality from real GDP per capita to energy consumption per capita for Boliva, Paraguay, Peru, and Venezuela; bidirectional causality for Argentina and Brazil; and the absence of causality for Ecuador. Yoo and Kwak (2010) reveal unidirectional causality from electricity consumption per capita to economic growth per capita for Argentina, Brazil, Chile, Columbia, and Ecuador; bidirectional causality for Venezuela; and the absence of causality for Peru. In a panel study of nine South American countries, Apergis and Payne (2010) find 1The brief overview of the energy outlook for South America is drawn from International Energy Outlook (United States Energy Information Administration, 2010) and Apergis and Payne (2010). D ow nl oa de d by [ U ni ve rs ity o f Io w a L ib ra ri es ] at 0 1: 24 0 5 O ct ob er 2 01 4 THE ELECTRICITY CONSUMPTION-GROWTH NEXUS IN SOUTH AMERICA 173 unidirectional causality from energy consumption to economic growth in both the short-run and long-run. In a recent study by Sadorsky (2012), results from a panel of seven South American countries show bidirectional causality between energy consumption and exports, and output and exports. However, there is the absence of a causal relationship between output and energy consumption. Alongside the energy consumption-growth literature for South America, recent work by Apergis and Payne (2011a,b; 2012a,b) differentiates between renewable and non-renewable energy con- sumption and the Granger-causal relationship of each with respect to economic growth. Apergis and Payne (2012a) find bidirectional causality between renewable and non-renewable energy consumption respectively and economic growth in both the short-run and long-run for a panel of 80 countries. With an emphasis on emerging market economies, Apergis and Payne (2011a) report unidirectional causality from economic growth to renewable electricity consumption in the short-run and bidirectional causality in the long-run whereas there is bidirectional causality between non-renewable electricity consumption and economic growth in both the short-run and long-run. In a panel study distinguishing between developed and developing countries, Apergis and Payne (2011b) show that irrespective of the level of economic development there is bidirectional causality between renewable and non-renewable energy consumption, respectively, and economic growth in both the short-run and long-run. For a panel of Central American countries, Apergis and Payne (2012b) find unidirectional causality from renewable electricity consumption to economic growth in the short-run, but bidirectional causality in the long-run. On the other hand, bidirectional causality exists between non-renewable electricity consumption and economic growth in both the short-run and long-run. 3. DATA, METHODOLOGY, AND RESULTS Annual data from 1990 to 2007 from the US Energy Information Administration and World Bank Development Indicators are used in the empirical analysis. A production model framework is specified as real GDP (Y, denoted in billions of constant 2000 US dollars) as a function of total renewable electricity consumption (RE, denoted in millions of kilowatt hours), total non- renewable electricity consumption (NRE, denoted in millions of kilowatt hours), real gross fixed capital formation (K, denoted in billions of constant 2000 US dollars), and total labor force (L, denoted in millions).2 The South American panel consists of Argentina, Bolivia, Brazil, Chile, Ecuador, Paraguay, Peru, Uruguay, and Venezuela.3 All variables are in natural logarithms in the subsequent empirical analysis. The analysis begins with an examination of the unit root and stationarity properties of the respective variables using several procedures: panel augmented Dickey-Fuller (ADF) unit root, nonparametric panel unit root, and panel stationarity tests. The panel ADF unit root test proposed by Levin et al. (2002) assumes homogeneity in the dynamics of the autoregressive coefficients for all panel units. The nonparametric panel unit root test of Maddala and Wu (1999) combines the p-values from individual unit root tests in the estimation of Fisher-ADF and Fisher-Phillips- Perron (PP) tests. The panel stationarity test by Carrion-i-Silvestre et al. (2005), based on the assumption that the long-run variance is either homogeneous or heterogeneous, is also estimated. The panel unit root and stationarity tests reported in Table 1 indicate that the respective variables are integrated of order one. 2See Apergis and Payne (2011a,b; 2012a,b) and citations therein regarding the use of a production model framework. 3Due to data availability for the variables included in the model, the following countries are excluded: Columbia, Guyana, and Suriname. D ow nl oa de d by [ U ni ve rs ity o f Io w a L ib ra ri es ] at 0 1: 24 0 5 O ct ob er 2 01 4 174 N. APERGIS AND J. E. PAYNE TABLE 1 Panel Unit Root and Stationarity Tests South America Variables LLC Fisher ADF Fisher PP CBL (HOM) CBL (HET) Y �0.42 17.83 16.94 32.15a 23.52a Y �5.17a 61.73a 94.13a 1.16 1.44 RE �0.38 16.77 12.36 20.06a 26.51a RE �5.41a 64.38a 65.71a 1.12 1.22 NRE �0.64 9.07 14.74 41.35a 20.43a NRE �5.04a 58.75a 68.91a 1.21 1.31 K �0.37 14.83 15.46 22.37a 11.04a K �4.94a 55.82a 90.45a 1.10 1.04 L �0.38 13.83 12.36 21.05a 23.44a L �5.36a 50.14a 82.38a 1.24 1.36 Notes: Under the Levin et al. (LLC), Fisher-ADF, and Fisher-PP tests the null hypothesis is a unit root while the alternative hypothesis is the absence of a unit root. The Carrion-i-Silvestre et al. (CBL) test assumes stationarity under the null hypothesis. Critical values at the 1% level denoted by “a”: LLC �0.84, Fisher-ADF 56.09, Fisher-PP 61.15, CBL (HOM) 6.73, and CBL (HET) 6.11. Next, the Larsson et al. (2001) panel cointegration procedure, a likelihood-based framework for the testing and estimation of cointegrated panels within an error correction model, is used to determine the long-run relationship among the variables. The panel framework consists of N cross-section (9 countries) observed over T time periods (18 years). Let i D 1; : : : ; N represent the number of countries, t D 1; : : : ; T the sample time period, and j D 1; : : : ; p the variables in each group. Thus, yij t represents the i th group and the j th variable at time t . The heterogeneous VAR.ki / model characterizes the data generating process for each group: Yi t D kiX kD1 …ikYi;t�k C "i t ; i D 1; : : : ; N (1) where for each group i the values Yi;�kiC1; : : : ; Yi;0 are fixed and the errors "i t are independently identically distributed Np.0; i /. The heterogeneous error correction model is given as: Yi t D …iYi;t�1 C ki�1X kD1 €ikYi;t�k C "i t ; i D 1; : : : ; N (2) where …i is order p�p. If …i is reduced rank, it is possible for …i D ˛iˇ 0 i where ˛i and ˇi are p�ri and full column rank. The reduced-rank estimation procedure allows for the estimation of…i and hypothesis testing of the cointegrating rank as well as estimates of the long-run coefficients, ˇi , and the adjustment parameters, ˛i . 4 If cointegration is determined using the trace statistic, the Larsson et al. (2001) procedure tests whether the cointegrating vector is homogeneous across countries. Moreover, the procedure allows for a robust test of cointegration that can be performed 4For specific details on the panel cointegration procedure see Larsson et al. (2001). The use of the Larsson et al. (2001) procedure parallels Apergis and Payne (2012b) and citations therein. D ow nl oa de d by [ U ni ve rs ity o f Io w a L ib ra ri es ] at 0 1: 24 0 5 O ct ob er 2 01 4 THE ELECTRICITY CONSUMPTION-GROWTH NEXUS IN SOUTH AMERICA 175 TABLE 2 Panel Cointegration Tests and Long-run Estimates South America Panel A: Panel Cointegration Tests Cointegrating Rank 1% Critical Values Null Hypothesis LR Test (Bootstrapped) H0 W r D 0 47.94 a 35.44 H0 W r D 1 22.84 71.06 Homogeneous Cointegration Vectors 1% Critical Values Null Hypothesis LR Test (Bootstrapped) H0 W b1 D b2 D : : : D bN 58.88 a 46.72 H0 W B block diagonal 38.82 68.94 Panel B: Long-Run Parameter Estimates Y D 0.264 C 0.182RE C 0.373NRE C 0.194K C 0.462L (6.55)a (5.09)a (14.5)a (5.80)a (16.5)a Adj. R2 D 0.61 LM D 1.04 HE D 1.24 RESTAT D 1.05 [0.25] [0.20] [0.23] Notes: t-statistics and probability values are reported in parentheses and brackets, respectively. LM is the Lagrange multiplier test for serial correlation. HE is White’s heteroscedasticity test. RESTAT denotes Ramsey’s misspecification test. Significance at the 1% level is denoted by “a”. with cross-sectional dependence in the error terms of the panel without arbitrary normalization assumptions. The panel cointegration test shown in Panel A of Table 2 reveals that the null of hypothesis of no cointegration is rejected in favor of panel cointegration with one cointegrating vector. Furthermore, the null hypothesis of homogenous cointegrating vectors is also rejected. Thus, a long-run equilibrium relationship exists between real GDP, renewable electricity consumption, non-renewable electricity consumption, real gross fixed capital formation, and the labor force. The long-run parameter estimates associated with the cointegrating vector are positive and statistically significant at the 1% level as reported in Panel B of Table 2. With the variables denoted in natural logarithms, the parameter estimates can be interpreted as long-run elasticity estimates: a 1% increase in renewable electricity consumption increases real GDP by 0.182%; a 1% increase in non-renewable electricity consumption increases real GDP by 0.373%; a 1% increase in real gross fixed capital formation increases real GDP by 0.194%; and a 1% increase in the labor force increases real GDP by 0.468%. With the determination of the long-run equilibrium relationship, the short-run and long-run causal relationships are examined using a panel vector error correction model. Defining the lagged residuals from the long-run cointegration equation given in Panel B of Table 2 as the error correction term, a dynamic error correction model is estimated as: Yi t D ı1i C qX kD1 '11ikYi t�k C qX kD1 '12ikREi t�k C qX kD1 '13ikNREi t�k C qX kD1 '14ikKi t�k C qX kD1 '15ikLi t�k C �1i"i t�1 C u1i t (3a) D ow nl oa de d by [ U ni ve rs ity o f Io w a L ib ra ri es ] at 0 1: 24 0 5 O ct ob er 2 01 4 176 N. APERGIS AND J. E. PAYNE REi t D ı2i C qX kD1 '21ikYi t�k C qX kD1 '22ikREi t�k C qX kD1 '23ikNREi t�k C qX kD1 '24ikKi t�k C qX kD1 '25ikLi t�k C �2i "i t�1 C u2i t (3b) NREi t D ı3i C qX kD1 '21ikYi t�k C qX kD1 '32ikREi t�k C qX kD1 '33ikNREi t�k C qX kD1 '34ikKi t�k C qX kD1 '35ikLi t�k C �3i"i t�1 C u3i t (3c) Ki t D ı4i C qX kD1 '41ikYi t�k C qX kD1 '42ikREi t�k C qX kD1 '43ikNREi t�k C qX kD1 '44ikKi t�k C qX kD1 '45ikLi t�k C �4i"i t�1 C u4i t (3d) Li t D ı5i C qX kD1 '51ikYi t�k C qX kD1 '52ikREi t�k C qX kD1 '53ikNREi t�k C qX kD1 '54ikKi t�k C qX kD1 '55ikLi t�k C �5i"i t�1 C u5i t (3e) where  is the first-difference operator; k is the lag length based on likelihood ratio tests; and u is the serially uncorrelated error term. Short-run causality is determined by the statistical significance of the partial F-statistic associated with the corresponding right hand side variables in Eqs. (3a)–(3e). Long-run causality is determined by the statistical significance of the respective error correction terms in Eqs. (3a)–(3e). Table 3 displays the results from the panel error correction model. As shown in Eq. (3a), both renewable and non-renewable electricity consumption each have a positive impact on economic growth in the short-run. Likewise in Eqs. (3b) and (3c), economic growth has a positive impact on renewable and non-renewable electricity consumption, respectively. Thus, the results indicate bidirectional causality between renewable and non-renewable electricity consumption, respectively, and economic growth in both the short-run and long-run.5 However, the negative bidirectional causality between renewable and non-renewable electricity consumption, as shown in Eqs. (3b) and (3c), indicate the potential substitutability between the two electricity consumption measures which parallels the results by Apergis and Payne (2011b; 2012a). In the short-run, real gross fixed capital formation has a positive and statistically significant impact on economic growth, renewable electricity consumption, and non-renewable electricity consumption. The labor force has a positive and statistically significant impact on economic growth and non-renewable electricity consumption, but statistically insignificant impact on renewable electricity consumption in the short-run. Yet, in Eqs. (3d) and (3e), non-renewable electricity 5The statistical significance of the error correction terms in Eqs. (3a)–(3c) indicate bidirectional causality in the long-run. D ow nl oa de d by [ U ni ve rs ity o f Io w a L ib ra ri es ] at 0 1: 24 0 5 O ct ob er 2 01 4 THE ELECTRICITY CONSUMPTION-GROWTH NEXUS IN SOUTH AMERICA 177 TABLE 3 Panel Causality Tests South America Sources of Causation (Independent Variables) Short-Run Long-Run Dependent Variable Y RE NRE K L ECT (3a) Y — 59.00 (0.266) 88.74 (0.383) 59.84 (0.363) 95.05 (0.282) �0.114 [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a (3b) RE 38.63 (0.128) — 49.73 (�0.112) 61.18 (0.149) 1.63 (0.051) �0.128 [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.30] [0.49] [0.00]a (3c) NRE 63.73 (0.264) 91.02 (�0.248) — 58.74 (0.130) 33.28 (0.093) �0.139 [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a (3d) K 45.94 (0.251) 45.61 (0.174) 56.77 (0.129) — 89.05 (0.184) �0.108 [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a (3e) L 79.84 (0.327) 37.81 (�0.094) 68.78 (0.153) 81.27 (0.177) — �0.086 [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a [0.00]a Notes: Partial F-statistics reported with respect to short-run changes in the independent variables. The sum of the lagged coefficients for the respective short-run changes is denoted in parentheses. ECT represents the coefficient of the error correction term. Probability values are in brackets and reported underneath the corresponding partial F-statistic and sum of the lagged coefficients, respectively. Significance at the 1% level denoted by “a”. consumption has a positive and statistically significant impact on real gross fixed capital formation and the labor force, respectively; however, renewable electricity consumption has a positive impact on real gross fixed capital formation, but a negative impact on the labor force in the short-run. This finding suggests that the technology associated with electricity generation from renewable energy sources may have labor-saving attributes. 4. CONCLUDING REMARKS Though South America is considered among the world’s leaders in oil, natural gas, hydroelectricity, and ethanol production, policymakers need to assess the sustainability of the region’s energy consumption mix for future growth. This study extends the literature on the energy consumption- growth nexus in the case of South America by examining the role of renewable and non-renewable electricity consumption in the economic growth process. Within a production model framework, the causal relationship between renewable and non-renewable electricity consumption, respectively, and economic growth is investigated using a panel error correction model. The panel error correction model reveals bidirectional causality between renewable and non-renewable electricity consumption, respectively, and economic growth in both the short-run and long-run. Also, note that the causality results suggest the potential substitutability between renewable and non-renewable electricity consumption as evidenced by the negative bidirectional causality between these two electricity measures, findings that parallel those reported by Apergis and Payne (2011b; 2012a). Given South America’s abundance of renewable and non-renewable energy resources, the interdependence between these energy resources and economic growth is not surprising. These results confirm the importance of each in the design of energy and conservation policies to ensure a diversified, sustainable energy consumption mix. Both renewable and non-renewable electricity consumption contribute to economic growth. Likewise, economic growth also contributes to the increased electricity consumption from both renewable and non-renewable energy sources. With D ow nl oa de d by [ U ni ve rs ity o f Io w a L ib ra ri es ] at 0 1: 24 0 5 O ct ob er 2 01 4 178 N. APERGIS AND J. E. 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