Energy Consumption and Economic Growth Revisited: a dynamic panel investigation for the OECD countries

Transcription

1 Energy Consumption and Economic Growth Revisited: a dynamic panel investigation for the OECD countries Iuliana Matei a1 Abstract: The aim of this paper is to investigate the energy consumption-economic growth nexus for 26 OECD countries for the period Using recently panel data techniques, we show that increases in real per capita GDP have a positive and statistically significant effect on per capita energy consumption (and vice-versa). In the long term, a 1% increase in real per capita GDP increases the energy consumption per capita by about 0,34% while a 1% increase in per capita energy use increases the real per capita GDP by about 1.28%. Thus, the impact of real GDP on energy consumption is less important than vice versa. Furthermore, the empirical evidence of a dynamic panel error-correction model reveals only a unidirectional causal linkage from the energy consumption to economic growth in the short-and-long run (both on the whole and on the selected sub-period). Key-words: the energy- growth nexus, panel cointegration methods, OECD countries 1 Adjunct professor at IESEG Paris. s: iuliana.matei@malix.univ-paris1.fr and i.matei@ieseg.fr. 1

2 1. Introduction Does increased energy consumption promote economic growth or does economic growth stimulates energy consumption in OCDE countries? This question has been greatly debated in the resource economics literature since the seminal article by Kraft and Kraft (1978). Three main reasons may well explain the growing interest for the link between economic growth and energy consumption. Firstly, theoretical studies involving the energy consumption - growth nexus are not constantly clear about the way of causality between these two factors. The supply-side research considers energy as a key production factor comparable to capital and labor and highlights that energy consumption positively impacts economic growth whereas the demand-side research views the energy use as a function of economic growth and identifies a direct effect from economic activity to energy consumption. Recently, this theoretical background was reviewed by Ozturk et al. (2010) and Apergis and Payne (2011) and structured in four major hypotheses: the growth hypothesis, the conservation hypothesis, the neutrality hypothesis and the feedback hypothesis. The first hypothesis assumes a unidirectional causality from energy consumption to economic growth; the second hypothesis imposes a unidirectional causality from economic growth to energy consumption, the third hypothesis points up no causality between energy consumption and economic growth while the last hypothesis supposes a bi-directional causality between the energy consumption and economic growth. Secondly, understanding the link between these two key factors is essential for energy policies. For example, the growth hypothesis implies that a decline in the energy consumption negatively influences economic growth or, in other words, that policies intended to restrict energy consumption will not promote economic growth. Conversely, under the conservation hypothesis and the neutrality hypothesis, adjustments in energy policies do not affect economic growth. Thirdly, although it is recognized that energy consumption and economic growth are related, available empirical evidence show that there is no consensus on the direction of this relationship. The diverging results could be explained by the employed model specifications (time series or panel data), the selected period or by the country sample. Until recently, most studies have focused on single countries by using annual data (because of data availability) and implicitly employed time series models (such as Augmented Dickey-Fuller unit root test - Dickey and Fuller (1979) and the Johansen (1991, 1995) cointegration test) such as: Kraft and Kraft (1978), Yu and Wang (1984) and so on. It is recognized that time series models may have low statistical power, mainly when the span of data is short (Campbell and Perron, 1991). To circumvent this drawback, a second strand of the empirical literature has focused on the panel data models (e.g., Lee (2005), Al- Iriani (2006), Lee and Chang (2007), Mehrara (2007), Apergis and Payne (2009, 2010, 2011), Omri 2

3 et al. (2014)). These models have high power because they add to the time series dimension, the cross-sectional dimension of data and, hence, arrive to exploit additional information. But, although these panel specifications are more innovative, they do not arrive to reach a full consensus on the causal link between energy consumption and economic growth. Finally, the OECD accounted in 2013 for 18% of global population, 47% of global GDP, 40% of total primary energy supply. These shares have significantly changed since 1971 when the region accounted for 61% of the global energy supply (International Energy Agency statistics, 2015). Furthermore, the OECD key demand trends indicate that the total final consumption increased by 2% in 2013 compared to previous year and that this drop differs across the three areas: Europe, Asia Oceania and Americas (the last region driving the OECD energy use growth by about 3.5% particularly because of industry sector). This paper re-examine the relationship between economic growth and energy consumption in OECD countries and makes four main contributions to the related literature. Firstly, it uses up-todate panel data techniques proposed by Kao and Chiang (2000) - the group-means fully modified ordinary least squares (FMOLS) estimator that incorporates a semi-parametric correction to the ordinary least squares (OLS) estimator, a parametric dynamic OLS (DOLS). Another candidate specification is the autodistributed lag (ARDL) model by Pesaran, Shin and Smith (1999) named also the Pooled Mean Group (PMG) estimator. The PMG model is used to identify the short-run and long-run causality among considered variables. Besides that, it allow intercept, slope coefficient and error variance to vary across countries and therefore, to identify heterogeneity among crosssection units of the panel. An alternative panel specification would be the Mean Group (MG) estimator by Pesaran and Smith (1995) that does not account for the fact that some economic conditions may be the same across countries in the long run. The efficiency gain of PMG estimator comes from the hypothesis of heterogeneous short-run dynamics and identical long-run coefficient across countries. Furthermore, the PMG model is robust to the choice of lag orders and appears to be consistent and efficient even in the presence of endogenous and non-stationary regressors (e.g., Fayad (2010)). Secondly, we analyze 26 OECD countries over the annual period from 1971 to 2013 implying a larger length (44 years) than previous panel data studies on OECD countries. At our best knowledge, there are only two candidate contributions by Constantini and Martini (2010) and Coers and Sanders (2013) that have a comparable time-span (more than 40 years) and country-sample (more than 25 countries). Moreover, this paper studies also a more recent time period covering the recent global financial crisis. Fourthly, since data on labor force is available starting with 1991, data necessitates estimating our models on sub-samples. By restricting the analysis to this sub-period, the study proposes not only robustness checks but, also extends the empirical literature on the 3

4 causal linkages between energy consumption and economic growth for a more recent period. Finally, the findings provide additional support for the economic approaches analyzing the energyincome nexus. Results show a unidirectional causality running from energy consumption to GDP growth (at the opposite side to those obtained by Coers and Sanders (2013), but in the same vein of unidirectional causality). Thus, the outcomes do not validate the feed-back hypothesis found in other studies where energy use and economic activity affect each other at the same time. The rest of the paper is constructed as follows. Section 2 presents a review of the existing literature on panel data. Section 3 describes the empirical approach that is used in this paper to investigate the relationship between the energy consumption and growth. Section 4 displays and discusses the obtained results. The section 5 provides some concluding results. 2. The review of the recent existing literature The energy consumption-economic growth nexus is a research topic that was widely studied for both developed and developing countries and for that the empirical evidence produced mixed and conflicting results with respect to the direction of causation. The purpose of this section is not to provide a complete overview of this empirical literature, but to focus on some recent panel data analyzing this relationship only in OECD countries (see table 1). Table 1: Overview of recent panel data studies on the non-renewable energy consumptioneconomic growth nexus in the OCDE countries Authors Period Countries Causality Lee et al. (2008) OECD countries energy growth Constantini and Martini (2010) OECD countries 45 non-ocde countries energy growth energy growth Lee and Lee (2010) OECD countries energy growth Belke et al. (2011) OECD countries energy growth Coers and Sanders (2013) OECD countries growth energy Salim et al. (2014) OECD countries energy growth Note: i) energy growth means that there is a bi-directional causality between the non-renewable energy consumption and economic growth; ii) growth energy means that the variable economic growth Grangercauses variable non-renewable energy consumption. The first recent panel data study on the relationship between energy consumption and growth in OECD countries was produced by Lee et al. (2008). The authors examined 22 OECD countries over the period from 1960 to 2001 and found that, by employing a Multivariate panel VECM 4

5 specification with output, energy consumption and fixed brut capital formation inside, a bidirectional causality between energy consumption and economic growth. They result validate the feed-back hypothesis that argues that energy consumption and real GDP impact each other simultaneously. In the same vein, four next studies presented in the table 1 find evidence in favour of bi-directional causality between these two factors. Among them, Salim et al. (2014) analyze the link between renewable and non-renewable energy consumption and GDP growth in 29 OECD countries from 1990 to 2012 and find, by using the PMG estimator and the Common Correlated Effects Mean Group (CCEMG) estimator for the long-run relationship, a bi-directional causality for both the short- and long-run. Conversely, the single study that finds a unidirectional causality running from output growth and capital formation to energy use is that of Coers and Sanders (2013). Their paper also uses panel unit root and cointegration techniques and specifies an appropriate vector error correction model to analyze the nexus between income and energy use. They also highlight some evidence of bi-causality over the very-short-run. Overall, the findings are very sensitive to the model misspecification and suggest that policies aiming to reduce energy usage or to promote energy efficiency do not harm GDP growth, except over the very short-run. To examine the causal relationship between economy and energy, Constantini and Martini (2010) focus on a large sample of developed and developing countries and four distinct energy sectors. They employ a Vector Error Correction Model for non-stationary and cointegrated panel and show that alternative country samples hardly affect the causality relations, particularly in a multivariate multisector framework. More precisely, when regarding the industry and transport sectors, their results show that the causality direction changes when different time horizons are accounted for. In the short-run, it is the economic growth process that determines the energy consumption trend so that it is mainly driven by production demand, and policies oriented towards promoting energy-saving do not seem to influence economic development negatively. Conversely, long-run causality is bidirectional, showing that changes in energy consumption could affect economic performance and vice versa. Belke et al. (2011) investigates the long-run relationship between energy consumption and real GDP, including energy prices, for 25 OECD countries from 1981 to They particularly focus on the distinction between common factors and idiosyncratic components using a principal component analysis to identify the drivers of the long-run relationship. The cointegration between the common components of selected variables indicates that international developments dominate the long-run relationship between energy consumption and real GDP. Furthermore, their results suggest that energy consumption is price-inelastic. Causality tests demonstrate the presence of a bi-directional causal relationship between energy consumption and economic growth. Coers and Sanders (2013) revisit this controversial relationship, by controlling for capital and labor 5

6 productivity for a larger panel of 30 OECD countries over the past 40 years (from 1960 to 2000). While for the short-run their results show a bi-directional causality, a strong unidirectional causality running from capital formation and GDP to energy consumption is found. The fact that in the longrun the reverse causality seems to be lost implies that results are very sensitive to models misspecification. Overall, their results suggest that policies aiming to promote energy efficiency or to reduce the energy usage are not likely to harm economic growth, except over the very short run. 3. Data and empirical specifications 3.1 Data This study uses annual data from 1971 to 2013 for 26 OECD countries. These are Australia, Austria, Belgium, Canada, Chile, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Israel, Italy, Japan, Luxembourg, Mexico, the Netherlands, New Zealand, Norway, Portugal, Spain, Sweden, Turkey, the United Kingdom, and the United States. Because the data on the other OECD member countries are available starting with , we decided to not include these seven countries in the sample. Data on real GDP per capita in constant 2000 U.S. dollars is used as a proxy for measuring the economic growth (G) and energy consumption is represented by energy use in kilograms of oil equivalent per capita (E). Data on labor force (referring to the total labor force comprising people ages 15 and older who meet the International Labour Organization definition of the economically active population: all people who supply labor for the production of goods and services during a specified period and includes both the employed and the unemployed), the CO2 emissions (in metric tons per capita) and the gross fixed capital formation (measured as percentage of GDP) are used as a set of control variables in the estimated models. All variables are in natural logarithms meaning that each estimated coefficient should be interpreted as a constant elasticity of the dependent variable with respect to the independent variable. All data have been obtained from the World Bank s World Development Indicators. Because data on the labor force variable is available starting with 1991, we compute the estimations on two panels: the whole period and the sub-period: Econometric approach Theoretical model vs. empirical model To investigate the nexus between energy use and economic growth in OECD countries, a Cobb- Douglas production function can be used as follows: (1) 6

7 In this function, Y is the real output per capita, E, K, L and C correspond to energy consumption, capital stock, labor force and CO2 emissions, respectively. The first right-term A is called the technology parameter. α, β, λ and γ are the production elasticities with respect to the capital, energy consumption, labor force and respectively, CO2 emissions. Overall, the model illustrates that the gross domestic product (GDP) is explained by a set of economic factors such as: the capital, the labor force and the energy consumption which can be directly related to CO2 emissions (e.g., Stern, 2000; Ang, 2008; Sharma, 2010; Omri, 2013). If the sum of production elasticities related to the capital, energy consumption, labor force and CO2 emissions equals 1 (i.e., α + β + λ + γ = 1) the Cobb-Douglas production function gets constant returns to scale. The log-linear form of this production function is given by: (2) Since our study works with panel data, the Eq. (2) can be re-written as follows: (3) where the subscript i=1,, 26 represents the country and t =1,,T denotes the time period (here, from 1971 until 2013). The Eq. (3) will be employed to estimates the link between energy consumption and economic growth in the 26 OECD countries. The parameter captures the possibility of country-specific fixed effects and deviations from the long-run equilibrium relationship are measured by the estimated residuals (assumed to be independent and identically distributed with zero mean and constant variance). The Eq. (3) assumes that the stock of capital (, energy consumption and CO2 emissions ( are the driving forces of economic growth (. We also construct a second equation where the same explanatory variables plus the GDP growth (in natural logarithms) can potentially affect the energy consumption. Estimation of eq. (3) gives us also the long-run elasticities by using panel cointegration techniques of Pedroni (1999, 2004) Cross-section dependence and unit root tests Numerous studies on the energy consumption-economic growth nexus often work with nonstationary variables in levels. To avoid spurious results, it is required to test for the cross-sectional independence in the errors, for the stationarity and the cointegration of variables. Before investigating for the presence of unit roots in the series, we apply two tests that rely on the assumption of cross-sectional dependence in the errors: the Pesaran (2004) test, and respectively, the Baltagi, Feng and Kao (2012) test. These tests help to identify the appropriate panel unit roots tests that need to be applied: the first generation unit root tests (relying on the assumption of cross- 7

8 sectional independence between countries) versus the second generation unit root tests (based on the hypothesis of cross-section independence between units) existing in the empirical literature. The cross-sectional dependence in the errors may generally arise because of the presence of common shocks (e.g., the recent global financial crisis, diverse oil shocks) and unobserved components. This hypothesis is more likely to be validated in panel data models because OECD countries have experienced a higher economic and financial integration process during the last decade. The Pesaran (2004) test is based on the pair-wise correlation coefficients and its statistics (CD) has the following form: (4) The Baltagi, Feng and Kao (2012) biais-corrected scaled LM offers a simple asymptotic bias correction for the scaled LM test statistic and is also asymptotically standard normal. Its LM statistic has the next form: (5) As the estimation results presented in the next section validate the cross-sectional hypothesis, we apply the Pesaran (2007) CIPS test allowing for heterogeneity in the autoregressive coefficient of the Dickey-Fuller (DF) regression and for the presence of a single unobserved common factor with heterogeneous factor loadings in the data. The test statistic is constructed from the results of panelmember-specific (A)DF regressions with the cross-section averages of the dependent and independent variables (with the lagged differences to account for serial correlation). The null hypothesis is (homogenous stationarity) H0: for all i (here meaning the countries) against the possibly heterogeneous alternatives: H1: or in the following cross-sectional augmented DF (CADF) regression: = (6) where i = 1,,N for each country of the panel and t=1,,t refers to the time period. Y represents the real per capita GDP. The parameters capture the possibility of country-specific fixed effects and the are the estimated residuals (assumed to be independent and identically distributed with zero mean and constant variance). The same equation is tested for each explanatory variable used in the selected panel data models. the Pesaran (2007) CIPS test proposes three specifications that can be tested gradually: i) models without constant and trend; ii) models with individual specific intercepts (i.e., Eq. (6)) and iii) models with incidental linear trends. The major benefit of applying this panel unit root test is its high power of exploring the cross-sectional dependence which induces strong interdependencies between the countries. 8

9 3.2.2 Long run estimators We first apply the Pedroni (1999, 2004) test allowing for cross-section interdependence with diverse individual effects and establishing whether a long-run equilibrium relationship exists. The equation that we have tested has the following form: = (7) where i = 1,,N for each country of the panel and t=1,,t refers to the time period. The parameters and capture the possibility of country-specific fixed effects and deterministic trends, and deviations from the long-run equilibrium relationship are measured here by the estimated residuals. By applying the unit root test on the residuals ( ), Pedroni (1999, 2004) tests the null hypothesis of no cointegration. The panel cointegration test of Pedroni (1999) provides four statistics in the within dimension: the panel - statistic, panel statistic, panel PP statistic and panel ADF statistic. All these statistics assembly the autoregressive coefficients across different countries for the unit root tests on the estimated residuals, accounting for common time factors and heterogeneity across countries. In the between dimension (i.e., group mean panel cointegration statistics), the test includes only three statistics: the group statistic, the group statistic, the group ADF statistic based on the averages of the individual autoregressive coefficients, and related with the unit root tests of the residuals for each country in the panel. All these tests are distributed asymptotically as standard normal, as highlighted by Pedroni (1999). Furthermore, to correct for endogeneity and serial correlation induced by the nuisance parameters as regressors in the ordinary least squares (OLS) model, Pedroni (2000, 2001) proposes two alternative specifications: a group-means fully modified OLS (FMOLS) estimator that incorporates a semi-parametric correction to the OLS estimator, and a parametric dynamic OLS (DOLS) estimator that corrects the OLS estimator in a parametric manner. Applying these two different models allow us to observe whether or not the coefficients are sensitive to the estimation technique. Since the unit root test shown evidence in favor of cross section dependence assumption, crosssection cointegration vectors is likely to take place. Thus, we also perform the Westerlund (2007) cointegration test relying on the cross-sectional dependence hypothesis. The test explores whether an error correction model has or not an error correction term (individual group or full panel) using the following model: = + ( - ) + + (8) 9

10 where i = 1,,N for each country of the panel and t=1,,t refers to the time period. Y is the real per capita GDP and X is the vector of our explanatory variables (the per capita energy use, capital stock and per capita CO2 emissions). The parameters capture the possibility of country-specific fixed effects and the are the estimated residuals (assumed to be independent and identically distributed with zero mean and constant variance). Imposing as null hypothesis the absence of cointegration, the test assumes the existence of an error correction for individual panel members (with the group-mean statistics - Gt and Ga) and/or for the panel as a whole (with the panel statistics - Pt and Pa) without any common-factor restriction. As explained by Westerlund (2007), the test is general enough to allow for a large degree of heterogeneity, both in the long-run cointegrating relationship and in the short-run dynamic, and for dependence within, as well as across, the crosssectional units Granger causality In the case where the cointegration hypothesis is validated for our sample, the next step is to search for the existence of causality between the output growth and the energy consumption. The finding of cointegration ensures that there exists an error correction mechanism by which changes in the dependent variable depend on the level of disequilibrium in the cointegrating relationship and on changes in the other explanatory variables. For this purpose, we apply the pooled mean group model (PMG) for dynamic heterogeneous panels by Pesaran, Shin and Smith (1999). Based on the autoregressive distributed lag (ARDL) model for time periods t = 1, 2,, 51 and groups i = 1, 2,, 26, the first model can be written as follows: = (9) Where Y is the per capita dependent variable, is the k 1 vector of explanatory variables for group i (including,, denotes the fixed effects, s are scalar coefficients of the lagged dependent variables, s are k 1 coefficient vectors. We re-parameterize the Eq. (9) and we obtain the following form: + + (10) where are independently distributed across i and t, with zero means and variances. ; ; j= 1,2,, p-1; and with j = 1, 2,, q-1. 10

11 The PMG model is used to identify the short-run and long-run causality among considered variables. Besides that, it allow intercept, slope coefficient and error variance to vary across countries and therefore, to identify heterogeneity among cross-section units of the panel. An alternative panel specification would be the Mean Group (MG) estimator by Pesaran and Smith (1995) that does not account for the fact that some economic conditions may be the same across countries in the long run. The efficiency gain of PMG estimator comes from the hypothesis of heterogeneous short-run dynamics and identical long-run coefficient across countries. Furthermore, the PMG model is robust to the choice of lag orders and appears to be consistent and efficient even in the presence of endogenous and non-stationary regressors (e.g., Fayad (2010), Salim et al. (2014)). The PMG estimator allows assessing two types of causality: a short-run causality by testing the significance of the coefficients related to the lagged differences of economic and energy variables ( and a long-run causality associated to the speed of adjustment coefficient or the error correction term that it is necessary to be negative to tell that variables exhibit a long-run equilibrium. A larger value of implies a stronger response of the variable to the deviation from long-run equilibrium while a low value indicates that any deviation from long-run equilibrium of the GDP growth or the energy consumption needs much longer time to force the variables back to the long-run equilibrium. If the speed of adjustment coefficient appears significant in both equations, bidirectional causality between energy consumption and economic growth takes place. We estimate the long-run and short-run effects of energy consumption on GDP growth (and vice-versa) over two periods: and (because of data availability regarding the labor force variable). 4. Results and discussions 4.1 Exploring cross-sectional dependence Because we think that there could be some interdependencies between the OECD countries especially after certain common shocks (e.g., the 2008 financial crisis, some oil shocks), we check for cross-sectional correlation in the data by applying the Pesaran (2004) test - based on pair-wise correlation coefficients, and the Baltagi, Feng and Kao (2012) biais-corrected scaled LM. The results shown in the table 2 (for the whole period and the sub-period) strongly reject the null hypothesis of no cross-sectional dependence at the 1% level of significance for all variables which reveals a potentially common dynamics to the countries. 11

12 Table 2: Cross section dependence results of Pesaran (CD) and Baltagi, Feng and Kao (LM) H0: no cross-section PANEL: VARIABLES IN LOG dependence in residuals Whole period CD p-value Biais corrected scaled LM p-value OECD 26 Energy consumption per cap GDP per capita CO2 emission per capita Capital stock (% GDP) a a a a a a a a PANEL: VARIABLES IN LOG Sub-period CD p-value Biais corrected scaled LM p-value OECD 26 Energy consumption per cap GDP per capita Labor force CO2 emission per capita Capital stock (% GDP) a a a a a Notes: a means significant at the 1% level. 4.2 Searching for the unit root in the panel a a a a a Since the Pesaran (2004) test shows evidence in favor of cross-section correlation, 2 nd generation panel unit root tests (PURT) has to be used in our investigation. Therefore, we apply the Pesaran (2007) CIPS test. Table 3 reports the results and shows that series are integrated of order 1 the series are stationary in first difference at 1%, 5% and 10% level of significance. Table 3: The Pesaran (2007) CIPS results Whole period : Sub-period: H0: series is I(1) Pesaran (2007) CIPS test Pesaran (2007) CIPS test Level First difference Level First difference Statistic p-value Statistic p-value Statistic p-value Statistic p-value OECD 26 Energy use (1) (0.243) *** (0) (0) (2) (0.729) *** (0) (0) GDP (1) (0.308) ***(0) (0) (0) (0.540) *** (0) (0) CO2 emissions (1) (0.837) ***(0) (0) (2) (0178) ***(0) (0) Capital stock (2) (0.480) ** (0) (0) (1) (0.821) ** (0) (0) Labor force (1) (0.981) *** (0) (0) (N, T) (26,43) (26,43) Notes: All panel unit roots include an intercept and a trend. But, specifications without trend are qualitatively similar. The Lag length of variables is displayed in first small parenthesis; the values in brackets are the associated probabilities; *, ** and *** indicate significance at the 1%, 5% and 10% levels, respectively. 12

13 4.3 Panel cointegration analysis Table 4 displays the results reported by the Pedroni (1999) test for the eq. (1). The results indicate that is some evidence of cointegration between the output growth per capita, the energy consumption per capita, the trade openness, CO2 emissions per capita, and the fixed brut capital formation (in % of GDP). More precisely, for the equation (1) the test statistics reject the null hypothesis of no cointegration (for the panel-adf, the panel-pp statistics and the group-pp and the group-adf statistics) at the 5% and 10% significance levels. Table 4: The Pedroni (1999) panel cointegration tests Panel test statistics Weighted Value Whole period Weighted Value Sub-period panel statistic (0.618) (0.601) panel statistic (0.530) (0.963) panel statistic ** (0.046) (0.121) panel statistic ** (0.021) ** (0.024) Group mean panel test statistic group statistic (0.876) (0.9998) group statistic * (0.055) (0.216) group ADF statistic **(0.034) **(2) Note: (i) the second column shows the statistic values; ii) the null hypothesis is no cointegration; all reported values are distributed N(0,1) under the null of unit root or no cointegration; iii) ***p<0.01, **p<0.05; *p<0.10; iv) the estimations based on AIC criterion with of max lag of 9; v) the models are without constant and trend. The table 5 reports the results of the Westerlund (2007) test for the model with constant. Only one statistics assumes the existence of an error correction for individual panel members at 10% significance level (with the group-mean statistics - Gt) while the statistics for the panel as a whole (Pt and Pa) accept the null hypothesis of the absence of cointegration. Table 5: The Westerlund (2007) cointegration test results for OECD: EC (dep. var) Statistics with constant and trend Value Z-value P-value Gt * Ga Pt Pa Note: i) ***p<0.01, **p<0.05, *p<0.10; ii) the p-values are based on the normal distribution; ii) The average AIC selected lag length is 2.12 and the average AIC selected lead length is Overall, the results shows that most of the variables are integrated of order one and are cointegrated. Hence, the results enable to test the long-run impact of the energy consumption, the CO2 emissions and the stock of capital on the economic growth. For this purpose, two techniques to estimate these 13

14 long-run relationships are used the Fully Modified Least Squares (FMOLS) and the Dynamic Ordinary Least Squares (DOLS). We present the results in the table 6 for the whole period. Table 6: Long-run panel estimators (dependent variable real GDP per capita) Variables FMOLS DOLS Energy consumption per cap 1.275*** (0.081) 1.257*** (0.097) CO2 emissions per capita *** (0.087) *** (0.103) Capital stock * (0.080) (0.108) R-squared Nb of panel observations Note: ***p<0.01, **p<0.05; *p<0.10; the time effects were not included in the models, only constant are integrated in the models with panel group FMOLS and DOLS (Pedroni cointegration techniques are employed); the standard errors are in the parenthesis; Fixed leads and lags specification (lead=1, lag=1). In summary, the results of this set of estimations show that the macroeconomic variables included in the models have a long-run impact on the GDP growth per capita. Both models (FMOLS and DOLS) indicate a positive and significant effect of energy consumption on the GDP growth. In the long run, the capital stock and the CO2 emissions per capita appear to have a negative effect on the economic growth. Overall, the results are qualitatively similar across these two techniques. Regarding the energy use per capita, the results suggest that a 1% increase in energy use per capita increases per capita GDP by a value between 1.28% (in the FMOLS) and 1.26% (in the DOLS). Table 7: Long-run panel estimators (dependent variable energy use per capita) Variables FMOLS DOLS Real GDP per cap 0.344*** (0.022) 0.327*** (0.026) CO2 emissions per capita 0.620*** (0.033) 0.644*** (0.038) Capital stock *** (0.041) *** (0.054) R-squared Nb of panel observations Note: ***p<0.01, **p<0.05; *p<0.10; the time effects were not included in the models, only constant are integrated in the models with panel group FMOLS and DOLS (Pedroni cointegration techniques are employed); the standard errors are in the parenthesis. The results reported in the table 7 show that a 1% increase in per capita GDP increases the per capita energy consumption by a value between 0.34% (in the FMOLS) and 0.33% (in the DOLS). The magnitude and the sign of coefficients are quasi-similar in both models. Furthermore, the coefficients of explanatory variables are significant in both models. 14

15 4.4 Investigating the causality relationships in the panel Table 8 reports the panel causality results from estimating the panel vector error correction model (PMG) presented in the eq. (3) and the eq.(4). The number of lags of the selected variables was set according to the Akaike criterion. Table 8: PMG model evaluating the effects on the GDP growth (column 2) and the energy use (column 3): Variables (in log) PMG 1 PMG 2 Long-run coefficients GDP growth *** (0.021) CO2 emissions *** (0.06) 0.746*** (0.025) Capital stock *** (0.07) -0.09*** (0.030) Trade openness 0.153*** (0.06) 0.152*** (0.025) 0.807*** (0.067) - Energy use Error correction term (ECT) *** (0.013) 0.134*** (0.034) Short-run coefficients Δ GDP growth Δ CO2 emissions Δ Capital stock Δ Trade openness Δ Energy use Intercept 0.110***(0.04) 0.028*** (0.02) 0.187*** (0.02) (0.02) 0.032* (0.02) 0.188*** (0.05) 0.395***(0.06) 0.558*** (0.04) (0.02) 8 (0.019) *** (0.14) No. obs No. groups Note: i) ECT the speed of adjustment coefficient, ii) * p<0.10, ** p<0.05, *** p<0.01; iii) the number of max lags were set of 4 (for PMG1) and 1 (for PMG 2) as identified by the AIC criterion; iv) numbers in parentheses are standard errors; v) the dynamic model in the column (2) is ARDL (2,2,2, 2) and in the column (3) is ARDL (1,1,1,1,1,1). Table 8 shows that the estimated coefficient of the error correction term is negative only in the case of the first model where the dependent variable is the real GDP growth per capita indicating the presence of a long-and short-run causality in the data: the energy consumption per capita has a stimulating effect on the real GDP growth per capita both in the long-and short-run. It shows that when disequilibrium happen adjustment back to equilibrium takes years (computed as ln(2)/ln(1+ect)). Table 9 reports the panel causality results from estimating the panel vector error correction model (PMG) on the sub-period This model includes also the labor force variable (available only for this period according to the World Bank database). The number of lags of the selected variables was set also according to the Akaike criterion. 15

16 Table 9: PMG model evaluating the effects on the GDP growth (column 2) and the energy use (column 3): Variables (in log) PMG 1 PMG 2 Long-run coefficients GDP growth *** (0.019) CO2 emissions *** (0.169) 0.743*** (0.020) Capital stock 0.215*** (0.061) *** (0.014) *** (0.131) 0.152*** (0.025) Labor force 2.031*** (0.199) - Energy use Error correction term (ECT) *** (0.020) 0.294*** (0.049) Short-run coefficients Δ GDP growth Δ CO2 emissions Δ Capital stock Δ Labor force Δ Energy use Intercept *** (0.057) 0.221*** (0.025) (0.108) (0.063) *** (0.065) 0.264***(0.089) 0.606*** (0.037) (0.028) (0.146) *** (0.247) No. obs No. groups Note: i) ECT the speed of adjustment coefficient, ii) * p<0.10, ** p<0.05, *** p<0.01; iii) the number of max lags were set of 4 (for PMG1) and 1 (for PMG 2) as identified by the AIC criterion; iv) numbers in parentheses are standard errors; v) the dynamic model in the column (2) is ARDL (1,2,2,2, 2) and in the column (3) is ARDL (1,1,1,1,1,1). With respect to the long-run causality relationship between variables, the error correction terms suggest that there is only a unidirectional causality running from the energy consumption to GDP growth in the long run (as the sign of the error correction term is negative). The magnitude of the disequilibrium is lower than on the whole period (-0,10<-0,048) indicating that any deviation from long-run equilibrium needs much longer time to force the variables back to the long-run equilibrium. Overall, the growth hypothesis is validated both in the long-run and short-run. However, the sign of the coefficients differ in the short-run. Overall, this implies that a decline in the energy consumption negatively influences economic growth or, in other words, that policies intended to restrict energy consumption will not promote economic growth. A 1% increase in energy consumption increases GDP growth by 2.03% in the long-run and reduces it by 0,32% in the short-run. In the second model (in the column (3)) the error correction terms is not validated. 5 Conclusions This paper presents new findings about the causal relationships between the energy consumption per capita and the economic growth per capita for 26 OECD countries over the period 16

17 and the period The paper used up-to-date panel techniques. Findings show evidence for a positive long-run causality running from the energy consumption per capita to real GDP growth per capita (as the error correction coefficients are negative and significant) both on the whole period and the sub-period considered. In the short-run the impacts are not clear: positive (on the whole period) and negative (in the sub-period). This finding validates the growth hypothesis meaning that a decline in the energy consumption can negatively influence the economic growth. These outcomes should be taken as evidence that the energy appears as a key input in the production function. 6 Some References Al-Iriani, M. (2006). Energy GDP relationship revisited: an example from GCC countries using panel causality. Energy Policy 34 (17), Apergis, N., Payne, J.E., Energy consumption and economic growth: evidence from the Commonwealth of Independent States. Energy Econ. 31, Apergis, N., Payne, J.E., 2010a. Energy consumption and growth in South America: evidence from a panel error correction model. Energy Econ. 32, Apergis, N., Payne, J.E., 2010b. The emissions, energy consumption, and growth nexus: evidence from the commonwealth of independent states. Energy Policy 38, Apergis, N., Payne, J.E., 2010c. Renewable energy consumption and growth in Eurasia. Energy Econ. 32 (6), Apergis, N., Payne, J.E., 2010d. Renewable energy consumption and economic growth: evidence from a panel of OECD countries. Energy Policy 38, Apergis, N., Payne, J.E., The renewable energy consumption-growth nexus in Central America. Appl. Energy 88, Apergis, N., Payne, J.E., Renewable and non-renewable energy consumption-growth nexus: evidence from a panel error correction model. Energy Econ. 34, Ang, J.B., Economic development, pollutant emissions and energy consumption in Malaysia. J. Policy Model 30, Apergis, N. and J. E. Payne (2009b). Energy consumption and economic growth in Central America: Evidence from a panel cointegration and error correction model. Energy Economics 31(2), Baltagi, B. H., Feng, Q., and C. Kao (2012). A Lagrange Multiplier test for cross-sectional dependence in a fixed effects panel data model, Journal of Econometrics, Elsevier, vol. 170 (1),

18 Belke, A,.F. Dobnik, and C. Dreger (2011). Energy consumption and economic growth: new insights into the cointegration relationship. Energy Economics 33(5), Campbell, J. Y. and P. Perron (1991). Pitfalls and opportunities: What macroeconomists should know about unit roots. In O. J. Blanchard and S. Fisher (Eds.), NBER Macroeconomics Annual, Volume 6, pp Cambridge: MIT Press. Coers, R. and M. Sanders (2014). The energy-gdp nexus; addressing an old question with new methods, Energy Economics 36, Constantini, V., and C. Martini (2010). The causality between energy consumption and economic growth: A multi-sectoral analysis using non-stationary cointegrated panel data. Energy Economics, 32 (3), Dickey, D. A. andw. A. Fuller (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74(366), Fayad, Ghada, Remittances and Dutch disease: a dynamic heterogeneous panel analysis on the Middle East and North Africa region. Working Paper. Centre for the Study of African Economies, Department of Economics, University of Oxford, UK. Johansen, S. (1995). Likelihood-based inference in cointegrated vector autoregressive models. Oxford: Oxford University Press. Kao, C. and Chiang, M. H. (2000). On the estimation and inference of a cointegrated regression in panel data, Advances in Econometrics, 15, Kraft, J. and A. Kraft (1978). On the relationship between energy and GNP. Journal of Energy and Development 3, Lee C., and C. Chang (2008). Energy consumption and economic growth in Asian economies: a more comprehensive analysis using panel data. Resource and Energy Economics 30(1), Lee, C., C. Chang, and P. Chen (2008). Energy-income causality in OECD countries revisited. The key role of capital stock. Energy Economics, 30(5), Lee, C., and J. Lee (2010). A panel data analysis of the demand for total energy and electricity in OECD countries. Energy Journal, 31(1), Lee, C. and C. Chang (2007). Energy consumption and GDP revisited: A panel analysis of developed and developing countries. Energy Economics 29(6), Lee, C. (2005). Energy consumption and GDP in developing countries: A cointegrated panel analysis. Energy Economics 27(3), Mehrara, M. (2007). Energy consumption and economic growth: The case of oil exporting countries. Energy Policy 35(5),

19 Omri, A., CO2 emissions, energy consumption and economic growth nexus in MENA countries: evidence from simultaneous equations models. Energy Econ. 40, Omri, A., Nguyen, D.K., Rault, C., Causal interactions between CO2 emissions, foreign direct investment, and economic growth: evidence from dynamic simultaneousequation models. Econ. Model. 42, Ozturk, I., A. Aslan, and H. Kalyoncu (2010). Energy consumption and economic growth relationship: Evidence from panel data for low and middle income countries. Energy Policy 38(8), Pedroni, P., Critical Values for Cointegrating Tests in Heterogeneous Panels with Multiple Regressors, Oxford Bulletin of Economics and Statistics 61, Pedroni, P. (2004). Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory 20(3), Pesaran, M. H. and R. Smith (1995). Estimating long-run relationships from dynamic heterogeneous panels. Journal of Econometrics 68(1), Pesaran, M. H., Y. Shin, and R. P. Smith (1999). Pooled mean group estimation of dynamic heterogeneous panels. Journal of the American Statistical Association 94(446), Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. CESifo Working No Pesaran, M. H. (2007) A simple panel unit root test in the presence of cross section dependence, Journal of Applied Econometrics 22, Salim R.A., Hassan, K., and S. Shafiei (2014). Renewable and non-renewable energy consumption and economic activities: Further Evidence from OECD countries, Energy Economics 44, Sharma, S.S., The relationship between energy and economic growth: empirical evidence from 66 countries. Appl. Energy 87, Stern, D.I. (2000). A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Economics 22, Yu, E.S.H., Hwang, B.K. (1984). The relationship between energy and GNP: further results. Energy Economics 6,