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Energy Consumption and Economic Growth Revisited: Empirical Evidence for Nigeria. Oduyoye A. O., Aderinto E. R. and Ejumedia P.E. E-mail; babyjasb@yahoo.com, esthercareer@yahoo.com & efe_patrick@yahoo.combabyjasb@yahoo.com

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Introduction Empirical Literature Review Model Specification and Methodology Empirical Results and Discussion Policy Implications and Conclusion

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The direction of “causality” between increased energy consumption and economic expansion has been an issue of debate for many years. The early study by Kraft and Kraft (1978) and recent studies by Payne (2010), Ouedrago (2010), Tsani (2010), Odhiambo (2009), Halicioglu (2009), Bellourni (2009), Olusegun (2008), Zamani (2007), Al-Irani (2006), Wolde-Rufael (2006), Narayan and Smith (2005), Yoo (2005),Oh and Lee ( 2004), Shiu and Lam (2004), Moritomo and Hope (2004), Jumbe (2004),Sari and Soytas (2003), and Ghosh (2002) have explored the direction of “causality” between increase energy consumption and economic growth for several countries. Different conclusions have been reached ranging from bi-directional, uni- directional and no causal relationship. Pradhan (2009) attributes the reason for different in conclusion to various structural frameworks and policies followed by different countries under different conditions and time periods.

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While, Apergis and Payne ( 2009), Balat (2008), Chiou-Wei et al., (2008), Lee and Chang ( 2007,2008), Mahadevan and Asafu- Adjaye, (2007); Hatemi-J and Irandoust, (2005) attributes the differences in conclusion to methodology issues, various proxies for energy consumption and economic growth, presence of omitted variables and varying energy consumption patterns. However, in recent times owing to the problem of climate change (Global warming) caused by increase carbon-dioxide emissions arising from traditional energy combustion, there is need to revisit the direction of causality between energy consumption and economic growth. This is important because when an economy heavily relies on energy consumption; energy conservation policy to mitigate climate change can negatively affect economic growth.

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Thus, it is pertinent for policy makers and analysts to understand the direction of causality between traditional energy consumption and economic growth in order to design appropriate energy policies. It is against this backdrop that this study examines direction of causality between traditional energy consumption and economic growth in Nigeria. Specifically, the study examines the direction of causality between components of traditional energy consumption (oil, coal and gas) and economic growth in Nigeria using recent data and Examines whether economic growth and energy consumption components have long-run relationship.

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As earlier said several studies have examine the direction of causality between energy consumption and growth. a).Energy Consumption Economic Growth Ighodaro and Ovenseri-Ogbomo (2008), Odhiambo (2009), Adeniran (2009), Okonkwo and Gbadebo (2009), Akinlo (2009) and Tsani (2010) b).No “Causality” Stern (2000), Altõnay and Karagöl (2004), Sica (2007), Olusegun (2008) c). Bi-directional Causality Glasure (2002), Oh and Lee (2004), Ghali and El-Sakka (2004), Omotor (2008), Magazzino (2011)

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Most of the econometric investigations on the direction of causality between energy consumption and economic growth in Nigeria have used total non renewable energy consumption. However, in order to examine the direction of causality between energy consumption and economic growth, we disaggregated energy consumption into oil, natural gas and coal consumption. The reason for this is to determine the components of energy consumption that drive economic growth most in Nigeria. Before carrying out our analysis, we first express our variables in logarithm form in order to put the variables in the same scale. This is necessary when analyzing the time series property of the variables before establishing their relationship (Eltony and Al-Awadi 2001).

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To avoid the problem of spurious regression, we examined the time series properties of the logged series using the Standard Augmented Dickey Fuller test by Dickey and Fuller (1979; 1981). The tests are conducted with intercept and no trend (t) and intercept and trend in each of the series. This can be determined as: Equation 1 above represents intercept and trend, while equation 2 represents intercept and no trend in series. α represents the drift, t represents deterministic trend and m is a lag length large enough to ensure that is a white noise process.

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The coefficient of interest in both equations above is δ. If δ <1, the series does not have unit root. The estimated t-statistic of the variable of interest is compared with the Dickey and Fuller Critical Values to determine if the null hypothesis is valid. If the variables are integrated of order one I (1). We test for the possibility of a co-integrated relationship using the Johansen multivariate co-integration test by Johansen 1988; johansen and juselius (1990), this is because of its superiority over the Engle and Granger two stage method. Once co-integration among the variables is established, a Vector Error Correction Model (VECM) which captures both the short run and long run dynamics can estimated.

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The VECM can be used to distinguish between two types of “causality” namely short run and long run “causality”. Short run “causality” exists from variable Y to variable X if all or some of the coefficients of the lags of the first difference of the variable Y are Statistically Significant in the equation of variable X, this is done using the Standard Wald Test. While long run “causality” in the presences of co-integration exists from variable Y to variable X if the coefficient of the Error Correction Variable in the equation of variable X is Statistically Significant. Thus, the VECM is written in a compacted form as follows;

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Where: X t is a column matrix of endogenous variables (OILC, NGC, CC, and GDP) co-integrated of order n, Δ is a symbol of difference operator, X t-I is the lagged values of each variable at time t-i. Y t-I is a vector of lagged value of the other variables in the model. Π t-1 is the error correction term. Uni-directional causality from Y to X exists both in short and long run if some or all of the coefficients of β 2 and Π t-1 are statistically significant and vice versa. If the coefficients of β 2 and Π t-1 are not statistically significant, then no short or long run “causality” exists between both variables, and lastly, if the coefficients of β 2 and Π t-1 are statistically significant, then there exist is a bi-directional relationship exists between both variables. With this background, the model that shows the direction of “causality” between the component of traditional energy consumption and growth can be specified as;

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Where GDP = Gross Domestic Product a proxy for economic growth. OILC = Oil Consumption, NGC = Natural Gas Consumption, CC = Coal Consumption, ECM t-1 = Error Correction Term. β 1i -β 6i and α 1i -α 6i are short run coefficients, while ᵞ 1 -ᵞ 6 are long run coefficients

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Data source Data are generated in line with the period covered by the study which is 1970-2011. The choice of this period is based on data availability. Particularly, it is also a period of strong government advocacy for increased traditional energy consumption, increase in natural gas and coal consumption and at the same time trying to mitigate climate change. The main sources of data for this study is from the Central Bank of Nigeria (CBN) Statistical Bulletin.

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Due to the nature of data, we began our analysis by examining the time series properties of the variables in the model. This is done using the Augmented Dickey Fuller (ADF) test. The result is summarized in table 1 below; Table 1 reports the results of the stationarity tests in the level as well as in first difference for all the variables. We included intercept and trend term in these tests, all of the variables are not stationary at level. However, after taking the first difference of the logged variables, each series became stationary. This is because the ADF calculated statistics for all the variables is more negative than the ADF critical values. Thus we accept the hypothesis that the series contain a Unit Root at level or the variables are integrated of order one I(1). Thus we proceeded to carrying out the co- integration test.

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The Johansen multivariate co-integration test by Johansen 1988; Johansen and Juselius1990 was used to carry out the co-integration test. The result is displayed in table 2 above. We allow for intercept (no trend) in the co- integration equation. The trace test statistics indicates at least two co- integrating equation, while the Maximum Eigen value suggest one co- integrating relationship. The implication of this is that there exist a long run relationship between Gross Domestic Product, total traditional energy consumption and its components which could be given some Error Correction representations ( Engle and Granger, 1987).

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Vector Error Correction Model causality tests The Vector Error Correction Model (VECM) enables us to determine the direction of causality of the variables both in the short-run and long-run. In this study, we estimate a multivariate VECM where we estimate the direction of causality between economic growth and total traditional energy consumption and between economic growth and traditional energy consumption components in the short run and long run. The result are displayed in table 3 and 4 below.

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The result suggests that in the short run, there exist a unilateral causality running from Oil Consumption to Economic Growth, but there exist no causality between natural gas consumption and gross domestic product, and between coal consumption and gross domestic product. Overall, the result indicates that total traditional energy consumption “Granger Cause” Economic Growth at 5 percent significance level in the short run.

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The result of the long run granger causality test indicates a bi-directional relationship between oil consumption and gross domestic product at 5 percent. But a unilateral relationship running from gross domestic product to gas consumption as well as from coal consumption to economic growth at 1 and 5 percent respectively. However, the result show that total energy consumption granger cause economic growth in Nigeria in the long run and not the other way round.

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The implication of this is that policy measure put in place to conserve traditional energy in order to mitigate climate can hamper economic growth in Nigeria. This is because the results show that both in the short run and long run, total traditional energy consumption “granger cause” economic growth in Nigeria.

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Thank You For Your Attention

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