Energy Consumption and Economic Growth

European Journal of Social Sciences – Volume 16, Number 2 (2010)

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Energy Consumption and Economic Growth: A Case Study of

Three SAARC Countries

Kashif Imran M. Phil Scholar; Applied Economics Research Centre, University of Karachi. Pakistan

E-mail: [email protected]

Masood Mashkoor Siddiqui Dr. Chairman, Department of Commerce

Federal Urdu University of Karachi. Pakistan

E-mail: [email protected]

Abstract Energy is a crucial component to economic growth and plays a vital role in economic development. This study inquires the causal relationships between energy consumption (EC) and the economic growth (EG) within a multivariate framework that includes capital stock and labor input for the panel of three SAARC countries by using modern panel unit root technique, residual based panel cointegration and panel based error correction models .The empirical results fully support a cointegration relationship between EC and EG in the long run. But from the causal point of view there is long run unidirectional causality running from EC to EG and no causality was found in the short run. Keywords: Energy consumption, Economic growth, Causality relationship, Panel

cointegration, SAARC countries JEL Classification Codes: O13; Q43; C33

Introduction Literature shows that macroeconomic growth theories focus on labor and capital frequently; researchers do not affix necessary importance to the role of energy which is important for economic growth and production (Stern and Cleveland 2004). The economists since Adam Smith have discussed the major inputs to economic activity as being land, labor, and capital. Neo-classical production function explains economic growth with enlargement in labor, capital and technology; total factor efficiency is used as technology. Growth of industrial nations in the nineteenth century can be seen in retrospect to have been the result of fourth major input energy. Energy can also mentioned as a production factor apart from labor and capital. By contrast, there are other energy economists who consider that energy is a significant factor of production as well as a key player in the production process, because it can directly be used to produce a final product (Stern 2000). All production and many consumption activities require energy as an essential input. It is the key source of economic growth, industrialization and urbanization.

In the light of above arguments we take the multivariate econometric model on the basis of aggregate production function which includes labor force and capital as controlled variables. This study inquires long run co-movement and the causal relationship between energy consumption and economic growth, for three SAARC countries like Bangladesh, India and Pakistan; collectively called sub continent, which have a large portion of world’s population. It has more than 94% population of South Asia, 35% of Asia and more than 21% of the world, thus the relationships between energy


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consumption (EC) and economic growth (EG) in such a dense populated area is the focus of this study. We determine the relationship between EC and EG within a multivariate framework. After checking the stationarity of variables through modern panel unit root test, we use the cointegration test which is more powerful than cross section approach, and then use the Dynamic OLS (DOLS) technique to estimate the cointegration vector for heterogeneous cointegrated panels. It makes possible to correct the standard OLS for bias induced by endogeneity and serial correlation of the regressors. Furthermore, we estimate a dynamic vector error correction model (VECM) which is suitable for heterogeneous panels and to differentiate between short and long run causality.

We find the EC and EG relationship by employing bi-variate causality procedure. But these tests give conflicting results because of a problem often fail to detect additional channels (such as capital and labor input). Thus, in the presence of cointegration among the series, what has been long overdue is an alternative, superior econometric method to the vector autoregressive (VAR) method (Asafu-Adjaye, 2000). Here, we use the VECM, because the VAR models may only be able to identify short run relationship and they are unable to determine cointegration among variables because a long run relationship is go missing with the first differencing; while the VECM have ability of distinguishing between short and long run relationship among variables as well as identify the sources of causation (Oh and Lee 2004b).

Literature Review By using of econometric techniques, the relationship between EC and EG has been analyzed for different countries and periods by various researchers and a broad literature is available in this field. The series between EC and EG has been the focus of extensive research for much of the past three decades. A large number of studies show the causal relationship between both variables; conducted for developing and developed economies. The first study in this regard was conducted by Kraft and Kraft (1978), in their study relationship between USA’s EC and GNP for the period of 1947-1974 was investigated; a unidirectional causality from GNP to EC was found. Later, Akarca and Long (1980) tested this relationship with the same variables for same country for 1947–1972 period; unlike Kraft and Kraft (1978) they could not found relationship between variables. Erol and Yu (1987) examined the relationship between EC and GDP for England, France, Italy, Germany, Canada and Japan with the data spanning 1952 to1982 and found bidirectional causality relationship for Japan, unidirectional from EC to GDP for Canada and unidirectional from GDP to EC for Germany and Italy. They could not found any causality relationship for France and England. A common strength of these studies is the use of bi-variate models.

Stern (1993) declared that causality relationship in bi-variate models is not healthy since the substitution effect of energy with other variables is ignored; he inquired the relationship between USA’s EC and GDP with a multivariate cointegration model and could not found any relationship. Ghali and El-Sakka (2004) reported the short run dynamics of the variables, which indicate the bidirectional Granger-causality between output growth and EC in the case of Canada. Various researchers focus on panel data to investigate the causality relationship between same variables. Al-Iriani (2006) used a bi-variate model for six countries making up the Gulf Co-operation Council, while the Lee (2005) used a tri-variate model with fixed capital formation for 18 developing countries. Mehrara (2007) found unidirectional causality from EG to EC for 11 oil exporting countries. Lee and Chang (2007) found bi-directional causality between EC and EG in case of twenty-two developed countries while unidirectional causality from EG to EC in eighteen developing countries. Lee and Chang (2008) found uni-directional causality running from EC to EG for Asian economies for the period of 1971 to 2002.

Most often literature shows a relationship between EC and EG. However, there is no clear trend in the literature. It is depending on the methodology used, the country and time period span used. It


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may bi-directional causality between both variables, uni-directional running from EC to EG or EG to EC, and no causality in either direction in accordance with the ‘neutrality hypotheses’.

Data and Methodology The econometric model used in present study is based on following production function.

Y = ƒ (EC, LF, K) So our model is as follows;

ititiitiitiiit KLFECGDP εψψψφ ++++= 321 (1)

Real GDP used for economic growth, while EC, LF, and K represent energy consumption, labor force, and capital stock respectively. Real gross fixed capital formation used for capital stock as proxy, as many researchers used1. All the variables are in natural log form.

Annual data used in the present study for three SAARC countries spanning the period of 1971 to 2008. The sample includes Bangladesh, India and Pakistan. Data for real GDP (2000 = 100), energy consumption, labor force and real gross fixed capital formation (2000 = 100) are obtained from the World Development Indicators (WDI) and annual survey reports of countries. The units are expressed in million US$ for GDP and capital stock, numbers in million for labor force, and kilotons of oil equivalent for energy consumption.

The first step is concerned to establish the degree of integration for each variable. So, for this purpose this study tests the existence of unit root at level and difference for each series in the sample. In the past decade or so, there has been much interest to test for the presence of unit root in panel data, a number of investigators, eminent Levin, Lin and Chu [LLC (2002)], Breitung (2000), Im, Pesaran, and Shin [IPS (2003)], and Maddala and Wu (1999)2 have involved panel based unit root tests that are resemblance to tests carried out on a single series. These investigators have shown that panel unit root tests are more powerful than others. Panel unit root tests lead to statistics with a normal distribution in the limit3. This study used Im, Pesaran, and Shin [IPS (2003)], unit root test. The mention test has null hypothesis of panel contain a unit root. Results of panel unit root test can be seen in Table: 1 Table 1: Panel Unit Root Test Results

GDP EC LF K

Level IPS -1.65 0.52 -1.27 -0.25 1st Difference IPS -13.06* -9.10* -6.33* -8.28*

All variables are in natural log form *, ** and *** indicate statistical significant at 1, 5 and 10% level of significance respectively.

Table (1) shows that variables are non stationary at level. But at first difference all the variables

are in stationary position. So, next step is to imply the cointegration test. Various researchers used different cointegration tests for panel data e.g. King and Hillier (1985) proposed residual based LM test. Engle and Granger (1987) also used residual based test. Pedroni (1997) and Philips and Moon (1999) proposed a FMOLS estimator for cointegration test4. Kao (1999) propose residual based DF and ADF for cointegration in panel data. Gutierrez (2003) and Banerjee et al. (2004) studied small sample performance of many of these panel tests using Monte Carlo simulations, and found that no one can be said to dominate the others. In terms of applied work, however the class of residual based tests has

1 Paul and Bhattacharya (2004), Beaudreau (2005), Lee (2005), Thompson (2006), and Sari & Soytas (2007) 2 Maddala and Wu proposed two types of non-parametric tests including Fisher-ADF and Fisher-PP statistics. In addition, for controlling

large sample size, Choi (2001) proposes two other test statistics besides Fisher’s inverse chi-square test statistics. 3 See Baltagi 2003 for detail. 4 Testing for error correction in panel data; January, 2005


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proven to be the most popular one5. This empirical work used residual based ADF and PP statistic for cointegration, results are shown in Table (2). Table 2: Residual Based Cointegration Results

Test Test statistic Level of Significance

ADF statistic 70.65 0.00 PP statistic 68.86 0.00

Null hypothesis: No cointegration.

Both ADF and PP test have same null hypothesis of no cointegration. Results from both tests

show that cointegration relationship exists in the variables. Thus, it can be predicted that GDP, EC, LF and K move together in the long run. So, there is a long run steady state relationship between EC and GDP for a cross section of selected economies.

Different researchers used different estimators to estimate panel cointegration vector. These estimators include OLS, Fully Modified OLS (FMOLS), and Dynamic OLS (DOLS). In these estimators DOLS has some advantages over OLS and FMOLS. It is proposed by Stock and Watson (1993). Dynamic OLS become better than OLS by coping with small sample and energetic sources of bias, while in FMOLS the Johansen method, being a full information technique, is exposed to the problem that parameter estimates in one equation are affected by any mis-specification in other equations. The Stock and Watson method is by contrast a robust single equation approach which corrects for regressors endogeneity by inclusion of leads and lags of first difference of the regressors, and for serially correlated errors by a GLS procedure, which is not well handled by OLS. Table (3) shows the results of the long run relationship by using DOLS estimator. All the variables in the model are highly significant in DOLS case. Table 3: DOLS Results

Variables DOLS

EC 0.14* (0.042) LF 0.21* (0.043) K 0.56* (0.049)

In parentheses standard error are given * indicates statistical significant at 1%.

After concluding that all the variables in model are cointegrated, we can implement a panel-

based error correction model to examine short run and long run causality between EC and economic growth,6 because cointegration results show that causality exists between the two series but it does not indicate the direction of causal relationship. Thus, the next step is to apply the Granger causality test. Granger causality is a two steps procedure. The first step relates to the estimation of the

residuals itε from long run relationship. Incorporating the residuals as a right hand side variable, the

short run error correction model is calculated at the second step. Defining the error term from Equation

(1) to be itECT , the dynamic error correction model of our concern is mentioned as follows:

∑ ∑ ∑∑ +∆Ω+∆Ω+∆Ω+∆Ω++Ω=∆ −−−−−

k k k

itkitikkitikkitikkit

k

ikitiijit KLFECGDPECTGDP 11413121111 µγ (2)

∑ ∑ ∑ ∑ µ+∆Ω+∆Ω+∆Ω+∆Ω+γ+Ω=∆ −−−−−

k k k k

it2kitik24kitik23kitik22kitik211iti2j2it KLFECGDPECTEC (3)

5 See “Mixed Signals among Tests for Panel Cointegration” By Westerlund and Basher (2007). 6 The VAR models may suggest a short run relationship between variables because long run information is removed in the first

differencing, but the VECM model can evade this deficiency. Further, the VECM can recognize sources of causation and can differentiate between a long run and a short run relationship in the series which the usual Granger causality test cannot do. Moreover, the VAR method may not be suitable in the presence of cointegration.


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The sources of causation can be discovered by testing the significance of coefficients of the dependent variables in Equations (2) and (3). First, the short run effects can be examined briefly. For

short run causality, we test 0: 120 =Ω ikH for all i and k in Equation (2) or 0: 210 =Ω ikH for all i

and k in Equation (3). Next, we test long run causality by looking at the significance of the speed of adjustment γ; the coefficient of the error correction term. The significance of γ determines the long run relationship in the cointegrated process, and movements along this path can be considered permanent.

For long run causality, we test 0: 10 =iH γ for all i in Equation (2) or 0: 20 =iH γ for all i in

Equation (3). Short run results of panel causality of our model are represent in Table: 4 Table 4: Panel Causality Test Results For Short Run

Dependent Variables Independent Variables

∆GDP ∆EC ∆LF ∆K

∆GDP - 1.29 1.5 0.17 ∆EC 1.29 - 2.54 0.24

Causality Results The major goal of this study is to test the causality between EC and EG for three SAARC countries over the period 1971 to 2008. The recently developed panel cointegration techniques are applied to explore the relationship between the two economic series; EC and economic growth. The cointegration tests can tell us that causality exist but can not the direction, to know the direction of causality we employ the Granger causality test, results for short run causality are represent in Table (4), results are showing that no variable is significant at even 10 % level of significance. So, we can conclude that there is no short run causality exists in either direction; from GDP to EC or from EC to GDP. To know the results for long run causality we see the coefficient of Error Correction Term (ECT), if the coefficient is significant and has negative sign then long run relationship exist between variables otherwise not, and ECT coefficient value is (-0.041) in our results and significant at 5% level of significance. So we can conclude that in the long run there is causality exist among variables running from EC to economic growth. But the coefficient is insignificant in reverse case; when EC take as dependent variable. So in this case no causality exists. It means a unidirectional causality exists in our model. In other words we can say that energy treat as an engine of accelerated economic growth and the changes in EC have a significant effect on growth. Our results are fully consistent with those of Stern (2000), Oh and Lee (2004a) and Paul and Bhattacharya (2004) who point out energy as one of the cornerstones of the aggregate production function in terms of supply; however compared with previous empirical studies, we note that the findings also support those of Yu and Choi (1985) for the Philippines, Masih and Masih (1998) for Thailand as well as those of Asafu-Adjaye (2000), Fatai et al. (2004) for Indonesia and Lee and Chang (2008) for Asian economies. Thus, there is every reason to believe that the results also deny the neutrality hypothesis for the energy-GDP relationship in three SAARC economies. For mentioned countries, in general it is clear that energy is an important component for economic growth in the long run. In fact, production in industries and agriculture also, really demands a sturdy amount of energy. Important too, the energy equations are not significant when EC is dependent variable, which indicates that there is no long run causal relationship running from GDP to EC.

Conclusions and Policy Implications This study aims to determine the causal relationship between energy consumption (EC) and economic growth (EG) in the case of three SAARC countries i.e. Bangladesh, India, and Pakistan. Since the energy is an important factor in production so in this paper we used the production side model to


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empirically re-investigate the causal relationship between EC and EG in a multivariate model by using the data from 1971 to 2008. Time series data may give inconsistent and unpredictable results with the short time period of typical data sets, This study applies new heterogeneous panel cointegration and panel based error correction model techniques to re-examine the relationship between EC and real GDP among mentioned countries.

On the basis of our short run and long run results we can reject the neutrality hypothesis that has previously been advanced. EC is found to Granger cause GDP in the long run, but not in short run. No any causal relationship exists running from GDP to EC or EC to GDP in short run, but exists in long run running from EC to EG. So, we can say high EC tends to come with high GDP, but not the reverse. In the light of above discussion it is reflecting that energy serves as an engine of economic growth and economic activity will be affected in the result of changes in EC. This means that continuous energy use does produce a continuous increase in output. Thus, GDP is basically determined by energy, but while energy conservation may be feasible. So the related authorities in SAARC economies should take a special interest in different sources of energy and invest more in this sector, and invite foreign investors to invest in this sector, make suitable policies in this regard and find new alternate and cheap sources of energy like other develop economies. More water reservoirs are needed in three mentioned economies to produce energy at cheap cost and at large scale. Improvement in or establishment of R&D departments and increase their efficiency is also need of time, so that it create multiplier effect on GDP and as a result prosperity will come in these economies. Short fall of energy sources may harm the economic growth and push back the economy in several ways. Through effective changes and better policies in this channel can change the living standards of people in developing economies like Bangladesh, India, and Pakistan.

References [1] Akarca,-A.T and Long,-T.V (1980) “On the Relationship between Energy and GNP: A Re-

examination”, Journal of Energy and Development, Vol. 5, pp. 326-31. [2] Al-Iriani,-M.A (2006) “Energy–GDP relationship revisited: an example from GCC countries

using panel causality”, Energy Policy Vol. 34 (17) pp. 3342–50. [3] Asafu-Adjaye,-J (2000) “The relationship between energy consumption, energy prices and

economic growth: time series evidence from Asian developing countries”, Energy Economics Vol. 22, pp. 615–625.

[4] Baltagi, H.B. (2003) “Econometric Analysis of Panel Data”, 2nd Edition, John Wiley & Sons

Ltd, New York. [5] Banerjee,-Anindya, Marcellino,-Massimiliano and Osbat,-Chiara (2004) “Some Cautions on the

Use of Panel Methods for Integrated Series of Macroeconomic Data”, Econometrics Journal, Vol. 7(2) pp. 322-40.

[6] Beaudreau, - B.C (2005) “Engineering and economic growth”, Structural Change and

Economic Dynamics, Vol. 16, pp. 211– 20. [7] Breitung,-J (2000) “The local power of some unit root tests for panel data. In: Baltagi, B.,

Fomby, T.B., Hill, R.C. (Eds.), Advances in Econometrics: Nonstationary Panels”, Panels

Cointegration and Dynamic Panels Vol. 15, pp. 161–78. [8] Choi,-I. (2001) “Unit root tests for panel data”, Journal of International Money and Finance,

Vol. 20, pp. 249-72. [9] Engle,-R. and Granger,-C (1987) “Cointegration and error correction: representation,

estimation, and testing”, Econometrica, Vol. 55, pp. 257–76. [10] Erol,-U and Yu,-E.S.H (1987) “Time series analysis of the causal relationships between U.S.

energy and employment”, Resources and Energy, Vol. 9, pp. 75–89.


212

[11] Fatai,-K, Oxley,-L and Scrimgeour,-F.G (2004) “Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, the Phillippines and Thailand”, Mathematics and Computers in Simulation, Vol. 64, pp. 431– 45.

[12] Ghali,-K.H, and El-Sakka,-M.I.T (2004) “Energy use and output growth in Canada: a multivariate cointegration analysis” Energy Economics, Vol. 26 (2), pp.225–38.

[13] Gutierrez,-Luciano (2003) “On the Power of Panel Cointegration Tests: A Monte Carlo Comparison”, Economics Letters, Vol. 80(1), pp. 105-11.

[14] Harris,-R.D and Tzavalis,-E (1999) “Inference for unit roots in dynamic panels where the time dimension is fixed”, Journal of Econometrics, Vol. 91 (2), pp. 201–26.

[15] Im,-K.S, Pesaran,-M.H and Shin,-Y (2003) “Testing for unit roots in heterogeneous panels”, Journal of Econometrics Vol.115, pp. 53– 74.

[16] Kao,-Chihwa (1999). “Spurious Regression and Residual-Based Tests for Cointegration in Panel Data”, Journal of Econometrics, Vol. 90(1), pp. 1-44.

[17] King,-M.A and Hiller,-G.H. (1985), “Locally Best Invariant Tests of the Error Covariance Matrix of the Linear Regression Model”, Journal of the Royal Statistical Society, Vol. 47, pp. 98-102.

[18] Kraft,-J and Kraft,-A (1978) “On the relationship between energy and GNP” Journal of Energy

and Development, Vol. 3, pp. 401–03. [19] Lee,-C.C (2005) “Energy consumption and GDP in developing countries: a cointegrated panel

analysis” Energy Economics, Vol. 27, pp. 415–27. [20] Lee,-C.C and Chang,-C.P (2007) “Energy consumption and GDP revisited: A panel analysis of

developed and developing countries”, Energy Economics, Vol.29 (2007) pp.1206–23. [21] Lee,-C.C and Chang,-C.P (2008) “Energy consumption and economic growth in Asian

economies: A more comprehensive analysis using panel data”, Resource and Energy

Economics, Vol. 30, pp. 50–65 [22] Levin,-A, Lin,-C.F and Chu,-C.S (2002) “Unit root tests in panel data: asymptotic and finite-

sample properties”, Journal of Econometrics, Vol. 108 (1) pp. 1–24. [23] Maddala,-G.S and Wu,-S.A (1999) “Comparative study of unit root tests with panel data and a

new simple test”, Oxford Bulletin of Economics and Statistics, Vol. 61, pp. 631–52. [24] Mehrara,-Mohsen (2007) “Energy consumption and economic growth: The case of oil

exporting countries”, Energy Policy, Vol. 35, pp. 2939– 45. [25] Masih,-A.M.M and Masih,-R (1998) “A multivariate cointegrated modeling approach in testing

temporal causality between energy consumption, real income and prices with an application to two Asian LDCs” Applied Economics, Vol.30 (10), pp. 1287–98.

[26] Oh,-W and Lee,-K (2004a) “Causal relationship between energy consumption and GDP revisited: the case of Korea 1970– 1999”, Energy Economics Vol. 26, 51–59.

[27] Oh,-W and Lee,-K (2004b) “Energy consumption and economic growth in Korea: testing the causality relation” Journal of Policy Modeling, Vol. 26, pp. 973–81.

[28] Paul,-S and Bhattacharya,-R.N (2004) “Causality between energy consumption and economic growth in India: a note on conflicting results”, Energy Economics, Vol. 26, pp. 977–83.

[29] Pedroni,-P (1997) “Panel Cointegration; Asymptotic and Finite Sample Properties of Pooled Time Series Tests, with an Application to the PPP Hypothesis: New Results”, working paper, Indiana University.

[30] Peter,-C-B-Phillips and Hyungsik,-R-Moon (1999). “Linear Regression Limit Theory for Nonstationary Panel Data”, Econometrica, Vol. 67(5), pp. 1057-1112.

[31] Sari,-R and Soytas,-U. (2007) “The growth of income and energy consumption in six developing countries”, Energy Policy Vol. 35 (2) pp. 889–98.

[32] Stern,-D. I (1993) “Energy and Economic Growth in the USA, A Multivariate Approach”, Energy Economics Vol.15, pp. 137-50.


213

[33] Stern,-D. I (2000) “Multivariate cointegration analysis of the role of energy in the U.S. macroeconomy”, Energy Economics, Vol. 22, pp. 267–83.

[34] Stern,-D. I and Cleveland,-C.J (2004) “Energy and Economic Growth”, Rensselaer Working

Papers in Economics 0410, Rensselaer Polytechnic Institute, Department of Economics. [35] Stock,-J. H. and Watson,-M. W. (1993) “A simple estimator of cointegrating vectors in higher

order integrated systems”, Econometrica, Vol. 61, pp. 783–820. [36] Thompson,-H. (2006) “The applied theory of energy substitution in production”, Energy

Economics Vol. 28 (4), pp. 410–25. [37] Yu,-S.H and Choi,-J.Y (1985) “The causal relationship between energy and GNP: an

international comparison”, Journal of Energy Development, Vol. 10, pp. 249–72. [38] Westerlund,-J. and Basher,-S. A. (2008). “Mixed Signals Among Tests for Panel

Cointegration”, Economic Modeling, Vol. 25(1), pp. 128-36

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Energy Consumption and Economic Growth