Link between Economic Growth and Energy

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running from GDP to EC for low and high income countries and bidirectional Granger ... The comprehension of the correlation study between energy consumption and ...... of no cointegration can be accepted at the 5% significance level.
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Link between Economic Growth and Energy Consumption in Over 90 Countries

Sahbi FARHANI * 1 Faculty of Economic Sciences and Management of Tunis, University of Tunis El Manar, TUNISIA E-mail: [email protected] Jaleleddine BEN REJEB Professor, Higher Institute of Management of Sousse, University of Sousse, TUNISIA E-mail: [email protected]

Abstract: This paper studies the relationship between economic growth (GDP) and energy consumption (EC) by using panel data for 95 countries from 1971 to 2008. The World Bank classification helps us to divide our 95 countries into four income groups of countries: low income group, lower-middle income group, upper-middle income group and high income group countries. To specify what matter, we use panel data analysis. The empirical results conclude that panel causality test results reveal that there is a long-run Granger causality running from GDP to EC for low and high income countries and bidirectional Granger causality between GDP and EC for the lower-middle and upper-middle income countries. Keywords: Income groups of countries, Economic Growth, Energy Consumption, Panel data analysis JEL classification: C33, O13, Q43 1. Introduction The comprehension of the correlation study between energy consumption and economic growth is considered like an important key of a development economy, and it drives to obtain the energy efficiency and to reduce the global energy consumption. But when we returned to the energy-economy literature, we were finding that the empirical results of the causal relationship between energy consumption (EC) and economic growth (GDP) for different panel data have been mixed or conflicting (see Table.1) due: firstly to different countries and periods of time used and secondly to different variables and econometric methodologies used, too. So by the results mentioned in Table.1, it is not possible to conclude definitely the way of causality between these two factors. Just we can indicate that Ouédraogo (2010) developed theoretically the idea of the causality direction way and he found that when the EC is caused by GDP, all countries based on the energy conservation policy will include a very little effect on economic growth, but when the direction runs from EC to GDP, all these types of countries will have a careful energy policy to avoid any negative effects on economic growth cause by a negative shock on energy supply. In other way, many authors using various econometric methodologies for different time periods to test for a short and long-run relationship. One of these methods is the causality technique of Engle and Granger (1987). This method has been used in many researches within the last two decades, which has become the important method for studying the relationship between these two variables in a large amount of empirical researches especially by introducing in the first time cointegration and in second time FMOLS and DOLS estimates respectively for solving the problem of the existence of endogeneity between regressors and estimating the cointegration vector for heterogeneous cointegrated panels.

*Corresponding author: Address: N°11, Balkiss Street, Erriadh City, 4023 Sousse, TUNISIA. Tel: +21622833253.

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2. Literature review This relationship between GDP and EC has been the focus of empirical investigations for many groups of countries which authors have researched to investigate the energy-economy nexus in developed countries (see for example: Lee (2006), and Narayan and Smyth (2008)), in developing countries (see for example: Lee (2005)) in the developing and developed countries in the same time (see for example: Lee and Chang (2007), and Mahadevan and Asafu-Adjaye (2007)), in Asian countries (see for example: Masih and Masih (1996, 1998), Glasure and Lee (1998), Asafu-Adjaye (2000), Fatai et al. (2004), Lee and Chang (2008), Lau et al. (2011), and Dahmardeh et al. (2012)) in African countries (see for example: Wolde-Rufael (2005), and Akinlo (2008)) or in specific regions like Gulf Cooperation countries (Al-Iriani, 2006) or oil exporting countries (Mehrara, 2007) or OECD countries (Lee at al., 2008; Lee and Lee, 2010 and Belke et al., 2011) or in countries based on the different level of income (Huang et al., 2008 and Ozturk et al., 2010) or in specific choice of countries (Soytas and Sari, 2003, 2006; Chontanawat et al., 2008; Apergis and Payne, 2009a; Apergis and Payne, 2009b; ChiouWei et al., 2009 and Farhani and Ben Rejeb, 2012). Some other viewpoints on research are based on one country like China (see for example: Wang and Shen, 2007; Yu and Meng, 2008; Wu et al., 2008 and Shuyun and Donghua (2011)). For more details about countries, methodology and results, see Table.1. Table.1: Summary of causal studies between energy consumption (EC) and Economic Growth (GDP) Authors Period Country Methodology Causality relationship Masih and 1955–1990 6 Asian countries Cointegration, ECM *EC  GDP (India) Masih (1996) *GDP  EC (Indonesia,Pakistan) 1961–1990

South Korea and Singapore

Bivariate VECM

*GDP~EC (Malaysia, Philippines, Singapore) *EC  GDP

1955–1991

Sri Lanka and Thailand

Trivariate VECM

*EC  GDP

1971–1995

Philippine and Thailand

*EC  GDP

1973–1995 1950–1992

India and Indonesia 8 countries

Cointegration and Granger causality based on ECM

1960–1999

Indonesia and India

Wolde-Rufael (2005)

1971–2001

Thailand and Philippines 19 African countries

Lee (2005) Lee (2006) Al-Iriani (2006) Soytas and Sari (2006)

1975–2001 1960–2001 1970–2002 1950–1992

18 developing countries 11 developed countries 6 countries of GCC Cooperation Countries) G-7 countries

Mehrara (2007) Lee and Chang (2007)

1971–2002

11 oil exporting countries

1965–2002

22 developed countries

1971–2002

18 developing countries

1971–2002

Developed countries

Glasure and Lee (1998) Masih and Masih (1998) Asafu-Adjaye (2000) Soytas and Sari (2003)

Fatai et (2004)

al.

Mahadevan and Asafu-

and

Bivariate VECM

Bivariate Toda and Yamamoto (1995) Toda and Yamamoto (1995)’s Granger causality

(Gulf

Trivariate VECM Granger causality Panel cointegration, GMM Cointegration and Granger causality based on ECM Panel cointegration Panel VARs GMM

developing

Panel ECM

based

and

on

*EC  GDP *EC  GDP (Argentina) *GDP  EC (South Korea) *EC  GDP (Turkey) *GDP~EC (Indonesia, Poland, Canada, USA & UK) *EC  GDP *EC  GDP *GDP  EC(Algeria, Congo DR, Egypt, Ghana, Ivory Coast) *EC  GDP(Cameroon, Morocco, Nigeria) *EC  GDP (Gabon, Zambia) *GDP~EC (Benin, Congo RP, Kenya, Senegal, South Africa, Sudan, Togo, Tunisia, Zimbabwe) *EC  GDP *Mixed results *GDP  EC *Mixed results *GDP  EC *GDP  EC(Developing countries) *EC  GDP (Developed countries) *EC  GDP(Developed countries in the short and long-run)

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Adjaye (2007)

Akinlo (2008) Lee et al. (2008) Chontanawat et al. (2008) Huang et al. (2008)

1980–2003 1960–2001

11 countries in sub-Saharan Africa 22 OECD countries

1971-2000

Over 100 countries

ARDL bounds test Panel cointegration, Panel VEC model Granger causality

1972–2002

82 low, middle and high income countries

Panel VAR, GMM model

Narayan and Smyth (2008) Lee and Chang (2008) Apergis and Payne (2009a)

1972–2002

G-7 countries

1971–2002

16 Asian Countries

1991–2005

11 countries of the Commonwealth of Independent States

Apergis and Payne (2009b)

1980–2004

6 Central American countries

Chiou-Wei et al. (2009)

1954–2006

8 Asian countries and USA

Panel cointegration, Granger causality Panel cointegration and Panel ECM Pedroni Panel cointegration, error correction model Pedroni Panel cointegration, error correction model Granger causality

Ozturk et al. (2010)

1971–2005

51 low countries

and

middle

income

*EC  GDP (Developing countries in the short-run) *GDP~EC(Developing countries in the short-run) *Mixed results *EC  GDP *Mixed results *GDP  EC (Middle and high income countries) *GDP–EC(Low income countries) *EC  GDP *EC  GDP (in the long-run) *GDP~EC (in the short run) *EC  GDP *EC  GDP

*GDP~EC (USA, Thailand, South Korea) *GDP  EC(Philippines, Singapor) *EC  GDP (Taiwan, Hong Kong, Malaysia, Indonesia) *GDP  EC(low income countries) *EC  GDP (middle income countries)

Pedroni Panel cointegration, Granger causality, panel FMOLS & DOLS estimates Lee and Lee 1978–2004 25 OECD countries Panel cointegration, *EC  GDP (2010) Granger causality Belke et al. 1981–2007 25 OECD countries Panel cointegration, *EC  GDP(in the long-run) (2011) Granger causality Lau et al. 1980–2006 17 Asian countries Pedroni Panel *EC  GDP (in the short-run) (2011) cointegration, *GDP  EC (in the long-run) Granger causality, FMOLS estimates Dahmardeh et 1980–2008 10 Asian developing countries Bivariate VECM, *EC  GDP al. (2012) Panel cointegration, Granger causality Farhani and 1973-2008 15 MENA countries Panel cointegration, *GDP~EC(in the short-run) Ben Rejeb Granger causality, *GDP  EC (in the long-run) (2012) panel FMOLS & DOLS estimates Notes: EC  GDP means that the causality runs from energy consumption to growth. GDP  EC means that the causality runs from growth to energy consumption. EC  GDP means that bidirectional causality exists between energy consumption and growth. EC~GDP means that no causality exists between energy consumption and growth. GDP: real Gross Domestic Product, EC: Energy Consumption, VAR: Vector AutoRegressive model, ECM: Error Correction Model, ARDL: AutoRegressive Distributed Lag, VECM: Vector Error Correction Model, GMM: Generalized Method of Moments, FMOLS: Fully modified Ordinary Least Squares, and DOLS: Dynamic Ordinary Least Squares.

We mentioned that the rest of the paper is organized as follows: Section.3 examines the data and the econometric methodology. Section.4 presents the empirical results. Section.5 concludes the paper.

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3. Data and econometric methodology 3.1 Data To investigate the relationship between energy consumption (EC) and economy growth (GDP), we use EC in kg of oil equivalent per capita and GDP per capita data with constant 2000 US$. The data are sourced from World Development Indicators (WDI, 2010) and SHERBROOKE University of Canada (2010) employed with their natural logarithms form to reduce the heteroscedasticity. According to the availability of data, we consider 95 countries for the annual period 1971–2008. In addition, by the World Bank countries classification (October 2009), these 95 countries are divided into four income groups. The first group contain low income countries (16 countries: Bangladesh, Benin, Congo, Ethiopia, Ghana, Haiti, Kenya, Mozambique, Nepal, Senegal, Tanzania, Togo, Vietnam, Yemen Rep., Zambia and Zimbabwe), the second group contain lower middle income countries (27 countries: Albania, Angola, Bolivia, Cameroon, China, Congo Rep., Côte d’Ivoire, Ecuador, Egypt Arab Rep., El Salvador, Guatemala, Honduras, India, Indonesia, Iran Islamic Rep., Jordan, Morocco, Nicaragua, Nigeria, Pakistan, Paraguay, Philippines, Sri Lanka, Sudan, Syrian Arab Rep., Thailand and Tunisia), the third group contain upper middle income countries (18 countries: Algeria, Argentina, Brazil, Bulgaria, Chile, Colombia, Costa Rica, Dominican Rep., Gabon, Jamaica, Malaysia, Mexico, Peru, Romania, South Africa, Turkey, Uruguay and Venezuela) and the last group contain high income countries (34 countries: Australia, Austria, Bahrain, Belgium, Canada, Cyprus, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea Rep., Kuwait, Luxembourg, Netherlands, New Zealand, Norway, Oman, Portugal, Saudi Arabia, Singapore, Spain, Sweden, Switzerland, Trinidad and Tobago, United Arab Emirates, United Kingdom, United States). 3.2 Econometric methodology The most adapted methodology is that which starts with a descriptive statistics of these two variables. So we will start by this methodology for all four income countries’ groups (See Table.2). After that we pass to apply panel unit root analysis, panel cointegration analysis, panel causality analysis and we finish by panel ordinary least square (OLS), panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOL) estimates. Table.2: Descriptive statistics Low income countries

Lower-middle income countries

Upper-middle income countries

High income countries

Mean Median Maximum Minimum Std. Dev.

GDP 331.7238 300.9000 804.4000 81.00000 153.7796

EC 392.1138 349.2550 1025.600 80.34000 187.2486

GDP 1022.095 993.5000 2759.900 127.3000 521.3971

EC 592.6363 517.0750 2643.445 165.8600 295.8734

GDP 3512.195 3159.950 9915.000 1181.100 1694.183

EC 1373.455 1122.697 3536.020 259.7800 746.4599

GDP 17821.83 16628.80 56189.00 2108.600 9359.767

EC 4702.883 4039.075 16238.00 111.8900 2656.258

Skewness Kurtosis

0.647459 2.748702

1.213891 4.425654

0.570081 2.994297

2.421887 12.32362

0.869997 3.147356

0.834406 2.689630

0.810945 3.733962

1.080704 3.853795

Jarque-Bera Probabilité

44.07910 0.000000

200.8075 0.000000

55.57504 0.000000

4719.258 0.000000

86.90485 0.000000

82.11601 0.000000

170.6099 0.000000

290.7348 0.000000

Observations Cross section

608 16

608 16

1026 27

1026 27

684 18

684 18

1292 34

1292 34

3.2.1 Panel unit root tests Several Panel unit root tests proposed to discover the stationary properties of panel data. This paper applied four tests proposed by Levin, lin and Chu (LLC, 2002), Im, Pesaran and Shin (IPS, 2003), Maddala and Wu (MW, 1999) and Hadri (2000) to test the presence of unit root.

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Levin, Lin and Chu (LLC, 2002) panel unit root test

This test is based on the conventional ADF test for the following regression equation: k

Yit  i  iYi ,t 1   i t  ij Yi ,t  j   it

(1)

j 1

Where  is the first difference operator,

Yit is the dependent variable,  it is a white-noise disturbance with a

variance of  , and t = 1, 2,..., T indexes time. 2

H 0 : i  0 H1 :  i  0

; Which alternative hypothesis corresponds to

The test is based on the test statistic

 ( ˆi )

Yit being stationary.

ti  ˆi /  (ˆi ) (where ˆi is the OLS estimate of  i in equation.1 and

is its standard error). Levin, Lin and Chu (LLC, 2002) found that the panel approach substantially

increases power in finite samples when compared with the equation ADF test, proposed a panel-based version of equation.2 that restricts

ˆi

by keeping it identical across cross-countries as follows: k

Yit   i   Yi ,t 1   i t  ij Yi ,t  j   it

(2)

j 1

Where i =1, 2,…, N indexes across cross-countries. Levin, Lin and Chu (LLC, 2002) tested:

H 0 : 1   2  ....    0 H1 : 1   2  ....    0

;

With the test based on the test statistic and

 ( ˆ )

t  ˆ /  (ˆ ) (where ˆ is the OLS estimate of  in equation.2

is its standard error).

 Im, Pesaran and Shin (IPS, 2003) panel unit root test Im, Pesaran and Shin (IPS, 2003) test is based on the mean group approach. They use the average of the t  i

Z statistic: Z  N [ t  E ( t )]/ V ( t )

statistics from equation.1 to perform the following

Where t 

1 N

(3)

N

 t i 1

i

,

E (t ) and V ( t ) are respectively the mean and variance of each t  i statistic, and they

are generated by simulations. This Z converges a standard normal distribution.  Maddala and Wu (MW, 1999) panel unit root test Maddala and Wu (MW, 1999) proposed to derive tests that combine the p-values from individual unit root tests. If we define pi as the p-value from any individual unit root test for cross-section, then under the null of unit root for all cross-sections, we have the asymptotic result that: N

PMW  2 ln( pi )   2 (2 N )

(4)

i 1

The null and alternative hypotheses are the same as for the as IPS (2003). 

Hadri (2000) panel unit root test

Like the Kwiatkowski, Phillips, Schmidt and Shin (KPSS, 1992) test, the Hadri (2000) test is based on the residuals from the individual OLS regressions of on a constant, or on a constant and a trend. If we include both the constant and a trend, we derive estimates from:

yit  i  i t  eit

(5)

Under The null hypothesis of stationarity, the asymptotic result is given by:

Z

N ( LM   )  N (0,1) C

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Where LM

ˆ e2 

1 NT

N

1 1 N T 2  S ˆ e2 NT 2 i 1 t 1 i ,t T

 eˆ i 1 t 1

2 i ,t

t

Si ,t   eˆi , j

,

is the cumulative sum of the residuals and

j 1

is the estimator of  e . 2

Hadri (2000) proposes two cases:

  1/ 6   1/15

and C  1/ 45 , if the model only includes constant, and C  11/ 6300 , if the model includes constant and trend. The Monte Carlos simulations examined by Hadri (2000) show that the results will be more exactly when T and N . 3.2.2 Panel cointegration tests To test the existence of a long run relationship between variables, we applied two panel cointegration tests proposed by Pedroni (1997, 1999) and Kao (1999). 

Pedroni (1999) panel cointegration tests

Pedroni (1999) developed a number of statistics based on the residuals of the Engle and Granger (1987) cointegration regression. Assuming a panel of N countries each with m regressors (Xm) and T observations, the long run model is written as: m

Yit   i  i t    j ,i X j ,it   it

t  1,, T

i  1, N

(7)

j 1

Where Yi ,t and X j ,i ,t are integrated of order one in levels, I(1). Pedroni (1999) proposed seven panel cointegration statistics. Four of these statistics, called panel cointegration statistics, are within-dimension based statistics. The other three statistics, called Group mean panel cointegration statistics, are between-dimension based statistics. Under null hypothesis, the seven tests are based on the absence of cointegration H 0 : i  0 ; i , where  i is the autoregressive term of the estimated residuals under the alternative hypothesis of the equation:

ˆi ,t  iˆi ,t 1  ui ,t

(8)

*Within-dimension based statistics (Panel cointegration statistics) Panel v-Statistic:

 N T 2 2  ˆ Z v    Lˆ1,1 i  i ,t 1   i 1 t 1 

 N T 2 2  ˆ Z     Lˆ1,1 i  i ,t 1   i 1 t 1 

(9) Panel non-parametric (PP) t-Statistic: N T   2 2 ˆ Z pp    2  Lˆ1,1 i i ,t 1   i 1 t 1 

1/ 2 N

 -Statistic:

Panel 1

 Lˆ ˆ T

2 1,1i

i 1 t 1

i ,t 1

1 N

 Lˆ ˆ T

i 1 t 1

ˆi ,t  ˆi

i ,t 1

1,1i

(10) Panel parametric (ADF) t-Statistic:

ˆi ,t  ˆi

(11)



N T  2 *2  ˆ Z ADF   Sˆ *2  Lˆ1,1 i i ,t 1  i 1 t 1  

1/ 2 N

T

 Lˆ i 1 t 1

ˆ

2 * 1,1i i ,t 1

ˆi*,t

(12)

*Between-dimension based statistics (Group mean panel cointegration statistics) Group

 -Statistic:

 T  Z      ˆi2,t 1  i 1  t 1  N

1 T

(13)

 ˆ t 1

i ,t 1



Group non-parametric (PP) t-Statistic:

ˆi ,t  ˆi



T   Z pp    ˆ 2  ˆi2,t 1  i 1  t 1  N

1/ 2 T

 ˆ t 1

ˆi ,t  ˆi

i ,t 1



(14) Group parametric (ADF) t-Statistic:

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Z ADF

 T      Sˆi2ˆi*2,t 1  i 1  t 1  N

1/ 2 T

 ˆ

ˆi*,t

* i ,t 1

t 1

(15) With ;

ˆi 

1 K  s  T 1     uˆi ,t uˆi ,t  s where uˆi ,t  ˆi ,t  ˆiˆi ,t 1 ; T s 1  Ki  1 t  s 1

M 1 K 2 2 K  s  T 2 ˆ ˆ ˆ ˆ Lˆ1,1,    1    where,    Y  bˆm,i X m,it ;  i ,t T      i , t i ,t  s i i ,t it T t 1 K  1 s 1  m 1 i  t  s 1

 2 

T 1 N ˆ2 2 2 ˆ 2  2ˆ and Sˆ 2  1 uˆ 2 ; ˆ ˆ   S where L   1,1i i  i ,t i i i i N i 1 T t 1

Ki 1 T *2 * *2 ˆ Si   uˆi ,t where uˆi ,t  ˆi ,t  ˆiˆi ,t 1   ˆi ,k ˆi ,t k . T t 1 k 1

Pedroni (1999) privileges that the seven statistics have a standard asymptotic distribution based on the independent movements in Brownian motions when T and N   :

Z  N



   N (0,1) N ,T 

 and are tabulated (Pedroni, 1999, Tab.2).

Where Z is one of the seven normalized statistics and 

(16)

Kao (1999) panel cointegration test

Kao (1999) proposed the following equation:

Yi ,t  i   X i ,t   i ,t

Where Yi ,t 

T

u t 1

i ,t

T

, Xi ,t   vi ,t ; t  1,, T

(17)

i  1, N .

t 1

One of test proposed by Kao (1999) is based on ADF test. This test is given by: p

ˆi ,t  ˆi ,t 1    j ˆi,t  j  ui,t , p

(18)

j 1

Where  is selected when ui ,t , p are not correlated under null hypothesis of absence of cointegration. Then the statistic test is:

t ADF  ADF 

Where

t ADF is the t-statictic of

6 N ˆ u 2ˆ 0u

  N (0,1) under H 0

ˆ 02u 3ˆ u2  2ˆ u2 10ˆ 02u  (equation.18),

and

 0u

(19)

comes from the covariance matrix

  0uv   of the bi-varied process (ui ,t , vi ,t ) ' . 2   0uv  0v  2 0u

3.2.3 FMOLS and DOLS estimates The estimator FMOLS is used by Pedroni (2001, 2004) to solve the problem of the existence of endogeneity between regressors. He considered the following equation:

Yi ,t  i  i X i ,t   i ,t

t  1,, T

i  1, N

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And he proposes that Yi ,t and X i ,t are cointegrated with slopes  i , which

i

may or may not be homogeneous

across i. So we will obtain the following equation:

Yi ,t  i  i X i ,t 

Ki



k  Ki

i ,k

X i ,t k   i ,t

t  1,, T

i  1, N

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' 1  T  T  We consider i ,t  (ˆi ,t , X i ,t ) and i ,t  lim E    i ,t   i ,t   is the long-run covariance for this T   T  t 1  t 1   0 ' 0 vector process which can be decomposed into i  i  i  i where i is the contemporaneous covariance and  i is a weighted sum of autocovariance.



Group-mean fully modified ordinary least square (FMOLS) estimators are given as:

ˆ *

FMOLS

Where Yi ,t  Yi ,t  Yi  *

1 T 2  1 N  T       X i ,t  X i      X i ,t  X i  Yi *,t  T ˆi   N i 1  t 1   t 1  

ˆ ˆ  2,1,i ˆ 0  2,1,i ˆ ˆ0 X i ,t and ˆi  ˆ 2,1,i   2,1,i 2,2,i   2,2,i . ˆ ˆ  



2,2,i



(22)



2,2,i

Group-mean panel dynamic ordinary least square (DOLS) estimator as:

ˆ *

DOLS

1 T 1 N  T  '      Zi ,t Zi ,t    Zi ,tYi ,t   N i 1  t 1   t 1  

Where Zi ,t   X i ,t  X i , X i ,t  Ki ,..., X i ,t  Ki  is vector of regressors, and  

(23)

Yi ,t  Yi ,t  Yi .

4. Empirical results 4.1 Panel unit root tests This paper employs four different unit root tests including LLC’s test (Levin et al., 2002), IPS-W-statistic (Im et al., 2003), MW-Fisher Chi-square (Maddala and Wu, 1999), and Hadri tests (Hadri, 2000). The results of these tests are reported in Table.3 indicating that the statistics significantly confirm that the level values of all series are non-stationary and all variables are stationary at the 5% significance level of the first difference, that is, all variables are I(1). Countries Method

Table.3: Panel unit root test results Lower-middle Upper-middle income income LNEC LNGDP LNEC LNGDP LNEC

Low income LNGDP

High income LNGDP

LNEC

LLC-t*

Level First difference

1.28 -12.1**

2.95** -12.9**

2.85** -12.3**

2.44** -12.0**

2.26** -12.4**

2.54** -12.2**

2.78** -12.2**

2.49** -12.5**

3.87** -9.86**

2.12** -7.92**

3.37** -12.5**

1.82 -9.07**

3.26** -9.31**

-0.04 -8.76**

3.33** -11.0**

-1.88** -11.8**

5.31** 60.23**

19.05** 22.28**

44.8** 8.99**

34.18** 18.64**

13.29** 75.32**

36.77** 66.23**

7.62** 76.04**

41.91** 60.44**

13.40** 3.21**

12.10** 2.15**

17.7** 3.34**

17.10** 2.20**

14.41** 2.04**

13.30** 2.05 **

20.02** 3.59**

20.54** 2.02**

IPS-W-stat

Level First difference MW-F-Chi-square

Level First difference Hadri

Level First difference

Notes: LLC, IPS, MW-Fisher and Hadri examine the null hypothesis of non-stationarity with intercept and trend, and ** indicates statistical significance at the 5% level. Probabilities for Fisher-type tests were computed by using an asymptotic χ2 distribution. All other tests assume asymptotic normality. The lag length is selected using the Modified Schwarz Information Criteria. All variables are in natural logarithms (LN).

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4.2 Panel cointegration tests To test the presence of cointegration between energy consumption (EC) and economic growth (GDP), we will employ two cointegration test introduced by Pedroni (1999) and Kao (1999). These two tests investigate the heterogeneous panels, in which heterogeneous slope coefficients, fixed effects and individual specific deterministic trends are permitted. A panel cointegration model of economic growth (GDP), which allows for considerable heterogeneity, is implemented as follows: (24) LNGDPi ,t  i  i LNECi ,t   i ,t t  1,, T i  1, N This equation tries to show the impact of energy consumption’s variations on economic growth.

Table.4: Pedroni (1999)’s residual cointegration test results (LNGDP as dependent variable) Countries Low income Lower-middle Upper-middle High income Method group income group income group group Statistic test Statistic test Statistic test Statistic test Within-dimension Panel υ-stat -2.25** -5.35** -4.25** -2.46** -13.23** -23.42** -15.50** -23.82** Panel  -stat Panel PP-stat -14.81** -24.32** -16.33** -17.38** Panel ADF-stat -13.42** -19.77** -15.63** -18.43** Between-dimension -21.71** -20.81** -17.71** -19.23** Group  -stat Group PP-stat -23.46** -23.72** -21.43** -24.48** Group ADF-stat -18.63** -20.69** -17.52** -18.62** Notes: The null hypothesis is that the variables are not cointegrated. Under the null tests, all variables are distributed normal (0, 1). ** indicates statistical significance at the 5% level.

As shown in Table.4, the results of Pedroni’s (1999) heterogeneous panel tests indicate that the null hypothesis of no cointegration can be accepted at the 5% significance level. Table.5: Kao (1999)’s residual cointegration test results (LNGDP as dependent variable) Countries Low income Lower-middle Upper-middle High income Method group income group income group group Statistic Prob Statistic Prob Statistic Prob Statistic Prob test Test test test -5.20** 0.0000 -4.32** 0.0000 -5.61** 0.0000 -5.78** 0.0000 ADF Note: ** indicates statistical significance at the 5% level.

Table.5 reports the results of Kao’s (1999) residual panel cointegration tests, which also reject the null of no cointegration at the 1% significance level. Thus, we conclude the existence of a panel long- run equilibrium relationship between these two variables, meaning that GDP and EC move together in the long run. 4.3 Panel causality test A panel-based on error correction model (ECM) followed by the two steps of Engle and Granger (1987) is employed to investigate the long-run and short-run dynamic relationships. The first step estimates the long-run parameters in equation.24 in order to obtain the residuals corresponding to the deviation from equilibrium. The second step estimates the parameters related to the short-run adjustment. After establishing cointegration test in the long run, we pass to examine the direction of causality between GDP and EC in a panel context which is based on the following regressions: m

m

k 1

k 1

LNGDPi ,t  1,i  1,1,i ,k LNGDPi ,t k  1,2,i ,k LNECi ,t k  1,i ECTi ,t 1  1,i ,t COPY RIGHT © 2012 Institute of Interdisciplinary Business Research

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m

m

k 1

k 1

LNECi ,t  2,i  2,1,k LNECi ,t k  2,2,i LNGDPi ,t k  2,i ECTi ,t 1   2,i ,t Where

 j ,i

(26)

(j=1,2) represents the fixed country effect; k (k=1,…,m) is the optimal lag length determined by the

Schwarz Information Criterion; and ECTi ,t 1 is the estimated lagged error correction term derived from the long-run cointegrating relationship of equation.24, in which

 j ,i

(j=1,2) is the adjustment coefficient and

 j ,i ,t

ECTi ,t  LNGDPi ,t  ˆi LNECi ,t . The term

is the disturbance term assumed to be uncorrelated with zero

means. To

investigate

H 0 : 1,2,i ,k

the

short-run causality from EC to GDP, we tested equation.25 based on  0 ; i , k . Similarly for equation.26, the null hypothesis based on H 0 : 2,2,i ,k  0 ; i, k ,

consists to test short-run causality from GDP to EC.

Table.6: Panel causality test results Countries Low income Lower-middle Upper-middle Method group income group income group LNGDP LNEC LNGDP causation’s LNEC LNGDP LNEC source Short-run ΔLNGDP # 0.13** # 0.35** # 0.53** ΔLNEC 0.04 # 0.43** # 0.55** # Long-run ECTt-1 0.85** 0.98** 0.83** 0.92** 1,02** 0.76**

High income group

LNGDP

LNEC

# 0.03

0.10** #

0.91**

1.17**

Notes: Δ denotes first differences. Figures denote F-statistic values (Fisher, 1932). P-values are in parentheses. ECT indicates the estimated error-correction term. ** indicates statistical significance at the 5% level.

As shown in Table.6, there is a short-run Granger causality running from GDP to EC for low and high income countries’ groups and bidirectional Granger causality for lower-middle and upper middle income countries’ groups. These results mean that EC is determined by economic growth in low and high income countries. Thus, the policy of energy conservation will not have an important affect on economic growth of these countries. Ozturk et al. (2010) investigated two reasons for this causality process. First, economic growth has resulted in a development in the commercial and industrial sectors where electricity is a fundamental input. Second, higher disposable income increases demand for electronic devices for entertainment and comfort for households. This idea was developed for the case of Middle Eastern countries by Narayan and Smyth (2009). For lower-middle and upper-middle income countries, the bidirectional Granger causality means that an increase in energy supply will give positive effects on economic growth, but any negative shock on energy supply will have negative effects on economic growth (Ozturk et al., 2010). 4.4. Panel OLS, FMOLS and DOLS estimates Pedroni (2001, 2004) investigated directly the condition on the cointegrating vector that is required for strong relation to hold. Furthermore, this condition allows us to pose the null hypothesis in a more natural form, so that we test whether or not strong relationship between GDP and EC holds consistently for all countries of the panel. Our models are based on the regression between these two factors as presented in equation.24, where GDP and EC slopes  i which may or may not be homogeneous across country i.

LNGDPi ,t  i  i LNECi ,t 

Ki



k  Ki

i ,k

LNECi ,t k   i ,t ; t  1,, T , i  1, N

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OLS, FMOLS and DOLS test results are reported respectively for low income countries, lower middle income countries, upper middle income countries and high income countries in Table.7, Table.8, Table.9 and Table.10. Individual OLS, FMOLS and DOLS estimates are provided in the first part of tables, while results for the panel estimators are shown at the bottom of the table. The results of OLS, FMOLS and DOLS for low income countries are presented in Table.7. Data from individual country tests produce the rejections at 5% level for OLS and FMOLS except Congo, Ethiopia, Haiti, Mozambique, Senegal, Vietnam, Zambia and Zimbabwe. But for DOLS, all countries produce rejections at the 5% level except Ethiopia, Ghana, Haiti, Kenya, Mozambique, Senegal, Vietnam and Zimbabwe. Moreover, we conclude a positive relationship between GDP and EC for all countries expect Mozambique, Nepal and Senegal. It means that as EC increases, GDP will decrease at least for these three countries. But for the rest of countries, if EC increases, GDP will be decreased. For the panel tests, it is observed that the strong relationship which runs from EC to GDP was overwhelmingly rejected at the 5% level for low income countries. The results of OLS, FMOLS and DOLS for lower-middle income countries are presented in Table.8. Data from individual country tests produce rejections at the 5% level for the OLS except Angola, Guatemala, Indonesia, Nicaragua and Sudan. But for FMOLS, data from individual country tests produce the rejections at the 5% level except Angola, Congo Rep., Ecuador, Guatemala, Indonesia, Nicaragua, Paraguay and Sudan. We note also that data from individual country tests produce the rejections at the 5% level for DOLS except Angola, Guatemala, Indonesia, Nicaragua, Paraguay, Philippines, Sudan and Thailand. In addition, there is a positive relationship between GDP and EC for all countries. It means that as EC increases, GDP will increase at least for all countries. For the panel tests, it is observed that the strong relationship which runs from EC to GDP was rejected at the 5% level for these countries. Test results of OLS, FMOLS and DOLS for upper-middle income countries are presented in Table.9. Data from countries produce rejections at the 5% level for OLS, FMOLS and DOLS except Argentina, Brazil, Bulgaria, Chile, Colombia, Costa Rica, Dominican Rep., Malaysia, Peru, Romania and Uruguay. In addition, there is a positive relationship between GDP and EC for all countries. It means that as EC increases, GDP will increase at least for all countries. For the panel tests, it is observed that the strong relationship which runs from EC to GDP was overwhelmingly rejected at the 5% level for these countries. For the last type of countries’ group (high income countries), test results of panel OLS, FMOLS and DOLS are presented in Table.10. For OLS estimates, data from countries produce rejections at the 5% level except Cyprus, Finland, Greece Ireland, Israel, Korea Rep., New Zealand, Singapore, Sweden, Trinidad and Tobago and UK. But for FMOLS estimates, the exception touches only Belgium, Cyprus Finland, Ireland, Israel, Netherlands, New Zealand, Singapore, Sweden, Trinidad and Tobago and UK. Moreover, for DOLS estimates, the exception touches only Cyprus Finland, Ireland, Israel, Japan, Korea Rep., New Zealand, Singapore, Sweden, Trinidad and Tobago and UK. In addition, there is a positive relationship between GDP and EC for all countries except for Bahrain with DOLS estimates. For the panel tests, it is observed that the strong relationship which runs from EC to GDP was overwhelmingly rejected at the 5% level for these countries. We conclude that results of estimates show that we cannot find a definitely strong relationship between EC and GDP for most countries because there is no evidence indicating that energy consumption leads economic growth in any of the four income countries’ group considered in this study. Thus, all countries should be guided by a stronger energy conservation policy.

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5. Conclusion and policy implications The principal aim of this paper is based on the linkages among economic growth and energy consumption in low income group, lower-middle income group, upper-middle income group and high income group countries during the period starting from 1971 to 2008. We employed the panel unit root tests, panel cointegration tests and panel causality test. Our panel cointegration tests reveal the existence of a panel long-run equilibrium relationship between GDP and EC. The empirical results of panel cointegration test show that RC and GDP are cointegrated for all four income countries’ groups. In addition, panel causality test results reveal that there is a long-run Granger causality running from GDP to EC for low and high income countries and bidirectional Granger causality between EC and GDP for the lower-middle and upper-middle income countries. After that, we have passed to test a strong relationship between economic growth and energy consumption holds consistently for all countries of the panel by doing individual and panel OLS, FMOLS and DOLS. The empirical results of this study provide to formulate energy policies in these countries. After obtaining these results, we can conclude some policy implications. Firstly, when EC leads GDP positively, it suggests that the benefit of energy use is greater than the externality cost of energy use. But, if an increase in GDP leads EC positively, the externality cost of energy use will set back economic growth. So, a conservation policy is necessary. Secondly, the policymakers should take into consideration the degree of economic growth in each country when energy consumption policy is formulated (Ozturk et al., 2010). In addition, some developed and developing countries (such as China) have also been increasing their GDP significantly faster than their energy consumption because they funded the systems and the best technologies which help to improve the relation between energy consumption and economic growth. These methods consist to use modern energy (electricity, gas, petroleum products and coal). For that, the energy conservation policies cannot be efficient without considering other economic and environmental factors (CO 2 emissions, energy price, capital, labor, trade, etc.) in order to develop theoretical and empirical results. So, our policy implications lead to include new variables for future research.

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Annexure Table.7: OLS, FMOLS DOLS estimates for low income countries Country Bangladesh Benin Congo Ethiopia Ghana Haiti Kenya Mozambique Nepal Senegal Tanzania Togo Vietnam Yemen Zambia Zimbabwe Panel Results

OLS 0,25** 0,28** 2,88 1,25 0,23** 0,15 0,44** -0,41 -0,21** -0,31 0,24** 0,31** 0,52 1,24** 1,97 0,42 0,56**

FMOLS 0,03** 0,05** 1,88 0,55 0,13** 0,27 0,43** -0,31 -0,14** -0,22 0,20** 0,38** 0,47 1,21** 1,15 0,82 0,88**

DOLS 0,15** 0,25** 3,88** 0,21 0,01 0,45 0,59 -0,41 -0,17** -0,34 0,28** 0,67** 0,88 1,77** 2,15** 0,29 0,72**

Table.8: OLS, FMOLS DOLS estimates for lowermiddle income countries Country OLS FMOLS DOLS Albania 1,32** 1,13** 1,15** Angola 0,48 0,55 0,27 Bolivia 0,18** 0,16** 0,21** Cameroon 1,20** 1,25** 1,61** China 0,23** 0,20** -0,14** Congo, Rep. 0,15** 0,37 0,45** Côte d'Ivoire 0,52** 0,43** 0,44** Ecuador 0,15** 0,37 0,45** Egypt 0,41** 0,33** 0,61** El Salvador 1,24** 0,21** 0,77** Guatemala 0,31 0,62 0,74 Honduras 0,24** 0,20** 0,28** India 0,31** 0,38** 0,67** Indonesia 0,42 0,47 0,68 Iran 1,24** 1,21** 1,77** Jordan 1,17** 1,05** 2,25** Morocco 0,42** 0,34** 0,72** Nicaragua 0,98 0,88 1,23 Nigeria 0,14** 0,28** 0,33** Pakistan 0,31** 0,38** 0,67** Paraguay 0,52** 0,49 0,51 Philippines 1,04** 0,41** 0,77 Sudan 0,97 1,25 1,53 Sri Lanka 0,42** 0,65** 0,39** Syria 0,24** 0,21** 0,77** Thailand 1,20** 0,47** 0,65 Tunisia 0,51** 0,42** 0,69** Panel Results 0,66** 0,53** 0,81** Sweden 0,15 0,37 0,45 Switzerland 0,94** 0,81** 0,97** Trinidad and Tobago 0,15 0,37 0,45 United Arab Emirates 0,52** 0,43** 0,44** United Kingdom 1,15 1,37 0,85 United States 1,24** 0,91** 0,67 0,86** 0,75** 0,81** Panel Results Notes: Asymptotic distribution of t-statistic is standard normal as T and N go to infinity. ** Indicates that the parameter is significant at the 5% level.

Table.9: OLS, FMOLS DOLS estimates for upper-middle income countries Country Algeria Argentina Brazil Bulgaria Chile Colombia Costa Rica Dominican, Rep. Gabon Jamaica Malaysia Mexico Peru Romania South Africa Turkey Uruguay Venezuela Panel Results

OLS 0,21** 1,65 1,28 0,88 1,05 0,88 0,15 0,43 0,32** 0,21** 0,37 0,94** 0,81 0,53 0,42** 1,14** 0,97 0,12** 0,56**

FMOLS 0,13** 1,73 1,54 1,28 0,95 0,83 0,27 0,33 0,11** 0,24** 0,22 1,20** 0,98 0,27 0,31** 1,21** 1,05 0,22** 0,88**

DOLS 0,25** 1,25 1,17 0,99 0,97 0,79 0,95 0,59 0,41** 0,17** 0,54 1,18** 0,87 0,18 0,46** 1,17** 1,15 0,29** 0,72**

Table.10: OLS, FMOLS DOLS estimates for high income countries Country Australia Austria

OLS 0,18** 1,20**

FMOLS 0,16** 1,25**

Bahrain Belgium Canada Cyprus Denmark Finland France Germany Greece Hungary Iceland Ireland Israel Italy Japan Korea, Rep. Kuwait Luxembourg Netherlands Norway New Zealand Oman Portugal Saudi Arabia Singapore Spain Sweden Switzerland Trinidad and Tobago United Arab Emirates United Kingdom United States Panel Results

0,23** 0,15** 0,52** 0,15 1,24** 0,42 1,24** 0,42** 1,17 0,42** 0,14** 0,98 0,31 0,58** 1,04** 0,78 0,97** 1,42** 0,52** 0,24** 0,29 0,51** 1,20** 0,23** 0,15 0,82** 0,15 0,94** 0,15 0,52** 1,15 1,24** 0,86**

0,20** 0,37 0,43** 0,37 0,21** 0,47 1,21** 0,47** 1,05** 0,34** 0,28** 0,88 0,38 0,49** 0,41** 0,48** 1,25** 1,35** 0,49 0,21** 0,47 0,42** 1,25** 0,20** 0,37 0,63** 0,37 0,81** 0,37 0,43** 1,37 0,91** 0,75**

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DOLS 0,21** 1,61** 0,14** 0,45** 0,44** 0,45 0,77** 0,68 1,77** 0,68** 2,25** 0,72** 0,33** 1,23 0,67 0,61** 0,77 0,63 1,53** 1,39** 0,51** 0,77** 0,35 0,69** 1,61** 0,14** 0,45 0,74** 0,45 0,97** 0,45 0,44** 0,85 0,67 0,81**

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