This paper uses panel data from 88 countries to examine the relationship between per capita GDP and per capita energy consumption. The results show that per capita GDP and per capita energy consumption are cointegrated. Also, there is a two-way short-run, long-run and strong causality between the growth of GDP and growth of energy consumption. These results are in contrast to almost all other existing studies.panel data; energy consumption; economic growth

Sinha, Dipendra

English

Research Papers in Economics

The Energy Consumption-GDP Nexus: Panel Data Evidence from 88 Countries 
 
Dipendra Sinha, Ritsumeikan Asia Pacific University, Japan and Macquarie University, 
Australia 
 
Abstract: 
 
This paper uses panel data from 88 countries to examine the relationship between per 
capita GDP and per capita energy consumption. The results show that per capita GDP 
and per capita energy consumption are cointegrated. Also, there is a two-way short-run, 
long-run and strong causality between the growth of GDP and growth of energy 
consumption. These results are in contrast to almost all other existing studies.  
 
 
JEL Codes: C23, O10  
I. Introduction 
There have been numerous studies on the relationship between energy consumption and 
GDP. This paper uses panel data from the following 88 countries to examine the 
relationship: Algeria, Argentina, Australia, Austria, Bangladesh, Belgium, Benin, 
Bolivia, Brazil, Cameroon, Canada, Chile, China, Colombia, Democratic Republic of the 
Congo,  , Republic of the Congo, Costa Rica, Cote d’Ivoire, Cyprus, Denmark, 
Dominican, Ecuador, Egypt, El Salvador, Finland, France, Gabon, Georgia, Germany, 
Ghana, Greece, Guatemala, Haiti, Honduras, Hong Kong, Hungary, Iceland, India, 
Indonesia, Iran, Ireland, Israel, Italy, Jamaica, Japan, Jordan, Kenya, Korea, Latvia, 
Luxembourg, Malaysia, Malta, Mexico, Morocco, Netherlands, New Zealand, Nicaragua, 
Nigeria, Norway, Oman, Pakistan, Panama, Paraguay, Peru, Philippines, Portugal, Saudi 
Arabia, Senegal, Singapore, South Africa, Spain, Sri Lanka, Sudan, Sweden, Switzerland, 
Syrian Arab Republic, Thailand, Togo, Trinidad and Tobago, Tunisia, Turkey, UAE, UK, 
USA, Uruguay, Venezuela, Zambia and  Zimbabwe.  This paper differs from the previous 
panel study in two important ways. First, we use the largest dataset by including a panel 
of 88 countries. Second, for per capita income, we use purchasing power adjusted data. 
Previous studies such as Lee (2005) and Mahadevan and Asafu-Adjaye (2007) use per 
capita income in constant US dollars. But, purchasing power parity adjusted data on per 
capita income are preferable for panel data.  
There is a large body of literature that examines the relationship between GDP 
and energy. One of the first studies was by Kraft and Kraft (1978). They use data for the 
USA for 1947-1974 to study the causal relationship between gross energy consumption 
and GNP.  They find uni-directional causality flowing from GNP to energy.  Their conclusion is that energy conservation would not adversely affect GNP.  This study was 
followed by many other studies such as Akarca and and Long II (1980), Abosedra and 
Baghestani (1991), Masih and Masih (1997) and Soytas and Sari (2003). These studies 
employ data for a single country or countries and find varied results. More recent studies 
use panel data.  Al-Iriani (2006) uses panel data for member countries of the Gulf 
Cooperation Council (GCC). He finds uni-directional causality from GDP to energy 
consumption. This result suggests that energy conservation policies may be adopted by 
the GCC without any adverse effects on the growth rate of GDP. Chien-Chiang (2005) 
uses panel data for 18 developing countries. He finds short-run and long-run uni-
directional causality flowing from energy to GDP. His result suggests that energy 
conservation may harm economic growth in the short-run and the long run.  Mahadevan 
and Asafu-Adjaye (2007) find that for the energy exporting developed countries, there is 
both short-run and long-run bi-directional causality between economic growth and energy 
consumption while for the energy exporting developing countries, energy consumption 
causes economic growth only in the short-run.    
 
II. Data, Methodology and Results 
This paper uses panel data for 88 countries for natural logarithms of per capita GDP in 
constant purchasing power parity terms (denoted by gdp) and per capita energy 
consumption in terms of kilograms of oil equivalent (denoted by energy). The first 
differences of these two variables (which give us the growth rates) are denoted by ∆gdp 
and ∆energy . Annual data are for 1975-2003 for all countries. Thus, we have a balanced 
panel. All data are from the World Development Indicators On-line (August 2008).  We use the following panel unit root tests: Im, Pesaran  and Shin (2003) test, and Levin, Lin 
and Chu (2002) test and Fisher type ADF and PP tests proposed by Maddala and Wu 
(1999). The results of the panel unit root tests for gdp and energy are in Tables 1 and 2, 
respectively. We find that both gdp and energy have unit roots according to LLC, IPS, 
Fisher ADF and Fisher PP unit root tests. The unit root tests for ∆gdp and ∆energy are in 
Tables 3 and 4 respectively. Both variables are stationary in their first differences. Since 
both variables are I(1), we proceed with Pedroni’s (1999, 2004) panel cointegration tests. 
We consider the following bivariate relationships: 
gdpit = αi +δt + βenergyit + εit           ( 1 )  
where α is the country effect, δ is the fixed effect and ε is the residual.    
[Tables 1-5, about here] 
The results of the panel ADF and panel PP cointegration tests are in Table 5. It shows 
that gdp and energy are cointegrated at the 5% level of significance. Since the two 
variables are cointegrated, we proceed with the dynamic panel VECM causality tests. 
Like Mahadevan and Asafu-Adjaye (2007), we use Granger causality model with a 
dynamic error term as proposed by Holz-Eakin, Newey and Rosen (1988). For the 
VECM tests, we use the error correction model as follows: 
∆gdpit = ∆θ1j +  θ ∑
m
k
11ik ∆gdpit-k + ∑  θ
m
k
12ik ∆energyit-k + λ1 εit-1 + u1it    (2) 
∆energyit  = ∆θ2j + ∑ θ
m
k
21ik ∆energyit-k  +   θ ∑
m
k
22ik ∆gdpit-k + + λ2 εit-1 + u2it  (3) 
Here, k is the lag length,  ∆ stands for first difference and εit-1 is the lagged residual from 
the cointegrating equation (1) . To test for short run causality running from the growth of 
energy consumption to economic growth, we have to whether the coefficients  θ12ik are equal to zero. Similarly, the reverse short run causality can be tested by testing the 
restriction that the coefficients θ22ik  are equal to zero. To test for long run causality 
flowing from the growth of per capita energy consumption to the growth rate of per 
capita GDP, we test the restriction λ1 =0 in (2). The reverse long run causality is 
examined by testing the restriction λ2 = 0 in (3). For the joint short run/long run causality 
(also known as the strong Granger causality) running from the growth of per capita 
energy consumption to growth of per capita GDP, we test the restrictions that θ12ik and λ1 
are equal to zero. The reverse causality is similarly defined.  
The results for short run causality, long run causality and strong causality for lags 
of 1, 2 and 3 are given in tables 6, 7 and 8, respectively. Tests with higher order lags up 
to 10 were also carried out. The results were the same. The Schwarz Bayesian criterion 
did not select lags higher than 3.  
[Tables 6-8, about here] 
  The results are clear-cut.  We find that there is evidence of short run, long run and 
strong two-way Granger causality between the growth rates of per capita GDP and 
energy consumption. These results are in contrast to many other studies which find 
causality in one direction or the other.  Our sample of countries consists of 58 developing 
and 30 developed countries. Although not reported here, we also performed cointegration 
and causality tests separately for developing and developed countries.  The results are 
quite similar for those of the panel of 88 countries.  
III. Conclusions 
We use panel data for 88 countries to test for cointegration between per capita real GDP 
and per capita energy consumption. We find that these two variables are cointegrated. Thus, we conduct tests for short-run, long run and strong Granger causality between the 
growth rates of per capita GDP and energy consumption. We find evidence for two-way 
short-run, long-run and strong causality between the two variables. These results 
contradict the results of a number of other studies using panel data.  References: 
 
Abosedra, S. and Baghestani, H. (1991), “New Evidence on the Causal Relationship 
United States Energy Consumption and Gross National Product,” Journal of Energy and 
Development, vol. 14, pp. 285-292. 
 
Akarca, A.T. and Long II, T.V. (1980), “On the Relationship between Energy and GNP: 
A reexamination,” Journal of Energy and Development, vol.5, pp. 326-331. 
 
Al-Iriani, M.A. (2006), “Energy-GDP Relationship Revisited: An Example from GCC 
Countries using Panel Causality,” vol. 34, pp. 3342-3350. 
 
Holz-Eakin, D., Newey, W. and Rosen, H. (1988), “Estimating Vector Autoregressions 
with Panel Data,” Econometrica, vol. 56, pp. 1731-1395.  
 
Im, K. S., Pesaran, M. H., and Y. Shin (2003), “Testing for Unit Roots in Heterogeneous 
Panels,” Journal of Econometrics, vol. 115, pp. 53–74. 
 
Kraft, J. and A. Kraft (1978), “On the Relationship between Energy and GNP,” Journal 
of Energy and Development, vol. 3, pp. 401-403. 
 
Lee, C-C (2005), “Energy consumption and GDP in developing countries: A cointegrated 
panel analysis,” Energy Economics, vol. 27, pp. 415-427.  
 
Masih, M. M and R. Masih (1997), “On the Temporal Causal Relationship between 
Energy Consumption, Real Income and Prices: Some New Evidence from Asian-energy 
Dependent NICs based on Multivariate Cointegration/Vector Error Correction 
Approach,” Journal of Policy Modeling, vol. 19, pp. 417-440.  
 
Levin, A., Lin, C. F., and C. Chu (2002), “Unit Root Tests in Panel Data: Asymptotic and 
Finite-Sample Properties,” Journal of Econometrics, vol. 108, pp. 1–24. 
 
Maddala, G. S. and S. Wu (1999), “A 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. 
 
Mahadevan, R. and Asafu-Adjaye, J. (2007), “Energy consumption, economic growth 
and prices: A reassessment using panel VECM for developed and developing countries,” 
Energy Policy, vol. 35, pp. 2481-2490. 
 
Pedroni, P. (1999) “Critical Values for Cointegration Tests for Heterogeneous Panel with 
Multiple Regressors” Oxford Bulletin of Economics and Statistics, vol. 61, pp. 653-70. 
 
Pedroni, P (2004) “Panel Cointegration: Asymptotic and Finite Sample Properties of 
Pooled Time Series Tests with an Application to the PPP Hypothesis” Econometric 
Theory, vol. 3, pp. 579-25.  
Soytas, U. and Sari, R. (2003), “Energy Consumption and GDP: Causality Relationship 
in G-7 Countries and Emerging Markets,” Energy Economics, vol. 25, pp. 33-37. 
 
 Table 1. Panel unit root tests for  gdp  
Test type  Test statistic  Probability 
LLC 2.5598  0.9948 
IPS 3.5493  0.9948 
Fisher ADF  133.526  0.9927 
Fisher PP  158.062  0.8302 
Note: gdp stands for the natural logarithm of real per capita GDP in purchasing power 
parity terms. LLC and IPS stand for Levin, Lin and Chu (2002) and Im, Pesaran and Shin 
(2003), respectively.  Table 2. Panel unit root tests for energy  
Test statistic  Test statistic  Probability 
LLC -1.1316  0.1289 
IPS 1.1362  0.8721 
Fisher ADF  153.894  0.8841 
Fisher PP  177.885  0.4461 
Note: energy stands for the natural logarithm of per capita energy consumption. LLC and 
IPS stand for Levin, Lin and Chu (2002) and Im, Pesaran and Shin (2003), respectively.   
Table 3.  Panel unit root tests for ∆gdp  
Test type  Test statistic  Probability 
LLC -18.9967  0.0000 
IPS -19.5765  0.0000 
Fisher ADF  766.630  0.0000 
Fisher PP  1060.00  0.0000 
Note: ∆gdp stands for the first difference of natural logarithm of real per capita GDP in 
purchasing power parity terms. LLC and IPS stand for Levin, Lin and Chu (2002) and 
Im, Pesaran and Shin (2003), respectively.  Table 4.  Panel unit root tests for ∆energy  
Test type  Test statistic  Probability 
LLC -21.6111  0.0000 
IPS -20.5191  0.0000 
Fisher ADF  800.926  0.0000 
Fisher PP  2120.16  0.0000 
Note: ∆energy stands for the first difference of natural logarithm of per capita energy 
consumption. LLC and IPS stand for Levin, Lin and Chu (2002) and Im, Pesaran and 
Shin (2003), respectively.   
Table 5. Pedroni’s panel cointegration tests for gdp and energy 
Test type  Test statistic  Probability 
Panel PP  -2.7793  0.0084 
Panel ADF   2.8356  0.0072 
Note: gdp and energy stand for the per capita constant GDP in purchasing power terms 
 and per capita energy consumption Table 6. Short run causality tests between the growth rates of real per capita GDP and per 
capita energy consumption 
Lag  Cause  Effect  F test statistic  Probability 
1  ∆energy   ∆gdp 630.44  0.0000 
1  ∆gdp  ∆energy 1949.92 0.0000 
2  ∆energy  ∆gdp 244.77  0.0000 
2  ∆gdp   ∆energy 1114.25 0.0000 
3  ∆energy  ∆gdp   45.10  0.0000 
3  ∆gdp  ∆energy 594.10  0.0000 
Note: ∆gdp  and ∆energy stand for the growth rates of real per capita purchasing power 
parity GDP and per capita energy consumption, respectively.  
Table 7. Long run causality tests between the growth rates of real per capita GDP and per 
capita energy consumption 
Lag  Cause  Effect  F test statistic  Probability 
1  ∆energy   ∆gdp 2432.28  0.0000 
1  ∆gdp  ∆energy 1664.60 0.0000 
2  ∆energy  ∆gdp 2025.75  0.0000 
2  ∆gdp   ∆energy 1309.00 0.0000 
3  ∆energy  ∆gdp 628.21  0.0000 
3  ∆gdp  ∆energy 1357.32 0.0000 
Note: ∆gdp and ∆energy  stand for the growth rates of real per capita purchasing power 
parity GDP and per capita energy consumption, respectively.  
Table 8. Joint short/long (strong Granger) causality tests between the growth rates of real 
per capita GDP and per capita energy consumption  
Lag  Cause  Effect  F test statistic  Probability 
1  ∆energy   ∆gdp 2145.75  0.0000 
1  ∆gdp  ∆energy 49450.68  0.0000 
2  ∆energy  ∆gdp 1263.59  0.0000 
2  ∆gdp   ∆energy 1258.60 0.0000 
3  ∆energy  ∆gdp 492.53  0.0000 
3  ∆gdp  ∆energy 2178.50 0.0000 
Note: ∆gdp and ∆energy  stand for the growth rates of real per capita purchasing power 
parity GDP and per capita energy consumption, respectively. 
 
 

Critical Values for Cointegration Tests for Heterogeneous Panel with Multiple Regressors”

Energy consumption and GDP in developing countries: A cointegrated panel analysis,”

Energy Consumption and GDP: Causality Relationship

Energy consumption, economic growth and prices: A reassessment using panel VECM for developed and developing countries,”

Energy-GDP Relationship Revisited: An Example from

New Evidence on the Causal Relationship United States Energy Consumption and Gross National Product,”

On the Relationship between Energy and

On the Relationship between Energy and GNP: A reexamination,”

On the Temporal Causal Relationship between Energy Consumption, Real Income and Prices: Some New Evidence from Asian-energy Dependent NICs based on Multivariate Cointegration/Vector Error Correction Approach,”

Panel Cointegration: Asymptotic and Finite Sample Properties of Pooled Time Series Tests with an Application to the PPP

Testing for Unit Roots in Heterogeneous Panels,”

Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties,”

The energy consumption-GDP nexus: Panel data evidence from 88 countries

The energy consumption-GDP nexus: Panel data evidence from 88 countries

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