revisiting the relationship between coal consumption and economic growth

Applied Econometrics and International Development Vol. 12-1 (2012)

REVISITING THE RELATIONSHIP BETWEEN COAL CONSUMPTION AND ECONOMIC GROWTH: COINTEGRATION AND CAUSALITY ANALYSIS IN

PAKISTAN SHAHBAZ, Muhammad1

DUBE, Smile2 Abstract. The paper re-visits the relationship between coal consumption and economic growth by including other supporting variables such as capital use and labor participation rate in Pakistan over the 1972-2009 period. The paper adopts an augmented neoclassical production framework. In doing so, for the long run relationship between the variables, the ARDL bounds testing approach to cointegration is applied. The VECM Granger causality procedure is used to detect the direction of causality between coal consumption and economic growth while an innovative accounting approach is used to check the robustness of causality results. Empirical exercise confirms a long run relationship between the variables. The results suggest that coal consumption, capital use and the labor participation rate have positive impact on economic growth. Causality analysis indicates bidirectional causal relation between coal consumption and economic growth and results are robust through innovative accounting approach. This implies that energy (coal) conservation policies may retard economic growth that in turn lowers the demand of coal. Keyword: Coal Consumption, Economic Growth, Cointegration JEL Classification: Q20, O11, C22 I. Introduction Since the nineteenth century coal has been a significant source of energy in promoting economic growth in developed economies. The preference for coal in developed and emerging economies is partly due to rising prices of alternative sources of energy such as petroleum and natural gas. With the rise in the prices of petroleum and natural gas, coal became competitive in economic expansion and growth. In planning for future energy needs and utilization, coal use, albeit, a source of carbon emissions is a major factor (Jinke et al., 2008). Coal is regarded as the most dependable source of energy. Unlike other forms of energy, coal is relatively abundant and cheap. Furthermore, the political volatility in a number of main oil producing countries makes dependence on coal a relatively safe source of energy. On the other hand, the dependency on coal is tempered by the ecological studies that point to coal use as leading to the discharge and production of greenhouse gases (Apergis and Payne, 2009a). In addition, there is belief that the utilization and consumption of coal is the most important cause of global warming because of the fact that power plants release carbon dioxide gas that is a major cause of global warming (Wolde-Rufael, 2010). This shows that coal consumption is an important source of energy not only for developed world but also for developing economies.

1 Corresponding Author: COMSATS Institute of Information Technology, Lahore, Pakistan /Corresponding author: email:[email protected] 2 Smile Dube, California State University, Sacramento (CSUS), Sacramento, CA, USA/Email: [email protected]


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Few studies in the energy literature on the relationship between coal consumption and economic growth employ advanced cointegration approaches and Granger causality tests for many countries.3. The importance of coal in energy supply and energy security invites further research as undertaken in this paper. We use a more generalized multivariate system that incorporates additional relevant variables in examining the relation between coal consumption and economic growth as indicated by Jinke et al. (2008). In this paper, we follow the approach by Jinke et al. (2008) and Wolde-Rufael, (2010) in their studies. The present paper fills this gap in energy literature by examining the nature of direction of causality between coal consumption and economic growth augmented by capital use and the labor participation rate in Pakistan for the period of 1972-2009. Lütkepohl (1982) has shown that the exclusion of relevant variables often yields inconsistent and biased results, and more importantly, that no casual relationship can be found due to these neglected variable(s). This shortcoming can be overcome by including additional variables which not only make findings less biased and consistent but also provides more reliable results on the causal relationship among the variables (Loizides and Vamvoukas, 2005). Thus, the current use of various types of Granger causality tests might not be valid with the exclusion of relevant variables from the production function. This empirical study leads to three main conclusions: (i) empirical evidence confirms a long run relationship between the variables, (ii) results indicate that coal consumption, capital use and the labor participation rate are the main contributors to economic growth, (iii) causality analysis shows bidirectional causality between coal consumption and economic growth and results are robust through innovative accounting approach. This implies that energy (coal) conservation policies may retard economic growth that in turn lowers the demand of coal. The present study contributes to the existing literature in four ways. First, we employ the ARDL bounds testing approach to cointegration that is valid regardless of whether a series is I(0) or I(1). Second, the paper provides plausible estimates of the impact of coal consumption on economic growth in the long and short-run. Third, we employ VECM Granger causality tests to examine the direction of long and short-run causality. Finally, an innovative accounting technique (forecast error variance decomposition and impulse response functions) is employed to measure feedback effects. The paper is organized as follows. Section II contains literature review. Section III has the methodology, model, and data description. Section IV is a discussion of the results from cointegration and Granger causality tests. Section V discusses VECM Granger causality results. Section VI presents feedback results from an innovative accounting technique. Section VII concludes and points to three areas for extending similar studies. II. Pakistani Context

3 See Yang (2000a, b), Wolde-Rufael (2004), Fatai et al. (2004), Lee and Chang (2005), Yoo (2006), Zhang and Li (2007), and Hu and Lin (2008).These studies used only coal consumption and economic growth to investigate the relation between said variables and ignored the importance of capital and labor force in production function. This leads their results bias and less helpful for policy makers.

Shahbaz,M.,Dube,S. Revisiting the Relationship between Coal Consumption and Growth in Pakistan

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Energy is considered the most important stimulus for economic growth and, consequently, for the development of quality of life in an economy. Exploration of stable energy supply with environmental conservation policies has become one of the main priorities of energy policy in Pakistan4. A rise in consistent electricity load-shedding further has put pressure on movement to explore alternative sources of energy. The acceleration of momentum of economic growth necessitates reliable, uninterrupted and affordable availability of energy resources. An increase in energy consumption per capita is considered a good indicator of economic development in an economy. Countries having higher per capita energy consumption are linked with high level of quality of human life. As is the case with some other developing countries, Pakistan is also facing problems to meet rising energy demand and prices of electricity, natural and oil are rising due to rising demand for energy to sustain economic development. Although, Pakistan has abundant reserves of natural gas but these reserves are running down due to hike in petroleum prices in the country5.

Table 1: Consumption and Production of Coal Coal Consumption (% share) Coal Production (000 tones) Year

Power Brick Kilns Cement Imports Production Total 2000-01 5.1 70.2 24.7 950 3, 095 4, 045 2001-02 5.7 58.5 35.9 1, 081 3, 328 4, 409 2002-03 4.2 53.3 42.5 1, 578 3, 312 4, 890 2003-04 3 42.7 54.2 2, 789 3, 275 6, 064 2004-05 2.3 49.5 48.2 3, 307 4, 587 7, 894 2005-06 1.9 54.7 43.3 2, 843 4, 871 7, 714 2006-07 2.1 41.5 56.4 4, 251 3, 643 7, 894 2007-08 2.0 37.2 61.2 5, 987 4, 124 10, 111 2008-09 2.3 N.A N. A 4, 652 3, 738 8, 390

Note: GOP (2010), N. A is not available Energy demand of Pakistan has been projected to 129 million tones of oil equalant (MTOE) in forthcoming 15 years (GOP, 2008). The country’s current and future economic growth is critically dependent upon the availability and exploration of new energy resources. Recently, energy demand has increased due to high per capita income and moderate rate of economic growth. On the other side of picture, the supply of energy (coal) is considerably low to meet the energy demand due to insufficient exploration and exploitation of energy resources in the country. To meet rising demand of coal due to the hike in prices of other energy fuels, government is offering incentives to foreign investors

4 A rise in consistent electricity load-shedding further has put pressure on movement to explore alternative sources of energy. 5 The nature of transportation has been converted to compressed gas consumption after hike in petroleum prices.


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in energy sector (coal). Pakistan uses coal, hydroelectric, natural gas, nuclear (to some extent) and oil sources to meet energy demand. Pakistan is a country who has huge coal reserves especially lignite coal resources and ranked 7th biggest country in list of top 20 countries of the globe having huge resources of coal. The coal resources estimated in Pakistan have been more than 185 billion tonnes while 175 billion tonnes discovered from Thar coalfields in Sindh province are also included. The supply of coal was low due to less demand for coal in local market in 2006-07. During July-March 2008-09, 28.8 percent coal production was decreased as compared to last year. Furthermore, supply of coal decreased by 15% from 4.12 million in 2007-08 to 3.49 million in 2008-09 due to hike in cost of mines operation. Brick kilns and Cement industries consume 60.4% and 37.3% of total coal production respectively, whereas the rest is consumed by power sector in the country. Table-1 indicates that share of coal consumption in brick kilns and power sector has increased to 37.2% and 2.0% in 2008 against 41.5% and 2.10% in 2007. The share of coal consumption has increased to 61.2% in 2008 from 56.4% in 2007. The rise in price of furnace oil led to shifting of more than 80% of cement industry to coal which enhanced the demand of coal in the respective industry by 2.5 – 3.0 million tones in 2008. This further enhanced the utilization of indigenous as well as imported coal. Utilization of domestic coal by cement manufacturing plants led the country to save scarce foreign reserves. Following rising demand of coal, the government is trying to develop infrastructure i.e. roads, supply of water, communication network, airstrips and railway tracks etc. to improve production of coal with special focus on the Thar coal fields in Singh. Subsequently, government of Sindh has established Thar Coal Energy Board to meet energy (coal) demand in the country. 3. Literature Review The literature review has three parts. Sub-section A discusses testable hypotheses, while B and C review the literature on single-country and cross-country studies respectively.

A. Testable Hypotheses The direction of causality between coal consumption and economic growth has four testable hypotheses. The first hypothesis tests for the importance of coal consumption for economic growth directly or indirectly through the use of capital and labor in economic activity where labor and capital are considered complements. That is, we test whether an increase in economic growth is linked to an increase in coal consumption or whether a causal relationship runs from coal consumption to economic growth. If confirmed, coal (energy) conservation policies may be harmful for economic growth. The energy economics literature also shows the adverse impact of coal consumption on economic growth (for example, Apergis and Payne, 2009a and b). This may be due to inefficient and extreme usage of coal. In addition, Wolde-Rufael (2010) indicates that over time, the usage of coal has become inefficient as it has been shown that it contributes a lot to the growth of carbon dioxides emissions. Second, we test if there is unidirectional causality from economic growth to coal consumption. If so, then the conservation-hypothesis postulates that coal consumption is determined by economic growth is plausible. Under such a scenario, energy (coal) conservation policies do not have adverse affects on economic growth. Wolde-Rufael (2010) has suggested that a rise in economic growth may be linked to reductions in coal consumption due to the low amount of electricity produced from coal. This implies that


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an economy could focus on other sources of energy which are safer, cleaner, and cheaper rather than coal. Third, we test the interdependent relationship between coal consumption and economic growth represented by the feedback hypothesis. The feedback hypothesis highlights the existence of a bidirectional causal relationship between coal consumption and economic growth. This hypothesis suggests that energy conservation policies may retard economic growth by reducing coal consumption in an economy, and that resulting fluctuations in economic growth may further reduce coal consumption. Finally, we test the neutrality hypothesis that suggests that there is minor role of coal consumption in economic growth. This is validated when there is no causal relation between coal consumption and economic growth. If confirmed, the neutrality hypothesis suggests that declining the use of coal through coal conservation policy will have adverse affects on economic growth. The summary of empirical literature on coal consumption and economic growth by applying various econometric approaches is shown in Tables 2a and 2b. B. Single Country Studies There is a voluminous literature in single country and cross-country using time series data. We discuss literature on single case country studies first, followed by that on cross-country studies.

Table 2a: Summary of Literature on Coal Consumption and Economic Growth:

Single Country Case Studies No. Authors Countries Sample

Period Methodology Variables Cointe

gration Causa lity

1. Yang (2000a)

Taiwan 1954-1997

Engle-Granger Coal Consumption, Real GNP per capita

No Y CC

2. Yang (2000b)

Taiwan 1954-1997

Engle-Granger Coal Consumption, Real GDP

No Y CC

3. Sari and Soytas (2004)

Turkey 1960-1999

Granger Causality, VAR Generalized Forecast Error Variance Decomposition Method

Coal Consumption, Real GDP, Employment

N.A Y CC

4. Wolde-Rufael (2004)

Shanghai 1952-1999

Toda-Yamamoto (1995) Granger Causality

Coal Consumption, Real GDP

N.A Y CC

5. Lee and Chang (2005)

Taiwan 1954-2003

Johansen-Juselius (1990) Cointegration Approach, Gregory-Hansen weak exogeneity

Coal Consumption, Real GDP per Capita

Yes Y CC


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test 6. Yoo

(2006) Korea 1968-

2002 Johansen-Juselius (1990) Cointegration Approach


Yes Y CC

7. Ewing et al. (2007)

United States

2001.m1-2005.m6

VAR Generalized Forecast Error Variance Decomposition Method

Coal Consumption, Industrial Production, Employment

N.A Y CC

8. Zhang and Li (2007)

China 1980-2004

Error Correction Method


Yes Y CC

9. Hu and Lin (2008)

Taiwan 1982.I-2006.IV

Hansen-Seo Asymmetric Cointegration Approach


Yes Y CC

10. Sari et al. (2008)

United States

2001.m1-2005.m6

ARDL bounds testing approach to Cointegration


Yes Y CC

11. Yuan et al. (2008)

China 1963-2005

Johansen-Juselius (1990) Cointegration, Generalized Impulse Response Analysis

Coal Consumption, Real GDP, capital, Labour

Yes Y CC

12. Payne (2009a)

United States

1949-2006

Toda-Yamamoto (1995) Granger Causality

Coal Consumption, Real GDP, Real Gross Capital formation, Employment

N.A Y CC

13. Ziramba (2009)

South Africa

1980-2005

ARDL bounds testing approach to Cointegration, Toda-Yamamoto (1995) Granger Causality


No Y CC CC EMP

14. Liu et al. (2009)

China 1978-2007

Engle-Granger Causality Test


No Y CC

15. Gurgul and Lach

Poland 2000-2009

Nonlinear Granger Causality


Yes Y CC


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Notes: Y, CC and EMP represent economic growth, coal consumption and employment. The unidirectional causality from economic growth to from coal consumption is indicated by Y CC, from coal consumption to economic growth by Y CC, bidirectional causality between coal consumption and economic growth by Y CC and no causal relation between both variables by Y CC, and N. A. represents not applied Yang (2000a) investigated cointegration and the direction of the causal relationship between coal consumption and real GNP per capita (proxies for economic growth) by applying a two-steps Granger cointegration and causality tests. The empirical results show both variables are not cointegrated, and that causality runs from economic growth to coal consumption in case of Taiwan. Yang (2000b) applied the two-step Granger cointegration and Granger causality tests to reinvestigate the association between economic growth (proxied by real GDP) and coal consumption for Taiwan. The results show no cointegration between coal consumption and economic growth while a bidirectional causal relationship is found between the both variables. Similarly, Lee and Chang (2005) reinvestigated the link between economic growth and aggregated and disaggregated energy consumption such as coal consumption, oil consumption, natural gas consumption and electricity consumption for Taiwan. They employed the Johansen (1988) cointegration approach to estimate long run relationships among the variables. They found cointegration among the variables and established that the direction of causality is two-way between coal consumption and economic growth. Wolde-Rufael (2004) used Shangai annual data to investigate the causal relation between coal consumption and economic growth over the period of 1952-1999. The modified version of the Granger (1969) causality test proposed by Toda and Yamamoto (1995) was applied. The findings suggest that uni-directional causality runs from coal consumption to economic growth. Sari and Soytas (2004) analyzed the relationship between energy consumption (coal), employment and economic growth by applying generalized forecast error variance decomposition approach. The empirical results show that employment makes contribution to economic growth by 23.5 percent to 26% through its innovative shocks. Hu and Lin (2008) probed the relationship between disaggregated energy consumption and economic growth in Taiwan using threshold cointegration approach developed by Hansen and Seo (2002). The empirical results confirm non-linear cointegration between the variables and bidirectional causal relation is found between coal consumption and economic growth. Sari et al. (2008) used an autoregressive distributive lag model (ARDL) with monthly data over the period of 2001-2005 to examine causality between disaggregated energy consumption, employment and economic growth (proxied by industrial production) for United States. They found no cointegration between coal consumption, employment and economic growth while coal consumption Granger causes economic growth. Zhang and Li (2007) analyzed causality between coal consumption and economic growth by applying Granger causality approach. Their findings show a one-way causal relation running from economic growth to coal consumption. Yuan et al. (2008) examined the relation between economic growth and aggregated and disaggregated energy consumption for Chinese economy by including other inputs such as labor and capital in production function. They employed the Johansen and Juselius (1990) cointegration and the two


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steps Granger causality approach. Their results confirm cointegration both at aggregated and disaggregated level between energy consumption and economic growth. Furthermore, they found bidirectional causality between coal consumption and economic growth in long run while in short-run, causality runs from economic growth to coal consumption, a result confirmed by Liu et al. (2009). Ziramba (2009) investigated the direction of causal relationship between disaggregated energy consumption, employment, and economic growth in South Africa over the period of 1980-2005, by applying the Toda and Yamamoto (1995) Granger causality approach. He employed an ARDL bound testing approach to test for cointegration between variables of interest. Results indicated no cointegration between coal consumption, employment and industrial production, and no causal relationship was found between coal consumption and economic growth. However, he found that causality runs from coal consumption to employment. Payne (2009a) investigated causality between coal consumption and economic growth by applying a modified version of Granger causality developed by Toda-Yamamoto (1995) for the United States. He found no evidence of Granger causality. In case of Polish economy, Gurgul and Lach (2011) examined the direction of causality between coal consumption, economic growth and employment using quarter frequency data over the period 2000-2009. They applied Johansen (1995) cointegration approach for long run and Toda-Yamamoto technique for direction of causality between coal consumption and economic growth. Their results confirmed cointegration between the variables while nonlinear granger causality analysis revealed unidirectional causality running from economic growth to all indicators of coal consumption such as hard coal, lignite coal and total coal consumption and to employment.

C. Cross-Country Studies

Table 2b: Summary of Literature on Coal Consumption and Economic Growth: Cross-Country Case Studies

No. Authors Countries Sample Period

Methodology Variables Cointe gration

Causality

16. Fatai et

al. (2004)

Australia 1960-1999

Johansen-Juselius (1990) Cointegration Approach, Engle-Granger Causality Test

Coal Consumption, Real GDP, Consumer prices

No Y CC

New Zealand

1960-1999

No Y CC

India No Y CC Indonesia No Y CC Philippines No Y CC Thailand No Y CC 17. Jinke et

al. (2008)

China 1980-2005

Engle-Granger Cointegration and Causality


Yes Y CC


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Tests India Yes Y CC Japan Yes Y CC South

Africa Yes Y CC

South Korea

Yes Y CC

18. Zahid (2008)

Pakistan 1973-2003

Toda-Yamamoto (1995) Granger Causality, VECM Granger Causality Approach

Coal Consumption, Real GDP per Capita

Yes Y CC

India No Y CC Bangladesh No Y CC Sri Lanka Yes Y CC Nepal No Y CC 19. Jinke et

al. (2009)

United States

1980-2005

Engle-Granger Cointegration and Causality Tests


No N.A

Japan Yes Y CC China Yes Y CC India Yes Y CC South

Africa Yes Y CC

20. Wolde-Rafeal (2010)

China 1965-2005

Toda-Yamamoto (1995) Granger Causality, Generalized Forecast Error Variance Decomposition Method

Coal Consumption, Real GDP, Real Gross Capital Formation, Employment

N.A Y CC

India N.A Y CC Japan N.A Y CC Korea N.A Y CC South

Africa N.A Y CC

United N.A Y CC


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States 21. Apergis

and Payne (2009a)

Panel of OECD Countries6

1980-2005

Panel Cointegration approach developed by Larsson et al. (2001) and Panel Causality Test

Coal Consumption, Real GDP, Real Gross Fixed Capital Formation, Labour Force

Yes Y CC

22. Apergis and Payne (2009b)

Emerging Economies7

1980-2006

Panel Cointegration approach developed by Larsson et al. (2001) and Panel Causality Test

Coal Consumption, Real GDP, Real Gross Fixed Capital Formation, Labour Force

Yes Y CC

Notes: Y, CC and EMP represent economic growth, coal consumption and employment. The unidirectional causality from economic growth to from coal consumption is indicated by Y CC, from coal consumption to economic growth by Y CC, bidirectional causality between coal consumption and economic growth by Y CC and no causal relation between both variables by Y CC, and N. A. represents not applied In cross-country case studies, Fatai et al. (2004) examined the relationship between energy (coal) consumption and economic growth relation over the period of 1960-1999 and included consumer prices as an additional variable. Their findings indicated bidirectional causality between coal consumption and economic growth for Philippines and Thailand while unidirectional causality was found to run from coal consumption to economic growth for Indonesia and India8. Jinke et al. (2008) applied the traditional Engle-Granger (1969) to investigate the relationship between coal consumption and economic growth. The results show unidirectional causality from economic growth to coal consumption for China and Japan9. Zahid (2008) applied an error correction model and the Toda and Yamamoto (1995) approach to detect the direction of causality between coal consumption and economic growth in SAARC [South Asian Agreement and Regional Cooperation] countries namely Pakistan, India, Sri Lanka, Bangladesh and Nepal, and found unidirectional Granger causality running from coal consumption to economic growth only in Pakistan.

6Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Portugal, South Korea, Spain, Sweden, Switzerland, UK, and the US. 7 Argentina, Brazil, Chile, China, Egypt, Hungary, India, Indonesia, Malaysia, Mexico, Morocco, Peru, Philippines, South Africa and Thailand 8 There is no causality between coal consumption and economic growth for Australia and New Zealand 9 For India, South Korean and South Africa coal consumption granger does not cause economic growth nor does economic growth granger cause coal consumption.


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Jinke et al. (2009) reexamined the link between coal consumption and economic growth by applying the traditional Engle-Granger (1969) approach. They found unidirectional causal link that runs from economic growth to coal consumption in China and Japan10. Wolde-Rafael (2010) examined causality between coal consumption and economic growth and his results indicate unidirectional causality running from coal consumption to economic growth in India and Japan while the opposite was found in China and South Korea. On the other hand, they found bi-directional causality between economic growth and coal consumption in South Africa and the United States. Finally, Apergis and Payne (2009a, b) conducted studies to investigate link between coal consumption and economic growth in OECD economies and emerging market economies using a panel approach. Their empirical exercise confirmed cointegration between the variables. They found negative causality running from coal consumption to economic growth using panel causality approach. 4. Modeling, Methodological Framework, and Data A large part of recent energy economic literature uses the traditional neo-classical production function framework to investigate relationship between coal consumption and economic growth where capital use and the labor participation rate are also considered as important contributors to production.11 The log-linear specification of an econometric to be estimated is:

itLtKtCCt LKCCY lnlnlnln 1 (1)

where, tY is real GDP per capita (a proxy for economic growth), tCC is for coal

consumption per capita, tK indicates capital stock per capita, tL is the labor participation

rate, and i is residual term is assumed to be normally distributed.

We employ the ARDL bounds testing approach to cointegration developed by Pesaran and Pesaran (1997), Pesaran et al. (2000), and Pesaran et al. (2001), to examine long run relationship between coal consumption, economic growth, capital use and the labor participation rate in Pakistan. The autoregressive distributive lag model can be applied without investigating the order of integration (Pesaran and Pesaran, 1997). Haug (2002) has shown that the ARDL approach to cointegration provides better results for a small sample data set as compared to traditional approaches such as the Engle and Granger (1987), the Johansen and Juselies (1990), and the Philips and Hansen, (1990) methods. Another advantage of ARDL bounds testing approach is that the unrestricted model of ECM accommodates lags that capture the data generating process in a general-to-specific

10 There is no causal relationship between coal consumption and economic growth in case of United States, India and South Africa. 11 For example see Stern, (2000); Ghali and El-Sakka, (2004); Beaudreau, (2005); Soytas et al.,

(2007); Lee and Chiang, (2008); Yuan et al. (2008) and Wolde-Rufael, (2010) etc.


176

framework (Laurenceson and Chai, 2003). The equation of an unrestricted error correction method is modeled as follows.

n

nintl

n

lltk

q

jjtj

p

iititLtKtCCtYT

LKCC

YLKCCYTY

000

11111

lnlnln

lnlnlnlnlnln

(2)

n

nintl

n

lltk

q

jjtj

p

iititLtKtCCtYT

LKY

CCLKCCYTCC

000

11111

lnlnln

lnlnlnlnlnln

(3)

n

nintl

n

lltk

q

jjtj

p

iititLtKtCCtYT

LYCC

KLKCCYTK

000

11111

lnlnln

lnlnlnlnlnln

(4)

n

nintl

n

lltk

q

jjtj

p

iititLtKtCCtYT

KYCC

LLKCCYTL

000

11111

lnlnln

lnlnlnlnlnln

(5)

The ARDL bounds testing approach to cointegration depends upon the tabulated critical values by Pesaran et al. (2001) to take decision about cointegration among the variables. The null hypothesis of no cointegration in model is 0 LKCCY ,

0 LKCCY , 0 LKCCY and 0 LKCCY .

The alternative hypothesis is 0 LKCCY , 0 LKCCY ,

0 LKCCY and 0 LKCCY . The next step is to compare

the calculated F-statistics with the LCB (lower critical bound) and the UCB (upper critical bound) in Pesaran et al. (2001). There is cointegration among the variables if the calculated F-statistics is more than the UCB. If the LCB is more than computed F-statistics then there is no cointegration. Finally, if calculated F-statistics is between lower and upper critical bounds then the decision about cointegration is inconclusive. The stability tests namely CUSUM and CUSUMSQ tests for the stability of coefficients from the ARDL bounds testing model. These stability tests were developed by Brown et al. (1975). VECM Granger Causality The concept of Granger causality states that ‘ X causes Y ’ if and only if the past values of X help to predict changes in Y . Thus, ‘Y causes X ’ if and only if the past values of Y help to predict the changes in X . The Vector Error Correction Model (VECM) is applied to detect the direction of causality among the variables. If the variables are integrated at I(1) and cointegration is found then there must be an error correction term that generates a speed of adjustment towards a long-run equilibrium. It implies that error


177

in preceding period 1t tends towards a long-run equilibrium path. The VECM

equations presented below has four variables. Model-A: Economic growth, coal consumption, capital and labor

it

rt

o

r

n

kkt

m

jjt

l

iit

ECM

LKCCYY

11

144

133

122

1111 lnlnlnlnln (6)

Model-B: Coal consumption, economic growth, capital and labor

it

rt

o

r

n

kkt

m

jjt

l

iit

ECM

LKYCCCC

12

144

133

122

1111 lnlnlnlnln (7)

Model-C: Capital, economic growth, coal consumption and labor

it

rt

o

r

n

kkt

m

jjt

l

iit

ECM

LCCYKK

13

144

133

122

1111 lnlnlnlnln (8)

Model-D: Labor, economic growth, coal consumption and capital

it

rt

o

r

n

kkt

m

jjt

l

iit

ECM

KCCYEL

14

144

133

122

1111 lnlnlnlnln (9)

where tY , tCC , tK and tL are real GDP per capita, coal consumption per capita, capital

per of coefficient of ECMs and coefficient of lagged terms of independent variables. In order to check the robustness of direction of causality results between coal consumption and economic growth and dynamic responses, we employ an innovative accounting approach. The representation of the VAR model provides a basis of impulse response functions and time horizons that are used to evaluate the response of the one variable to the other variables. Impulse response functions also trace the impact of an innovation shock of an endogenous variable on the other variables included in the VAR system. Variance decomposition informs us about the relative importance of innovative shocks on endogenous variables. The generalized forecast error variance decomposition approach developed by Koop et al. (1996), and Pesaran and Shin (1999) are employed since it is not sensitive to the order of the variables in a VAR system. This study uses annual data of real gross domestic per capita (Yt), coal consumption per capita tons (CCt), real capital per capita (Kt) (proxied by real gross fixed capital formation per capita), and employment (proxied by the labor participation rate (Lt)). It covers the sample period 1972- 2009. The data on coal consumption per capita, real GDP per capita, capital per capita and the labor participation rate is obtained from various Government of Pakistan (GOP) (2009-10) publications.12

12 Economic literature suggests that results are sensitive to functional form of empirical model (Cameron, 1994 and Ehrlich, 1996). Bowers and Pierce (1975) have criticized the findings of


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5. Discussion of Results The long-run relationship among coal consumption, economic growth, capital, and the labor participation rate is examined through ARDL bounds testing approach to cointegration. Johansen and Juselius (1990) and Johansen (1995) have indicated that all variables in the vector auto-regression (VAR) are dynamically interrelated. Formally, the existence of cointegration indicates the presence of common non-stationary trends among the variables. Several procedures for cointegration such as Engle–Granger’s (EG) (1987), Johansen’s (1992) maximum likelihood, Pesaran and Shin’s (1999), and Pesaran’s et al. (2001) ARDL. The Engle-Granger (1987) is least preferred if there is more than one cointegrating vector (Seddighi et al. 2006). For the ARDL, pre-testing for non-stationarity of the series is not necessary although this is often undertaken to check that none of the series are I(2). The ARDL cointegration approach assumes that either variables are integrated at I(0) or I(1). If any variable in the model is integrated at I(2) then ARDL approach to cointegration is not applicable. Table-1 reports descriptive statistics and confirms that all the four series are normal as shown by the Jarque-Bera statistics. The stationarity of variables is checked by applying the ADF, DF-GLS, and P-P unit root tests. The results are reported in Table-A2, in the Annex, and show that all variables at level form have unit roots. However, with first differences, variables are stationary which means that variables are integrated at I(1) so that we can employ the ARDL bounds testing approach to cointegration. The AIC statistic is used to choose appropriate lag length and to capture the dynamic relationship to choose a best ARDL model. The existence of a cointegrating relationship among variables employs a joint significance F-test for the null hypothesis of no cointegrating relationship. The calculated F-statistics i.e.

),,( ttttY LKCCYF =10.62, ),,/( ttttCC LKYCCF = 7.94, ),,/( ttttK LCCYKF = 6.43

),,( ttttY LKCCYF =10.622 is higher than upper critical bounds (8.922) at 1%, (6.504)

at 5% and (5.462) at 10% significance level tabulated by Turner (2006) reported in Table A3 in the Annex13. The lower critical bound is more than our calculated F-statistics i.e. 2.8879 when labor is dependent variable i.e. ),,/( ttttL KCCYLF . The critical bounds

developed by Pesaran et al. (2001) and Narayan (2005) are not efficient in small sample data sets. The results show that there is long run cointegration when real income per capita (Y), coal consumption (CC), capital (K) and labor participation (L) are dependent variables. This means that there is long run relationship among the variables over the period of 1972-2009. We turn next to the discussion of the impact of coal consumption, capital and labor participation on economic growth in the long-run and short-run. The long-run results from OLS and FMOLS are reported in Table 3.

Ehrlich’s (1975) based on functional form of empirical model. Furthermore, Ehrlich (1977) and Layson (1983) have argued on basis of the theory and empirical evidence that log-linear functional form provides better results as compared to linear specification. In case of Pakistan, Shahbaz (2010) has shown that the log-linear specification provides superior results than a simple linear specification. 13 Calculation of F-statistics for cointegration is based on formula developed by Pesaran et al. (2001)


179

Table 3: Long Run Results from OLS and FMOLS: Dependent Variable = tYln

OLS Regression FMOLS Regression Variable Coefficient T-Statistic Coefficient T-Statistic Constant 5.2041 9.7463* 4.9839 10.8653*

tCCln 0.1187 2.2482** 0.1194 1.7169***

tKln 0.2462 2.9703* 0.3387 3.2531*

tLln 0.5790 6.0086* 0.5218 3.7666*

Note: *, ** and *** show significance at 1%, 5% and 10% level of significance. The results reveal that coal consumption is positively linked with economic growth and it is statically significant. It indicates that a 1 per cent increase in coal consumption leads to a 0.12 percent increase in economic growth. These findings contradict results in Apergis and Payne (2009a, b) for OECD countries and emerging market economies where they find a significant negative impact of coal consumption on economic growth. The impact of capital is positive and statistically significant at 1% level of significance. A 1 percent increase in capital use increases economic growth by 0.2462 – 0.3387 percent. A 1 percent increase in the labor participation rate leads to a 0.5790 – 0.5218 increase in economic growth. The short-run results are reported in Table 4.

Table 4: Short Run Results

Panel-I .Dependent Variable = tYln

Variable Coefficient Std. Error T-Statistic Constant 0.0208 0.0031 6.6031*

tCCln 0.0537 0.0171 3.1313*

1ln tCC 0.0723 0.0205 3.5194*

tKln 0.0661 0.0469 1.4093

tEln -0.1401 0.0795 -1.7626***

1tECM -0.1098 0.0519 -2.1149**

R-squared = 0.4852; Adjusted R-squared = 0.3994; S.E. of regression = 0.0148; Akaike info criterion = -5.4344; Schwarz criterion = -5.1705; Durbin-Watson = 2.2069

F-statistic = 5.6552; Prob(F-statistic) = 0.0008 Panel-II: Diagnostic Tests.Statistics (Prob-values) J-B Normality test 1.1488 (0.5630) Breusch-Godfrey LM test 0.7874 (0.4648) ARCH LM test 1.7676 (0.1928) Ramsey RESET 1.8237 (0.1873) White Heteroskedasticity Test 1.5707 (0.1985)

Note: *, ** and *** show significance at 1%, 5% and 10% level of significance respectively. Empirical evidence indicates that coal consumption affects economic growth positively and it is significant at 1% level of significance. A 0.0537 percent economic growth is increased due to 1 percent increase in coal consumption. The impact of the lagged coal


180

consumption also has a positive and significant impact on economic growth. Although the short-run impact of increasing capital increasing on economic growth is positive, it is, nonetheless, statistically insignificant. Finally, a rise in the labor participation rate is negatively associated with economic growth and statistically significant at 10% level of significance. The sign of coefficient of lagged ECM term is negative and significant at 5% level of significance. This corroborates the established long-run relationship among the variables from cointegration. Furthermore, the value of lagged ECM term entails that change in coal consumption from short run to long span of time is corrected by almost 10.98 percent within a year and it is significant. The diagnostic tests such as LM test for serial correlation, ARCH test, normality test of the residual term, White Heteroscedasticity, and the model specification test are reported in the lower segment of Table-6. The evidence indicates no serial correlation, the residual term is normally distributed and the functional form of the model is well specified. There is no evidence of autoregressive conditional heteroskedasticity and same inference can be drawn for White heteroskedasticity. The stability tests using CUSUM and CUSUMsq point to stable long-run and short-run parameters. The graphs of both CUSUM and CUSUMsq are presented above in the Annex (see figure 1 and 2) and show that all values lie within critical boundaries at 5 % level of significance. This confirms the stability of long-run and short-run parameters. Granger Causality (VECM) Results Given cointegration among the variables, we now perform Granger causality tests to provide a clearer picture for policymakers in the formulation of energy, environment, and economic policies. We apply the VECM framework to detect direction of causality between coal consumption, economic growth, capital use, and the labor participation rate. Table-6 reports the results of Granger causality tests. The long-run is indicated by the significance of coefficient of the one period lagged error-correction term, 1tECM in

equations (6) to (9) using t-test. The joint significance LR test of the lagged explanatory variables is for the short-run causality. Our empirical results suggest that the ECMt-1 coefficient carries negative sign and it is statistically significant in all VECMs except in equation-9. In equation-6, one can conclude that in long run coal consumption, capital and labor rate all Granger cause economic growth but in the short run, causality runs from coal consumption and labor to economic growth. Thus, our results indicate that the coal consumption led-growth hypothesis is valid both for the long-run and the short-run. There is long run causality from economic growth, capital and labor to coal consumption but in the short-run, only economic growth Granger causes coal consumption shown by the results of equation-7. This shows that economic growth also leads coal consumption in Pakistan. The results from equation-8 reveal that long-run causality runs from economic growth, coal consumption and labor to capital but uni-directional causality is also found from economic growth to capital is short run. The results of equation-9 indicate unidirectional causality from capital to labor in the short-run as confirmed by significance of F-statistics for joint significance while feedback effect is insignificant having positive sign. Overall, empirical evidence shows bi-directional causal relationships between coal consumption and economic growth in long and short runs. This shows the importance of coal consumption to economic growth and how economic growth also induces more demand for coal. In such an environment, coal


181

consumption and economic growth seem to be complements with implications that coal conservation policies may be harmful for economic growth. These findings are consistent with Yang (2000b), Lee and Chang (2005) and Hu and Lin (2008) for Taiwan, Yoo (2006) for Korea, Yuan et al. (2008) for China, Wolde-Rufael (2010) for South Africa and United States, Apergis and Payne (2009a, b) for OECD and emerging economies but they contradict with the results of Zahid (2008), and Saten and Shahbaz (2010) who found unidirectional causality that runs from coal consumption to economic growth in the case of Pakistan. Furthermore, economic growth and capital Granger cause each other in the long-run as well as in the short-run. Unidirectional causality is found from capital to labor in short-run. In addition to that, the significance of the ECMt-1 term also points to the fact that if the system exposed to shocks, it will convergence to the long-run equilibrium (in terms of absolute values) at a relatively slow speed for economic growth (-0.1728), coal consumption (-0.2791) but high convergence speed of capital (-0.3205) (vector error correction terms)14. A summary of short-run and long-run causality relationships is reported in Table 6 (based on Table 5). That is, a more intuitive presentation of Table-6 results is reported in Table-7. Table 5: The Results of Granger Causality (VECM)

Type of Granger causality

Short-run Long-run

Joint (short and long-run)

l

ln

ln

ln

ECM

,ln t EY

,ln tCC

,ln t EK

,ln t EL

Dependent variable

F-statistics [p-values] (t-statistics)

F-statistics [p-values]

Yln

– 7.86* [0.002

0]

1.05 [0.3636]

3.9954**

[0.0302]

–0.17

* (–

2.94)

– 7.56* [0.0008]

3.96** [0.0183]

3.55*** [0.0274]

Cln

2.35***

[0.1045]

– 0.07 [0.9323]

0.09 [0.9088]

-0.27

* (-

3.36)

3.73** [0.0229]

– 3.92** [0.0190]

4.01** [0.0174]

Kln

2.56***

[0.0953]

0.40 [0.672

0]

– 0.02 [0.9778]

-0.32**

[2.60]

3.75** [0.0225]

2.47*** [0.0833]

– 2.45*** [0.0843]

14 The coefficient of 1tECM i.e. equation-9 has a positive sign but it is statistically

insignificant.


182

Lln

1.60 [0.2202]

2.08 [0.143

6]

3.52**

[0.0436]

– 0.05 [0.56

]

1.06 [0.3790]

1.74 [0.1815]

2.44*** [0.0855]

–

Note: *, ** and *** denote the significant level at the 1, 5 and 10 per cent respectively15.

Table-6: Summary of Long Run and Short Causality (derived from Table-6)

Variables Short Run Long Run

tCCln causes to tYln At 1% significance level At 1% significance level

tKln causes to tYln No At 1% significance level

tLln causes to tYln No At 1% significance level

tYln causes to tCCln At 10% significance level At 1% significance level

tKln causes to tCCln No At 1% significance level

tLln causes to tCCln No At 1% significance level

tYln causes to tKln At 5% significance level At 5% significance level

tCCln causes to tKln No At 5% significance level

tLln causes to tKln No At 5% significance level

tYln causes to tLln No No

tCCln causes to tLln No No

tKln causes to tLln At 5% significance level No

Innovative Accounting Technique As expected, Granger causality tests do not determine the relative strength of causality effects beyond the selected time span. In such circumstances, causality tests are inappropriate because these tests are unable to indicate how much feedback is transmitted from one variable to another. To examine the feedback from one variable to another and to check the relative effectiveness of causality affects out-of-sample period, Shahbaz and Khan (2010), Lorde et al. (2010) and Paul and Uddin (2010) have applied Innovative Accounting Technique (Variance Decompositions and Impulse Response Functions) to detect the strength of causality among the variables. Results are presented in the Annex. 6. Summary and Conclusion The paper re-investigates the relationship between coal consumption and economic growth in case of Pakistan by including additional variables such as capital use and the labor participation rate within multivariate framework over the period of 1972-2009. The continued use of coal as an energy source has economic and environmental impacts in an

15 There is no need to find out joint long-and-short runs direction of causality when coefficient of

1tECM is having positive sign and statistically insignificant.


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economy. To address these issues we need information about the direction of causality between coal consumption and economic growth for making decisions on how and to what extent energy (coal) conservation policies may have inverse impact on economic growth. In doing so, we have applied the ARDL bounds testing approach to cointegration to examine long-run association among economic growth, coal consumption, capital, and the labor participation rate in a VECM to detect nature of long-run and short-run causal relationships. We used unit root tests such as the ADF, DF-GLS, and P-P to test for stationarity of the series. Finally, an innovative accounting technique was applied to examine the extent of causality between the variables of interest.

Our findings indicate the existence of a long run relationship among economic growth, coal consumption, capital and the labor participation rate over the study period. The results show that coal consumption, capital and the labor participation rate have a positive impact on economic growth. This implies that coal and capital are critical inputs in stimulating economic growth. When we augmented the basic model by including labor as an additional variable, we found that labor also contributes to economic growth. Causality analysis reveals that there is bi-directional causality between economic growth and coal consumption. Thus, energy (coal) conservation efforts may affect economic growth negatively resulting in a low rate of economic growth which in turn lowers the demand for coal. Government should also explore other environment friendly sources of energy to meet demand of energy to sustain economic growth rate.

To the extent that any paper is never a final word on any research topic, this paper is no different. There are clearly three areas that are worth pursuing further. This study can be further extended to India and China that are heavy users of coal to meet their energy demand. The CO2 emissions could be incorporated in a multivariable framework to study the coal consumption-growth relationship and explore consequences of coal consumption on the environment highlighted by Coondoo and Dinda (2002) and Dinda (2004). Furthermore, this model can be applied to South Asian Agreement and Regional Cooperation (SAARC) countries by using non-linear structural break unit root tests and non-linear cointegration approaches that would be more useful to examine the threshold dynamics of coal consumption and growth and in Asian countries and other developing economies.

References Apergis, N., Payne, J.E., 2009a. Energy consumption and economic growth in Central America: evidence from a panel cointegration and error correction model. Energy Economics 8, 211–216.

Apergis, N., Payne, J.E., 2009b. Energy consumption and economic growth: evidence from the commonwealth of independent states. Energy Economics 31, 641–647.

Beaudreau, B. 2005. Engineering and economic growth. Energy Economics 16, 211–220.

Bowers, W. and Pierce, G., 1975. The illusion of deterrence in Isaac Ehrlich’s work on the deterrent effect of capital punishment. Yale Law Journal 85, 187-208.

Cameron, S., 1994. A review of the econometric evidence on the effects of capital punishment. Journal of Socio-economics 23, 197-214.


184

Coondoo, D., Dinda, S., 2002. Causality between income and emissions: a country group-specific econometric analysis. Ecological Economics 40, 351–367.

Dinda, S., 2004. Environmental Kuznets curve hypothesis: a survey. Ecological Economics 49, 431–455.

Ehrlich, I., 1977. The deterrent effect of capital punishment: reply. American Economics Review 67, 452–458.

Ehrlich, I., 1975. The deterrent effect of capital punishment – A question of life and death. American Economic Review 65, 397-417.

Ehrlich, I., 1996. Crime, punishment and the market for offences. Journal of Economic Perspectives 10, 43-67.

Engle, RF and Granger CWJ., 1987. Cointegration and error correction representation: estimation and testing. Econometrica 55, 251-276.

Ewing, B.T., Sari, R., Soytas, U., 2007. Disaggregate energy consumption and industrial output in the United States. Energy Policy 35, 1274–1281.

Fatai, K., Oxley, L., Scrimgeour, F.G., 2004. Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, the Philippines, and Thailand. Mathematics and Computers in Simulation 64, 431–445.

Ghali,-K.H., El-Sakka,-M.I.T., 2004. Energy use and output growth in Canada: a multivariate cointegration analysis. Energy Economic,s 26, 225-238

Granger, C W J, 1969. Investigating causal relations by econometric models and cross-spectral methods. Econometrica, Econometric Society 37, 424-438.

Hansen, B.E. and Seo, B., 2002. Testing for two-regime threshold cointegration in vector error-correction models. Journal of Econometrics,110, 293-318.

Haug, A., 2002. Temporal aggregation and the power of cointegration tests: a Monte Carlo study. Oxford Bulletin of Economics and Statistics 64, 399-412.

Holtz-Eakin, D., Newey, W., Rosen, H., 1985. Implementing causality tests with panel data with an example from local public finance, NBER Technical Working Paper, No. 48.

Hu, J.L., Lin, C.H., 2008. Disaggregated energy consumption and GDP in Taiwan: a threshold co-integration analysis. Energy Economics 30, 2342–2358.

Jinke, L., Feng-hua, W and Hua-ling, S., 2009. Differences in coal consumption patterns and economic growth between developed and developing countries. Procedia Earth and Planetary Science 1, 1744-1750.

Jinke, L., Hualing, S., Dianming, G., 2008. Causality relationship between coal consumption and GDP: difference of major OECD and non-OECD countries. Applied Energy 85, 421–429.

Johansen, S and Juselius, K., 1990. Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bulletin of Economics and Statistics 52, 169-210.

Johansen, S., 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12, 231–254.

Johansen, S., 1995. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press, Oxford.


185

Koop, G., M. H. Pesaran and S. M. Potter., 1996. Impulse response analysis in nonlinear multivariate models. Journal of Econometrics 74, 119-147.

Laurenceson, J and J, C.H. Chai., 2003. Financial reform and economic development in China. Cheltenham, UK, Edward Elgar.

Layson, S., 1983. Homicide and deterrence: another view of the Canadian time series evidence. Canadian Journal of Economics 16, 52-73.

Lee, C.C., Chang, C.P., 2005. Structural breaks, energy consumption and economic growth revisited: evidence from Taiwan. Energy Economics 27, 857–872.

Loizides, J and Vamvoukas, G., 2005. Government expenditure and economic growth: Evidence from trivariate causality testing. Journal of Applied Economics, 125-152.

Lorde. T., Waithe, K and Francis, B., 2010. The importance of electrical energy for economic growth in Barbados. Energy Economics 32, 1411-1420.

Lutkepohl, H., 1982. Non-causality due to omitted variables. Journal of Econometrics 19, 267–378.

Narayan, P. K., (2005). The saving and investment nexus for China: evidence from cointegration tests. Applied Economics 37, 1979-1990.

Paul, BP and Uddin, G. S., 2010. Energy and output dynamics in Bangladesh. Energy Economics, doi:10.1016/j.eneco.2010.11.011.

Payne, J.E., 2009a. Survey of the international evidence on the causal relationship between energy consumption and growth. Journal of Economic Studies 37, 53-95.

Pesaran, M. H and Pesaran, B., 1997. Working with Microfit 4.0: interactive econometric analysis. Oxford: Oxford University Press.

Pesaran, M. H. and Y. Shin., 1999. An autoregressive distributed lag modeling approach to cointegration analysis. Chapter 11 in Econometrics and Economic Theory in the 20th Century: The Ragnar Frisch Centennial Symposium, Strom S. (ed.). Cambridge University Press: Cambridge.

Pesaran, M.H., Shin, Y., Smith, R.J., 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16, 289-326.

Pesaran, MH., Shin, Y and Smith, RJ., 2000. Structural analysis of vector error correction models with exogenous I(1) variables. Journal of Econometrics 97, 293–343.

Phillips, P C B and BE, Hansen., 1990. Statistical inference in instrumental variables regression with I(1) processes. Review of Economic Studies 57, 99-125.

Sari, R., Ewing, B.T., Soytas, U., 2008. The relationship between disaggregate energy consumption and industrial production in the United States: an ARDL approach. Energy Economics 30, 2302–2313.

Sari, R., Soytas, U., 2004. Disaggregate energy consumption, employment, and income in Turkey. Energy Economics 26, 335–344.

Saten, K and Shahbaz, M., 2010. Coal Consumption and Economic Growth Revisited: Structural Breaks, Cointegration and Causality Tests for Pakistan. mpra.ub.uni-muenchen.de/26151.


186

Seddighi. H. R, A. Lawler, A. V. Katos., 2006. Applied Econometrics: A Practical Approach. Routledge, London and New York.

Shahbaz. M and Khan, R, I., 2010. Old wine in new bottles: saving-growth nexus: innovative accounting technique in Pakistan.Theoretical and Applied Economics 7,49-60.

Shahbaz, M., 2010. Income inequality-economic growth and non-linearity: A case of Pakistan. International Journal of Social Economics 37, 613-636.

Soytas, U., Sari, R., Ewing, B.T., 2007. Energy consumption, income, and carbon emissions in the United States. Ecological Economics 62, 482–489.

Stern, D. I., 2000. A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Economics 22, 267-283.

Toda, H.Y. and T. Yamamoto., 1995. Statistical inferences in vector auto-regressions with possibly integrated processes. Journal of Econometrics 66, 225-250.

Turner, P., 2006. Response surfaces for an F-test for cointegration. Applied Economics Letters 13, 479-482.

Wolde-Rufael, Y., 2004. Disaggregated industrial energy consumption and GDP: the case of Shanghai, 1952–1999. Energy Economics 26, 69–75.

Wolde-Rufael, Y., 2010. Coal consumption and economic growth revisited. Applied Energy 87, 160–167.

Yang, H.Y., 2000a. Coal consumption and economic growth in Taiwan. Energy Sources 22, 109–115.

Yang, H.Y., 2000b. A note on the causal relationship between energy and GDP in Taiwan. Energy Economics 22, 309–317.

Yoo, S.H., 2006. Causal relationship between coal consumption and economic growth in Korea. Applied Energy 83, 1181–1189.

Yuan, J.H., Kang, J.G., Zhao, C.H., Hu, Z.G., 2008. Energy consumption and economic growth: evidence from China at both aggregated and disaggregated levels. Energy Economics 30, 3077–3094.

Zahid, A., 2008. Energy-GDP relationship: A causal analysis for the five countries of South Asia. Applied Econometrics and International Development 8, 167-180.

Zhang, Y. and Li, W., 2007. Study on causal relationship between coal consumption and economic growth in China. Resources & Industries 9, 89–93.

Ziramba, E., 2009. Disaggregate energy consumption and industrial production in South Africa. Energy Policy 37, 2214–2220.

On line Annex at the journal Website: http://www.usc.es/economet/aeid.htm


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Annex

Table-A1: Descriptive Statistics

Variable tYln tCCln tKln tLln

Mean 10.0303 14.8122 4.4200 3.4297 Median 10.1010 14.9301 4.4485 3.4053 Maximum 10.5047 16.2106 4.9632 3.9680 Minimum 9.5491 13.8782 4.0004 2.9569 Std. Dev. 0.2801 0.6144 0.2184 0.2741 Skewness -0.2780 0.4822 0.0756 0.1448 Kurtosis 1.9190 2.6491 3.0576 2.2251 Jarque-Bera 2.3396 1.6677 0.0414 1.0835 Probability 0.3104 0.4343 0.9794 0.5817

Table-A2: Unit Root Tests

ADF Test with Intercept and Trend

ADF-GLS Test with Intercept and Trend

P-P Test with Intercept and Trend

Variables

T-Statistic Lag T-Statistic Lag T-Statistic Lag

tYln -1.4111 1 -1.4571 1 -1.4907 3

tCCln -0.9673 1 -1.2673 1 -1.3675 2

tKln -1.6737 1 -1.8960 1 -1.5122 3

tLln -0.7263 1 -1.1486 1 -1.5083 3

tYln -4.3214* 0 -4.4120* 0 -4.4607* 3

tCCln -3.5151*** 1 -3.6105** 1 -6.6253* 3

tKln -3.9841** 2 -4.1041* 0 -3.9804** 2

tLln -4.1485** 2 -4.2695* 1 -8.0115* 3

Note: *,**, *** represent significance at 1%, 5%, 10% level of significance

Table A3: The ARDL Cointegration Tests

Bounds Testing to Cointegration Estimated Models

,,( tttY KCCYF

,,/( tttCC KYCCF

,,/( tttK CCYKF

,,/( tttL KCCYLF

Lag Length (2, 2, 1, 2) (2, 1, 1, 2) (2, 1, 2, 2) (2, 2, 2, 2) F-statistics 10.622* 7.948** 6.4324*** 2.8879


188

Critical values (T = 38)#

Lower bounds I(0)

Upper bounds I(1)

1 per cent level

7.397 8.926

5 per cent level

5.296 6.504

10 percent level

4.401 5.462

Diagnostic tests 2R 0.8517 0.7865 0.7569 0.6853

F-statistics 6.1021* 4.4231* 3.9230* 2.0502*** J-B Normality test

2.4345 (0.2960) 0.1666 (0.9200) 0.9248 (0.6297) 1.0106 (0.6032)

Breusch-Godfrey LM test

1.8893 (0.1854) 1.2911 (0.3021) 1.5313 (0.2482) 0.2999 (0.7455)

ARCH LM test

0.7043 (0.4078) 0.7085 (0.5007) 0.6973 (0.5061) 1.1574 (0.2903)

Heteroskedasticity Test

0.5045 (0.9108) 0.6097 (0.8311) 2.0158 (0.0811) 1.4229 (0.2428)

Ramsey RESET

0.2038 (0.6577) 0.3898 (0.5407) 0.0104 (0.9199) 2.9362 (0.0634)

Note: The asterisks *, ** and*** show significant at 1%, 5% and 10% level respectively. The optimal lag structure is determined by AIC. # Critical value bounds were computed by a surface response procedure in Turner (2006).


189

Figure 1. Plot of Cumulative Sum of Recursive Residuals

-16

-12

-8

-4

0

4

8

12

16

1980 1985 1990 1995 2000 2005

CUSUM 5% Significance

The straight lines represent critical bounds at 5% significance level.

Figure 2. Plot of Cumulative Sum of Squares of Recursive Residuals

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1980 1985 1990 1995 2000 2005

CUSUM of Squares 5% Significance

The straight lines represent critical bounds at 5% significance level.


190

Table-8.I: Variance Decomposition Approach

Variance Decomposition of tCCln

Period tYln tCCln tKln tEln

1 100.0000 0.0000 0.0000 0.0000 2 91.0411 4.5093 0.0001 4.4494 3 83.0004 9.2462 0.0269 7.7264 4 77.3586 13.0004 0.1137 9.5273 5 73.4898 15.8055 0.2663 10.4381 6 70.8219 17.8414 0.4781 10.8585 7 68.9763 19.2733 0.7374 11.0128 8 67.7080 20.2362 1.0318 11.0239 9 66.8532 20.8379 1.3491 10.9596

10 66.2986 21.1647 1.6786 10.8578 Variance Decomposition of tYln

Period tYln tCCln tKln tEln

1 16.9601 83.0398 0.0000 0.0000 2 20.0425 78.6727 0.0846 1.2001 3 22.4814 74.8839 0.2941 2.3404 4 24.5984 71.6350 0.6132 3.1532 5 26.5299 68.7547 1.0158 3.6995 6 28.3328 66.1285 1.4727 4.0658 7 30.0297 63.6973 1.9563 4.3165 8 31.6289 61.4341 2.4428 4.4940 9 33.1339 59.3273 2.9132 4.6254

10 34.5469 57.3709 3.3544 4.7277

Variance decomposition is applied to investigate the response of shock in the dependent variable due to shocks stemming from independent variables. The variance decomposition method is an alternate approach to the impulse response function method. This process explains how much of the predicted error variance for any variable is described by innovations generated throughout each independent variable in a system over various time-horizons. The results reported in Table-8 and show that economic growth is explained predominantly by its own innovative shocks (66.29%) while coal consumption, capital and labor explain economic growth through their innovative shocks accounts for 21.16%, 1.35%, and 10.95% respectively.


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On the other hand, evidence indicates that economic growth explains a substantial portion of coal consumption by its innovative shocks i.e. 34.54% while. 49.15% of coal consumption is due to its own innovative shocks. Capital and labor also explain coal consumption through their shocks but their impact is minimal. This implies that a bidirectional causal relationship between coal consumption and economic growth. However, there is a strong causal relationship from economic growth to coal consumption. This finding is consistent with VECM Granger causality results from our analysis for the long-run.

Table-8.II: Variance Decomposition Approach

Variance Decomposition of tKln

Period tYln tCCln tKln tLln

1 0.9770 0.0336 98.9893 0.0000 2 5.0413 11.0626 78.7867 5.1093 3 8.1105 23.9245 58.9937 8.9711 4 10.178 33.8939 45.0801 10.8476 5 11.6893 41.0210 35.7003 11.5892 6 12.9202 46.0023 29.2983 11.7790 7 14.0135 49.4206 24.8475 11.7183 8 15.0398 51.6950 21.7106 11.5545 9 16.0331 53.1242 19.4842 11.3583

10 17.0076 53.9264 17.9031 11.1626 Variance Decomposition of tEln

Period tYln tCCln tKln tLln

1 11.9624 1.1249 10.0378 76.8747 2 11.6737 1.2376 11.7146 75.3739 3 11.5023 1.5565 12.9111 74.0299 4 11.3959 2.1310 13.6636 72.8092 5 11.3239 2.9651 14.0640 71.6468 6 11.2756 3.9931 14.2108 70.5203 7 11.2499 5.1124 14.1922 69.4454 8 11.2477 6.2222 14.0790 68.4509 9 11.2690 7.2462 13.9234 67.5612

10 11.3117 8.1391 13.7599 66.7891

A substantial portion (53.92%) of capital is explained by shocks in coal consumption while 17.90% is due to its innovative shocks. Economic growth and labor contribute to explain capital by 17.00% and 11.16% respectively. This implies that coal consumption


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Granger causes capital. Finally, 66.78% of labor is explained by its own innovative shocks while 13.75%, 11.31% and 8.13% is due to capital, economic growth and coal consumption respectively. There is uni-directional causality that runs from coal consumption to labor given that 26.34% portion of labor is explained by coal consumption. The generalized impulse response functions show how long, and to what extent a dependent variable reacts due to shocks in an independent variable. The results show positive response of economic growth from shocks in coal consumption. It implies that unanticipated shocks to coal consumption leads economic growth to experience sizeable positive response, and the same inference can be drawn from economic growth to coal consumption. This shows bidirectional causal relationship between economic growth and coal consumption.

Figure-3 Generalized Impulse Response Function

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Response to Generalized One S.D. Innovations

revisiting the relationship between coal consumption and economic growth

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