1

Energy Consumption and Economic Growth – The Case of Australia

Hong To a, *

, Albert Wijeweera a, Michael B. Charles

a

a Business School, Southern Cross University,

Locked Mail Bag 4, Coolangatta, QLD, 4225, Australia

* Corresponding author. Tel.: +61 7 55893207;

E-mail addresses: hong.to@scu.edu.au (H. To), albert.wijeweera@scu.edu.au (A.

Wijeweera), michael.charles@scu.edu.au (M.B. Charles).

Abstract:

This study integrates neoclassical growth, endogenous growth, and ecological-economics

viewpoints to examine how energy consumption affects economic growth in Australia. It

utilizes four decades of data ranging from 1970 to 2011 and the bound testing cointegration

approach along with multivariate Granger causality test to examine probable statistical

relationships between the variables. Results based on bound test approach suggest that

energy consumption and Australian economic growth, despite being positively related, are

not statistically significant either in the short run or the long run. The weak relationship

between the two variables is further ascertained by the multivariate Granger causality test

results. These findings suggest, at least ostensibly, that much debated carbon pricing policies

may not necessarily have an adverse effect on Australia’s economic growth.

Key Words: Energy and Growth, Australia, Bound Testing Approach

JEL: O13, Q43, Q48

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1. Introduction

Over the years, much research has been carried out to determine the key factors

impacting on economic growth, with energy being a relatively new factor, and one not

included in traditional economic growth models (Stern, 2011; Pirlogea and Cicea, 2012). A

majority of studies have explained economic activity and growth in terms of a production

function. Neoclassical growth models usually regard capital, labour and land as the primary

factors of production, while energy is regarded as an intermediate input eventually produced

by the primary factors of production. Furthermore, neoclassical economists often assume that

energy and capital are perfectly substitutable (Solow, 1974). A decline in energy use does

not, under conditions of economic efficiency, result in a reduction in economic growth. These

viewpoints have led to a focus in the mainstream growth theory on the primary inputs, and in

particular, capital and labour, more so given that land is usually subsumed as a subcategory

of capital. Energy is assumed to have a relatively minor role in economic production in the

mainstream theory of growth. This has been strongly criticised by proponents of ecological

economics, which is grounded in the biophysical theory of the role of energy. The law of

thermodynamics implies that a minimum quantity of energy is required to carry out the

transformation of matter. Since all production involves the transformation or movement of

matter in some way, energy is therefore necessary for economic production and, as a result,

economic growth. That said, there must be limits to the substitution of other factors of energy

production. Furthermore, econometric studies (e.g., Berndt and Wood, 1979; Apostolakis,

1990; Stern, 1993; Frondel and Schmidt, 2002) have employed various functional forms to

estimate elasticities of substitution between energy and capital. These studies have shown

that capital and energy are, at best weak, substitutes, and are quite possibly complements.

As discussed above, energy is an input in the production process, since it is used in other

economic activities. Many countries such as Japan lack energy resources and generally

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depend on imports of crude oil, natural gas, and coal for their industrial and residential

energy needs, transportation, and electricity generation. In these cases, there is likely to be a

positive relationship between energy consumption and economic growth. Peak oil, energy

security and climate change have become key concerns in recent decades. Given the changes

in energy policies in response to these issues, the causal relationships between energy

consumption and economic growth has become a compelling area of investigation. From an

economic point of view, this relationship lies in two aspects: i) the growing dependence of

economic growth on energy, and ii) economic growth promoting energy technology advances

and large-scale development and utilization of energy. Various studies (e.g., Akarca and

Long, 1979; 1980; Glasure and Lee, 1998; Masih and Masih, 1996; 1997; 1998) have shown

i) that the relationship between energy consumption and economic growth varies depending

on the country, and ii) the relationship varies in the same country at different times.

The discrepancy in results results from a number of factors. These include: i) the

different structures and stages of economic development, ii) the use of different econometric

methods, iii) the varying time horizon of the analysis, and iv) the type and number of

variables employed (Yu and Choi, 1985; Ferguson et al., 2000; Toman and Jemelkova, 2003;

Karanfil, 2009; Payne, 2010). Earlier studies relied on the OLS model of log-linear to

estimate parameters and conduct statistical tests, all without taking into consideration the

special features of time series data. These traditional estimation methods are often associated

with several empirical problems, such as the possible endogeneity of regressors and the non-

stationarity of the variables. All of these lead to spurious regressions with misleading

statistical results (Granger and Newbold, 1974). There have been important advances in the

past decade, with new time series econometric techniques such as cointegration, error

correction and vector autoregressive (VAR) methods being developed. As a result, it is

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necessary to revisit and statistically re-examine the relationship between energy consumption

and economic growth using modern time series analysis.

Furthermore, the existing literature on the relationship between energy consumption and

economic growth has suffered from two major limitations: i) a lack of a synthesis of energy-

based and mainstream models as a result of different theoretically-based approaches on

economic growth (i.e., mainstream growth theory vs. ecological-economics viewpoints); and

ii) the possibility of omitted-variable biases, which arises when variables known to be

important are omitted from the models. In this study, we attempt to address these issues by

examining the relationship between energy consumption and economic growth in the case of

Australia, where relatively little research using a multivariate approach in this area has been

conducted. Earlier Australian studies are based primarily on bivariate models, which could

hamper an accurate analysis owing to the omitted variable biases (Shahiduzzaman and Alam,

2012). Most recent endogenous growth models hold that investment in human capital,

innovation, and knowledge are significant contributors to economic growth (Aghion and

Howitt, 1997). Furthermore, energy is necessary for economic production and economic

growth from ecological-economics viewpoints. This study adds to the literature by

augmenting the model specification with human capital and energy variables, together with

the classical determinants of growth, i.e., labour force and capital stock. In other words, the

model used herein enables us to integrate neoclassical and endogenous growth with

ecological-economics viewpoints so as to study the relationship between energy consumption

and economic growth in Australia. Although there have been some attempts to integrate

neoclassical growth theory with the ecological-economics approach, such as that of Stern

(2011) and Ayres and Warr (2009), there has been, as yet, no synthesis of these approaches

and endogenous growth theory.

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The remainder of this paper is organized as follows: the following section provides a

brief overview of the related literature. The third section discusses variables and data sources,

while the fourth outlines the methodology employed in this study. Results are presented in

the fifth section. Some concluding remarks and policy implications complete the article.

2. Literature Review

There has been a growing literature on the causal relationship between energy

consumption and economic growth. These studies have employed a variety of time series

econometric techniques. This research interest on energy and growth stems from the earlier

oil crisis in the 1970s to the more recent concerns on energy prices, energy security and the

impact of environmental policy to conserve energy and reduce greenhouse gas emissions.

The empirical results on the energy consumption-growth nexus have yielded mixed and

inconsistent results in terms of their causal relationships. In this literature review,

international based studies are discussed first before moving to Australian studies.

2.1. International studies

The first relevant study on energy and growth dates back to the late 1970s. In their

pioneering work, Kraft and Kraft (1978) used annual U.S. data from 1947 to 1974 to study

the relationship between gross national product (GNP) and gross energy inputs. They

employed the Sims causality test procedure to infer the causal relationship, and discovered

that increased GNP leads to increased energy consumption. Using employment to substitute

for economic growth, Akarca and Long (1979) showed that increased energy consumption

leads to higher levels of employment. However, when using different methodology (i.e., Sims

causality test) and different data set (i.e., annual U.S. data from 1950 to 1970), Akarca and

Long (1980) found no causal relationship between energy consumption and GNP. As per

Akarca and Long (1979), Erol and Yu (1987a), together with Murray and Nan (1992), used

employment to substitute for economic growth. Erol and Yu (1987a) applied the Sims

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causality technique to monthly U.S. data from 1973 to 1984 and found no causal relationship

between energy consumption and employment. Yet Murray and Nan (1992) used the Granger

causality procedure and monthly U.S. data from 1974 to 1988. They found that increased

employment results in increased energy consumption. In other research, Erol and Yu (1987b)

applied both the Sims and Granger causality procedures to examine the causal relationships

between energy consumption and real GNP for Japan, Germany, Italy, Canada, France and

the U.K. The results show that there is bidirectional causality between the two variables in

Japan. For the case of Germany and Italy, increased GNP leads to increased energy

consumption. Increased energy consumption leads to increased GNP in Canada, but there are

no causal relationships between the two in France and the U.K.

The feature of the model specification in the above studies is the reliance on bivariate

causality test of energy consumption and output or employment. However, a common

problem of a bivariate analysis is the possibility of omitted variables bias, which could result

in misleading statistical results (Stern, 2000; Payne, 2010). Recognizing the problem, Yu and

Hwang (1984), together with Stern (1993), incorporated additional variables in their analyses

for the case of the U.S. Yu and Hwang (1984) included employment when examining the

relationship between energy consumption and GNP. They employed both Sims and Granger

causality tests and found that increased employment leads to increased energy consumption,

while there is no causal relationship between energy consumption and GNP. Stern (1993)

incorporated employment and capital in the analysis and found that increased energy

consumption results in growth in real GDP.

In the previous studies, traditional OLS method was usually used to estimate parameters

and to conduct statistical tests. These traditional estimation methods do not take into

consideration the special features of time series data, such as the possible endogeneity of

regressors and the non-stationarity of the variables, both of which could result in spurious

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regressions, together with misleading statistical results (Granger and Newbold, 1974). With

advances in time series econometrics in the past decade, new time series econometric

techniques such as Engle-Granger (1987) / Johansen-Juselius (1990) cointegration and error-

correction models have been applied to re-investigate the relationship between energy

consumption and growth.

Results of the studies utilizing the Engle-Granger cointegration and error-correction

model follow. Glasure and Lee (1998) found that there is bidirectional causality between

energy consumption and real GDP in South Korea and Singapore. Francis et al. (2007) found

similar results for the case of Haiti, Jamaica and Trinidad and Tobago. Yet Cheng and Lai

(1997) demonstrated unidirectional relationship from energy consumption to employment

and from real GDP to energy consumption in Taiwan. Taking into account the possibility of

omitted-variable biases, Yu and Jin (1992), Cheng (1996), Paul and Bhattacharya (2004) and

Pirlogea and Cicea (2012) all incorporated measures of capital and/or labour in the context of

a production model framework. Glasure and Lee included wages and energy prices (1995)

and, later, wages and energy prices, real money supply and real government spending (1996)

into their models to examine the relationship between energy consumption and growth. Yu

and Jin (1992) and Cheng (1996) found no long-term cointegration relation and no causal

relationship between the two, while Glasure and Lee (1995, 1996) and Paul and Bhattacharya

(2004), by way of contrast, found bidirectional relationship between energy consumption and

growth. The majority of these studies have focused on the causal relationship between energy

consumption and economic growth using aggregate energy consumption data. Given that the

use of aggregate energy consumption could mask the differential impact associated with

various types of energy consumption, as well as by end use and sector, Yang (2000a, 2000b),

Yoo and Kim (2006), Jinke et al. (2008) and Pirlogea and Cicea (2012) attempted to examine

the impact of various disaggregated measures of energy consumption such as electricity, coal,

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natural gas, oil and renewables, as well as by sector. Again, there is no consensus on the

causal relationship between the two factors within and across countries.

Johansen-Juselius cointegration and error-correction model has been more widely

employed. The majority of these studies are based on the bivariate model, which includes

only energy and output or employment, as per Masih and Masih (1996), Soytas and Sari

(2003), Yoo (2005, 2006a, 2006b, 2006c), Yoo and Jung (2005), Chen et al. (2007) and

Zachariadis (2007). Other studies included i) measures of capital and/or labour, as per Stern

(2000), Ghali and El-Sakka (2004), Oh and Lee (2004a, 2004b), Paul and Bhattacharya

(2004), Soytas and Sari (2006a, 2007), Yuan et al. (2008); or ii) consumer prices, as per

Masih and Masih (1997, 1998) and Asafu-Adjaye (2000). Glasure (2002), however,

incorporated various variables, including real government expenditure, real money supply,

real oil prices and dummy variable oil price shocks. While most of the studies have used

aggregate energy consumption data, Ghosh (2002), Hondroyiannis et al. (2002), Shiu and

Lam (2004), Yoo (2005, 2006a, 2006b, 2006c), Yoo and Jung (2005), Chen et al. (2007),

Soytas and Sari (2007), Zachariadis (2007) and Yuan et al. (2008) all employed various

disaggregated measures of energy consumption by source and by sector.

Inconsistent and contradictory results are still reported across studies. For example,

Masih and Masih (1996, 1997, 1998) found no causal relationship between energy

consumption and growth in Malaysia, Singapore and the Philippines, while there is a

bidirectional relationship between the two in Pakistan, South Korea and Taiwan. In addition,

they found that increased energy consumption causes growth in India, Thailand and Sri

Lanka, while economic growth leads to increased energy consumption in Indonesia. Stern

(2000) found that greater energy consumption results in growth in the United States, while

Soytas and Sari (2003) discovered i) no causal relationship in Canada, Indonesia, Poland, the

United Kingdom, and the United States; ii) bidirectional causality in Argentina and Turkey;

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iii) unidirectional causality with greater energy consumption leading to increased GDP in

France, West Germany and Japan; and iv) causality with increased GDP leading to increased

energy consumption in Italy and South Korea. In contrast to Soytas and Sari’s result (2003),

Ghali and El-Sakka (2004) established bidirectional relationship between energy

consumption and growth in Canada. Finally, Oh and Lee (2004a, 2004b) found inconsistent

conclusions for the case of Korea when using different data sets and models.

While the Engle-Granger/Johansen-Juselius cointegration procedures and corresponding

error-correction models have been widely used to study a causal relationship between energy

consumption and economic growth, these methods have been criticized owing to the low

power and size properties of small samples associated with conventional unit root and

cointegration tests (Harris and Sollis, 2003). In response, more recent studies have employed

the autoregressive distributed lag (ARDL) model and bounds testing approach, together with

the Toda-Yamamoto (1995) and Dolado-Lütkepohl (1996) long-run causality tests, which can

be performed irrespective of whether the variables possess a unit root and whether

cointegration exists among the variables. Altinay and Karagol (2005) used the Dolado-

Lütkepohl test of long-run causality between electricity consumption and real GDP for the

case of Turkey and found unidirectional causality, with increased electricity consumption

leading to higher GDP. Lee (2006) employed the Toda-Yamamoto causality test and found

no causal relationship between energy usage and real GDP per capita in Germany, Sweden

and the United Kingdom; bidirectional causality between the two in the United States;

increased energy consumption leading to increases in real GDP per capita in Belgium,

Canada and Switzerland; and increases in real GDP per capita leading to greater energy

consumption in France, Italy and Japan. Soytas and Sari (2006b) also used the Toda-

Yamamoto causality test for their model including energy usage, real GDP, real gross fixed

capital formation and labour force variables to discover the causal relationship between

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energy consumption and growth in China. Their results showed no causal relationship

between the two. Zachariadis (2007) employed different approaches, including ARDL

bounds test and the Toda-Yamamoto causality test, to study the causal relationship between

the disaggregated measures of energy consumption by sector and income/output measures in

Canada, France, Germany, Italy, Japan, the United Kingdom and the United States.

Inconsistent and conflicting results were found in the research when applying different

econometric methods. Bowden and Payne (2010) also studied the causal relationship between

the disaggregated measure of energy consumption by sector and real GDP in the United

States using the Toda-Yamamoto causality test. They incorporated real gross fixed capital

formation and employment variables in their analysis and found no causal relationship

between commercial/industrial renewable energy consumption and real GDP; bidirectional

causality between commercial/residential non-renewable energy consumption and real GDP;

and unidirectional causality, with residential renewable/industrial non-renewable energy

consumption leading to an increase in real GDP. Another U.S. study reported by Sari et al.

(2008) included the employment variable and employed the ARDL bounds test to investigate

the causal relationship between the disaggregated measures of energy consumption by

sources and industrial production. The results showed unidirectional causality, with increased

industrial production leading to greater energy consumption, except for the case of coal

consumption, which was found to lead growth.

Another approach that addresses the concerns of the low power and size properties of

small samples associated with conventional unit root and cointegration tests is the panel

cointegration tests. Panel unit root and cointegration tests provide additional power by

combining the cross-section and time series data allowing for the heterogeneity across

countries (Payne, 2010). Lee (2005), Chen et al. (2007), Mehrara (2007), Narayan and Smyth

(2007), Lee and Chang (2008) and Lee et al. (2008) employed this approach, while Huang et

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al. (2008) and Sharma (2010) applied dynamic panel estimation to infer a causal relationship

between energy consumption and economic growth. Lee (2005) included real gross capital

formation in the analysis and found unidirectional causality, with increased energy

consumption leading to real GDP growth for the developing countries panel. Yet Chen et al.

(2007) discovered bidirectional causality between electricity consumption and real GDP for a

ten-country panel including China, Hong Kong, Indonesia, India, Korea, Malaysia, the

Philippines, Singapore, Taiwan, and Thailand. Mehrara (2007), however, found that real

GDP per capita growth led commercial energy usage per capita for the oil-exporting

countries panel. Narayan and Smyth (2007) included real gross fixed capital formation in the

estimation and found that energy consumption per capita causes real GDP growth per capita

for the G7 panel. For OECD countries, Lee et al. (2008) found bidirectional causality

between the two variables in question, while Lee and Chang (2008) incorporated both real

gross fixed capital formation and labor force and found unidirectional causality, with

increased energy consumption leading to real GDP growth for the Asian panel, APEC panel,

and the ASEAN panel. Huang et al. (2008) classified data into four income groups and

discovered i) no causal relationship between energy consumption and real GDP per capita for

the low-income panel; ii) economic growth leading energy consumption positively in the

middle-income group; and iii) economic growth leading energy consumption negatively for

the high-income panel. Mixed results on the impact of electricity and non-electricity

consumption on economic growth for a global panel as well as for four regional panels

(East/South Asian and the Pacific region, Europe and Central Asian region, Latin America

and Caribbean region, and Sub-Saharan, North Africa and Middle Eastern region) were also

found by Sharma (2010). The analysis is based on a model consisting of inflation, capital

stock, labour force, trade, and energy.

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2.2. Australian studies

Earlier studies using Australian data to examine the causal relationship between energy

consumption and economic growth are based primarily on bivariate models. Using a bivariate

approach, Fatai et al. (2004) applied different time series econometric methods (Toda-

Yamamoto causality, ARDL bounds test, and Johansen-Juselius procedure) to annual data

from 1960 to 1999, and concluded that real GDP growth leads to increased energy

consumption. The authors also studied the impacts of various disaggregated measures of

energy consumption by sources (i.e., coal, electricity, oil, natural gas consumption). Narayan

and Smyth (2005), by way of contrast, used a trivariate model (electricity consumption per

capita, real GDP per capita, and manufacturing employment index) and applied ARDL

bounds test to discover the causality relationship during 1966-1999. The results also showed

that there is unidirectional causality, with growth leading to increased electricity

consumption. Using a bivariate model and Johansen-Juselius test procedure, Chontanawat et

al. (2008) demonstrated causality from real per capita GDP to per capita energy consumption

for the period 1960-2000. These test results are in contrast to those of Narayan and Prasad

(2008), who found a long-run causality from electricity consumption to output in Australia

for the period 1960–2002 using a bootstrapped Granger causality test. To reduce potential

omitted-variable biases, Mahadevan and Asafu-Adjaye (2007) included the consumer price

index as a third variable in their study. They found evidence of cointegration and

bidirectional causality between per capita energy consumption and real per capita GDP for

the period 1971-2002. Shahiduzzaman and Alam (2012) incorporated capital and labour in

their study, in addition to energy consumption and real GDP, and used both Johansen-

Juselius and Toda-Yamamoto causality tests to determine a causal relationship for the years

1961-2009. They also found evidence of cointegration and bidirectional causality between

GDP and energy usage, consistent with the results of Mahadevan and Asafu-Adjaye (2007).

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3. Variable and Data Sources

Neoclassical growth models, such as Solow’s growth model (Solow, 1956), usually

consider capital and labour as the primary factors of production and, therefore, energy is

assumed to have a relatively minor role. Yet most ecological-economics viewpoints consider

only the role of energy and ignore the roles of other classical inputs such as capital and labour

(Stern, 2011). Endogenous growth models have emphasized the role of human capital in

economic growth (Galor and Weil, 2000; Lucas, 2002). To synthesize these approaches, we

use a production function approach, which enables to incorporate capital and labour inputs as

considered in neoclassical growth theory, energy as used in ecological economics models,

and capital input as discussed in endogenous growth models. The production function

approach provides a more comprehensive methodology that avoids the ad hoc selection of

additional variables (Stern, 1993; Stern, 2000; Shahiduzzaman and Alam, 2012).

Following the literature, we use gross domestic product (GDP), real values in $AUD, as

the dependent variable. As explained above, there are four explanatory variables: capital,

labor, energy consumption, and human capital. The capital input (K) in the model is

measured by gross capital formation (real values in $AUD), which consists of outlays on

additions to the fixed assets of the economy plus net changes in the level of inventories. The

labour factor (L) is measured by total labour force comprising people aged 15 and older who

supply labour for the production of goods and services. Energy input (E) refers to the use of

primary energy before transformation to other end-use fuels, which is equal to indigenous

production plus imports and stock changes, minus exports and fuels supplied to ships and

aircraft engaged in international transport. Energy consumption data are aggregated and

measured by kilotonnes of oil equivalent. Human capital refers to expertise or know-how

embodied in people through processes of education and training. The most commonly used

measure of human capital is the level of school attainment in a country. Here, human capital

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(H) is measured by the total enrollment in tertiary education, regardless of age, which is

expressed as a percentage of the total population of the five-year age group following on

from secondary school leaving. We use annual time series data from 1970 to 2011 sourced

from the World Bank (2012) to estimate the model.

4. Methodology

The following model is used to examine the relationship between energy consumption

and economic growth.

)1( )( tttttt LogELogHLogKLogLYLog

where, Yt is Australian gross domestic product in constant Australian dollars. Lt, Kt, Ht, and Et

refer to labour, capital, human capital, and energy consumption as explained in the data

section. In general, we expect that β, γ, λ and θ will all be positive because an increase in

factors of production should, under normal circumstances, lead to a higher output. The model

is in log-log form. Hence, coefficients can directly be interpreted as elasticities. For instance,

β measures the labour elasticity. In specific terms, β shows percentage change in real GDP in

response to a one per cent change in labour force. Other coefficients can also be interpreted in

a similar way. To illustrate, λ shows percentage change in real GDP in response to a one per

cent change in human capital. However, our focus would be on the direction and the

magnitude of θ or the energy elasticity.

One of the limitations of the model given in equation (1) is that it only provides

information on the long-run relationship between the factors of production and national

output in Australia. However, in this paper, we aim to analyse both the short-run and long-run

elasticities, and the energy input elasticities in particular. For that purpose, the study uses the

bound testing cointegration approach suggested by Pesaran et al. (2001). The bound testing

method utilizes the autoregressive distributed lag (ARDL) model, while the ARDL model

used is given in equation (2).

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)2( )log()log(

)log()log()log()log(

5

0

4

0

3

0

2

0

1

1

tttttkt

n

k

kkt

n

k

k

kt

n

k

kkt

n

k

kkt

n

k

kt

uLogELogHLogKLogLEH

KLYY

Compared to other known methods of cointegration such as Engle and Granger Two-

Step approach (1987) and the system-based reduced rank approach of Johansen (1991), the

bound testing approach has several advantages. To illustrate, in other cointegration methods,

researchers are required to know unit root properties of each series before using them in the

estimation. As explained by Pesaran et al. (2001), both the Engle-Granger method and the

Johansen method are concentrated on variables integrated of order one. But in the bound

testing approach, the order of integration (order zero or order one) does not matter. Bound

testing method has a further advantage because it performs better in small samples (Narayan,

2005). More importantly, the ADRL method can be used to estimate both short- and long-run

estimates in one step. To test for cointegration, we should test the null hypothesis of all long-

run coefficients being zero. Pesaran et al (2001) advise using a F-test, but with modified

critical values, depending on whether all variables are integrated or order one, or order zero.

5. Results

Given that the original data sample contains only 42 observations and that the degrees of

freedom is further curtailed by the differencing, we have confined the model to the lag one

first differenced data and the long run relationships. The results are shown in Table 1 below.

Short-run elasticities are given by the estimated coefficients of DLL, DLK, DLH, and DLE,

while the long-run elasticities are given by LL, LK, LH, and LE. As the results show, the

short-run elasticity of the energy consumption has the expected positive sign, but is

statistically insignificant at conventional levels. As far as the other variables are concerned,

the estimate of the coefficient of labour variable is positive and significant at 1 per cent level

of significance. This suggests that there is statistical evidence to support the assertion that

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labour exerts a positive impact on growth in the short run. It is impossible to comment on the

estimate of the human capital variable or capital variable because both are statistically

insignificant at conventional levels of significance. With respect to the long run coefficients,

all four factors of production have the expected a positive relationship with economic growth,

but only capital and human capital variables coefficients are statistically significant at 5 per

cent level of significance.

Table 1: Short-run and long-run elasticities using ARDL bound test

Dependent Variable: DLY

Sample (adjusted): 1971 2010

Included observations: 40 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C –5.035383 2.524488 –1.994616 0.0552

DLL(1) 0.921455 0.272004 3.387653 0.0020

DLK(1) –0.002522 0.039260 -0.064245 0.9492

DLH(1) 0.008440 0.029027 0.290774 0.7732

DLE(1) 0.007450 0.110182 0.067616 0.9465

LL 0.101931 0.135306 0.753335 0.4571

LK 0.081865 0.033083 2.474507 0.0192

LH 0.051259 0.017329 2.957945 0.0060

LE 0.118404 0.088220 1.342153 0.1896

TREND –0.010115 0.003800 –2.661509 0.0124

R-squared 0.481812 Mean dependent var 0.031562

Adjusted R-squared 0.326356 S.D. dependent var 0.015282

S.E. of regression 0.012543 Akaike info criterion –5.706989

Sum squared resid 0.004720 Schwarz criterion –5.284769

Log likelihood 124.1398 Hannan-Quinn criter. –5.554327

F-statistic 3.099341 Durbin-Watson stat 2.377331

Prob(F-statistic) 0.009417

Now we perform diagnostics tests to demonstrate that our findings are robust. There are

important steps to this diagnostics review. First, as Pesaran et al. (2001) showed, results

based on equation (2) are valid only if the level variables are in fact a part of the estimated

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model. We perform F-test to test the null hypothesis that β=γ=λ=0 or no level relationship

between the variables under consideration. However, Pesaran et al. (2001) suggest a bound

testing approach for the F-test, which contains two critical values for two bands, upper bound

assuming I(1) variables and lower bound assuming I(0) variables. If the computed F-statistics

fall outside the critical values of these bounds, a conclusive decision can be made without

knowing the order of integration of the variables. However, if the calculated F-statistics falls

within the upper and lower bound, the knowledge of integration is necessary or the inferences

are inconclusive (Pesaran et al. 2001). The upper bound value for our specification is 3.25

and the F- test statistics give 3.51, which is outside this range. As a result, we can make

conclusive inferences from the results based on the ARDL modelling framework shown in

equation (2).

The second diagnostic test involves estimating the ARDL model by substituting an error

correction term for the variables in levels. The significance of the error correction term is

regarded as a further proof for the long-run relationship between the chosen variables. As

shown by Pesaran et al. (2001), this method should be used in the subsequent estimation of

short-run dynamics because it has a more parsimonious specification than the version given

in equation (2). The model with an error correction term is given in equation (3). Here, ECTt-1

is the one period lag residuals saved from equation (1). As shown in Table 2, the error

correction term for the economic activity and growth in terms of a production equation is

estimated as –0.23. This is significant at the 10 per cent level of significance. The value

suggests that, after a shock, economic growth converges to the equilibrium. Approximately

23 per cent of the deviation is therefore corrected within one year.

)3( )log(

)log()log()log()log()log(

1

5

0

4

0

3

0

2

0

1

1

ttkt

n

k

k

kt

n

k

kkt

n

k

kkt

n

k

kkt

n

k

kt

uECTE

HKLYY

18

Table 2: ARDL bound test with an error correction term (ECT)

Dependent Variable: DLY

Sample (adjusted): 1971 2010

Included observations: 40 after adjustments

Variable Coefficient Std. Error t-Statistic Prob.

C 0.025523 0.005641 4.524305 0.0001

DLL(1) 0.434682 0.272450 1.595458 0.1199

DLK(1) –0.043286 0.033986 –1.273625 0.2114

DLH(1) –0.039493 0.025595 –1.542976 0.1321

DLE(1) 0.046163 0.100996 0.457078 0.6505

ECT(-1) –0.227068 0.122454 1.854317 0.0724

R-squared 0.301297 Mean dependent var 0.031562

Adjusted R-squared 0.198546 S.D. dependent var 0.015282

S.E. of regression 0.013681 Akaike info criterion –5.608100

Sum squared resid 0.006364 Schwarz criterion –5.354768

Log likelihood 118.1620 Hannan-Quinn criter. –5.516503

F-statistic 2.932313 Durbin-Watson stat 1.958128

Prob(F-statistic) 0.026328

Results based on bound testing cointegration method suggest that energy consumption

and Australian economic growth, despite being positively related, are not statistically

significant either in the short run or the long run. To ascertain this finding, we have also

conducted a multivariate Granger causality test to see whether there are any feed-in effects

between energy use and economic growth. According to the multivariate Granger causality

test, energy (Et) is said to Granger cause GDP, if the prediction error of current GDP

declines, as we include lagged values of energy in addition to lagged values of GDP. In other

words, the coefficients of lagged energy terms are statistically significant. One of the

requirements of Granger causality test is that series are stationary. On account of the fact that

19

all variables are first differenced stationary, we conduct the multivariate Granger causality

test in the following form:

)8(

)7(

)6(

)5(

)4(

15141312

11

11111

1

15141312

11

11111

1

15141312

11

11111

1

15141312

11

11111

1

15141312

11

11111

1

HttHtHtHtH

tHit

n

i

iHit

n

i

iHit

n

i

iHit

n

i

iHit

n

i

iHHt

KttKtKtKtK

tKit

n

i

iKit

n

i

iKit

n

i

iKit

n

i

iKit

n

i

iKKt

LttLtLtLtL

tLit

n

i

iLit

n

i

iLit

n

i

iLit

n

i

iLit

n

i

iLLt

EttEtEtEtE

tEit

n

i

iEit

n

i

iEit

n

i

iEit

n

i

iEit

n

i

iEEt

YttYtYtYtY

tYit

n

i

iYit

n

i

iYit

n

i

iYit

n

i

iYit

n

i

iYYt

LHaLKaLLaLEa

LYaLHLKLLLELYLH

LHaLKaLLaLEa

LYaLHLKLLLELYLK

LHaLKaLLaLEa

LYaLHLKLLLELYLL

LHaLKaLLaLEa

LYaLHLKLLLELYLE

LHaLKaLLaLEa

LYaLHLKLLLELYLY

Results of the Granger causality test vary according to the lag length used in the

estimation. Optimum lag length is decided by the Akaike information criterion. The test

results using 7 lags are given in Table 3. According to the results, the null hypothesis that

DLE does not Granger cause DLY, or that DLY does not Granger cause DLE, cannot be

rejected. The interrelationship between the two variables seems not strong. This finding is in

contrast to earlier Australian studies, which showed either bidirectional causality between the

two (i.e., Mahadevan and Asafu-Adjaye, 2007; Shahiduzzaman and Alam, 2012), or

unidirectional causality with economic growth leading energy consumption (i.e., Fatai et al.,

2004; Narayan and Smyth, 2005; Chontanawat et al., 2008), or unidirectional causality with

energy consumption leading economic growth (i.e., Narayan and Prasad, 2008).

20

Table 3: Results of Granger causality test

Sample: 1970 2011

Included observations: 39

Dependent variable: DLY

Dependent variable: DLE

Excluded Chi-sq df Prob. Excluded Chi-sq df Prob.

DLL 1.409789 2 0.4942 DLY 0.061295 2 0.9698

DLK 0.718177 2 0.6983 DLL 7.596891 2 0.0224

DLH 2.410475 2 0.2996 DLK 2.191732 2 0.3342

DLE 1.209791 2 0.5461 DLH 4.850036 2 0.0885

All 5.246247 8 0.731 All 13.35655 8 0.1002

6. Conclusion and policy implications

In sum, we have applied ARDL bound test to time series data from 1970 to 2011 to infer the

causal relationship between energy consumption and economic growth in Australia. To

reduce potential omitted-variable biases, we have considered a multivariate model including

labour, capital, human capital, in addition to energy consumption and real GDP. The model is

based on the production function framework, which is formulated to synthesize the

approaches from neoclassical and endogenous growth models, as well as from an ecological

economics viewpoint. The main finding is that there is no causality between energy

consumption and economic growth in Australia. The results in this paper support the

‘neutrality’ hypothesis, which views energy consumption as a small component of real GDP

(Payne, 2010). As a result, energy consumption should not have a significant impact on

economic growth. Furthermore, the finding is in line with structural change of the Australian

economy toward a more service-intensive economy, which requires less energy intensity than

an economy relying on a large manufacturing industry. Although energy remains important,

energy-saving technical progress in the manufacturing industry has allowed less energy to be

21

used per unit output and has reduced the constraint that energy resources place on the output

of the economy and economic growth (Stern, 2011). This has important consequences for

energy conservation and climate change policies, especially as Australia grapples with

measures to improve energy security and concomitantly reduce greenhouse gases emissions.

Our results suggest, at least ostensibly, that energy conservation and carbon pricing policies

may not necessarily have an adverse effect on Australia’s economic growth.

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