1. Introduction
Even though neoclassical theory states that energy is neutral to economic growth, historically, energy shocks have affected global economic growth. This observation shows the key role of energy consumption in economic growth [
1,
2]. According to the ecological and biophysical models, energy is the main factor of production [
3,
4] . The production of goods is straightly related to the use and the availability of energy, which is considered as the primary factor of production. The main aim of this study is to examine the role of energy consumption in economic growth for an oil producing and exporting country (OPEC), such as Saudi Arabia, using the autoregressive distributed lag (ARDL) Bounds testing to cointegration over the period 1971–2012. In order to control for potential omitted-variable biases and take into account the neoclassical, endogenous growth and ecological economics view points, we consider a model where real GDP is explained by employment, capital, and human capital in addition to energy consumption (for more details, see [
4,
5,
6]). Generally, it is known that the level of energy consumption is very high in the majority of OPEC members, and mainly in Saudi Arabia, due to the low levels of national fuel prices and energy efficiency. Energy is over subsidized in Saudi Arabia. Nowadays, the country is under pressure to increase fuel prices and reduce fuel subsidization. If we find that energy consumption boosts economic growth in Saudi Arabia, a policy aiming to reduce energy consumption might lead to undesirable results concerning economic growth. We should suggest some other measures such as energy efficiency, investment in renewable energies such as solar and wind energies and capital formation that could lead to economic growth and increase employment, where the demand for labor is increasing.
The causal relationship between energy consumption and economic growth has been well studied since the seminal paper of [
7]. However, until now the empirical findings are divergent sometimes, even for the same country. Different empirical research has used a variety of econometric techniques and variables over various periods for different countries or groups of countries at aggregated and disaggregated levels. In this context, by using a meta-analysis of 51 works on energy-growth nexus, Menegaki (2014) [
8] found that the long run impact of energy consumption on economic growth is dependent of the variables investigated, the data span, and the cointegration technique used.
The role of energy in economic growth has interested researchers since the first oil crisis of 1973. It continues to interest researchers for many reasons. Firstly, the high volatility of fossil fuel prices has largely affected global economic activities and even food security. Secondly, since the industrial revolution, energy has been used in all economic activities. It has become the main engine of the global economy. Thirdly, the use of energy (mainly fossil fuels) is not without negative effects on the global environment and its consequences on climate change has adversely affected human activities.
As reviewed and reported by previous research, there is a large literature on the causal relationship between energy use and economic growth. In this study, we survey mainly the studies including employment and capital as inputs in the energy-growth nexus. The first work on this subject is that of Kraft and Kraft (1978) [
7] who studied the relationship between gross national product (GNP) and energy for the United States over the period 1947–1974. By using the Sims causality test, they showed that energy consumption does not cause GNP but the reverse holds. Comparatively to the work of Kraft and Kraft (1978) [
7], Akarca and Long (1980) [
9] changed only the period of study but they did not find the same result. Their finding showed the absence of causality between the gross national product and energy use for the U.S. over the period 1950–1970. Eight years after the first work of Kraft and Kraft (1979) [
7], Erol and Yu (1987) [
10] studied the causal relationship between energy consumption and GNP for another six developed countries (Canada, France, Germany, Italy, Japan and the United Kingdom). The authors used both tests of causality of Sims and Granger. The neutral hypothesis of energy was verified for both countries of France and the United Kingdom, whereas the energy-led growth hypothesis held only for Canada. The feedback hypothesis was verified for Japan while the conservation hypothesis was found for Germany and Italy.
The above studies considered only the tests of causality between two macroeconomic series. Later many researchers showed that these types of models suffer from the problem of omission of some other variables, which could affect the results. In order to resolve this problem, when studying the relationship between energy consumption and economic growth, many studies have included other variables such as employment, capital, carbon dioxide emissions,
etc. For example, when investigating the causal relationship between energy consumption and GNP in United States, Yu and Hwang (1984) [
11] included employment as a control variable. Their findings showed the absence of causality in both directions between energy consumption and GNP. The results confirmed the neutral hypothesis.
However, all these previous studies did not check for the non-stationarity of the macroeconomic time series. Hence, since the work of Yu and Hwang (1984) [
11], many studies have used the cointegration approaches of Engle and Granger (1987) [
12], and Johansen and Juselius (1990) [
13] to resolve this problem. As an example of work using the Engle–Granger cointegration technique, Cheng and Lai (1997) [
14] studied the relationship between real GDP, energy consumption and employment in the case of Taiwan. They found unidirectional causality running from real GDP to energy consumption. This means that conservative hypothesis holds.
Contrarily to the previous technique, the Johansen–Juselius cointegration approach has been widely used (e.g., [
15,
16,
17,
18,
19]). For example, Chang
et al. (2001) [
15] studied the dynamic causal relationships between economic output, energy consumption and employment for Taiwan for the period covering January 1982 to November 1997. Their results supported the energy-led growth hypothesis. Ghali and El-Sakka (2004) [
16] used a neo-classical production function where economic output is explained by capital, labor and energy use for Canada. They found bidirectional causality between economic growth and energy use. Yuan
et al. (2008) [
18] studied the causal relationships between energy consumption, labor, capital and economic output for China using a neo-classical aggregate production function. They used energy consumption at both aggregated and disaggregate levels (electricity consumption, oil consumption and coal consumption). The authors found that the series are cointgrated at both aggregated and disaggregated levels. The results of short run causality are mixed. Stern and Enflo (2013) [
19] studied the relationship between energy, capital, labor and economic growth for Sweden over the three periods of 1900–2000, 1950–2000 and the whole period of 1850–2000. Using a VECM, they found that energy Granger causes economic growth for the whole period of 1850–2000, whereas economic growth causes energy for the two other periods.
However, the Johansen’s cointegration approach has been criticized by many researchers when using small samples, because the usual unit root and cointegration tests are not powerful in this case. In order to control for the problems associated with small samples, some researchers have made appeal to other methods such as the ARDL bounds testing approach (e.g., [
20,
21]) and panel unit root and cointegration tests (e.g., [
22]).
Sari
et al. (2008) [
20] studied the relationships between disaggregate energy consumption (coal, natural gas, fossil fuels, conventional hydroelectric power, solar energy, wind energy, wood, and waste), employment and industrial output in the United States using monthly data covering the period of January 2001 to June 2005. They found unidirectional causality running from industrial production to disaggregate energy consumption; except for the case of coal consumption, the reverse holds. Shahbaz
et al. (2011) [
21] included employment as an additional variable to electricity consumption and economic growth when studying the causal relationships between the three variables for Portugal over the period of 1971–2009. Their results showed that there is bidirectional causality between the three variables in the long run. The feedback hypothesis was validated. Lee
et al. (2008) [
22] analyzed the relationships between energy consumption, capital stock and economic output for a set of 22 OECD countries over the period 1960–2001. They found that the three series are cointegrated and there is a bi-directional causality between economic growth and energy consumption. By studying the energy-growth nexus in 17 Arab countries using the ARDL approach over the period 1980–2011, Shahateet (2014) [
23] found that neutrality hypothesis is verified in 16 out of 17 Arab countries including Saudi Arabia.
By taking into account the non-normality assumption, Yildirim and Aslan (2012) [
24] studied the causal relationships between energy use, employment, gross fixed capital formation, and economic growth for 17 highly developed OECD countries using the Toda–Yamamoto test framework and the leveraged bootstrapped simulation technique. Their findings indicate the neutrality in the case of Australia, Canada, and Ireland. In the same line, Yalta and Cakar (2012) [
25] used a maximum entropy bootstrap to investigate the relationship between energy consumption and economic growth over the period 1971–2007 in the case of China. Overall, their results support the neutrality hypothesis. Salamaliki and Venetis (2013) [
4] argued that no previous studies took into account the indirect causality. To do so, they studied the causal relationships between energy consumption, real GDP and capital stock in the group of seven advanced countries by employing two recent methods of Dufour–Pelletier–Renault (2006) [
26] and Hill (2007) [
27] in order to detect direct and indirect causal effects. They found that in general, real GDP causes energy consumption for this group of countries. Ayres and Voudouris (2014) [
28] studied the nonlinear relationships between economic growth with energy use, labor and capital stock in Japan, United Kingdom and United States. They argued that since the industrial revolution, capital stock and energy have played an important role in economic growth.
For the case of Saudi Arabia, empirical studies on energy consumption-economic growth nexus are very restricted. To our knowledge, there are only two studies (Alqudair [
29,
30]) that focused on Saudi Arabia as a single-country, whereas many other studies ([
31,
32,
33,
34,
35,
36,
37,
38]) focused on Saudi Arabia in the context of a multi-country study. The results of these studies are mixed and sometimes contradictory. The results of [
30,
31,
32,
37] are conforming to the conservative hypothesis that implies economic growth Granger causes energy consumption in the long run. Squalli (2007) [
33], Narayan and Smyth (2009) [
34], Sadorsky (2011) [
35] and Mohammadi and Parvaresh (2014) [
38] validated the feedback hypothesis that states bidirectional causality between energy consumption and economic growth in the long run. Contrarily, Hossein
et al. (2012) [
29] and Alkhathlan
et al. (2012) [
36] found an absence of causality between energy consumption and economic growth in the long run. Their findings are conforming to the neutrality hypothesis.
Our study complements the work of Alkhathlan
et al. (2012) [
29], which is the only one of these studies that used the production function approach. The study of Alqudair [
30] is limited to a two-variable specification that investigated the energy use-economic growth nexus. Alkhathlan
et al. (2012) [
29] studied the relationship between economic growth, carbon dioxide emissions, employment and energy consumption in Saudi Arabia over the period 1980–2008 using both approaches of ARDL Bounds testing and Johansen multivariate cointegration. The authors used a production function framework where they explained economic output by energy consumption, employment and CO
2 emissions. Compared to this previous study, we do not integrate CO
2 emissions; we add real fixed capital formation and human capital as explanatory variables of economic growth and extending the period from 1971 to 2012.
The remainder of our paper is organized as follows. In
Section 2, we present the material and methods used to investigate the relationship between real GDP, energy consumption, real fixed capital formation, employment and human capital in the case of Saudi Arabia. We present the data and the different procedures of ARDL cointegration Bounds testing framework.
Section 3 presents the empirical results. In
Section 4, we discuss our different findings. Finally,
Section 5 concludes by some policy implications and recommendations.
3. Empirical Results
Contrarily to previous studies using the ARDL bounds test to cointegration in the context of VECM, our study is based on a single equation VAR model. As the ARDL bounds test is based on the assumption that the variables are I(0) or I(1), so, before applying this test, we determine the order of integration of all variables using the unit root tests. For all the variables, we use their natural logarithms. The objective is to ensure that none of the variables is integrated of order two because of spurious results and we cannot interpret the values of F-statistics provided by [
44] or [
50] when we have I(2) variables.
In order to check that none of the variables is integrated of order two, we apply the ADF, PP and DF-GLS tests. The results of the three tests applied to the levels and first differences of the variables are shown in
Table 2. According to the three tests of ADF, PP and DF-GLS, we find that real GDP is not stationary at its level, whereas its first difference is found to be stationary. In addition, the three-unit root tests show that energy consumption is integrated of order one. The level of real gross fixed capital formation is found to be not stationary due to ADF and DF-GLS tests whereas it is shown to be stationary due to PP test at a 10% level. According to the three tests, the first difference of real gross fixed capital formation is stationary. The variable employment is found to be integrated of order one according to ADF and DF-GLS tests, while it is shown to be integrated of order zero due to PP test. Finally, human capital is found to be integrated of order zero according to ADF test, while it is shown to be integrated of order one due to PP and DF-GLS tests. Apparently, there are sometimes conflicts between the outcomes of tests; however, we can conclude that neither of variables is integrated of order two.
Table 2.
Results of unit root tests.
Table 2.
Results of unit root tests.
Variables | ADF | PP | DF-GLS |
---|
Y | 2.467 (0) | −2.067 (0) | 1.021 (0) |
x1 | −2.167 (0) | −1.910 (2) | −0.423 (1) |
x2 | −2.148 (1) | 2.819 (4) *** | −0.561 (1) |
x3 | −2.538 (1) | −3.545 (4) ** | −0.201 (1) |
x4 | −3.730 (1) ** | −2.756 (0) | −1.592 (0) |
ΔY | −3.337 (0) * | −3.337 (0) * | −1.648 (1) *** |
Δx1 | −4.485 (0) * | −4.497 (1) * | −2.896 (0) * |
Δx2 | −2.495 (0) ** | −2.448 (1) ** | −1.832 (0) *** |
Δx3 | −3.219 (0) *** | −3.188 (2) | −3.205 (0) ** |
Δx4 | −3.894 (0) * | −3.937 (2) * | −3.936 (0) * |
The results shown by the different unit root tests suggest the use of bounds test for cointegration framework. In doing so, we estimate a 1-equation VAR model (called also unrestricted ECM) given by Equation (5). This needs to implement the information criteria for selecting the lag-lengths. This is somewhat difficult for the lags of exogenousregressors.
Looking at the different values of Akaike information criterion (AIC), Schwarz criterion (SC) and Hannan–Quinn information criterion (HQ), we see that SC suggests a maximum lag of one. The results of lag order selection criteria are shown in
Table 3.
Table 3.
Results of lag order selection criteria for unrestricted ECM.
Table 3.
Results of lag order selection criteria for unrestricted ECM.
Lag Order | AIC | SC | HQ |
---|
1 | −3.051 | −2.418 * | −2.822 |
2 | −3.026 | −2.173 | −2.720 |
3 | −3.441 * | −2.364 | −3.058 * |
After that, we check for the significance of exogenous variables. As the span of the period is small and finite (41 years), we estimate the model given in Equation (5) by OLS by taking three years as the maximum lag length order for the exogenous regressors. After that, we choose only the regressors, which are significant. The selected ARDL model is of order (1, 1, 3, 0, 0).
The results of diagnostic tests applied to ARDL (1, 1, 3, 0, 0) are shown in
Table 4.When we check for the serial independence of the errors of this model using the Breush-Godfrey Lagrange multiplier (LM) test, we find that they are serially independent at the 5% level up to order twelve.
Table 4.
Results of diagnostic tests applied to ARDL (1, 1, 3, 0, 0).
Table 4.
Results of diagnostic tests applied to ARDL (1, 1, 3, 0, 0).
Breush-Godfrey LM Test | Normality Test | ARCH Test |
---|
Lag | LM stat | p-value | | p-value | | p-value |
---|
1 | 2.994 | 0.083 | 0.812 | 0.666 | 0.061 (1) | 0.803 |
2 | 1.529 | 0.216 | |
3 | 3.127 | 0.077 |
4 | 3.566 | 0.058 |
5 | 0.041 | 0.839 |
6 | 0.001 | 0.970 |
7 | 1.509 | 0.219 |
8 | 0.384 | 0.535 |
9 | 0.0001 | 0.990 |
10 | 0.829 | 0.362 |
11 | 0.933 | 0.333 |
12 | 0.121 | 0.727 |
The
p-values corresponding to the Jarque–Bera normality test indicate that we accept at a 5% level the normality of the error disturbances. The result of the autoregressive conditional heteroskedasticity (ARCH) test shows that the errors of the ARDL model are not heteroskedastic as its
p-value is equal to 0.803. Finally, before proceeding to the bounds testing, we verify for the inverse roots of the autoregressive characteristic of our unrestricted ECM. As shown by
Figure 2, all these roots are inside the unit circle, which implies that the ARDL model is dynamically stable.
Figure 2.
The inverse roots of the associated characteristic ARDL model.
Figure 2.
The inverse roots of the associated characteristic ARDL model.
Since the ARDL model passes all diagnostic tests without problem, we apply the bounds testing by checking for the null hypothesis, H
0: θ
0 = θ
1 = θ
2 = θ
3 = θ
4 = 0. Results of the test are shown in
Table 5. The value of F-statistic is equal to 5.355. Therefore, when we go to the bounds test tables of critical values, we use the number of regressors (k = 4). We have not constrained the intercept of our model, and there is no linear trend term included in the ARDL model. As the number of observations is small (T = 42), critical values are collected from [
50] for T = 40. The lower and upper bounds at the 10%, 5%, and 1% significance levels are equal to [2.483, 3.647], [2.962, 4.268], and [4.045, 5.898], respectively.
Table 5.
The results of ARDL bounds test.
Table 5.
The results of ARDL bounds test.
Bounds Testing for Cointegration |
F-statistic | Optimal lag length | F-statistic |
FY(Y/x1, x2, x3, x4) | 1, 1, 3, 0, 0 | 5.355 ** |
Critical Values |
Significance level | Lower bounds I(0) | Upper bounds I(1) |
1% level | 4.045 | 5.898 |
5% level | 2.962 | 4.268 |
10% level | 2.483 | 3.647 |
As the value of the F-statistic exceeds the upper bound at the 5% significance level, we can conclude that there is a relationship between economic growth and the various explanatory variables (energy consumption, real gross fixed capital formation, employment and human capital) in the long run.
For testing the dynamic causal relationships between economic growth and the various explanatory variables, we estimate the long run equilibrium model given in Equation (3) by OLS, construct the residuals series, deduce the error correction terms given in Equation (4) and estimate by OLS the restricted ECM given in Equation (2). The results show that the coefficient of the error-correction term, ECT
t−1, is negative and significant at 1% level. If the result of the ARDL bounds test shows that there is cointegration between the variables, but the coefficient of the error correction term is positive or negative, but not significant, the model may be misspecified. In addition, if the coefficient of the error-correction term is smaller than −1, it makes no sense and it implies that there is an “over-correction” towards equilibrium ([
45]). This is expected because there is cointegration between the various investigated variables. The coefficient of the error-correction term represents the speed of adjustment per period after a short-run shock to the equilibrium relationship. It is equal to −0.637. The magnitude of this coefficient implies that the speed of adjustment is rapid. Nearly 64% of any disequilibrium between economic growth and the inputs is corrected within one year. Thus, given the negative ECT sign, real GDP “moves” to close the disequilibrium gap within two years.
The restricted error correction model, which represents short run dynamics, passes also the diagnostic tests without problems. Results of diagnostic tests are shown in
Table 6.
Figure 3 shows that the model is dynamically stable because the only root is inside the unit circle and it lies on the X-axis. Accordingly, the short-run dynamics associated with the model are not complicated.
Table 6.
Results of diagnostic tests applied to residuals of ECM.
Table 6.
Results of diagnostic tests applied to residuals of ECM.
LM test | Normality test | ARCH test |
---|
Lag | | p-value | | p-value | | p-value |
---|
1 | 1.964 | 0.161 | 1.686 | 0.430 | 0.693 (1) | 0.405 |
2 | 5.48 × 10−5 | 0.994 | |
3 | 2.044 | 0.152 |
4 | 2.507 | 0.113 |
5 | 1.177 | 0.277 |
6 | 0.013 | 0.906 |
7 | 2.416 | 0.120 |
8 | 0.901 | 0.342 |
9 | 0.225 | 0.634 |
10 | 0.679 | 0.409 |
11 | 0.701 | 0.402 |
12 | 0.264 | 0.606 |
As shown in
Table 6, the results of LM test applied to residuals of ECM imply that the error disturbances are serially independent up to order twelve.
Figure 3.
The inverse roots of the associated characteristic RECM.
Figure 3.
The inverse roots of the associated characteristic RECM.
As we find that the variables are cointegrated when the economic output is the dependent variable, we should employ the Granger causality test in the ECM to determine which exogenous variables can cause economic growth for Saudi Arabia. Long-run causality is determined by the t-significance of the one period lagged error-correction term ECT
t-1, whereas short run causality is determined by the joint significance F tests of the lagged explanatory variables. The results of different types of short and long run Granger causality are shown in
Table 7. In the short run, the conventional inputs (capital and employment) Granger cause economic growth at 1% level, whereas energy consumption and human capital Granger cause economic growth at a level of 5%. For the long-run causality, the estimated coefficient of ECT
t−1 is negative and significant at a level of 1%, implying that there is unidirectional causality running from energy consumption, capital, employment and human capital to economic output in the long run. When checking for the strong long run causality, we find that all inputs Granger cause economic growth at even 1% level.
Table 7.
The results of Granger causality tests.
Table 7.
The results of Granger causality tests.
Depend. Variable: Δy | ∑Δx1j | ∑Δx2j | ∑Δx3j | ∑Δx4j | Long-run causality |
---|
F statistics [p-values] | ECTt-1 [t-statistic] |
---|
Short-run causality | 5.98 ** [0.05] | 17.21 *** [0.00] | 6.88 * [0.00] | 4.50 ** [0.03] | –0.637 * [–4.44] |
Strong Long-run causality | 20.19 * [0.00] | 35.50 * [0.00] | 21.63 * [0.00] | 19.96 * [0.00] | |
For robustness, as the unit root tests suggest that the five time series can be I(1), we can also use the multivariate cointegration approach of [
52,
53]. Before applying this test, we determine the lag length for the VAR model. Due to the criterion of AIC, the maximum lag length is equal to 1 whereas it is equal to 5 according to the criteria of SC.
For a lag length equal to 1, the results of trace and Max-Eigen tests are summarized for the five cases in
Table 8. According to Max–Eigen test, there is only one cointegrating relationship between the five variables for all the five cases. However, the trace test gives that there are 3 cointegrating relationships for the first case (no intercept or trend in CE or test VAR), and only 1 cointegrating relationship for the four remaining cases. We can conclude that there is significant evidence in favor of only one cointegrating relationship between the five variables, as the second case (intercept but no trend in CE, no intercept in VAR) is more suitable for our study.
Table 8.
Results of trace and Max–Eigen tests.
Table 8.
Results of trace and Max–Eigen tests.
Data Trend | None | None | Linear | Linear | Quadratic |
---|
Rank or No. of CEs | No Intercept No Trend | Intercept No Trend | Intercept No Trend | Intercept Trend | Intercept Trend |
Number of Cointegrating relations by model at 5% level |
Trace test | 3 | 1 | 1 | 1 | 1 |
Max-Eigen test | 1 | 1 | 1 | 1 | 1 |
When estimating the vector error correction model (VECM), the coefficient of error correction term is negative and statistically significant in only the equation where the dependent variable is economic growth. This implies that the only long run relationship exists when economic growth is the dependent variable. The results given by the multivariate cointegration approach of [
52,
53] confirm those obtained using the ARDL bounds testing to cointegration framework.
5. Conclusions
This study tests the energy-led growth hypothesis by examining the relationship between real GDP and energy consumption, employment, real fixed capital formation and human capital using the ARDL bounds testing to cointegration approach for the case of Saudi Arabia. Our results show the presence of a long run relationship between the various variables. Robustness of the results of ARDL testing to cointegration is tested by employing the Johansen multivariate cointegration technique. The two approaches give approximately the same results. All the inputs (conventional and non-conventional) Granger cause economic growth in both the short and long runs. While our results show the importance of energy use to economic activity through the various channels of production, consumption and investment in Saudi Arabia, this must not neglect its environmental consequences and the investment in renewable energy sources. Our findings reaffirm the previous studies stating that energy is not neutral to economic growth.
We are motivated by testing the energy-led growth hypothesis due to the impact of the various conservation policies on economic growth. Energy policies are based, in part, on the energy-growth nexus. Knowing the relationship between economic growth and energy use can help policy makers taking the best decisions of energy management, which are important for environmental and climate changes.
As we find that energy consumption Granger causes economic growth, the adoption of energy conservation policies can limit Saudi economic growth. Hence, policy measures should be based on energy efficiency and the investment in renewable energy sources such as solar and wind where Saudi Arabia has also abundant resources.
Energy efficiency plays both roles. It saves money that can be used in the investment in renewable energies and it reduces greenhouse gases to meet the international commitments on climate change. The investment in renewable energies should help Saudi Arabia to be prepared in the long run to the low carbon economy that is based on clean technologies. Adding to that as suggested by the International Monetary Fund, energy subsidy reforms should be planned in Saudi Arabia.
As energy is overused in Saudi Arabia, future researches on this subject should be dedicated to energy efficiency and environmental efficiency. Reducing the conventional energy use in developed and developing countries is considered an important element in the world’s ability to grow sustainably. Likewise, reducing energy consumption is considered a practical solution to many of today’s common challenges including global energy shortages; mitigating against further changes in the climate; and health impacts of local air and water pollution. Understanding the factors that influence fluctuations in energy use are of first-order importance for academics and policymakers.