The nexus between oil consumption, economic growth, and crude oil prices in Saudi Arabia

Alkofahi, Kolthoom; Bousrih, Jihen Article The nexus between oil consumption, economic growth, and crude oil prices in Saudi Arabia Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Alkofahi, Kolthoom; Bousrih, Jihen (2024) : The nexus between oil consumption, economic growth, and crude oil prices in Saudi Arabia, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 5, pp. 1-16, https://doi.org/10.3390/economies12050105 This Version is available at: https://hdl.handle.net/10419/329031 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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Economies 12: 105. https://doi.org/10.3390/ economies12050105 Academic Editor: Brantley T. Liddle Received: 22 March 2024 Revised: 17 April 2024 Accepted: 24 April 2024 Published: 29 April 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article The Nexus between Oil Consumption, Economic Growth, and Crude Oil Prices in Saudi Arabia Kolthoom Alkofahi 1and Jihen Bousrih 2,3,* 1Finance Department, College of Business Administration, Prince Sultan University, Rafha Street, P.O. Box 66833, Riyadh 11586, Saudi Arabia; [email protected] 2Department of Economics, College of Business and Administration, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia 3BestMod Laboratory, High Institute of Management-Tunis, Le Bardo 2000, Tunisia *Correspondence: [email protected] Abstract: The energy revolution in Saudi Arabia has accelerated significantly since 2016, driven by the National Vision 2030. Significant changes to energy subsidies took place, and the renewable energy sector has seen rapid growth. The paper presents an empirical analysis of the Saudi energy transition by emphasizing the drivers of fuel consumption in KSA. It primarily attempts to explore the long-run (LR) connection between oil consumption and several economic variables such as economic growth, crude oil prices, investment, and the labor force in Saudi Arabia (KSA) from 1991 up to 2021. The paper implemented the vector error correction model (VECM) and performed different diagnostic tests to provide more evidence about the validity and robustness of the tests. The empirical findings highlighted how important the labor force, savings, GDP, and crude oil price are in determining oil consumption for KSA. The law of demand is significantly present, which negatively affects oil consumption for KSA as an oil exporting country. The results also supported the existence of a long-term direct correlation between the variables and oil consumption. Furthermore, the short-term estimation highlighted that only saving has a negative impact on oil consumption for a single lagged period. Our findings provide governments and regulators with further incentive to slow the expansion in oil consumption, as a larger labor force is demanding more oil to attain the target, faster economic growth, and increased savings are all contributing factors. Our findings are significant because they can assist policymakers, investors, and regulators in generating more efficient oil substitutes and making them affordable for the economy. Keywords: fuel energy consumption; oil exporter country; renewable energy; crude oil price; energy transition 1. Introduction The energy shift is already well underway and picking up speed (Xiao et al. 2021). In most of the world, wind and solar energy are the most affordable sources of electricity; renewable energy is responsible for almost all increases in electrical capacity worldwide; the use of electric vehicles (EVs) is growing quickly; and manufacturers are shifting to all-electric vehicles. The advancement of technology and the implementation of policies like China’s most recent Five-Year Initiative for Renewable Energy Development, the European REPowerEU initiative, and the US Inflation Reduction Act are going to reinforce these trends. The transition has also been further accelerated by the rise in fossil energy prices since Russia’s invasion of Ukraine. Conventional oil and gas businesses are facing a difficult situation as they attempt to adjust their business models to the new realities of the energy industry while defending the significance of their operations. The world will continue to rely on fossil fuels for some time to come. Today, many nations believe that geothermal, tidal, solar, and wind energy will dominate our energy supplies in the future (Abban et al. 2023). We currently Economies 2024,12, 105. https://doi.org/10.3390/economies12050105 https://www.mdpi.com/journal/economies Economies 2024,12, 105 2 of 16 rely excessively on energy sources that will inevitably run out. Our houses, workplaces, educational institutions, public structures, and manufacturing facilities are all powered by fossil fuels. While these types of energy provide these sectors with a dependable and cost-effective means of operation, they also contribute to daily emissions of damaging CO 2 into the atmosphere. To slow down and eventually reverse the harm done to the world, we hope that renewable energy will completely replace fossil fuels one day. While renewable energy offers an excellent way to counteract the negative consequences of fossil fuels, other reasons need to be considered as well. A more sustainable system must be adopted to combat climate change, as fossil fuels are the primary cause of this issue. Beyond that, it is also essential if we want to supply the growing market with affordable and easily available energy (De La Peña et al. 2022). Growing environmental concerns have been penetrating both public policy discussions and business activities. International and national legal actions have tightened environmental regulations, while rapid advancements in energy conservation and green energy have accelerated innovation in these fields (Nu¸tăet al. 2024). Despite these concerns, the fossil fuel market continues to be altered in more and more obvious ways. In 2023 1 , fossil fuels accounted for more than 60% of the world’s electricity generated to date. This is true even if every major economy is actively implementing renewable energy sources. When examining the macroeconomic effects of the energy transition, it is convenient to concentrate on exporters of fossil fuels, as they would see a significant erosion of their primary source of export and fiscal revenue, necessitating a change to a new growth model. Therefore, the goal of the paper is to further the discussion by exploring the main drivers of the fossil fuel industry for one of the most important oil exporters in the world, which is Saudi Arabia. This paper will investigate the relationship in the short run and the long run of some main components of energy consumption for the Saudi economy. The capacity of the oil sector in the Kingdom of Saudi Arabia (KSA) has been growing due to technical advancements. The output of traditional oil reserves has been increasing both in volume and productivity, and it has been supported by the shale oil industry, which is expanding quickly. It is expected that global pressure to reduce the use of fossil fuels will never stop. Although conventional energy sources are scarce, their negative effects outweigh their positive effects in terms of economic growth (Zhao et al. 2022). Nevertheless, conventional energy is vital to achieving output levels that support economic growth. Furthermore, the widespread use and high consumption rates of fossil fuels are contributing factors to the present worries about their depletion (Madaleno and Nogueira 2023). Oil energy continues to play a crucial role for most countries, despite the debate surrounding the usage of substitute renewable energy resources like water, solar, and nuclear power, which is becoming increasingly heated. So, changes in the price of oil could have a significant macroeconomic impact on nations that export as well as those that import (Bouri et al. 2020). Oil is a key factor in determining production costs for the first group, and it is the second main source of government revenue. Oil price fluctuations have differing effects on exporting and importing nations. Exporting nations rely heavily on income from oil. Therefore, the money to finance development projects will increase as oil prices rise. Academics and policymakers have long debated domestic fuel prices in oil-exporting nations, and it continues to be a crucial factor in these nations’ public policy decisions (Coady et al. 2018;Atalla et al. 2018;Aune et al. 2017). Although governments may reduce the price of domestic gasoline for some social and commercial reasons, this practice is sometimes criticized because it encourages excessive and inefficient domestic energy usage and costs governments money by reducing oil exports. However, this approach may be less effective than pricing domestic fuels according to the worldwide market. Hence, the effectiveness of fiscal and monetary policies is greatly impacted by fluctuations in oil prices (Siddiqui et al. 2022). Yet, the negative influence increases the uncertainty of the financial and real aggregates because of the oil price volatility, particularly when there are imperfect capital markets (Kitous et al. 2016). Governments, on the other hand, cannot abruptly reduce their spending and then confront a significant budget deficit. Economies 2024,12, 105 3 of 16 In the last 10 years, KSA’s domestic oil consumption has increased nine times, on average by 16% a year, to around 4 million barrels daily. Only the US, China, India, Japan, and Russia consume more oil than KSA, which is currently the world’s sixth-largest oil consumer. More than one-fourth of KSA’s production is now consumed domestically. If this rise persists, which we anticipate will be the case, it will significantly affect KSA’s oil exports and production. The main purpose of this research is to explore the factors that impact the consumption of oil and, therefore, formulate strategies to reduce the dependence on fuel energy, as the consumption of oil is the primary energy source in the region, and we expect that it will decline in the future owing to the widespread use of renewable energy supplies. In fact, in the short term, there are several possible directions for the changeover. Investment restrictions on fossil fuels might cause energy prices to spike rapidly for a lengthy period, and obstacles in the markets for metals and minerals could cause the transition’s costs to increase or slow down. The long-term scenario could look like this: Most of the world, especially East and South Asia, should benefit from switching to more affordable, locally derived energy sources in place of costly, environmentally damaging fossil fuels. The decrease in current energy supplies is likely to outweigh the economic benefits of clean energy for major fossil fuel producers, particularly those in the Middle East and North Africa (Pearce 2023). This paper is an attempt to explore the impact in the short run and long run of economic growth, saving, and the labor force on crude oil consumption. By far, this work represents the opening attempt to examine this relationship for the Kingdom of Saudi Arabia to better understand the transition toward green energies. The other parts of the paper will appear as follows: Section 2will investigate the theoretical background and the review of the literature. We present the data and techniques that are implemented in the next section. Section 4shows the results, and the final section will include recommendations and a conclusion. 2. Theoretical Background and Literature Review Energy is a necessary component of economic growth (Liu and Hao 2018). Concurrently, the primary cause of greenhouse gas emissions, which contribute to climate change, is energy consumption (Chang 2012;Alshehry and Belloumi 2015). One of the main issues facing the coming years will be how to simultaneously accomplish energy sustainability, economic development, and climate change mitigation. Decoupling rising energy consumption from economic growth and other macroeconomic variables means it is crucial to know the underlying causes of this trend (Wang et al. 2014). According to the theory, Uri (1982) and Beenstock and Dalziel (1986) demonstrate that, under certain assumptions, an expression for energy demand is constructed using the production equation and includes income and energy costs as explanatory variables. However, depending on the topic being investigated, the typical function of energy demand may not be an appropriate paradigm in a variety of scenarios, according to Bhattacharyya and Timilsina (2010) and others. The energy demand equation was reworked and presented as follows: Nordhaus (1975), followed by Beenstock and Willcocks (1981), and Beenstock and Dalziel (1986). Q=F(K,L,E)(1) where Qrepresents the production output, Krepresents capital, Lrepresents Labor, Erepresents Energy and Fis a function that indicates the innovation or manufacturing procedure that converts inputs to output. The Taylor expansion can be used to state Equation (1). The following equation represents the Cobb-Douglas production function (Cobb and Douglas 1928;Nordhaus 1975): Q=AKαLβEγ(2) where Ais the overall factor of productivity and, α , β , and γ , respectively, are the production elasticities for capital, labor, and energy. Economies 2024,12, 105 4 of 16 Numerous studies have been conducted on the earlier connection between labor, capital, and energy, utilizing various data frequencies, methodologies, countries, and periods. The results have varied widely. Many scholars have investigated the connection between the use of energy and economic growth since Apergis and Payne (2010). But no conclusion has been reached. This undoubtedly reveals that further energy research is still required. This results from the fact that the causal chain’s orientation has important policy ramifications. Energy conservation implies that lower energy consumption could hurt real GDP if energy plays a major role in economic growth (Eggoh et al. 2011). Several temporal and spatial scales have been used to investigate the factors that influence energy use. Using structural decomposition analysis (SDA), Lan et al. (2016) broke down the global energy footprint from 1990 to 2010 and discovered that GDP per capita contributed significantly to the rise in global energy consumption during this time. Omri et al. offered a thorough investigation of the factors influencing the use of renewable energy for a global panel of 64 countries and subgroups between 1990 and 2011 based on income levels (Shahbaz et al. 2015). Bilgen (2014) studied the worldwide energy consumption of various fuel types, industry sectors, and their effects on the environment. He recommended increasing energy efficiency and implementing related innovations to lower energy consumption. Ramanathan (2006) examined the relationship between energy consumption and economic growth using data from 1980 to 2001. Based on the assumption of a fixed GDP in 2025, he projected the association between non-fossil energy consumption and GDP growth. A wider range of studies examine the energy use of a particular nation, while others focus on regions like the OECD and the European Union. Between 1995 and 2000, the energy intensity trend that affected Lithuania’s various industries as well as the country was examined by Baležentis et al. (2011). The biggest influence on the decline in energy intensity came from energy savings in the residential and service sectors. However, this study did not examine different types of energy; rather, it concentrated solely on sectors. Furthermore, studies conducted in developing nations primarily concentrate on China and India, in contrast to studies conducted on energy use in developed countries. Gorus (2017) looked at the connection between KSA’s economic growth and oil production between 1970 and 2013. It also uses the Bootstrap Granger causality test and the ARDL Bound test to explore the causal association between oil consumption and economic growth. Results show the long-run sensitivity of economic growth to oil consumption and the cointegration of variables. However, the Bootstrap Granger Causality Test results demonstrate the absence of a causal connection between rising economic activity and oil consumption. We also find that (Osiobe 2019) used VECM techniques to examine the connection between energy consumption and GDP growth in Malaysia from 1970 to 2014. Their results show that an increase in GDP increases CO2 emissions through a surge in energy use. Wang and Li’s comparative study in 2016 revealed the most important variables influencing China and India’s changes in energy consumption between 1970 and 2012. Wang et al. (2020) analyzed China, India, and the US and revealed that, whereas income and oil intensity were the main drivers in the US, coal intensity was a driving factor in China and India for rising energy consumption. Numerous academics have conducted in-depth examinations of the factors that influence the energy consumption of specific industries in China, such as transportation (Zhang et al. 2011), logistics (Dai and Gao 2016), and nonferrous metals (Wang and Feng 2018). Nonetheless, it is uncommon to find an in-depth look that addresses the energy consumption of multiple nations. According to research by Pao and Tsai (2011), FDI and energy use are causally related in both directions. Zeng et al. (2020) conclude that whereas FDI influx stimulates energy consumption, it stimulates FDI inflow. Through energy consumption, FDI indirectly supports economic growth. Wahyudi and Palupi (2023) reveal a bidirectional connection between FDI and energy consumption, as well as a two-way relationship between FDI and labor force participation rate (LFR). While LFR has a negligible inverse impact on energy Economies 2024,12, 105 5 of 16 consumption over the LR, FDI has a considerable beneficial impact on energy consumption. LFR and FDI, however, have no bearing in the near run. This study aids in the making of decisions about energy, foreign direct investment, and increasing the number and caliber of workers in OECD nations. Typically, market efficiency and energy market volatility are taken into consideration when analyzing energy pricing for fossil fuels, such as coal, natural gas, and crude oil (Khan et al. 2022;Olubusoye et al. 2021;Wang et al. 2022). As fossil fuels are mostly used upfront in the supply chain as intermediate inputs, rising energy prices have a knock-on impact that raises costs throughout the whole global supply chain. Such supply-chain disruptions are the subject of numerous published studies (Durugbo et al. 2020). Another essential component of the economy is labor, along with capital and inputs like energy. According to Çetin (2019), the labor force participation rate is thought to be a better indicator of the labor market. (Kusairi et al. 2023) study claims that automation is currently taking over from technology, which has an impact on the structure of the labor market. Consequently, it can lead to a drop in the function of the workforce, which might cause a change in investment that turns an industry into one that is more capital-intensive than labor-intensive. Therefore, a rise in income and consequently an increase in energy consumption have an impact on a decrease in labor force participation. The findings of Kalantzis and Niczyporuk’s research in 2022 indicate a causal relationship between labor productivity and energy efficiency. The authors demonstrate how an increase in energy efficiency, induced by a decrease in energy consumption, will lead to an increase in labor productivity. Rising productivity is influenced by increased labor force participation, and rising productivity can increase energy demand. A policy including the public and private sectors is required to address this problem and raise the standard of industrial inputs that employ renewable energy sources to boost energy efficiency. 3. Methodology This section is designed to demonstrate the econometric model the study will implement and elaborate on the databases used to extract the statistics. It also reveals the empirical model used in the analysis, and hence, it attempts to assess the empirical results for the LR connection amongst energy consumption and the chosen economic variables. Energy consumption remains a crucial source of energy in KSA, as it is one of the world’s top oil-extracting and distributing nations. However, with the widespread use of green energy and other energy substitutes, we need to research if the usage of oil as the key source of energy will diminish in the future with the rise of other energy substitutes. 3.1. Data and Descriptive Statistics This study has chosen some economic indicators based on the expected relative importance of these indicators to KSA’s use of energy. The data were selected from two different sources: the “Statistical Review of World Energy”, where the energy consumption (OilC) and crude oil prices (Pcrude) data were obtained, and the WBT (World Bank Tables) to retrieve the data of GDP, the gross domestic saving (S), and the labor force (L). Refer to Table 1. To gain a better understanding of the transition from fossil fuel consumption to clean energy consumption, and as the KSA announced numerous reforms to reduce its dependence on oil or fossil fuels as well as introduce new clean energy sources, a longer time span was selected. However, the period was chosen based on the availability of the data for all the indicators. As the labor force for KSA has only been available from 1991 until 2021, the data were collected over that period. For the estimation, we used data on annual frequency. The estimation is performed using EViews 12. An analysis of transforming the logs was employed for the indicators (taking natural logarithms) to stabilize the variance of the series and reduce potential heteroskedasticity. The log transformation not only induces stationarity in the matrix of variance-covariance, but it is also helpful as the growth rate for the variable is represented by taking the log difference of the variable (Tang and Tan 2013). Economies 2024,12, 105 6 of 16 In addition, it should be noted that the period was selected because it is restricted by the overparameterization problem, which refers to the problem that exists when the number of observations used is fewer than the number of estimated parameters. If it persists, the estimation technique will be inadequate, and the used model will not be compatible (Hapsari et al. 2021). Table 1. Variable definitions and sources. Factor Abbreviation Description Source Dependent Variable Oil Consumption lOilC Oil Consumption in thousands barrel per day Statistical Review of World Energy (2023) Independent Variables GDP lGDP GDP per capita (current USD) World Bank (2023) Crude oil prices lPcrude Global crude oil prices, are measured in current US dollars per barrel. Statistical Review of World Energy (2023) Gross domestic savings (% GDP) lS Calculated by subtracting the expenditure on financial consumption from GDP. World Bank (2023) Labor Force participation rate lL Labor force participation rate, male (% of male population ages 15–64) World Bank (2023) 3.2. Model This study examines the determinants of energy consumption at the SR and LR for KSA. Theoretically, the energy consumption function relates crude oil prices to the total output of the economy. Therefore, the energy consumption equation could be modeled as in Equation (3): OilCt=F(Pcrudet,GDPt)(3) where OilC, Pcrude, and GDP are explained in Table 1. In line with the purpose of the current analysis, more variables were added, such as savings and labor force. Based on the research techniques of Tang and Tan (2013) and Shahbaz and Rahman (2010), the suggested research model can now be written as follows: lOilCt=θ0+θ1lGDPt+θ2lPcrudet+θ3lSit +θ4lLt+εt(4) where θi is the coefficient to be estimated; i= 0, . . . , 4. And subscripts t denote year; t=1, 2, . . . ., 24. The first model to estimate the joint dynamic behavior of a system of time series equations is vector autoregression analysis (VAR). However, for the results to be legitimate, this model requires all the series to satisfy the stationarity condition in level in addition to the first difference. Unfortunately, there is a possibility of losing important insight into the interrelation among these series, especially for the LR stochastic trend, mostly known as the cointegration between the levels, caused by differencing the data. An alternative method initiated by Johansen (1995) is VECM, which is widely used to assess the credibility of level regressions and whether LR relationships (cointegration) amongst these indicators occur (Indrajaya 2021). If estimation shows at least one cointegration relationship, then VECM is a better estimate than the VAR, as it was created to predict the LR association and to adjust to SR variation from LR stability. In the VECM, all the variables are treated as endogenous, including the levels of the variables as well as the first differences. Therefore, estimation using VECM was created to function well with nonstationary data that has some cointegration relationship (Enders 2008); as a result, it will outpace the VAR estimation. Before we move on, some diagnostic tests must be checked and conducted to ensure the legitimacy of the variables used and the appropriate model that could be employed. Economies 2024,12, 105 7 of 16 In the coming subsections, several important tests and their outcomes are stated in the subsequence tables. 3.2.1. Descriptive Statistics and Correlation Matrix More information about the statistics used in this study can be obtained through the analysis of the descriptive statistics. It summarizes the features of the time series used in this study and gives insight about how far the observation of each data point is from the mean. Referring to Table 2, it is reported that the descriptive statistics’ output comprises three main categories: measuring the sample’s mean, the median, and the standard deviation from the mean. It also reveals if the data are distributed normally or not through the Jarque-Bera test. Table 2. Descriptive statistics. Variable loilCtlGDPtlPcrudetlStlLt Mean 7.7275 9.4975 3.9880 3.6080 3.4565 Median 7.6914 9.6057 3.9594 3.5912 3.4015 Max 8.2846 10.099 4.8521 4.0148 3.8988 Min 7.0522 8.8776 3.0052 3.0866 3.2108 S.D. 0.4298 0.4761 0.5255 0.2568 0.1901 Jarque-Bera 2.9072 3.8072 1.7036 1.387 6.3488 Probability 0.2337 0.1490 0.4266 0.5103 0.2700 Obs. 31 31 31 31 31 As can be seen, the standard deviation of all the variables under investigation ranges from 0.1901 to 0.5255. The results of the Jarque-Bera diagnostic test failed to reject the hypothesis that the variables are normally distributed (probability is greater than 0.05). Another important test that is used to check the pair correlation between the variables is the correlation test (correlation matrix), represented in Table 3below. According to the data, the highest correlation coefficient is observed between energy consumption and the GDP, and a strong and positive correlation can be predicted between savings and crude oil prices. Table 3. Correlation Matrix. Variable loilC lGDP lPcrude lS lL loilC 1.0000 lGDP 0.9589 1.0000 lPcrude 0.7275 0.8432 1.0000 lS 0.4508 0.5097 0.8022 1.0000 lL −0.7517 −0.6497 −0.6267 −0.7053 1.0000 3.2.2. Unit Root Tests The unit root test is very important, and we cannot proceed with the study without providing enough evidence that the series is appropriate for the study. The test is designed to test if the series used in the model are stationary in level (integrated of order 0; I(0) ), or whether they are stationary in the first difference; I(1) , but the series must not be stationary in the second difference I(2) . The ADF test (Dickey and Fuller 1979) and the PP test (Phillips and Perron 1988) are used to test stationarity. The results for the unit root test are only reported for the first difference of the variables, as the unit root test for all the series in Economies 2024,12, 105 8 of 16 level was found not to be stationary and is suggested to be integrated of order one I(1) , see Table 4. Table 4. Unit root test for the variables (in first-difference). Variable ADF Test (t-Test) p-Value Phillips-Perron Test p-Value loilC −4.6779 *** 0.0008 −4.7186 *** 0.0007 lGDP −5.0423 *** 0.0003 −5.0201 *** 0.0003 lPcrude −4.8148 *** 0.0006 −4.7380 *** 0.0007 lS −5.4401 *** 0.0001 −5.5340 *** 0.0001 lL −4.2273 *** 0.0026 −4.4171 *** 0.0016 Note: *** denote 1%, 5%, and 10% significant levels. The intercept and trend are included in the estimation. 3.2.3. Lag Length Selection Criterion The outcomes of the three lag-length selection criteria are reported in Table 5: the Akaike (AIC), Schwarz (SIC), and Hannan Quinn tests (HQ). As can be seen, all three selected criteria indicated a one-lag length for the use of the analysis (VECM). Table 5. Lag Length Selection Test. Lag Length Test Akaik Information Criterion Schwarz Information Criterion Hannan-Quinn Information Criterion 0−4.9429 −4.7072 −4.8691 1−13.7286 * −12.3142 * −13.2856 * Note: * denote 1% significant level. 3.2.4. Cointegration Test and Long-Run (LR) Stability The cointegration test is a statistical method that every study must conduct as it examines the possible presence of a LR correlation amongst the non-stationary variables over the given period of the study. This test helps in identifying the LR parameters or equilibrium, and it sure helps in examining whether two of the series are cointegrated or not and therefore cannot depart from LR stationarity. Table 6below reports the results of the Johansen Cointegration test. Table 6. Results of the Cointegration test. Hypothesized No. of Cointegration Equations Trace Test Statistic Critical Value None * 88.6059 *** 69.8189 At most 1 * 58.2548 *** 47.8651 At most 2 * 34.7753 ** 29.7971 At most 3 * 16.0911 ** 15.4947 At most 4 * 5.9697 ** 3.8415 Note: *** and ** denote the rejection of the null hypothesis of no cointegration at 1% and 5% significant levels, respectively. * denote 1% significant level. As can be seen in Table 6, the results indicate the presence of four cointegration equations among the variables at a 5% level of significance. This validates the existence of a LR relationship between oil consumption, economic growth, crude oil prices, savings, and the labor force in the suggested research model. We have previously stated that if the data shows a LR association amongst the selected indicators, then VAR is not the legitimate model to use. 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