A Panel Analysis on the Nexus between Financial Development, Oil Production, and Trade-Openness and Its Impact on Sustainable Economic Growth: Evidence from Selected Arab Economies (2024)

1. Introduction

Globally, countries strive to increase their economy in terms of output; these actions are often driven by the need to improve the living standard of the populace and solve macroeconomic problems, such as inflation, unemployment, and economic [1]. Thus, various economies employ a variety of economic tools and machinery to attain these much-desired levels in their economies. These economic tools and systems employed by these countries include production, trade and breaking into the financial market through financial development.

Thus, the synergy between trade, production, and financial development, alongside economic growth, has become a topical issue among policymakers and scholars. With a keen interest in the Arab economies, oil production and export have continued to be the mainstay of their economies, as these countries are rich in crude oil deposits [2,3,4,5,6,7,8]. As a result of decades of oil extraction and export, the Arab region’s economy has grown substantially, meeting up with other advanced countries. However, it is still being determined as to whether or not this expansion in their economies has been sustainable and inclusive, driven only by oil production, while leaving aside financial development. Previous studies [4] agree that oil production and financial development have a pathway in increasing the growth of nations.

An understanding of financial development concepts which describe a country’s level of advancement in terms of its financial infrastructure [9] is crucial because it helps direct people’s savings into investments that develop the economy, improve the efficiency with which resources are used, and generally boosts the economy [10]. Recent decades have seen substantial changes in the financial sectors of Arab economies, making it necessary to examine the effect of financial development on economic expansion in the region [11].

Modernised financial systems greatly enhance the ability to efficiently allocate resources, mobilise funds, and provide access to credit for firms and individuals [12]. Investment and entrepreneurship are bolstered by stable financial systems, boosting output and creating more employment opportunities [13]. According to [14], banking and financial markets that are both stable and liquid are key to fostering capital accumulation and luring in FDI. Insurance and risk management are only two examples of how innovative financial services help keep the economy steady.

However, oil production is a major factor in the economies of the chosen Arab countries. These nations’ full oil reserves have influenced how their economies are structured and how quickly they have developed. Revenues, jobs, and foreign exchange profits for governments are all boosted by oil exports [15]. Economic growth or decline in these nations is directly related to oil prices and production levels. The specific constraints and opportunities these economies pose can only be comprehended by examining the connection between oil production and economic growth.

From Figure 1 below, it is obvious that there are noticeable fluctuations in the level of oil production in the countries. The fluctuations in oil production, which is the mainstay of these Arab countries, depict the uncertainties in the level of economic growth in the countries. Hence, the inclusion of oil production is analysed within the current study.

Another variable of concern is the trade openness in the selected Arab countries. According to [11], the extent to which a country participates in international trade (trade openness) is a major determinant of economic development. When diversifying their economies and generating foreign cash, Arab economies, especially those with oil deposits, rely significantly on international trade. The opening up of trade is advantageous because it encourages economic integration, boosts competitiveness, and spreads information and technology. It opens up more business opportunities, encourages the development of specialised skill sets, and simplifies the learning of cutting-edge technologies and management skills [16,17]. To evaluate the advantages and disadvantages of globalisation in these Arab economies, it is crucial to analyse the connection between trade openness and economic growth.

Recent years have seen a flurry of research into the connections between improved banking infrastructure, oil output, freer trade, and increased GDP in a few carefully chosen Arab states. Several studies have highlighted improved financial infrastructure’s role in fostering expansion in Arab economies. For instance, [18] research analysed how developments in the financial sector affected GDP expansion in Saudi Arabia. According to their research, a country’s economic growth is influenced favourably by a financially developed financial system with effective intermediation, credit access, and deepening capital markets. A similar inquiry into the connection between financial development and economic growth in the United Arab Emirates was conducted by [19]; increases in financial intermediation, market liquidity and bank safety were proven to affect economic expansion majorly.

In addition, research on the impact of oil production on Arab economies’ growth patterns has been extensive. Kuwait’s oil industry was the focus of [20] research into the country’s economy. Their results indicated a causal link between oil output and economic growth, underlining the crucial role of oil profits in fuelling economic growth. Another study that looked into how oil production affected Iraq’s GDP was conducted by [4]. They found that oil output was positively related to GDP growth over the long term, highlighting the significance of oil income in fostering economic growth.

Recent research into the causes of economic growth in Arab economies has also focused heavily on trade openness’s role. The effect of freer trade on the Qatari economy was studied by [21]. Trade openness, as measured by the ratio of trade to GDP, was found to positively affect economic growth, highlighting the significance of international commerce in fostering economic progress. Similarly, [22] examined how Saudi Arabia’s trade liberalisation influenced GDP growth. Their findings pointed to a positive correlation, highlighting the part trade plays in encouraging economic growth and diversification.

The rationale behind investigating these Arab nations stems from the fact that their economies possess distinctive inherent characteristics such as formal financial markets, reduced inflation, oil production for oil-rich countries, and a closely stable economy. As a result, these facts, among others, tend to distinguish these countries from others.

Going forward, this study aims to contribute meaningfully to knowledge. Unlike previous views, [23] contends that financial development follows economic expansion. This suggests that, when an economy grows, so does the demand for financial services, leading to an increase in the number of financial institutions, financial instruments and services available on the market. Therefore, rather than the other way around, the degree of economic development influences financial development. The causality test once again tests the idea that financial development and economic growth have a reciprocal or two-way causal relationship.

Lastly, there is the belief that there is no causal relationship between financial progress and economic growth. This perspective holds that growth does not lead to financial development, or vice versa. Although [24] first proposed this viewpoint, our research indicates that it is appropriate to carefully re-evaluate it using the causality test. Once again, by emphasising the long-term effects and the causal relationship, our study aims to achieve its goal of examining the role that oil production plays in defining the economic level within the Arab economies. Based on the above synopsis, the current study leverages on the classical growth model as motivation for the present study’s theoretical foundation. This study seeks to explore the nexus between oil production and economic growth while accounting for other key growth drivers like gross capital formulation accumulation, labour, trade openness and financial development for a balanced panel of selected Arab economies towards sustainable economic growth. In particular, this study aims to answer the following questions: (a) What effect does gross capital formulation accumulation have on Arab economic growth? (b) Does oil production augment Arab economic growth? (c) What effect does labour participation have on Arab economic growth?

In summary, the current study seeks to fulfil the objectives of this study, thus, we gathered panel data for selected Arab economies from 1994 to 2020. The study employed the common correlated effects mean group (CCEMG) methodology and augmented mean group (AMG) estimation methods, a robust econometric methodology, to tackle common panel data challenges like heterogeneity, endogeneity, and cross-sectional dependency.

The remainder of this study is organised as follows: Section 2 outlines both the theoretical foundation and the empirical literature, while Section 3 presents the data and methodology. Section 4 renders the results and discussion, while Section 5 provides the conclusion and policy suggestions.

2. Review of the Related Literature

This section provides a theoretical underpinning for the present study. The present study draws motivation from the classical Solow growth model proposed by [25], where gross capital formation and labour are perceived as key drivers of economic growth. Subsequently, technological innovation, or Solow residual, was incorporated into the Solow growth model as drivers for economic growth. However, since Solow’s pioneering study, several theoretical and empirical studies have been documented in the extant growth literature to corroborate his position.

2.1. Financial Development and Economic Growth Nexus

Analysing the relationship between financial development and economic growth in developing countries, Ekanayake and Thaver [26] employed the panel least squares and panel fully modified least squares approach; a panel Granger causality test was also included in the approach of analysis to analyse the data collected from 138 developing countries. The study’s findings corroborate previous research linking FD to economic expansion in poor nations. Additionally, samples from Europe, Central Asia, and South Asia, as well as all countries that show evidence that there is bi-directional causality going from FD to GROWTH and from GROWTH to FD, whereas this is not the case in East Asia, the Pacific, or Latin America, including the Caribbean. This region includes the Middle East, North Africa, and Sub-Saharan Africa.

Exploring the relationship between financial development and economic growth in Saudi Arabia, Ibrahim [27] employed a fully modified ordinary least squares approach. The findings show that domestic bank loans to the private sector affect GDP growth significantly and positively in the long run but have no effect, or even a negative one, in the short run. However, the stock market index has a favourable but negligible short-term influence and an unexpected and negligible long-term effect. An increase in industrial output is likely to have a favourable and sizable impact on economic expansion in the medium- to long-term.

In a similar study conducted in Saudi Arabia, Bogart [28] investigated how financial institution quality and financial development affect the economy’s growth. Employing a dynamic panel framework, the study analysed the data using the generalised method of moments. This study proved that Saudi Arabia’s economy benefits from increased financial sophistication. Further, empirical findings confirm the existence of a positive and statistically significant link between the quality of financial institutions and economic expansion. The study’s findings point to the importance of bolstering the Saudi Arabian banking sector’s financial development initiatives of the past three decades and strengthening the financial institutions’ role in promoting saving and investment, which in turn promotes long-term economic growth.

Yahyaoui et al. [29] studied financial development and its relationship with economic growth from the institutional approach and examined the connection between financial and economic growth in six emerging Arab states, focusing on institution quality impact. The generalised method of moments (GMM) was used to generate several estimates for 1990–2016. The selected countries’ results demonstrate that the quality of their institutions is to blame for the spread of ideas between the financial and real worlds. To hasten economic expansion, the financial sector requires higher-quality institutions brought about by improved socioeconomic conditions, corruption prevention measures, and a reliable legal structure.

Another similar study was conducted by [30] on the relationship between financial development and the economic growth of the Gulf Cooperation Council (GCC) countries. The study employed a panel fully modified and dynamic estimation approach, where the data were subjected to the Pedroni test for co-integration and the Granger causality test. The study concluded that the growth of economies in the GCC has a strongly favourable effect on the growth of the financial sectors in those nations. The research also shows that oil revenues can only achieve so much for economic growth due to low-quality institutions. The findings suggest that, in oil-producing nations, bettering the quality of institutions is important for advancing the cause of economic growth. Despite contributing to empiricism on the connection between economic growth and financial development, the study stands out due to its new incorporation of institutions and oil rents.

2.2. Oil Production and Economic Growth Nexus

The relationship between oil production and economic growth is another aspect and serves as one of the objectives the current study seeks to analyse. Some of the studies conducted in this regard are reviewed. One such is the study of [31], which investigated the effect of oil production on the stock market development in the Southeast Asian economy. The study adopted a panel approach to the research. It employed both the linear auto-regressive distributed lag model and the non-linear auto-regressive distributed lag model to analyse the data obtained. The results of this study show that higher oil prices have a detrimental effect on both the long- and short-term growth of stock markets. The long-term asymmetric relationship between oil price and stock market growth was established using the non-linear analysis. Regarding the stock market growth and development metrics, the study found a negative and statistically significant correlation with asymmetric shocks in oil prices.

In a similar study, Raifu [32] conducted a study on how institutional quality plays a role in the nexus of oil production and unemployment, exploring the African and Asian oil-producing countries. The study employed the panel ordinary least squares technique and panel auto-regressive distributed lag model. The POLS outcome shows that, while unemployment decreases in Asian oil-exporting countries, it fails to decrease in countries that export oil in Africa.

However, the interaction between the price of oil and institutional quality factors like democratic accountability alongside the rule of law reduces the rate of unemployment in oil-exporting countries in Africa. In contrast, the relationship between oil prices and factors like government stability and corruption control reduces unemployment even more in oil-exporting nations in Asia. The Panel ARDL results, however, demonstrate that an increase in oil prices only reduces unemployment in the short term for oil-exporting African countries. In contrast, it only reduces unemployment in the long term for oil-exporting Asian countries. Even though it marginally alters when PARDL is used, the interaction between institutional quality and oil price mostly stays the same compared to POLS. Based on our findings, we conclude that, to ensure that an increase in oil price has the desired impact on unemployment, each oil-exporting country needs to strengthen some components of its institutional quality apparatuses. Specifically, in African oil exporting nations, the battle against corruption and political instability must receive the utmost priority, whereas, in Asian oil exporting nations, the rule of law and democratic accountability must take precedence.

Investigating the relationship between oil revenue and economic growth in Ghana, Adabora and Buabeng [33] employed the auto-regressive distributed lag model to analyse the data for the study. According to the ARDL estimations, a rise in Ghana’s oil revenue resulted in a considerable increase in the country’s economic growth, indicating that oil revenue fosters economic growth. Another supplemental conclusion of the study indicates that, while interest rates have a negative impact on Ghana’s economic growth, non-oil revenue, capital, and foreign direct investment (FDI) have a positive impact.

In a separate study, Adekoya [34] examined the relationship between oil consumption and economic growth. The study employed a panel framework of the auto-regressive distributed lag model and dynamic heterogeneous panel estimators. The long-term impact of oil consumption on the economies of resource-rich countries was negative, according to the baseline model, but the short-term impact was positive. In contrast, resource-poor nations do not see any noticeable impact from oil usage in the short or long term. The AMG estimator consistently provides non-significant results, lending credence to the findings of the reference model. Despite a few examples when positive coefficients are seen at specific threshold levels, the threshold regression model’s generally negative coefficients nevertheless suggest that oil consumption has a minimal impact on growth for both sets of countries.

Analysing the impact of shocks in the price of crude oil and economic growth in South Asian countries, Ahmad et al., [35] employed the vector auto-regressive approach where the impulse response function was also adopted to analyse the variations of the shocks in the oil price. The result of the impulse response function explained a lot of the variation between macroeconomic indicators in reaction to shocks in the price of crude oil. As a result, even small shifts in oil prices can profoundly affect the region’s economy and social conditions, which are reflected in the region’s macroeconomic statistics. Distinctions in macroeconomic fundamentals, independent policy, sector structure and national distinctions all affect how each country in the area responds to fluctuations in crude oil prices, as shown by the variance decomposition results.

Finally, on the relationship between oil production and economic growth, the study by [36] on the relationship between energy consumption and economic growth in Nigeria employed the auto-regressive distributed lag model to analyse the data. The results demonstrate that the variables related to petroleum and power are positive and significant to growth, while the ones related to coal are positive but not significant.

According to the results, the correlation between energy use and economic expansion is positive. Coal must be mined and burnt to meet the rising demand for electricity and fuel the economy’s expansion.

2.3. Trade Openness and Economic Growth Nexus

The relationship between trade openness and the economy’s growth is reviewed in this section. Sumbal [17] analysed the role of human capital accumulation in the relationship between trade openness and economic growth. The study developed a dynamic panel data model and estimated the data using the generalised method of moment estimator. The data outline a fascinating indirect relationship between trade liberalisation and GDP expansion. When countries display a low level of HCA, trade may negatively affect GDP growth if HCA is considered an intervening variable.

Investigating the relationship between trade openness and the growth of the economy in Madagascar, Rasoanomenjanahary et al., [37] employed the vector error correction mechanism to analyse the country’s data between 1993 and 2020. The findings additionally demonstrate that a country’s degree of trade openness is inversely related to the rate at which its economy grows. In addition, the analysis shows that inflation, FDI net inflow and labour force participation all work together for stronger economic growth. The research concluded with the suggestion that trade rules be drafted and implemented to control the country’s many ports of entry and exit.

Analysing the relationship between trade liberalisation and financial development on economic growth in Asia, Amar, and Ichiro [38] employed a meta-analytical approach. According to a meta-analysis of 748 estimates drawn from 75 prior studies, the growth-enhancing effect of finance is economically significant. The synthesis results also show that the relationship between finance and economic growth in South Asia is more robust than in East Asia. We use linear and non-linear methods to investigate the likelihood of bias in the publication process, and our findings suggest that such prejudice may exist. Using cutting-edge meta-analysis techniques, we determine that the aggregated estimates provide substantial empirical evidence of the effect of finance on economic growth across Asia and its subregions.

Lastly, in the review, Abdul and Nazia [39] employed a panel method to research the commonly correlated effects mean group estimator and the generalised method of moment estimator to analyse the relationship between trade openness and economic growth. The econometric evidence suggests that greater trade freedom is associated with a higher GDP. Several panel data specifications, such as a common correlated effect mean group (CCEMG) estimator and a generalised method of moments (GMM) estimator, which accommodates endogeneity between trade and growth, lead to these conclusions. Previous empirical research has found that limiting international trade helps the economy expand, but this new study challenges that notion. Based on our data, the study concludes that trade liberalisation might promote economic expansion.

3. Methodology

3.1. Data and Model Specification

In the current study, the impact of oil production, financial development index and trade openness on economic growth was analysed for selected Arab economies. The selection of variables in the regression model reflects the key determinants of economic growth identified by the Solow–Swan model. These variables capture the influence of savings, investment, technological progress, labour dynamics, and resource endowments on long-run economic growth, providing insights into the factors driving variations in gross domestic product across Arab countries. The study focuses on eight Arab countries—Algeria, Egypt, Kuwait, Oman, Qatar, Saudi Arabia, Sudan, and Tunisia. These countries were chosen due to their varying degrees of reliance on oil production and financial development, as well as their economic importance within the Arab region. We used annual data spanning the period from 1994 to 2020. The selected time period enables us to investigate long-term trends and potential dynamic nexus between the variables over time. However, it is important to note that the choice of countries included, and the time period, are primarily based on the data availability of consistent data for the bloc countries in Arab economies. The data used for this study have been obtained from World Bank Development Indicators (WDI) and British Petroleum (BP). Gross domestic product (constant 2015 USD $) is used as a proxy for economic growth and retrieved from WDI. Oil production provides crucial information about how much each country participates in the oil sector, which is a key factor in determining economic growth, especially in oil-exporting countries. Thus, we used oil production (in million tonnes) as an independent variable. The oil production is obtained from BP. Also, we used the financial development index which is also important factor in determining a country’s capacity to promote investment and allocate capital efficiently, both of which can impact economic growth. This index was obtained from WDI. Labour force size and gross capital formation represent the contributions of labour and physical capital accumulation, respectively, to economic expansion. For labour, we used total labour force while gross capital formation as a percentage of GDP was used as a proxy for capital and both data are derived from WDI. A country’s involvement in international trade affects its potential economic growth through trade activities. To this end, we used trade as a percentage of GDP from WDI. Table 1 shows the data sources and their abbreviations. The economic and econometric models are given in Equations (1) and (2).

$G D P = f (F D, O I L, T O, L A B O R, G C F)$

(1)

${L N G D P}_{i t} = β_{0} + β_{1} {F D}_{i t} + β_{2} {L N O I L}_{i t} + β_{3} {L N L A B O R}_{i t} + β_{4} {G C F}_{i t} + β_{5} {T O}_{i t} + ε_{i t}$

3.2. Methodological Sequence

The study used a multi-step panel data econometric technique to examine the relationship between the independent variables, namely oil production, financial development index, labour, gross capital formation, and trade openness on economic growth for the selected eight Arab economies. In Figure 2, the flow of the empirical analysis is illustrated.

3.2.1. Cross-Sectional Dependency and Slope hom*ogeneity Tests

It is necessary to perform some preliminary analysis, such as cross-sectional dependency (CD) and hom*ogeneity tests, in order to select the most suitable econometric approaches before beginning to investigate the relationship between the variables of interest. The first step is to check cross-sectional dependencies within the chosen panel bloc. Cross-sectional dependency refers to the interdependence between the observations of each unit in the panel bloc. The standard statistical techniques may cause biased estimates of the model parameters and invalid references under the presence of cross-sectional dependency. To address this problem, it is important to account for cross-sectional dependency in panel time series to ensure that statistical inferences are robust and reliable. Thus, we employ the [40] CD test to check cross-sectional dependency within the panel bloc.

${C D}_{l m} = \sqrt{\frac{2 T}{N (N - 1)}} \sum_{i = 1}^{N - 1} \sum_{j = i + 1}^{N} {\hat{ρ}}_{i j}^{2}$

(3)

where T is the time period, N is the number of cross-sectional units, and $ρ$ indicates the pair-wise correlation of the residuals.

The second step is to check the slope hom*ogeneity, which is an important assumption in the panel time series. The slope hom*ogeneity assumption indicates that the relationship between the independent variable and dependent variable is constant for all cross-sectional units and over time. This implies that the effect of a change in the independent variables on the dependent variable is the same over time. The violation of the slope hom*ogeneity assumption may also lead to biased and inefficient estimates of the estimated regression. Thus, there is a need to test for slope hom*ogeneity to conduct an appropriate panel time series analysis. This study uses Swamy’s slope hom*ogeneity test, as proposed by [41]. The null hypothesis of this test is $H_{0} : β_{i} = β$ for all i, slope coefficients are constant or hom*ogenous across the countries.

3.2.2. Panel Co-Integration and Long-Run Estimates

After confirming the integration order of all the selected variables, there is a need for a co-integration analysis to examine the long-run relationship among non-stationary variables over a time period. If non-stationary variables are found to be co-integrated, it implies that they share a common long-term trend. To investigate the long-run relationship between variables, we used [42] co-integration approach because it leads to more reliable and robust results in panel studies by considering the presence of cross-sectional dependency and slope heterogeneity. Thus, this method is appropriate for our study. The [42] co-integration test provides two group mean (Gt, Ga) and two panel (Pt, Pa) test statistics. These test statistics can be defined in the terms of an error correction model as follows:

$∆ Y_{i t} = δ_{i}^{'} d_{t} + α_{i} Y_{i t - 1} + λ_{i}^{'} X_{i t - 1} + \sum_{j = 1}^{p_{i}} φ_{i j} ∆ Y_{i t - j} + \sum_{j = 0}^{p_{i}} γ_{i j} ∆ X_{i t - j} + ε_{i t}$

(4)

where $d_{t}$ is a deterministic component, $λ_{i}$ is long-term coefficient, and $φ_{i j}$ and $γ_{i j}$ are short-term coefficients.

If variables are co-integrated, a long-run coefficient estimate is required to determine the size and direction of the effect of independent variables on the dependent variable over the long term. To this end, we employ second-generation long-run estimation methods which are the common correlated effects mean group (CCEMG) estimator and augmented mean group (AMG) estimator in the current study. The CCE estimator approach was developed by [43] and the CCEMG estimator is used for estimating parameters in the existence of common correlated effects. Thus, it can effectively address unobserved time-invariant heterogeneity that may exist across cross-sectional units. Furthermore, the CCEMG estimator performs well with small samples, thus the estimator result is consistent. The estimation of the CCE estimator is as below:

$Y_{i t} = α_{i} + β_{i} X_{i t} + δ_{i} {\bar{Y}}_{i t} + θ_{i} {\bar{X}}_{i t} + φ_{i} f_{t} + ε_{i t}$

(5)

where $α_{i}$ groups fixed effects, $β_{i}$ measures the slope of country-specific, $f_{t}$ indicates the unobserved common factor, and $ε_{i t}$ is the error term. The mean group estimator of CCE is calculated by taking the average of each coefficient over each individual regression as follows:

$C C E M G = \frac{1}{N} {\sum_{i = 1}^{N} \hat{β}}_{i}$

(6)

Another method which is used in this current study is the AMG estimator developed by [44]. The AMG estimator is also robust to slope heterogeneity and cross-sectional dependency. The fundamental difference between AMG and CCEMG estimators is the estimation of the unobserved common factor ( $f_{t}$ ) in Equation (6). The CCEMG estimator uses linear combinations of the cross-sectional averages of the dependent and independent variables. Subsequently, ordinary least squares (is used to estimate each individual coefficient. On the other hand, the AMG estimator utilises a two-step measurement procedure to calculate the unobserved common dynamic factor. First, it augments the equation by adding time dummies and then estimates using first difference OLS.

${∆ Y}_{i t} = α_{i} + β_{i} {∆ X}_{i t} + φ_{i} f_{t} \sum_{t = 2}^{T} τ_{t} {D U M M Y}_{t} + ε_{i t}$

(7)

where $∆$ indicates the difference operator and $τ_{t}$ is the coefficient of time dummies. Second, either an individual unit coefficient or an explicit variable is included in the group-specific regression model. The mean group estimator for AMG is also calculated by using the same method we used for the CCEMG estimator.

4. Results and Discussion

This section proceeds with the discussion of the empirical results in a stylised manner. This section proceeds with emphasis on the preliminary analysis and subsequently the mainstream regression accordingly.

Preliminary Analysis

This current study advances with preliminary analysis, which includes the measure of central tendencies and measures of dispersions, in order to operationalise the objectives of the study variables as outlined in Table 2 over the sampled period. Subsequently, Table 2 highlights the basic summary statistics that comprise means, maximum, minimum, kurtosis, skewness, and correlation analysis. Trade openness emerges as the variable with the highest averages over the sampled period, while the financial development index ranks with the lowest average over the sampled period. Subsequently, the main moments of the series show normal distribution of the variables, as outlined by the Jarque–Bera statistics. To further explore the one–one relationship over study variables, the pairwise correlation analysis is investigated. The correlation analysis gives a preliminary insight into the causal relationship between the study variables, as outlined by [45]. We observed a positive and statistically significant relationship between oil production and economic growth. This outcome is expected as the countries explored are oil rich and dependent countries. It is a well-known fact that most Arab economies are oil-driven economies. Furthermore, labour and economic growth also show significant positive statistical relationships over the investigated study span. On the contrary, financial development and trade openness exhibit an inverse relationship between each other. These outcomes require further investigation. Thus, the need for more empirical analysis given that pairwise analysis is not sufficient, although revealing. The subsequent section advances with econometric tests to either refute or establish the correlations test result relationships.

Furthermore, the current study is fitted in a balanced panel environment. To this end, there is a need to explore common-shock effect, i.e., cross-sectional dependency test and slope hom*ogeneity test, as presented in Table 3 for each variable. The results show that all variables have traits of cross-sectional as the study rejects the null hypothesis of no cross-sectional dependency and accepts the alternative of presence of cross-sectional dependency. Similarly, the slope hom*ogeneity test confirms that the fitted model is heterogeneous. To this end, with the presence of cross-sectional dependency and heterogeneity, there is a need for estimators that account for these issues. Thus, the present study leveraged on second-generational panel estimators which are robust and provide consistent coefficient and estimates.

The results of the CIPS panel unit root test results are presented in Table 4 for each variable in the level, I(0), and the first difference, I(1), model. CIPS unit root test results show that all the variables except financial development became stationary after taking their first difference. Furthermore, to evaluate the long-run equilibrium relationship between the study variables, the Westerlund’s panel co-integration test is employed, which circumvents cross-sectional dependency and heterogeneity. Table 5 presents the co-integration test with four test statistics. All test statistics affirm the presence of long-run traits and a relationship between the study variables over the investigated period.

Furthermore, Table 6 presents the baseline regression for the study variables and entire panel. To this end, CCEMG and AMG are used to explore the relationship between the outlined variables. The use of two estimators is for robustness and sensitivity analysis. On the relationship between oil production and economic growth, there is a positive and statistical relationship seen for the Arabian economies with the exception for Egypt. The plausible explanation for the positive relationship between economic growth and oil production observed in Algeria, Kuwait, Oman, Saudi Arabia, Sudan, Tunisia, and Qatar lies in the economy’s commitment to its oil sector as a key driver for its economic growth. These outcomes highlight that these economies’ economic growth is majorly driven by the oil sector, i.e., oil production. Most of the countries, like Saudi Arabia, Oman, and Kuwait, are top exporters of oil and its by-product. The gains from the oil business are used to drive economic activities in these Arab economies. However, it is worth mentioning that most of these economies have diversified their economy from oil dependent or mono-driven by oil to other sectors like manufacturing, service, and industries to engender economic growth. This proposition resonates the vision and mission of the Arab economies as they are on the trajectory for a multifaceted economy not reliant on oil production alone. The positive relationship observed between oil production and economic growth aligns with the studies by [46,47] for the Kingdom of Saudi Arabia and [48] for Sudan, with empirical evidence from VAR econometrics tools. Subsequently, the financial development of these investigated countries displays an inverse relationship with economic growth. Most of the economies, for instance Algeria, Kuwait, Qatar, Saudi Arabia, and the entire panel, affirm the significant inverse nexus with economic growth. This suggests that the economies have somewhat weak financial institutions. The plausible explanation for this inverse relationship is tied to the closed nature of these economies in the past. However, more recently the Arab economies are now open to the rest of the world and more intertwined. Furthermore, the present study affirms the proposition of the Solow–Swan hypothesis which highlights the positive impact of gross capital stock accumulation and labour as presented in overall panel analysis. This outcome is also consistent with other studies [49,50,51], although some of the specific countries show negative impact. Similarly, openness to rest of the world in the form of trade openness engenders economic growth in the study bloc. Thus, our study corroborates the mercantilism school of thought on the need for trade for economic growth. This position is also supported by the empirical studies by [52,53]. Additionally, there is a need to explore the direction of each variable on the other, i.e., causality analysis, which highlights the predictability power of one variable on the other.

Furthermore, in Table 7, we observe a feedback relationship running between economic growth (GDP) and gross capital formation. A similar trend is observed in a bi-direction causality relationship between GDP and labour. These results corroborate the validation of baseline regression in Table 5 and the Solow–Swan growth hypothesis. In the same fashion, a feedback causality is seen between trade openness and economic growth. This reinforces the fact that capital stock accumulation, labour and trade are key determinants of predicting growth in the study area, and vice versa. On the other hand, a uni-direction causality is seen flowing from GDP to financial development index. That is, as the economy grows the financial system in those economies opens and develops. A policy perspective on the growth quest for the investigated bloc is insightful, as there is a need for policy intervention for promotion and pursuit of growth drivers, such as openness to trade, especially export activities to enjoin the gains of trade that engenders economic growth. There is also need for diversification from mono-economic policies to pursue strategies that promote diversification for economies to other sectors like industrial, service, and manufacturing, which will require investments and pragmatic action steps in capital accumulation stock, as well and labour inputs, which are known drivers for economic growth.

5. Conclusions and Policy Direction

Conventional growth theories, such as endogenous and exogenous economic growth theories, have been proposed over the years, in the extant macroeconomic literature, to underscore the determinants of economic growth. The pursuit of economic growth aligns with the vision of the United Nations Sustainable Development Goals (UN-SDGs-8), which highlight the need for decent economic growth for a sustainable economy. It is on this premise that the present study explores the nexus between oil production and economic growth for selected Arab economies. The present study circumvents other key growth drivers such as gross capital formulation accumulation, labour, trade openness, and financial development in a balanced panel environment, while leveraging on the classical growth model as a basis for theoretical underpinning. The study position outlines that all mentioned variables have a long-run equilibrium relationship over the sampled period.

The present study’s empirical results show and resonate with a priori expectations, which have inherent policy for economic growth and implications. This study validates the role of the positive nexus between oil production and economic growth. This result proposes the following suggestions. From a policy lens, there is a need for diversification of these investigated economies from their reliance on oil production to circumvent the risk associated with the volatile oil prices and market. There is a need for policy administrators to prioritise strategies into diversification to other non-oil sectors. This proposition could involve pragmatic action steps in the service, industry, and tourism sectors. There is also a need for strategic long-term planning and a stabilisation agenda on the part of the Arab economies to circumvent the effect of dwindling oil prices. On the nexus between trade openness and economic growth, there is a need for government officials to liberalise their economy to promote trade by reduction or total elimination of trade blocks such as tariffs, quotas, and standards across trading partners and borders. There is also a need for the development of trade infrastructure, which, by extension, advances economic growth in the region and specific countries.

Furthermore, during the study period, we observed a positive relationship between oil production and economic growth for the selected countries and the entire balanced panel. This finding aligns with the a priori expectation that the natural endowment of oil drives the economic growth of Arab economies, supporting the oil-economic growth hypothesis. This finding suggests that increased oil production in the region leads to economic growth. Therefore, we encourage the investigated countries to intensify their oil production efforts; we see this as a key driver of economic growth. Thus, Arab economies are enjoined to incorporate the use of new, modern technologies in oil exploration and production for more sustainable economic growth. Additionally, the mixed results emanating from an individual countries’ analysis of the nexus between opened trade and economic growth are insightful and have inherent implications for economic sustainability. The economic structure of the investigated blocs may be a plausible explanation. From a policy perspective, there is a need for openness to trade, as outlined by the mercantilism school of thought, to foster sustainable economic growth. While, on the relationship between labour per capita and economic growth among the investigated countries, as well as the entire panel, this outcome is in line with the conventional classical growth model, which posits that labour and capital are the primary determinants of growth. The policy taken from this result is that, for these countries to advance economic growth, they are required to formulate strategies and policies that will improve labour force participation by both genders in the labour force and the quality of labour through education and training activities.

Furthermore, from a policy perspective, there is a need for investment in physical capital accumulation, which can be improved by investment in research and development and in education, skill, and improvement in labour flexibility to contribute to economic growth by reducing barriers to the labour market and its regulations. Generally, it is pertinent for policymakers to explore distinctive characteristics and issues in their economy when formulating policy implications. There is a need for regular evaluation, appraisal, and adaptation of strategies to ensure the effectiveness of policies in promoting capital accumulation, labour and trade export, and economic growth in the long run.

In conclusion, to maximise the full gains from oil production, it is imperative that the Arab bloc open its economies to the rest of the globe. Following the findings of our baseline regression, it is necessary to involve the private sector in the trajectory of economic growth. Both labour and capital continue to play essential roles in economic growth; nevertheless, even more work needs to be done in the art of the economies that are being researched in order to achieve the desired Sustainable Development Goal-8. The applicability of the study lies on the results-driven policy direction for stakeholders and government officials on the validation of the Solow and classical growth model proposition.

Although the present study explores growth drivers for Arab economies in a balanced framework, there is a need for other studies to evaluate the theme while accounting for other growth factors not captured in the study, like foreign direct investment, which could be FDI inflow and outflow, and other demographic indicators like population growth, industrialisation, and the like. There is also a gap on the econometric front to explore the theme with generalised method of moments (GMM) that shows more dynamics and characterisations of the study variables to either refute or validate them.

Author Contributions

Conceptualization, E.M.A.D.; Methodology, W.K.; Validation, W.K.; Formal analysis, W.K.; Investigation, W.K.; Writing—original draft, W.K.; Writing—review and editing, E.M.A.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research receives no funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data were sourced from WDI and British Petroleum (BP)as reported in the data section.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Oil production in selected Arab countries.

Figure 2. Empirical Analysis Flow.

Table 1. Data Description.

Name of Variable	Abbreviation	Source
Real Gross Domestic Product	GDP	WDI
Financial Development Index	FD	WDI
Oil production (in million tonnes)	OIL	BP
Trade Openness (% of GDP)	TO	WDI
Labour Force (Total)	LABOR	WDI
Gross Capital Formation (% of GDP)	GCF	WDI

Note: WDI—World Development Indicators, BP—BP Statistical Review of World Energy.

Table 2. Descriptive statistics and correlation analysis.

A: Descriptive Statistics
	GDP	FD	GCF	LABOR	OIL	TO
Observations	243	243	243	243	243	243
Mean	24.160	0.274	24.160	15.280	3.506	70.577
Median	25.040	0.280	23.868	15.468	3.757	76.685
Maximum	27.244	0.571	50.780	17.247	6.375	114.343
Minimum	15.382	0.033	6.873	12.618	−2.318	0.757
Std. Dev.	3.105	0.155	8.919	1.146	1.676	25.989
Skewness	−2.062	0.184	0.665	−0.351	−0.670	−0.729
Kurtosis	6.038	1.678	3.182	2.324	3.628	2.834
Sum	5870.980	66.533	6074.554	3713.102	852.038	17,150.22
Sum Sq. Dev.	2333.417	5.816	19,254.39	317.834	679.883	153,463.2
Jarque–Bera	265.601	19.080	18.226	9.614	22.165	21.826
Probability	0.000	0.000	0.018	0.008	0.000	0.000
B: Correlation Analysis
GDP	1.000
FD	−0.273 *	1.000
GCF	−0.269 *	0.080	1.000
LABOR	0.630 *	−0.417 *	−0.041	1.000
OIL	0.115 ***	0.584 *	0.248 **	−0.029	1.000
TO	−0.193 **	0.506 *	−0.047	−0.433 *	0.358	1.000

Note: The asterisks *, ** and *** represent the significance level at 1%, 5%, and 10% significance levels, respectively.

Table 3. Cross-section dependence and slope hom*ogeneity.

Variable	CD-Test	p-Value	corr	abs(corr)
GDP	25.55	0.000	0.929	0.929
FD	9.04	0.000	0.329	0.390
GCF	4.94	0.000	0.18	0.559
LABOR	26.71	0.000	0.971	0.971
OIL	−0.21	0.832	−0.008	0.519
TRADE	9.25	0.000	0.336	0.416
Slope hom*ogeneity
		Delta	p-value
adj		3.412	0.001

Table 4. Panel CIPS unit root test.

Variables	I(0)		I(1)		Decision
	C	C and T	C	C and T
GDP	−1.833	−2.178	−3.391 *	−3.143 *	I(1)
FD	−2.346 **	−2.592	−4.980 *	−5.005 *	I(0)
GCF	−1.634	−1.962	−4.223 *	−4.247 *	I(1)
LABOR	−1.520	−2.035	−2.946 **	−2.924 **	I(1)
OIL	−1.823	−2.196	−3.844 *	−3.669 *	I(1)
TRADE	−2.197	−2.550	−4.797 *	−4.197 *	I(1)

Note: C = constant, and C and T = constant and trend. * and ** denote significance level at 1% and 5%.

Table 5. Westerlund panel co-integration test results.

Statistic	Value	Z-Value	p-Value
Gt	−2.572 *	1.361	0.913
Ga	−3.404 *	−3.726	0.000
Pt	−4.143 *	3.752	1.000
Pa	−2.469 *	4.142	1.000

Note: * indicates the rejection of the null hypothesis of co-integration at 1% significance level.

Table 6. Long-run estimation results.

		OIL	FD	TO	GCF	LABOR
Algeria	CCEMG	0.131 **	1.594 ***	0.001	0.004 *	0.428 **
	AMG	0.119 **	−1.430 **	0.000	0.002 **	0.267 **
Egypt	CCEMG	−0.115	0.138	0.001 **	0.009 ***	0.269 *
	AMG	−0.092	0.172	−0.001	0.009 *	0.447 **
Kuwait	CCEMG	1.345 *	0.283	0.006 ***	−0.002	−0.483 ***
	AMG	0.617 *	−0.109	0.001	0.000	−0.622 *
Oman	CCEMG	0.339 *	0.198	0.002 **	0.001	0.198 **
	AMG	0.505 *	0.063	0.001	0.000	0.067
Qatar	CCEMG	−0.016 ***	−0.254 ***	0.000	0.000	−0.103 ***
	AMG	−0.172 ***	−0.059	0.000	−0.002 ***	−0.160 **
Saudi Arabia	CCEMG	0.696 *	−0.168	0.000	0.006 *	0.967 *
	AMG	0.650 *	−0.189	0.000	0.005 **	1.018 *
Sudan	CCEMG	0.077 *	2.178 **	0.003	0.003	0.998
	AMG	0.076 *	0.216	0.001	−0.007	3.119 **
Tunisia	CCEMG	0.140 **	−1.559 *	0.000	0.010	0.493
	AMG	0.101 ***	−1.579 *	−0.000	0.013 **	0.182
Panel	CCEMG	0.324 ***	−0.097	−0.000	0.004 **	0.345 ***
	AMG	0.225 **	−0.365	0.000	0.003 ***	0.494

Note: *, **, and *** denote the rejection of the null hypothesis at 1%, 5%, and 10% significance levels, respectively.

Table 7. Dumitrescu–Hurlin Granger causality test results.

	W-Bar	Z-Bar	p-Value	Granger Causality Flow
GDP≠>FD	3.525 *	5.050	0.000	GDP→FD
FD≠>GDP	1.383	0.766	0.444	GDP→FD
GDP≠>OIL	2.769 *	3.539	0.000	GDP↔OIL
OIL≠>GDP	3.016	4.031	0.000	GDP↔OIL
GDP≠>TO	2.023 **	2.046	0.041	GDP↔TO
TO≠>GDP	2.750 *	3.499	0.000	GDP↔TO
GDP≠>GCF	2.964 *	3.928	0.000	GDP↔GCF
GCF≠>GDP	1.993 **	1.986	0.047	GDP↔GCF
GDP≠>LABOUR	3.102 *	4.201	0.000	GDP↔LABOR
LABOUR≠>GDP	1.566	1.132	0.258	GDP↔LABOR

Notes: * and ** show the rejection of the null hypothesis at 1% and 5% significance levels. ≠> represents “does not Granger causality”.

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