Modeling finance-growth nexus in MENA region economies: A panel data analysis

Kamal, Abdelmonem Lotfy Mohamed; AboElsoud, Mostafa E.; Abdella, Khaled Article Modeling finance-growth nexus in MENA region economies: A panel data analysis Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Kamal, Abdelmonem Lotfy Mohamed; AboElsoud, Mostafa E.; Abdella, Khaled (2023) : Modeling finance-growth nexus in MENA region economies: A panel data analysis, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 11, Iss. 12, pp. 1-18, https://doi.org/10.3390/economies11120290 This Version is available at: https://hdl.handle.net/10419/328914 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 11: 290. https://doi.org/10.3390/ economies11120290 Academic Editor: Joydeep Bhattacharya Received: 12 October 2023 Revised: 10 November 2023 Accepted: 16 November 2023 Published: 30 November 2023 Copyright: © 2023 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 Modeling Finance–Growth Nexus in MENA Region Economies: A Panel Data Analysis Abdelmonem Lotfy Mohamed Kamal 1, Mostafa E. AboElsoud 2,3,* and Khaled Abdella 2,4 1Department of Finance and Investment, Faculty of Business Administration, Economics, and Information System, Misr University for Science and Technology (MUST), 6th of October City, Giza 11556, Egypt; [email protected] 2Department of Economics, Faculty of Business Administration, Economics & Political Science, The British University in Egypt, Cairo 11837, Egypt; [email protected] 3Department of Economics, Faculty of Commerce, Suez Canal University, Ismailia 41522, Egypt 4Department of Economics, Faculty of Management, Sadat Academy for Management Sciences, Cairo 11837, Egypt *Correspondence: [email protected] Abstract: The primary objective of this paper is to examine the relationship between finance and economic growth in a cohort of 16 economies within the MENA region spanning a four-decade period from 1980 to 2021. This study employs panel unit root and panel co-integration analyses to investigate this long-term nexus. The fully modified and dynamic Ordinary Least Squares (OLS) approaches are utilized to estimate the long-run coefficients. The findings underscore the existence of cross-sectional interdependence among these nations. Furthermore, Pedroni’s panel co-integration research robustly supports the idea of a long-term co-integrating relationship between financial development and economic growth. Our long-run panel estimations reveal a positive and statistically significant impact of financial development on GDP per capita income growth. In addition to this broad analysis, this paper conducts a detailed time-series examination focused on a specific country to validate the robustness of the results. These findings further substantiate the favorable influence of financial development on income growth in the majority of MENA nations. Notably, private sector participation in these economies is found to be alarmingly low. As a result, a significant policy implication of this study underscores the urgent need for policymakers to prioritize measures conducive to private sector expansion. Moreover, enhancing financial inclusion, addressing the crowd-out effect, and tackling non-performing loans are critical areas requiring attention within the MENA region. Furthermore, our research highlights the potential benefits of developing stock markets as part of an optimal strategy to enhance both economic and income growth rates. In conclusion, this study contributes valuable insights into the finance–growth nexus in the MENA region, emphasizing the importance of financial development as a driver of economic prosperity and the need for targeted policy initiatives to support private sector growth and financial stability. Keywords: financial development; income growth; finance–growth nexus; cross-sectional dependence; panel co-integration; MENA region economies 1. Introduction A nation’s economic growth, when accelerated, leads to the enhancement of the overall well-being of its population by amplifying productive capacity and advancing its fundamental infrastructure framework. Numerous scholars and policymakers have dedicated significant efforts to analyzing a diverse range of tools that foster economic growth. In recent years, there has been a growing emphasis on recognizing the pivotal role of the financial industry in contributing to overall economic growth. The literature extensively acknowledges the significance of financial sector growth in facilitating economic progress, encompassing the development of both the stock market and Economies 2023,11, 290. https://doi.org/10.3390/economies11120290 https://www.mdpi.com/journal/economies Economies 2023,11, 290 2 of 18 the banking industry. A robust financial system plays a crucial role in capital development and efficient resource allocation, promoting economic growth. To comprehensively understand and thoroughly analyze the impact of the financial sector on economic growth, it is imperative to grasp the essential functions and significant contributions of this sector to the economy. Financial intermediaries are instrumental in facilitating technical innovation, economic growth, and development by overseeing managerial activities, mobilizing savings, managing risks, and facilitating transactions (Schumpeter and Opie 1934). According to Rajan and Zingales (1998), the financial market reduces borrowing costs, enabling organizations to expand their operations. The investigation into the extent and direction of the relationship between financial development and economic growth holds utmost importance due to its potential to inform policy decisions that can significantly impact growth and improve living standards. Two perspectives emerge: the ‘demand pull’ theory posits that financial market expansion results from higher economic growth due to increased demand for financial services (Adu et al. 2013;Alhassan et al. 2022), while the supply-leading stance argues that economic growth follows financial development (Jalil and Feridun 2011;King and Levine 1993; Rajan and Zingales 1998;Sehrawat and Giri 2018). The premise that ‘financial development matters for economic growth’ was confirmed via comprehensive research conducted by the World Bank (1989) on developing nations implementing financial development programs. A robust financial market efficiently allocates financial resources to productive endeavors, fostering economic expansion, but opposing viewpoints also exist. This paper aims to investigate the relationship between finance and economic growth in the Middle East and North Africa (MENA) region. It employs advanced quantitative approaches and the latest available data to address these challenges. This study contributes in three significant ways. Firstly, we utilize a log–log linear regression model to examine the relationship between finance and economic growth while considering the influence of other factors for reliable results. Second, despite the growing significance of the MENA region in the global economic landscape, research on the relationship between finance and economic growth in this region remains limited. To address this gap, we employ a comprehensive dataset spanning the years from 1980 to 2021, comprising panel data from 16 nations. Finally, we utilize the broad-based financial development index developed by Svirydzenka (2016) from the International Monetary Fund (IMF) to investigate the relationship between finance and economic growth in the MENA region, enhancing the robustness of our empirical results. The structure of this paper is as follows: Following this introduction, Section 2discusses and synthesizes a review of the relevant literature. Section 3presents the models adopted in this paper and the data. The estimation techniques are discussed in Section 4. Empirical findings are presented in Section 5, followed by the conclusions in Section 6. 2. Literature Review A substantial body of literature has delved into the significance of the relationship between financial markets and economic growth. It was Schumpeter and Opie (1934) who first recognized finance’s central role in economic growth, igniting a new wave of scholarly discourse. Their argument posited that advancing economies stemmed from improved financial infrastructure, enabling greater capital accumulation and technological innovations. Since the groundbreaking work of King and Levine (1993), several research studies have explored the link between financial development and growth. However, discrepancies in the supporting data have persisted. Variations in sample sizes, timeframes, and quantitative methodologies may account for some of these discrepancies. Nevertheless, a cadre of scholars have cast doubt on the purported growth-enhancing effects of improved financial infrastructure. Robinson (1979) contended that economic growth primarily drives financial development, reversing the cause-and-effect relationship. The financial sector’s pivotal role in economic growth, as articulated in Lucas’s (1988) stylized statement, stands in contrast to the findings of Modigliani and Miller (1958), who, Economies 2023,11, 290 3 of 18 assuming information symmetry and the absence of transaction costs, argued that the expansion of real sectors is unrelated to the growth of financial sectors. Some economists, such as Morck and Nakamura (1999), even suggest that banks may hinder rather than promote economic development. Jahfer and Inoue (2014) found that both the development of the financial sector and economic growth are causal factors. Durusu-Ciftci et al. (2017), drawing data from 40 nations over the long term (1989 to 2011), attributed the observed rise in their study to a flourishing financial sector, advocating government support for the financial system. Arestis et al. (2015) discovered a robust positive correlation between financial growth and economic expansion. Pradhan et al. (2018) found that growth and financial development are mutually causal across 35 countries from 1961 to 2015. Other studies have highlighted the indirect impact of financial development on growth. Yang (2019) demonstrated that financial development contributes to GDP growth in both highand middle-income countries. Bist (2018) explored the long-term relationship between financial development and economic growth using panel unit root and panel cointegration analysis in 16 low-income countries over two decades (1995 to 2014). Cross-sectional interdependence among the nations was evident, and the empirical findings from the long-term panel analysis revealed a robust and statistically significant relationship between financial development and economic growth. Tran et al. (2020), examining over 40,000 Vietnamese enterprises, assessed the impact of local financial development on firm growth, identifying corruption as a major impediment. Mengesha and Berde (2023) examined the effect of improvements in a country’s financial infrastructure on GDP growth from 1980 to 2021, uncovering a reverse causality linking economic growth to financial sector development. Economic growth, as emphasized by Barro (1991), is closely linked to human capital improvement, reduced government spending, and macroeconomic stability. King and Levine (1993) expanded upon Barro’s framework to include financial indicators. Levine (1997) introduced the standard indicator of financial depth, defined as the ratio of total liquid liabilities of the financial system to GDP. Financial development positively affecting economic growth led to financial depth becoming the standard measure for studying the interaction between finance and economic growth. Ang (2008) studied how progress in the banking sector influenced GDP expansion in Malaysia from 1960 to 2003, showing a long-term positive effect of financial development on economic growth. Nguyen et al. (2019) employed the generalized method of moments (GMM) technique, highlighting the positive impact of stock and bond markets on economic growth in middle-income economies. Pradhan et al. (2017), analyzing GDP growth and four financial development measures from 1991 to 2011, identified unidirectional and bidirectional causation between the variables, advocating increased access to investment capital and stock market development. Nevertheless, there is growing evidence challenging the conventional belief that deeper financial markets invariably lead to faster economic growth. Klein and Olivei (2008) concluded that capital flow liberalization’s beneficial effects are primarily seen in industrialized economies. However, Caporale et al. (2015) and Stolbov (2017) discovered that a causal relationship between financial depth and economic growth is not universal. Polemis et al. (2020) found no robust or linear influence of conventional measures of financial depth, such as broad money and domestic credit to GDP ratios, on economic development. Isiaka et al. (2021) observed a detrimental impact of financial depth on economic growth, irrespective of the measurement parameter. Alfaro et al. (2004) emphasized the vital role of finance in facilitating foreign direct investment (FDI) contributing to economic growth. Kutan et al. (2017) focused on the roles of FDI and institutional quality in MENA nations, revealing that financial development benefits these countries, subsequently boosting economic growth. Using the VECM method, Biplob and Halder (2018) found a one-way relationship between financial loans and growth when examining the connection between capital flow Economies 2023,11, 290 4 of 18 liberalization, financial depth, and economic growth in Bangladesh. They emphasized the importance of a robust financial sector for overall economic growth, particularly the role of private sector credit and domestic investment. Mohanty and Bhanumurthy (2019) and Aziz et al. (2023) identified a strong correlation between financial development and economic growth, along with a bidirectional causal link between the two. Furthermore, Mohanty and Bhanumurthy identified finance as the leading predictor of the Indian economy, emphasizing private savings and investment as key factors influencing development. Anwar and Nguyen (2011) employed a panel GMM model, analyzing data from 61 Vietnamese provinces between 1997 and 2006, revealing a causal link between improved access to capital and economic development, where both a large money supply and gross domestic savings played pivotal roles. Shahbaz et al. (2013) conducted a multivariate framework analysis, exploring the dynamic relationships between economic expansion, energy consumption, financial advancement, and international trade. Their findings indicated long-term interconnections among these variables, using ARDL bounds testing, and highlighted a two-way connection between financial development and economic growth. Additionally, Shahbaz et al. (2015) found that the financial sector positively impacted economic expansion, with trade openness also fostering economic development. Bist and Bista (2018), using the ARDL model over a 30-year period (1984 to 2014), identified a strong positive unidirectional relationship between economic growth and financial stability in Nepal, while observing a negative correlation between growth and both trade openness and gross domestic credit. Furthermore, Rahman et al. (2020), studying Pakistan using the Markov switching model and data from 1980 to 2018, affirmed the role of finance in fostering development across highand low-growth nations, with highincome regions experiencing more rapid expansion. Government expenditure and trade liberalization were identified as contributors to economic growth. Zhang and Zhou (2021), explore various theoretical schools of thought and empirical discoveries on this nexus, with the goal of developing a cohesive, microfounded model in a small open-market scenario to accommodate multiple theoretical possibilities and actual data. The model is then adjusted to reflect some well-documented stylized facts. Numerical models reveal that in the long term, the welfare-maximizing level of financial development is lower than its growth-maximizing level. In the near run, the price channel (via the global interest rate) outweighs the quantity channel (via financial productivity), highlighting the critical importance of international collaboration in addressing systemic risk. Despite the ongoing debate among academics regarding the causative link between financial development and economic growth, this study seeks to contribute to the literature by testing the hypothesis of a causal relationship between financial development and economic growth across a broader sample of MENA countries. The primary objectives of this research are (a) to determine the association between financial development and economic growth and (b) to quantify the existence and direction of causality between financial development indicators and economic growth in the MENA region. 3. Methodology In accordance with recent advancements in second-generation panel unit-root testing, exemplified by Bai and Ng (2004), Bist (2018), Dumitrescu and Hurlin (2012), Moon and Perron (2004), and Pesaran (2007), there is a pressing need for innovative panel non-causality tests that explicitly account for various forms of dependencies among panel members. To address this need, this paper aligns with the methodological approach proposed by Bist (2018), Mengesha and Berde (2023), and Pradhan et al. (2017) in developing a log–log linear regression model. This model is designed to investigate the finance–growth nexus while considering the presence of other covariates. Recent literature on the finance–growth nexus underscores the significance of examining this relationship through the lens of endogenous growth theory, as advocated by Bist and Bista (2018), Guru and Yadav (2019), Haque et al. (2022), Mengesha and Berde Economies 2023,11, 290 5 of 18 (2023), and Pradhan et al. (2017). In our model, the endogenous variable is economic growth, calculated as the natural logarithm of a nation’s GDP per capita at time t. Per capita income, determined by dividing a country’s gross domestic product by its population ( Beylik et al. 2022 ), serves as a crucial economic metric for assessing a country’s level of development. While acknowledging that economic development depends on a multitude of factors (Aye and Edoja 2017) , our analysis focuses solely on factors for which empirical data are readily available. The primary variable of interest in our investigation is the degree of financial development (FD), represented using a proxy variable due to its indirect measurability. To enhance accuracy, we adopt a comprehensive measure of financial development developed by Svirydzenka (2016), moving away from earlier studies that relied solely on domestic lending to the private sector as a share of GDP as a proxy for financial development. This encompassing index, recognized as the IMF financial development index, assesses financial depth, access, and efficiency. Recent empirical research strongly supports its superiority as a measure of financial development (Chen et al. 2020;Mengesha and Berde 2023;Raifu et al. 2023). Trade openness, quantified as the ratio of total trade value to gross domestic product, is posited to contribute to an increase in the technology index and subsequently stimulate economic growth (Jalil and Rauf 2021). This linkage can be attributed to trade’s facilitation of technology diffusion, information and skills transfer, leading to more efficient resource utilization and increased factor productivity, all of which support a nation’s economic growth (Islam et al. 2022;Mtar and Belazreg 2023). Furthermore, the foundations of economic development rest on capital and labor. Investment has a positive impact on economic growth, as indicated by both the Cobb Douglas production function and other models (Bist 2018;Narayan and Narayan 2013). Additionally, a nation’s overall development hinges on its labor force (Bist 2018). Conversely, inflation exerts an influence on both economic growth and a nation’s financial operations by altering interest rates, which directly affect the activities of financial institutions, including deposit mobilization and lending (Beck et al. 2000;Bist 2018; Christopoulos and Tsionas 2004;Levine et al. 2000). These variables feature prominently in the literature for regulating the connection between finance and growth. Therefore, the primary aim of this study is to provide empirical evidence regarding the relationship between financial sector growth and economic expansion in MENA region countries, a task undertaken via our log–log regression model. After a comprehensive review of the literature, we have selected investment, trade openness, inflation, and the labor force as control variables. As a result, the model takes the following form: LGDPPCit =β0i+β1iLFDIit +β2iLINVit +β3iLOPEit +β4iLINFit +β5iLLFit +µit We use the natural log of these variables in our estimation. (LGDPPC): Economic growth, defined as the natural log of gross domestic product per capita, measured in constant US dollars using the purchasing power parity approach and 2017 international dollars. This variable serves as the dependent variable. (LFDI): A proxy for the development of the financial sector, represented as the natural log of the IMF Financial Development Index. (LINV): The natural log of the ratio of total investment to GDP. (LOPE): Trade openness, as the natural log of the import plus export to GDP ratio. (LINF): The natural log of the inflation rate. (LLF): The natural log of the labor force as a percentage of the total population. (µit): The error term in our model. The model accounts for the heterogeneity among the various nations by allowing for distinct intercepts and slope coefficients for each country. In this context, the countryspecific fixed effect is denoted as β 0i, while the long-run coefficients for private credit, investment, trade openness, consumer price index, and labor force are represented by β 1i, β2i, β3i, β4i, and β5i, respectively. Economies 2023,11, 290 6 of 18 Data for this model are sourced from the World Economic Outlook publication published by the World Bank and the Penn World Table (PWT 10.01) for the period spanning the years from 1980 to 2021. Consequently, due to data availability constraints, our study is limited to 16 nations. The countries included in our analysis are Algeria, Bahrain, Egypt, the Islamic Republic of Iran, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar, Saudi Arabia, Tunisia, Turkey, and the United Arab Emirates. 4. Empirical Analysis 4.1. Descriptive Analysis Table 1shows the descriptive statistics for the overall sample of 16 nations in the panel. The logarithmic values of LGDPPC, LFDI, LINV, LOPE, LINF and LLF were used. Table 1. Descriptive Statistics for Countries in the MENA Region. Descriptive Statistics Countries LGDPPC LFDI LINV LOPE LINF LLF Algeria 9.2 −2.09 3.53 0.72 2.14 −1.52 Bahrain 10.75 −0.99 3.2 0.16 0.51 −0.90 Egypt 9.04 −1.26 3.05 −0.30 2.46 −1.39 Iran 9.32 −1.21 3.59 0.44 3.03 −1.35 Israel 10.29 −0.71 3.17 −2.81 3.66 −0.84 Jordan 9.19 −0.73 3.29 1.62 1.49 −1.50 Kuwait 10.77 −1.04 2.95 2.94 1.18 −0.73 Lebanon 9.66 −1.36 3.17 −2.95 3.50 −1.37 Libya 10.57 −2.09 4.40 1.25 1.86 3.32 Morocco 8.55 −1.47 3.35 −1.26 1.29 −1.14 Oman 10.25 −1.14 3.10 0.28 0.63 −0.99 Qatar 11.41 −0.76 3.66 3.05 1.19 −0.54 Saudi Arabia 10.76 −0.99 3.15 1.25 0.21 −1.14 Tunisia 8.93 −1.61 3.17 −1.74 1.69 −1.2 Turkey 9.75 −1.04 3.23 0.8 3.67 −1.13 UAE 11.45 −1.17 3.2 2.29 1.32 −0.59 Average 9.993 −1.229 3.326 0.359 1.864 −0.813 Standard Deviation 0.909 0.420 0.346 1.819 1.118 1.144 (Source: Authors’ own calculations). This table presents a 41-year average of the six variables used in this research. For the dependent variable LGDPPC, it seems that the oil-rich Arab countries, Bahrain, and Israel have the highest average GDP per capita, the highest ever being the UAE with 11.45, then Qatar with 11.41. The lowest ever is Morocco with 8.55, then Tunisia with 8.93. These results are applicable to the outcomes of huge boom of global oil prices during the era of study. For the independent variables, it was found that two variables have negative signs; LFDI has negative coefficients for all countries and LIF has negative coefficients for all countries except for Libya. On the other hand, the other three explanatory variables have positive coefficients for the majority of countries; LINV and LINF have positive coefficients for all nations. These results are applicable to the economic theory since GDP per capita is positively impacted by investment, inflation, and trade openness. The countries which benefited most from investment are Libya with 4.4, then Qatar with 3.66, Iran with 3.59, and Algeria with 3.53; the lowest is Kuwait with 2.95, then Egypt with 3.05. The countries which benefited most from trade openness are Qatar with 3.05 then Kuwait with 2.94 and UAE with 2.29; where the lowest are Lebanon with − 2.95, then Israel with − 2.81 and Tunisia with − 1.74. These results are applicable to the situation of these countries’ tendency towards more openness, in the case of Arab Gulf countries, and more restrictions in countries such as Lebanon and Tunisia. For inflation, it is noted that countries with high and persistent inflation rates, such as Turkey, Irael, Lebanon, Iran, and Egypt, have the highest positive impact of inflation on GDP per capita. On the other hand, this positive Economies 2023,11, 290 7 of 18 impact of inflation reached its minimum in countries having low levels of inflation, such as Saudi Arabia, Bahrain, and Oman, as a result of being rich nations. 4.2. Measuring Financial Development Numerous measures have been proposed in the literature to assess the development of a country’s financial sector. Initially, these measures primarily included monetary aggregates such as M1 and M2. However, these metrics are more indicative of the financial system’s capacity to provide transaction services rather than its ability to facilitate the transfer of funds from savers to borrowers (Hashmi and Bhatti 2019). Similarly, commonly used variables in the literature encompass credit to the private sector ( Beck et al. 2000;Levine et al. 2000 ), liquid liabilities (King and Levine 1993), and deposit liabilities (Christopoulos and Tsionas 2004). Additionally, stock market indicators have been employed by various researchers as proxies for measuring financial development. More recently, the International Monetary Fund (IMF) has introduced a set of indicators (Svirydzenka 2016) to assess a nation’s financial development comprehensively. These indicators collectively constitute the Financial Development Index (FDI), comprising the Financial Institutions Efficiency [FIE] Index, Financial Institutions Depth [FID] Index, and Financial Institutions Access [FIA] Index, which evaluate the accessibility, depth, and efficiency of financial institutions. Furthermore, the Financial Markets Efficiency [FMEI] Index, Financial Markets Depth [FMDI] Index, and Financial Markets Access Index [FMAI] are three financial market indices that employ similar metrics to assess the state and pace of development in financial markets. Consequently, this paper employs the IMF’s Financial Development Index (FDI) to conduct econometric analyses for the MENA region countries under study. To account for the finance–growth nexus, this paper incorporates macroeconomic variables, including trade openness (measured as the sum of imports and exports as a percentage of GDP), investment as a percentage of GDP, labor force (defined as the proportion of the economically active population aged 15 and older to the total population), and inflation (measured as the consumer price index). The ratio of GDP from imports plus exports, reflecting trade openness, provides insight into a country’s economic status, as trade connects nations to technological advancements achieved by their trading partners. Furthermore, as argued by Yanikkaya (2003), trade offers developing nations access to crucial investment and intermediate goods essential for their developmental processes. In a manner akin to its influence on growth, inflation also affects a nation’s financial activities by altering interest rates, thereby directly impacting the operations of banking and financial institutions, including deposit collection and mobilization. Alongside these factors, capital and labor are fundamental pillars in any theory of economic development. In various models, capital stock is shown to have a favorable impact on economic growth ( Narayan and Narayan 2013 ). Similarly, a nation’s overall development hinges on its labor force. These variables have been extensively utilized in the literature to explore the finance– growth nexus, as evidenced by studies such as those conducted by Beck et al. (2000), Christopoulos and Tsionas (2004), Levine et al. (2000), Menyah et al. (2014), Narayan and Narayan (2013), Salahuddin and Gow (2016), and Samargandi et al. (2014). 4.3. Data Analysis Procedures Our data analysis encompasses four essential steps. First, it involves determining the integration levels of the variables. Second, it examines whether these variables exhibit longterm co-integration. Third, it entails estimating the parameters associated with long-term co-integration. Finally, the fourth step involves testing the short-term causal relationship between financial development and economic growth. Economies 2023,11, 290 8 of 18 4.4. Integration Levels In heterogeneous panel data analysis, Im et al. (2003) and Maddala and Wu (1999) panel unit root tests are the most commonly employed approaches, as indicated in the literature. While these tests allow for individual unit root processes within a panel, they do not address the issue of cross-sectional dependence (Pesaran 2007). Therefore, before conducting first-generation unit root tests akin to those conducted by Im et al. (2003) and Maddala and Wu (1999), it is imperative to assess cross-sectional dependence. To tackle this concern, the study employs the Cross-Sectional Augmented IPS (CIPS) test, a second-generation panel unit root test developed by Pesaran (2007). The results of the second-generation panel unit root (CIPS) test and the investigation of cross-sectional dependence in the series are presented in Table 2. Table 2. Results of second-generation panel unit root (CIPS) and cross-sectional dependence Pesaran CD test. Variables Tests LGDPPC LFDI LINV LOPE LINF LLF Pesaran CD 14.51 * 23.7 * 7.63 * 16.32 * 15.39 * 50.18 * p-value 0.000 0.000 0.001 0.002 0.000 0.003 CIPS Level −2.903 −2.29 1.258 0.16 0.51 −0.90 p-value 0.213 0.146 0.251 0.09 0.149 0.111 CIPS (First Difference) −3.637 * −2.430 * −2.517 * −2.043 * −2.956 * −2.045 * p-value 0.001 0.000 0.003 0.005 0.000 0.000 (Source: Authors’ own calculations). * Indicates significance at 1 percent. To evaluate cross-sectional dependence for all variables, Pesaran’s CD test is used where the following hypothesis test is applied: H0: There is no cross-sectional dependence. H1: There is cross-sectional dependence. For all variables, the Pesaran CD test results yield p-values below 0.05, leading to the rejection of the null hypothesis. This indicates the presence of cross-sectional dependence within the dataset. Consequently, it becomes imperative to apply a second-generation panel unit root test to address the limitations associated with cross-sectional dependence, thus ensuring more accurate results compared to the first-generation unit root tests when cross-sectional dependence among variables is present. The second-generation panel unit root test is formulated as follows: H0: The series is not stationary (Unit Root Test is Present). H1: The series is stationary. Upon reviewing the second row of Table 2, it is evident that all variables are nonstationary at the level, as the null hypothesis is not rejected. Subsequently, the CIPS test is conducted, which results in the rejection of the null hypothesis, with p-values consistently below 0.05 for all variables in the series. This signifies that the variables under examination are stationary at the first difference. Consequently, the investigation confirms that these variables are integrated at order one, denoted as ‘I(1) variables’. 4.5. Co-Integration and Long-Run Relationship Estimation Having established that the variables are integrated at order one I(1), the next step involves conducting a co-integration test among these variables. In accordance with Pedroni (2004), this paper employs a panel co-integration test, which computes seven test statistics, as detailed Economies 2023,11, 290 15 of 18 Informed Consent Statement: This article does not contain any studies with human participants or animals performed by any of the authors. Data Availability Statement: The data that support part of the findings of this study are available and freely accessed from World Bank, World Development Indicators available at https://databank. worldbank.org/source/world-development-indicators (accessed on 1 December 2022). Conflicts of Interest: The authors declare no conflict of interest. In addition, the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. References Adu, George, George Marbuah, and Justice Tei Mensah. 2013. 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