Determinants of foreign direct investment in the Middle East and North Africa region: evidence from balanced panel data analysis

Determinants of foreign direct investment in the Middle East and North Africa region: evidence from balanced panel data analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Determinants of foreign direct investment in the Middle East and North Africa region: evidence from balanced panel data analysis Ahmed Nazzal, María-Victoria Sánchez Rebull, Angels Monserrat Niñerola This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5319811/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose : this study investigates factors influencing foreign direct investment (FDI) inflows to the Middle East and North Africa (MENA) region. Approach/design: using a balanced Panel data from 24 MENA economies covering 1980-2022 we employ econometric techniques (unit root, cointegration, FMOLS, DOLS, AMG, CCEMG) to evaluate the impacts of market and resource-seeking motives, institutional, government policies (macroeconomic level), and political risk on FDI inflows into MENA region. Findings: The long-term results suggest that market size, low corruption, government effectiveness and exchange rate contribute to FDI inflows while natural resources availability, role of law and voice and accountability deters FDI inflows to MENA. Contrary, political stability and regulatory quality factors has no significant effects on FDI inflows. Originality/Value: the study has potential implications for boosting FDI inflows into MENA region MENA such as: Policymakers of MENA economies should adopt stable monetary policies, implement policies and regulations to Promote private sector development, improve institutional quality and maintain macroeconomic stability. Diversification of economies and reduction of reliance on natural resources are essential for long-term FDI attraction. Additionally, green innovation should be encouraged by spending more on R&D, green technology and improving policies regarding the rule of law to create a good investment environment. Future Research: Further research is needed to explore the specific mechanisms through which these factors influence FDI inflows and to assess the potential impact of other variables, such as technological advancements and regional integration. Business and commerce/Economics Social science/Business and management Foreign Direct Investment MENA Political Risk Market Size Resources Seeking 1. Introduction Foreign direct investment (FDI) is defined as capital movements (inflows/outflows) that occur as a result of multinational corporations (MNCs) activities in international markets (Agiomirgianakis et al., 2004 ). It has been a relatively vital and preferred international finance and capital generation instrument for the past 40 years. Despite the global economic crisis (Susic et al., 2017 ), FDI has played an important and decisive role in economic development and internationalization over the past four decades (Janicki & Wunnava, 2004 ). FDI has positive effects on the host economy's income level, economic growth, and productivity improvement (Sokang, 2018 ), in addition to reducing income inequality among individuals (Ravinthirakumaran & Ravinthirakumaran, 2018 ). Therefore, FDI inflows can help host economies improve their overall well-being and economic standards (Moudatsou, 2003 ) and attract more intangible assets, such as managerial skills and technologies (Gupta & Singh, 2016 ; Jadhav, 2012 ). Many countries in North Africa and the Middle East (MENA) are trying to attract more FDI inflows to their economies by adopting new policies and improving and adjusting their existing policies to achieve this goal (Abdelkarim et al., 2012 ). MENA economies account for approximately 60% of the world's oil reserves and 45% of the world's natural gas reserves, indicating that MENA plays a significant and evolving role in the global economy (Emara et al., 2019 ). Statistics reveal that MENA economies account for 10% of the world's population, 4.30% of the world's labor force, and 29.55% of the world's natural resource rents (WDR, 2019 ). Moreover, the MENA economies share $ 1.295 trillion, which represents 2.66% of the world trade (exports and imports amount to $ 566 billion and $ 729 billion, respectively), compared with $ 800 billion in 2005 (WDR, 2005 ). Data also show that FDI inflows into MENA were the highest in 2008 ( $ 96,210,683,505), as Saudi Arabia attracted more FDI to the kingdom (Saudi policymakers improved and reformed their policies related to real estate, petrochemicals, refining, construction, and trade) (World Investment Report, 2010). Despite MENA countries' attempts to attract more FDI inflows, the MENA region suffers from low levels of FDI flows compared with other regions worldwide. In 2020, its net FDI inflows represented 3.63% of the world FDI net inflows, a low value compared with 28.93% of BRICS FDI inflows (Brazil, Russia, India, China, and South Africa) (BRICS), 5.32% of MINT inflows (Mexico, Indonesia, Nigeria, and Turkey) and 24.05% of the European Union (EU) (Giroud & Ivarsson, 2020 ). There may be multiple reasons, such as the low level of MENA integration and reforms (Makdisi et al., 2006 ), poorly structured and low-quality institutions attracting more capital flows (Jabri & Brahim, 2015 ), and other reasons related to transparency and corruption levels (Hassan, 2017 ). Therefore, the main motivation of this study is to identify and quantify the effects of the factors that determine FDI inflows to the MENA economies (Afghanistan, Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, the State of Palestine, Yemen) (Abed & Davoodi, 2003 ). Many studies have examined the impact of individual factors, such as bureaucracy, corruption, employment protection, and legal institutions, on FDI flows (Bénassy-Quéré et al., 2007 ), measuring the relationship between macroeconomic instability and FDI inflows (Chan & Gemayel, 2004 ). Other factors that have received attention as FDI determinants are market openness, infrastructure, the rate of return on investment, inflation, economic growth, political stability, the human capital index, and the availability of natural resources (Globerman & Shapiro, 2002 ; Mosallamy & Abbas, 2016 ). Other studies have attempted to develop and build a dynamic model of the determinants of FDI flows (Abonazel & Shalaby, 2020 ; Rogmans & Ebbers, 2013 ). There is no common accord on a generally approved set of factors that can be described as the best determinants influencing FDI inflows into a specific region or economy. However, the abovementioned studies show the different sets of regressors adopted by each study, and there is no agreed-upon consensus on the perspectives, methodologies, sample selection, and analytical tools of the potential determinants of FDI (Chakrabarti, 2001 ). In this sense, the prime focus of the present study is to assess the factors affecting FDI inflows into MENA economies, with a particular emphasis on market and resource-seeking motives, as well as institutional, policy, and political risk variables. This study contributes to the literature by performing a systematic analysis to identify the significant determinants of FDI inflows into the MENA economies, which consider that more resources and market seeking affect the behavior of MNCs to invest more FDI (Caccia et al., 2018 ; Mohamed & Sidiropoulos, 2010 ). The study adopts a holistic and comprehensive procedure using recent and updated data. The study acknowledges the cross-sectional dependence (CSD) and slope heterogeneity (SH) panel data selection (countries) problems by using CSD and SH tests. It then employs a proper heterogeneous panel analysis to compare recent methods with traditional methods to confirm the robustness of the results. Furthermore, this study is innovative in that it evaluates the market and resource-seeking motives; institutional, policy, and political risk variables; and FDI by considering a heterogeneous panel estimation for MENA economies. This study is organized as follows: the first section is the present introduction; the second section provides the theoretical literature related to determinants of FDI inflow. While the third section presents the study methodology and model development, the fourth section discusses the empirical results. Finally, the conclusions are presented. 2. Literature Review Researchers started researching the FDI phenomenon and MNC behavior in international markets after the second world war, specifically during the second globalization era (Piketty, 2014 ). Although MNCs have numerous motives for undertaking FDI, no general theory of FDI explains the existence of international production, FDI, and MNCs. Wilkinson & Kindleberger ( 1969 ) applied capital market and portfolio investment theories to explain and describe FDI initiation, as FDI was considered an international capital movement only. This approach states that the vital reason for capital flows is interest rate differentials (when the uncertainties or risks approach zero, capital is more likely to flow to other markets where capital gains the highest returns). Nevertheless, this approach has failed to differentiate between investment control and FDI and portfolio, because whenever the interest rate is higher abroad, investors can lend their capital abroad without the necessity of using their control power over the corporation that received their money. Hymer ( 1960 ) was the first to introduce the ownership advantage concept by stating that to compete with domestic firms, MNCs should have firm-specific advantages, which include superior technology, brand name, managerial skills, and scale economies, yet Hymers' approach failed to explain the actual FDI decision (where and when FDI takes place). With respect to domestic competition and outflow FDI, Kojima ( 1975 ) argued that when a firm fails to compete in the domestic market, it will seek investment opportunities abroad (the case of Japan) and, specifically, in developing countries. However, Kojima's arguments do not explain why domestic firms (competent locally) tend to expand their business activities in international markets. The product life cycle theory of Vernon ( 1966 ) attempted to answer the question of when FDI takes place (firms go abroad when their products have already been standardized and matured in the home markets). The eclectic approach, the Ownership Location and Internalization (OLI) paradigm, and the knowledge-capital model provide the framework to answer the question of when MNCs make FDI decisions by investing abroad (Dunning, 1980 ). According to Dunning (1988), MNCs decide to invest abroad mainly to achieve resource/asset-seeking, efficiency-seeking, and market-seeking goals. Markusen ( 1997 ), or the knowledge-capital model, argued that MNCs investing in abroad decisions are primarily based on knowledge capital rather than licensing to minimize the risk of firm-specific advantages and to provide strong incentives for ownership-specific advantages, which results in greater quantities of FDI. In terms of the firm internationalization process, the Uppsala model provides a better understanding of how firms invest and internationalize gradually, starting from the domestic market, then moving to closer markets, and gradually moving to more distant markets on the basis of their attained general knowledge and market-specific knowledge (Johanson & Vahlne, 1977 ). According to Johanson & Vahlne ( 1977 ), firms internationalize to foreign markets starting from a low-resource-commitment mode and then moving to higher-commitment modes as they gain experiential knowledge in the foreign market. Moreover, firms’ international expansion, which is based on their entry mode strategy, can introduce a unique set of uncertainties. These uncertainties encompass behavioral and environmental dimensions, which often arise from a firm's superior efficiency relative to the target market (Williamson, 1986 ). On the one hand, firm expansion may create market transaction costs and control costs (Brouthers & Nakos, 2004 ). On the other hand, firms’ ultimate goal is to minimize their cost at all points during their operations and decision making. Therefore, firms should consider a suitable entry mode strategy to minimize their market transaction costs and balanced organizational costs. Transaction cost theory (TCT) provides a framework that suggests that firms tend to select entry modes that balance the advantages of integration with the additional costs of control (Williamson, 1979 ). To study the determinants of FDI inflows, many studies have been conducted to examine variables that determine and influence FDI inflows. Several of them focused on economic variables related to market size as a significant factor for FDI because it is an indicator of growth in the host economy and maximizes MNCs' capital returns (Asongu et al., 2018 ; Leitão, 2010 ). There is a general consensus on what determines the flow of FDI to one country rather than another. Hunya ( 2000 ) reported that countries that draw substantial FDI typically exhibit robust economic fundamentals, high macroeconomic and political stability levels, well-developed infrastructure, a skilled labor force, a sound legal system that effectively upholds laws, and favorable growth prospects. Furthermore, factors such as geographic location, market size, and the abundance of natural resources also generally hold significant importance. Aziz & Mishra ( 2016 ) reported that market size, trade openness, and trade agreements are significant variables (positive impact) that determine FDI inflow to Arab economies. Moreover, their analysis revealed that Arab world economies are considered resource seeking for MNCs because of the availability of natural resources. Ranjan & Agrawal ( 2011 ) examined FDI determinants in BRICS countries from 1975–2009 and reported that market size (measured by national income), the cost of labor, trade openness, and economic development are potential factors determining FDI inflows. Jadhav ( 2012 ) also focused their research on BRICS countries from 2000–2009, highlighting that natural resource availability, trade openness, regulation of rules, and voice and responsibility were the most important determinants of FDI in those countries. Moreover, the analysis revealed that most investments in BRICS countries are motivated by market-seeking purposes due to their market size. In their study, Sufian & Moise (2010) analyzed the determinants of FDI in MENA economies via panel data, and the results revealed that the size of the host economy, government size, natural resources, and institutional variables are the key determinants of FDI inflows in MENA countries. Moreover, Okafor ( 2015 ); Okafor et al. ( 2017 ) revealed that FDI inflows to Sub-Saharan Africa are influenced by the availability of natural resources, infrastructure development, market size, and completion rates in primary education. Numerous studies depict FDI as a prospective undertaking that heavily depends on investors' future expectations, particularly in relation to economic and political circumstances. Asiedu ( 2006 ) suggest that macroeconomic instability, investment restrictions, corruption, and political instability have a negative effect on FDI in Africa. Nevertheless, natural resources and market size positively impact FDI, and lower inflation, good infrastructure, an educated population, openness to FDI, less corruption, political stability, and a reliable legal system have similar effects. In addition, the results of Brada et al. (2006) suggest that political stability directly impacts investments, as domestic instability can significantly impede operations and future cash flows. The last decade has seen a notable increase in scholarly attention to investigating the effects of corruption and financial development levels on inward FDI in host countries (Shakib, 2016 ). Numerous empirical studies support the claim that corruption in the host economy negatively affects FDI inflows (Hoang & Bui, 2015 ; Smarzynska & Wei, 2000 ). Additionally, corruption reduces FDI investment efficiency (Smarzynska & Wei, 2000 ; Wei, 1999 ) and is considered an FDI determent variable (Azam et al., 2019 ). Therefore, the literature indicates that various factors influence FDI. However, there is no empirical consensus on the drivers that attract FDI inflows to the host economy; hence, further research is needed. It is not possible to investigate all those factors, as some data may not be available, and their combined impact may be inconsequential. Therefore, on the basis of the literature and global economic circumstances, it is presumed that the availability of natural resources, market size, institutional factors, policy factors and political risk factors could be utilized to assess and investigate the determinants that affect FDI inflows into the MENA region. The fourth section describes the methods and the proposed model under consideration in our study. 3. Methodology 3.1. Variables and data collection On the basis of the discussed literature review, this study estimates a set of potential determinant variables that influence FDI flows into 24 MENA economies. FDI inflows into MENA in the current USD price are the regressand. The regressors included are the availability of natural resources, market size, institutional factors (control of corruption, role of law and voice and accountability), policy factors (inflation, exchange rate and real interest rate), and political risk factors (political stability and absence of violence/terrorism, government effectiveness, and regulatory quality). The World Development Indicators are used to gather information on FDI, the availability of natural resources, market size, and policy factors. The Worldwide Governance Indicators are the source of information on institutional factors and political factors. Moreover, the present study covers balanced panel data from 1980–2022 for MENA economies. Table 1 classifies the variables, their expected signs, and the data source for each set. Table 1 List of explanatory variables Regressor Indicators Expected sign Data source Natural resources "ANR" -Ore and metal exports as a % of total merchandise exports. -The sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents and forest rents. -Renewable energy consumption (% of total final energy consumption). -Fuel exports (% of merchandise exports). +/- World Development Indicators. Market size "MS" -GDP per capita. -Population growth. -Trade openness. + World Development Indicators. Institutional factors -Control of corruption "CC" -Role of law "RL" -Voice and accountability "VA" + +/- +/- World Governance Indicators. Policy factors -Inflation "INF" -Exchange rate "EXR" -Real interest rate "RIR" - - - World Development Indicators. Political risk factors Political Stability No Violence/Terrorism "PS" Government Effectiveness "GE" Regulatory Quality "RQ" +/- + +/- World Governance Indicators. 3.2. Model specification The proposed regression model is based on the OLI paradigm proposed by Dunning ( 1980 ); notably, a similar model was also adopted by scholars in addressing this topic (Azam & Haseeb, 2021 ; Jadhav, 2012 ; Khan et al., 2020 ). The study employed the following functional model for FDI inflows to MENA: FDI inflows = \(\:f\left(Natural\:resources+market\:size+insttitutional\:factors+policy\:factors+politcal\:risk\:factors\right)\) ………………………………………………………………………………………………(1) The econometric form of Eq. (1) can be expressed as follows: FDI it = α + β 1i MS it + β 2i NRA it + β 3i CC it + β 4i RL it + β 5i VA it + β 6i INF it + β 7i EXR it + β 8i RIR it + β 9i + PS it + β 10i GE it + β 11i RQ it + ε it …..(2) where α, β1 to β11 are the coefficients, i represents the number of countries, t represents time, and ε is the error term. Notably, "i" explains the cross-sectional number of units, and "t" explains the time dimension. 3.3. Estimation techniques This study follows Ahmed et al. ( 2021 ) and Azam & Haseeb ( 2021 ) panel econometric analysis approaches to describe the long-term impact of the variables affecting FDI inflows to MENA economies. The estimations and tests employed are detailed in the following subsections. Subsection 3.3.1 includes cross-sectional dependence (CSD) to incorporate any unobserved and global common shocks. Failure to address this issue results in a spurious and misspecified model, second-generation panel unit root test to address highly persistent series, which has the potential to influence model estimation and lead to spurious and biased statistical inferences, and slope homogeneity (SH) in large panels tests verifies whether the slopes of the cross-sections exhibit heterogeneity. Panel cointegration tests to investigate the structural long-run relationships among the variables used in the study are included in subsection 3.3.2. Moreover, heterogeneous panel estimators, commonly known as fully modified order least squares (FMOLS) and dynamic order least squares (DOLS) for solving endogeneity problems, serial correlation presence and elimination of small sample bias, are described in subsection 3.3.3. Finally, robustness estimators for heterogeneous, endogenous, and cross-sectional panels using augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators for qual, stable, and efficient results in the long run in the presence of SH and CSD issues are included in subsection 3.3.4. 3.3.1. Cross-sectional dependence (CSD), slope homogeneity (SH), and unit root analyses This study adopts the Pesaran ( 2015 ) cross-sectional dependence (CSD) test as the first step in the analysis to detect cross-sectional dependence in the panel data and identify whether the panel data have any unobserved and global common shocks (Khalid & Shafiullah, 2021 ), avoid biased estimates and inferences (Li et al., 2021 ) and avoid any stationarity results or cointegration bias (Khan et al., 2020 ; Salim et al., 2017 ). The CSD statistics can be represented as follows: \(\:\sqrt{\frac{2T}{N\left(N-1\right)}}\left(\sum\:_{i=0}^{N-1}\sum\:_{j=i+1}^{N}{\widehat{p}}_{ij}\right),N\left(\text{0,1}\right),N\to\:{\infty\:}\) ……………………………………………………………………...3 Once CSD results are gathered, the next step is checking the panel data stationarity, commonly known as the unit root of panel data, and confirming whether the series could generate consistent and reliable outcomes (Li et al., 2021 ) or generate spurious and biased statistical inferences. In the presence of CSD issues, traditional unit root methods are considered inappropriate or insufficient because those methods are based on the cross-sectional independence hypothesis (Azam & Haseeb, 2021 ; Su et al., 2021 ). Therefore, this study employs the second-generation unit root test, mainly the Pesaran (2007) or cross-sectionally augmented IPS (CIPS) unit root tests, to highlight CSD concerns. The results of the CIPS unit root test (level, 1st difference, or 2nd difference) determine whether the variables have stationary or nonstationary features. In addition, this test helps identify whether the variables have an order of cointegration over time. Following Shariff & Hamzah ( 2015 ), CIPS statistics can be represented as follows: \(\:CIPS=\sum\:_{i=1}^{N}\frac{CAD{F}_{i}}{N}\) ……………………………………………………………………………………………………………..…4 where \(\:CAD{F}_{i}\) represents the augmented Dickey‒Fuller (ADF) statistic for an i th cross-sectional unit. The third test is the slope homogeneity test (SH), which is used to confirm whether the cross-sectional slope is homogeneous. This study implements Pesaran & Yamagata ( 2008 ) to test slope heterogeneity by testing the null hypothesis that slopes are homogenous against the alternative hypothesis that slopes are heterogeneous (Su et al., 2021 ). The SH test involves an estimation of the following equations: \(\:\stackrel{\sim}{\varDelta\:}=\sqrt{N}\left(\frac{{N}^{-1}\stackrel{\sim}{s}-k}{\sqrt{2k}}\right)\) ………………………………………………………………………………………………………5 \(\:{\stackrel{\sim}{\varDelta\:}}_{adj}=\sqrt{N}\left(\frac{{N}^{-1}-\stackrel{\sim}{s}-E\left({\stackrel{\sim}{z}}_{i}T\right)}{\sqrt{{var}\left({\stackrel{\sim}{z}}_{i}T\right)}}\right)\) ……………………..……………………………………………………………………….6 where \(\:\stackrel{\sim}{s}\) represents the Swamy statistic test, N signifies the cross-sectional dimension, \(\:E\left({\stackrel{\sim}{z}}_{i}T\right)\) =k, \(\:{var}\left({\stackrel{\sim}{z}}_{i}T\right)\) = \(\:\frac{2\text{k}(\text{T}-\text{k}-1)}{\text{T}+1}\) , N denotes the cross-sectional dimension, T is the time series dimension, and k represents the finite positive constant. 3.3.2. Panel cointegration test To test the long-term linkages and dynamic cointegration among the variables and following prior studies such as Ali et al. ( 2020 ); Azam & Haseeb ( 2021 ), and Su et al. ( 2021 ), this study employed the error correction-based panel cointegration test or bootstrap panel cointegration technique introduced by Westerlund ( 2007 ) and advanced panel cointegration methods by Persyn & Westerlund ( 2008 ). The Westerlund ( 2007 ) bootstrap technique is helpful in providing more evidence by calculating accurate and reliable results against CSD and SH issues (Ahmed et al., 2021 ). It provides more accurate and consistent results than other residual techniques, such as Pedroni (2000, 2001 ), as it limits the panel cointegration tests to a normal distribution (Ali et al., 2020 ). Persyn & Westerlund ( 2008 ) assess the panel cointegration against H 0 : no cointegration among the panel and H 1 : cointegration exists among the panel variables, using two types of tests: the 1st panel association test and the 2nd group means test. On the basis of the error correction model, the advanced bootstrap has four test statistics normally distributed: group mean tests (Ga and Gt) to test the alternative hypothesis that at least one unit is cointegrated and panel association tests (Pa and Pt) to test the alternative hypothesis that the whole panel is cointegrated. The Pa and Ga statistics consist of the standard errors introduced by Newey & West ( 1994 ), which are adjusted to address autocorrelation and heteroskedasticity issues. Moreover, Pt and Gt are calculated with the help of the standard error parameter of the error correction model. The bootstrap panel cointegration method results for the variables under investigation are expected to show stationarity at the first differential level (Persyn & Westerlund, 2008 ). Later, the analysis detects and recognizes whether the cointegration exists individually (at least in one unit) or in the entire panel. Thus, the estimates for the dynamic cointegration or long-run association are interpreted. 3.3.3. FMOLS and DOLS estimators This study adopts the Pedroni ( 1999 and 2001 ) and FMOLS estimators of Phillips & Hansen ( 1990 ) to address issues of endogeneity, heteroskedasticity, and serial correlation with the explanatory variables (Ali et al., 2020 ) and produces reliable long-term effects of the selected variables on FDI into MENA economies. In addition, this study employed the DOLS long-run estimator of Saikkonen ( 1992 ) and Stock & Watson ( 1993 ). Although the FMOLS and DOLS techniques are broadly used in the literature, specifically in empirical studies related to heterogeneous panels, some studies have criticized these techniques for their inability to handle both cross-sectional dependence and slope heterogeneity efficiently in the presence of endogeneity (Apergis et al., 2007 ; Khan et al., 2020 ). 3.3.4. Robustness estimators To ensure the consistency and robustness of the results, the augmented mean group (AMG) by Eberhardt & Teal ( 2010 ) and the common correlated effect mean group (CCEMG) by Pesaran ( 2006 ) estimators were adopted in this study to control for serial correlation and heterogeneous, cross-sectional and endogenous data in panel units and produce a reliable output (Azam & Haseeb, 2021 ; Su et al., 2021 ). 4. Results 4.1. Cross-sectional dependence, slope heterogeneity, and unit root test Table 2 shows the findings for both the CSD and CIPS unit root tests. The results of the CD test delineate the cross-sectional dependency across the MENA economies as it rejects the null hypothesis at a 1% level of significance, implying that our model variables are cross-sectionally interdependent (the transmission of shock in one country against the other). To address CSD issues, this study employs the CIPS unit root test by checking for cross-sectional dependence and the integration order of variables. The CIPS unit root test results reject the null hypothesis at the 1st difference, which means that the variables are nonstationary at the level but become stationary at the first difference. The results from the CIPS test show that the variables in the model exhibit a unique order of integration at the 1st difference, which implies that there is a possibility of a long-run relationship among the variables under consideration. Table 2 Results of the CSD and CIPS unit root tests CD-test CIPS Variable Coefficient p-value level 1st difference FDI 42.659*** 0.0000 -3.245*** -6.099*** MS 11.952*** 0.0000 -3.633*** -6.236*** ANR 12.529*** 0.0000 -4.890*** -6.413*** CC 6.104*** 0.0000 -5.012*** -6.420*** RL 2.515** 0.0120 -4.939*** -6.294*** VA -0.392 0.6950 -4.688*** -6.384*** RIR 13.223*** 0.0000 -5.146 *** -6.420*** INF 14.54*** 0.0000 -4.907*** -6.390*** EXR 15.612*** 0.0000 -2.287 -4.226 *** PS 5.824*** 0.0000 -5.034*** -6.416*** GE 3.956*** 0.0000 -5.136*** -6.305*** RQ 1.575 0.1150 -5.252*** -5.888*** Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively, Table 3 confirms that the slope coefficient parameters are not homogenous throughout the panel data. The statistical results reject the null hypothesis of slope homogeneity at the 1% significance level, meaning that the model has heterogeneity problems. Table 3 Hashem Pesaran & Yamagata ( 2008 ) Test for slope heterogeneity Stats p-value Delta 4.729*** 0.000 Delta adjusted 5.661*** 0.000 Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively This means that while there is a long-run relationship among the variables, the nature of this relationship may differ across individual panel units (countries or regions). 4.2. The results of panel cointegration 4.2.1. Pedroni panel cointegration test (1st generation test) Table 4 reports the Pedroni ( 1999 ) Engle–Granger-based panel cointegration. The statistical outcomes of both within or inside the panel (Panel PP statistic and Panel ADF statistic) and between the panel dimensions (group PP measurements and group ADF measurements). Within- or inside-panel measurements reject the null hypothesis at the 1% significance level (no cointegration). Additionally, the panel measurements also confirm the rejection of the null hypothesis. Therefore, the Pedroni ( 1999 ) test results (Panel PP statistic, Panel ADF statistic, Group PP statistic, and Group ADF statistic) confirm that the variables in our model are moving together over the long-term dynamic cointegration. Table 4 Results of Pedroni ( 1999 ) panel cointegration (Engle-Granger-based) Estimates Stats. p-value Panel v-statistic -8.868 0.698 Panel σ-statistic 7.889 0.998 Panel PP statistic -17.907 *** 0.000 Panel ADF statistic -14.898 *** 0.000 Alternative Hypothesis: Individual AR Coefficient Group σ-statistic 4.354 0.999 Group PP statistic -13.358 *** 0.000 Group ADF statistic -13.651 *** 0.000 Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively 4.2.2. Westerlund bootstrap panel cointegration results (2nd generation test) The Westerlund ( 2007 ) bootstrap panel cointegration (2nd generation cointegration) test was applied to test the cointegration between the model variables (long-term relationships between the variables). The outcomes of the bootstrap panel cointegration are reported in Table 5 , which displays both within- and between-dimensional outcomes. The statistical results report the rejection of the null hypothesis and acknowledge the alternative hypothesis. For this reason, the variables in our model are cointegrated and move over a long run of FDI determinants into the MENA model. Table 5 Westerlund ( 2007 ) Bootstrap panel cointegration results Statistic Value Z value p-value Robust p-value Deterministic chosen: Constant Gt -2.676 -2.299** 0.011 0.02 Ga -10.922 0.031 0.513 0.11 Pt -13.627 -3.992* 0 0.085 Pa -12.689 -3.906* 0 0.07 Deterministic chosen: constant and trend Gt -3.2110 -1.8740** 0.0310 0.0300 Ga -14.3440 1.7120** 0.0434 0.0390 Pt -17.2850 -4.4160** 0.0000 0.0200 Pa -16.9070 -1.7170** 0.0430 0.0200 Note: The Westerlund panel long-run relationship null hypothesis states that there is no cointegration. The Westerlund bootstrap approach was deployed to test the cross-sectional dependence, and the number of replications was 400. P values are for a one-sided test based on a normal distribution. The robust p value is for a one-sided test based on 400 bootstrap replications. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively Therefore, the cointegration panel test results obtained from 1 st− and 2nd -generation tests (traditional and new cointegration tests) acknowledge the existence of a long-run bond among the variables in our model (variables in our model have a unique order of cointegration and move together over a long-term dynamic cointegration pattern). The results are in line with those of previous studies that followed the same panel cointegration procedure, such as Alharthi et al. ( 2024 ); An et al. ( 2021 ); Basher & Westerlund ( 2009 ); Khan et al. ( 2020 ); Ponce & Alvarado ( 2019 ) and Tachie et al. ( 2020 ). The statistical results of the panel cointegration tests are mainly cross-sectional dependance. The CIPS unit root test and slope heterogeneity test confirm that the dataset used to test the proposed model has interrelationships or interactions between different cross-sectional units, which leads to correlations in the error terms across different cross-sectional units and heterogeneity issues, which means that traditional estimation techniques, namely, fixed effects and random effects, remain consistent However, they may not be efficient, and estimated standard errors can be biased. Therefore, we introduce the FMOLS and DOLS estimation techniques to solve endogeneity, heteroskedasticity and serial correlation issues with the explanatory variables and produce reliable long-term effects. 4.3. Results from traditional estimators Table 6 shows the statistical results of both the FMOLS and DOLS estimators. The statistical findings show that the variables have a significant positive effect on FDI, but availability of natural resources, inflation, real interest rate, role of law and voice and accountability, while political stability and regulatory quality does not have any statistically significance. The findings from FMOLS indicate that a 1% increase in available natural resources decreases FDI inflows by 3.496%. Furthermore, FDI increases by 4.271% if the market size increases by 1%; if control of corruption is improved by 1%, FDI inflows increase by 9.5%, and a 1% increase in the real interest rate decreases FDI inflows by 11.67%, and a 1% improvement in government effectiveness leads to FDI inflows of 14.2%. FMOLS and DOLS suggest that MENA policymakers need to adopt policies and economic reforms that target corruption, remove all trade barriers, improve regulations regarding which enhances the government effectiveness and the financial systems and create appropriate institutions. The empirical assessments of the negative link between availability of natural resources with FDI is align with Elheddad ( 2018 ); Lu et al. ( 2020 ); Mina ( 2007 ) and Rogmans & Ebbers ( 2013 ) findings which argued that availability of natural resources generates an adverse effect on FDI in MENA economies. The positive links between market size and FDI inflows to MENA economies are in accordance with the findings of Abonazel & Shalaby ( 2020 ); Mosallamy & Abbas ( 2016 ) and Saad Alshehry ( 2020 ). Moreover, the empirical results of the policy factors (inflation, real interest rates and exchange rates) are in accordance with the findings of Aziz & Mishra ( 2016 ) and Jabri et al. ( 2013 ). The results concerning political stability, government effectiveness, and institutional factors support the conclusions of (Alharthi et al., 2024 ; Awdeh & Jomaa, 2022 ; Rogmans & Ebbers, 2013 ) Table 6 Long-run panel results through FMOLS and DOLS estimators FMLOS DOLS Variable Coefficient t-Statistic p-value Coefficient t-Statistic p-value ANR -3.496*** -4.551 0.000 -3.160** -2.3059 0.021 MS 4.271*** 6.311 0.000 2.263** 2.4211 0.016 EXR 0.005*** 3.515 0.005 0.005* 1.7962 0.073 INF -8.217*** -9.303 0.000 -7.027*** -4.4656 0.000 RIR -11.670*** -11.099 0.000 -9.922*** -5.2822 0.000 GE 14.249*** 5.623 0.000 14.013*** 3.1005 0.002 CC 9.544*** 3.872 0.001 9.1189** 2.0649 0.039 PS -23.664** -2.502 0.012 -22.273 -1.3150 0.189 RL -9.049*** -3.549 0.000 -10.176** -2.2070 0.028 RQ -18.372 -0.845 0.398 -8.0463 -0.2041 0.838 VA -1.569*** -9.434 0.000 -1.3648*** -4.5701 0.000 Note: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively 4.4. Results from the CCEMG and AMG estimators FMOLS and DOLS estimators provided results that dealt with endogeneity and serial correlation issues. However, CD issues still exist. Therefore, this section provides proper and reliable stable statistical results in the presence of CD, SH, and endogeneity using CCEMG and AMG estimator robustness estimators. The results of the CCEMG and AMG estimators are shown in Table 7 . Notably, applying both CCEMG and AMG estimators did not affect the sign of the estimated coefficient (positive or negative effect on FDI), but the coefficient value and its significance level changed. Table 7 CCEMG and AMG estimators long-term results. CCEMG AMG FDI Coef. P value Coef. P value ANR -3.006* 0.094 -4.418*** 0.000 MS 2.809** 0.018 0.020** 0.045 EXR 0.006* 0.066 0.005*** 0.000 INF -8.685*** 0.000 -6.700*** 0.000 RIR -12.128*** 0.000 -9.338*** 0.000 VA -1.6263*** 0.000 -1.7784*** 0.000 RL -4.754** 0.038 -13.104*** 0.000 CC 5.593** 0.045 10.208*** 0.001 PS -27.467 0.234 -8.760 0.402 GE 16.796*** 0.008 17.449*** 0.000 RQ -28.552 0.596 -3.319 0.893 Note: Asterisks ***, **, *, level of significance at 1%, 5%, and 10%, respectively. (CCEMG) Common correlated effects mean group estimator (AMG) Augmented mean group estimator. The CCEMG and AMG estimator results support the findings of the traditional estimator results (FMOLS and DOLS). The results of the CCEMG test show that a 1% increase in natural resources, inflation, real interest rate and rule of law deters FDI inflows by 3%, 8.6%, 12.1% and 4.7%, respectively. However, if exchange rates, market size, control of corruption and government effectiveness increase by 1%, FDI inflows will be encouraged by .006%, 2.8%, 5.5% and 16.7%, respectively. The AMG estimator results support the CCEMG, as the direction of the variables' coefficients is the same (impact on FDI). The methodological approach in this study and the results from the statistical tests indicate that CCEMG and AMG are more reliable, efficient, and robust estimators for estimating the coefficients of the variables in our model. Therefore, both the CCEMG and AMG results were considered the final results in this study, which solved the issues of endogeneity, serial correlation, CD, and SH in our panel data. Our results are consistent with the findings of (Azzaki et al., 2023 ; Bhujabal et al., 2024 ; Burger et al., 2016 ; Cieślik & Hamza, 2022 ; Mim & Saïdane, 2023; Onyeiwu, 2003 ). 5. Discussion FDI has an important and decisive role in economic development, economic growth, productivity improvement and income inequality reduction among individuals in host economies. Several studies have documented the positive effects of FDI on the host economy's employment, manufacturing, and consumption. Numerous scholars and legislators agree that FDI inflows have significant positive impacts on host economies in terms of improving their overall well-being and economic standards and attracting more intangible assets, such as managerial skills and pioneering technologies. However, various variables can help host economies attract FDI inflows into their markets, whereas natural resource availability, market size, institutions, governmental policies, and political risk factors play important key roles in determining specific advantages, locations and investment choices (Dunning & Lundan, 2008 ) and attracting FDI. Therefore, their roles cannot be neglected when investigating the variables that attract FDI into an economy. In general, economies with large market size, good institutions, low rates of corruption, and good governmental policies are attractive for MNCs to invest in and benefit the economy by increasing local production and reducing unemployment. The obtainability of resources is vital in triggering and encouraging economic growth. It has been observed that natural resource availability, voice and accountability and role of law variables discourage FDI inflows to MENA economies, this result is aligned with the hypothesis of MNCs tend to invest their money in economies where laws are weak and less accountability which allow them more access to the natural resource and low taxes (Chiyaba & Singleton, 2024 ). On the basis of the OLI paradigm, the main objective of this study is to investigate the factors affecting FDI flows into MENA economies, with a special focus on natural resource availability, market size, institutions, governmental policies and political risk factor impacts on FDI inflows from 1980–2022. This study is different from previous studies in the sense that it covers 24 MENA countries, which are relatively more FDI-attracted countries. Additionally, empirically, the study uses FMOLS and DOLS as traditional estimators and CCEMG and AMG estimators as the main robustness estimators to provide proper and reliable stable statistical results in the presence of CSD, SH and endogeneity issues in panel data. The results reveal some insights into the factors that trigger FDI in the MENA region. First, a 'nation's natural resources availability reserves deters FDI inflows (Dutch disease or resource curse phenomenon) this finding is in line with Rogmans & Ebbers ( 2013 ). Regarding market size are positively associated with FDI inflows, our findings are in line with those of Dunning (1988) in terms of the locational advantages of countries, Rugman & Verbeke ( 1989 ) in terms of country-specific advantages, and Bannaga et al. ( 2013 ), who suggested that foreign investors are more likely attracted to the host economy with larger markets. The results indicate that MENA economies are sought after for their location. When a host economy is characterized by a large market size, more FDI inflows and more MNCs invest in that economy, whereas the accumulated FDI inflows in host economies attract more FDI inflows (Aziz, 2018 ). This finding supports the claim that MNCs exhibit a greater propensity to be drawn toward countries that have already amassed substantial FDI. This discernible pattern underscores the pivotal role played by the success of MNCs within the host countries as a compelling impetus for subsequent investments by foreign corporations (Mina, 2012 ; Tun et al., 2012 ). Second, with respect to policy factors, the empirical results show that inflation negatively affects FDI because it triggers the tax system and discourages investors in the long run due to the money illusion (Omankhanlen, 2011 ). Moreover, according to the general theory of investment introduced by Keynes ( 1937 ), real interest rates play an important role in investment decisions; lower interest rates mean a lower cost of investment than investment potential returns do. The empirical results show that interest rates have an inverse relationship with FDI flows into MENA economies. At the same time, real interest rate fluctuations significantly affect exchange rates in the host economy and, therefore, host economy attractiveness, which in turn affects FDI inflows in the long run (Al-Shakrchy et al., 2023 ). Therefore, a stable exchange rate improves FDI inflow, and high FDI inflow enhances the stability of the exchange rate in the country. The empirical results show that the coefficient of exchange rate is positive and significant, indicating that the appreciation of exchange rate triggers FDI flows to accumulate in MENA economies. This positive sign for exchange rate coefficient indicates that when local currency appreciated against the US dollars, it means better economic condition, therefore more FDI inflows to MENA economies. Our findings are in line (Aziz & Mishra, 2016 ; Khatabi et al., 2020 ; Marial, 2009 ), but in contrary to (Al-Shakrchy et al., 2023 ; Habash, 2006 ; Kamal et al., 2023 ). The study results show that institutional quality (control of corruption, role of law and voice and accountability) factors have significant impacts on FDI inflows into MENA economies. The quality of institutions can affect and improve FDI inflows into MENA economies by increasing the host economy's productivity, enhancing research and development, and facilitating good financial capital with the ability to lend large-scale projects. High-quality institutions can reduce both time and restrictions when doing business. Moreover, low corruption and high voice and accountability levels are important determinants that attract more FDI inflows (Sabir et al., 2019 ; Sufian & Moise, 2010). When MNCs consider investing in a foreign economy, transaction cost calculations are an important instrument for deciding whether to carry out the investment. In this context, according to Dunning ( 2005 ), high-quality institutions play a fundamental role in reducing those transaction costs that may arise because insufficient safeguarded property rights, the lack of a robustly regulated institutional framework, pervasive corruption, underdeveloped financial markets, and ineffective incentive structures contribute to the observed challenges. As a result, more efficient and qualified institutions reduce transaction costs and increase investor trust by ensuring that the host country is committed to providing a stable and developed business environment (Tomassen et al., 2012 ; Tomassen & Benito, 2009 ). These findings, which are in line with those of Aziz ( 2022 ), Mina ( 2012 ), and Rachdi & Brahim ( 2014 ), support the idea that FDI inflows are optimized in economies with low levels of corruption. Moreover, higher-quality institutions in host economies attract and optimize FDI inflows. The empirical results suggest that institutional quality improvements attract greater FDI inflows to the market. Finally, with respect to political risk factors, low political risk (stability, good government effectiveness, and good regulatory quality) can influence more FDI inflows into MENA economies (Gani, 2007 ) and reduce all unwanted transaction costs that might arise (Asiedu, 2006 ; Morisset, 1999 ). Political risk variables are crucial elements in defining the host economy's regulations, especially investment regulations, as well as shaping the MNCs’ investment decisions and activities, which significantly affects the business climate in terms of whether to encourage or discourage FDI inflows (Chalençon & Mayrhofer, 2021 ; Triki et al., 2022 ). The empirical results regarding the insignificance of political stability and regulatory quality suggesting that economies required to focus on improving the quality of their institutions, also foreign investors are concerned more about the domestic business environment and economic freedom rather than democracy, political stability and regulations in MENA region. Our empirical results are in accordance with Gani & Al-Abri ( 2013 ); Kobeissi ( 2005 ); Rogmans & Ebbers ( 2013 ). 6. Conclusions, implications, and future research This study investigates and assesses the factors affecting FDI inflows into MENA economies, with a particular emphasis on market and resource-seeking motives; institutional and government policies (macroeconomic level); and political risk variables. The results imply that FDI flows are derived mainly through, large market sizes, qualified institutions, good governmental policies, and low levels of corrupted economies. The results reveal that FDI inflows are also attracted to economies where their local currencies are appreciated against US dollar, high-quality public services and the credibility of the government's commitment, economies that can formulate and implement policies and regulations that promote private sector development, and economies with good quality of governance, which positively affects private sector development. The results of this study have several implications for academics and policymakers and provide some promising research paths. At a theoretical level, this study contributes to the literature by performing a systematic analysis to identify the significant determinants of FDI inflows into MENA economies, which consider that more market seeking affect the behavior of MNCs to invest more FDI. Methodologically, it employs proper cointegration techniques for testing the structural long-run relationships among the variables in the proposed model. Furthermore, CCEMG and AMG are more reliable, efficient, and robust estimators for estimating the coefficients of econometric models in panel data that have issues with endogeneity, serial correlation, CSD, and SH. The conclusions have several important policy and managerial implications that might help host economy governments and law makers determine the factors that encourage and discourage FDI inflows. First, natural resource availability, market size, control of corruption, institutional quality, and political stability have proven to be significant determinants of FDI inflow into the MENA region while political stability and regulatory quality are insignificant. Thus, efforts aimed at formulating strong policies and reforms, paying particular attention to their institutional quality and effectiveness, and stable monetary policies should be adopted to control inflation and real interest rates and improve regional trade by reducing all barriers and restrictions and diversifying their economies. Second, investment strategies that help promote and enhance intraregional FDI should be adopted, creating suitable investment protection and insurance instruments that attract foreign investors to do business. Third, policies regarding the rule of law and regulatory quality should be improved to create a good investment climate; reduce taxes, capital and profit transfer restrictions; and build qualified dispute resolution entities. Despite the contributions of this study, there are several limitations to highlight. First, we do not include variables related to the reliability of local infrastructure, total factor productivity (TFP), or capital openness. The question of whether those variables promote FDI in the MENA region requires further investigation. Second, the study considered the availability of natural resources in MENA economies and did not acknowledge climate change or the rise of global environmental awareness. The impacts of environmental regulations on FDI inflows into MENA economies require further investigation to help MNCs and policymakers develop environmentally friendly FDI strategies that improve the attractiveness of FDI without harming the environment. Third, the study employs traditional institutional factors. Future research may look further into factors such as conflict intensity, corruption distance, and religious tensions and how these factors can influence NNC investment decisions in MENA economies. Future research should investigate whether different types of violent conflict have different impacts on MNCs investing in MENA economies. More research is needed to understand the effects of political Islam on government effectiveness in terms of formulating foreign investment policies in MENA economies. Additionally, more emphasis should be placed on the impact of the distance of corruption between the home country and the host MENA country. Fourth, different theoretical frameworks, such as transaction cost theory (TCT), the Uppsala model, network theory, the market imperfection approach, and the resource-based view (RBV), could be used to test and validate these theories against the MENA economies. Finally, another area of future research is possible by applying the cross-sectionally augmented autoregressive distributed lag model (CS-ARDL) estimator, to solve any undiscovered issues of endogeneity, non-stationarity, mixed-order integration, SH, and CSD issues and panel-corrected standard error (PCSE) to control auto correlation and heteroskedasticity in the panel dataset under investigation and obtain accurate and robust estimation results in both long and short-run coefficients by applying dynamic common correlated impact predictors. Declarations Funding The authors have not received any funding to accomplish this study. Supplementary Material The data used in this study for empirical analysis were obtained from World Development Indicators, World Bank database, and world governance indicators. Ethics declarations Competing interests The authors report no affiliation or involvement in an organization or entity with a financial or nonfinancial interest in the subject matter or materials discussed in this manuscript. Ethical approval This article does not contain any studies with human participants performed by any of the authors. Informed consent This article does not contain any studies with human participants performed by any of the authors. Author Contributions, All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by correspondent Author. The first draft of the manuscript was written by correspondent Author. Second and third authors (respectively) commented on previous versions of the manuscript. All authors read and approved the final manuscript. References Abdelkarim, J., Abid, I., & Guesmi, K. (2012). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5319811","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":388952971,"identity":"e28e9f4a-5c48-4779-a9a8-6ef390502049","order_by":0,"name":"Ahmed Nazzal","email":"data:image/png;base64,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","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":true,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Nazzal","suffix":""},{"id":388952972,"identity":"126104cb-a179-4239-839a-3c108a8eff74","order_by":1,"name":"María-Victoria Sánchez Rebull","email":"","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":false,"prefix":"","firstName":"María-Victoria","middleName":"Sánchez","lastName":"Rebull","suffix":""},{"id":388952973,"identity":"18cd6273-31c0-447a-97ff-dc2a098c6c21","order_by":2,"name":"Angels Monserrat Niñerola","email":"","orcid":"","institution":"Rovira i Virgili University","correspondingAuthor":false,"prefix":"","firstName":"Angels","middleName":"Monserrat","lastName":"Niñerola","suffix":""}],"badges":[],"createdAt":"2024-10-23 14:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5319811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5319811/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74040089,"identity":"fa705e54-897e-4b54-a3ba-a074cb05b321","added_by":"auto","created_at":"2025-01-17 08:20:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1299873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5319811/v1/054dbcce-1f1d-40d0-afd6-7e91e47bd6b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of foreign direct investment in the Middle East and North Africa region: evidence from balanced panel data analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForeign direct investment (FDI) is defined as capital movements (inflows/outflows) that occur as a result of multinational corporations (MNCs) activities in international markets (Agiomirgianakis et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). It has been a relatively vital and preferred international finance and capital generation instrument for the past 40 years. Despite the global economic crisis (Susic et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), FDI has played an important and decisive role in economic development and internationalization over the past four decades (Janicki \u0026amp; Wunnava, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). FDI has positive effects on the host economy's income level, economic growth, and productivity improvement (Sokang, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), in addition to reducing income inequality among individuals (Ravinthirakumaran \u0026amp; Ravinthirakumaran, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, FDI inflows can help host economies improve their overall well-being and economic standards (Moudatsou, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and attract more intangible assets, such as managerial skills and technologies (Gupta \u0026amp; Singh, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Jadhav, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany countries in North Africa and the Middle East (MENA) are trying to attract more FDI inflows to their economies by adopting new policies and improving and adjusting their existing policies to achieve this goal (Abdelkarim et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMENA economies account for approximately 60% of the world's oil reserves and 45% of the world's natural gas reserves, indicating that MENA plays a significant and evolving role in the global economy (Emara et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Statistics reveal that MENA economies account for 10% of the world's population, 4.30% of the world's labor force, and 29.55% of the world's natural resource rents (WDR, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, the MENA economies share \u003cspan\u003e$\u003c/span\u003e1.295 trillion, which represents 2.66% of the world trade (exports and imports amount to \u003cspan\u003e$\u003c/span\u003e566\u0026nbsp;billion and \u003cspan\u003e$\u003c/span\u003e729\u0026nbsp;billion, respectively), compared with \u003cspan\u003e$\u003c/span\u003e800\u0026nbsp;billion in 2005 (WDR, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Data also show that FDI inflows into MENA were the highest in 2008 (\u003cspan\u003e$\u003c/span\u003e96,210,683,505), as Saudi Arabia attracted more FDI to the kingdom (Saudi policymakers improved and reformed their policies related to real estate, petrochemicals, refining, construction, and trade) (World Investment Report, 2010).\u003c/p\u003e \u003cp\u003eDespite MENA countries' attempts to attract more FDI inflows, the MENA region suffers from low levels of FDI flows compared with other regions worldwide. In 2020, its net FDI inflows represented 3.63% of the world FDI net inflows, a low value compared with 28.93% of BRICS FDI inflows (Brazil, Russia, India, China, and South Africa) (BRICS), 5.32% of MINT inflows (Mexico, Indonesia, Nigeria, and Turkey) and 24.05% of the European Union (EU) (Giroud \u0026amp; Ivarsson, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There may be multiple reasons, such as the low level of MENA integration and reforms (Makdisi et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), poorly structured and low-quality institutions attracting more capital flows (Jabri \u0026amp; Brahim, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and other reasons related to transparency and corruption levels (Hassan, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the main motivation of this study is to identify and quantify the effects of the factors that determine FDI inflows to the MENA economies (Afghanistan, Algeria, Bahrain, Djibouti, Egypt, Iran, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Pakistan, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates, the State of Palestine, Yemen) (Abed \u0026amp; Davoodi, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMany studies have examined the impact of individual factors, such as bureaucracy, corruption, employment protection, and legal institutions, on FDI flows (B\u0026eacute;nassy-Qu\u0026eacute;r\u0026eacute; et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), measuring the relationship between macroeconomic instability and FDI inflows (Chan \u0026amp; Gemayel, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Other factors that have received attention as FDI determinants are market openness, infrastructure, the rate of return on investment, inflation, economic growth, political stability, the human capital index, and the availability of natural resources (Globerman \u0026amp; Shapiro, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Mosallamy \u0026amp; Abbas, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Other studies have attempted to develop and build a dynamic model of the determinants of FDI flows (Abonazel \u0026amp; Shalaby, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rogmans \u0026amp; Ebbers, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). There is no common accord on a generally approved set of factors that can be described as the best determinants influencing FDI inflows into a specific region or economy. However, the abovementioned studies show the different sets of regressors adopted by each study, and there is no agreed-upon consensus on the perspectives, methodologies, sample selection, and analytical tools of the potential determinants of FDI (Chakrabarti, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this sense, the prime focus of the present study is to assess the factors affecting FDI inflows into MENA economies, with a particular emphasis on market and resource-seeking motives, as well as institutional, policy, and political risk variables. This study contributes to the literature by performing a systematic analysis to identify the significant determinants of FDI inflows into the MENA economies, which consider that more resources and market seeking affect the behavior of MNCs to invest more FDI (Caccia et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mohamed \u0026amp; Sidiropoulos, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study adopts a holistic and comprehensive procedure using recent and updated data. The study acknowledges the cross-sectional dependence (CSD) and slope heterogeneity (SH) panel data selection (countries) problems by using CSD and SH tests. It then employs a proper heterogeneous panel analysis to compare recent methods with traditional methods to confirm the robustness of the results. Furthermore, this study is innovative in that it evaluates the market and resource-seeking motives; institutional, policy, and political risk variables; and FDI by considering a heterogeneous panel estimation for MENA economies.\u003c/p\u003e \u003cp\u003eThis study is organized as follows: the first section is the present introduction; the second section provides the theoretical literature related to determinants of FDI inflow. While the third section presents the study methodology and model development, the fourth section discusses the empirical results. Finally, the conclusions are presented.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eResearchers started researching the FDI phenomenon and MNC behavior in international markets after the second world war, specifically during the second globalization era (Piketty, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Although MNCs have numerous motives for undertaking FDI, no general theory of FDI explains the existence of international production, FDI, and MNCs.\u003c/p\u003e \u003cp\u003eWilkinson \u0026amp; Kindleberger (\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e1969\u003c/span\u003e) applied capital market and portfolio investment theories to explain and describe FDI initiation, as FDI was considered an international capital movement only. This approach states that the vital reason for capital flows is interest rate differentials (when the uncertainties or risks approach zero, capital is more likely to flow to other markets where capital gains the highest returns). Nevertheless, this approach has failed to differentiate between investment control and FDI and portfolio, because whenever the interest rate is higher abroad, investors can lend their capital abroad without the necessity of using their control power over the corporation that received their money. Hymer (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1960\u003c/span\u003e) was the first to introduce the ownership advantage concept by stating that to compete with domestic firms, MNCs should have firm-specific advantages, which include superior technology, brand name, managerial skills, and scale economies, yet Hymers' approach failed to explain the actual FDI decision (where and when FDI takes place).\u003c/p\u003e \u003cp\u003eWith respect to domestic competition and outflow FDI, Kojima (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1975\u003c/span\u003e) argued that when a firm fails to compete in the domestic market, it will seek investment opportunities abroad (the case of Japan) and, specifically, in developing countries. However, Kojima's arguments do not explain why domestic firms (competent locally) tend to expand their business activities in international markets.\u003c/p\u003e \u003cp\u003eThe product life cycle theory of Vernon (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) attempted to answer the question of when FDI takes place (firms go abroad when their products have already been standardized and matured in the home markets). The eclectic approach, the Ownership Location and Internalization (OLI) paradigm, and the knowledge-capital model provide the framework to answer the question of when MNCs make FDI decisions by investing abroad (Dunning, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). According to Dunning (1988), MNCs decide to invest abroad mainly to achieve resource/asset-seeking, efficiency-seeking, and market-seeking goals. Markusen (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), or the knowledge-capital model, argued that MNCs investing in abroad decisions are primarily based on knowledge capital rather than licensing to minimize the risk of firm-specific advantages and to provide strong incentives for ownership-specific advantages, which results in greater quantities of FDI.\u003c/p\u003e \u003cp\u003eIn terms of the firm internationalization process, the Uppsala model provides a better understanding of how firms invest and internationalize gradually, starting from the domestic market, then moving to closer markets, and gradually moving to more distant markets on the basis of their attained general knowledge and market-specific knowledge (Johanson \u0026amp; Vahlne, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). According to Johanson \u0026amp; Vahlne (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1977\u003c/span\u003e), firms internationalize to foreign markets starting from a low-resource-commitment mode and then moving to higher-commitment modes as they gain experiential knowledge in the foreign market.\u003c/p\u003e \u003cp\u003eMoreover, firms\u0026rsquo; international expansion, which is based on their entry mode strategy, can introduce a unique set of uncertainties. These uncertainties encompass behavioral and environmental dimensions, which often arise from a firm's superior efficiency relative to the target market (Williamson, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). On the one hand, firm expansion may create market transaction costs and control costs (Brouthers \u0026amp; Nakos, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). On the other hand, firms\u0026rsquo; ultimate goal is to minimize their cost at all points during their operations and decision making. Therefore, firms should consider a suitable entry mode strategy to minimize their market transaction costs and balanced organizational costs. Transaction cost theory (TCT) provides a framework that suggests that firms tend to select entry modes that balance the advantages of integration with the additional costs of control (Williamson, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e1979\u003c/span\u003e). To study the determinants of FDI inflows, many studies have been conducted to examine variables that determine and influence FDI inflows. Several of them focused on economic variables related to market size as a significant factor for FDI because it is an indicator of growth in the host economy and maximizes MNCs' capital returns (Asongu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Leit\u0026atilde;o, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). There is a general consensus on what determines the flow of FDI to one country rather than another. Hunya (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) reported that countries that draw substantial FDI typically exhibit robust economic fundamentals, high macroeconomic and political stability levels, well-developed infrastructure, a skilled labor force, a sound legal system that effectively upholds laws, and favorable growth prospects. Furthermore, factors such as geographic location, market size, and the abundance of natural resources also generally hold significant importance.\u003c/p\u003e \u003cp\u003eAziz \u0026amp; Mishra (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) reported that market size, trade openness, and trade agreements are significant variables (positive impact) that determine FDI inflow to Arab economies. Moreover, their analysis revealed that Arab world economies are considered resource seeking for MNCs because of the availability of natural resources. Ranjan \u0026amp; Agrawal (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) examined FDI determinants in BRICS countries from 1975\u0026ndash;2009 and reported that market size (measured by national income), the cost of labor, trade openness, and economic development are potential factors determining FDI inflows. Jadhav (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) also focused their research on BRICS countries from 2000\u0026ndash;2009, highlighting that natural resource availability, trade openness, regulation of rules, and voice and responsibility were the most important determinants of FDI in those countries. Moreover, the analysis revealed that most investments in BRICS countries are motivated by market-seeking purposes due to their market size. In their study, Sufian \u0026amp; Moise (2010) analyzed the determinants of FDI in MENA economies via panel data, and the results revealed that the size of the host economy, government size, natural resources, and institutional variables are the key determinants of FDI inflows in MENA countries.\u003c/p\u003e \u003cp\u003eMoreover, Okafor (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2015\u003c/span\u003e); Okafor et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) revealed that FDI inflows to Sub-Saharan Africa are influenced by the availability of natural resources, infrastructure development, market size, and completion rates in primary education.\u003c/p\u003e \u003cp\u003eNumerous studies depict FDI as a prospective undertaking that heavily depends on investors' future expectations, particularly in relation to economic and political circumstances. Asiedu (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) suggest that macroeconomic instability, investment restrictions, corruption, and political instability have a negative effect on FDI in Africa. Nevertheless, natural resources and market size positively impact FDI, and lower inflation, good infrastructure, an educated population, openness to FDI, less corruption, political stability, and a reliable legal system have similar effects. In addition, the results of Brada et al. (2006) suggest that political stability directly impacts investments, as domestic instability can significantly impede operations and future cash flows.\u003c/p\u003e \u003cp\u003eThe last decade has seen a notable increase in scholarly attention to investigating the effects of corruption and financial development levels on inward FDI in host countries (Shakib, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Numerous empirical studies support the claim that corruption in the host economy negatively affects FDI inflows (Hoang \u0026amp; Bui, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Smarzynska \u0026amp; Wei, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Additionally, corruption reduces FDI investment efficiency (Smarzynska \u0026amp; Wei, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Wei, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and is considered an FDI determent variable (Azam et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTherefore, the literature indicates that various factors influence FDI. However, there is no empirical consensus on the drivers that attract FDI inflows to the host economy; hence, further research is needed. It is not possible to investigate all those factors, as some data may not be available, and their combined impact may be inconsequential. Therefore, on the basis of the literature and global economic circumstances, it is presumed that the availability of natural resources, market size, institutional factors, policy factors and political risk factors could be utilized to assess and investigate the determinants that affect FDI inflows into the MENA region.\u003c/p\u003e \u003cp\u003eThe fourth section describes the methods and the proposed model under consideration in our study.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Variables and data collection\u003c/h2\u003e \u003cp\u003eOn the basis of the discussed literature review, this study estimates a set of potential determinant variables that influence FDI flows into 24 MENA economies. FDI inflows into MENA in the current USD price are the regressand. The regressors included are the availability of natural resources, market size, institutional factors (control of corruption, role of law and voice and accountability), policy factors (inflation, exchange rate and real interest rate), and political risk factors (political stability and absence of violence/terrorism, government effectiveness, and regulatory quality). The World Development Indicators are used to gather information on FDI, the availability of natural resources, market size, and policy factors. The Worldwide Governance Indicators are the source of information on institutional factors and political factors. Moreover, the present study covers balanced panel data from 1980\u0026ndash;2022 for MENA economies. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e classifies the variables, their expected signs, and the data source for each set.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of explanatory variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegressor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExpected sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural resources \"ANR\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-Ore and metal exports as a % of total merchandise exports.\u003c/p\u003e \u003cp\u003e-The sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents and forest rents.\u003c/p\u003e \u003cp\u003e-Renewable energy consumption (% of total final energy consumption).\u003c/p\u003e \u003cp\u003e-Fuel exports (% of merchandise exports).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicators.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarket size \"MS\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-GDP per capita.\u003c/p\u003e \u003cp\u003e-Population growth.\u003c/p\u003e \u003cp\u003e-Trade openness.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicators.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInstitutional factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-Control of corruption \"CC\"\u003c/p\u003e \u003cp\u003e-Role of law \"RL\"\u003c/p\u003e \u003cp\u003e-Voice and accountability \"VA\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e+/-\u003c/p\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Governance Indicators.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolicy factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-Inflation \"INF\"\u003c/p\u003e \u003cp\u003e-Exchange rate \"EXR\"\u003c/p\u003e \u003cp\u003e-Real interest rate \"RIR\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Development Indicators.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical risk factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolitical Stability No Violence/Terrorism \"PS\"\u003c/p\u003e \u003cp\u003eGovernment Effectiveness \"GE\"\u003c/p\u003e \u003cp\u003eRegulatory Quality \"RQ\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+/-\u003c/p\u003e \u003cp\u003e+\u003c/p\u003e \u003cp\u003e+/-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Governance Indicators.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Model specification\u003c/h2\u003e \u003cp\u003eThe proposed regression model is based on the OLI paradigm proposed by Dunning (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1980\u003c/span\u003e); notably, a similar model was also adopted by scholars in addressing this topic (Azam \u0026amp; Haseeb, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jadhav, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The study employed the following functional model for FDI inflows to MENA:\u003c/p\u003e \u003cp\u003eFDI inflows =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:f\\left(Natural\\:resources+market\\:size+insttitutional\\:factors+policy\\:factors+politcal\\:risk\\:factors\\right)\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;(1)\u003c/p\u003e \u003cp\u003eThe econometric form of Eq.\u0026nbsp;(1) can be expressed as follows:\u003c/p\u003e \u003cp\u003eFDI\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;α\u0026thinsp;+\u0026thinsp;β\u003csub\u003e1i\u003c/sub\u003eMS\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e2i\u003c/sub\u003eNRA\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e3i\u003c/sub\u003eCC\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e4i\u003c/sub\u003eRL\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e5i\u003c/sub\u003eVA\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e6i\u003c/sub\u003eINF\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e7i\u003c/sub\u003eEXR\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e8i\u003c/sub\u003eRIR\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e9i +\u003c/sub\u003ePS\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e10i\u003c/sub\u003eGE\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;β\u003csub\u003e11i\u003c/sub\u003eRQ\u003csub\u003eit\u003c/sub\u003e\u0026thinsp;+\u0026thinsp;ε\u003csub\u003eit\u003c/sub\u003e\u0026hellip;..(2)\u003c/p\u003e \u003cp\u003ewhere α, β1 to β11 are the coefficients, i represents the number of countries, t represents time, and ε is the error term. Notably, \"i\" explains the cross-sectional number of units, and \"t\" explains the time dimension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Estimation techniques\u003c/h2\u003e \u003cp\u003eThis study follows Ahmed et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Azam \u0026amp; Haseeb (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) panel econometric analysis approaches to describe the long-term impact of the variables affecting FDI inflows to MENA economies.\u003c/p\u003e \u003cp\u003eThe estimations and tests employed are detailed in the following subsections.\u003c/p\u003e \u003cp\u003eSubsection 3.3.1 includes cross-sectional dependence (CSD) to incorporate any unobserved and global common shocks. Failure to address this issue results in a spurious and misspecified model, second-generation panel unit root test to address highly persistent series, which has the potential to influence model estimation and lead to spurious and biased statistical inferences, and slope homogeneity (SH) in large panels tests verifies whether the slopes of the cross-sections exhibit heterogeneity. Panel cointegration tests to investigate the structural long-run relationships among the variables used in the study are included in subsection 3.3.2. Moreover, heterogeneous panel estimators, commonly known as fully modified order least squares (FMOLS) and dynamic order least squares (DOLS) for solving endogeneity problems, serial correlation presence and elimination of small sample bias, are described in subsection 3.3.3. Finally, robustness estimators for heterogeneous, endogenous, and cross-sectional panels using augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators for qual, stable, and efficient results in the long run in the presence of SH and CSD issues are included in subsection 3.3.4.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Cross-sectional dependence (CSD), slope homogeneity (SH), and unit root analyses\u003c/h2\u003e \u003cp\u003eThis study adopts the Pesaran (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) cross-sectional dependence (CSD) test as the first step in the analysis to detect cross-sectional dependence in the panel data and identify whether the panel data have any unobserved and global common shocks (Khalid \u0026amp; Shafiullah, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), avoid biased estimates and inferences (Li et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and avoid any stationarity results or cointegration bias (Khan et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Salim et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The CSD statistics can be represented as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{\\frac{2T}{N\\left(N-1\\right)}}\\left(\\sum\\:_{i=0}^{N-1}\\sum\\:_{j=i+1}^{N}{\\widehat{p}}_{ij}\\right),N\\left(\\text{0,1}\\right),N\\to\\:{\\infty\\:}\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;...3\u003c/p\u003e \u003cp\u003eOnce CSD results are gathered, the next step is checking the panel data stationarity, commonly known as the unit root of panel data, and confirming whether the series could generate consistent and reliable outcomes (Li et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) or generate spurious and biased statistical inferences.\u003c/p\u003e \u003cp\u003eIn the presence of CSD issues, traditional unit root methods are considered inappropriate or insufficient because those methods are based on the cross-sectional independence hypothesis (Azam \u0026amp; Haseeb, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, this study employs the second-generation unit root test, mainly the Pesaran (2007) or cross-sectionally augmented IPS (CIPS) unit root tests, to highlight CSD concerns. The results of the CIPS unit root test (level, 1st difference, or 2nd difference) determine whether the variables have stationary or nonstationary features. In addition, this test helps identify whether the variables have an order of cointegration over time. Following Shariff \u0026amp; Hamzah (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), CIPS statistics can be represented as follows:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:CIPS=\\sum\\:_{i=1}^{N}\\frac{CAD{F}_{i}}{N}\\)\u003c/span\u003e \u003c/span\u003e \u003cem\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;4\u003c/em\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:CAD{F}_{i}\\)\u003c/span\u003e\u003c/span\u003e represents the augmented Dickey‒Fuller (ADF) statistic for an i\u003csup\u003eth\u003c/sup\u003e cross-sectional unit.\u003c/p\u003e \u003cp\u003eThe third test is the slope homogeneity test (SH), which is used to confirm whether the cross-sectional slope is homogeneous. This study implements Pesaran \u0026amp; Yamagata (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) to test slope heterogeneity by testing the null hypothesis that slopes are homogenous against the alternative hypothesis that slopes are heterogeneous (Su et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The SH test involves an estimation of the following equations:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{\\varDelta\\:}=\\sqrt{N}\\left(\\frac{{N}^{-1}\\stackrel{\\sim}{s}-k}{\\sqrt{2k}}\\right)\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;5\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\stackrel{\\sim}{\\varDelta\\:}}_{adj}=\\sqrt{N}\\left(\\frac{{N}^{-1}-\\stackrel{\\sim}{s}-E\\left({\\stackrel{\\sim}{z}}_{i}T\\right)}{\\sqrt{{var}\\left({\\stackrel{\\sim}{z}}_{i}T\\right)}}\\right)\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.6\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{\\sim}{s}\\)\u003c/span\u003e\u003c/span\u003e represents the Swamy statistic test, N signifies the cross-sectional dimension, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left({\\stackrel{\\sim}{z}}_{i}T\\right)\\)\u003c/span\u003e\u003c/span\u003e=k, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{var}\\left({\\stackrel{\\sim}{z}}_{i}T\\right)\\)\u003c/span\u003e\u003c/span\u003e =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{2\\text{k}(\\text{T}-\\text{k}-1)}{\\text{T}+1}\\)\u003c/span\u003e\u003c/span\u003e, N denotes the cross-sectional dimension, T is the time series dimension, and \u003cem\u003ek\u003c/em\u003e represents the finite positive constant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Panel cointegration test\u003c/h2\u003e \u003cp\u003eTo test the long-term linkages and dynamic cointegration among the variables and following prior studies such as Ali et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Azam \u0026amp; Haseeb (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Su et al. (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), this study employed the error correction-based panel cointegration test or bootstrap panel cointegration technique introduced by Westerlund (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and advanced panel cointegration methods by Persyn \u0026amp; Westerlund (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The Westerlund (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) bootstrap technique is helpful in providing more evidence by calculating accurate and reliable results against CSD and SH issues (Ahmed et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). It provides more accurate and consistent results than other residual techniques, such as Pedroni (2000, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), as it limits the panel cointegration tests to a normal distribution (Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePersyn \u0026amp; Westerlund (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) assess the panel cointegration against H\u003csub\u003e0\u003c/sub\u003e: no cointegration among the panel and H\u003csub\u003e1\u003c/sub\u003e: cointegration exists among the panel variables, using two types of tests: the 1st panel association test and the 2nd group means test. On the basis of the error correction model, the advanced bootstrap has four test statistics normally distributed: group mean tests (Ga and Gt) to test the alternative hypothesis that at least one unit is cointegrated and panel association tests (Pa and Pt) to test the alternative hypothesis that the whole panel is cointegrated. The Pa and Ga statistics consist of the standard errors introduced by Newey \u0026amp; West (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), which are adjusted to address autocorrelation and heteroskedasticity issues. Moreover, Pt and Gt are calculated with the help of the standard error parameter of the error correction model.\u003c/p\u003e \u003cp\u003eThe bootstrap panel cointegration method results for the variables under investigation are expected to show stationarity at the first differential level (Persyn \u0026amp; Westerlund, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Later, the analysis detects and recognizes whether the cointegration exists individually (at least in one unit) or in the entire panel. Thus, the estimates for the dynamic cointegration or long-run association are interpreted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. FMOLS and DOLS estimators\u003c/h2\u003e \u003cp\u003eThis study adopts the Pedroni (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1999\u003c/span\u003e and \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) and FMOLS estimators of Phillips \u0026amp; Hansen (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) to address issues of endogeneity, heteroskedasticity, and serial correlation with the explanatory variables (Ali et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and produces reliable long-term effects of the selected variables on FDI into MENA economies. In addition, this study employed the DOLS long-run estimator of Saikkonen (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) and Stock \u0026amp; Watson (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough the FMOLS and DOLS techniques are broadly used in the literature, specifically in empirical studies related to heterogeneous panels, some studies have criticized these techniques for their inability to handle both cross-sectional dependence and slope heterogeneity efficiently in the presence of endogeneity (Apergis et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Khan et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. Robustness estimators\u003c/h2\u003e \u003cp\u003eTo ensure the consistency and robustness of the results, the augmented mean group (AMG) by Eberhardt \u0026amp; Teal (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and the common correlated effect mean group (CCEMG) by Pesaran (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) estimators were adopted in this study to control for serial correlation and heterogeneous, cross-sectional and endogenous data in panel units and produce a reliable output (Azam \u0026amp; Haseeb, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Su et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Cross-sectional dependence, slope heterogeneity, and unit root test\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the findings for both the CSD and CIPS unit root tests. The results of the CD test delineate the cross-sectional dependency across the MENA economies as it rejects the null hypothesis at a 1% level of significance, implying that our model variables are cross-sectionally interdependent (the transmission of shock in one country against the other).\u003c/p\u003e \u003cp\u003eTo address CSD issues, this study employs the CIPS unit root test by checking for cross-sectional dependence and the integration order of variables. The CIPS unit root test results reject the null hypothesis at the 1st difference, which means that the variables are nonstationary at the level but become stationary at the first difference. The results from the CIPS test show that the variables in the model exhibit a unique order of integration at the 1st difference, which implies that there is a possibility of a long-run relationship among the variables under consideration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the CSD and CIPS unit root tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCD-test\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCIPS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003elevel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1st difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.659***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.245***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.099***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.952***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.633***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.236***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.529***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.890***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.413***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.104***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.420***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.515**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.939***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.294***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.688***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.384***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.223***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.146 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.420***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.54***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.907***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.390***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.612***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.226 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.824***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.416***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.956***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6.305***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.252***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.888***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively,\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e confirms that the slope coefficient parameters are not homogenous throughout the panel data. The statistical results reject the null hypothesis of slope homogeneity at the 1% significance level, meaning that the model has heterogeneity problems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHashem Pesaran \u0026amp; Yamagata (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) Test for slope heterogeneity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStats\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.729***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDelta adjusted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.661***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis means that while there is a long-run relationship among the variables, the nature of this relationship may differ across individual panel units (countries or regions).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The results of panel cointegration\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.2.1. Pedroni panel cointegration test (1st generation test)\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the Pedroni (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) Engle\u0026ndash;Granger-based panel cointegration. The statistical outcomes of both within or inside the panel (Panel PP statistic and Panel ADF statistic) and between the panel dimensions (group PP measurements and group ADF measurements). Within- or inside-panel measurements reject the null hypothesis at the 1% significance level (no cointegration). Additionally, the panel measurements also confirm the rejection of the null hypothesis. Therefore, the Pedroni (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) test results (Panel PP statistic, Panel ADF statistic, Group PP statistic, and Group ADF statistic) confirm that the variables in our model are moving together over the long-term dynamic cointegration.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of Pedroni (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) panel cointegration (Engle-Granger-based)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEstimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStats.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel v-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel σ-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel PP statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-17.907\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanel ADF statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14.898\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAlternative Hypothesis: Individual AR Coefficient\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup σ-statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup PP statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.358\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup ADF statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.651\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.2.2. Westerlund bootstrap panel cointegration results (2nd generation test)\u003c/h2\u003e \u003cp\u003eThe Westerlund (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) bootstrap panel cointegration (2nd generation cointegration) test was applied to test the cointegration between the model variables (long-term relationships between the variables). The outcomes of the bootstrap panel cointegration are reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which displays both within- and between-dimensional outcomes. The statistical results report the rejection of the null hypothesis and acknowledge the alternative hypothesis.\u003c/p\u003e \u003cp\u003eFor this reason, the variables in our model are cointegrated and move over a long run of FDI determinants into the MENA model.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWesterlund (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) Bootstrap panel cointegration results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZ value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDeterministic chosen: Constant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.299**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-13.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.992*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.906*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDeterministic chosen: constant and trend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.2110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.8740**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14.3440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.7120**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0390\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePt\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-17.2850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.4160**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16.9070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.7170**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: The Westerlund panel long-run relationship null hypothesis states that there is no cointegration. The Westerlund bootstrap approach was deployed to test the cross-sectional dependence, and the number of replications was 400. P values are for a one-sided test based on a normal distribution. The robust p value is for a one-sided test based on 400 bootstrap replications. ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTherefore, the cointegration panel test results obtained from 1\u003csup\u003est\u0026minus;\u003c/sup\u003e and 2nd -generation tests (traditional and new cointegration tests) acknowledge the existence of a long-run bond among the variables in our model (variables in our model have a unique order of cointegration and move together over a long-term dynamic cointegration pattern). The results are in line with those of previous studies that followed the same panel cointegration procedure, such as Alharthi et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); An et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); Basher \u0026amp; Westerlund (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e); Khan et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Ponce \u0026amp; Alvarado (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Tachie et al. (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe statistical results of the panel cointegration tests are mainly cross-sectional dependance. The CIPS unit root test and slope heterogeneity test confirm that the dataset used to test the proposed model has interrelationships or interactions between different cross-sectional units, which leads to correlations in the error terms across different cross-sectional units and heterogeneity issues, which means that traditional estimation techniques, namely, fixed effects and random effects, remain consistent However, they may not be efficient, and estimated standard errors can be biased. Therefore, we introduce the FMOLS and DOLS estimation techniques to solve endogeneity, heteroskedasticity and serial correlation issues with the explanatory variables and produce reliable long-term effects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Results from traditional estimators\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the statistical results of both the FMOLS and DOLS estimators. The statistical findings show that the variables have a significant positive effect on FDI, but availability of natural resources, inflation, real interest rate, role of law and voice and accountability, while political stability and regulatory quality does not have any statistically significance. The findings from FMOLS indicate that a 1% increase in available natural resources decreases FDI inflows by 3.496%. Furthermore, FDI increases by 4.271% if the market size increases by 1%; if control of corruption is improved by 1%, FDI inflows increase by 9.5%, and a 1% increase in the real interest rate decreases FDI inflows by 11.67%, and a 1% improvement in government effectiveness leads to FDI inflows of 14.2%.\u003c/p\u003e \u003cp\u003eFMOLS and DOLS suggest that MENA policymakers need to adopt policies and economic reforms that target corruption, remove all trade barriers, improve regulations regarding which enhances the government effectiveness and the financial systems and create appropriate institutions. The empirical assessments of the negative link between availability of natural resources with FDI is align with Elheddad (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Lu et al. (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Mina (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Rogmans \u0026amp; Ebbers (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) findings which argued that availability of natural resources generates an adverse effect on FDI in MENA economies. The positive links between market size and FDI inflows to MENA economies are in accordance with the findings of Abonazel \u0026amp; Shalaby (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Mosallamy \u0026amp; Abbas (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Saad Alshehry (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, the empirical results of the policy factors (inflation, real interest rates and exchange rates) are in accordance with the findings of Aziz \u0026amp; Mishra (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and Jabri et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The results concerning political stability, government effectiveness, and institutional factors support the conclusions of (Alharthi et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Awdeh \u0026amp; Jomaa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rogmans \u0026amp; Ebbers, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong-run panel results through FMOLS and DOLS estimators\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eFMLOS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eDOLS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.496***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.160**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.3059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.271***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.311\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.263**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.4211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7962\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.217***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.4656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.670***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-11.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.922***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-5.2822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.249***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.013***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.1005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.544***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.1189**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.0649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-23.664**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-22.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-1.3150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-9.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.176**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.2070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-18.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-8.0463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.2041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.569***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.3648***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4.5701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: ***, ** and * indicate statistical significance at the 1%, 5% and 10% levels, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Results from the CCEMG and AMG estimators\u003c/h2\u003e \u003cp\u003eFMOLS and DOLS estimators provided results that dealt with endogeneity and serial correlation issues. However, CD issues still exist. Therefore, this section provides proper and reliable stable statistical results in the presence of CD, SH, and endogeneity using CCEMG and AMG estimator robustness estimators.\u003c/p\u003e \u003cp\u003eThe results of the CCEMG and AMG estimators are shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Notably, applying both CCEMG and AMG estimators did not affect the sign of the estimated coefficient (positive or negative effect on FDI), but the coefficient value and its significance level changed.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCCEMG and AMG estimators long-term results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCCEMG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eAMG\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eANR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-4.418***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.809**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.685***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.700***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.128***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-9.338***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.6263***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.7784***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-4.754**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-13.104***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.593**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.208***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-27.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.796***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.449***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-28.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.319\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Asterisks ***, **, *, level of significance at 1%, 5%, and 10%, respectively. (CCEMG) Common correlated effects mean group estimator (AMG) Augmented mean group estimator.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe CCEMG and AMG estimator results support the findings of the traditional estimator results (FMOLS and DOLS). The results of the CCEMG test show that a 1% increase in natural resources, inflation, real interest rate and rule of law deters FDI inflows by 3%, 8.6%, 12.1% and 4.7%, respectively. However, if exchange rates, market size, control of corruption and government effectiveness increase by 1%, FDI inflows will be encouraged by .006%, 2.8%, 5.5% and 16.7%, respectively. The AMG estimator results support the CCEMG, as the direction of the variables' coefficients is the same (impact on FDI).\u003c/p\u003e \u003cp\u003eThe methodological approach in this study and the results from the statistical tests indicate that CCEMG and AMG are more reliable, efficient, and robust estimators for estimating the coefficients of the variables in our model. Therefore, both the CCEMG and AMG results were considered the final results in this study, which solved the issues of endogeneity, serial correlation, CD, and SH in our panel data. Our results are consistent with the findings of (Azzaki et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bhujabal et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Burger et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cieślik \u0026amp; Hamza, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mim \u0026amp; Sa\u0026iuml;dane, 2023; Onyeiwu, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eFDI has an important and decisive role in economic development, economic growth, productivity improvement and income inequality reduction among individuals in host economies.\u003c/p\u003e \u003cp\u003eSeveral studies have documented the positive effects of FDI on the host economy's employment, manufacturing, and consumption. Numerous scholars and legislators agree that FDI inflows have significant positive impacts on host economies in terms of improving their overall well-being and economic standards and attracting more intangible assets, such as managerial skills and pioneering technologies. However, various variables can help host economies attract FDI inflows into their markets, whereas natural resource availability, market size, institutions, governmental policies, and political risk factors play important key roles in determining specific advantages, locations and investment choices (Dunning \u0026amp; Lundan, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and attracting FDI. Therefore, their roles cannot be neglected when investigating the variables that attract FDI into an economy.\u003c/p\u003e \u003cp\u003eIn general, economies with large market size, good institutions, low rates of corruption, and good governmental policies are attractive for MNCs to invest in and benefit the economy by increasing local production and reducing unemployment. The obtainability of resources is vital in triggering and encouraging economic growth.\u003c/p\u003e \u003cp\u003eIt has been observed that natural resource availability, voice and accountability and role of law variables discourage FDI inflows to MENA economies, this result is aligned with the hypothesis of MNCs tend to invest their money in economies where laws are weak and less accountability which allow them more access to the natural resource and low taxes (Chiyaba \u0026amp; Singleton, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the basis of the OLI paradigm, the main objective of this study is to investigate the factors affecting FDI flows into MENA economies, with a special focus on natural resource availability, market size, institutions, governmental policies and political risk factor impacts on FDI inflows from 1980\u0026ndash;2022. This study is different from previous studies in the sense that it covers 24 MENA countries, which are relatively more FDI-attracted countries. Additionally, empirically, the study uses FMOLS and DOLS as traditional estimators and CCEMG and AMG estimators as the main robustness estimators to provide proper and reliable stable statistical results in the presence of CSD, SH and endogeneity issues in panel data.\u003c/p\u003e \u003cp\u003eThe results reveal some insights into the factors that trigger FDI in the MENA region. First, a 'nation's natural resources availability reserves deters FDI inflows (Dutch disease or resource curse phenomenon) this finding is in line with Rogmans \u0026amp; Ebbers (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Regarding market size are positively associated with FDI inflows, our findings are in line with those of Dunning (1988) in terms of the locational advantages of countries, Rugman \u0026amp; Verbeke (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) in terms of country-specific advantages, and Bannaga et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), who suggested that foreign investors are more likely attracted to the host economy with larger markets. The results indicate that MENA economies are sought after for their location. When a host economy is characterized by a large market size, more FDI inflows and more MNCs invest in that economy, whereas the accumulated FDI inflows in host economies attract more FDI inflows (Aziz, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This finding supports the claim that MNCs exhibit a greater propensity to be drawn toward countries that have already amassed substantial FDI. This discernible pattern underscores the pivotal role played by the success of MNCs within the host countries as a compelling impetus for subsequent investments by foreign corporations (Mina, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tun et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, with respect to policy factors, the empirical results show that inflation negatively affects FDI because it triggers the tax system and discourages investors in the long run due to the money illusion (Omankhanlen, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, according to the general theory of investment introduced by Keynes (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1937\u003c/span\u003e), real interest rates play an important role in investment decisions; lower interest rates mean a lower cost of investment than investment potential returns do. The empirical results show that interest rates have an inverse relationship with FDI flows into MENA economies. At the same time, real interest rate fluctuations significantly affect exchange rates in the host economy and, therefore, host economy attractiveness, which in turn affects FDI inflows in the long run (Al-Shakrchy et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, a stable exchange rate improves FDI inflow, and high FDI inflow enhances the stability of the exchange rate in the country.\u003c/p\u003e \u003cp\u003eThe empirical results show that the coefficient of exchange rate is positive and significant, indicating that the appreciation of exchange rate triggers FDI flows to accumulate in MENA economies. This positive sign for exchange rate coefficient indicates that when local currency appreciated against the US dollars, it means better economic condition, therefore more FDI inflows to MENA economies. Our findings are in line (Aziz \u0026amp; Mishra, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Khatabi et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Marial, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), but in contrary to (Al-Shakrchy et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Habash, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Kamal et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study results show that institutional quality (control of corruption, role of law and voice and accountability) factors have significant impacts on FDI inflows into MENA economies. The quality of institutions can affect and improve FDI inflows into MENA economies by increasing the host economy's productivity, enhancing research and development, and facilitating good financial capital with the ability to lend large-scale projects. High-quality institutions can reduce both time and restrictions when doing business. Moreover, low corruption and high voice and accountability levels are important determinants that attract more FDI inflows (Sabir et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Sufian \u0026amp; Moise, 2010). When MNCs consider investing in a foreign economy, transaction cost calculations are an important instrument for deciding whether to carry out the investment. In this context, according to Dunning (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), high-quality institutions play a fundamental role in reducing those transaction costs that may arise because insufficient safeguarded property rights, the lack of a robustly regulated institutional framework, pervasive corruption, underdeveloped financial markets, and ineffective incentive structures contribute to the observed challenges. As a result, more efficient and qualified institutions reduce transaction costs and increase investor trust by ensuring that the host country is committed to providing a stable and developed business environment (Tomassen et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tomassen \u0026amp; Benito, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These findings, which are in line with those of Aziz (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Mina (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Rachdi \u0026amp; Brahim (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), support the idea that FDI inflows are optimized in economies with low levels of corruption. Moreover, higher-quality institutions in host economies attract and optimize FDI inflows. The empirical results suggest that institutional quality improvements attract greater FDI inflows to the market.\u003c/p\u003e \u003cp\u003eFinally, with respect to political risk factors, low political risk (stability, good government effectiveness, and good regulatory quality) can influence more FDI inflows into MENA economies (Gani, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and reduce all unwanted transaction costs that might arise (Asiedu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Morisset, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Political risk variables are crucial elements in defining the host economy's regulations, especially investment regulations, as well as shaping the MNCs\u0026rsquo; investment decisions and activities, which significantly affects the business climate in terms of whether to encourage or discourage FDI inflows (Chalen\u0026ccedil;on \u0026amp; Mayrhofer, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Triki et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The empirical results regarding the insignificance of political stability and regulatory quality suggesting that economies required to focus on improving the quality of their institutions, also foreign investors are concerned more about the domestic business environment and economic freedom rather than democracy, political stability and regulations in MENA region. Our empirical results are in accordance with Gani \u0026amp; Al-Abri (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); Kobeissi (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); Rogmans \u0026amp; Ebbers (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e"},{"header":"6. Conclusions, implications, and future research","content":"\u003cp\u003eThis study investigates and assesses the factors affecting FDI inflows into MENA economies, with a particular emphasis on market and resource-seeking motives; institutional and government policies (macroeconomic level); and political risk variables. The results imply that FDI flows are derived mainly through, large market sizes, qualified institutions, good governmental policies, and low levels of corrupted economies.\u003c/p\u003e \u003cp\u003eThe results reveal that FDI inflows are also attracted to economies where their local currencies are appreciated against US dollar, high-quality public services and the credibility of the government's commitment, economies that can formulate and implement policies and regulations that promote private sector development, and economies with good quality of governance, which positively affects private sector development.\u003c/p\u003e \u003cp\u003eThe results of this study have several implications for academics and policymakers and provide some promising research paths.\u003c/p\u003e \u003cp\u003eAt a theoretical level, this study contributes to the literature by performing a systematic analysis to identify the significant determinants of FDI inflows into MENA economies, which consider that more market seeking affect the behavior of MNCs to invest more FDI. Methodologically, it employs proper cointegration techniques for testing the structural long-run relationships among the variables in the proposed model. Furthermore, CCEMG and AMG are more reliable, efficient, and robust estimators for estimating the coefficients of econometric models in panel data that have issues with endogeneity, serial correlation, CSD, and SH.\u003c/p\u003e \u003cp\u003eThe conclusions have several important policy and managerial implications that might help host economy governments and law makers determine the factors that encourage and discourage FDI inflows. First, natural resource availability, market size, control of corruption, institutional quality, and political stability have proven to be significant determinants of FDI inflow into the MENA region while political stability and regulatory quality are insignificant. Thus, efforts aimed at formulating strong policies and reforms, paying particular attention to their institutional quality and effectiveness, and stable monetary policies should be adopted to control inflation and real interest rates and improve regional trade by reducing all barriers and restrictions and diversifying their economies. Second, investment strategies that help promote and enhance intraregional FDI should be adopted, creating suitable investment protection and insurance instruments that attract foreign investors to do business. Third, policies regarding the rule of law and regulatory quality should be improved to create a good investment climate; reduce taxes, capital and profit transfer restrictions; and build qualified dispute resolution entities.\u003c/p\u003e \u003cp\u003eDespite the contributions of this study, there are several limitations to highlight. First, we do not include variables related to the reliability of local infrastructure, total factor productivity (TFP), or capital openness. The question of whether those variables promote FDI in the MENA region requires further investigation. Second, the study considered the availability of natural resources in MENA economies and did not acknowledge climate change or the rise of global environmental awareness. The impacts of environmental regulations on FDI inflows into MENA economies require further investigation to help MNCs and policymakers develop environmentally friendly FDI strategies that improve the attractiveness of FDI without harming the environment. Third, the study employs traditional institutional factors. Future research may look further into factors such as conflict intensity, corruption distance, and religious tensions and how these factors can influence NNC investment decisions in MENA economies. Future research should investigate whether different types of violent conflict have different impacts on MNCs investing in MENA economies. More research is needed to understand the effects of political Islam on government effectiveness in terms of formulating foreign investment policies in MENA economies. Additionally, more emphasis should be placed on the impact of the distance of corruption between the home country and the host MENA country. Fourth, different theoretical frameworks, such as transaction cost theory (TCT), the Uppsala model, network theory, the market imperfection approach, and the resource-based view (RBV), could be used to test and validate these theories against the MENA economies. Finally, another area of future research is possible by applying the cross-sectionally augmented autoregressive distributed lag model (CS-ARDL) estimator, to solve any undiscovered issues of endogeneity, non-stationarity, mixed-order integration, SH, and CSD issues and panel-corrected standard error (PCSE) to control auto correlation and heteroskedasticity in the panel dataset under investigation and obtain accurate and robust estimation results in both long and short-run coefficients by applying dynamic common correlated impact predictors.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have not received any funding to accomplish this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study for empirical analysis were obtained from World Development Indicators, World Bank database, and world governance indicators.\u003c/p\u003e\n\u003cp\u003eEthics declarations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no affiliation or involvement in an organization or entity with a financial or nonfinancial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions,\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by correspondent Author. The first draft of the manuscript was written by correspondent Author. Second and third authors (respectively) commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdelkarim, J., Abid, I., \u0026amp; Guesmi, K. (2012). Determinants Effects of Foreign Direct Investment in Middle East and North Africa Region: Panel Cointegration Analysis. \u003cem\u003eSSRN Electronic Journal\u003c/em\u003e. https://doi.org/10.2139/ssrn.2071187\u003c/li\u003e\n \u003cli\u003eAbed, G., \u0026amp; Davoodi, H. (2003). Challenges of Growth and Globalization in the Middle East and North Africa. In \u003cem\u003eInternational Monetary Fund, Publication Services\u003c/em\u003e. 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The Economic Institutions of Capitalism. \u003cem\u003eThe Antitrust Bulletin\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(3), 827\u0026ndash;834. https://doi.org/10.1177/0003603X8603100308\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Foreign Direct Investment, MENA, Political Risk, Market Size, Resources Seeking","lastPublishedDoi":"10.21203/rs.3.rs-5319811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5319811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: this study investigates factors influencing foreign direct investment (FDI) inflows to the Middle East and North Africa (MENA) region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eApproach/design:\u003c/strong\u003e using a balanced Panel data from 24 MENA economies covering 1980-2022 we employ econometric techniques (unit root, cointegration, FMOLS, DOLS, AMG, CCEMG) to evaluate the impacts of market and resource-seeking motives, institutional, government policies (macroeconomic level), and political risk on FDI inflows into MENA region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e The long-term results suggest that market size, low corruption, government effectiveness and exchange rate contribute to FDI inflows while natural resources availability, role of law and voice and accountability deters FDI inflows to MENA. Contrary, political stability and regulatory quality factors has no significant effects on FDI inflows.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOriginality/Value: \u003c/strong\u003ethe study has potential implications for boosting FDI inflows into MENA region MENA such as: Policymakers of MENA economies should adopt stable monetary policies, implement policies and regulations to Promote private sector development, improve institutional quality and maintain macroeconomic stability. Diversification of economies and reduction of reliance on natural resources are essential for long-term FDI attraction. Additionally, green innovation should be encouraged by spending more on R\u0026amp;D, green technology and improving policies regarding the rule of law to create a good investment environment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuture Research:\u003c/strong\u003e Further research is needed to explore the specific mechanisms through which these factors influence FDI inflows and to assess the potential impact of other variables, such as technological advancements and regional integration.\u003c/p\u003e","manuscriptTitle":"Determinants of foreign direct investment in the Middle East and North Africa region: evidence from balanced panel data analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-17 08:12:46","doi":"10.21203/rs.3.rs-5319811/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5427adab-af7f-4b3d-a5f6-070a06f7ae30","owner":[],"postedDate":"January 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41452059,"name":"Business and commerce/Economics"},{"id":41452060,"name":"Social science/Business and management"}],"tags":[],"updatedAt":"2025-01-17T08:12:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-17 08:12:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5319811","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5319811","identity":"rs-5319811","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}