Asymmetric Growth Dynamics: The Performance-Based Growth-Nexus of Investment, Trade, and Energy in Middle-Income Countries

Asymmetric Growth Dynamics: The Performance-Based Growth-Nexus of Investment, Trade, and Energy in Middle-Income Countries | 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 Research Article Asymmetric Growth Dynamics: The Performance-Based Growth-Nexus of Investment, Trade, and Energy in Middle-Income Countries Aya Khater, Dina Yousri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8377513/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 This study investigates the impact of gross capital formation, foreign direct investment, trade openness, and energy consumption on economic growth in 97 middle-income countries, adopting a performance-based analytical framework. Unlike conventional studies that rely solely on income classifications, this research divides middle-income countries into “Overachievers” and “Underperformers” to capture intra-group divergence in growth dynamics. Employing second-generation panel techniques, including the Cross-Sectionally Augmented IPS (CIPS) test, panel ARDL modeling, and Granger causality analysis for 1993–2020, the findings reveal distinct performance-driven patterns. Gross capital formation consistently exerts a positive and significant long-run effect on growth across both groups, emphasizing the central role of domestic investment. Energy consumption also contributes positively in the long-run, with short-run bidirectional causality observed only among Overachievers. Conversely, foreign direct investment demonstrates a negative effect for Underperformers and an insignificant effect for Overachievers, largely explained by sectoral concentration in low-value-added, enclave, or service-oriented investments with limited linkages and spillovers. Trade openness supports growth in Underperformers but is insignificant for Overachievers, reflecting structural and compositional differences in trade dynamics. These results underscore the importance of performance-based classifications rather than the use of conventional income categories. Lastly, this paper emphasizes the significance of adopting a performance-based approach for capturing heterogeneous growth effects and highlights the need for policies that enhance domestic investment efficiency, strengthen institutional quality, and channel foreign direct investment into sectors with higher spillover potential. JEL Classification: C33, E22, F43, O11, O47 Economic Growth Gross Capital Formation Foreign Direct Investment Trade Openness Energy Consumption Middle-Income Countries Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Economic growth remains a central objective in middle-income countries, as it drives enhancements in living standards, poverty alleviation, and the creation of broader economic opportunities. In recent decades, growth patterns have shifted under the influence of globalization, technological advancement, institutional reforms, and environmental pressures, reshaping the conventional determinants of growth. The literature identifies various key drivers of economic expansion, notably domestic investment, foreign direct investment (FDI), trade openness, and energy consumption (Omri & Kahouli, 2014 ; Kalu et al., 1991; Alsamara et al., 2019 ; Dinh et al., 2019 ). Trade openness spurs economic growth by improving resource allocation, promoting specialization, and enabling access to larger markets (Kalu et al., 2016 ), although it can have adverse effects on growth in developing economies experiencing trade deficits (Bibi et al., 2014 ; Yanikkaya, 2003 ). Additionally, it facilitates technology diffusion, increases competition, and improves access to intermediate goods (Adhikary, 2011 ). On the other hand, FDI enhances long-term growth through channels such as technology transfer, research and development, and managerial efficiency gains (Shinwari et al., 2024 ; Kalu et al., 2016 ; Iamsiraroj, 2016 ). Also, domestic investment remains a fundamental pillar, particularly when complemented by human capital development, infrastructure expansion, and adequate credit provision (Dinh et al., 2019 ; Bakari, 2017 ). Furthermore, energy consumption plays a pivotal role, especially within industrial and service sectors and exhibits a bidirectional relationship with economic growth (Mezghani & Haddad, 2017 ; Acaravci et al., 2015 ). Economic growth drivers have always been a center of discussion in economic literature, where the majority of research has been conducted focusing on subsets of countries using traditional income categories. Using the latter, the intra-income divergence is commonly neglected, as it cannot be captured through the use of rigid income classifications proposed by the World Bank or United Nations (Lencucha & Neupane, 2022 ; Fantom & Serajuddin, 2016 ; Fialho & Van Bergeijk, 2017 ). Hence, this paper aims to answer the research question “What is the impact of gross capital formation, foreign direct investment, trade openness and energy consumption on economic growth in middle-income countries?”. Concurrently, it aims to shed light on intra-group differences in growth performance by adopting a performance-based econometric framework, inspired by the approach of Barreto and Hughes ( 2004 ), which emphasizes actual economic performance rather than income category. By doing so, middle-income countries are further classified as “Overachievers” and “Underperformers,” allowing an assessment of how these key variables influence growth depending on performance level. The results provide support for the previously explained performance-based classification, by showing intra-income divergence, specifically regarding the effect of FDI and trade openness middle-income countries’ economic growth. Thus, this study contributes to the literature by concentrating solely on middle-income economies and demonstrating the intra-dimensional divergence that conventional income classifications often overlook. The remainder of this paper is organized as follows. Section 2 provides a comprehensive literature review, examining the concept of economic growth and its principal determinants (gross capital formation, foreign direct investment, trade openness, and energy consumption) while synthesizing the most relevant empirical findings. Section 3 then presents the research methodology and describes the dataset employed in the analysis. Sections 4 and 5 report and interpret the empirical results. Finally, Section 6 translates these findings into policy recommendations and provides concluding remarks. 2. Literature Review The literature identifies multiple determinants of economic growth. Classical and neoclassical frameworks emphasize capital accumulation as a primary source of growth, whereby expanding physical capital in combination with labor raises output, particularly in the short term (Smith, 1776 ; Ricardo, 1821 ; Solow, 1956 ; Swan, 1956 ). Empirical studies consistently show that gross capital formation is positively linked to growth, particularly in developing economies (Aslan & Altinoz, 2021 ; Kim & Loayza, 2021 ). However, the principle of diminishing returns to capital underscores the need for complementary inputs, such as human capital and technology. Human capital accumulation enhances productivity and innovation, as Lucas ( 1988 ) notes, and educational investment is associated with long-run economic growth (Hanushek & Woessmann, 2012 ; Barro, 2001 ). Better-educated economies are also better positioned to adopt foreign technologies (Rodriguez-Segura, 2022 ; Borensztein et al., 1998 ). Sustained growth further relies on technological innovation. With respect to the latter, Romer ( 1990 ) highlights endogenous innovation and knowledge spillovers as central drivers of long-term expansion. Countries with strong innovation systems, research and development, patents and advanced digital infrastructure, tend to achieve faster growth (Gomes et al., 2022 ; Aghion & Howitt, 2006 ; Coe et al., 2009 ). Also, trade openness, as argued by Frankel and Romer ( 1999 ), promotes growth by facilitating economies of scale, enhancing competition, and improving access to technology, with the strongest gains observed in countries with sound institutions and infrastructure (Wacziarg & Welch, 2008 ). Nonetheless, its impact remains context-specific, depending on national competitiveness and structural conditions. Similarly, foreign direct investment contributes to growth by providing capital and technology, though its effectiveness depends on absorptive capacity (Borensztein et al., 1998 ), institutional strength, and financial sector development (Hayat, 2019 ). Ongoing disparities in cross-country growth continue to raise questions about which structural and policy conditions best support development, particularly in the context of globalization (Rodrik, 2003 ). Consequently, examining growth determinants within specific economic contexts remains a central focus of contemporary research. 2.1 The Effect of Gross Capital Formation on Economic Growth Gross capital formation (GCF) is widely recognized as a fundamental engine of economic growth, encompassing investments in physical assets, such as infrastructure, machinery, equipment, and land improvements (World Bank Metadata Glossary, 2025). These investments increase productive capacity, facilitate the diffusion of technology, and support long-term structural transformation. GCF also accounts for changes in inventories and, under the 1993 System of National Accounts, includes the acquisition of valuables. One of the primary channels through which GCF stimulates growth is capital deepening , which enhances labor productivity, particularly in capital-scarce economies (Solow, 1956 ; Barro & Sala-i-Martin, 2004 ; Kim & Loayza, 2021 ). Sectoral investments in agriculture and manufacturing contribute to economies of scale and higher output (OECD, 2015 ), while the adoption of advanced capital goods generates knowledge spillovers and promotes learning-by-doing (Romer, 1990 ; Lucas, 1988 ; Moretti, 2004 ; Wang & Choi, 2023 ). Furthermore, infrastructure development enhances market efficiency and reduces transaction costs, thereby encouraging trade, mobility as well as private-sector investment (Calderón & Servén, 2010 ; Pradhan & Bagchi, 2013 ). GCF also facilitates structural transformation by reallocating resources from low- to high-productivity sectors, fostering industrial upgrading and economic diversification (Rodrik, 2013 ; McMillan et al., 2017 ). It stimulates employment in construction and related industries, raises demand for skilled labor and fosters investment in human capital (Lucas, 1988 ). Nevertheless, the growth impact of GCF is contingent upon the quality of institutional and financial systems, as weak governance can lead to resource misallocation and reduced returns (Dabla-Norris et al., 2013 ; Levine, 2005 ). Such growth-enhancing role of GCF is consistently supported by empirical evidence (Pasara & Garidzirai, 2020 ; Rani & Kumar, 2019 ; Kesar et al., 2023 ; Maune et al., 2023 ; Gibescu, 2013 ). Hence, this study examines the effect of GCF on economic growth in high-income countries, hypothesizing a positive effect of GCF on economic growth in underperforming MICs (H1.1) as well as in overachieving MICs (H1.2). 2.2 The Effect of Foreign Direct Investment on Economic Growth According to the World Bank (World Bank Metadata Glossary, 2025), foreign direct investment is defined as the equity inflows into an economy, including reinvested earnings and long-term capital. It is commonly viewed as a growth driver in economic literature through channels such as capital injection, technology transfer and enhanced management practices (Borensztein et al., 1998 ). Nevertheless, its contribution to growth is not guaranteed and is largely contingent on the host country’s absorptive capacity, in particular the level of human capital. As Azam and Emirullah ( 2014 ) note, education enables economies to integrate more effectively into global production networks and promotes knowledge transfer. Likewise, Alfaro et al. ( 2004 ) argue that well-developed financial markets ensure the efficient allocation of FDI toward high-productivity sectors, whereas weak financial systems may result in misallocation or crowding out of domestic firms. Complementing the latter, Azman-Saini and Law ( 2010 ) demonstrate that FDI exerts a positive growth effect only after financial development reaches a critical threshold. Moreover, institutional quality is equally important in determining the growth effects of FDI. Strong governance frameworks enhance its effectiveness, whereas corruption and weak legal systems can undermine or even eliminate its benefits (Ofori & Asongu, 2024 ; Herzer, 2012 ; Sunde, 2017 ; Van Bon, 2019 ). The sectoral distribution of FDI also influences its growth impact. Chaudhury et al. ( 2020 ) show that FDI in manufacturing does not necessarily promote industrialization or productivity gains, particularly when domestic linkages are limited. Building on these insights, this paper investigates the effect of FDI on economic growth in middle-income countries, assuming a positive impact in underperforming MICs (H2.1) and in overachieving MICs (H2.2). 2.3 The Effect of Trade Openness on Economic Growth Trade openness (TO) refers to the extent to which an economy allows the cross-country flow of goods, services, and capital, and it is widely recognized as an important driver of economic growth. Classical and neoclassical theories emphasize that trade promotes specialization according to comparative advantage, enhancing resource allocation and raising aggregate output (Krugman & Obstfeld, 2009 ). From the perspective of endogenous growth models, trade further contributes to growth by providing access to foreign technologies, fostering innovation, and generating knowledge spillovers, particularly through imports of intermediate goods and exposure to international competition (Grossman & Helpman, 1991; Coe & Helpman, 1995 ). These benefits are reinforced through robust institutions, adequate infrastructure and well-developed human capital (Bhaumik et al., 2025 ; Keller, 2004 ; Falvey et al., 2004 ). Moreover, greater openness enables firms to access larger markets, exploit economies of scale, and improve efficiency (Melitz & Ottaviano, 2008 ). Empirical evidence consistently supports these growth-enhancing effects (Kong et al., 2021; Imoisi, 2018 ; Tahir & Azid, 2015 ; Ramanayake & Lee, 2015 ; Adeel-Farooq et al., 2017 ; Vamvakidis, 2002 ). However, the benefits of TO are not uniform across countries. In economies with undiversified exports or fragile domestic industries, trade can increase vulnerability to external global shocks and cause premature deindustrialization. Openness may also increase income inequality, as benefits are often gained by skilled labor and capital-intensive sectors only (Liu et al., 2022 ; Goldberg & Pavcnik, 2007 ). Moreover, high reliance on commodity exports can slow down long-term economic growth through the “resource curse” (Hausmann & Rigobon, 2003 ). The previously mentioned risks are highlighted in empirical studies, emphasizing that the growth effects are highly context-dependent and affected by domestic conditions (Bibi et al., 2014 ; Yanikkaya, 2003 ; Adhikary, 2011 ; Caceres, 2017 ; Musila & Yiheyis, 2015 ). Nevertheless, this study investigates the effect of TO on economic growth in MICs, hypothesizing a positive effect in both underperforming as well as overachieving MICs (H3.1 and H3.2, respectively). 2.4 The Effect of Energy Consumption on Economic Growth Energy consumption is widely acknowledged as a fundamental driver of economic growth, serving as the backbone of production, transportation, and modern service activities (Stern, 2011 ). While classical growth models focus primarily on capital and labor, contemporary frameworks recognize energy as a third essential input, necessary for operating industrial machinery, facilitating mobility, and expanding productive capacity. Incorporating energy into production functions highlights its critical role in sustaining long-term economic expansion (Stern, 2011 ). Moreover, energy use increasingly drives technological advancement and structural transformation. Furthermore, the global transition toward renewable energy not only aligns with climate objectives but also stimulates investment, fosters job creation, and promotes economic diversification (Dirma et al., 2024 ; Apergis & Payne, 2010 ). The significance of energy consumption has grown due to its rising global demand, caused by population growth, rapid urbanization and industrialization, particularly in developing economies. Access to reliable energy has consequently become essential to enhancing productivity, improving efficiency and maintaining competitiveness (Alshami & Sabah, 2019 ; Lee & Chang, 2008 ). The energy-growth nexus has been extensively explored in the literature through four main hypotheses: the growth hypothesis, conservation hypothesis, feedback hypothesis and neutrality hypothesis. The growth hypothesis assumes that energy drives GDP (Lee & Chang, 2008 ; Fareed & Pata, 2022 ; Tang et al., 2016 ; Li et al., 2011 ; Chiou-Wei et al., 2008 ), while the conservation hypothesis states that GDP causes energy use (Magazzino et al., 2021 ; Karanfil, 2008 ; Ahmed et al., 2015 ; Alper & Oguz, 2016 ; Destek & Aslan, 2017 ). The feedback hypothesis indicates a bidirectional causality between both variables (Phukon & Konwar, 2019 ; Behmiri & Manso, 2012 ) and the neutrality hypothesis suggests no causal relationship (Narayan, 2016 ; Menegaki, 2011 ; Payne, 2009 ; Faisal et al., 2016 ). This paper examines the effect of energy consumption on economic growth in MICs, hypothesizing a positive effect on economic growth in both underperforming and overachieving MICs (H4.1 and H4.2, respectively). Furthermore, the author assumes energy consumption to Granger cause economic growth in underperforming MICs, while a bi-directional relationship is expected for overachieving MICs (H4.3 and H4.4, respectively). 3. Data & Methodology This study seeks to provide deeper insights into the relationship between economic growth, gross capital formation, foreign direct investment, trade openness, and energy consumption across 97 middle-income countries 3 during the period 1993–2020. The employed model is presented in Eq. 1 . $$\:{GDP\_gr}_{t}\:=\:{\beta\:}_{0}+\:{\beta\:}_{1}{GCF\_gr}_{t}\:\:+\:{\beta\:}_{2}\:{FDI\_gr}_{t}\:+\:{\beta\:}_{3}{TO\_gr}_{t}\:+\:{\beta\:}_{4}\:{EC\_gr}_{t}\:+\:{\mu\:}_{t}$$ 1 Data on economic growth, measured by GDP growth, and GCF are sourced from UNCTAD. FDI and TO (proxied by the sum of exports and imports as a percentage of GDP) are obtained from the World Bank. Energy consumption per capita is retrieved from Our World in Data (OWID). Income classification follows the definitions and methodology adopted by the World Bank. To account for inflation, the CPI deflator from UNCTAD is applied, whenever reported data does not account for inflationary effects. All variables are expressed in growth rates. Table 1 presents the variables along with their definitions and respective sources. Table 1 Variables' Definitions and Sources Variable Definition Source GDP_gr Growth in Real Gross Domestic Product (constant 2015) United Nations Conference on Trade and Development Data Hub GCF_gr Growth in Real Gross Capital Formation (constant 2015) United Nations Conference on Trade and Development Data Hub FDI_gr Growth in Real Foreign Direct Investment Inflows (CPI deflated) World Bank TO_gr Growth in Real Trade Openness (Total Trade in Goods and Services/GDP) (CPI deflated) World Bank EC_gr Growth in Primary Energy Consumption/capita (kW/h per capita) OWID Data Bank The World Bank classifies all economies based on income per capita to facilitate the assessment of national performance and the formulation of economic policies. Such income-based groupings enable researchers to address the most pressing challenges within each category and to ensure that policy recommendations are contextually relevant. Nonetheless, this classification system has been widely criticized for overlooking disparities in economic performance among countries within the same income category (Lencucha & Neupane, 2022 ; Fantom & Serajuddin, 2016 ; Fialho & Van Bergeijk, 2017 ). To address this limitation, the present study introduces a performance-based subdivision by further splitting each income category into two groups: “Overachievers” and “Underperformers.” This classification is derived through an Ordinary Least Squares regression in which GDP growth is explained by changes in GCF, FDI, TO and EC. The resulting residuals for each country are averaged and ranked according to their magnitude and sign. Countries with consistently high positive residuals are identified as “Overachievers,” indicating stronger-than-expected growth performance, whereas those with highly negative residuals are classified as “Underperformers,” reflecting weaker-than-expected outcomes. The top ten and bottom ten performers are subsequently selected for in-depth analysis to highlight intra-income divergence to examine how the selected explanatory variables influence economic growth. Various econometric techniques can be employed to examine the relationship between GCF, FDI, TO, EC and economic growth, including the Generalized Method of Moments (GMM), Dynamic Ordinary Least Squares (DOLS), and Fully Modified Ordinary Least Squares (FMOLS). However, both DOLS and FMOLS require the variables to be cointegrated, which is unsuitable in this study since, as demonstrated by the stationarity tests, most variables are not integrated of order one. GMM, while robust to endogeneity, heteroskedasticity, and serial correlation, is primarily suited for short panels, where the cross-sectional dimension exceeds the time dimension (N > T), a condition not met in this analysis. Although instrumental variable and GMM estimators, such as the widely applied Arellano and Bond (1991) approach, are consistent and asymptotically efficient in large samples, their efficiency is lower in panels with a limited number of cross-sections, as shown in Monte Carlo simulations (Bruno, 2005 ; Judson & Owen, 1999 ; Kiviet, 1995 ). In this study, middle-income countries are subdivided into ten “Overachievers” 4 and ten “Underperformers” 5 to capture intra-group performance divergence, resulting in panels with N = 10 and T = 97, which violates the standard GMM requirements. Consequently, the Autoregressive Distributed Lag (ARDL) model is adopted as it is better suited to this data structure and does not impose such restrictions. Furthermore, the Granger causality test is applied to assess the direction of causality between energy consumption and economic growth, consistent with the energy–growth nexus literature. All estimations are conducted using EViews 13. 3.1 Cross-Sectional Dependence With the rise of globalization and increasingly interconnected financial systems, cross-sectional dependence has become a critical consideration in panel data analysis, yet it is often overlooked by researchers. Cross-sectional units are frequently influenced by common unobserved shocks, such as global economic crises, technological advancements, or policy changes, which induce correlations in the error terms across units (Pesaran, 2006 ). Failing to account for this contemporaneous correlation can lead to biased or spurious results, ultimately weakening the reliability of inferences and policy recommendations (Tackie et al., 2022 ; EViews 12 User’s Guide, 2020). To address this, EViews 13 provides three primary tests for detecting cross-sectional dependence in panel datasets: the Breusch-Pagan LM test (1980), Pesaran’s Scaled LM test (2004), and Pesaran’s CD test (2004). 3.2 Panel Stationarity Test The initial stage of the analysis involves performing stationarity tests on each variable to identify potential trends or seasonal effects. A stationary time series is characterized by a constant mean and variance over time, with the covariance between observations depending solely on the lag between them rather than the specific points in time at which it is measured (Gujarati & Porter, 2010 ). As highlighted by Granger and Newbold (as cited in Akinwale & Grobler, 2019 ), non-stationary series pose the risk of generating spurious regressions, which can produce misleading interpretations. Such series are only reliable for short-term behavior and cannot be generalized to other periods, making them unsuitable for forecasting (Gujarati & Porter, 2010 ). In the presence of cross-sectional dependence, it is necessary to employ second-generation panel unit root tests that explicitly account for interdependencies across units. EViews offers two main approaches for this purpose: the PANIC test by Bai and Ng (2004) and the Cross-sectionally Augmented IPS (CIPS) test by Pesaran ( 2007 ). This study utilizes the CIPS test, as it enhances the conventional IPS test by incorporating cross-sectional averages to address unobserved common factors within the panel. The null hypothesis assumes that all series are non-stationary (contain a unit root), whereas the alternative suggests that at least one is stationary. Unlike the PANIC test, which is primarily suited for explicitly modeling latent factors, the CIPS test is more appropriate for the present context, given the moderate panel size and the objective of assessing stationarity under cross-sectional dependence across heterogeneous units (Choi, 2006 ; Pesaran, 2007 ). 3.3 Multicollinearity Test (VIF) Multicollinearity arises when independent variables in a model are highly correlated, creating substantial redundancy in the explanatory information they provide. While it does not inherently bias the model’s overall fit, it severely limits the ability to isolate the unique contribution of each independent variable to the dependent one. This phenomenon produces unstable and inaccurate coefficient estimates, inflates standard errors, and diminishes statistical power, ultimately complicating the identification of significant relationships. In more severe forms, multicollinearity undermines the interpretability and credibility of the model, thereby weakening the robustness and validity of any inferences or policy recommendations derived from the analysis. To detect and address such concerns, a Variance Inflation Factor test is conducted, to help quantify the extent of shared variance among predictors (Kyriazos & Poga, 2023 ; Salmerón et al., 2022). 3.4 Autoregressive Distributed Lag Model The Auto Regressive Distributed Lags Model (ARDL) was initially proposed by Pe-saran and Shin in (1995) and later expanded by Pesaran et al. (2001). This model employs standard ordinary least squares regression and incorporates lags of both dependent and independent variables. As noted by Farhani et al. ( 2014 ), Acaravci and Ozturk ( 2010 ), Ahmad et al. ( 2017 ), Al-Mulali et al. ( 2015 ) and Ghosh ( 2010 ), ARDL has recently gained traction in co-integration testing due to several advantages over other methods. First, it allows for variables integrated of different orders (i.e., I(0) or I(1)). Second, it provides efficient and unbiased estimators for both large and small sample sizes. Third, it effectively addresses endogeneity issues within the variables. Additionally, ARDL permits the use of varying lag orders for each variable, deter-mined by criteria such as the Akaike Information Criterion (AIC) and the Schwarz Bayesian Criterion (SBC), all within a single reduced equation framework. Lastly, the ARDL model overcomes the problem of autocorrelation (Pumphrey & Salah, 2022 ), provides unbiased estimates accounting for nonlinearity, heterogeneity and nonstationarity (Anoruo et al., 2024 ), and is also suitable in case of Cross-Sectional Dependence (Tackie et al., 2022 ; Anoruo et al., 2024 ; Tabash et al., 2022 ; Oyinlola et al., 2023 ). The lag length was determined through an iterative process, guided by diagnostic checks and empirical robustness. Several lag structures were estimated and assessed based on the consistency of coefficient signs, statistical significance and the overall model stability. 3.5 Granger Causality Test In economic literature, specifically the energy-growth nexus is commonly addressed through investigating the direction of causality between these two variables (Adams et al., 2016 ; Alper and Oguz, 2016 ; Behmiri and Manso, 2012 , Belke et al., 2011 ; Chiou-Wei et al., 2008 ; Destek and Aslan, 2017 ; Faisal et al., 2016 ; Payne, 2009 ). In order to do that, the Granger causality test is applied, to examine whether past values of energy consumption statistically improve the prediction of economic growth, or vice versa. A lag length of two was selected to capture only the most recent dynamics in the data, thereby minimizing the risk of distortion in the causality direction. Extending the lag structure further may artificially suggest the presence of causality where none exists, potentially leading to misleading inferences. 4. Results 4.1 Data Preparation In order to ensure robustness of the findings, the authors started by first running cross-sectional dependence tests, followed by stationarity tests and VIF analysis, as shown in the below sections. 4.1.1 Cross-Sectional Dependence Test Results With regards to Tables 1 and 2 , we do not reject H 0 of having no cross-sectional dependence for underperforming MICs. In other words, we conclude that data for underperforming MICs is free of cross-sectional dependence. Regarding overachieving MICs, we fail to reject H 0 , which means that there is substantial evidence for cross-sectional dependence among MICs’ Overachievers (Table 2 ). Therefore, we have to account for the latter in the coming tests. Table 2 Cross-Sectional Dependence - Underperformers MICs Residual Cross-Section Dependence Test Null hypothesis: No cross-section dependence (correlation) in residuals Equation: Untitled Periods included: 28 Cross-sections included: 10 Total panel observations: 280 Note: non-zero cross-section means detected in data Cross-section means were removed during computation of correlations Test Statistic d.f. Prob. Breusch-Pagan LM 49.23628 45 0.3075 Pesaran scaled LM 0.446543 0.6552 Pesaran CD 1.546885 0.1219 Table 3 Cross-Sectional Dependence - Overachievers MICs Residual Cross-Section Dependence Test Null hypothesis: No cross-section dependence (correlation) in residuals Equation: Untitled Periods included: 28 Cross-sections included: 10 Total panel observations: 280 Note: non-zero cross-section means detected in data Cross-section means were removed during computation of correlations Test Statistic d.f. Prob. Breusch-Pagan LM 139.2025 45 0.0000 Pesaran scaled LM 9.929818 0.0000 Pesaran CD 5.337382 0.0000 4.1.2 Stationarity Test Results According to Table 3 , we can conclude that all variables for the MICs’ Underperformers as well as Overachievers are stationary at levels. Table 4 Summary of Stationarity Test Results MICs Underperformers Overachievers IPS ADF CIPS GDP Levels 0.0000*** 0.0000*** Constant - 1st Difference 0.0000*** 0.0000*** Constant & Trend < 0.01*** GCF Levels 0.0000*** 0.0000*** Constant - 1st Difference 0.0000*** 0.0000*** Constant & Trend < 0.05** FDI Levels 0.0000*** 0.0000*** Constant < 0.01*** 1st Difference 0.0000*** 0.0000*** Constant & Trend - TO Levels 0.0000*** 0.0000*** Constant < 0.01*** 1st Difference 0.0000*** 0.0000*** Constant & Trend - EC Levels 0.0000*** 0.0000*** Constant < 0.01*** 1st Difference 0.0000*** 0.0000*** Constant & Trend - *, **, *** indicate a significance at 10%, 5% and 1% levels, respectively. 4.1.3 Multicollinearity Test Results According to the VIF test (Table 5 and 6 ), it is evident that there is no multicollinearity between independent variables, for both, HICs’ Underperformer as well as Overachievers, as the VIF statistic lies around one for all variables (Khan et al., 2023 ; Chien et al., 2022 ). Table 5 VIF - Underperformers MICs Variance Inflation Factors Date: 07/20/25 Time: 12:54 Sample: 1993 2020 Included observations: 280 Coefficient Uncentered Centered Variable Variance VIF VIF GCF_GR 7.56E-05 1.037925 1.025433 FDI_GR 4.19E-07 1.027918 1.003136 TO_GR 0.000983 1.025125 1.023481 EC_GR 0.001860 1.038067 1.024883 C 0.269047 1.049213 NA Table 6 VIF - Overachievers MICs Variance Inflation Factors Date: 07/20/25 Time: 12:54 Sample: 1993 2020 Included observations: 280 Coefficient Uncentered Centered Variable Variance VIF VIF GCF_GR 1.60E-05 1.110075 1.089946 FDI_GR 2.40E-06 1.050648 1.001493 TO_GR 0.001614 1.097950 1.092136 EC_GR 4.74E-05 1.028909 1.008307 C 0.372642 1.091382 NA 4.2 Autoregressive Distributed Lag Model Results As shown in the ARDL results (table 17), for MICs’ Underperformers have a 5% significant long-run relationship between the independent and dependent variables, since the cointegrating coefficient is -0.425175. Hence, it can be concluded that approximately 43% of the short-run disequilibrium is corrected in each period. Also, all independent variables show a 1% significant impact on GDP growth. In particular, the results for MICs’ Underperformers explain an approximate 0.2% increase in GDP growth due to a 1% change in domestic investment, while FDI is shown to decrease economic growth by roughly 0.004%. As for TO and EC, they both boost GDP growth by nearly 0.8% and 0.21%, respectively. Regarding MIC’s Overachievers (Table 8 ), the cointegrating coefficient is also negative and significant at the 1%, showing a slightly faster speed of adjustment, namely, around 52% of the deviation from the long-run equilibrium is corrected each period. Again, GCF and EC show a positive and significant effect on economic growth with a magnitude of 0.25% and 0.03%, respectively. However, there is insufficient evidence for a significant effect of FDI and TO on GDP growth. Table 7 ARDL (MICS - Underperformers) Dependent Variable: D(GDP_GR) Method: ARDL Date: 08/26/24 Time: 17:04 Sample: 1997 2020 Included observations: 240 Number of cross-sections: 10 Dependent lags: 4 (Automatic) Automatic-lag linear regressors (4 max. lags): GCF_GR FDI_GR EC_GR TO_GR Deterministics: Restricted constant and no trend (Case 2) Model selection method: Akaike info criterion (AIC) Number of models evaluated: 2500 Selected model: PMG(3,4,3,4,4) Variable Coefficient Std. Error t-Statistic Prob. Long-run (Pooled) Coefficients GCF_GR 0.198243 0.022755 8.711933 0.0000 FDI_GR -0.004316 0.000800 -5.397345 0.0000 EC_GR 0.206813 0.027446 7.535307 0.0000 TO_GR 0.791363 0.072462 10.92106 0.0000 C 1.279442 0.191060 6.696537 0.0000 Short-run (Mean-Group) Coefficients COINTEQ -0.425175 0.205308 -2.070908 0.0395 D(GDP_GR(-1)) 0.030379 0.330758 0.091846 0.9269 D(GDP_GR(-2)) 0.013603 0.231548 0.058748 0.9532 D(GCF_GR) 0.044115 0.050943 0.865972 0.3874 D(GCF_GR(-1)) -0.095611 0.100323 -0.953037 0.3416 D(GCF_GR(-2)) -0.021428 0.067076 -0.319456 0.7497 D(GCF_GR(-3)) 0.014898 0.029865 0.498838 0.6184 D(FDI_GR) -0.004018 0.004400 -0.913301 0.3621 D(FDI_GR(-1)) 0.003619 0.001646 2.197928 0.0290 D(FDI_GR(-2)) 0.019914 0.016024 1.242804 0.2153 D(EC_GR) 0.238674 0.245601 0.971796 0.3322 D(EC_GR(-1)) 0.436908 0.311306 1.403470 0.1619 D(EC_GR(-2)) 0.338153 0.252593 1.338725 0.1820 D(EC_GR(-3)) 0.349170 0.293852 1.188251 0.2360 D(TO_GR) -0.284134 0.188873 -1.504370 0.1339 D(TO_GR(-1)) -0.172082 0.142944 -1.203841 0.2299 D(TO_GR(-2)) -0.149027 0.150454 -0.990520 0.3230 D(TO_GR(-3)) -0.058375 0.083077 -0.702664 0.4830 Log-Likelihood: -379.5410 Table 8 ARDL (MICs - Overachievers) Dependent Variable: D(GDP_GR) Method: ARDL Date: 04/15/25 Time: 15:32 Sample: 1995 2020 Included observations: 260 Number of cross-sections: 10 Dependent lags: 3 (Automatic) Automatic-lag linear regressors (3 max. lags): GCF_GR FDI_GR TO_GR EC_GR Deterministics: Restricted constant and no trend (Case 2) Model selection method: Akaike info criterion (AIC) Number of models evaluated: 768 Selected model: PMG(2,1,2,2,0) Variable Coefficient Std. Error t-Statistic Prob. Long-run (Pooled) Coefficients GCF_GR 0.246911 0.031053 7.951193 0.0000 FDI_GR 0.000218 0.000236 0.924780 0.3560 TO_GR 0.055498 0.042104 1.318112 0.1886 EC_GR 0.032672 0.009866 3.311656 0.0011 C 3.812547 0.428952 8.888060 0.0000 Short-run (Mean-Group) Coefficients COINTEQ -0.518257 0.093949 -5.516386 0.0000 D(GDP_GR(-1)) 0.192037 0.199252 0.963792 0.3361 D(GCF_GR) -0.024061 0.045128 -0.533164 0.5944 D(FDI_GR) 0.001357 0.003815 0.355739 0.7223 D(FDI_GR(-1)) 0.000348 0.004741 0.073370 0.9416 D(TO_GR) -0.018723 0.019383 -0.965935 0.3350 D(TO_GR(-1)) -0.080593 0.084623 -0.952384 0.3418 Log-Likelihood: -646.0676 4.3 Granger Causality Test Results According to the Granger causality test results for MICs, there is evidence for the neutrality hypothesis regarding underperforming MICs, meaning that there is no causality between GDP growth and EC (Table 9 ). As for overachieving MICs, we can conclude that there is a bidirectional relationship between GDP growth and EC (at a significance level of 5% and 1%), highlighting evidence for the feedback hypothesis (Table 10 ). Table 9 Granger Causality Test – MICs’ Underperformers Pairwise Granger Causality Tests Date: 04/27/25 Time: 11:29 Sample: 1993 2020 Lags: 4 Null Hypothesis: Obs F-Statistic Prob. EC_GR does not Granger Cause GDP_GR 240 1.17187 0.3239 GDP_GR does not Granger Cause EC_GR 1.26047 0.2864 Table 10 Granger Causality Test - MICs' Overachievers Pairwise Granger Causality Tests Date: 04/27/25 Time: 11:31 Sample: 1993 2020 Lags: 4 Null Hypothesis: Obs F-Statistic Prob. EC_GR does not Granger Cause GDP_GR 240 3.36977 0.0105 GDP_GR does not Granger Cause EC_GR 18.3170 4.E-13 5. Discussion With respect to MICs’ ARDL results underscore the critical role of gross capital formation in driving long-run economic growth across both underperforming and overachieving MICs. Consequently, we do not reject H1.1 and H1.2, which is in line with Kesar et al. (2022), Maune et al. ( 2023 ) and Yang and Shafiq ( 2020 ). For MICs’ Underperformers, GCF demonstrates a statistically significant and positive effect on GDP growth at the 1% level, with a 1% increase in domestic investment associated with a 0.2% rise in economic output. This finding suggests that domestic investment remains a key engine of growth in underperforming MICs. It highlights the importance of capital accumulation, particularly in infrastructure, industry, and public services, for stimulating productive activity and fostering long-term development. Similarly, in overachieving MICs, GCF continues to exhibit a positive and significant impact on GDP growth, with an even slightly higher effect of approximately 0.25% for every 1% increase in investment, which is the highest coefficient among all independent variables. This reinforces the idea that investment-driven growth is a common feature of overachieving MICs. However, the stronger magnitude of the coefficient in Overachievers may reflect more efficient allocation of capital, better investment climates, or more productive use of resources. The faster speed of adjustment toward long-run equilibrium in Overachievers, as shown by the ECT term, further supports the interpretation that these economies are better equipped to translate investment inflows into sustained growth. For MICs, as countries climb the development ladder, the relationship between FDI and economic growth becomes more nuanced. MICs’ Underperformers show a negative effect of FDI on growth, while such impact appeared to be insignificant for overachieving MICs. Accordingly, H2.1 is rejected, in line with Ramzan et al. ( 2019 ), Chaudhury et al., ( 2020 ) and Saqib et al., ( 2013 ). This counterintuitive result can be explained by diminishing marginal returns and structural mismatch. While in LICs FDI can bring basic infrastructure, technology, and job creation filling large existing gaps, in MICs FDI often shifts to real estate, finance, or extractive industries with fewer linkages to the broader economy. This means MICs may receive FDI that doesn’t boost productivity or create positive spillovers (Alfaro et al., 2004 ). In particular, in underperforming MICs, like Libya, Argentina and Ukraine, FDI often targets resource extraction, real estate, or low-value services, which generate little to no technology transfer or productivity gains and hence cause a negative impact on economic growth (Görg & Greenway, 2004). Additionally, MICs with weak institutions can cause FDI to crowd out domestic investment or foster corruption (Jude & Levieuge, 2017 ). Moreover, some of the underperforming MICs, such as Argentina and Ukraine, suffer from economic volatility, inflation, political swings, which can make FDI pro-cyclical and destabilizing and hence have detrimental effects on growth (Lensink & Morrissey, 2001 ). Concerning overachieving MICs, results showed no significant effect of FDI on economic growth, leading us to reject H2.2, similar to Ozili ( 2025 ) and Falki ( 2009 ). Such insignificance can largely be attributed to sectoral composition of FDI. In Vietnam for example, an overachieving middle-income country, FDI has played a key role in the manufacturing and processing sectors, particularly electronics and textiles. However, it has led to an economy dominated by low value-added, labor-intensive production, with limited domestic linkages and minimal technology transfer (Bui et al., 2019 ). Similarly, China's shift in FDI composition toward service-oriented industries such as finance, real estate, and IT has contributed less to productivity growth than earlier manufacturing-based FDI inflows, thus reducing the overall developmental impact (Ross & Fleming, 2022 ). Likewise, Cambodia has attracted FDI primarily in real estate, financial services, and low-tech garment manufacturing, sectors that offer limited industrial upgrading and few knowledge spillovers into the domestic economy (Tang & Wong, 2023 ). Accordingly, if FDI inflows are concentrated in sectors with constrained capacity to stimulate sustained and broad-based economic development, it may lack significant impact on GDP growth. Moving on to the effect of trade openness on economic growth in MICs, results again showed a significant difference within the same income category. For Underperformers, a significant positive impact of TO on GDP growth is confirmed. In other words, H3.1 is not rejected. Accordingly, it can be concluded that TO boosts economic growth in underperforming MICs, as supported by Hye et al. ( 2016 ), Karras ( 2003 ), Keho ( 2017 ), Malefane and Odhiambo ( 2018 ), as well as Raghutla ( 2020 ). As cited by Idris et al. ( 2016 ), Imoisi ( 2018 ) and Silajdzic and Mehic ( 2018 ), trade allows access to foreign technology, machinery, and intermediate goods that spur productivity and ultimately economic growth. These aforementioned goods are often not available domestically or are too costly to produce locally. Openness also facilitates the import of high-tech and ICT (information and communication technology) goods, which help modernize industries, improve productivity, and support integration into global value chains. Indicators such as ICT goods imports (% of total imports) available from the World Bank and UNCTAD, can provide empirical support for the positive effect of trade openness on economic growth. For example, Tonga 6 experienced a sharp increase in ICT goods imports as a percentage of total imports between 2000 and 2014, indicating a growing reliance on foreign technology to modernize its economy and enhance productivity across key sectors. In particular, the Tongan ICT goods imports accounted for approximately 0.98% in 2000 and reached its peak of 10.22% in 2014, representing a tenfold increase over the period. A similar upward trend in ICT goods imports is observed in Ukraine, another underperforming MICs, where ICT goods accounted for approximately 2.5% of total imports in 2000. This share increased steadily, reaching a peak of 6.59% in 2019. Likewise, the Republic of the Congo, a third underperforming MIC, experienced growth in the share of ICT goods within its total imports, although the magnitude of this increase was more modest compared to that of Ukraine and Tonga (Fig. 1 ). Furthermore, in several underperforming MICs, such as Fiji, Tonga, and Dominica, the economy is heavily reliant on tourism and related service exports. For such countries, trade openness plays a pivotal role in enabling tourism-led growth by facilitating the cross-border movement of people, goods, and capital. Trade openness reduces barriers to international mobility and logistics, allowing countries to tap into global tourism markets. By improving diplomatic, trade, and travel relations, often through bilateral or multilateral agreements, these economies can attract a higher volume of international visitors. Fiji, for example, has long promoted an open visa policy and liberal air access, which have enabled sustained growth in tourist arrivals from Australia, New Zealand, and North America, accounting for 46.3%, 23.0% and 11.0% in 2024, respectively (Fiji Bureau of Statistics, 2025 ). Also, the Fig. 2 below shows the increasing trend in tourism export in Fiji, Tonga and Dominica, highlighting the importance of higher trade openness for substantial economic growth in their context. Moving on to the effect of TO on economic growth for overachieving MICs, we have to reject H3.2, as results showed insignificant coefficients, which may be explained by the relatively stable TO levels in this group. According to the below Fig. 3 , on average, TO grew at a modest rate of − 0.23% annually, indicating minimal change over time. In contrast, LICs and HICs experienced higher average TO growth rates of 1.02% and 1.00% respectively, indicating more dynamic trade environments. The limited change observed in overachieving MICs likely reduced the model’s ability to detect a statistically significant effect, even if trade openness remains economically relevant. With reference to the results for the energy-growth nexus in MICs, Granger causality findings suggest that the role of energy consumption in economic growth varies significantly based on the country’s performance trajectory. In underperforming MICs, the neutrality hypothesis is confirmed, in line with Narayan ( 2016 ), Menegaki ( 2011 ) and Payne ( 2009 ), while the feedback hypothesis is supported for overachieving MICs, as found by Phukon and Konwar ( 2019 ), Behmiri and Manso ( 2012 ) and Shahbaz et al. ( 2013 ). Accordingly, H4.3 is rejected, while we do not reject H4.4. The lack of causality between EC and GDP growth in underperforming MICs may be attributed to several country-level factors, such as inefficient energy use and infrastructural deficiencies. As shown in Fig. 4 , underperforming MICs exhibit an average GDP per unit of energy use of approximately 10.78 constant 2021 PPP dollars per kg of oil equivalent, compared to 11.82 for overachievers (based on data from 2000 to 2022). This disparity suggests relatively lower energy efficiency and less productive use of energy in underperforming MICs, offering a potential explanation for the observed neutrality hypothesis in the energy-growth nexus. Moreover, infrastructural quality appears to be a key differentiating factor. According to Fig. 5 , MICs’ Underperformers experience average electric power transmission and distribution losses of 22.36% of output, nearly double the 12.24% observed in Overachievers. These elevated losses point to significant inefficiencies in energy delivery systems, which can further diminish the economic impact of energy consumption. Combined, these structural differences help to contextualize why energy consumption fails to Granger-cause economic growth in underperforming MICs, in contrast to the bidirectional relationship found in Overachievers. However, the ARDL model findings for MICs indicate that energy consumption has a statistically significant and positive long-run effect on economic growth for both Underperformers as well as Overachievers, leading us not reject H4.1 and H4.2. This suggests that, over time, increased energy use contributes to higher levels of output, regardless of the MICs' performance category. Importantly, these findings do not contradict the Granger causality results discussed earlier. While the Granger test assesses short-run predictive relationships, the ARDL model captures long-run equilibrium dynamics. Therefore, the absence of short-run causality in underperforming MICs (as per Granger) can coexist with a significant long-run relationship (as found in ARDL), implying that energy consumption may influence growth only with a time lag or under certain structural conditions. In contrast, the MICs’ Overachievers not only exhibit a strong long-run impact of energy consumption on growth, but also show short-run bidirectional causality, reinforcing the robustness of the energy-growth linkage in these economies. 6. Implications and Conclusion In light of the previously discussed findings, it is recommended for MICs to increase domestic investment, as GCF consistently demonstrated a positive and significant effect on economic growth, underscoring its structural role in enhancing productive capacity, stimulating employment, and supporting long-term development. Hence, MICs’ policy makers are strongly encouraged to reorient their growth strategies toward strengthening domestic investment. This may include increasing public and private capital formation, expanding access to finance for domestic firms, improving infrastructure, and ensuring a stable and transparent policy environment that fosters local investment. Targeted measures could involve large-scale infrastructure projects in transport, energy, and digital connectivity to crowd in private sector investment and expand the productive capital stock. Since the results for TO are nuanced in MICs, it is of high importance to pursue context-specific trade strategies that align with a country's stage of development and its economic performance characteristics. In the case of underperforming MICs, the evidence confirms that trade openness can act as a catalyst for economic growth. Policymakers in these countries are therefore encouraged to pursue targeted trade liberalization policies aimed at facilitating the import of productivity-enhancing goods, particularly capital and ICT equipment. These imports can support industrial modernization, enhance technological capabilities, and enable integration into global value chains. Policy measures such as tariff reductions on high-tech inputs, restructuring of customs procedures, and the establishment of trade facilitation mechanisms would be instrumental in realizing these gains. Furthermore, investments in trade-related infrastructure, such as ports, transportation networks, and digital connectivity, should be prioritized to reduce transaction costs and improve trade efficiency. In addition, in countries with tourism-dependent economies, such as Fiji, Tonga, and Dominica, trade openness plays a vital role in facilitating tourism-led growth. For such nations, the liberalization of cross-border movement through open visa regimes, air service agreements, and enhanced diplomatic relations is critical. Policymakers should also invest in tourism-supporting infrastructure, including airports, accommodation services, in order to strengthen international competitiveness in this sector. Conversely, for overachieving MICs, the absence of a statistically significant relationship between TO and economic growth suggests diminishing marginal returns to openness in environments, where trade levels have remained relatively constant. In this context, policy attention should shift from the quantity to the quality of trade. Rather than aiming to further liberalize already open economies, overachieving MICs should focus on diversifying their export baskets and upgrading their participation in global value chains. This includes promoting high-value-added sectors such as advanced manufacturing, technology-driven services, and green industries. Governments should support firms in transitioning toward more complex production through targeted research and development incentives, innovation policies, and capacity-building programs. Additionally, enhancing the scope and depth of trade agreements to include modern elements such as digital trade, services liberalization, and intellectual property rights could create new growth avenues. Addressing the challenges of low energy efficiency and high losses in electric power transmission and distribution in underperforming MICs is essential for improving the developmental impact of energy use in. These inefficiencies constrain both short-term productivity and long-term growth, as highlighted by the significantly higher average T&D losses and lower energy output per unit consumed in these countries. To tackle these issues, governments should prioritize the modernization of energy infrastructure. This includes replacing aging transmission lines and substations with higher-capacity, smart grid technologies that allow for real-time monitoring, load optimization, and preventative maintenance. Investing in decentralized energy solutions, such as mini-grids in remote areas, can also reduce reliance on long-distance transmission, lowering technical losses. simultaneously, strengthening regulatory frameworks is crucial. Introducing and enforcing energy efficiency standards industrial processes can reduce unnecessary consumption. On the other hand, MIC’s Overachievers are well-positioned to invest in energy innovation, including the deployment of smart grids, real-time monitoring systems, and energy storage technologies that can enhance system efficiency and resilience. Policymakers should also maintain efforts to improve industrial energy efficiency, which is essential for preserving competitiveness in increasingly complex global value chains. To conclude, this paper examined the relationship between gross capital formation, foreign direct investment, trade openness, energy consumption and economic growth, employing a performance-based framework that moves beyond the conventional income classifications. This approach provides robust evidence of intra-group divergence, offering a more nuanced understanding of growth dynamics across different performance groups. The results confirm that GCF consistently operates as a primary driver of economic expansion, enhancing productive capacity, facilitating structural transformation, and supporting long-term development. Energy consumption also exerts a positive influence on growth, with short-run neutrality observed in Underperformers and a feedback relationship in Overachievers. Conversely, FDI demonstrates a negative effect on growth in Underperformers and remains statistically insignificant for Overachievers, largely reflecting its concentration in low-value-added or enclave sectors with minimal domestic spillovers. Similarly, TO promotes growth in Underperformers but is insignificant for Overachievers, emphasizing that the growth impact is contingent upon the quality and structure of trade, rather than its sheer volume. Thus, these findings highlight that growth strategies in MICs should be tailored according to actual performance levels, rather than relying solely on broad income-based classifications. Declarations Competing Interest Information The authors declare no competing interests. Author Contribution All authors contributed to this work. Aya Khater conceived and designed the study. Data were collected and analyzed by Aya Khater. The original draft was prepared by Aya Khater. 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Metadata glossary: World Development Indicators [BX.KLT.DINV.CD.WD]. https://databank.worldbank.org/metadataglossary/world-development-indicators/series/BX.KLT.DINV.CD.WD Yang, X., & Shafiq, M. N. (2020). The Impact of Foreign Direct Investment, Capital Formation, Inflation, Money Supply and Trade Openness on Economic growth of Asian Countries. International Research Association for Sustainable Development: Journal of Economics, 2 (1), 25-34. Yanikkaya, H. (2003). Trade Openness and Economic Growth: A Cross-Country Empirical Investigation. Journal of Development Economics, 72 (1), 57-89. Footnotes raising the capital-to-labor ratio The resource curse refers to the paradox in which countries endowed with abundant natural resources often experience slower economic growth, weaker industrial development, and institutional deterioration. 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Albania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia (Plurinational State of), Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Cabo Verde, Cambodia, Cameroon, China, Colombia, Comoros, Congo, Costa Rica, Côte d'Ivoire, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eswatini, Fiji, Gabon, Georgia, Ghana, Grenada, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, Iran (Islamic Republic of), Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kyrgyzstan, Lao People's Dem. Rep., Lebanon, Lesotho, Libya, Malaysia, Maldives, Mauritania, Mauritius, Mexico, Mongolia, Morocco, Myanmar, Namibia, Nepal, Nicaragua, Nigeria, North Macedonia, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Russian Federation, Samoa, Sao Tome and Principe, Senegal, Solomon Islands, South Africa, Sri Lanka, Suriname, Tajikistan, Tanzania, United Republic of, Thailand, Tonga, Tunisia, Turkmenistan, Turkey, Ukraine, Uzbekistan, Vanuatu, Viet Nam, Zambia, Zimbabwe. The MICs’ Overachievers in this study are: Azerbaijan, Bhutan, Bosnia and Herzegovina, Cambodia, China, Equatorial Guinea, Guyana, Lao People's Dem. Rep., Myanmar, Viet Nam The MICs’ Underperformers in this study are: Argentina, Bulgaria, Congo, Dominica, Fiji, Jamaica, Kiribati, Libya, Tonga, Ukraine one of the underperforming MICs Additional Declarations No competing interests reported. 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figure created by author)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8377513/v1/3d1257d326023c678412aa0d.png"},{"id":98810493,"identity":"91d87222-6157-4721-b683-1cf2b810f144","added_by":"auto","created_at":"2025-12-22 15:22:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82693,"visible":true,"origin":"","legend":"\u003cp\u003eInternational Tourism Receipts (% of Total Exports) during 2000-2020 in Fiji, Tonga and Dominica (Data Source: World Bank, figure created by author)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8377513/v1/23781bff77b052500dd52644.png"},{"id":99307268,"identity":"321fea29-ecf3-41cf-a242-43bcd4637fdc","added_by":"auto","created_at":"2025-12-31 16:05:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":41014,"visible":true,"origin":"","legend":"\u003cp\u003eaverage TO Growth - 2001-2023 (Overachieving MICs, HICs and LICs) (Data Source: World Bank, figure created by author)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8377513/v1/35d977f585aa404ce31332de.png"},{"id":99307028,"identity":"32e56f39-0c51-4eb9-9d1a-8adcf53deaab","added_by":"auto","created_at":"2025-12-31 16:05:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47600,"visible":true,"origin":"","legend":"\u003cp\u003eMICs' Average Energy Efficiency (2000-2022) (Data Source: World Bank; figure created by author)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8377513/v1/e6785d3c319494fb57bb86a8.png"},{"id":98810495,"identity":"f333fb31-886a-46ae-9f37-9bf2e8085b8b","added_by":"auto","created_at":"2025-12-22 15:22:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":54600,"visible":true,"origin":"","legend":"\u003cp\u003eMICs' Average Infrastructure Efficiency (2000-2022) (Data Source: World Bank; figure created by author)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8377513/v1/6fac118c01b3af6aab71bf06.png"},{"id":99322171,"identity":"fe92b03b-2aa8-4647-ab02-3abc550eb7c9","added_by":"auto","created_at":"2025-12-31 16:43:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1674839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8377513/v1/29e99a63-e0c8-4bd0-88fc-b497d12078ba.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Asymmetric Growth Dynamics: The Performance-Based Growth-Nexus of Investment, Trade, and Energy in Middle-Income Countries","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEconomic growth remains a central objective in middle-income countries, as it drives enhancements in living standards, poverty alleviation, and the creation of broader economic opportunities. In recent decades, growth patterns have shifted under the influence of globalization, technological advancement, institutional reforms, and environmental pressures, reshaping the conventional determinants of growth. The literature identifies various key drivers of economic expansion, notably domestic investment, foreign direct investment (FDI), trade openness, and energy consumption (Omri \u0026amp; Kahouli, \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Kalu et al., 1991; Alsamara et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dinh et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Trade openness spurs economic growth by improving resource allocation, promoting specialization, and enabling access to larger markets (Kalu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), although it can have adverse effects on growth in developing economies experiencing trade deficits (Bibi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yanikkaya, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Additionally, it facilitates technology diffusion, increases competition, and improves access to intermediate goods (Adhikary, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). On the other hand, FDI enhances long-term growth through channels such as technology transfer, research and development, and managerial efficiency gains (Shinwari et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kalu et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Iamsiraroj, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Also, domestic investment remains a fundamental pillar, particularly when complemented by human capital development, infrastructure expansion, and adequate credit provision (Dinh et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bakari, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Furthermore, energy consumption plays a pivotal role, especially within industrial and service sectors and exhibits a bidirectional relationship with economic growth (Mezghani \u0026amp; Haddad, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Acaravci et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEconomic growth drivers have always been a center of discussion in economic literature, where the majority of research has been conducted focusing on subsets of countries using traditional income categories. Using the latter, the intra-income divergence is commonly neglected, as it cannot be captured through the use of rigid income classifications proposed by the World Bank or United Nations (Lencucha \u0026amp; Neupane, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fantom \u0026amp; Serajuddin, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fialho \u0026amp; Van Bergeijk, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Hence, this paper aims to answer the research question \u0026ldquo;What is the impact of gross capital formation, foreign direct investment, trade openness and energy consumption on economic growth in middle-income countries?\u0026rdquo;. Concurrently, it aims to shed light on intra-group differences in growth performance by adopting a performance-based econometric framework, inspired by the approach of Barreto and Hughes (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), which emphasizes actual economic performance rather than income category. By doing so, middle-income countries are further classified as \u0026ldquo;Overachievers\u0026rdquo; and \u0026ldquo;Underperformers,\u0026rdquo; allowing an assessment of how these key variables influence growth depending on performance level.\u003c/p\u003e \u003cp\u003eThe results provide support for the previously explained performance-based classification, by showing intra-income divergence, specifically regarding the effect of FDI and trade openness middle-income countries\u0026rsquo; economic growth. Thus, this study contributes to the literature by concentrating solely on middle-income economies and demonstrating the intra-dimensional divergence that conventional income classifications often overlook.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section 2 provides a comprehensive literature review, examining the concept of economic growth and its principal determinants (gross capital formation, foreign direct investment, trade openness, and energy consumption) while synthesizing the most relevant empirical findings. Section 3 then presents the research methodology and describes the dataset employed in the analysis. Sections 4 and 5 report and interpret the empirical results. Finally, Section 6 translates these findings into policy recommendations and provides concluding remarks.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe literature identifies multiple determinants of economic growth. Classical and neoclassical frameworks emphasize capital accumulation as a primary source of growth, whereby expanding physical capital in combination with labor raises output, particularly in the short term (Smith, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e1776\u003c/span\u003e; Ricardo, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e1821\u003c/span\u003e; Solow, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Swan, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). Empirical studies consistently show that gross capital formation is positively linked to growth, particularly in developing economies (Aslan \u0026amp; Altinoz, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kim \u0026amp; Loayza, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the principle of diminishing returns to capital underscores the need for complementary inputs, such as human capital and technology. Human capital accumulation enhances productivity and innovation, as Lucas (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) notes, and educational investment is associated with long-run economic growth (Hanushek \u0026amp; Woessmann, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Barro, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Better-educated economies are also better positioned to adopt foreign technologies (Rodriguez-Segura, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Borensztein et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Sustained growth further relies on technological innovation. With respect to the latter, Romer (\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) highlights endogenous innovation and knowledge spillovers as central drivers of long-term expansion. Countries with strong innovation systems, research and development, patents and advanced digital infrastructure, tend to achieve faster growth (Gomes et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Aghion \u0026amp; Howitt, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Coe et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Also, trade openness, as argued by Frankel and Romer (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), promotes growth by facilitating economies of scale, enhancing competition, and improving access to technology, with the strongest gains observed in countries with sound institutions and infrastructure (Wacziarg \u0026amp; Welch, \u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Nonetheless, its impact remains context-specific, depending on national competitiveness and structural conditions. Similarly, foreign direct investment contributes to growth by providing capital and technology, though its effectiveness depends on absorptive capacity (Borensztein et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), institutional strength, and financial sector development (Hayat, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Ongoing disparities in cross-country growth continue to raise questions about which structural and policy conditions best support development, particularly in the context of globalization (Rodrik, \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Consequently, examining growth determinants within specific economic contexts remains a central focus of contemporary research.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Effect of Gross Capital Formation on Economic Growth\u003c/h2\u003e \u003cp\u003eGross capital formation (GCF) is widely recognized as a fundamental engine of economic growth, encompassing investments in physical assets, such as infrastructure, machinery, equipment, and land improvements (World Bank Metadata Glossary, 2025). These investments increase productive capacity, facilitate the diffusion of technology, and support long-term structural transformation. GCF also accounts for changes in inventories and, under the 1993 System of National Accounts, includes the acquisition of valuables. One of the primary channels through which GCF stimulates growth is capital deepening\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e, which enhances labor productivity, particularly in capital-scarce economies (Solow, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; Barro \u0026amp; Sala-i-Martin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Kim \u0026amp; Loayza, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sectoral investments in agriculture and manufacturing contribute to economies of scale and higher output (OECD, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), while the adoption of advanced capital goods generates knowledge spillovers and promotes learning-by-doing (Romer, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Lucas, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Moretti, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Wang \u0026amp; Choi, \u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, infrastructure development enhances market efficiency and reduces transaction costs, thereby encouraging trade, mobility as well as private-sector investment (Calder\u0026oacute;n \u0026amp; Serv\u0026eacute;n, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Pradhan \u0026amp; Bagchi, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGCF also facilitates structural transformation by reallocating resources from low- to high-productivity sectors, fostering industrial upgrading and economic diversification (Rodrik, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; McMillan et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It stimulates employment in construction and related industries, raises demand for skilled labor and fosters investment in human capital (Lucas, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). Nevertheless, the growth impact of GCF is contingent upon the quality of institutional and financial systems, as weak governance can lead to resource misallocation and reduced returns (Dabla-Norris et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Levine, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Such growth-enhancing role of GCF is consistently supported by empirical evidence (Pasara \u0026amp; Garidzirai, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rani \u0026amp; Kumar, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kesar et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Maune et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gibescu, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Hence, this study examines the effect of GCF on economic growth in high-income countries, hypothesizing a positive effect of GCF on economic growth in underperforming MICs (H1.1) as well as in overachieving MICs (H1.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The Effect of Foreign Direct Investment on Economic Growth\u003c/h2\u003e \u003cp\u003eAccording to the World Bank (World Bank Metadata Glossary, 2025), foreign direct investment is defined as the equity inflows into an economy, including reinvested earnings and long-term capital. It is commonly viewed as a growth driver in economic literature through channels such as capital injection, technology transfer and enhanced management practices (Borensztein et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Nevertheless, its contribution to growth is not guaranteed and is largely contingent on the host country\u0026rsquo;s absorptive capacity, in particular the level of human capital. As Azam and Emirullah (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) note, education enables economies to integrate more effectively into global production networks and promotes knowledge transfer. Likewise, Alfaro et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) argue that well-developed financial markets ensure the efficient allocation of FDI toward high-productivity sectors, whereas weak financial systems may result in misallocation or crowding out of domestic firms. Complementing the latter, Azman-Saini and Law (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) demonstrate that FDI exerts a positive growth effect only after financial development reaches a critical threshold.\u003c/p\u003e \u003cp\u003eMoreover, institutional quality is equally important in determining the growth effects of FDI. Strong governance frameworks enhance its effectiveness, whereas corruption and weak legal systems can undermine or even eliminate its benefits (Ofori \u0026amp; Asongu, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Herzer, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sunde, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Van Bon, \u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The sectoral distribution of FDI also influences its growth impact. Chaudhury et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) show that FDI in manufacturing does not necessarily promote industrialization or productivity gains, particularly when domestic linkages are limited. Building on these insights, this paper investigates the effect of FDI on economic growth in middle-income countries, assuming a positive impact in underperforming MICs (H2.1) and in overachieving MICs (H2.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The Effect of Trade Openness on Economic Growth\u003c/h2\u003e \u003cp\u003eTrade openness (TO) refers to the extent to which an economy allows the cross-country flow of goods, services, and capital, and it is widely recognized as an important driver of economic growth. Classical and neoclassical theories emphasize that trade promotes specialization according to comparative advantage, enhancing resource allocation and raising aggregate output (Krugman \u0026amp; Obstfeld, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). From the perspective of endogenous growth models, trade further contributes to growth by providing access to foreign technologies, fostering innovation, and generating knowledge spillovers, particularly through imports of intermediate goods and exposure to international competition (Grossman \u0026amp; Helpman, 1991; Coe \u0026amp; Helpman, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). These benefits are reinforced through robust institutions, adequate infrastructure and well-developed human capital (Bhaumik et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Keller, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Falvey et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Moreover, greater openness enables firms to access larger markets, exploit economies of scale, and improve efficiency (Melitz \u0026amp; Ottaviano, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Empirical evidence consistently supports these growth-enhancing effects (Kong et al., 2021; Imoisi, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tahir \u0026amp; Azid, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ramanayake \u0026amp; Lee, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Adeel-Farooq et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Vamvakidis, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, the benefits of TO are not uniform across countries. In economies with undiversified exports or fragile domestic industries, trade can increase vulnerability to external global shocks and cause premature deindustrialization. Openness may also increase income inequality, as benefits are often gained by skilled labor and capital-intensive sectors only (Liu et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Goldberg \u0026amp; Pavcnik, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Moreover, high reliance on commodity exports can slow down long-term economic growth through the \u0026ldquo;resource curse\u0026rdquo;\u003ca class=\"FNLink\" href=\"#Fn2\" id=\"#FNLinkFn2\"\u003e\u003c/a\u003e (Hausmann \u0026amp; Rigobon, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The previously mentioned risks are highlighted in empirical studies, emphasizing that the growth effects are highly context-dependent and affected by domestic conditions (Bibi et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Yanikkaya, \u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Adhikary, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Caceres, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Musila \u0026amp; Yiheyis, \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Nevertheless, this study investigates the effect of TO on economic growth in MICs, hypothesizing a positive effect in both underperforming as well as overachieving MICs (H3.1 and H3.2, respectively).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 The Effect of Energy Consumption on Economic Growth\u003c/h2\u003e \u003cp\u003eEnergy consumption is widely acknowledged as a fundamental driver of economic growth, serving as the backbone of production, transportation, and modern service activities (Stern, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). While classical growth models focus primarily on capital and labor, contemporary frameworks recognize energy as a third essential input, necessary for operating industrial machinery, facilitating mobility, and expanding productive capacity. Incorporating energy into production functions highlights its critical role in sustaining long-term economic expansion (Stern, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, energy use increasingly drives technological advancement and structural transformation. Furthermore, the global transition toward renewable energy not only aligns with climate objectives but also stimulates investment, fosters job creation, and promotes economic diversification (Dirma et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Apergis \u0026amp; Payne, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe significance of energy consumption has grown due to its rising global demand, caused by population growth, rapid urbanization and industrialization, particularly in developing economies. Access to reliable energy has consequently become essential to enhancing productivity, improving efficiency and maintaining competitiveness (Alshami \u0026amp; Sabah, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lee \u0026amp; Chang, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The energy-growth nexus has been extensively explored in the literature through four main hypotheses: the growth hypothesis, conservation hypothesis, feedback hypothesis and neutrality hypothesis. The growth hypothesis assumes that energy drives GDP (Lee \u0026amp; Chang, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Fareed \u0026amp; Pata, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chiou-Wei et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), while the conservation hypothesis states that GDP causes energy use (Magazzino et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Karanfil, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ahmed et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Alper \u0026amp; Oguz, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Destek \u0026amp; Aslan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The feedback hypothesis indicates a bidirectional causality between both variables (Phukon \u0026amp; Konwar, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Behmiri \u0026amp; Manso, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and the neutrality hypothesis suggests no causal relationship (Narayan, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Menegaki, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Payne, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Faisal et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This paper examines the effect of energy consumption on economic growth in MICs, hypothesizing a positive effect on economic growth in both underperforming and overachieving MICs (H4.1 and H4.2, respectively). Furthermore, the author assumes energy consumption to Granger cause economic growth in underperforming MICs, while a bi-directional relationship is expected for overachieving MICs (H4.3 and H4.4, respectively).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data \u0026 Methodology","content":"\u003cp\u003eThis study seeks to provide deeper insights into the relationship between economic growth, gross capital formation, foreign direct investment, trade openness, and energy consumption across 97 middle-income countries\u003csup\u003e3\u003c/sup\u003e during the period 1993\u0026ndash;2020. The employed model is presented in Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{GDP\\_gr}_{t}\\:=\\:{\\beta\\:}_{0}+\\:{\\beta\\:}_{1}{GCF\\_gr}_{t}\\:\\:+\\:{\\beta\\:}_{2}\\:{FDI\\_gr}_{t}\\:+\\:{\\beta\\:}_{3}{TO\\_gr}_{t}\\:+\\:{\\beta\\:}_{4}\\:{EC\\_gr}_{t}\\:+\\:{\\mu\\:}_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eData on economic growth, measured by GDP growth, and GCF are sourced from UNCTAD. FDI and TO (proxied by the sum of exports and imports as a percentage of GDP) are obtained from the World Bank. Energy consumption per capita is retrieved from Our World in Data (OWID). Income classification follows the definitions and methodology adopted by the World Bank. To account for inflation, the CPI deflator from UNCTAD is applied, whenever reported data does not account for inflationary effects. All variables are expressed in growth rates. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the variables along with their definitions and respective sources.\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\u003eVariables' Definitions and Sources\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\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP_gr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth in Real Gross Domestic Product (constant 2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited Nations Conference on Trade and Development Data Hub\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCF_gr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth in Real Gross Capital Formation (constant 2015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnited Nations Conference on Trade and Development Data Hub\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI_gr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth in Real Foreign Direct Investment Inflows (CPI deflated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO_gr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth in Real Trade Openness (Total Trade in Goods and Services/GDP) (CPI deflated)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC_gr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth in Primary Energy Consumption/capita (kW/h per capita)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOWID Data Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe World Bank classifies all economies based on income per capita to facilitate the assessment of national performance and the formulation of economic policies. Such income-based groupings enable researchers to address the most pressing challenges within each category and to ensure that policy recommendations are contextually relevant. Nonetheless, this classification system has been widely criticized for overlooking disparities in economic performance among countries within the same income category (Lencucha \u0026amp; Neupane, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Fantom \u0026amp; Serajuddin, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Fialho \u0026amp; Van Bergeijk, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To address this limitation, the present study introduces a performance-based subdivision by further splitting each income category into two groups: \u0026ldquo;Overachievers\u0026rdquo; and \u0026ldquo;Underperformers.\u0026rdquo; This classification is derived through an Ordinary Least Squares regression in which GDP growth is explained by changes in GCF, FDI, TO and EC. The resulting residuals for each country are averaged and ranked according to their magnitude and sign. Countries with consistently high positive residuals are identified as \u0026ldquo;Overachievers,\u0026rdquo; indicating stronger-than-expected growth performance, whereas those with highly negative residuals are classified as \u0026ldquo;Underperformers,\u0026rdquo; reflecting weaker-than-expected outcomes. The top ten and bottom ten performers are subsequently selected for in-depth analysis to highlight intra-income divergence to examine how the selected explanatory variables influence economic growth.\u003c/p\u003e \u003cp\u003eVarious econometric techniques can be employed to examine the relationship between GCF, FDI, TO, EC and economic growth, including the Generalized Method of Moments (GMM), Dynamic Ordinary Least Squares (DOLS), and Fully Modified Ordinary Least Squares (FMOLS). However, both DOLS and FMOLS require the variables to be cointegrated, which is unsuitable in this study since, as demonstrated by the stationarity tests, most variables are not integrated of order one. GMM, while robust to endogeneity, heteroskedasticity, and serial correlation, is primarily suited for short panels, where the cross-sectional dimension exceeds the time dimension (N\u0026thinsp;\u0026gt;\u0026thinsp;T), a condition not met in this analysis. Although instrumental variable and GMM estimators, such as the widely applied Arellano and Bond (1991) approach, are consistent and asymptotically efficient in large samples, their efficiency is lower in panels with a limited number of cross-sections, as shown in Monte Carlo simulations (Bruno, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Judson \u0026amp; Owen, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Kiviet, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). In this study, middle-income countries are subdivided into ten \u0026ldquo;Overachievers\u0026rdquo;\u003csup\u003e4\u003c/sup\u003e and ten \u0026ldquo;Underperformers\u0026rdquo;\u003csup\u003e5\u003c/sup\u003e to capture intra-group performance divergence, resulting in panels with N\u0026thinsp;=\u0026thinsp;10 and T\u0026thinsp;=\u0026thinsp;97, which violates the standard GMM requirements. Consequently, the Autoregressive Distributed Lag (ARDL) model is adopted as it is better suited to this data structure and does not impose such restrictions. Furthermore, the Granger causality test is applied to assess the direction of causality between energy consumption and economic growth, consistent with the energy\u0026ndash;growth nexus literature. All estimations are conducted using EViews 13.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Cross-Sectional Dependence\u003c/h2\u003e \u003cp\u003eWith the rise of globalization and increasingly interconnected financial systems, cross-sectional dependence has become a critical consideration in panel data analysis, yet it is often overlooked by researchers. Cross-sectional units are frequently influenced by common unobserved shocks, such as global economic crises, technological advancements, or policy changes, which induce correlations in the error terms across units (Pesaran, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Failing to account for this contemporaneous correlation can lead to biased or spurious results, ultimately weakening the reliability of inferences and policy recommendations (Tackie et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; EViews 12 User\u0026rsquo;s Guide, 2020). To address this, EViews 13 provides three primary tests for detecting cross-sectional dependence in panel datasets: the Breusch-Pagan LM test (1980), Pesaran\u0026rsquo;s Scaled LM test (2004), and Pesaran\u0026rsquo;s CD test (2004).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Panel Stationarity Test\u003c/h2\u003e \u003cp\u003eThe initial stage of the analysis involves performing stationarity tests on each variable to identify potential trends or seasonal effects. A stationary time series is characterized by a constant mean and variance over time, with the covariance between observations depending solely on the lag between them rather than the specific points in time at which it is measured (Gujarati \u0026amp; Porter, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). As highlighted by Granger and Newbold (as cited in Akinwale \u0026amp; Grobler, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), non-stationary series pose the risk of generating spurious regressions, which can produce misleading interpretations. Such series are only reliable for short-term behavior and cannot be generalized to other periods, making them unsuitable for forecasting (Gujarati \u0026amp; Porter, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the presence of cross-sectional dependence, it is necessary to employ second-generation panel unit root tests that explicitly account for interdependencies across units. EViews offers two main approaches for this purpose: the PANIC test by Bai and Ng (2004) and the Cross-sectionally Augmented IPS (CIPS) test by Pesaran (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This study utilizes the CIPS test, as it enhances the conventional IPS test by incorporating cross-sectional averages to address unobserved common factors within the panel. The null hypothesis assumes that all series are non-stationary (contain a unit root), whereas the alternative suggests that at least one is stationary. Unlike the PANIC test, which is primarily suited for explicitly modeling latent factors, the CIPS test is more appropriate for the present context, given the moderate panel size and the objective of assessing stationarity under cross-sectional dependence across heterogeneous units (Choi, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Pesaran, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Multicollinearity Test (VIF)\u003c/h2\u003e \u003cp\u003eMulticollinearity arises when independent variables in a model are highly correlated, creating substantial redundancy in the explanatory information they provide. While it does not inherently bias the model\u0026rsquo;s overall fit, it severely limits the ability to isolate the unique contribution of each independent variable to the dependent one. This phenomenon produces unstable and inaccurate coefficient estimates, inflates standard errors, and diminishes statistical power, ultimately complicating the identification of significant relationships. In more severe forms, multicollinearity undermines the interpretability and credibility of the model, thereby weakening the robustness and validity of any inferences or policy recommendations derived from the analysis. To detect and address such concerns, a Variance Inflation Factor test is conducted, to help quantify the extent of shared variance among predictors (Kyriazos \u0026amp; Poga, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Salmer\u0026oacute;n et al., 2022).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Autoregressive Distributed Lag Model\u003c/h2\u003e \u003cp\u003eThe Auto Regressive Distributed Lags Model (ARDL) was initially proposed by Pe-saran and Shin in (1995) and later expanded by Pesaran et al. (2001). This model employs standard ordinary least squares regression and incorporates lags of both dependent and independent variables. As noted by Farhani et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Acaravci and Ozturk (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Ahmad et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Al-Mulali et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Ghosh (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), ARDL has recently gained traction in co-integration testing due to several advantages over other methods. First, it allows for variables integrated of different orders (i.e., I(0) or I(1)). Second, it provides efficient and unbiased estimators for both large and small sample sizes. Third, it effectively addresses endogeneity issues within the variables. Additionally, ARDL permits the use of varying lag orders for each variable, deter-mined by criteria such as the Akaike Information Criterion (AIC) and the Schwarz Bayesian Criterion (SBC), all within a single reduced equation framework. Lastly, the ARDL model overcomes the problem of autocorrelation (Pumphrey \u0026amp; Salah, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), provides unbiased estimates accounting for nonlinearity, heterogeneity and nonstationarity (Anoruo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and is also suitable in case of Cross-Sectional Dependence (Tackie et al., \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Anoruo et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tabash et al., \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Oyinlola et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe lag length was determined through an iterative process, guided by diagnostic checks and empirical robustness. Several lag structures were estimated and assessed based on the consistency of coefficient signs, statistical significance and the overall model stability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Granger Causality Test\u003c/h2\u003e \u003cp\u003eIn economic literature, specifically the energy-growth nexus is commonly addressed through investigating the direction of causality between these two variables (Adams et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Alper and Oguz, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Behmiri and Manso, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Belke et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chiou-Wei et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Destek and Aslan, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Faisal et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Payne, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In order to do that, the Granger causality test is applied, to examine whether past values of energy consumption statistically improve the prediction of economic growth, or vice versa. A lag length of two was selected to capture only the most recent dynamics in the data, thereby minimizing the risk of distortion in the causality direction. Extending the lag structure further may artificially suggest the presence of causality where none exists, potentially leading to misleading inferences.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Preparation\u003c/h2\u003e \u003cp\u003eIn order to ensure robustness of the findings, the authors started by first running cross-sectional dependence tests, followed by stationarity tests and VIF analysis, as shown in the below sections.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Cross-Sectional Dependence Test Results\u003c/h2\u003e \u003cp\u003eWith regards to Tables \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we do not reject H\u003csub\u003e0\u003c/sub\u003e of having no cross-sectional dependence for underperforming MICs. In other words, we conclude that data for underperforming MICs is free of cross-sectional dependence. Regarding overachieving MICs, we fail to reject H\u003csub\u003e0\u003c/sub\u003e, which means that there is substantial evidence for cross-sectional dependence among MICs\u0026rsquo; Overachievers (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Therefore, we have to account for the latter in the coming tests.\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\u003eCross-Sectional Dependence - Underperformers MICs\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eResidual Cross-Section Dependence Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNull hypothesis: No cross-section dependence (correlation) in residuals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEquation: Untitled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePeriods included: 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCross-sections included: 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTotal panel observations: 280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: non-zero cross-section means detected in data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCross-section means were removed during computation of correlations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed.f.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreusch-Pagan LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.23628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran scaled LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.446543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.6552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.546885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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\u003eCross-Sectional Dependence - Overachievers MICs\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eResidual Cross-Section Dependence Test\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNull hypothesis: No cross-section dependence (correlation) in residuals\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEquation: Untitled\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePeriods included: 28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCross-sections included: 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTotal panel observations: 280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNote: non-zero cross-section means detected in data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCross-section means were removed during computation of correlations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed.f.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreusch-Pagan LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e139.2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran scaled LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.929818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePesaran CD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.337382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0000\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=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Stationarity Test Results\u003c/h2\u003e \u003cp\u003eAccording to Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we can conclude that all variables for the MICs\u0026rsquo; Underperformers as well as Overachievers are stationary at levels.\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\u003eSummary of Stationarity Test Results\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 \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMICs\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eUnderperformers\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eOverachievers\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eIPS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eADF\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eCIPS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1st Difference\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant \u0026amp; Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eGCF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1st Difference\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant \u0026amp; Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eFDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1st Difference\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant \u0026amp; Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eTO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1st Difference\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant \u0026amp; Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eLevels\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003e1st Difference\u003c/em\u003e\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\u003e0.0000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eConstant \u0026amp; Trend\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003e*, **, *** indicate a significance at 10%, 5% and 1% levels, respectively.\u003c/em\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Multicollinearity Test Results\u003c/h2\u003e \u003cp\u003eAccording to the VIF test (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), it is evident that there is no multicollinearity between independent variables, for both, HICs\u0026rsquo; Underperformer as well as Overachievers, as the VIF statistic lies around one for all variables (Khan et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chien et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\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\u003eVIF - Underperformers MICs\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVariance Inflation Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDate: 07/20/25 Time: 12:54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSample: 1993 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIncluded observations: 280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncentered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCF_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.56E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.037925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.025433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.19E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.027918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.003136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.025125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.023481\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.038067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.024883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.269047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.049213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003eVIF - Overachievers MICs\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVariance Inflation Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDate: 07/20/25 Time: 12:54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSample: 1993 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIncluded observations: 280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUncentered\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCF_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.60E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.110075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.089946\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.40E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.050648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.001493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.097950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.092136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.74E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.028909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.008307\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.372642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.091382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\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 \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Autoregressive Distributed Lag Model Results\u003c/h2\u003e \u003cp\u003eAs shown in the ARDL results (table 17), for MICs\u0026rsquo; Underperformers have a 5% significant long-run relationship between the independent and dependent variables, since the cointegrating coefficient is -0.425175. Hence, it can be concluded that approximately 43% of the short-run disequilibrium is corrected in each period. Also, all independent variables show a 1% significant impact on GDP growth. In particular, the results for MICs\u0026rsquo; Underperformers explain an approximate 0.2% increase in GDP growth due to a 1% change in domestic investment, while FDI is shown to decrease economic growth by roughly 0.004%. As for TO and EC, they both boost GDP growth by nearly 0.8% and 0.21%, respectively.\u003c/p\u003e \u003cp\u003eRegarding MIC\u0026rsquo;s Overachievers (Table \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), the cointegrating coefficient is also negative and significant at the 1%, showing a slightly faster speed of adjustment, namely, around 52% of the deviation from the long-run equilibrium is corrected each period. Again, GCF and EC show a positive and significant effect on economic growth with a magnitude of 0.25% and 0.03%, respectively. However, there is insufficient evidence for a significant effect of FDI and TO on GDP growth.\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\u003eARDL (MICS - Underperformers)\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: D(GDP_GR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMethod: ARDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDate: 08/26/24 Time: 17:04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSample: 1997 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIncluded observations: 240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of cross-sections: 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDependent lags: 4 (Automatic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAutomatic-lag linear regressors (4 max. lags): GCF_GR FDI_GR EC_GR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTO_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDeterministics: Restricted constant and no trend (Case 2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModel selection method: Akaike info criterion (AIC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNumber of models evaluated: 2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSelected model: PMG(3,4,3,4,4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\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\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLong-run (Pooled) Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCF_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.198243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.711933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.397345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.206813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.535307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.791363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.072462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.92106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.279442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.191060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.696537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eShort-run (Mean-Group) Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOINTEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.425175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.205308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.070908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GDP_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.030379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.330758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9269\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GDP_GR(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.013603\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.231548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GCF_GR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.044115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.050943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GCF_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.095611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.100323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.953037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GCF_GR(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.021428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.067076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.319456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GCF_GR(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.014898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.498838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.6184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(FDI_GR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.004018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.913301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3621\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(FDI_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.197928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0290\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(FDI_GR(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.019914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.016024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.242804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EC_GR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.238674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.245601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.971796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EC_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.436908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.311306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.403470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1619\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EC_GR(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.338153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.252593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.338725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(EC_GR(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.349170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.188251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TO_GR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.284134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.188873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.504370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TO_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.172082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.142944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.203841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TO_GR(-2))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.149027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.150454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.990520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3230\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TO_GR(-3))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.058375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.083077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.702664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.4830\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-Likelihood:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-379.5410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eARDL (MICs - Overachievers)\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDependent Variable: D(GDP_GR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMethod: ARDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDate: 04/15/25 Time: 15:32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSample: 1995 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIncluded observations: 260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of cross-sections: 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDependent lags: 3 (Automatic)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAutomatic-lag linear regressors (3 max. lags): GCF_GR FDI_GR TO_GR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEC_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDeterministics: Restricted constant and no trend (Case 2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eModel selection method: Akaike info criterion (AIC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eNumber of models evaluated: 768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSelected model: PMG(2,1,2,2,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\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\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLong-run (Pooled) Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCF_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.246911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.031053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.951193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFDI_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.924780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTO_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.055498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.318112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1886\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.032672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.311656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.812547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.428952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.888060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eShort-run (Mean-Group) Coefficients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOINTEQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.518257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.516386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GDP_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.192037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.199252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.963792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3361\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(GCF_GR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.024061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.045128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.533164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.5944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(FDI_GR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.355739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(FDI_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.073370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TO_GR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.018723\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.965935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD(TO_GR(-1))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.080593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.084623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.952384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3418\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-Likelihood:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-646.0676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Granger Causality Test Results\u003c/h2\u003e \u003cp\u003eAccording to the Granger causality test results for MICs, there is evidence for the neutrality hypothesis regarding underperforming MICs, meaning that there is no causality between GDP growth and EC (Table \u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). As for overachieving MICs, we can conclude that there is a bidirectional relationship between GDP growth and EC (at a significance level of 5% and 1%), highlighting evidence for the feedback hypothesis (Table \u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGranger Causality Test \u0026ndash; MICs\u0026rsquo; Underperformers\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePairwise Granger Causality Tests\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDate: 04/27/25 Time: 11:29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSample: 1993 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLags: 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC_GR does not Granger Cause GDP_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGDP_GR does not Granger Cause EC_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGranger Causality Test - MICs' Overachievers\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\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePairwise Granger Causality Tests\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDate: 04/27/25 Time: 11:31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSample: 1993 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLags: 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNull Hypothesis:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProb.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC_GR does not Granger Cause GDP_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.36977\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGDP_GR does not Granger Cause EC_GR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.3170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.E-13\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"},{"header":"5. Discussion","content":"\u003cp\u003eWith respect to MICs\u0026rsquo; ARDL results underscore the critical role of gross capital formation in driving long-run economic growth across both underperforming and overachieving MICs. Consequently, we do not reject H1.1 and H1.2, which is in line with Kesar et \u003cem\u003eal.\u003c/em\u003e (2022), Maune et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Yang and Shafiq (\u003cspan citationid=\"CR141\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For MICs\u0026rsquo; Underperformers, GCF demonstrates a statistically significant and positive effect on GDP growth at the 1% level, with a 1% increase in domestic investment associated with a 0.2% rise in economic output. This finding suggests that domestic investment remains a key engine of growth in underperforming MICs. It highlights the importance of capital accumulation, particularly in infrastructure, industry, and public services, for stimulating productive activity and fostering long-term development. Similarly, in overachieving MICs, GCF continues to exhibit a positive and significant impact on GDP growth, with an even slightly higher effect of approximately 0.25% for every 1% increase in investment, which is the highest coefficient among all independent variables. This reinforces the idea that investment-driven growth is a common feature of overachieving MICs. However, the stronger magnitude of the coefficient in Overachievers may reflect more efficient allocation of capital, better investment climates, or more productive use of resources. The faster speed of adjustment toward long-run equilibrium in Overachievers, as shown by the ECT term, further supports the interpretation that these economies are better equipped to translate investment inflows into sustained growth.\u003c/p\u003e \u003cp\u003eFor MICs, as countries climb the development ladder, the relationship between FDI and economic growth becomes more nuanced. MICs\u0026rsquo; Underperformers show a negative effect of FDI on growth, while such impact appeared to be insignificant for overachieving MICs. Accordingly, H2.1 is rejected, in line with Ramzan et al. (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Chaudhury et al., (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Saqib et al., (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This counterintuitive result can be explained by diminishing marginal returns and structural mismatch. While in LICs FDI can bring basic infrastructure, technology, and job creation filling large existing gaps, in MICs FDI often shifts to real estate, finance, or extractive industries with fewer linkages to the broader economy. This means MICs may receive FDI that doesn\u0026rsquo;t boost productivity or create positive spillovers (Alfaro et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In particular, in underperforming MICs, like Libya, Argentina and Ukraine, FDI often targets resource extraction, real estate, or low-value services, which generate little to no technology transfer or productivity gains and hence cause a negative impact on economic growth (G\u0026ouml;rg \u0026amp; Greenway, 2004). Additionally, MICs with weak institutions can cause FDI to crowd out domestic investment or foster corruption (Jude \u0026amp; Levieuge, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, some of the underperforming MICs, such as Argentina and Ukraine, suffer from economic volatility, inflation, political swings, which can make FDI pro-cyclical and destabilizing and hence have detrimental effects on growth (Lensink \u0026amp; Morrissey, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConcerning overachieving MICs, results showed no significant effect of FDI on economic growth, leading us to reject H2.2, similar to Ozili (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and Falki (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Such insignificance can largely be attributed to sectoral composition of FDI. In Vietnam for example, an overachieving middle-income country, FDI has played a key role in the manufacturing and processing sectors, particularly electronics and textiles. However, it has led to an economy dominated by low value-added, labor-intensive production, with limited domestic linkages and minimal technology transfer (Bui et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Similarly, China's shift in FDI composition toward service-oriented industries such as finance, real estate, and IT has contributed less to productivity growth than earlier manufacturing-based FDI inflows, thus reducing the overall developmental impact (Ross \u0026amp; Fleming, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Likewise, Cambodia has attracted FDI primarily in real estate, financial services, and low-tech garment manufacturing, sectors that offer limited industrial upgrading and few knowledge spillovers into the domestic economy (Tang \u0026amp; Wong, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Accordingly, if FDI inflows are concentrated in sectors with constrained capacity to stimulate sustained and broad-based economic development, it may lack significant impact on GDP growth.\u003c/p\u003e \u003cp\u003eMoving on to the effect of trade openness on economic growth in MICs, results again showed a significant difference within the same income category. For Underperformers, a significant positive impact of TO on GDP growth is confirmed. In other words, H3.1 is not rejected. Accordingly, it can be concluded that TO boosts economic growth in underperforming MICs, as supported by Hye et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Karras (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), Keho (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Malefane and Odhiambo (\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), as well as Raghutla (\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As cited by Idris et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Imoisi (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Silajdzic and Mehic (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), trade allows access to foreign technology, machinery, and intermediate goods that spur productivity and ultimately economic growth. These aforementioned goods are often not available domestically or are too costly to produce locally.\u003c/p\u003e \u003cp\u003eOpenness also facilitates the import of high-tech and ICT (information and communication technology) goods, which help modernize industries, improve productivity, and support integration into global value chains. Indicators such as ICT goods imports (% of total imports) available from the World Bank and UNCTAD, can provide empirical support for the positive effect of trade openness on economic growth. For example, Tonga\u003csup\u003e6\u003c/sup\u003e experienced a sharp increase in ICT goods imports as a percentage of total imports between 2000 and 2014, indicating a growing reliance on foreign technology to modernize its economy and enhance productivity across key sectors. In particular, the Tongan ICT goods imports accounted for approximately 0.98% in 2000 and reached its peak of 10.22% in 2014, representing a tenfold increase over the period. A similar upward trend in ICT goods imports is observed in Ukraine, another underperforming MICs, where ICT goods accounted for approximately 2.5% of total imports in 2000. This share increased steadily, reaching a peak of 6.59% in 2019. Likewise, the Republic of the Congo, a third underperforming MIC, experienced growth in the share of ICT goods within its total imports, although the magnitude of this increase was more modest compared to that of Ukraine and Tonga (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, in several underperforming MICs, such as Fiji, Tonga, and Dominica, the economy is heavily reliant on tourism and related service exports. For such countries, trade openness plays a pivotal role in enabling tourism-led growth by facilitating the cross-border movement of people, goods, and capital. Trade openness reduces barriers to international mobility and logistics, allowing countries to tap into global tourism markets. By improving diplomatic, trade, and travel relations, often through bilateral or multilateral agreements, these economies can attract a higher volume of international visitors. Fiji, for example, has long promoted an open visa policy and liberal air access, which have enabled sustained growth in tourist arrivals from Australia, New Zealand, and North America, accounting for 46.3%, 23.0% and 11.0% in 2024, respectively (Fiji Bureau of Statistics, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Also, the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below shows the increasing trend in tourism export in Fiji, Tonga and Dominica, highlighting the importance of higher trade openness for substantial economic growth in their context.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMoving on to the effect of TO on economic growth for overachieving MICs, we have to reject H3.2, as results showed insignificant coefficients, which may be explained by the relatively stable TO levels in this group. According to the below Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, on average, TO grew at a modest rate of \u0026minus;\u0026thinsp;0.23% annually, indicating minimal change over time. In contrast, LICs and HICs experienced higher average TO growth rates of 1.02% and 1.00% respectively, indicating more dynamic trade environments. The limited change observed in overachieving MICs likely reduced the model\u0026rsquo;s ability to detect a statistically significant effect, even if trade openness remains economically relevant.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWith reference to the results for the energy-growth nexus in MICs, Granger causality findings suggest that the role of energy consumption in economic growth varies significantly based on the country\u0026rsquo;s performance trajectory. In underperforming MICs, the neutrality hypothesis is confirmed, in line with Narayan (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Menegaki (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Payne (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), while the feedback hypothesis is supported for overachieving MICs, as found by Phukon and Konwar (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Behmiri and Manso (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and Shahbaz et al. (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Accordingly, H4.3 is rejected, while we do not reject H4.4. The lack of causality between EC and GDP growth in underperforming MICs may be attributed to several country-level factors, such as inefficient energy use and infrastructural deficiencies. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, underperforming MICs exhibit an average GDP per unit of energy use of approximately 10.78 constant 2021 PPP dollars per kg of oil equivalent, compared to 11.82 for overachievers (based on data from 2000 to 2022). This disparity suggests relatively lower energy efficiency and less productive use of energy in underperforming MICs, offering a potential explanation for the observed neutrality hypothesis in the energy-growth nexus. Moreover, infrastructural quality appears to be a key differentiating factor. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, MICs\u0026rsquo; Underperformers experience average electric power transmission and distribution losses of 22.36% of output, nearly double the 12.24% observed in Overachievers. These elevated losses point to significant inefficiencies in energy delivery systems, which can further diminish the economic impact of energy consumption. Combined, these structural differences help to contextualize why energy consumption fails to Granger-cause economic growth in underperforming MICs, in contrast to the bidirectional relationship found in Overachievers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, the ARDL model findings for MICs indicate that energy consumption has a statistically significant and positive long-run effect on economic growth for both Underperformers as well as Overachievers, leading us not reject H4.1 and H4.2. This suggests that, over time, increased energy use contributes to higher levels of output, regardless of the MICs' performance category. Importantly, these findings do not contradict the Granger causality results discussed earlier. While the Granger test assesses short-run predictive relationships, the ARDL model captures long-run equilibrium dynamics. Therefore, the absence of short-run causality in underperforming MICs (as per Granger) can coexist with a significant long-run relationship (as found in ARDL), implying that energy consumption may influence growth only with a time lag or under certain structural conditions. In contrast, the MICs\u0026rsquo; Overachievers not only exhibit a strong long-run impact of energy consumption on growth, but also show short-run bidirectional causality, reinforcing the robustness of the energy-growth linkage in these economies.\u003c/p\u003e"},{"header":"6. Implications and Conclusion","content":"\u003cp\u003eIn light of the previously discussed findings, it is recommended for MICs to increase domestic investment, as GCF consistently demonstrated a positive and significant effect on economic growth, underscoring its structural role in enhancing productive capacity, stimulating employment, and supporting long-term development. Hence, MICs\u0026rsquo; policy makers are strongly encouraged to reorient their growth strategies toward strengthening domestic investment. This may include increasing public and private capital formation, expanding access to finance for domestic firms, improving infrastructure, and ensuring a stable and transparent policy environment that fosters local investment. Targeted measures could involve large-scale infrastructure projects in transport, energy, and digital connectivity to crowd in private sector investment and expand the productive capital stock.\u003c/p\u003e \u003cp\u003eSince the results for TO are nuanced in MICs, it is of high importance to pursue context-specific trade strategies that align with a country's stage of development and its economic performance characteristics. In the case of underperforming MICs, the evidence confirms that trade openness can act as a catalyst for economic growth. Policymakers in these countries are therefore encouraged to pursue targeted trade liberalization policies aimed at facilitating the import of productivity-enhancing goods, particularly capital and ICT equipment. These imports can support industrial modernization, enhance technological capabilities, and enable integration into global value chains. Policy measures such as tariff reductions on high-tech inputs, restructuring of customs procedures, and the establishment of trade facilitation mechanisms would be instrumental in realizing these gains. Furthermore, investments in trade-related infrastructure, such as ports, transportation networks, and digital connectivity, should be prioritized to reduce transaction costs and improve trade efficiency. In addition, in countries with tourism-dependent economies, such as Fiji, Tonga, and Dominica, trade openness plays a vital role in facilitating tourism-led growth. For such nations, the liberalization of cross-border movement through open visa regimes, air service agreements, and enhanced diplomatic relations is critical. Policymakers should also invest in tourism-supporting infrastructure, including airports, accommodation services, in order to strengthen international competitiveness in this sector. Conversely, for overachieving MICs, the absence of a statistically significant relationship between TO and economic growth suggests diminishing marginal returns to openness in environments, where trade levels have remained relatively constant. In this context, policy attention should shift from the quantity to the quality of trade. Rather than aiming to further liberalize already open economies, overachieving MICs should focus on diversifying their export baskets and upgrading their participation in global value chains. This includes promoting high-value-added sectors such as advanced manufacturing, technology-driven services, and green industries. Governments should support firms in transitioning toward more complex production through targeted research and development incentives, innovation policies, and capacity-building programs. Additionally, enhancing the scope and depth of trade agreements to include modern elements such as digital trade, services liberalization, and intellectual property rights could create new growth avenues.\u003c/p\u003e \u003cp\u003eAddressing the challenges of low energy efficiency and high losses in electric power transmission and distribution in underperforming MICs is essential for improving the developmental impact of energy use in. These inefficiencies constrain both short-term productivity and long-term growth, as highlighted by the significantly higher average T\u0026amp;D losses and lower energy output per unit consumed in these countries. To tackle these issues, governments should prioritize the modernization of energy infrastructure. This includes replacing aging transmission lines and substations with higher-capacity, smart grid technologies that allow for real-time monitoring, load optimization, and preventative maintenance. Investing in decentralized energy solutions, such as mini-grids in remote areas, can also reduce reliance on long-distance transmission, lowering technical losses. simultaneously, strengthening regulatory frameworks is crucial. Introducing and enforcing energy efficiency standards industrial processes can reduce unnecessary consumption. On the other hand, MIC\u0026rsquo;s Overachievers are well-positioned to invest in energy innovation, including the deployment of smart grids, real-time monitoring systems, and energy storage technologies that can enhance system efficiency and resilience. Policymakers should also maintain efforts to improve industrial energy efficiency, which is essential for preserving competitiveness in increasingly complex global value chains.\u003c/p\u003e \u003cp\u003eTo conclude, this paper examined the relationship between gross capital formation, foreign direct investment, trade openness, energy consumption and economic growth, employing a performance-based framework that moves beyond the conventional income classifications. This approach provides robust evidence of intra-group divergence, offering a more nuanced understanding of growth dynamics across different performance groups. The results confirm that GCF consistently operates as a primary driver of economic expansion, enhancing productive capacity, facilitating structural transformation, and supporting long-term development. Energy consumption also exerts a positive influence on growth, with short-run neutrality observed in Underperformers and a feedback relationship in Overachievers. Conversely, FDI demonstrates a negative effect on growth in Underperformers and remains statistically insignificant for Overachievers, largely reflecting its concentration in low-value-added or enclave sectors with minimal domestic spillovers. Similarly, TO promotes growth in Underperformers but is insignificant for Overachievers, emphasizing that the growth impact is contingent upon the quality and structure of trade, rather than its sheer volume. Thus, these findings highlight that growth strategies in MICs should be tailored according to actual performance levels, rather than relying solely on broad income-based classifications.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interest Information\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to this work. Aya Khater conceived and designed the study. Data were collected and analyzed by Aya Khater. The original draft was prepared by Aya Khater. All authors provided critical revisions and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data sources supporting the findings of this study are available within the paper . They are provided in Table 2, along with the variables' definitions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcaravci, A., \u0026amp; Ozturk, I. (2010). On the relationship between energy consumption, CO2 emissions and economic growth in Europe. \u003cem\u003eEnergy\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(12), 5412-5420.\u003c/li\u003e\n\u003cli\u003eAcaravci, A., Erdogan, S., \u0026amp; Akalin, G. (2015). 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National Bureau of Economic Research.\u003c/span\u003e\u003cdiv id=\"Par19\" class=\"Para\"\u003eAuty, R. M. (2001). Resource abundance and economic development. Oxford University Press.\u003c/div\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Albania, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Belarus, Belize, Benin, Bhutan, Bolivia (Plurinational State of), Bosnia and Herzegovina, Botswana, Brazil, Bulgaria, Cabo Verde, Cambodia, Cameroon, China, Colombia, Comoros, Congo, Costa Rica, C\u0026ocirc;te d'Ivoire, Djibouti, Dominica, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eswatini, Fiji, Gabon, Georgia, Ghana, Grenada, Guatemala, Guyana, Haiti, Honduras, India, Indonesia, Iran (Islamic Republic of), Iraq, Jamaica, Jordan, Kazakhstan, Kenya, Kiribati, Kyrgyzstan, Lao People's Dem. Rep., Lebanon, Lesotho, Libya, Malaysia, Maldives, Mauritania, Mauritius, Mexico, Mongolia, Morocco, Myanmar, Namibia, Nepal, Nicaragua, Nigeria, North Macedonia, Pakistan, Panama, Papua New Guinea, Paraguay, Peru, Philippines, Romania, Russian Federation, Samoa, Sao Tome and Principe, Senegal, Solomon Islands, South Africa, Sri Lanka, Suriname, Tajikistan, Tanzania, United Republic of, Thailand, Tonga, Tunisia, Turkmenistan, Turkey, Ukraine, Uzbekistan, Vanuatu, Viet Nam, Zambia, Zimbabwe.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The MICs\u0026rsquo; Overachievers in this study are: Azerbaijan, Bhutan, Bosnia and Herzegovina, Cambodia, China, Equatorial Guinea, Guyana, Lao People's Dem. Rep., Myanmar, Viet Nam\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The MICs\u0026rsquo; Underperformers in this study are: Argentina, Bulgaria, Congo, Dominica, Fiji, Jamaica, Kiribati, Libya, Tonga, Ukraine\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e one of the underperforming MICs\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Economic Growth, Gross Capital Formation, Foreign Direct Investment, Trade Openness, Energy Consumption, Middle-Income Countries","lastPublishedDoi":"10.21203/rs.3.rs-8377513/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8377513/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of gross capital formation, foreign direct investment, trade openness, and energy consumption on economic growth in 97 middle-income countries, adopting a performance-based analytical framework. Unlike conventional studies that rely solely on income classifications, this research divides middle-income countries into “Overachievers” and “Underperformers” to capture intra-group divergence in growth dynamics. Employing second-generation panel techniques, including the Cross-Sectionally Augmented IPS (CIPS) test, panel ARDL modeling, and Granger causality analysis for 1993–2020, the findings reveal distinct performance-driven patterns. Gross capital formation consistently exerts a positive and significant long-run effect on growth across both groups, emphasizing the central role of domestic investment. Energy consumption also contributes positively in the long-run, with short-run bidirectional causality observed only among Overachievers. Conversely, foreign direct investment demonstrates a negative effect for Underperformers and an insignificant effect for Overachievers, largely explained by sectoral concentration in low-value-added, enclave, or service-oriented investments with limited linkages and spillovers. Trade openness supports growth in Underperformers but is insignificant for Overachievers, reflecting structural and compositional differences in trade dynamics. These results underscore the importance of performance-based classifications rather than the use of conventional income categories. Lastly, this paper emphasizes the significance of adopting a performance-based approach for capturing heterogeneous growth effects and highlights the need for policies that enhance domestic investment efficiency, strengthen institutional quality, and channel foreign direct investment into sectors with higher spillover potential.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification: C33, E22, F43, O11, O47\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":"Asymmetric Growth Dynamics: The Performance-Based Growth-Nexus of Investment, Trade, and Energy in Middle-Income Countries","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 15:21:58","doi":"10.21203/rs.3.rs-8377513/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":"5d2b78b0-94b9-4323-b7cb-a7ed9b50b399","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T20:38:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 15:21:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8377513","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8377513","identity":"rs-8377513","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}