Resource Rents, Institutions, and Growth in Sub-Saharan African OIC Countries: A Panel Quantile Analysis

Resource Rents, Institutions, and Growth in Sub-Saharan African OIC Countries: A Panel Quantile Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Resource Rents, Institutions, and Growth in Sub-Saharan African OIC Countries: A Panel Quantile Analysis Mousse Abdi Mohamoud, Eid Ibrahim Daud, Abdiaziz Ali Nour, Khadar Abdi Mohamed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8892786/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract This research investigates the nexus among natural resource rents, control of corruption, and GDP per capita growth in 21 Sub-Saharan African OIC member states for the period 2005–2024. Applying panel Fixed Effects estimation as well as panel quantile regression (25th, 50th, and 75th percentiles), the study assesses heterogeneities along with the growth distribution. The findings suggest that control of corruption is positively associated with growth in all specifications. The interaction variable of natural resource rents and corruption control is found to be negative and significant, implying that the marginal effect of corruption control on growth decreases with increases in resource reliance. Quantile regression results indicate that the negative relationship between resource rents and growth is most pronounced at the lowest end of the growth scale, while foreign direct investment has a positive association with growth mostly in the median and upper quantiles. These results indicate that there is distributional diversity in the resource, governance, and growth nexus across SSA-OIC countries. Business and commerce/Economics Social science/Economics Earth and environmental sciences/Environmental social sciences Resource Curse Control of Corruption SSA-OIC Economic Growth Quantile Regression Figures Figure 1 1.0 Introduction The "resource curse" describes the empirical pattern where countries rich in natural resources, such as oil, gas, and minerals, often grow more slowly than resource-poor countries, contrary to the intuition that natural riches should guarantee prosperity (Alssadek & Benhin, 2023 ; Saeed, 2021 ). This paradox was formalized by Sachs and Warner’s early work, which demonstrated a robust negative association between natural-resource dependence and GDP per capita growth across 90–100 countries between 1970 and 1990 (Alssadek & Benhin, 2023 ; Manzano & Rigobon, 2001 ). Subsequent literature has highlighted several mechanisms for this "curse," including "Dutch disease," where resource booms appreciate the real exchange rate and crowd out manufacturing, and the erosion of institutions as large rents make corruption and rent-seeking more attractive (Collier & Goderis, 2008 ; Mehlum et al., 2006 ). Sub-Saharan African (SSA) members of the Organization of Islamic Cooperation (OIC) sit at a unique intersection between the resource-curse debate and distinct developmental constraints. Approximately half of the OIC members are in Africa, and approximately 30% of the OIC’s population resides in sub-Saharan countries that remain heavily exposed to arid climates and are dependent on low-productivity subsistence agriculture (Ahsan, 2020). This specific bloc faces a "double bind": many are resource-dependent, but they also struggle with a longstanding crisis of governance, weak rule of law, and fragile state capacity (Anheier et al., 2023 ; Maxwele et al., 2024 ). For these OIC members, institutional quality, specifically stable government and effective regulation, is a central determinant of growth, yet many score poorly in these dimensions (Slesman et al., 2015 ). While institutions are known to matter, their interaction with resource abundance in the SSA-OIC context is complex and often damaging. In these states, resource rents can create powerful incentives for corruption, meaning that governance must be substantially stronger in resource-rich SSA than in resource-poor SSA to deliver similar developmental outcomes (Coulibaly et al., 2018 ; Ringold et al., 2017 ). Evidence suggests that resource wealth increases the bar" for governance; modest improvements in institutions can be overwhelmed by rent-seeking and political incentives tied to resource income, blunting the potential growth payoff (Adika, 2020 ; Eregha & Mesagan, 2016 ). This raises the question of whether resource wealth actively hinders good governance by promoting growth in a specific region. There is a significant gap in the literature regarding how these factors differ between low and high-growth countries. Most existing studies on the growth of the OIC and African countries rely on mean-based panel models, which miss important distributional heterogeneity (Tarchoun & Mili, 2024 ; Uddin et al., 2017 ). While quantile regression has been applied to other determinants, such as how tourism, aid, or health variables affect countries differently at the lower and upper tails of the growth distribution, there is no known study that applies quantile regression to the sample of OIC member states in Sub-Saharan Africa to examine the joint role of resource abundance and institutional quality (Bila et al., 2024 ; Sahni et al., 2021 ). This leaves the question of whether "average" resource–institution effects mask critical differences across the growth distribution open. The selection of the SSA-OIC sub-group is theoretically significant because these nations operate under a dual institutional framework. As members of the OIC, they are signatories to the OIC-2025 Program of Action , which emphasizes 'Good Governance' and 'Accountability' as religious and developmental imperatives. However, they also face the traditional 'Resource Curse' challenges common in Sub-Saharan Africa. This study seeks to understand if OIC membership, and the associated developmental support from the Islamic Development Bank (IsDB), helps or hinders these nations in overcoming the structural drag of resource rents. While much research has focused on the wealthy, resource-rich OIC members in the Middle East (the GCC countries), the SSA-OIC members represent the 'frontier' of the OIC's development agenda. These countries are often the most vulnerable to price volatility and have the weakest 'absorptive capacity' for Foreign Direct Investment (FDI). Studying this specific bloc reveals the 'governance bottlenecks' that prevent OIC-led development initiatives from succeeding in resource-dependent African states. This study contributes to the literature by providing evidence that the benefits of fighting corruption diminish as resource reliance increases. Based on the negative coefficient of the interaction term between resource reliance and corruption control, our findings imply that the marginal growth payoff for anti-corruption policies falls in more resource-intensive economies. This is consistent with theories in which natural resource abundance expands rent-seeking opportunities and weakens incentives to invest in costly monitoring institutions, making corruption more resilient to standard governance improvements (Dávid-Barrett & Fazekas, 2020 ; Knutsen et al., 2017 ; Leite & Weidmann, 2001 ). 2.0 Literature Review 2.1 Theoretical Framework 2.1.1 The Resource Curse Theory: Volatility and "Dutch Disease." The "resource curse" framework suggests that natural resource abundance can paradoxically undermine long-run economic growth. The primary mechanism for this is "Dutch Disease," which operates through two main channels: the spending effect and the resource movement effect (Alssadek & Benhin, 2021 ; Brahmbhatt et al., 2010 ). Resource booms trigger real exchange rate appreciation, shifting capital and labor toward the booming resource sector and non-tradable services while reducing manufacturing output and net exports (Botta, 2017 ; Gylfason et al., 1999 ). This deindustrialization leads to a "learning-by-doing loss," where the shrinkage of high-skill, export-oriented sectors depresses innovation and long-term productivity (Magud & Sosa, 2013; Tressel et al., 2006). Furthermore, price and revenue volatility inherent in commodities increases macroeconomic instability. This volatility discourages investment in tradable sectors because of heightened uncertainty, further cementing the negative link between resource dependence and growth (Al-Jomard et al., 2025 ; Gylfason et al., 1999 ). In OIC member states, 'Control of Corruption' is not just a secular metric but is closely linked to the cultural and religious concept of Amanah (trust and accountability). Theoretically, the 'Rentier State' hypothesis suggests that resource wealth allows elites to bypass these traditional social contracts. In the SSA-OIC context, huge resource rents may create a 'moral hazard' where the availability of easy money from oil and minerals undermines the integration of ethical governance standards promoted by OIC developmental charters, leading to a unique form of institutional erosion. 2.1.2 Institutional Theory: The Role of Governance Institutional theory explains developmental variance by focusing on the "rules of the game," the laws, norms, and structures that shape economic incentives. North ( 1990 ) defines institutions as humanly devised constraints that determine transaction and production costs. Good institutions reduce uncertainty and secure property rights, thereby raising investments (North, 2003 ). Building on this, Acemoglu & Robinson ( 2010 ) distinguish between "inclusive" and "extractive" institutions. Inclusive institutions foster broad participation and economic opportunities, while extractive institutions concentrate on power and rent among a small elite. Persistently, extractive institutions arise because powerful groups block reforms that would erode their personal rents, even if such reforms would benefit society as a whole (Acemoglu & Robinson, 2006 , 2019 ). Consequently, the impact of resources on an economy is often a function of whether the underlying institutional framework is inclusive or extractive or not. For Organization of Islamic Cooperation (OIC) countries, institutions are often conceptualized through the Islamic principle of Amanah, which encompasses notions of trust and accountability. This perspective aligns institutional theory with Islamic ethical values, emphasizing stewardship, fulfillment of trust, and moral responsibility at the core of governance and institutional performance. The principle of Amanah underpins governance mechanisms in OIC countries, stressing that institutions must operate with accountability, honesty, and ethical integrity in fulfilling their duties. This view contrasts with conventional Western governance models and instead integrates foundational Islamic values such as shiddiq (honesty) and mas’uliyah (accountability), reflecting a culturally-rooted approach to institutional design and performance (Hirsanuddin & Martini, 2023 ). This incorporation of Amanah into institutional frameworks also strengthens trust between stakeholders and fosters alignment in regulatory and administrative practices, which is essential for institutional credibility and effectiveness in OIC contexts (Bin-Nashwan, 2025 ). 2.1.3 The Rentier State Hypothesis: Accountability and Revenue The Rentier State Hypothesis posits that resource rents weaken government accountability by loosening the link between taxation and representation. When budgets are financed by external rents (oil/mineral payments) rather than domestic taxes, the state’s need to bargain with and respond to its citizens is reduced (Barma, 2014 ; Canuto, 2019 ). This detachment manifests in several ways: a weaker demand for accountability, as citizens are less likely to monitor windfall spending compared to tax-funded spending (Hoem Sjursen, 2018 ); the erosion of fiscal capacity and parliamentary oversight (Zhuang & Zhang, 2016 ); and the use of rents for patronage and repression to "buy off" or silence opposition (Sandbakken, 2006 ; Schwarz, 2008 ). Thus, resource wealth can stabilize authoritarianism and deteriorate the quality of governance. Sub-Saharan African nations that are members of the Organization of Islamic Cooperation (SSA-OIC) are particularly vulnerable to the rentier state phenomenon because they are "frontier economies" characterized by high dependence on natural resources and relatively underdeveloped tax systems. This resource reliance creates economic vulnerabilities, including fiscal instability and challenges in mobilizing domestic revenues through taxation. As these economies rely heavily on resource rents, their tax systems remain nascent and limited, restricting sustainable fiscal capacity and heightening susceptibility to the adverse effects of commodity price volatility and external shocks. The developing institutional quality and financial systems further compound these challenges, making SSA-OIC nations especially prone to the pitfalls of rentierism seen in resource-dependent economies (Jeppesen et al., 2023 ; Taylor, 2025 ; Zallé, 2023 ). 2.2 Empirical Review: The African Context Empirical evidence regarding the resource curse in Africa is mixed, but follows specific patterns. Many studies confirm a curse, on average, finding that resource rents correlate with lower economic growth, reduced life expectancy, and hindered democratic transitions (Achuo, 2023 ; Jensen & Wantchekon, 2004 ; Ongo Nkoa et al., 2024 ). In Sub-Saharan Africa (SSA), higher resource dependence often reduces non-resource tax revenues and increases vulnerability to fiscal stress (Taylor, 2025 ). However, a growing body of research has emphasized the concept of "conditionality." Studies have found that resource-rich African economies can outgrow resource-poor ones when institutions and infrastructure are robust (Adika, 2020 ; Mlambo, 2022 ). For the continent as a whole, resources appear to be a curse when institutional quality is low, but a blessing when it is high, suggesting the existence of institutional "thresholds" that determine the growth outcome (Amare et al., 2024 ; Asongu et al., 2024 ). 3.0 Data and Methods 3.1 Data Sources and Variables Table 1 shows that this study utilizes a panel dataset comprising data from 2005 to 2024 for the selection of Sub-Saharan African OIC nations. The variables were sourced from primary international databases to ensure cross-country comparability and data integrity. Table 1 Variable Definitions and Data Sources Variables Description Exp. Sign Source Dependent variable GDP GDP per capita growth (annual %) : Annual percentage growth rate of GDP per capita based on constant local currency. Aggregates are based on constant 2015 U.S. dollars. World Bank (WDI) Independent variables CC Control of Corruption : Captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests. Range is approximately − 2.5 (weak) to 2.5 (strong). + WGI FDI Foreign direct investment, net inflows (% of GDP) : Net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor. +/- World Bank (WDI) GFCF Gross fixed capital formation (% of GDP) : Formerly gross domestic fixed investment, it includes land improvements; plant, machinery, and equipment purchases; and the construction of roads, railways, schools, and hospitals. + World Bank (WDI) NR Total natural resources rents (% of GDP) : The sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. It serves as the primary proxy for resource dependence. - World Bank (WDI) Trade Trade (% of GDP) : The sum of exports and imports of goods and services measured as a share of gross domestic product. + World Bank (WDI) Interaction variable IN_CNR Interaction of Corruption and Resources : The product of the Control of Corruption index and Total Natural Resource Rents. These variables test the "dampening" hypothesis, which posits that corruption moderates the impact of resources on growth. - Authors' Calculation Source: Authors, 2025 3.2 Sample Selection The sample consists of 21 African countries representing a diverse range of resource endowments and growth trajectories. Specific countries are included in the panel analysis. West Africa: Benin, Burkina Faso, Cote d'Ivoire, Gambia (The), Guinea, Guinea-Bissau, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo. East and Central Africa: Cameroon, Chad, Comoros, Mozambique, Somalia, and Uganda. North & East Africa: Mauritania and Sudan. Southern Africa: Gabon. 3.3 Econometric Methodology This study employs a multi-stage econometric approach to ensure the robustness of the findings. The analysis began with Fixed Effects (FE) and Random Effects (RE) models. We apply the Hausman (1978) specification test to determine the most efficient and consistent estimator. The omitted variable bias is frequently guarded against when using fixed effects. However, they are not suitable for evaluating the effects of time-varying characteristics. The fixed-effect model is: $${\varvec{G}\varvec{D}\varvec{P}}_{\varvec{i}\varvec{t}}={{\varvec{\beta}}_{1}\varvec{C}\varvec{C}}_{\varvec{i}\varvec{t}}+{{\varvec{\beta}}_{2}\varvec{F}\varvec{D}\varvec{I}}_{\varvec{i}\varvec{t}}+{\varvec{G}\varvec{F}\varvec{C}\varvec{F}}_{\varvec{i}\varvec{t}}+{{\varvec{\beta}}_{4}\varvec{N}\varvec{R}}_{\varvec{i}\varvec{t}}+{\varvec{\beta}}_{5}{\varvec{T}\varvec{r}\varvec{a}\varvec{d}\varvec{e}}_{\varvec{i}\varvec{t}}+{\varvec{\beta}}_{6}{\varvec{I}\varvec{N}\_\varvec{C}\varvec{N}\varvec{R}}_{\varvec{i}\varvec{t}}+{\varvec{\mu}}_{\varvec{i}}+{\varvec{\epsilon}}_{\varvec{i}\varvec{t}}$$ 1 …………………………… However, because they are also related to each other and to the error terms of other groups, this gives rise to a large variance, with statistical inference becoming questionable. In this case, we can perform better by employing a random effects model. In the effects model, the variation among entities is assumed to be random and independent of the independent variables. The random effect model is expressed as. $${\varvec{G}\varvec{D}\varvec{P}}_{\varvec{i}\varvec{t}}={{\varvec{\beta}}_{1}\varvec{C}\varvec{C}}_{\varvec{i}\varvec{t}}+{{\varvec{\beta}}_{2}\varvec{F}\varvec{D}\varvec{I}}_{\varvec{i}\varvec{t}}+{{\varvec{\beta}}_{3}\varvec{G}\varvec{F}\varvec{C}\varvec{F}}_{\varvec{i}\varvec{t}}+{{\varvec{\beta}}_{4}\varvec{N}\varvec{R}}_{\varvec{i}\varvec{t}}+{\varvec{\beta}}_{5}{\varvec{T}\varvec{r}\varvec{a}\varvec{d}\varvec{e}}_{\varvec{i}\varvec{t}}+{\varvec{\beta}}_{6}{\varvec{I}\varvec{N}\_\varvec{C}\varvec{N}\varvec{R}}_{\varvec{i}\varvec{t}}+{\varvec{\tau}}_{\varvec{t}}+{\varvec{\epsilon}}_{\varvec{i}\varvec{t}}$$ 2 ……………………… Where is the error for the between-group and is a random intercept. The strength of the random effects model rests in its allowance for variation across subjects, allowing time-invariant factors, such as gender, ethnicity, or race, to be examined as part of the model. The other thing to stress is that the random effects method involves variation at two levels, within an individual and between individuals. The amount of sampling variability in a given mixed model is generally lower than that observed in its fixed-effects form (Allison, 2005). However, this difficulty arises when one wants to account for all important observable variables. Due to the unavailability of data on some variables, we might expect omitted variable bias in the model. The decision to use the fixed effects or random effects model is based on whether it is correlated with any of the other explanatory variables in the model (Wooldridge, 2002 ). If such a correlation is present, we recommend the fixed effects method. Otherwise, the random effect is less wide and leads to more efficient estimates (Wooldridge, 2002 ). Cross-Sectional Dependence (CSD) tests (Pesaran, Friedman, and Frees) were conducted to address potential issues inherent in the panel data. To account for distributional heterogeneity, specifically how factors affect low-growth versus high-growth countries differently. The study utilizes Panel Quantile Regression at the 25th, 50th, and 75th percentiles . This method provides a more granular view than mean-based estimates, revealing whether the "dampening" effect of corruption is uniform across different levels of economic performance. $${{\varvec{Q}}_{\varvec{G}\varvec{D}\varvec{P}}}_{\varvec{i}\varvec{t}}\left(\frac{\varvec{\tau}}{{\varvec{\alpha}}_{\varvec{i}}}\right)={{\varvec{\alpha}}_{\varvec{i}}{+\varvec{\alpha}}_{1\varvec{\tau}}\varvec{C}\varvec{C}}_{\varvec{i}\varvec{t}}+{{\varvec{\alpha}}_{2\varvec{\tau}}\varvec{N}\varvec{R}}_{\varvec{i}\varvec{t}}+{{\varvec{\alpha}}_{3\varvec{\tau}}(\varvec{C}\varvec{C}\_\text{N}\text{R})}_{\varvec{i}\varvec{t}}+{{{\varvec{\alpha}}_{4\varvec{\tau}}\varvec{\beta}}_{4}\varvec{F}\varvec{D}\varvec{I}}_{\varvec{i}\varvec{t}}+{{\varvec{\alpha}}_{5\varvec{\tau}}\varvec{G}\varvec{F}\varvec{C}\varvec{F}}_{\varvec{i}\varvec{t}}+{\varvec{\alpha}}_{6\varvec{\tau}}{\varvec{T}\varvec{r}\varvec{a}\varvec{d}\varvec{e}}_{\varvec{i}\varvec{t}}+{\varvec{\epsilon}}_{\varvec{i}\varvec{t}}$$ Where \({\varvec{Q}}_{\varvec{\tau}}\) denotes the \({\varvec{\tau}}^{\varvec{t}\varvec{h}}\) conditional quantile of the growth rate given its determining factors, and α τ represents the regression parameters of the \({\varvec{\tau}}^{\varvec{t}\varvec{h}}\) quantile of the growth rate (Daud et al., 2025 ). 4.0 Results and Discussion Summary of the mean values for key variables across the countries. Table 2 Descriptive statistics - by (country) Country GDP CC FDI GFCF NR Trade IN_CNR Benin 1.92875 − .492322 1.455707 21.38073 3.405952 50.97922 -1.744657 Burkina Faso 2.428663 − .2660514 1.249466 20.39844 10.62943 54.14592 -2.792999 Cameroon .771994 -1.143125 1.737358 18.9128 6.891751 44.3963 -7.859464 Chad .5532006 -1.424857 2.111447 21.07596 22.66449 54.28798 -32.35334 Comoros 1.968909 − .8635139 .6017029 15.13705 1.779442 39.71184 -1.517172 Cote d'Ivoire 2.584631 − .7498674 1.547591 20.69751 4.09342 55.86556 -3.229493 Gabon − .346644 − .8849028 5.725714 23.93276 27.49673 83.45023 -24.73491 Gambia .4804703 − .5872659 5.770827 20.79439 4.09227 47.00985 -2.490973 Guinea 2.489542 -1.060744 4.462321 24.01061 14.31169 84.99003 -15.35613 Guinea-Bissau 1.621305 -1.327854 1.784951 19.47543 14.92993 47.0439 -19.87888 Mali 1.377435 − .6937659 2.622248 24.01555 8.613327 50.57869 -6.188464 Mauritania 1.184526 − .7686517 8.758733 38.80429 17.29863 90.02357 -12.6953 Mozambique 2.416294 − .6769133 17.84316 35.33213 12.13094 96.09719 -8.506995 Niger 2.040488 − .6595102 5.217585 26.94419 8.174785 42.88138 -5.3975 Nigeria 1.511558 -1.109093 1.234113 13.87355 11.46041 33.631 -12.70984 Senegal 1.665148 − .1855635 4.26171 28.33317 3.237861 61.10125 − .5848032 Sierra Leone 1.678513 − .7701097 4.53554 18.88626 10.32875 39.97213 -8.035233 Somalia, Fed. Re 2.915673 -1.714643 4.314429 16.78817 15.70368 80.75855 -26.8459 Sudan -3.42648 -1.391398 3.016999 13.8836 9.337896 21.34978 -12.64246 Togo 1.591604 − .8545901 1.8164 20.01032 10.98121 64.108 -9.750782 Uganda 2.667719 − .9933705 4.112768 24.77006 10.83456 40.1571 -10.59969 Total 1.43349 − .8865767 4.010975 22.22037 10.87606 56.23094 -10.75786 Source : Authors, 2025 Table 2 is the descriptive statistics. The data, representing a set of African nations, show that, on average, the countries experienced a Gross Domestic Product (GDP) growth of 1.43349 and an overall negative control of corruption (CC) balance of -0.8865767. The average Foreign Direct Investment (FDI) is strong at 4.010975, and the average Gross Fixed Capital Formation (GFCF), an indicator of investment, is 22.22037. The average Natural Resources (NR) contribution is 10.87606, and the average Trade openness is high at 56.23094. However, the average Interaction of National resource and Control of corruption (IN_CNR) is significantly negative at -10.75786. The high standard deviation in natural resources (NR) and GDP across countries, such as Nigeria and The Gambia, illustrates significant diversity in economic and resource endowments within the sample. This variability reflects the wide differences in natural resource abundance, industrial capacity, economic structure, and overall development. The stark contrast in natural resource endowments and GDP structures between Nigeria and Gambia contributes to a high standard deviation in metrics measuring natural resources and GDP within the sample. Nigeria’s large-scale resource base, industrial output, and infrastructure investment underlie its higher GDP levels and greater economic complexity, whereas Gambia’s limited natural resources, vulnerability to environmental stressors, reliance on imports, and economic fragility translate into lower GDP and greater economic vulnerability. This diversity underscores the challenges of formulating broad generalizations about natural resources and GDP growth, as country-specific contexts, including resource management, economic structure, institutional quality, and external dependence, drive different economic outcomes (Ali et al., 2023 ; Ibitoye et al., 2022 ; Jallow et al., 2025 ). Source: Authors’ computation, 2025 Figure 1 is a correlation matrix that gives a scientific summary about such linear relationships of macroeconomic and institutional quality, and their interactions. Traditional economic variables have the expected patterns, such as the strong positive relationship between FDI and GFCF at 0.656, both of which also show moderate positive relationships with Trade. But the more important point is the findings on the "resource-governance nexus." There is a negative but moderate correlation between NR and CC (-0.367), indicative of the fact that higher resource dependence goes hand in hand with less institutional control. The interaction term IN_CNR: (NR × CC) has a significantly high negative correlation with NR (-0.878) and a strong positive correlation with CC (0.688). It follows that with the level of resource abundance, the combined effect of rent and governance is exponentially marginal; as a country grows more dependent on natural resources, the stability-enhancing potential for a mechanism to control corruption encounters formidable structural changes. Finally, the matrix indicates that although investment and trade are ambiance together, natural resource wealth and relative capability of control of corruption exhibit the most dynamic relationship within the dataset. Table 3 Fixed and Random Effect Models Variables Fixed Effect model Random Effect model GDP per capita growth Coef. (p-value) Coef. (p-value) Control of Corruption 5.7 (0.000)*** 3.965 (0.000)*** Foreign Direct Investment -0.031 (0.562) -0.04 (0.414) Gross Capital Formation 0.07 (0.057)* 0.063 (0.063)* Total Natural Resources -0.104 (0.272) -0.16 (0.042)** Trade (% of GDP) 0.038 (0.044)** 0.02 (0.134) Corruption × Resource -0.195 (0.028)** -0.214 (0.004)*** Constant 1.95 (0.216) 2.034 (0.102) Number of obs F-test Prob > F 418 7.790 0.000 418 χ2 = 36.367 Prob > χ2 = 0.000 ***p<.01, **p<.05, *p<.1 Source: Authors’ computation, 2025 Table 3 shows the fixed and random effects. The Fixed Effects model indicates that control of corruption is positively and statistically significantly associated with GDP per capita growth (p < 0.01). Gross capital formation is positively associated with growth at the 10% significance level. Total natural resource rents are not statistically significant in the Fixed Effects specification. The interaction term between corruption control and natural resource rents is negative and statistically significant (p < 0.05), indicating that the marginal association between corruption control and growth decreases as resource rents increase. Foreign direct investment and trade openness are not consistently significant across specifications. These results describe conditional associations and do not imply causality. Table 4 Hausman (1978) specification test Chi-square test value Coef. 17.02 P-value 0.0092 Source : Authors, 2025 Table 4 is the Hausman specification test result, with a Chi-square test value of 17.02 and a highly significant P-value of 0.0092, leading to the rejection of the null hypothesis (H 0 ), which states that the Random Effects (RE) model is consistent and efficient. Because the p-value (0.0092) is less than the conventional significance level of 0.05, the test strongly indicates that there is a systematic difference between the coefficients estimated by the RE and the Fixed Effects (FE) models. This means that unobserved country-specific effects are correlated with the independent variables, causing RE estimates to be biased and inconsistent. Therefore, based on statistical evidence from the Hausman test, the Fixed Effects (FE) model is a more appropriate and robust specification for analyzing panel data, as it accounts for these time-invariant, country-specific characteristics. Table 5 Cross-sectional independence of fixed-effect model CSD Tests CSD value P-value Pesaran 5.347 0.0000 Friedman 46.469 0.0007 CSD Tests CSD value Critical value Frees 0.174 0.1782 Source : Authors, 2025 Table 5 shows that the cross-sectional independence of fixed-effect model results from the cross-sectional dependence (CSD) tests strongly indicate that the assumption of independence across countries in the Fixed Effects (FE) panel data model is violated. Specifically, both the Pesaran CSD test (CSD value of 5.347, p-value of 0.0000) and the Friedman test (CSD value of 46.469, p-value of 0.0007) have highly significant p-values (less than 0.05), leading to the rejection of the null hypothesis of no cross-sectional dependence. This finding suggests that country-specific shocks or unobserved factors are correlated across nations in the sample, meaning that the error terms are not independent. While the Frees test (CSD value of 0.174 compared to a critical value of 0.1782) shows marginal non-rejection of the null hypothesis, the strong and consistent evidence from the Pesaran and Friedman tests confirms the existence of cross-sectional dependence. Therefore, standard fixed-effects estimation is inefficient and potentially inconsistent, necessitating the use of more sophisticated models that can handle cross-sectional dependence, such as the quantile regression model. Table 6 Quantile Regression Models Variables 25th panel quantile regression 50th panel quantile regression 75th panel quantile regression GDP per capita growth (annual) Coefficient (P-value) Coefficient (P-value) Coefficient (P-value) Control of Corruption Estimate 3.977 (0.000) 2.484 (0.000) 3.762 (0.000) Foreign Direct Investment, net inflows -0.001 (0.777) 0.021 (0.000) 0.045 (0.000) Gross Capital Formation (% of GDP) 0.031 (0.000) 0.004 (0.000) -0.016 (0.000) Total Natural Resources Rents (% of GDP) -0.313 (0.000) -0.106 (0.000) -0.180 (0.000) Trade (% of GDP) 0.003 (0.000) 0.007 (0.000) 0.006 (0.000) Corruption × Resource Interaction -0.246 (0.000) -0.118 (0.000) -0.238 (0.000) Source : Authors, 2025 Table 6 shows the Quantile Regression . Panel quantile regression reveals heterogeneity across the growth distribution. At the 25th percentile, natural resource rents are negatively and significantly associated with growth (p < 0.001). The interaction term remains negative and significant, indicating that the conditional association between corruption control and growth weakens in more resource-intensive economies, particularly among low-growth countries. At the 50th and 75th percentiles, corruption control remains positively and significantly associated with growth (p < 0.001). Foreign direct investment is positively and significantly associated with growth in the median and upper quantiles but not in the lower quantiles. Gross capital formation shows a positive association at lower quantiles but becomes negative at the 75th percentile, suggesting diminishing returns at higher growth levels. 5.0 Discussion The empirical findings indicate that the association between corruption control and growth is conditional on the level of natural resource dependence. The negative interaction coefficient suggests that as the share of resource rents in GDP increases, the positive association between corruption control and growth weakens. This pattern is consistent with frameworks in which high resource rents alter incentive structures, potentially affecting the economic returns to institutional improvements. However, the analysis does not establish causal mechanisms and should be interpreted as identifying conditional correlations within the sample. This finding suggests a "double-curse" mechanism, as proposed by Leite & Weidmann ( 2001 ), wherein abundant rents make it more difficult to eradicate corruption and render governance reforms less transformative. Conceptually, this result reverses the logic posited by Mehlum et al. ( 2006 ), who argued that high-quality institutions can neutralize the resource curse. While Mehlum et al. ( 2006 ) found that better institutions eventually enhance resource abundance, the evidence suggests that greater resource dependence actually erodes the growth gains typically expected from better corruption control. This positioning aligns with recent evidence from the OIC and MENA regions, where resource rents have been found to influence the growth of governance, blunting the effectiveness of institutional upgrades (Belloumi & Almashyakhi, 2025 ; Erum & Hussain, 2019 ). The lack of statistical significance regarding the impact of Foreign Direct Investment (FDI) on growth in low-growth countries further underscores the conditional nature of development. A broad consensus in the literature suggests that FDI-led growth is not automatic, but rather contingent upon host-country absorptive capacity and institutional quality (Li & Tanna, 2019 ). In many low-growth economies, critical thresholds in human capital, financial development, and infrastructure have not yet been met, preventing these nations from capturing spillovers and technological transfers typically associated with FDI (Gupta et al., 2022 ; Kariuki & Kabaru, 2022 ). Furthermore, high macroeconomic volatility and a weak rule of law often distort the type of FDI attracted to these regions, frequently resulting in "enclave-type" resource extraction, which offers limited linkages to the broader economy (Adegboye et al., 2020 ; Sultana & Turkina, 2020 ). Consequently, without surpassing these institutional and capacity thresholds, FDI remains a marginal or even insignificant contributor to sustainable growth in the low-growth subsample (Asafo-Agyei & Kodongo, 2022 ; Hayat, 2019 ). The lack of significance for the direct NR variable, coupled with the significant negative interaction term, supports the 'Institutional Threshold' theory. In SSA-OIC countries, the resource curse is not a direct mechanical link; rather, it is an institutional bypass. High resource rents create a 'dampening effect' where even if a country improves its Control of Corruption, the presence of massive rents creates such strong incentives for rent-seeking that the institutional improvements fail to translate into GDP growth. This "dampening effect" is particularly pronounced in OIC Sub-Saharan African (SSA) countries, where high natural-resource rents create a structural drag on the economy. Resource rents in these regions are strongly associated with rent-seeking behavior rather than productive entrepreneurship, as talent and capital are often diverted toward capturing resource revenue (Munemo, 2022 ). This environment fosters a persistent state of institutional erosion, in which inefficient public administration and political instability remain entrenched despite formal improvements in corruption indices (Henri, 2019 ). Furthermore, resource dependence has been shown to encourage capital flight and expansion of the shadow economy, significantly reducing the investable surplus available for development (Ngondjeb & Nlom, 2017 ; Ogashenko et al., 2025 ). Even as corruption controls improve, the resource sector continues to generate powerful incentives for state capture and sophisticated forms of bureaucratic rent seeking, such as off-budget deals and licensing manipulation (Asiamah et al., 2022 ; Mlambo, 2022 ). Ultimately, the "resource-rent state" structure acts as a powerful countervailing force, ensuring that institutional improvements do not translate into proportionate efficiency gains or broad-based economic growth (Dramani et al., 2022 ; Kutlu & Mao, 2023 ). 6.0 Limitations This study has several limitations. The first is that results arise from observational panel data, so they identify conditional correlations rather than causal effects; institutional quality and growth may be determined at the same time, and reverse causation cannot be excluded. Second, governance indicators, including control of corruption, are based on perception and contain measurement error that can attenuate estimated associations. Third, any omitted time-varying factors (e.g., intensity of conflict and non-resource commodity price shocks; macro-stabilization policy and structural reforms) that correlate with resource rents/institutions on the one hand and growth on the other may bias estimated coefficients. Fourth, tests for cross-sectional dependence imply that shocks and unobserved factors are correlated across countries; this will bias inference in standard panel settings, so caution is warranted in interpretation. Fifth, the external validity is restricted to both the SSA-OIC sample and the 2005–2024 period. 7.0 Conclusion and Policy Recommendations This study finds that control of corruption has a positive and significant relationship with GDP per capita growth in SSA-OIC nations, whereas natural resource rents and their interaction with corruption control are negative and statistically significant. This suggests that the marginal linkage between institutions and growth weakens with rising resource dependence. Quantile regression analysis reveals that the association between resource rents and growth also differs across the distribution of growth, with the largest negative effect of resource rents on growth occurring in low-growth countries. These findings pertain to conditional correlations in the sample, and should not be taken to reflect causal effects. These results would support the view that economic diversification and governance reforms specifically targeted at the extractive sector could lead to different growth outcomes, especially in resource-rich countries. More generally, additional studies with dynamic panel data methods, instrumental variables, or common-factor adjustments would offer enhanced identification of such causal mechanisms. From a policy perspective, these findings underscore that SSA-OIC nations cannot rely on resource wealth as a primary engine for long-term prosperity. Economic diversification must be treated as a prerequisite for institutional efficacy, as reducing resource dependence is essential for restoring the full growth-enhancing potential of anti-corruption reforms. Furthermore, governance policies must move beyond general mandates to specifically target resource rent management. This includes implementing rigorous transparency standards for mining and oil contracts, adhering to international extractive industries’ transparency frameworks, and creating ring-fenced mechanisms for windfall revenues to prevent them from being diverted into patronage networks. By addressing the specific incentives for rent-seeking inherent in the extractive sector, governments can ensure that institutional upgrades translate into productivity gains. This study also highlights the necessity of a nuanced, staged strategy for attracting Foreign Direct Investment. As the quantile regression results indicate that FDI primarily benefits countries already in higher-growth brackets, low-growth nations should prioritize the "basics" of development, such as physical infrastructure, human capital, and basic legal stability, before expecting foreign capital to act as a primary driver of growth. Without reaching these critical thresholds, FDI in resource-rich regions often remains confined to "enclaves" that offer limited spillover benefits to the broader economy. Finally, it is important to acknowledge the limitations of this research, particularly regarding the availability of consistent data across all OIC-specific metrics and the difficulty in capturing the full extent of informal economic activities within these states. Future investigations should aim to incorporate more localized institutional data to refine these regional growth strategies further. Declarations Ethical Approval This article does not contain any studies with human participants performed by any of the authors. Informed Consent This article does not contain any studies with human participants performed by any of the authors. Funding No external funding was received for this work. Disclosure statement We have no conflicts of interest to disclose. 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The Journal of Chinese Sociology , 3 (1), 20. https://doi.org/10.1186/s40711-016-0040-3 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviews received at journal 06 Apr, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor assigned by journal 02 Mar, 2026 Editor invited by journal 27 Feb, 2026 Submission checks completed at journal 25 Feb, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8892786","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601175760,"identity":"2b760611-f730-449a-8373-6544a0cd94bc","order_by":0,"name":"Mousse Abdi Mohamoud","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYNACnho5fhCdUEC0FpljxpINIC0GRGuxYU7ccADEIEaL7owc4w8fctiMjc+vTvzwwIBBnl/sAH4tZjdyDAxnnJGRM7vxdrME0GGGM2cnENaSzNvDZmx24+wGkJYEg9tEaDn89x9z4uYZZzf/IFaLYTMDD9D7/L3biLTlzLNixh6eY8YSN3i3WSQYSBDhl+PJmz/8AEVl/9nNN39U2MjzSxPQggASYJUSxCoHAf4DpKgeBaNgFIyCkQQAXz5E/hOIb4EAAAAASUVORK5CYII=","orcid":"","institution":"HISER Institute","correspondingAuthor":true,"prefix":"","firstName":"Mousse","middleName":"Abdi","lastName":"Mohamoud","suffix":""},{"id":601175761,"identity":"0a0cf4ee-7d47-45c2-be44-f48c25d5edb4","order_by":1,"name":"Eid Ibrahim Daud","email":"","orcid":"","institution":"University of Hargeisa","correspondingAuthor":false,"prefix":"","firstName":"Eid","middleName":"Ibrahim","lastName":"Daud","suffix":""},{"id":601175762,"identity":"0847af94-4c65-4c8e-9c97-7f0934bfa78c","order_by":2,"name":"Abdiaziz Ali Nour","email":"","orcid":"","institution":"HISER Institute","correspondingAuthor":false,"prefix":"","firstName":"Abdiaziz","middleName":"Ali","lastName":"Nour","suffix":""},{"id":601175763,"identity":"988d2585-5bb6-49a8-b73a-cc7cacdd64e3","order_by":3,"name":"Khadar Abdi Mohamed","email":"","orcid":"","institution":"Civil Service Institute","correspondingAuthor":false,"prefix":"","firstName":"Khadar","middleName":"Abdi","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2026-02-16 11:38:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8892786/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8892786/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104068517,"identity":"0844f1b2-b128-400e-9093-1247d5cbb5b0","added_by":"auto","created_at":"2026-03-06 11:21:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Authors’ computation, 2025\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8892786/v1/22a63a0a5e9b7f64734f1939.png"},{"id":104403337,"identity":"500cc47e-d1a8-4d44-8008-ad210a0d512b","added_by":"auto","created_at":"2026-03-11 12:18:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1539873,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8892786/v1/d4ba0f2c-4b14-4547-820e-c860d367b650.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Resource Rents, Institutions, and Growth in Sub-Saharan African OIC Countries: A Panel Quantile Analysis","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eThe \"resource curse\" describes the empirical pattern where countries rich in natural resources, such as oil, gas, and minerals, often grow more slowly than resource-poor countries, contrary to the intuition that natural riches should guarantee prosperity (Alssadek \u0026amp; Benhin, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Saeed, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This paradox was formalized by Sachs and Warner\u0026rsquo;s early work, which demonstrated a robust negative association between natural-resource dependence and GDP per capita growth across 90\u0026ndash;100 countries between 1970 and 1990 (Alssadek \u0026amp; Benhin, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Manzano \u0026amp; Rigobon, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Subsequent literature has highlighted several mechanisms for this \"curse,\" including \"Dutch disease,\" where resource booms appreciate the real exchange rate and crowd out manufacturing, and the erosion of institutions as large rents make corruption and rent-seeking more attractive (Collier \u0026amp; Goderis, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Mehlum et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSub-Saharan African (SSA) members of the Organization of Islamic Cooperation (OIC) sit at a unique intersection between the resource-curse debate and distinct developmental constraints. Approximately half of the OIC members are in Africa, and approximately 30% of the OIC\u0026rsquo;s population resides in sub-Saharan countries that remain heavily exposed to arid climates and are dependent on low-productivity subsistence agriculture (Ahsan, 2020). This specific bloc faces a \"double bind\": many are resource-dependent, but they also struggle with a longstanding crisis of governance, weak rule of law, and fragile state capacity (Anheier et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Maxwele et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For these OIC members, institutional quality, specifically stable government and effective regulation, is a central determinant of growth, yet many score poorly in these dimensions (Slesman et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile institutions are known to matter, their interaction with resource abundance in the SSA-OIC context is complex and often damaging. In these states, resource rents can create powerful incentives for corruption, meaning that governance must be substantially stronger in resource-rich SSA than in resource-poor SSA to deliver similar developmental outcomes (Coulibaly et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ringold et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Evidence suggests that resource wealth increases the bar\" for governance; modest improvements in institutions can be overwhelmed by rent-seeking and political incentives tied to resource income, blunting the potential growth payoff (Adika, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Eregha \u0026amp; Mesagan, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This raises the question of whether resource wealth actively hinders good governance by promoting growth in a specific region.\u003c/p\u003e \u003cp\u003eThere is a significant gap in the literature regarding how these factors differ between low and high-growth countries. Most existing studies on the growth of the OIC and African countries rely on mean-based panel models, which miss important distributional heterogeneity (Tarchoun \u0026amp; Mili, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Uddin et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While quantile regression has been applied to other determinants, such as how tourism, aid, or health variables affect countries differently at the lower and upper tails of the growth distribution, there is no known study that applies quantile regression to the sample of OIC member states in Sub-Saharan Africa to examine the joint role of resource abundance and institutional quality (Bila et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sahni et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This leaves the question of whether \"average\" resource\u0026ndash;institution effects mask critical differences across the growth distribution open.\u003c/p\u003e \u003cp\u003eThe selection of the SSA-OIC sub-group is theoretically significant because these nations operate under a dual institutional framework. As members of the OIC, they are signatories to the \u003cem\u003eOIC-2025 Program of Action\u003c/em\u003e, which emphasizes 'Good Governance' and 'Accountability' as religious and developmental imperatives. However, they also face the traditional 'Resource Curse' challenges common in Sub-Saharan Africa. This study seeks to understand if OIC membership, and the associated developmental support from the Islamic Development Bank (IsDB), helps or hinders these nations in overcoming the structural drag of resource rents.\u003c/p\u003e \u003cp\u003eWhile much research has focused on the wealthy, resource-rich OIC members in the Middle East (the GCC countries), the SSA-OIC members represent the 'frontier' of the OIC's development agenda. These countries are often the most vulnerable to price volatility and have the weakest 'absorptive capacity' for Foreign Direct Investment (FDI). Studying this specific bloc reveals the 'governance bottlenecks' that prevent OIC-led development initiatives from succeeding in resource-dependent African states.\u003c/p\u003e \u003cp\u003eThis study contributes to the literature by providing evidence that the benefits of fighting corruption diminish as resource reliance increases. Based on the negative coefficient of the interaction term between resource reliance and corruption control, our findings imply that the marginal growth payoff for anti-corruption policies falls in more resource-intensive economies. This is consistent with theories in which natural resource abundance expands rent-seeking opportunities and weakens incentives to invest in costly monitoring institutions, making corruption more resilient to standard governance improvements (D\u0026aacute;vid-Barrett \u0026amp; Fazekas, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Knutsen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Leite \u0026amp; Weidmann, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e"},{"header":"2.0 Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Theoretical Framework\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 The Resource Curse Theory: Volatility and \"Dutch Disease.\"\u003c/h2\u003e \u003cp\u003eThe \"resource curse\" framework suggests that natural resource abundance can paradoxically undermine long-run economic growth. The primary mechanism for this is \"Dutch Disease,\" which operates through two main channels: the spending effect and the resource movement effect (Alssadek \u0026amp; Benhin, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Brahmbhatt et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Resource booms trigger real exchange rate appreciation, shifting capital and labor toward the booming resource sector and non-tradable services while reducing manufacturing output and net exports (Botta, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gylfason et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This deindustrialization leads to a \"learning-by-doing loss,\" where the shrinkage of high-skill, export-oriented sectors depresses innovation and long-term productivity (Magud \u0026amp; Sosa, 2013; Tressel et al., 2006).\u003c/p\u003e \u003cp\u003eFurthermore, price and revenue volatility inherent in commodities increases macroeconomic instability. This volatility discourages investment in tradable sectors because of heightened uncertainty, further cementing the negative link between resource dependence and growth (Al-Jomard et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Gylfason et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn OIC member states, 'Control of Corruption' is not just a secular metric but is closely linked to the cultural and religious concept of Amanah (trust and accountability). Theoretically, the 'Rentier State' hypothesis suggests that resource wealth allows elites to bypass these traditional social contracts. In the SSA-OIC context, huge resource rents may create a 'moral hazard' where the availability of easy money from oil and minerals undermines the integration of ethical governance standards promoted by OIC developmental charters, leading to a unique form of institutional erosion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Institutional Theory: The Role of Governance\u003c/h2\u003e \u003cp\u003eInstitutional theory explains developmental variance by focusing on the \"rules of the game,\" the laws, norms, and structures that shape economic incentives. North (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1990\u003c/span\u003e) defines institutions as humanly devised constraints that determine transaction and production costs. Good institutions reduce uncertainty and secure property rights, thereby raising investments (North, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on this, Acemoglu \u0026amp; Robinson (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) distinguish between \"inclusive\" and \"extractive\" institutions. Inclusive institutions foster broad participation and economic opportunities, while extractive institutions concentrate on power and rent among a small elite. Persistently, extractive institutions arise because powerful groups block reforms that would erode their personal rents, even if such reforms would benefit society as a whole (Acemoglu \u0026amp; Robinson, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Consequently, the impact of resources on an economy is often a function of whether the underlying institutional framework is inclusive or extractive or not.\u003c/p\u003e \u003cp\u003eFor Organization of Islamic Cooperation (OIC) countries, institutions are often conceptualized through the Islamic principle of Amanah, which encompasses notions of trust and accountability. This perspective aligns institutional theory with Islamic ethical values, emphasizing stewardship, fulfillment of trust, and moral responsibility at the core of governance and institutional performance. The principle of Amanah underpins governance mechanisms in OIC countries, stressing that institutions must operate with accountability, honesty, and ethical integrity in fulfilling their duties. This view contrasts with conventional Western governance models and instead integrates foundational Islamic values such as shiddiq (honesty) and mas\u0026rsquo;uliyah (accountability), reflecting a culturally-rooted approach to institutional design and performance (Hirsanuddin \u0026amp; Martini, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This incorporation of Amanah into institutional frameworks also strengthens trust between stakeholders and fosters alignment in regulatory and administrative practices, which is essential for institutional credibility and effectiveness in OIC contexts (Bin-Nashwan, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 The Rentier State Hypothesis: Accountability and Revenue\u003c/h2\u003e \u003cp\u003eThe Rentier State Hypothesis posits that resource rents weaken government accountability by loosening the link between taxation and representation. When budgets are financed by external rents (oil/mineral payments) rather than domestic taxes, the state\u0026rsquo;s need to bargain with and respond to its citizens is reduced (Barma, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Canuto, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis detachment manifests in several ways: a weaker demand for accountability, as citizens are less likely to monitor windfall spending compared to tax-funded spending (Hoem Sjursen, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); the erosion of fiscal capacity and parliamentary oversight (Zhuang \u0026amp; Zhang, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); and the use of rents for patronage and repression to \"buy off\" or silence opposition (Sandbakken, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Schwarz, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Thus, resource wealth can stabilize authoritarianism and deteriorate the quality of governance.\u003c/p\u003e \u003cp\u003eSub-Saharan African nations that are members of the Organization of Islamic Cooperation (SSA-OIC) are particularly vulnerable to the rentier state phenomenon because they are \"frontier economies\" characterized by high dependence on natural resources and relatively underdeveloped tax systems. This resource reliance creates economic vulnerabilities, including fiscal instability and challenges in mobilizing domestic revenues through taxation. As these economies rely heavily on resource rents, their tax systems remain nascent and limited, restricting sustainable fiscal capacity and heightening susceptibility to the adverse effects of commodity price volatility and external shocks. The developing institutional quality and financial systems further compound these challenges, making SSA-OIC nations especially prone to the pitfalls of rentierism seen in resource-dependent economies (Jeppesen et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Taylor, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zall\u0026eacute;, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Review: The African Context\u003c/h2\u003e \u003cp\u003eEmpirical evidence regarding the resource curse in Africa is mixed, but follows specific patterns. Many studies confirm a curse, on average, finding that resource rents correlate with lower economic growth, reduced life expectancy, and hindered democratic transitions (Achuo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jensen \u0026amp; Wantchekon, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Ongo Nkoa et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In Sub-Saharan Africa (SSA), higher resource dependence often reduces non-resource tax revenues and increases vulnerability to fiscal stress (Taylor, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, a growing body of research has emphasized the concept of \"conditionality.\" Studies have found that resource-rich African economies can outgrow resource-poor ones when institutions and infrastructure are robust (Adika, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mlambo, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For the continent as a whole, resources appear to be a curse when institutional quality is low, but a blessing when it is high, suggesting the existence of institutional \"thresholds\" that determine the growth outcome (Amare et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Asongu et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Data and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data Sources and Variables\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that this study utilizes a panel dataset comprising data from 2005 to 2024 for the selection of Sub-Saharan African OIC nations. The variables were sourced from primary international databases to ensure cross-country comparability and data integrity.\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\u003eVariable Definitions and Data Sources\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExp. Sign\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\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\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGDP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGDP per capita growth (annual %)\u003c/b\u003e:\u0026nbsp;Annual percentage growth rate of GDP per capita based on constant local currency. Aggregates are based on constant 2015 U.S. dollars.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIndependent variables\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003e\u003cb\u003eCC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eControl of Corruption\u003c/b\u003e:\u0026nbsp;Captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as \"capture\" of the state by elites and private interests. Range is approximately\u0026thinsp;\u0026minus;\u0026thinsp;2.5 (weak) to 2.5 (strong).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWGI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFDI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eForeign direct investment, net inflows (% of GDP)\u003c/b\u003e:\u0026nbsp;Net inflows of investment to acquire a lasting management interest (10 percent or more of voting stock) in an enterprise operating in an economy other than that of the investor.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+/-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGFCF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGross fixed capital formation (% of GDP)\u003c/b\u003e:\u0026nbsp;Formerly gross domestic fixed investment, it includes land improvements; plant, machinery, and equipment purchases; and the construction of roads, railways, schools, and hospitals.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTotal natural resources rents (% of GDP)\u003c/b\u003e:\u0026nbsp;The sum of oil rents, natural gas rents, coal rents (hard and soft), mineral rents, and forest rents. It serves as the primary proxy for resource dependence.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrade\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTrade (% of GDP)\u003c/b\u003e:\u0026nbsp;The sum of exports and imports of goods and services measured as a share of gross domestic product.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e+\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank (WDI)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteraction variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003e\u003cb\u003eIN_CNR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eInteraction of Corruption and Resources\u003c/b\u003e:\u0026nbsp;The product of the Control of Corruption index and Total Natural Resource Rents. These variables test the \"dampening\" hypothesis, which posits that corruption moderates the impact of resources on growth.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAuthors' Calculation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource: Authors, 2025\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Sample Selection\u003c/h2\u003e \u003cp\u003eThe sample consists of 21 African countries representing a diverse range of resource endowments and growth trajectories. Specific countries are included in the panel analysis.\u003c/p\u003e \u003cp\u003eWest Africa: Benin, Burkina Faso, Cote d'Ivoire, Gambia (The), Guinea, Guinea-Bissau, Mali, Niger, Nigeria, Senegal, Sierra Leone, and Togo. East and Central Africa: Cameroon, Chad, Comoros, Mozambique, Somalia, and Uganda. North \u0026amp; East Africa: Mauritania and Sudan. Southern Africa: Gabon.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Econometric Methodology\u003c/h2\u003e \u003cp\u003eThis study employs a multi-stage econometric approach to ensure the robustness of the findings.\u003c/p\u003e \u003cp\u003eThe analysis began with Fixed Effects (FE) and Random Effects (RE) models. We apply the Hausman (1978) specification test to determine the most efficient and consistent estimator. The omitted variable bias is frequently guarded against when using fixed effects. However, they are not suitable for evaluating the effects of time-varying characteristics.\u003c/p\u003e \u003cp\u003eThe fixed-effect model is:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$${\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{i}\\varvec{t}}={{\\varvec{\\beta}}_{1}\\varvec{C}\\varvec{C}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\beta}}_{2}\\varvec{F}\\varvec{D}\\varvec{I}}_{\\varvec{i}\\varvec{t}}+{\\varvec{G}\\varvec{F}\\varvec{C}\\varvec{F}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\beta}}_{4}\\varvec{N}\\varvec{R}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta}}_{5}{\\varvec{T}\\varvec{r}\\varvec{a}\\varvec{d}\\varvec{e}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta}}_{6}{\\varvec{I}\\varvec{N}\\_\\varvec{C}\\varvec{N}\\varvec{R}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\mu}}_{\\varvec{i}}+{\\varvec{\\epsilon}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e \u003cp\u003eHowever, because they are also related to each other and to the error terms of other groups, this gives rise to a large variance, \u0026ensp;with statistical inference becoming questionable. In this case, we can perform better by employing a random effects model. In the effects model, the variation among entities is assumed to be random and independent of the independent variables.\u003c/p\u003e \u003cp\u003eThe random effect model is expressed as.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\varvec{G}\\varvec{D}\\varvec{P}}_{\\varvec{i}\\varvec{t}}={{\\varvec{\\beta}}_{1}\\varvec{C}\\varvec{C}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\beta}}_{2}\\varvec{F}\\varvec{D}\\varvec{I}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\beta}}_{3}\\varvec{G}\\varvec{F}\\varvec{C}\\varvec{F}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\beta}}_{4}\\varvec{N}\\varvec{R}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta}}_{5}{\\varvec{T}\\varvec{r}\\varvec{a}\\varvec{d}\\varvec{e}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\beta}}_{6}{\\varvec{I}\\varvec{N}\\_\\varvec{C}\\varvec{N}\\varvec{R}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\tau}}_{\\varvec{t}}+{\\varvec{\\epsilon}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u003c/p\u003e \u003cp\u003eWhere is the error for the between-group and is a random intercept. The strength of the random effects model rests in its allowance for variation across subjects, allowing time-invariant factors, such as gender, ethnicity, or race, to be examined as part of the model. The other thing to stress is that the random effects method involves variation at two levels, within an individual and between individuals. The amount of sampling variability in a given mixed model is generally lower than that observed in its fixed-effects form (Allison, 2005). However, this difficulty arises when one wants to account for all important observable variables. Due to the unavailability of data on some variables, we might expect omitted variable bias in the model.\u003c/p\u003e \u003cp\u003eThe decision to use the fixed effects or random effects model is based on whether it is correlated with any of the other explanatory variables in the model (Wooldridge, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). If such a correlation is present, we recommend the fixed effects method. Otherwise, \u0026ensp;the random effect is less wide and leads to more efficient estimates (Wooldridge, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCross-Sectional Dependence (CSD) tests (Pesaran, Friedman, and Frees) were conducted to address potential issues inherent in the panel data.\u003c/p\u003e \u003cp\u003eTo account for distributional heterogeneity, specifically how factors affect low-growth versus high-growth countries differently. The study utilizes \u003cb\u003ePanel Quantile Regression\u003c/b\u003e at the \u003cb\u003e25th, 50th, and 75th percentiles\u003c/b\u003e. This method provides a more granular view than mean-based estimates, revealing whether the \"dampening\" effect of corruption is uniform across different levels of economic performance.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${{\\varvec{Q}}_{\\varvec{G}\\varvec{D}\\varvec{P}}}_{\\varvec{i}\\varvec{t}}\\left(\\frac{\\varvec{\\tau}}{{\\varvec{\\alpha}}_{\\varvec{i}}}\\right)={{\\varvec{\\alpha}}_{\\varvec{i}}{+\\varvec{\\alpha}}_{1\\varvec{\\tau}}\\varvec{C}\\varvec{C}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\alpha}}_{2\\varvec{\\tau}}\\varvec{N}\\varvec{R}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\alpha}}_{3\\varvec{\\tau}}(\\varvec{C}\\varvec{C}\\_\\text{N}\\text{R})}_{\\varvec{i}\\varvec{t}}+{{{\\varvec{\\alpha}}_{4\\varvec{\\tau}}\\varvec{\\beta}}_{4}\\varvec{F}\\varvec{D}\\varvec{I}}_{\\varvec{i}\\varvec{t}}+{{\\varvec{\\alpha}}_{5\\varvec{\\tau}}\\varvec{G}\\varvec{F}\\varvec{C}\\varvec{F}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\alpha}}_{6\\varvec{\\tau}}{\\varvec{T}\\varvec{r}\\varvec{a}\\varvec{d}\\varvec{e}}_{\\varvec{i}\\varvec{t}}+{\\varvec{\\epsilon}}_{\\varvec{i}\\varvec{t}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{Q}}_{\\varvec{\\tau}}\\)\u003c/span\u003e\u003c/span\u003edenotes the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{\\tau}}^{\\varvec{t}\\varvec{h}}\\)\u003c/span\u003e\u003c/span\u003e conditional quantile of the growth rate given its determining factors, and \u003cb\u003eα\u003c/b\u003e\u003csub\u003e\u003cb\u003eτ\u003c/b\u003e\u003c/sub\u003e represents the regression parameters of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\varvec{\\tau}}^{\\varvec{t}\\varvec{h}}\\)\u003c/span\u003e\u003c/span\u003e quantile of the growth rate (Daud et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Results and Discussion","content":"\u003cp\u003eSummary of the mean values for key variables across the countries.\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\u003eDescriptive statistics - by (country)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGDP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFDI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGFCF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrade\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIN_CNR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBenin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.92875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.492322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.455707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.38073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.405952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.97922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.744657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurkina Faso\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.428663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.2660514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.249466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.39844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.62943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.14592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.792999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCameroon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.771994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.143125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.737358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.9128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.891751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44.3963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-7.859464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.5532006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.424857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.111447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21.07596\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.66449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54.28798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-32.35334\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComoros\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.968909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.8635139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.6017029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.13705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.779442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.71184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.517172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCote d'Ivoire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.584631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.7498674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.547591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.69751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.09342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e55.86556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-3.229493\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGabon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.346644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.8849028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.725714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.93276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27.49673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83.45023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-24.73491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGambia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.4804703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.5872659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.770827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.79439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.09227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.00985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-2.490973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuinea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.489542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.060744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.462321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.01061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.31169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e84.99003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-15.35613\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuinea-Bissau\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.621305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.327854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.784951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.47543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.92993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47.0439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-19.87888\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.377435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.6937659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.622248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.01555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.613327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50.57869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-6.188464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMauritania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.184526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.7686517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.758733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e38.80429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.29863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90.02357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-12.6953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMozambique\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.416294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.6769133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.84316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.33213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.13094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e96.09719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-8.506995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNiger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.040488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.6595102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.217585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.94419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.174785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e42.88138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-5.3975\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.511558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.109093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.234113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.87355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.46041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-12.70984\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSenegal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.665148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1855635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.26171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.33317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.237861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e61.10125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.5848032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSierra Leone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.678513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.7701097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.53554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.88626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.32875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e39.97213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-8.035233\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSomalia, Fed. Re\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.915673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.714643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.314429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.78817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.70368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e80.75855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-26.8459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSudan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.42648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.391398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.016999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.8836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.337896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.34978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-12.64246\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTogo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.591604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.8545901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.01032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.98121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-9.750782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUganda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.667719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.9933705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.112768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.77006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.83456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40.1571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-10.59969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.8865767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.010975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.22037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.87606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56.23094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-10.75786\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is the descriptive statistics. The data, representing a set of African nations, show that, on average, the countries experienced a Gross Domestic Product (GDP) growth of 1.43349 and an overall negative control of corruption (CC) balance of -0.8865767. The average Foreign Direct Investment (FDI) is strong at 4.010975, and the average Gross Fixed Capital Formation (GFCF), an indicator of investment, is 22.22037. The average Natural Resources (NR) contribution is 10.87606, and the average Trade openness is high at 56.23094. However, the average Interaction of National resource and Control of corruption (IN_CNR) is significantly negative at -10.75786.\u003c/p\u003e \u003cp\u003eThe high standard deviation in natural resources (NR) and GDP across countries, such as Nigeria and The Gambia, illustrates significant diversity in economic and resource endowments within the sample. This variability reflects the wide differences in natural resource abundance, industrial capacity, economic structure, and overall development.\u003c/p\u003e \u003cp\u003eThe stark contrast in natural resource endowments and GDP structures between Nigeria and Gambia contributes to a high standard deviation in metrics measuring natural resources and GDP within the sample. Nigeria\u0026rsquo;s large-scale resource base, industrial output, and infrastructure investment underlie its higher GDP levels and greater economic complexity, whereas Gambia\u0026rsquo;s limited natural resources, vulnerability to environmental stressors, reliance on imports, and economic fragility translate into lower GDP and greater economic vulnerability. This diversity underscores the challenges of formulating broad generalizations about natural resources and GDP growth, as country-specific contexts, including resource management, economic structure, institutional quality, and external dependence, drive different economic outcomes (Ali et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ibitoye et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Jallow et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSource: Authors\u0026rsquo; computation, 2025\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is a correlation matrix that gives a scientific summary about such linear relationships of macroeconomic and institutional quality, and their interactions. Traditional economic variables have the expected patterns, such as the strong positive relationship between FDI and GFCF at 0.656, both of which also show moderate positive relationships with Trade. But the more important point is the findings on the \"resource-governance nexus.\" There is a negative but moderate correlation between NR and CC (-0.367), indicative of the fact that higher resource dependence goes hand in hand with less institutional control. The interaction term IN_CNR: (NR \u0026times; CC) has a significantly high negative correlation with NR (-0.878) and a strong positive correlation with CC (0.688). It follows that with the level of resource abundance, the combined effect of rent and governance is exponentially marginal; as a country grows more dependent on natural resources, the stability-enhancing potential for a mechanism to control corruption encounters formidable structural changes. Finally, the matrix indicates that although investment and trade are ambiance together, natural resource wealth and relative capability of control of corruption exhibit the most dynamic relationship within the dataset.\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\u003eFixed and Random Effect Models\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\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed Effect model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRandom Effect model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP per capita growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef. (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoef. (p-value)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl of Corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.7 (0.000)***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.965 (0.000)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign Direct Investment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.031 (0.562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04 (0.414)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Capital Formation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.07 (0.057)*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.063 (0.063)*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Natural Resources\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.104 (0.272)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.16 (0.042)**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.038 (0.044)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02 (0.134)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorruption \u0026times; Resource\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.195 (0.028)**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.214 (0.004)***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 (0.216)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.034 (0.102)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of obs\u003c/p\u003e \u003cp\u003eF-test\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;F\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e418\u003c/p\u003e \u003cp\u003e7.790\u003c/p\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e418\u003c/p\u003e \u003cp\u003eχ2\u0026thinsp;=\u0026thinsp;36.367\u003c/p\u003e \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;χ2\u0026thinsp;=\u0026thinsp;0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e***p\u0026lt;.01, **p\u0026lt;.05, *p\u0026lt;.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Authors\u0026rsquo; computation, 2025\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the fixed and random effects. The Fixed Effects model indicates that control of corruption is positively and statistically significantly associated with GDP per capita growth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Gross capital formation is positively associated with growth at the 10% significance level. Total natural resource rents are not statistically significant in the Fixed Effects specification. The interaction term between corruption control and natural resource rents is negative and statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that the marginal association between corruption control and growth decreases as resource rents increase. Foreign direct investment and trade openness are not consistently significant across specifications. These results describe conditional associations and do not imply causality.\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\u003eHausman (1978) specification test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChi-square test value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoef.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.02\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.0092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e is the Hausman specification test result, with a Chi-square test value of 17.02 and a highly significant P-value of 0.0092, leading to the rejection of the null hypothesis (H\u003csub\u003e0\u003c/sub\u003e), which states that the Random Effects (RE) model is consistent and efficient. Because the p-value (0.0092) is less than the conventional significance level of 0.05, the test strongly indicates that there is a systematic difference between the coefficients estimated by the RE and the Fixed Effects (FE) models. This means that unobserved country-specific effects are correlated with the independent variables, causing RE estimates to be biased and inconsistent. Therefore, based on statistical evidence from the Hausman test, the Fixed Effects (FE) model is a more appropriate and robust specification for analyzing panel data, as it accounts for these time-invariant, country-specific characteristics.\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\u003eCross-sectional independence of fixed-effect model\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\u003eCSD Tests\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCSD value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePesaran\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFriedman\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSD Tests\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCSD value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eCritical value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrees\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1782\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the cross-sectional independence of fixed-effect model results from the cross-sectional dependence (CSD) tests strongly indicate that the assumption of independence across countries in the Fixed Effects (FE) panel data model is violated. Specifically, both the Pesaran CSD test (CSD value of 5.347, p-value of 0.0000) and the Friedman test (CSD value of 46.469, p-value of 0.0007) have highly significant p-values (less than 0.05), leading to the rejection of the null hypothesis of no cross-sectional dependence. This finding suggests that country-specific shocks or unobserved factors are correlated across nations in the sample, meaning that the error terms are not independent. While the Frees test (CSD value of 0.174 compared to a critical value of 0.1782) shows marginal non-rejection of the null hypothesis, the strong and consistent evidence from the Pesaran and Friedman tests confirms the existence of cross-sectional dependence. Therefore, standard fixed-effects estimation is inefficient and potentially inconsistent, necessitating the use of more sophisticated models that can handle cross-sectional dependence, such as the quantile regression model.\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\u003eQuantile Regression Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25th panel quantile regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50th panel quantile regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75th panel quantile regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDP per capita growth (annual)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient (P-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoefficient (P-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient (P-value)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eControl of Corruption Estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.977 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.484 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.762 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForeign Direct Investment, net inflows\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.001 (0.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.021 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.045 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGross Capital Formation (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.016 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Natural Resources Rents (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.313 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.106 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.180 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrade (% of GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorruption \u0026times; Resource Interaction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.246 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.118 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.238 (0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Authors, 2025\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the \u003cb\u003eQuantile Regression\u003c/b\u003e. Panel quantile regression reveals heterogeneity across the growth distribution. At the 25th percentile, natural resource rents are negatively and significantly associated with growth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The interaction term remains negative and significant, indicating that the conditional association between corruption control and growth weakens in more resource-intensive economies, particularly among low-growth countries.\u003c/p\u003e \u003cp\u003eAt the 50th and 75th percentiles, corruption control remains positively and significantly associated with growth (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Foreign direct investment is positively and significantly associated with growth in the median and upper quantiles but not in the lower quantiles. Gross capital formation shows a positive association at lower quantiles but becomes negative at the 75th percentile, suggesting diminishing returns at higher growth levels.\u003c/p\u003e"},{"header":"5.0 Discussion","content":"\u003cp\u003eThe empirical findings indicate that the association between corruption control and growth is conditional on the level of natural resource dependence. The negative interaction coefficient suggests that as the share of resource rents in GDP increases, the positive association between corruption control and growth weakens. This pattern is consistent with frameworks in which high resource rents alter incentive structures, potentially affecting the economic returns to institutional improvements. However, the analysis does not establish causal mechanisms and should be interpreted as identifying conditional correlations within the sample. This finding suggests a \"double-curse\" mechanism, as proposed by Leite \u0026amp; Weidmann (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), wherein abundant rents make it more difficult to eradicate corruption and render governance reforms less transformative. Conceptually, this result reverses the logic posited by Mehlum et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), who argued that high-quality institutions can neutralize the resource curse. While Mehlum et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) found that better institutions eventually enhance resource abundance, the evidence suggests that greater resource dependence actually erodes the growth gains typically expected from better corruption control. This positioning aligns with recent evidence from the OIC and MENA regions, where resource rents have been found to influence the growth of governance, blunting the effectiveness of institutional upgrades (Belloumi \u0026amp; Almashyakhi, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Erum \u0026amp; Hussain, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe lack of statistical significance regarding the impact of Foreign Direct Investment (FDI) on growth in low-growth countries further underscores the conditional nature of development. A broad consensus in the literature suggests that FDI-led growth is not automatic, but rather contingent upon host-country absorptive capacity and institutional quality (Li \u0026amp; Tanna, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In many low-growth economies, critical thresholds in human capital, financial development, and infrastructure have not yet been met, preventing these nations from capturing spillovers and technological transfers typically associated with FDI (Gupta et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kariuki \u0026amp; Kabaru, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, high macroeconomic volatility and a weak rule of law often distort the type of FDI attracted to these regions, frequently resulting in \"enclave-type\" resource extraction, which offers limited linkages to the broader economy (Adegboye et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sultana \u0026amp; Turkina, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, without surpassing these institutional and capacity thresholds, FDI remains a marginal or even insignificant contributor to sustainable growth in the low-growth subsample (Asafo-Agyei \u0026amp; Kodongo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hayat, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe lack of significance for the direct NR variable, coupled with the significant negative interaction term, supports the 'Institutional Threshold' theory. In SSA-OIC countries, the resource curse is not a direct mechanical link; rather, it is an institutional bypass. High resource rents create a 'dampening effect' where even if a country improves its Control of Corruption, the presence of massive rents creates such strong incentives for rent-seeking that the institutional improvements fail to translate into GDP growth.\u003c/p\u003e \u003cp\u003eThis \"dampening effect\" is particularly pronounced in OIC Sub-Saharan African (SSA) countries, where high natural-resource rents create a structural drag on the economy. Resource rents in these regions are strongly associated with rent-seeking behavior rather than productive entrepreneurship, as talent and capital are often diverted toward capturing resource revenue (Munemo, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This environment fosters a persistent state of institutional erosion, in which inefficient public administration and political instability remain entrenched despite formal improvements in corruption indices (Henri, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, resource dependence has been shown to encourage capital flight and expansion of the shadow economy, significantly reducing the investable surplus available for development (Ngondjeb \u0026amp; Nlom, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ogashenko et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Even as corruption controls improve, the resource sector continues to generate powerful incentives for state capture and sophisticated forms of bureaucratic rent seeking, such as off-budget deals and licensing manipulation (Asiamah et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mlambo, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Ultimately, the \"resource-rent state\" structure acts as a powerful countervailing force, ensuring that institutional improvements do not translate into proportionate efficiency gains or broad-based economic growth (Dramani et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kutlu \u0026amp; Mao, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e"},{"header":"6.0 Limitations","content":"\u003cp\u003eThis study has several limitations. The first is that results arise from observational panel data, so they identify conditional correlations rather than causal effects; institutional quality and growth may be determined at the same time, and reverse causation cannot be excluded. Second, governance indicators, including control of corruption, are based on perception and contain measurement error that can attenuate estimated associations. Third, any omitted time-varying factors (e.g., intensity of conflict and non-resource commodity price shocks; macro-stabilization policy and structural reforms) that correlate with resource rents/institutions on the one hand and growth on the other may bias estimated coefficients. Fourth, tests for cross-sectional dependence imply that shocks and unobserved factors are correlated across countries; this will bias inference in standard panel settings, so caution is warranted in interpretation. Fifth, the external validity is restricted to both the SSA-OIC sample and the 2005\u0026ndash;2024 period.\u003c/p\u003e"},{"header":"7.0 Conclusion and Policy Recommendations","content":"\u003cp\u003eThis study finds that control of corruption has a positive and significant relationship with GDP per capita growth in SSA-OIC nations, whereas natural resource rents and their interaction with\u0026ensp;corruption control are negative and statistically significant. This suggests that the marginal linkage between institutions\u0026ensp;and growth weakens with rising resource dependence. Quantile regression analysis reveals that the association between resource rents and growth also differs across the distribution of growth, with the largest negative\u0026ensp;effect of resource rents on growth occurring in low-growth countries.\u003c/p\u003e \u003cp\u003eThese findings pertain to conditional correlations in the sample, and should not be taken\u0026ensp;to reflect causal effects. These results would support the view that economic diversification and governance reforms specifically targeted at the extractive sector could lead to different growth\u0026ensp;outcomes, especially in resource-rich countries. More generally, additional studies with dynamic panel data methods, instrumental variables, or common-factor adjustments would offer enhanced identification of such causal\u0026ensp;mechanisms.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, these findings underscore that SSA-OIC nations cannot rely on resource wealth as a primary engine for long-term prosperity. Economic diversification must be treated as a prerequisite for institutional efficacy, as reducing resource dependence is essential for restoring the full growth-enhancing potential of anti-corruption reforms. Furthermore, governance policies must move beyond general mandates to specifically target resource rent management. This includes implementing rigorous transparency standards for mining and oil contracts, adhering to international extractive industries\u0026rsquo; transparency frameworks, and creating ring-fenced mechanisms for windfall revenues to prevent them from being diverted into patronage networks. By addressing the specific incentives for rent-seeking inherent in the extractive sector, governments can ensure that institutional upgrades translate into productivity gains.\u003c/p\u003e \u003cp\u003eThis study also highlights the necessity of a nuanced, staged strategy for attracting Foreign Direct Investment. As the quantile regression results indicate that FDI primarily benefits countries already in higher-growth brackets, low-growth nations should prioritize the \"basics\" of development, such as physical infrastructure, human capital, and basic legal stability, before expecting foreign capital to act as a primary driver of growth. Without reaching these critical thresholds, FDI in resource-rich regions often remains confined to \"enclaves\" that offer limited spillover benefits to the broader economy. Finally, it is important to acknowledge the limitations of this research, particularly regarding the availability of consistent data across all OIC-specific metrics and the difficulty in capturing the full extent of informal economic activities within these states. Future investigations should aim to incorporate more localized institutional data to refine these regional growth strategies further.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo external funding was received for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe have no conflicts of interest to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available upon request because the data link is redacted to preserve anonymity during the review process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAcemoglu, D., \u0026amp; Robinson, J. 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Natural resources, rent dependence, and public goods provision in China: Evidence from Shanxi\u0026rsquo;s county-level governments. \u003cem\u003eThe Journal of Chinese Sociology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1), 20. https://doi.org/10.1186/s40711-016-0040-3\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Resource Curse, Control of Corruption, SSA-OIC, Economic Growth, Quantile Regression","lastPublishedDoi":"10.21203/rs.3.rs-8892786/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8892786/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research investigates the\u0026ensp;nexus among natural resource rents, control of corruption, and GDP per capita growth in 21 Sub-Saharan African OIC member states for the period 2005\u0026ndash;2024. Applying panel Fixed Effects estimation as well as\u0026ensp;panel quantile regression (25th, 50th, and 75th percentiles), the study assesses heterogeneities along with the growth distribution. The findings suggest that control of corruption is positively associated\u0026ensp;with growth in all specifications. The interaction variable of natural resource rents and corruption control is found to be negative and significant, implying that the marginal effect of corruption control on growth decreases with increases in resource reliance. Quantile regression results indicate that\u0026ensp;the negative relationship between resource rents and growth is most pronounced at the lowest end of the growth scale, while foreign direct investment has a positive association with growth mostly in the median and upper quantiles. These results indicate that there is distributional diversity in the resource, governance, and growth nexus across SSA-OIC countries.\u003c/p\u003e","manuscriptTitle":"Resource Rents, Institutions, and Growth in Sub-Saharan African OIC Countries: A Panel Quantile Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 11:21:31","doi":"10.21203/rs.3.rs-8892786/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-28T12:13:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T03:42:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T17:54:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166361749013963443288042775213654868586","date":"2026-03-11T17:01:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104069697922664671673473935317410271608","date":"2026-03-10T17:56:00+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T10:03:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-02T14:57:59+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-27T11:54:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-25T20:43:18+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2026-02-25T20:38:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6ff77d63-b849-4281-b543-230b1038b0cb","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63979738,"name":"Business and commerce/Economics"},{"id":63979739,"name":"Social science/Economics"},{"id":63979740,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-18T13:23:42+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 11:21:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8892786","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8892786","identity":"rs-8892786","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}