Financial inclusion and natural resource rents: non-linear effects on economic growth in Sub-Saharan Africa

This study investigates the relationship between financial inclusion, natural resource rents, and economic growth across 21 Sub-Saharan African countries from 2014 to 2022. It employs the System Generalized Method of Moments (System GMM) alongside Panel Quantile-on-Quantile Regression (Panel QQR). The analysis uncovers robust non-linear dynamics and distributional heterogeneities. The SGMM results robustly confirm a statistically significant U-shaped relationship, whereby financial inclusion initially impedes economic growth but subsequently fosters it—under various interactions that exert positive and significant influences on performance. These findings underscore that achieving sustainable growth and circumventing the resource curse is conditional upon the effective moderating role of governance in concert with the contribution of financial inclusion. The QQR results reveal a nuanced landscape: financial inclusion exerts its strongest positive effects at the mid-level quantiles of the growth distribution, while it exhibits adverse impacts at the extreme lower and upper tails—reflecting the dual threats of financial exclusion and excessive inclusion. Similarly, natural resource rents show a negative association with growth at lower quantiles, which supports the resource curse hypothesis; however, they enhance growth at higher quantiles, in line with stronger governance capacity and institutional reforms. Governance consistently exerts a positive and intensifying influence across all quantiles, reinforcing its essential role in promoting sustainable economic development. These insights underscore the urgency of implementing finely tuned, context-sensitive financial inclusion strategies alongside transparent and accountable resource revenue management. The study concludes with actionable policy recommendations aligned with key Sustainable Development Goals (SDGs) and a resilient growth trajectory across the region.

1 Introduction

Historically, natural resources have played a pivotal role in supporting economic growth in developed countries such as the United States, Canada, and Australia. However, many resource-rich developing nations encounter what is referred to as the “resource curse,” wherein the abundance of natural resources, contrary to expectations, results in weaker developmental outcomes and slower economic growth. This paradox is frequently attributed to several key factors, including the “Dutch disease”, poor institutional quality, insufficient investment in human capital, and the slow evolution of the financial sector [1–9].

Given the historical trajectory of resource extraction in Sub-Saharan Africa (SSA) and the region's relatively underwhelming economic performance, a fundamental question emerges: Does SSA suffer from the “resource curse”? [10]. Empirically, the bulk of the relevant literature has concentrated on the role of institutional quality and financial development in mitigating the detrimental effects of natural resource revenues on economic growth. Nevertheless, a significant gap exists in the research concerning the role of financial inclusion in alleviating the impact of the resource curse, as it has not yet garnered sufficient attention in this context, as highlighted by Adabor and Mishra [11].

Financial inclusion has become an increasingly important topic for both researchers and policymakers, evolving into a key development strategy in numerous economies [12, 13]. It was recognized as a fundamental pillar of global progress at the 2010 G20 Summit in Toronto and Seoul [14, 15]. Access to affordable financial services is seen as a vital component in reducing poverty, fostering economic growth, and enhancing environmental quality [16–20], particularly in developing nations where 65% of adults still lack basic financial accounts [13].

Sub-Saharan Africa provides a compelling setting for examining the interconnections between financial inclusion, natural resource rents, and economic growth [21]. Several economies in the region—such as Nigeria and Angola (oil), Niger and the Democratic Republic of the Congo (minerals), and Ghana and South Africa (gold)—remain highly dependent on the export of natural resources. However, this resource abundance has not translated into sustained and inclusive economic development, lending further support to the “resource curse” hypothesis [22]. A substantial body of empirical literature documents the negative impact of resource rents on crucial economic and institutional factors, including economic growth, investment, foreign direct investment, structural transformation, financial sector development, human capital accumulation, social welfare, and governance quality [23–31].

According to Ampofo et al. [32], the continent is a classic case of a wealthy beggar that mostly relies on external aid, loans, and grants for survival instead of its national wealth. Political leaders are often blamed for their rent-seeking behaviors and economic mismanagement, which contribute to the continent's ongoing economic struggles. In general, countries or regions with a heavy reliance on natural resources are more susceptible to rent-seeking behavior and corruption, which significantly hampers the development of their financial systems [33, 34].

Furthermore, the SSA region has witnessed remarkable progress in financial inclusion, driven largely by mobile financial services. Between 2011 and 2021, the share of adults with financial accounts grew from 23% to 55%—the fastest global increase—compared to a rise from 51% to 76% worldwide. By 2022, twelve (12) SSA countries had more adults with digital money accounts than with accounts in traditional financial institutions [35, 36]. Kenya stands out with M-Pesa, which has revolutionized financial access, enabling secure, low-cost transactions. Over 75% of its population is financially included, and 76.7% live within 5 kilometers of a financial access point. The country reports 161.9 access points per 100,000 people—well ahead of Uganda (63.1), Tanzania (48.9), and Nigeria (11.4) [37–40].

In this vein, Boachie and Adu-Darko [41], Ndombi Avouba et al. [42], Ugwuanyi et al. [43], Ifediora et al. [13], and Adedokun and Aga [44] found that financial inclusion had a positive impact on economic growth in Sub-Saharan African countries. Similarly, Adabor and Mishra [11] concluded that enhancing financial inclusion could contribute to mitigating the “resource curse paradox” in Ghana and other resource-rich developing economies. Nonetheless, many countries in the region continued to fall short of the financial inclusion levels necessary to fully harness their benefits, due to persistent challenges such as inadequate infrastructure, regulatory constraints, and socio-economic barriers, as noted by Osuma [45] and Eshun and Kočenda [46]. In this regard, Chinoda and Kapingura [47] observed that SSA remained below the global average in all financial inclusion metrics, except for mobile money usage.

Due to data limitations, this study selects a purposive sample of 21 Sub-Saharan African (SSA) countries to ensure the effective attainment of its objectives. This deliberate selection allows for a focused and in-depth analysis of the impact of financial inclusion and natural resource rents on economic growth in the region over the period 2014–2022. The study specifically examines the mediating role of governance, alongside financial inclusion, in alleviating the adverse effects of the “resource curse” and turning it into an opportunity for sustainable economic growth.

It is noteworthy that while SSA constitutes just 16% of the global population, the region accounts for approximately 67% of the world's extreme poor, according to World Bank data for 2024. This stark regional disparity highlights the significant development challenges facing countries in the region [48]. Nigeria and Angola exemplify the paradox between heavy reliance on oil and poor developmental outcomes. In Nigeria, oil revenues account for 80% of government income, yet approximately 40% of the population lives in extreme poverty [49]. Similarly, despite Angola's significant dependence on oil, which constitutes 28.9% of its GDP and 95% of its exports, its GDP growth in 2023 was only 0.9%, a sharp decline from 3% in 2022 and well below the projected 3.5%, as noted by Schoch et al. [50].

Consequently, ensuring institutional transparency and reinforcing governance frameworks emerge as critical imperatives for mitigating the detrimental consequences of excessive reliance on natural resource rents, thereby laying the groundwork for inclusive, sustainable, and long-term economic development in the region [51–54].

To ensure the reliability of the results, the study employs two approaches. First, it utilizes a dynamic panel data method, specifically the System Generalized Method of Moments (System GMM), to address issues of endogeneity and to examine the potential U-shaped relationship between financial inclusion and economic growth under different interaction effects, thereby providing more accurate and consistent parameter estimates. Second, the study adopts a non-linear approach through the Panel Quantile-on-Quantile Regression (QQR) model, which allows for exploring heterogeneous relationships across various conditional quantiles of both the dependent and independent variables.

Overall, this study distinguishes itself from existing literature through two key contributions. First, as emphasized in the preceding paragraph, it constitutes one of the pioneering endeavors to integrate dynamic and non-linear methodologies in examining the intricate interdependencies that define the Sub-Saharan African context. Second, it offers a novel and explicit linkage between its economic and policy findings and multiple Sustainable Development Goals (SDGs), notably SDG 8 (Decent Work and Economic Growth) and SDG 16 (Peace, Justice, and Strong Institutions)—a connection that has largely been addressed only implicitly in prior research. Within this framework, expanding access to finance emerges as a critical enabler of SDG attainment, particularly for Goals 1 and 8 [16, 33, 47, 55]. Additionally, fostering the diversification of productive sectors and reducing reliance on resource rents are identified as key strategies for advancing SDG 12 [56]. Historical evidence further underscores the pivotal role of institutional quality as both a determinant and a moderating factor in shaping the impact of natural resource abundance, thereby reinforcing the broader sustainability agenda [57, 58]. In recent years, circular economy practices have gained growing prominence as strategic instruments for simultaneously advancing environmental and sustainable development goals [59].

The paper proceeds as follows: Section 2 reviews the literature on financial inclusion, resource rents, economic growth, and transparency. Section 3 details our methodology, including model specification, variable definitions, estimation strategy, and data sources. Section 4 presents empirical results from dynamic SGMM and panel QQR analyses. Section 5 concludes with policy recommendations.

2 Related literature

This section offers a concise overview of literature examining the interplay between natural resource wealth, financial inclusion, and economic growth. Scholars broadly agree that over-reliance on natural resources can turn an economic asset into a liability—commonly referred to as the “resource curse”—especially in settings characterized by weak institutions, corruption, and insufficient investment in education and human capital [31, 54, 60].

Accordingly, this section focuses on four main explanatory aspects. It begins by analyzing the relationship between natural resource rents and economic growth. Next, it explores the link between financial inclusion and economic growth. Then, it examines the interaction effect between financial inclusion and natural resource rents. Lastly, it assesses the moderating role of institutional transparency.

2.1 Natural resource rents and economic growth

Natural resource abundance—whether renewable or non-renewable—is generally perceived as a strategic asset for countries endowed with such wealth [61]. Nevertheless, in many low- and middle-income countries, excessive dependence on resource extraction has impeded long-term economic growth [62, 63]. This paradox—where resource wealth leads to adverse economic outcomes—is frequently linked to a combination of structural imbalances and institutional weaknesses. One of the most cited explanations is the “Dutch disease”, which undermines economic competitiveness by appreciating the exchange rate and promoting rent-seeking behavior over productive innovation [64–70]. Sub-Saharan Africa, despite being richly endowed with natural resources, exemplifies this economic paradox [32, 49, 71–73]. Some scholars have vividly described this scenario as akin to a child born with a silver spoon but raised as a beggar in Africa [74]. However, the experience of Botswana demonstrates that the resource curse is not an inevitable fate. With sound institutions and prudent management, natural resource wealth can serve as a catalyst for sustainable development [75].

To achieve sustainable development, it is crucial that current generations use resources in a way that does not compromise the wellbeing of future generations. In resource-dependent economies, this requires diversifying both the economy and income sources to offset the depletion of resource rents, while also implementing environmental policies aimed at mitigating ecological degradation. However, the enduring effects of Dutch disease may impede these efforts by dampening economic growth and reducing efficiency [76, 77].

The relationship between natural resources and economic growth has been widely examined over the past three decades; however, the debate continues to evolve without a definitive consensus [78]. Ofori and Grechyna [79] examined the relationship between remittances, natural resource rents, and economic growth in Sub-Saharan Africa (SSA), using data from 43 countries between 1990 and 2017. Their findings indicate that while forest rents positively influence economic growth, both oil and natural gas rents have adverse effects. Similarly, Dramani et al. [80], employing threshold analysis for the period 1990–2019, identified a double-threshold effect of natural resource dependence on economic growth in SSA. Specifically, when total natural resource rents account for less than 6% of GDP, they exert a significant negative effect on growth. As rents rise to between 6% and approximately 15% of GDP, the negative impact significantly reduces, and beyond the 15% threshold, natural resource rents exhibit a substantial and significant positive effect on economic growth.

In a related study, Mumuni and Mwimba [81] assessed the joint effects of green energy consumption and natural resource rents on economic growth in 24 African countries from 1990 to 2020. Their results indicate that while green energy consumption constrains economic growth in the short run, it contributes positively to long-run expansion. In terms of natural resource rents, total rents were found to stimulate growth in the short term but hinder it over the long term. Moreover, both forest and mineral rents exerted a significant negative impact on short-run growth, whereas only mineral rents showed a long-run growth-enhancing effect—albeit generally lacking statistical significance. Li et al. [82] emphasized that although the availability of natural resources can significantly promote growth, mismanagement may result in economic instability, conflict, and poor economic outcomes. Fajrian et al. [83] found that both foreign direct investment (FDI) and natural resources positively affect economic growth. However, an increase in natural resources reduces the overall impact of FDI on economic growth.

2.2 Financial inclusion and economic growth

Financial inclusion has been widely recognized as a catalyst for economic growth in developing countries (e.g., SSA) by expanding opportunities for lower-income individuals and fostering broader development [46, 84–92, 210].

Recently, digital financial inclusion has garnered considerable scholarly attention due to its potential to forecast and promote sustainable economic growth in developing economies, especially given the increasing integration of information and communication technologies (ICTs) into financial systems [42, 93–96]. In this regard, Zehri et al. [97] provide compelling empirical evidence using a dynamic fixed-effect panel data approach and the Generalized Method of Moments (GMM) from 2010 to 2021. Their findings reveal that conducive environments for decent work, coupled with enhanced access to digital financial services, have a significant positive impact on economic growth. The synergistic effect of the components of Sustainable Development Goal 8 (SDG 8) is especially pronounced in high-income countries. Dong et al. [98] emphasized the importance of financial institutions focusing on the development of financial products that align with the principles of green finance in BRICS countries.

In the context of SSA countries, empirical evidence suggests a positive relationship between financial inclusion and economic growth. For instance, Osuma [45] highlighted that enhancing digital financial services in Sub-Saharan Africa holds substantial potential to significantly improve financial inclusion, foster economic growth, and reduce poverty levels. Similarly, Boachie and Adu-Darko [41] identified a positive correlation between financial inclusion and economic growth in Sub-Saharan Africa during the period 2010–2020, employing the Driscoll-Kraay standard error estimation technique. The study further emphasizes that financial inclusion fosters economic growth primarily through its positive influence on human capital development. Boachie et al. [99] found that financial inclusion exerts a positive and statistically significant effect on both the stability of the banking sector and economic growth in Sub-Saharan African countries.

Moreover, Ugwuanyi et al. [43] found that both traditional and digital forms of financial inclusion exert a positive and statistically significant influence on economic growth in Sub-Saharan African countries during the period 2012–2020. Notably, traditional financial inclusion demonstrates a stronger impact, particularly within the access-related sub-dimensions. Conversely, in the usage sub-dimension, the relative effect of traditional vs. digital finance appears largely comparable, indicating no significant difference in their influence. This finding aligns with Ifediorae et al. [13], who discovered that the availability and outreach dimensions of financial services, along with the composite financial inclusion index, exert a positive and statistically significant effect on economic growth in Sub-Saharan Africa during the period 2012–2018. By contrast, while the usage dimension contributes to economic growth, its impact is not statistically significant.

Adedokun et al. [100] investigated the impact of financial inclusion on economic growth in 41 Sub-Saharan African countries over the period 2004–2019, utilizing the Generalized Method of Moments (GMM) and Granger causality analysis. The results indicated a positive relationship between financial inclusion and economic growth. Furthermore, Adedokun and Aga [44] examined this relationship over the period 2004–2017, using the Generalized Method of Moments (GMM) and Dumitrescu–Hurlin causality tests. Their findings also revealed a positive and statistically significant effect of financial inclusion on economic growth in the region, with short-run causality running from economic growth to financial inclusion.

2.3 Financial inclusion and natural resource rents

Despite the extensive literature on the relationship between economic growth and digital financial inclusion, a notable gap persists in studies exploring the interlinkages between financial inclusion, natural resource rents, and economic development, as emphasized by Imran and Jijian [101] and Gao [102]. The emerging field of resource-based finance is still evolving [14]. This study addresses a significant gap by examining the complex dynamics between financial inclusion and economic growth, taking into account the key interactions specific to the Sub-Saharan African (SSA) context. Financial inclusion is an important channel linking natural resource rent and economic growth [11].

Since the emergence of the “resource curse” hypothesis, researchers have shown growing interest in the macroeconomic and financial implications of natural resources [34], helping to explain why some resource-rich countries have underdeveloped financial sectors, while others have effectively leveraged their resources to develop capital markets, banking, and insurance services [103].

Although the advantages that financial inclusion offers, many Sub-Saharan African economies still fall short of the required levels needed to reap its benefits. The region remains one of the most financially marginalized in the world [47, 104, 105, 209]. Nigeria, the continent's largest country by land area and population, and the leading African country in terms of proven oil reserves, has an estimated 55% of its adult population unbanked—the highest rate globally [14, 211]. Over-reliance on natural resources leads to rent-seeking and corruption, thereby weakening financial development and the evolution of financial systems [34, 106, 107]. Consequently, the lack of income diversification and wealth concentration hinders inclusive financial access [108].

In a more recent contribution, Qamruzzaman [109] investigated the interaction between natural resource rents, education, financial services, and technological progress in the context of achieving Sustainable Development Goal 7 (affordable and clean energy) in eight resource-rich countries. Key findings reveal that financial inclusion fosters economic growth and contributes to environmental degradation, necessitating regulatory measures. Similarly, Li et al. [82] found that natural resource rents significantly and negatively impact financial inclusion in a sample of 109 countries from 1996 to 2020, supporting the resource curse hypothesis. Furthermore, Khan et al. [33] concluded that total natural resource rents impede access to finance through both financial institutions and markets. Specifically, rents from oil, coal, minerals, and forests negatively affect access to finance in financial institutions and markets, whereas gas rents surprisingly have a positive effect on both.

In line with these findings, Imran and Jijian [101] reported a negative association between financial inclusion and total natural resource rents (TNRR) in QUAD countries. On the other hand, Pang et al. [110] identified green investment and natural resource rents as the two most influential drivers of financial inclusion in China. Han et al. [111] found that eco-digitalization, green technology, green finance, and renewable energy significantly promote environmental sustainability in China by mitigating CO₂ emissions and ecological footprints.

Adedokun and Aga [44] highlighted that the economies of Sub-Saharan Africa, characterized by large populations, expanding markets, and abundant natural resources, provide a strong rationale for studying the impact of financial inclusion on economic growth. This is especially relevant given the growing participation of marginalized and low-income groups in financial systems. Adabor and Mishra [11] noted that developing countries endowed with abundant natural resources have faced slower economic growth rates. However, to their knowledge, no previous studies have examined whether financial inclusion could address this paradox. Their empirical findings from Ghana (1970–2020) show that enhancing financial literacy, accessibility, and the usage of financial services can help overcome the “resource curse paradox” in resource-rich developing nations. Nkoa et al. [112], using GMM analysis on panel data from 44 Sub-Saharan African countries (1990–2021), found that natural resource rents negatively impact life expectancy, supporting the resource-curse-health hypothesis. However, this relationship becomes positive once financial system stability surpasses certain thresholds, highlighting its moderating effect.

Zhu et al. [113] also affirmed the presence of the resource curse in Sub-Saharan Africa (SSA), though its effect lessens at higher levels of sustainable development (SD). While renewable energy (RE) and financial technology (Fintech) each have a negative impact on SD when considered individually, their combined interaction with natural resource rents yields a significantly positive outcome. These findings indicate that effective management of natural resources, in conjunction with RE and Fintech, could potentially turn the resource curse into a developmental advantage for SSA.

Finally, Huang and Meng [114] examined how digital finance helps mitigate the resource curse in China's resource-dependent cities, using data from 219 cities (88 resource-based) between 2011 and 2019. Their study confirmed a strong resource curse effect, which digital finance effectively reduces. The mediation analysis showed that digital finance enhances financial depth, access, and efficiency, improves urban capital allocation, reduces enterprise financing constraints, and ultimately promotes economic growth.

2.4 Governance as a moderating factor between natural resource rents and economic growth

In an effort to mitigate the adverse effects of the so-called “resource curse”, the Extractive Industries Transparency Initiative (EITI) has emerged as a globally recognized standard aimed at promoting transparency and accountability in resource-rich economies [22, 52, 115–117]. A growing body of empirical literature generally concurs that institutional quality is a pivotal determinant in explaining the heterogeneity of economic outcomes among these countries [118–120].

Within this framework, the seminal theory proposed by Mehlum et al. [54]—building upon the foundational work of Sachs and Warner—posits that the impact of natural resource abundance on economic growth fundamentally depends on the quality of institutions. The balanced institutionalist perspective suggests that resource-dependent countries are not inherently doomed to fail, but rather face specific challenges in implementing effective growth policies; ultimately, success is largely contingent on the strength of institutional frameworks [51]. Sarmidi et al. [121] concluded that countries with low institutional quality rely heavily on natural resources as a source of growth, whereas those with high-quality institutions are relatively less dependent on natural resources to achieve economic growth. The ongoing effort to elucidate the mechanisms through which resource rents influence economic performance continues to fuel debates regarding the fundamental causes of the resource curse phenomenon [122, 123].

Numerous studies have explored how institutional quality moderates the effects of the resource curse. Evidence indicates that institutional quality positively reinforces the contribution of various mineral resource categories to economic growth in Sub-Saharan African countries [124, 212]. As mentioned previously, Sub-Saharan Africa, despite its wealth of natural resources, faces a pronounced economic paradox marked by entrenched poverty, structural inequalities, and sluggish overall growth [72]. Gaining insight into the role of corruption in shaping the relationship between natural resource wealth and economic growth is essential for advancing sustainable development goals in the region [125].

Chung and Jin [126] highlight the complex interplay between resource types, institutional quality, geopolitical risk, and economic growth. They find that oil, mineral, and forest rents negatively affect growth, partially mediated by institutional quality. In contrast, natural gas rents support the resource blessing hypothesis via full institutional mediation, suggesting institutional improvements can reverse the resource curse. Likewise, Ashraf et al. [127] found that natural resources and economic openness lead to increased CO₂ emissions; however, their interaction with institutional quality contributes to improved environmental quality. Fagbemi and Kotey [123] also reveal a unidirectional causal relationship running from the interaction between resource rents and institutional quality to economic growth in Nigeria, suggesting that the country's growth trajectory may be jointly determined by these two factors. Adabor [128] found that the results derived from ARDL model—both in the short and long run—indicate that while the direct effect of gas revenue on economic growth in Ghana was positive, it was statistically insignificant. In contrast, the interaction between gas revenues and government effectiveness had a positive and statistically significant impact on economic growth.

Khan et al. [33] emphasize that institutional quality plays a pivotal role in moderating the influence of natural resource rents on access to finance, with a positive effect evident in countries with strong institutional frameworks, and a negative effect in those with weak institutions. In a similar vein, Li et al. [82] highlight that the association between resource rents and financial inclusion is highly dependent on institutional robustness—positive under strong governance and negative under institutional fragility. Supporting this perspective, Ofoeda et al. [129] find that institutional quality strengthens the beneficial impact of financial inclusion on economic growth. Additionally, the study by Chernor Momodu and Unisa [130] reveals that incorporating institutional quality as an interaction term in their models leads to positive developmental effects from both mineral and forest resource revenues. Collectively, these studies underscore the indispensable role of institutional quality in leveraging natural resource wealth to achieve inclusive and sustainable economic development.

3 Methods, data, and variables

3.1 Methodology

This study employs data spanning 2014–2022, covering a sample of 21 Sub-Saharan African countries. The research methodology is specifically designed to investigate the potential linkages among financial inclusion, natural resource rents, and economic growth. The selection of the System Generalized Method of Moments (SGMM) estimator, as supported by Eshun and Kočenda [46], Akpa et al. [131], Asongu et al. [132], Ullah et al. [133], and Adewale [134], is justified by three key considerations: (i) the number of cross-sectional units (countries) exceeds the number of time-series observations per unit; (ii) the dependent variables demonstrate persistence over time; and (iii) the estimation strategy effectively addresses endogeneity by employing internal instruments constructed from lagged values of the variables.

In addition to these considerations, System GMM (SGMM) is highly effective in handling heteroskedasticity, where the variance of the error terms varies across observations. In the presence of such heteroskedasticity, conventional estimators may produce biased or inconsistent results, undermining statistical inference. SGMM ensures that parameter estimates remain consistent and reliable even under heteroskedastic conditions, in a manner comparable to White's heteroskedasticity-consistent standard errors [135–137].

The System Generalized Method of Moments (SGMM) uses a dynamic panel data approach with equations in differences and levels, employing lagged variables as instruments to address reverse causality, temporal persistence, and unobserved heterogeneity [138, 139].

The model's specification is validated through several diagnostic tests. The Hansen J and Sargan tests assess the overall validity of the instruments, where insignificant results indicate that the instruments are appropriate. Additionally, a test for autocorrelation examines whether the differenced error term exhibits first- or second-order serial correlation. Failure to reject the null hypothesis suggests that the original error term is serially uncorrelated and that the moment conditions are correctly specified [AR(2) > 0.10] [140, 141].

Building on its widespread application in the empirical literature, this study employs the two-step System GMM (SGMM) estimator due to its superior consistency and efficiency, which significantly enhance the precision and credibility of the estimated coefficients [142–144]. To further address potential endogeneity, we implement the forward orthogonal deviations approach proposed by Arellano and Bover [214]. Moreover, we conduct Arellano and Bond [213] autocorrelation tests [AR(1) and AR(2)] to verify the absence of serial correlation, while the Sargan test is used to assess the validity of over identifying restrictions [145, 146]. The estimation is executed using the xtabond2 command for the two-step GMM system, following the robust methodology outlined by Roodman [146]. The empirical analysis relies on dynamic panel data models, complemented by comprehensive robustness checks, ensuring the validity and reliability of the findings.

To capture the potential non-linear dynamics between variables, this study employs the Panel Quantile-on-Quantile Regression (Panel QQR) methodology. In this context, Sim and Zhou [147] introduced a nonparametric QQR framework specifically designed to estimate non-linear relationships between two variables, highlighting that such relationships may vary across different points of their respective distributions. The QQR approach is widely recognized as one of the most robust techniques for analyzing subtle and non-linear interactions between independent and dependent variables across their full distributional ranges. Its ability to capture heterogeneous, quantile-specific effects has led to its increasing adoption in empirical research [148–154, 215], providing policymakers with deeper insights for designing precise, evidence-based policies.

Nevertheless, studies applying the Panel QQR methodology remain very scarce, along the lines of Tugcu and Menegaki [155], who employed a panel QQR framework to examine the effect of renewable energy generation on energy security in G7 countries. Accordingly, the present study seeks to address this critical research gap.

This study employs a panel-adapted QQR approach to capture the non-linear and heterogeneous effects between real GDP (RGDP) and key independent variables—namely Financial Inclusion, Natural Resource Rents, and Governance—across the conditional distributions of both dependent and independent variables, denoted by τ and θ, respectively. To address unobserved individual heterogeneity and cross-sectional dependence, fixed effects are first removed through the within transformation. Subsequently, latent common factors are extracted via Principal Component Analysis (PCA) applied to the residuals. These extracted components are included as control variables in both the QQR and standard Quantile Regression models. Estimation is conducted through kernel-weighted local quantile regression, with bootstrap resampling techniques applied to ensure robust statistical inference.

All estimations were conducted using a suite of statistical software, including STATA, RStudio, MATLAB, EViews, and Python, thereby enhancing the reliability and robustness of the empirical results.

In this context, the present study contributes to the existing literature in several key ways. It investigates the presence of a U-shaped relationship between financial inclusion and economic growth under critical interactions involving natural resource rents, governance, and financial inclusion, utilizing the System Generalized Method of Moments (SGMM) approach to address endogeneity concerns. Furthermore, the study adopts a non-linear framework through the Panel Quantile-on-Quantile Regression (QQR) model, which enables the analysis of heterogeneous effects across different quantiles of both dependent and independent variables. This positions our research among the first to combine dynamic and non-linear methodologies to explore the complex interdependencies shaping the Sub-Saharan African context. In addition, the study introduces a novel and explicit linkage between its economic and policy findings and the Sustainable Development Goals (SDGs)—a connection that previous literature has largely addressed only implicitly.

Moreover, we employ a multidimensional composite index for financial inclusion and governance, serving as moderating variables within the estimations. Developing a robust and accurate measure of financial inclusion is particularly crucial for researchers and policymakers [84], especially in the SSA region, where data availability continues to pose a persistent challenge. This comprehensive approach enhances both the analytical depth and the practical relevance of our findings.

3.2 Selection of dependent, main variables of Interest, moderating and control variables

3.2.1 Dependent variables

The study measures economic growth using Real Gross Domestic Product (RGDP) calculated at constant 2015 market prices in USD, as reported in the World Bank's World Development Indicators. GDP is defined as the total value added by all resident producers within the economy, inclusive of product taxes and excluding subsidies not incorporated in the value of the products. It is calculated without adjustments for depreciation of fixed assets or the depletion and degradation of natural resources. This variable is considered appropriate for measuring economic growth as it comprehensively captures economic activity across all production sectors [12, 79–81, 83, 156].

3.2.2 Main variables of interest with a moderating variable

To ensure the robustness of the model and the reliability of econometric estimates, each variable is represented by an index. The variable FIG refers to the Composite Financial Inclusion Index, which provides a multidimensional perspective by integrating three key dimensions—access, penetration, and usage—based on data from the FAS database. This composite index covers a sample of 21 Sub-Saharan African (SSA) countries over the 2014–2022 period, following the framework proposed by Ifediora et al. [13]. It is constructed using Principal Component Analysis (PCA), which normalizes the constituent variables through a weighted geometric mean.

Given the methodological limitations of individual financial inclusion indicators, the literature increasingly supports multidimensional approaches that more accurately capture the complexity of financial inclusion [43]. The adopted framework builds on the conceptual foundation introduced by Clamara et al. [216], who were among the first to apply PCA in developing composite indices for financial inclusion, effectively capturing the multidimensional structure of financial services across developing economies.

The methodological approach of this study aligns with a growing body of empirical literature, including the contributions of Yakubu et al. [157], Boachie and Adu-Darko [41], Odame et al. [12], Hussain et al. [96], Ndombi Avouba et al. [42], Boachie et al. [99], Ugwuanyi et al. [43], Ozili et al. [158], Adedokun et al. [100], Nguyen [92], and Allen et al. [159].

NAT refers to the total natural resource rents as a percentage of GDP. This variable represents the aggregate rents derived from natural resources, including oil rents, natural gas rents, coal rents (both hard and soft), mineral rents, and forest rents. It is employed as a key independent variable to examine its impact on economic growth, supported by a substantial body of empirical literature [83, 88, 113, 160, 217].

We incorporate the Composite Governance Index as a moderating variable, estimated using data from the Worldwide Governance Indicators (WGI), in line with Basnayake et al. [84]. Governance (GOV) is thus employed as a moderating factor, enabling a nuanced analysis of how governance conditions shape the relationship between financial inclusion and resource rents in promoting economic growth.

3.2.3 Control variables

In identifying the control variables, we relied on applied empirical literature to select key macroeconomic indicators widely acknowledged for their influence on economic growth. These variables are included to mitigate the risk of omitted variable bias and to account for potential confounding factors in the relationship between the independent and dependent variables. Inflation (INF) is included as a proxy for macroeconomic stability, reflecting the general price level trends in the economy [161–164]. Trade Openness (TR), measured as the sum of exports and imports relative to GDP, promotes growth by facilitating market access, enhancing competition, and encouraging innovation [165–168].

Domestic Credit to the Private Sector (DCPS) captures the role of credit in supporting economic activity and fostering growth [169–173]. School Enrollment (SE), represented by primary school enrollment (% of total enrollment), serves as a proxy for early-stage education and human capital development [11, 174–176, 218]. Urban Population (URB), measured by the share of the urban population in the total population, is recognized as a key factor in economic development and aligns with the monitoring of progress toward Sustainable Development Goals (SDGs) [162, 177–181]. Age Dependency Ratio (AGED), reflecting the proportion of dependents to the working-age population, influences both societal dynamics and economic performance [86, 182–185].

All variables in the study are transformed into their natural logarithmic forms, except for the Financial Inclusion index (FIG) and the Governance index (GOV), which remain in their original forms. A detailed description of all variables is provided in Table 1.

Table 1

Table 1. Variables description.

3.3 Empirical model

To estimate the impact of financial inclusion, natural resource rents, governance, and their interactions on economic growth, the following econometric models are proposed:

$\begin{array}{l}RGD{P}_{i,t}={\propto }_0+{\propto }_{1}FI{G}_{i,t}+{\propto }_2{FI{G}^2}_{i,t}+{\propto }_{3}NA{T}_{i,t}+{\propto }_4\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}GO{V}_{i,t}+{\propto }_5{(NAT}_{i,t}^{*}GO{V}_{i,t})+{\sum }_{k=1}^k{p}_k{X}_{k,i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+u_{i,t}\dots & (1)\end{array}$

$\begin{array}{l}RGD{P}_{i,t}={\propto }_0+{\propto }_{1}FI{G}_{i,t}+{\propto }_2{FI{G}^2}_{i,t}+{\propto }_{3}NA{T}_{i,t}+{\propto }_4\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}GO{V}_{i,t}+{\propto }_5{(NAT}_{i,t}^{*}GO{V}_{i,t}^{*}FI{G}_{i,t})+{\sum }_{k=1}^k{p}_k{X}_{k,i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+u_{i,t}\dots .& (2)\end{array}$

$\begin{array}{l}RGD{P}_{i,t}={\propto }_0+{\propto }_{1}FI{G}_{i,t}+{\propto }_2{FI{G}^2}_{i,t}+{\propto }_{3}NA{T}_{i,t}+{\propto }_4\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}GO{V}_{i,t}+{\propto }_5{(NAT}_{i,t}^{*}FI{G}_{i,t})+{\sum }_{k=1}^k{p}_k{X}_{k,i,t}\\ \hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}\hspace{1em}+u_{i,t}\dots .& (3)\end{array}$

These empirical models build upon the theoretical frameworks proposed by Khan et al. [33] and Adabor and Mishra [11] to investigate the triangular interaction between financial inclusion (FIG), natural resource rents (NAT), and governance (GOV) in shaping economic growth (RGDP). The model specification considers FIG and NAT as the primary explanatory variables, with GOV serving as a key institutional moderator. To control for other macroeconomic determinants of growth, the model also incorporates a vector of control variables, X_{k, i, t} including inflation, trade openness, domestic credit, primary school enrollment, urbanization, and age dependency, as illustrated in Figure 1.

Figure 1

Figure 1. Conceptual framework.

This approach is empirically grounded in two main strands of the literature: (1) the non-linear relationship between financial inclusion and economic growth, as emphasized by Siddiki and Bala-Keffi [87]; and (2) the conditional nature of the resource–growth nexus, often referred to as the “Resource Blessing Hypothesis” [67, 122, 124].

The impact of financial inclusion on economic growth remains a pivotal issue in development economics. While extensive research exists, the non-linear dynamics of this relationship—particularly across heterogeneous institutional contexts and resource endowments in Sub-Saharan Africa—remain underexplored. Building on Mwita et al. [186], Ullah et al. [187], and Ndombi Avouba et al. [42], this study introduces a quadratic term of financial inclusion in Equations 1–3 to test for a U-shaped relationship, following the empirical strategy of Environmental Kuznets Curve analyses [188]. The relationship's form depends on the coefficients ∞1 and ∞₂: ∞1 > 0 and ∞₂ < 0 indicate an inverted U, whereas ∞1 < 0 and ∞₂ > 0 indicate a U-shaped curve. The analysis incorporates interaction effects and panel data from 21 Sub-Saharan African countries over 2014–2022.

Figure 2 presents an exploratory analysis of the financial inclusion–economic growth nexus, revealing a pronounced U-shaped pattern, where growth declines at low inclusion levels but rises sharply beyond the 0.51 turning point, highlighting the relationship's non-linear nature.

Figure 2

Figure 2. U-shaped relationship between financial inclusion and economic growth in SSA countries.

4 Evidence of empirical investigation and result discussion

4.1 Preliminary analysis

The study begins with a comprehensive set of preliminary analyses, including descriptive statistics, correlation matrix evaluation, multicollinearity diagnostics through the Variance Inflation Factor (VIF), slope homogeneity testing, cross-sectional dependence assessment, and panel unit root tests. These diagnostic checks ensure the validity and suitability of the data for subsequent econometric modeling.

Table 2 presents descriptive statistics, providing essential measures of central tendency and dispersion for the variables during the period 2014–2022, as emphasized by Ampofo et al. [32]. While most variables exhibit acceptable levels of skewness and kurtosis, Ln_INF and Ln_NAT notably deviate from normality, characterized by heavy-tailed distributions. Furthermore, the Jarque-Bera test confirms the presence of non-normality in several variables, thereby justifying the use of nonparametric methods.

Table 2

Table 2. Descriptive statistics.

Figure 3 illustrates the correlation matrix, capturing the interrelationships among the study variables. Financial inclusion (FIG), natural resource rents (Ln_NAT), inflation (Ln_INF), urbanization (Ln_URB), domestic credit to the private sector (Ln_DCPS), and age dependency (Ln_AGED) are positively correlated with economic growth (Ln_RGDP). Conversely, governance (GOV), trade openness (Ln_TR), and primary school enrollment (Ln_ENRSP) exhibit negative correlations with economic growth. Although these relationships are not indicative of strong multicollinearity, their significance and direction will be further investigated through regression analysis.

Figure 3

Figure 3. Correlation matrix.

Figure 4 (scatter plots) indicates the presence of non-linear relationships between economic growth and financial inclusion, natural resource rents, and governance, emphasizing the relevance of non-linear models in capturing these complex interactions.

Figure 4

Figure 4. Overview of the relationship between FIG (a), Ln_NAT (b), GOV (c), and Ln_RGDP.

To more precisely assess multicollinearity, VIF values are computed, following the guidance of Zhang et al. [189]. The values range from 1.25 to 6.04, with a mean of 3.16—all well below the conventional threshold of 10. These results affirm that multicollinearity is not a critical issue, thereby reinforcing the reliability of the coefficient estimates (see Table 3).

Table 3

Table 3. Variance Inflated Factor (VIF) results.

The results of the slope homogeneity test presented in Table 4, following the procedure proposed by Blomquist and Westerlund [190], yield p-values greater than the 0.05 level. This indicates that the null hypothesis of slope homogeneity cannot be rejected, suggesting that the slope coefficients are statistically homogeneous across the panel units.

Table 4

Table 4. Slope homogeneity analysis.

Before proceeding with the panel regression, the study evaluates both cross-sectional dependence (CD) and the stationarity properties of the variables, in line with the methodology suggested by Ike et al. [191]. CD tests are particularly pertinent in panels characterized by a large cross-sectional dimension (N) and a relatively short time span (T), as emphasized by Jeon [192] and De Hoyos and Sarafidis [193]. As noted by Khan et al. [194], addressing cross-sectional dependence is fundamental to ensuring unbiased parameter estimates, valid standard errors, and robust regression inferences.

The CD test outcomes, presented both in aggregate form (Table 5a) and for individual variables (Table 5b), show p-values below the 0.01 significance level. This leads to the rejection of the null hypothesis at the 99% confidence level and confirms the presence of substantial cross-sectional dependence in the dataset. Accordingly, the study adopts second-generation panel unit root tests, as recommended by Wang et al. [195].

Table 5a

Table 5a. Cross-sectional dependency test.

Table 5b

Table 5b. Cross-sectional dependency test.

Table 6 reports the results of the CIPS unit root test. The variables Ln_RGDP, GOV, Ln_TR, and Ln_URB are found to be stationary at levels [I(0)], whereas ΔFIG, ΔLn_NAT, ΔLn_INF, ΔLn_DCPS, ΔLn_ENRSP, and ΔLn_AGED become stationary after first differencing [I(1)].

Table 6

Table 6. Unit root analysis.

Considering potential issues of slope heterogeneity and endogeneity, this study employs the System Generalized Method of Moments (GMM), a dynamic panel data estimation technique [196]. Using this approach, three different regression models are estimated, and the corresponding results are presented in Tables 4, 5a.

4.2 Dynamic SGMM results

The dynamic System GMM estimation results presented in Tables 7a and 7b reveal the effects of economic and institutional variables—as well as their interaction terms—on economic growth (Ln_RGDP) within a dynamic panel data context. Table 7a shows the comprehensive (full) model specification, while Table 7b provides more parsimonious (reduced-form) model versions with a streamlined set of explanatory variables.

Table 7a

Table 7a. System GMM estimation—full specification.

Table 7b

Table 7b. System GMM estimation—reduced specification.

The estimation results from the dynamic System GMM model in Table 7a, across all three specifications, demonstrate a statistically significant relationship between economic growth and various economic and institutional determinants, particularly emphasizing the interactions among natural resource rents, governance quality, and financial inclusion. In all models, the lagged real GDP variable [Ln_RGDP(-1)] is positive and statistically significant at the 1% level, indicating strong dynamic persistence in economic growth over time.

Regarding financial inclusion (FIG) and its squared term (FIG²), the estimated coefficients are not statistically significant at conventional levels, suggesting that the effect of financial inclusion on economic growth may be indirect or conditional on specific contextual or institutional factors. Nonetheless, the consistently positive coefficient of the squared term across all models provides empirical support for a non-linear relationship. This pattern implies that the growth-enhancing impact of financial inclusion becomes more pronounced after a certain threshold of financial inclusion is reached.

As for natural resource rents (Ln_Nat), the results consistently show a negative and statistically significant effect across all model specifications, reflecting the well-documented “resource curse” phenomenon. This suggests that an abundance of natural resources hinders economic growth. These results support the findings of Mumuni and Mwimba [81] and Ofori and Grechyna [79], and align with the theoretical framework by Sachs and Warner [197], which associates resource abundance with diminished economic performance. However, the inclusion of interaction terms provides a more nuanced understanding of this relationship.

In Model 1, the interaction term between resource rents and governance (Inter) has a positive and statistically significant effect at the 5% level, indicating that improved governance effectively turns the usually adverse impact of resource rents on economic growth into a positive outcome.

Our findings are consistent with the empirical evidence of Chung and Jin [126] and Adabor [128]. This outcome also corroborates the results of Hemed and Albiman [124], who demonstrated that institutional quality significantly amplified the growth-enhancing effects of mineral resources in a panel of 46 Sub-Saharan African countries (1998–2018). Similarly, our results align with Adebanjo and Adeoye [115], who found that improved transparency mitigated the adverse effects of resource wealth and supported stronger economic outcomes across SSA. Collectively, these insights reinforce the institutional quality hypothesis, underscoring governance as a pivotal factor in overcoming the resource curse [54, 198, 199].

In Model 2, the interaction between financial inclusion and resource rents reveals a positive and statistically significant effect at the 1% level. This highlights the pivotal role of strengthening financial inclusion as an effective mechanism for channeling natural resource revenues into sustainable economic growth. Our findings are consistent with those of Adabor et al. [200] and Huang and Meng [114], who emphasize the importance of financial inclusion in enhancing the developmental gains from resource wealth. However, our findings contrast with those reported by Khan et al. [194], Bosah et al. [14], and Li and Wu [88].

In Model 3, the triple interaction among resource rents, governance quality, and financial inclusion remains positive and statistically significant at the 5% level. This suggests that the combined integration of these factors produces an effect exceeding the individual impact of each variable.

Several control variables perform as expected according to theory and prior literature. Specifically, inflation (Ln_INF) exhibits a partially significant negative effect in Model 2, emphasizing its potential destabilizing impact on economic stability. Trade openness (Ln_TR) consistently demonstrates a positive and statistically significant influence across all models, reaffirming its key role as a driver of economic growth. Conversely, domestic credit to the private sector (Ln_DCPS) shows a negative and significant effect in Models 2 and 3, possibly reflecting inefficiencies in financial resource allocation within the studied economies.

Table 7b, which presents more parsimonious model specifications, indicates that the lagged dependent variable remains statistically significant and robust across all estimations. The non-linear relationship between financial inclusion and economic growth becomes more pronounced, with FIG exhibiting a negative coefficient, while FIG² is positive and statistically significant. These findings confirm the presence of a U-shaped relationship between financial inclusion and economic growth. Specifically, at lower levels of financial inclusion, economic growth appears limited or even hindered; however, beyond a certain threshold, higher financial inclusion serves as a strong catalyst for sustained economic expansion.

Thus, our findings are broadly in line with those of Ullah et al. [187], who found a U-shaped relationship between financial development and economic growth or volatility. This pattern was observed in both developing and developed countries, moderated by regulatory quality. Similarly, our results align with Ndombi Avouba et al. [42], who explored this dynamic in the WAEMU region. Their study, covering the 2014–2018 period, identified a U-shaped link between banking service penetration and growth. However, our findings differ from those of Eshun and Kočenda [46], who reported that while financial inclusion is economically beneficial, it initially contributes to environmental degradation. Nevertheless, they observed an inverted U-shaped relationship, whereby beyond a certain threshold, further financial inclusion enhances environmental sustainability—a pattern that is also reflected in the estimated effects on aggregate output.

Along similar lines, Mwita et al. [186] found a non-linear, inverted U-shaped relationship in 12 SADC countries, where financial inclusion initially increases carbon intensity but subsequently reduces emissions through improved access to clean technologies and green finance.

Taken together, these findings underscore the presence of non-linear relationships in financial sector–growth dynamics. In general, these results align with extensive prior research emphasizing financial inclusion's pivotal role in fostering economic growth, especially in Sub-Saharan Africa [13, 14, 43, 45, 99, 100].

Meanwhile, resource rents consistently exhibit a negative and statistically significant effect, whereas governance remains statistically insignificant in its direct form. Nevertheless, the interaction term (Inter) retains its positive and significant effect, reinforcing the idea that the combined influence of resource rents, governance, and financial inclusion has a more pronounced impact on economic growth than each variable individually.

Model validity checks—including AR(1), AR(2), Hansen, and Sargan tests—confirm the appropriateness of using the System GMM (SGMM) method. In Tables 7a, 7b, the AR(1) test produces statistically significant results, while AR(2) is not significant, suggesting no second-order autocorrelation. Furthermore, Hansen and Sargan tests show no over-identification of instruments, reinforcing the consistency and reliability of the SGMM estimators.

In summary, the findings demonstrate that economic growth is significantly shaped by both economic and institutional factors, especially through the intricate interplay among natural resource rents, governance quality, and financial inclusion. This study addresses a critical gap in the literature by emphasizing the vital importance of harmonizing institutional and financial policies to promote prudent and sustainable management of resource revenues. Such alignment is crucial for transforming natural resource rents from a potential constraint into a catalyst for sustained economic growth.

4.3 Panel QQR results

The empirical findings presented in Tables 7a, 7b demonstrate that financial inclusion positively influences economic growth when represented in its squared form, indicating a non-linear relationship between the two variables. To comprehensively capture this non-linear heterogeneity across different quantiles, the Panel Quantile-on-Quantile Regression (QQR) methodology is employed, with the results illustrated in Figure 4.

Figure 5 illustrates the panel QQR results across three parts (a–c), presenting 3D surfaces that capture the relationships between financial inclusion, natural resource rents, governance, and economic growth in 21 SSA countries from 2014 to 2022. The X-axis denotes the quantiles of the independent variable, the Y-axis represents the quantiles of the dependent variable, and the Z-axis displays the estimated coefficients. The color bar encodes coefficient magnitude, ranging from dark blue (low or negative effects) to dark red (high or positive effects), thereby highlighting peaks and troughs and providing a clear, nuanced understanding of the dynamic interactions among the examined variables.

Figure 5

Figure 5. Graphical representation of the panel Quantile-on-Quantile Regression (QQR) estimates (a–c).

Figure 5a illustrates the results of the Panel Quantile-on-Quantile Regression (Panel QQR), revealing a non-linear and heterogeneous relationship between financial inclusion (FIG) and economic growth (Ln RGDP). At very low quantiles—particularly at (0.05, 0.05)—the effect of financial inclusion on economic growth is negative. As financial inclusion increases to low and moderate levels (starting from the 0.2 quantile), its impact becomes progressively positive, reaching a peak of 0.124 at the (0.3, 0.05) quantiles. This pattern, consistent with SGMM results, highlights the dynamic nature of the relationship. Conversely, at higher quantiles, the effect becomes negative again, suggesting diminishing returns. This finding is broadly consistent with the results of Tabash et al. [201], who demonstrated that the positive impact of financial inclusion on economic growth, poverty reduction, and inequality reaches its optimum at a 42% threshold. Beyond this level, further expansion in financial inclusion may yield diminishing or even adverse effects. These insights are also aligned with SDG 8, which advocates for inclusive and sustainable economic growth and decent work for all.

The findings of our study are consistent with a growing body of literature emphasizing the role of financial inclusion and financial technology in fostering sustainable economic development [17, 85, 86, 93–97, 109, 202, 203]. For instance, Choudhary et al. [202] highlighted that financial inclusion and fintech contribute significantly to GDP growth, innovation, and infrastructure development, thereby supporting SDGs 2, 4, 8, and 9. Similarly, Daud and Trinugroho [95] linked these factors to broader financial access and enhanced environmental sustainability. Zehri et al. [97] underscored the importance of digital finance in promoting decent work and economic expansion, particularly in high-income countries (SDG 8). Meanwhile, Hussain et al. [96] emphasized the combined impact of financial inclusion and information and communication technologies (ICT) in driving sustainable development across developing nations, with a notable focus on achieving SDG 8.

Figure 5b presents results from the Panel Quantile-on-Quantile Regression model, revealing a non-linear and heterogeneous relationship between natural resource rents (Ln NAT) and economic growth (Ln RGDP). The impact of resource rents varies across quantiles: at lower growth quantiles, the effect is weak or negative, supporting the resource curse hypothesis; conversely, at higher growth quantiles, resource rents exert a more positive influence, reflecting improved resource management in relatively developed economies. These findings underscore the critical role of institutional quality, governance, and economic structure in shaping growth trajectories. Furthermore, they align with Sustainable Development Goal 12 (SDG 12), which advocates for responsible consumption and production. Realizing this goal necessitates prudent management of resource revenues, reduced reliance on non-renewable resources, and a strategic transition toward sustainable economic models [2, 32, 81, 204].

Figure 5c illustrates that governance positively impacts economic growth across all quantiles, though the magnitude of this effect varies with governance levels. The strongest effect occurs at higher quantiles, peaking at 0.73 at the (0.85, 0.05) quantile. The effect is moderate at lower quantiles, as indicated by the green, yellow, and orange areas, and declines at middle quantiles, ranging from 0.19 to 0.35, as shown by the dark blue region. These findings underscore that good governance is essential for sustainable growth, aligning with SDG 16′s goal of establishing effective, accountable, and inclusive institutions.

Our findings align with previous empirical research [58, 185, 205, 219]. Omri and Omri [219] emphasized that governance dimensions—transparency, accountability, and the rule of law—alongside foreign direct investment (FDI), complemented natural resource rents in fostering sustainable development. Kuttu et al. [205] demonstrated a strong link between reduced corruption, improved taxation policies, and enhanced sustainable development across African sectors. According to Mondjeli et al. [125], understanding how corruption affects the relationship between natural resources and growth has profound implications for achieving the Sustainable Development Goals (SDGs) in Sub-Saharan Africa.

Following the empirical literature [148, 206, 207], we validate the robustness and reliability of the proposed Panel QQR model by benchmarking its findings against those from the conventional Quantile Regression (QR) framework. Figure 6 presents the comparison across three panels (a–c), with blue dotted lines representing the Panel QQR estimates and red dotted lines depicting the Panel QR estimates. It is essential that the average slope coefficients from Panel QQR closely align with those from QR, confirming the validity of the results. Trend analysis reveals strong concordance in panels (a) and (c) and moderate alignment in panel (b). Importantly, even with minor deviations, Panel QQR captures non-linear and distribution-dependent effects, demonstrating its clear methodological superiority over standard QR.

Figure 6

Figure 6. Comparison of panel QQR and panel QR estimates (a–c).

5 Conclusion and policy recommendations

Policymakers increasingly acknowledge financial inclusion as a vital catalyst for poverty reduction and economic growth, especially in Sub-Saharan Africa (SSA) [14, 45, 46, 208]. Despite consensus on adopting sustainable financial systems and effective resource governance, the link between financial inclusion, natural resource rents, and economic growth in SSA remains underexplored. Understanding additional growth drivers beyond natural capital is crucial, with financial inclusion emerging as a key—yet non-linear—factor [42].

This study provides a comprehensive examination of the nexus between financial inclusion, natural resource rents, and economic growth across 21 Sub-Saharan African countries, using data from 2014 to 2022. The analysis employs both the System GMM and the Panel Quantile-on-Quantile Regression (Panel QQR) methods.

The SGMM estimation results confirm the existence of a U-shaped relationship between financial inclusion and economic growth in the SSA region, under various interaction effects that are consistently positive and statistically significant across all models. These findings underscore the moderating role of governance and the critical contribution of financial inclusion in transforming natural resource dependence from a developmental obstacle into a catalyst for sustainable growth. Overall, the results emphasize the presence of non-linear dynamics in the relationship between the financial sector and economic performance, and are broadly consistent with the growing body of literature that highlights the pivotal role of financial inclusion in fostering long-term, inclusive, and sustainable economic development.

The Panel Quantile-on-Quantile Regression (Panel QQR) results reveal a non-linear and heterogeneous relationship between financial inclusion and economic growth across different quantiles. Financial inclusion has a stronger positive influence on growth at moderate quantiles, suggesting that a moderate level of financial access promotes economic expansion. However, at very low and very high quantiles, the effect turns negative, indicating that insufficient financial access or excessive inclusion may hinder growth. Similarly, the effect of natural resource rents on economic growth varies across quantiles. At lower growth quantiles, the impact is negative, which supports the resource curse hypothesis; whereas at higher quantiles, the effect becomes positive, reflecting more efficient resource utilization and gradual improvements in institutional quality in several countries in the region. In parallel, governance exerts a consistently positive influence on economic growth across all quantiles, with its magnitude increasing at higher quantiles, highlighting the pivotal role of effective governance in fostering sustainable economic development.

This outcome aligns closely with the Sustainable Development Goals (SDGs), particularly Goal 8 (Decent Work and Economic Growth), Goal 12 (Responsible Consumption and Production), and Goal 16 (Peace, Justice, and Strong Institutions).

The study's findings yield a set of strategic policy recommendations critical for advancing inclusive and sustainable economic growth across Sub-Saharan Africa. First, financial inclusion must be promoted through a calibrated and progressive approach—eschewing both underdevelopment and overextension—by investing in digital infrastructure and ensuring equitable access to financial services for marginalized and vulnerable populations, particularly women and youth. This must occur within a robust regulatory framework that fosters innovation and inclusive finance. Second, the transparent and accountable management of natural resource revenues is imperative. Third, reinforcing institutional quality, upholding good governance standards, and intensifying anti-corruption efforts are indispensable to achieving fair wealth distribution and comprehensive development outcomes. These policy directions must be coherently aligned with the Sustainable Development Goals (SDGs), extending beyond economic and institutional dimensions (Goals 8, 12, and 16) to encompass environmental sustainability (Goal 13: Climate Action; Goal 15: Life on Land) and social equity (Goal 10: Reduced Inequalities). Collectively, these actions are vital to securing a more just, inclusive, and resilient growth trajectory amid evolving global and regional challenges.

Data availability statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Requests to access these datasets should be directed to Yy50aWRqYW5pQGNyZWFkLmR6.

Author contributions

AM: Conceptualization, Data curation, Investigation, Methodology, Project administration, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing. CT: Conceptualization, Formal analysis, Investigation, Methodology, Resources, Validation, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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Appendix

Table A1

Table A1. List of panel countries.