Nations endowed with abundant natural resources are often assumed to experience faster and more sustained development than resource-scarce countries. However, a paradox arises in many mineral-rich countries, particularly those classified as low, lower-middle, and middle income, that continue to face widespread poverty, poor health outcomes, and significant deficiencies in living conditions ([8]). This contradiction is commonly described as the natural resource curse, a phenomenon in which countries rich in natural resources tend to grow more slowly than their resource-poor counterparts ([7]; [40]). Conversely, several countries with limited resource endowments have achieved remarkable economic success. In rare cases, some resource-rich nations have managed to avoid the curse and benefit from what is known as the resource blessing. A notable example is Botswana, which progressed from middle-income to upper-middle-income status by adopting strategic policies to develop its mining sector, invest in education, and diversify its economy ([43]).

One of the primary factors contributing to inadequate economic growth and the persistence of the resource curse in resource-rich countries is the systematic export of raw natural resources from low and lower-middle-income countries to high-income economies. This process significantly benefits the latter due to their advanced manufacturing capacities and sustained industrial development ([35]), while also fueling global demand for natural resources ([37]).

Over the past three decades, both scholars and policymakers have paid significant attention to the relationship between natural resource rents and economic growth. Two key concepts—resource abundance and resource dependence—have been extensively examined in the literature ([4]; [10]; [11]; [12]; [18]). Many studies argue that nations with abundant mineral resources have struggled to achieve sustainable development ([5]; [8]; [27]). Conversely, other research suggests that natural resources can indeed support economic growth, provided a country has a strong political economy and high-quality institutions ([3]; [11]). Some studies ([9]; [26]) assert that natural resources positively contribute to economic growth. However, others find that in the world’s wealthiest and most resource-rich countries, an increase in total natural resources often correlates with reduced economic growth ([33]). According to [2], there is a two-way causal relationship between natural resource exploitation and economic growth in Iran, with resource extraction having a detrimental effect. Similarly, [10] suggest that resource revenues negatively impact financial development in countries with weak political institutions, but not in those with stronger governance frameworks.

Recently, spatial econometric methods have gained traction in the resource curse literature, recognising that economic outcomes in one country may be influenced by those in neighbouring countries through trade, investment, or policy diffusion. In a pioneering study, [36] applied spatial autoregressive (SAR), spatial error (SEM), and spatial Durbin (SDM) models to cross-sectional data from 53 African countries. Their results confirmed the persistence of the resource curse and highlighted the importance of governance, human capital, and trade openness. However, their analysis was limited to a single time period and could not account for unobserved heterogeneity across countries or temporal dynamics. The present study builds on and extends the work of [36] by employing spatial panel data models over the period 2005-2024, thereby controlling for both spatial dependence and country-specific effects, and by providing sub-period and regional analyses that reveal how the resource-growth relationship has evolved.

The novel contribution of this study is threefold. First, it extends the analysis to a panel framework covering two decades (2005-2024), allowing it to capture both cross-sectional and temporal variations. Second, it employs spatial panel econometric models (SAR, SEM, SDM) to account for spatial dependence across countries—an approach rarely used in previous research. Third, it provides a sub-period analysis and a regional comparison (ECOWAS vs SADC) to assess the heterogeneity of the resource-growth relationship.

Furthermore, Sub-Saharan Africa continues to grapple with structural challenges including rapid population growth, underdeveloped infrastructure, and rising social inequality ([39]), all of which may limit the continent’s capacity to fully benefit from its natural wealth.

Within this context, the present study aims to examine the determinants of GDP per capita across 54 African nations over the 2005-2024 period. The goal is to assess the relative impact of natural resource endowments, governance quality, human capital, and trade openness on economic performance by leveraging recent data and spatial panel methods. The findings are expected to provide valuable insights for policymakers and development practitioners in the region.

There has been much debate on how Africa’s economic progress is impacted by its natural resources. Some studies emphasize that resource abundance can be an engine of growth by providing revenues that support public investment, diversification, and improved living standards. For instance, [9] argued that natural resource rents contribute positively to economic growth. Successful examples such as Botswana demonstrate how natural wealth can be transformed into sustainable development outcomes when accompanied by good governance, economic diversification strategies, and investments in human capital and education ([13]). Similarly, research by [3] and [11] suggests that natural resources can indeed support growth when countries benefit from strong political economies and robust institutional frameworks.

Conversely, a large body of literature points to the so-called resource curse, where countries with abundant natural resources experience slower economic growth than resource-poor nations ([23]; [34]). Several mechanisms explain this paradox. One is Dutch disease, where booming resource revenues appreciate the real exchange rate, eroding competitiveness in other sectors such as manufacturing and agriculture ([15]; [41]). Others stress how resource rents encourage rent-seeking, corruption, and weak institutional development, which in turn hampers economic performance ([14]). Resource abundance has also been closely associated with conflict, particularly over high-value commodities such as diamonds and gold, which have fueled civil wars in several African countries ([14]; [32]).

Moreover, the uneven geographic distribution of natural resources often creates enclave economies characterized by spatial inequalities, where revenues accrue to national elites and multinational corporations, while local populations gain few benefits ([16]; [21]; [30]; [31]; [42]).

Other studies adopt a more nuanced perspective, emphasizing that outcomes are not predetermined but depend on institutional quality, governance, and policy decisions. For instance, [12] argues that resource abundance in itself is not harmful; it is resource dependence combined with weak institutions that leads to adverse effects. Likewise, [41] highlights the role of political economy factors in shaping outcomes.

Empirical analyses also show that resource wealth may generate positive or negative effects depending on the broader development strategy pursued. Unequal spatial distribution of resources can exacerbate disparities in income, infrastructure, and social services. Case studies such as Zambia ([22]), Nigeria ([42]), and Angola ([25]) provide further evidence of how institutional frameworks and governance quality condition the impact of resources on growth.

A relatively recent development in the literature is the recognition that economic outcomes in one country may be influenced by those in neighbouring countries. Spatial spillovers can arise through trade, investment, migration, or the diffusion of policies and technologies. Ignoring such interdependencies may lead to biased estimates and incomplete policy conclusions.

Several studies have applied spatial econometric methods to examine the resource-growth relationship. For instance, [4] use panel cointegration techniques but do not explicitly model spatial dependence. In the African context, [36] provide the first systematic application of spatial autoregressive (SAR), spatial error (SEM), and spatial Durbin (SDM) models to cross-sectional data from 53 African countries. Their results reveal significant spatial dependence in both the dependent variable and the error term, and highlight the importance of governance and human capital. However, their cross-sectional design cannot account for unobserved heterogeneity or temporal dynamics.

The present study extends this line of inquiry by employing spatial panel data models over the period 2005-2024. This approach allows us to control for country fixed effects, capture temporal variation, and decompose direct and indirect effects. In doing so, we provide a more nuanced understanding of how natural resources, governance, and other factors interact across space and time to shape economic development in Africa.

Recent panel data studies have enriched this literature by exploiting both cross-country and temporal variations. For example, [4] use panel cointegration techniques to examine the resource-growth nexus in African countries. However, most panel studies have overlooked the spatial dimension, assuming that observations are independent across countries—a strong assumption given the evidence of spatial spillovers in economic development.

In summary, the literature indicates that the relationship between natural resources and economic development in Africa is highly complex. While in some contexts resources have supported growth and prosperity, in others they have reinforced poverty, inequality, and conflict. Ultimately, governance, institutional quality, human capital, and the spatial distribution of resources play a decisive role in determining whether resource abundance becomes a blessing or a curse.

The primary objective of this research was to identify the determinants of per capita income in Africa over the 2005-2024 period, while explicitly accounting for spatial dependence and unobserved heterogeneity. Our analysis reveals that the Spatial Durbin Model (SDM) provides the best fit for the data, based on AIC/BIC criteria and its ability to capture both direct effects and spatial spillovers.

A central result concerns the role of natural resources. The coefficient on natural resource rents (log TNRR) is negative and statistically significant in all spatial specifications, confirming the persistence of the “resource curse” across African countries. This finding aligns with [24], who highlight the risks of rent-seeking, Dutch disease, and institutional weakening associated with natural wealth. Importantly, the sub-period analysis reveals that this negative effect has intensified in the more recent period (2015-2024), suggesting that the resource curse has become more pronounced, possibly due to increased commodity price volatility or governance challenges.

Governance emerges as a critical determinant of economic performance. The positive and significant effect of Overall Governance on GDP per capita in all specifications confirms that institutional quality is essential for fostering development. This finding is consistent with [20], who argue that sound institutions mitigate the adverse effects of resource rents and enable countries to channel them into productive uses. The negative spatial lag of governance in the SDM model suggests competition effects: better governance in neighboring countries may attract investment away from the domestic economy, highlighting the need for regional coordination in governance improvements.

Human capital shows mixed results across specifications. While it is not significant in the SAR and SEM models, it becomes marginally significant in the SDM direct effect and positive in the sub-period analysis for 2015-2024. This aligns with [1] and [38], who emphasize the role of quality education and health for long-term growth. The lack of strong significance may reflect the slow-changing nature of human capital and the difficulty of capturing its effects in panel data with country fixed effects.

Population size exhibits a complex pattern. In non-spatial models and basic spatial models, population has a positive effect, possibly reflecting market size effects. However, in the SDM specification, the direct effect becomes negative, suggesting that once spatial spillovers are controlled for, domestic population growth exerts downward pressure on per capita income. This is consistent with [17], who argues that population can have negative short-term effects if not matched by productive investments. The positive spatial lag of population indicates that larger populations in neighboring countries generate demand that benefits domestic producers, pointing to regional market integration as a potential buffer against domestic demographic pressures.

Gross fixed capital formation shows a positive and significant effect in all specifications, contrary to the negative coefficient found in cross-sectional OLS analyses. This highlights the importance of panel data for controlling for unobserved heterogeneity and suggests that when country-specific factors are accounted for, investment does contribute positively to growth. The positive spatial lag of investment in the SDM model indicates spillover effects, possibly through regional infrastructure or value chains.

Exports consistently exhibit strong positive effects across all models, confirming that trade openness and integration into global markets foster economic growth. This finding underscores the importance of export diversification and trade facilitation policies for African development.

The spatial parameters are positive and significant in all models, confirming the presence of spatial dependence in African economic development. The increase in spatial dependence over time suggests growing economic integration or contagion effects across the continent. This finding has important policy implications: national development strategies cannot be designed in isolation; regional coordination and cooperation are essential.

The regional comparison reveals important heterogeneity:

In ECOWAS, governance plays an even stronger role, while human capital’s negative coefficient is puzzling and warrants further investigation (possibly due to poor education quality or brain drain). The resource curse is not statistically significant in this region, possibly because resource wealth is more diversified or because institutional weaknesses are more severe, masking any direct effect. In SADC, the resource curse is clearly present, and human capital, investment, and exports are all powerful drivers of growth. The negative effect of population suggests that demographic pressures are more acute in this region, despite its higher income levels.

These regional differences highlight the need for tailored policy approaches. In West Africa, strengthening governance and regional market integration should be priorities. In Southern Africa, managing demographic pressures, investing in human capital, and promoting export diversification are critical.

This study investigates the nexus between natural resource rents and economic development across 54 African countries from 2005 to 2024, employing spatial panel econometric models to account for cross-country interdependencies. The results confirm the persistence of the resource curse, while also revealing the crucial roles of governance, human capital, and regional spillovers in shaping this relationship. Our analysis yields several important findings.

First, natural resource rents exert a significant negative effect on GDP per capita, confirming the persistence of the resource curse across the continent. This negative effect has strengthened in the more recent period (2015-2024), suggesting that the challenges associated with resource dependence have intensified. Second, governance, exports, and gross fixed capital formation consistently show positive effects, underscoring the importance of institutional quality, trade openness, and productive investment for economic development. Third, spatial dependence is significant and has increased over time, indicating growing economic integration or contagion effects across African countries. The Spatial Durbin Model (SDM) emerges as the preferred specification, revealing complex spillover patterns: negative spillovers from governance and exports (suggesting competition) and positive spillovers from resources, population, and investment (suggesting complementarities).

The regional comparative analysis highlights stark differences between ECOWAS and SADC. In ECOWAS, governance is paramount, while human capital shows an unexpected negative association. In SADC, the resource curse is clearly present, and human capital, investment, and exports are powerful growth drivers. These findings underscore the heterogeneity of African economies and the need for region-specific policy interventions.

The Spatial Error Model (SEM) also performs well in eliminating residual spatial dependence, but the SDM’s lower AIC/BIC and richer insights into spillover mechanisms make it the preferred choice. The sub-period analysis reveals important temporal dynamics, with the resource curse intensifying and spatial dependence increasing over time.

These findings have important policy implications. First, African countries should focus on strengthening governance and institutions, as these not only directly enhance growth but also mitigate the adverse effects of resource dependence. Second, investments in human capital and productive infrastructure should be prioritized to transform resource wealth into sustainable development. Third, export diversification and trade facilitation policies are essential to harness the benefits of global integration. Fourth, regional coordination is crucial: given the significant spatial spillovers, national development strategies should be aligned with regional initiatives to maximize positive externalities and minimize competitive dynamics.

Several limitations of this study should be acknowledged. First, the imputation of missing data, while necessary for balanced panel estimation, may introduce some measurement error. Second, the governance index excludes Voice and Accountability due to data availability, potentially omitting an important dimension of institutional quality. Third, the analysis focuses on total natural resource rents without distinguishing between different types of resources (e.g., oil, minerals, forests), which may have heterogeneous effects.

Future research should explore several extensions. First, disaggregating resource rents by type could reveal whether certain resources are more prone to the curse than others. Second, incorporating additional variables such as conflict indicators, foreign direct investment, or institutional quality measures could provide a more complete picture. Third, exploring non-linear effects and threshold models could help identify conditions under which resource wealth becomes a blessing rather than a curse. Fourth, dynamic spatial panel models could shed light on the adjustment processes and long-run equilibrium relationships.

In conclusion, this study provides robust evidence that natural resources alone do not drive development in Africa. Strengthening governance, investing in human capital, promoting trade openness, and fostering regional cooperation are essential to transform Africa’s resource wealth into sustainable and inclusive economic growth.

The authors state that no funds, grants, or other financial support were received during the preparation of this manuscript. There are no declared competing interests of the authors that are relevant to the subject matter of this article.

Natural resource rents negatively affect GDP per capita in Africa.Governance and exports have strong positive effects on economic performance.Spatial spillovers are significant and have increased over time.The resource curse is more pronounced in SADC than in ECOWAS.The negative resource effect intensified after the end of the commodity super-cycle (2014).

Soma led the study conception, data curation, formal analysis, software development, visualization, and original drafting. K. Traoré, D. Traoré, and S.Y.A. Ouedraogo contributed to methodology development, formal analysis, and result validation. N.V.A. Koassi co-conceived the study, supervised the project, oversaw methodological design and validation, and handled journal correspondence. W.A. Ouédraogo co-conceived the study, provided resources, and validated findings. M. Kontiliguissonko handled data curation, investigation, and validation. All authors critically revised and approved the final manuscript.