Research Article |
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Corresponding author: Oluwafemi Adeboje ( femiadeboje@gmail.com ) Academic editor: Marina Sheresheva
© 2025 Oluwafemi Adeboje, Frank Ogbeide, Isiaka Akande Raifu.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Adeboje O, Ogbeide F, Raifu IA (2025) Modelling Financial Sector Reform and Resource Dependence Effects on Macroeconomic Stability In SSA: Re-Enacting Africa’s Quest for Long-Term Development. BRICS Journal of Economics 6(4): 119-148. https://doi.org/10.3897/brics-econ.6.e162459
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This paper examines the influence of financial sector reform on macroeconomic stability in 14 SSA countries by employing a traditional panel, dynamic panel framework, and causality tests on data from 2000 to 2021. It explores whether income groupings of the sampled countries in line with the World Bank classification matter for the outcomes of the analysis. The results suggest that financial reform policies can both induce and prevent economic instability. They increase instability in the lower-middle and upper-middle-income countries, as seen in the overall estimated dynamic panel models, but they reduce it in low-income economies. The static panel models produced similar results. It has also been shown that the rent from natural resources had uniformly damaging effects on the macroeconomic stability of all income groups in SSA, effectively confirming the “resource curse” thesis. Yet, the findings of the panel as a whole contradicted this, suggesting that revenue from natural resources can effectively play a role in stabilizing macroeconomic conditions. The results also suggest the existence of what can be called “a human capital-misery trap”, in which higher human capital development can lead to macroeconomic instability. Inflation was found to have a detrimental effect, and the impact of government interventions appeared to be mixed. This paper emphasizes the need for robust financial reforms and comprehensive policy measures in Sub-Saharan Africa (SSA), aiming to enhance the effectiveness, competitiveness, and stability of the financial sector and the broader economic landscape, which will require prudent management of natural resources.
Financial Sector Reform, Macroeconomic Stability, Natural Resource Dependence, Human Capital-Misery Trap Syndrome.
Long-term development is predicated on achieving relative sustainability, according to researchers and policy-makers alike. Most African economies rely on natural resource wealth to drive fiscal stability and development, but they are often buffeted by economic instability and uncertainty due to the inelastic nature of their export commodities on the international market. Any changes in commodity prices can lead to misalignments in the economy, potentially hindering economic development (
Moreover, as globalization has made nations relatively interdependent, most national economies are becoming increasingly susceptible to external shocks that affect their growth rate, either positively or negatively (
Sub-Saharan Africa’s average annual GDP growth is projected to increase from 3.3% in 2024 to 3.5% in 2025, and then to 4.3% during the period 2026 to 2027, driven by stronger private consumption and investment, as inflation moderates and exchange rates stabilize (
Recent data show that private banking credit in Sub-Saharan Africa increased from the early 1980s through to the 1990s. This was partly due to accelerated financial reform during 1986-1996 when numerous financial restrictions were removed, promoting access to long-term investment financing. Some scholars have recognized financial reform as an essential component of inclusive development, as it facilitates access to credit (
Numerous studies have examined the correlation between resource availability and economic growth (
This study aims to determine whether the presence of natural resources has a detrimental effect on the macroeconomic stability of Sub-Saharan African countries. This study focuses on examining the Staples theory of export-led growth, which emphasizes the significance of traditional commodities or staple products in the development of resource-rich economies (
A systematic review of recent empirical literature has not identified any study that examines directly whether a country’s income level determines the combined effect of natural resources dependence and financial sector reforms on macroeconomic stability in Sub-Saharan Africa. Recent research has closely examined related moderation channels. For example,
The paper is divided into five sections. Following this introductory section, section 2 reviews the relevant literature. Section 3 discusses the fundamental theory, methodology, and model specifications of the study. Section 4 presents the empirical results, and section 5 concludes with final remarks and policy suggestions.
Financial sector reforms have long been seen as an integral part of the policy agenda in developing countries. It was previously thought that they would improve the efficiency of resource mobilization and allocation in the real economy, leading to higher growth rates. Recent research has also considered financial reforms crucial for macroeconomic stability (
Resource abundance
Inadequate management of a single natural resource that is narrow and inelastic can lead to institutional weakness and civil conflict. It can also promote greedy rent-seeking, which often neglects poor and disadvantaged people in the economy (
In contrast,
Several studies have shown that the abundance of resources has a negative impact on economic performance. These include
In light of these findings,
Numerous studies in the field of finance and development have shown that countries with more advanced financial systems are likely to experience greater and faster increases in economic growth (
The process of liberalizing domestic financial sector has the potential to increase the depth and resilience of a country’s financial system, making it better able to withstand various types of economic shocks (
Other researchers, however, have expressed skepticism regarding the veracity of the effects of financial liberalization, arguing that they have been overemphasized.
A body of scholarly literature establishes a connection between banking crises and financial deregulation. Moreover, recent research has progressively shown a correlation between the frequency of financial crises and the increasing resolve of monetary authorities to promote liberalization within the domestic financial system. The objective of the present study is to examine the association between banking crises and policies of financial liberalisation, as explored by
The study conducted by
In their study,
Demirguc-Kunt and Detragiache (1998) and Kaminsky and Smukler (2001 and 2002), have demonstrated that domestic financial liberalization has the potential to increase economic volatility and make economies more vulnerable to external shocks. One possible explanation is that financial liberalization can lead to increased risk-taking behaviors often resulting in higher default rates on loans. This happens because financial institutions may be more willing to finance businesses that are considered risky in order to have larger profits. The findings also indicate that information asymmetries arising from market distortions reduce the allocative efficiency of financial systems, thus adversely affecting economic growth. Recent analysis of Sub-Saharan Africa has found that rapid credit expansion not supported by regulatory infrastructure can sow the seeds of banking stress, as seen in Nigeria’s boom-and-bust lending cycles. (
Financial reform has had varying effects on macroeconomic stability in different countries. Having been successful in some of them, it could have failed in others. The Indian economy has exhibited a sustained pattern of robust development. This has helped mitigate the adverse effects of South East Asian crises, and India has accumulated substantial foreign exchange reserves. It has also proactively repaid part of its external debt and implemented measures to restructure domestic debt. Similarly, sub-Saharan African economies with prudent reserve management and fiscal buffers have fared comparatively well during the process of financial sector liberalisation (
In summary, the literature provides valuable insights into the link between financial reforms, natural resource use, and economic performance. However, there is a noticeable gap in explicitly modelling these interactions within a coherent growth framework. To address this challenge, the next section outlines the theoretical framework and methodology adopted in this study.
Building on the insights from the literature review, which highlighted both the contributions and gaps in understanding how natural resources, financial reforms, and country-specific factors influence macroeconomic stability, this study now turns to the theoretical and methodological framework. To formally investigate these relationships, the analysis employs the standard Cobb–Douglas production function as the starting point. In line with the traditional Solow growth model, the following aggregate CD production function with two-factor inputs as specified in Equation 1:
where
0 < α < 1 (1)
Labour (L) and capital stock (K) as input into the production process are well known in economic theory to contribute significantly to achieving the desired level of output. In Equation (1), output (Y) denotes macroeconomic instability. We quantified macroeconomic instability by creating a misery index
We use an index derived from the financial reform database created by
To modify the CD function, we include a finance variable as an input to the production process to create output. The modelling technique is based on the idea that financial intermediation is a significant input that leads to widespread economic benefits. Financial reform policies play a crucial role in mobilization of savings within the financial sector, which enables the provision of essential credit to individuals and businesses seeking funds for investment purposes. These policies boost income and economic performance. Financial reform is added to the Comprehensive Development (CD) strategy to achieve macroeconomic stability. Equation (1) above has been modified to take into account the typical economic structures of most countries in SSA, which are broadly driven by revenue from natural resources (NR) that is used to finance growth-inducing economic activities.
Y = f (FINR, K, L, NR) (2)
Using the dynamics of K and L in a typical economy, we know that, at time t, K is determined by savings (a function of interest rate) and income from labour, whereas Labour, L, is determined by the population’s natural growth rate. Equations (3) and (4) depict the Capital and Labour, K and L, dynamics over a 2-period case.
(3)
(4)
Functional form Equation (2) is modified by introducing Capital and Labour, K and L dynamics, and our estimated model is put in a natural log-form, including an error term and a constant, which results in Equation (5):
(5)
Where InY is the extent of macroeconomic stability (captured by Misery index); In (FinRt) represents financial reform index computed from the financial reform database for the 14 Sub-Saharan African economies. We expect policies of financial reform to positively influence macroeconomic stability outcomes for included countries; In (St) is national savings growth; In (Rt) is interest-rate variables; In (Wt)is growth in wealth-related variables; In (Ht) is proxy by secondary school enrolment rate, capturing human capital development at time t; In (NRt) is rent from natural resource export. All variables except those expressed in growth rates, which are already in percentage form, are expressed in their absolute values and then transformed using natural logarithms. The present analysis uses yearly data spanning from 2000 to 2021 for a panel of 14 African nations and their corresponding income classifications, as detailed in the Appendix.
The financial reform index was derived from a financial reform database developed by
We used the GMM technique to obtain reliable results by including lagged dependent variables as regressors. Because highly biased and inconsistent OLS (ordinary least squares) can lead to unsatisfactory results, we employed both the fixed-effects model and random-effects model. The Hausman model specification test was conducted to determine the preferred model, which was then compared with system GMM estimates.
Furthermore, this study investigated the extent to which financial reform is associated with macroeconomic stability, by examining whether income levels matter. We accounted for the income effect by conducting independent panel data estimations for countries with different income levels. The sample used in the study covers three out of four income classes in the World Bank classification (2023). The list of countries and their income classes can be found in Appendix
Table
| Details | Mean | Median | Skewness | Kurtosis | Jarque-Bera | Prob. |
| Financial reform | 1.13 | 1 | 0.33 | 1.79 | 36.72 | 0.00 |
| Economic Misery Index | 15.51 | 9.46 | 3.96 | 23.4 | 9221.91 | 0.00 |
| Real Interest Rate | 12.25 | 10.6 | 4.76 | 46.2 | 37665.17 | 0.00 |
| Natural Resource Rent | 7.69 | 4.84 | 2.77 | 10.4 | 1565.6 | 0.00 |
| Inflation | 13.74 | 8.11 | 4.35 | 28.34 | 13370.41 | 0.00 |
| Government Consumption Expenditure | 13.79 | 13.25 | 0.29 | 2.49 | 11.01 | 0.00 |
| Secondary School Enrolment | 26.44 | 23.13 | 1.47 | 5.5 | 193.74 | 0.00 |
The correlations between the variables employed are reported in Table
| VARIABLES | FINR | NATR | INF | GCON | SSE | RINTR | MISR |
| Financial reform (FINR) | 1 | 0.07 | -0.21 | 0.07 | 0.43 | 0.14 | -0.12 |
| Natural Resource Rent (NATR) | 0.07 | 1 | 0.11 | -0.38 | -0.02 | -0.17 | 0.09 |
| Inflation (INF) | -0.21 | 0.11 | 1 | -0.34 | -0.04 | -0.35 | 0.25 |
| Government Consumption (GCON) | 0.07 | -0.38 | -0.34 | 1 | 0.16 | 0.36 | -0.27 |
| Secondary School Enrolment(SSE) | 0.43 | -0.02 | -0.04 | 0.16 | 1 | -0.18 | 0.14 |
| Real Interest Rate (RINTR) | 0.14 | -0.17 | -0.35 | 0.36 | -0.18 | 1 | -0.55 |
| Economic Misery (MISR) | -0.12 | 0.09 | 0.25 | -0.27 | 0.14 | -0.35 | 1 |
The Im, Pasaran, and Shin, (2003) panel unit root tests procedure was employed to examine the time series properties of the variables employed. Because it relies on average individual unit-root statistics, the IPS test can account for heterogeneity in the coefficients of the variables. The IPS method for panel data and cross-sectional analysis is unique because it allows for heterogeneity in coefficients, in contrast to
Table
| Details | IPS (2003) Stationarity Tests | Remarks | |
| Value | Prob. | ||
| MISR | -5.81 | 0.000 | I(1) |
| FINR | -6.51 | 0.000 | I(1) |
| NATR | -7.40 | 0.000 | I(1) |
| RINTR | -15.55 | 0.000 | I(1) |
| INF | -5.44 | 0.000 | I(1) |
| GCON | -3.39 | 0.004 | I(1) |
| SSE | -4.72 | 0.000 | I(1) |
The Pedroni cointegration test uses the significance of the “between” and “within” relationships to confirm the existence of a cointegrating relationship in a panel cointegration test. Table
| Categories (Statistic) | Common AR Coefficients (Within Dimension) | |||
| Statistic | Probability | Weighted Statistic | Probability | |
| Panel v | -1.12 | 0.06 | -1.70 | 0.09 |
| Panel rho | 1.00 | 0.04 | 1.74 | 0.05 |
| Panel PP | 0.84 | 0.00 | 1.94 | 0.01 |
| Panel ADF | 0.52 | 0.01 | 1.43 | 0.02 |
| Categories (Statistic) | Individual AR Coefficients (Between Dimension) | |||
| Statistic | Probability | |||
| Group rho | 1.87 | 0.06 | ||
| Group PP | 2.06 | 0.03 | ||
| Group ADF | 1.66 | 0.05 | ||
Using the Pedroni cointegration test, we obtained useful results that provided assurance of a long-term relationship between the variables. The Panel Within Dimension statistics, including v, rho, PP, and ADF, were statistically significant for the common autoregressive coefficients. Similarly, the Group Between Dimension statistics, such as rho, PP, and ADF, were also significant for the individual autoregressive coefficients at their respective significance levels. The test was conducted to evaluate the reliability of the pooled panel and the analysis of country data; its results support the use of pooled panel methods in this research.
The Hausman test, displayed in Table
| Parameters | Fixed Effects | Random Effects | Dynamic Panel Model: (first Period Lag) | Dynamic-Panel Model: (Second Period Lagged) |
| Coeff. | Coeff. | Coeff. | Coeff. | |
| Constant term | -1.892 (-2.223**) | -0.711 (-2.503**) | -3.742 (-3.788***) | -4.865 (-5.059***) |
| Lagged MISR (-1) | - | - | 0.051 (3.991) | 0.038 (2.062**) |
| Lagged MISR (-2) | - | - | - | 0.022 (1.821*) |
| Financial Reform (FINR) | -0.161 (-0.306) | -0.844 (-2.557**) | 0.553 (1.914*) | 0.607 (2.201**) |
| Real Interest Rate (RINTR) | 0.001 (0.151) | 0.005 (0.661) | 0.002 (0.323) | 0.002 (0.292) |
| Inflation (INF) | 0.936 (9.381***) | 0.941 (9.755***) | 0.923 (7.236***) | 0.940 (7.417***) |
| Government Consumption (GCON) | -0.084 (-1.224) | -0.097 (-1.494) | 0.055 (0.963) | 0.101 (1.904*) |
| Natural Resource Rent (NATR) | -0.141 (-1.905*) | -0.157 (-2.853**) | -0.025 (-0.916) | -0.018 (-0.671) |
| Secondary School Enrolment | 0.221 (8.644***) | 0.163 (7.947***) | 0.129 (9.762***) | 0.129 (4.161***) |
| R-Square | 0.973 | 0.961 | 0.959 | 0.662 |
| Adjusted R-Square | 0.971 | 0.96 | 0.958 | 0.661 |
| F-Statistics (Probability) | 29.65 (0.000***) | 18.49 (0.000***) | 98.02 (0.000***) | 134.32 (0.000***) |
| Hausman Tests | Chi^2 (9) = 24.87 (0.0004) | |||
| Sargan Tests | N/A | N/A | Chi^2 (18) = 59.04 (0.756) | |
In terms of the model’s fit, the R-squared values indicate that about 97.1% and 96% of the observed fluctuations in macroeconomic instability can be accounted for by the explanatory variables used in the fixed- and random-effects models, respectively. The one-step Generalized Method of Moments (GMM) and the two-step GMM models explain approximately 95.8% and 66.1%, respectively, of the changes in the dependent variable. The F-Statistics were employed to conduct an overall model specification test, which indicated that both our specified fixed-effects model and random-effects model were accurately defined. The F-statistic of 98.02 and a probability value of 0.000 indicate a high level of significance for the specified dynamic panel models. Consequently, the included variables demonstrate noteworthy joint statistical significance.
The findings of the dynamic panel model are presented in Table
The coefficient of financial reform (FINR) displayed varying results when examining individual variables. In the traditional panel model, the coefficient is negative, indicating that domestic financial reforms have the potential to strengthen a country’s economy’s capacity to withstand financial shocks and decrease uncertainty in the business environment. However, the dynamic panel model found that the coefficient was statistically significant at a 5% significance level and is positive. This suggests that a complete domestic financial liberalisation leads to a deterioration of 55.3% and 60.7% in the macroeconomic situation, as indicated by the results from the one-step and two-step systems GMM models, respectively.
Based on the findings from the static panel model, it can be seen that a 100% level of financial liberalization reduces macroeconomic instability in the region by 84.4%. These findings are consistent with the results from the random effects model and in line with conclusions drawn by
The coefficient of natural resource rent, which reflects the level of resource dependence in sub-Saharan Africa (SSA), shows a negative value in both dynamic and traditional models, suggesting that the use of revenue from natural resources has the potential to improve stability of countries in SSA. This finding contradicts the widely accepted resource-curse hypothesis but aligns with previous studies by
For robustness checks, the study conducts panel analyses to ascertain whether the effects of income groupings matter in the relationship between financial reform, natural resource dependence and macroeconomic misery of studied countries. The results presented in Table
| Variables | Low-Income Countries | Lower Middle-Income Countries | Upper Middle Income Country | ||||
| Fixed Effect | Random Effect | Dynamic Panel | Fixed Effect | Random Effect | Dynamic Panel | OLS Estimation | |
| Constant | 1.2901 (0.76) | 1.6814 (1.75)* | 1.1864 (1.61) | 2.1143 (0.60) | 23.9210 (8.27)*** | 8.7816 (0.69) | 9.9870 (0.57) |
| MISR (-1) | - | - | 0.0171 (1.39) | - | - | 0.0775 (1.19) | - |
| FINR | -0.2918 (-2.064)** | -0.1984 (-1.61) | -0.2133 (-0.69) | 0.1112 (2.125)** | 2.7935 (1.82)* | 0.9295 (2.22)** | 11.7030 (5.66)*** |
| NATR | 0.1535 (-2.46)** | -0.0391 (-1.53) | -0.0404 (-1.49) | -0.0692 (-1.73) | -0.0938 (-1.47) | -0.3281 (-1.54) | -0.5159 (-1.23) |
| INF | 0.9827 (2.09)** | 0.9963 (4.03)*** | 0.9848 (8.66)*** | 1.001 (3.35)** | 0.954 (2.04)** | 0.683 (1.981)* | 0.7583 (1.91) * |
| GCON | 0.0519 (0.77) | 0.0402 (0.81) | 0.0601 (1.69)* | 0.0421 (0.47) | 0.0083 (1.945)* | 0.0056 (2.012)** | -2.553 (-3.36)** |
| SSE | 0.0366 (1.08) | 0.0308 (1.928)** | 0.0332 (1.883)* | 0.0357 (0.50) | 0.1523 (1.93)* | 0.6281 (2.02)** | 0.4139 (3.23)** |
| RINTR | 0.0022 (0.19) | 0.0056 (0.55) | 0.0050 (0.47) | 0.0345 (0.88) | -1.1547 (-4.07)*** | -1.295 (-3.90)** | 0.2049 (0.72) |
| R2 | 0.9862 | 0.9815 | 0.9819 | 0.9887 | 0.655 | 0.7636 | 0.7965 |
| F-Statistics | 337.55 (0.000)*** | 1947.47 (0.000)*** | 1829.45 (0.000)*** | 247.96 (0.000)*** | 75.95 (0.000)*** | 9.61 (0.000)*** | 13.98 (0.000)*** |
To further appreciate the link between the reform of the financial sector and macroeconomic stability, the study conducted a causality test in a country-specific context. The Augmented Dickey-Fuller technique was employed to carry out the unit root tests for each of the 14 sampled countries; their results are presented in Table
| S/N | Countries | Financial Reform | Macroeconomic Instability | Overall Remarks | ||||||||
| ADF- Statistics (t-statistic) | Prob | Critical Values | ADF Statistics (t-statistic) | Prob. | Critical Values | |||||||
| 1% | 5% | 10% | 1% | 5% | 10% | |||||||
| 1 | Burkina Faso | -4.64 | 0.00 | -4.27 | -3.55 | -3.21 | -5.262 | 0.00 | -4.31 | -3.57 | -3.22 | I(1) |
| 2 | Cameroon | -3.61 | 0.04 | -4.28 | -3.56 | -3.21 | -7.1 | 0.00 | -4.29 | -3.56 | -3.22 | I(1) |
| 3 | Cote d’Ivoire | -4.43 | 0.007 | -4.27 | -3.55 | -3.12 | -7.889 | 0.00 | -4.29 | -3.56 | -3.22 | I(1) |
| 4 | Ethiopia | -3.99 | 0.02 | -4.28 | -3.56 | -3.21 | -8.171 | 0.00 | -4.29 | -3.56 | -3.22 | I(1) |
| 5 | Ghana | -5.04 | 0.00 | -4.29 | -3.56 | -3.21 | -6.936 | 0.00 | -4.30 | -3.57 | -3.22 | I(1) |
| 6 | Kenya | -4.67 | 0.00 | -4.28 | -3.56 | -3.21 | -8.774 | 0.00 | -4.30 | -3.57 | -3.22 | I(1) |
| 7 | Madagascar | -5.26 | 0.00 | -4.31 | -3.57 | -3.22 | -6.134 | 0.00 | -4.30 | -3.57 | -3.22 | I(1) |
| 8 | Mozambique | -4.67 | 0.00 | -4.27 | -3.55 | -3.21 | -4.667 | 0.00 | -4.27 | -3.56 | -3.21 | I(1) |
| 9 | Nigeria | -5.68 | 0.00 | -4.28 | -3.56 | -3.21 | -7.072 | 0.00 | -4.30 | -3.57 | -3.22 | I(1) |
| 10 | Senegal | -4.11 | 0.02 | -4.29 | -3.56 | -3.21 | -8.113 | 0.00 | -4.29 | -3.56 | -3.22 | I(1) |
| 11 | South Africa | -5.92 | 0.00 | -4.28 | -3.56 | -3.21 | -8.734 | 0.00 | -4.29 | -3.56 | -3.22 | I(1) |
| 12 | Tanzania | -5.20 | 0.01 | -4.28 | -3.56 | -3.21 | -5.838 | 0.00 | -4.39 | -3.56 | -3.23 | I(1) |
| 13 | Uganda | -4.71 | 0.00 | -4.28 | -3.56 | -3.21 | -3.401 | 0.07 | -4.30 | -3.57 | -3.22 | I(1) |
| 14 | Zimbabwe | -3.52 | 0.06 | -4.39 | -3.61 | -3.24 | -7.414 | 0.00 | -4.29 | -3.56 | -3.22 | I(1) |
| Country | Eigen value | Trace Statistic | Critical Value (5%) | Number of CE(s) | Eigen value | Max. Eigen Statistic | 5% Critical Value | No of CE(s) |
| Burkina Faso | 0.46 | 42.12 | 47.86 | 1 | 0.46 | 18.67 | 27.58 | 1 |
| Cameroon | 0.54 | 47.80 | 47.86 | 2 | 0.54 | 23.88 | 27.58 | 2 |
| Cote d’Ivoire | 0.73 | 89.17 | 99.82 | 1 | 0.60 | 24.38 | 27.58 | 2 |
| Ethiopia | 0.62 | 39.74 | 39.80 | 2 | 0.62 | 25.44 | 26.13 | 2 |
| Ghana | 0.62 | 93.75 | 95.75 | 0 | 0.62 | 29.64 | 40.08 | 0 |
| Kenya | 0.67 | 66.26 | 69.82 | 1 | 0.70 | 37.71 | 40.08 | 0 |
| Madagascar | 0.52 | 36.93 | 47.86 | 1 | 0.52 | 20.80 | 27.58 | 1 |
| Mozambique | 0.13 | 2.95 | 3.84 | 5 | 0.13 | 2.95 | 3.84 | 5 |
| Nigeria | 0.50 | 43.22 | 47.86 | 2 | 0.67 | 33.12 | 33.88 | 1 |
| South Africa | 0.53 | 41.84 | 47.86 | 2 | 0.53 | 23.34 | 27.58 | 2 |
| Senegal | 0.45 | 27.91 | 29.80 | 3 | 0.66 | 33.16 | 33.88 | 1 |
| Tanzania | 0.53 | 29.68 | 29.80 | 3 | 0.53 | 16.03 | 21.13 | 3 |
| Uganda | 0.96 | 109.06 | 119.82 | 1 | 0.69 | 23.36 | 27.58 | 2 |
| Zimbabwe | 0.77 | 79.15 | 89.82 | 1 | 0.57 | 20.27 | 27.58 | 2 |
The Granger causality findings in Table
| Country | Null Hypothesis | Chi-Square | P-Value | Conclusion |
| Burkina Faso | H01 H02 | 0.1380 0.0679 | 0.9333 0.9666 | No Causality |
| Cameroon | H01 H02 | 1.0110 1.2949 | 0.6032 0.5234 | No Causality |
| Cote d’ Ivore | H01 H02 | 4.5858 1.6046 | 0.1010 0.4483 | No Causality |
| Ethiopia | H01 H02 | 0.6450 2.5150 | 0.7243 0.2844 | No Causality |
| Ghana | H01 H02 | 1.6250 0.8333 | 0.4437 0.6593 | No Causality |
| Kenya | H01 H02 | 7.3831 2.8863 | 0.0249 0.2362 | FINR → MIS |
| Madagascar | H01 H02 | 5.4223 1.3526 | 0.0665 0.5085 | FINR → MIS |
| Mozambique | H01 H02 | 3.3146 3.1629 | 0.1907 0.2057 | No Causality |
| Nigeria | H01 H02 | 0.6749 5.4967 | 0.7136 0.0640 | FINR ← MIS |
| Senegal | H01 H02 | 0.0827 5.8384 | 0.9595 0.0540 | FINR ← MIS |
| South Africa | H01 H02 | 1.5962 1.0582 | 0.4502 0.5891 | No Causality |
| Tanzania | H01 H02 | 27.5585 0.9794 | 0.0000 0.6128 | FINR → MIS |
| Uganda | H01 H02 | 0.6785 0.9574 | 0.7123 0.6196 | No Causality |
| Zimbabwe | H01 H02 | 0.4747 1.3523 | 0.7887 0.5086 | No Causality |
This study aimed to examine the effects of domestic financial reform on macroeconomic stability in 14 countries in Sub-Saharan Africa (SSA). To do this, it used both static and dynamic panel data modelling techniques. The sample size was determined by the number of SSA countries that have implemented gradual financial reform, as outlined in the study conducted by
The evidence on natural resource rents further reinforces the resource-curse thesis, as dependence on resource revenues was shown to have a generally destabilizing effect across SSA income groups. However, the overall panel results suggest that, when resource rents are effectively managed, they can serve as buffers to stabilize the economy. The contrasting effects emphasize that resource abundance in itself is not inherently harmful; rather, the quality of governance, fiscal institutions, and the capacity to implement counter-cyclical policies determine whether resource revenues translate into stability or volatility. This result is particularly relevant given recent commodity price shocks that have once again exposed the vulnerability of resource-dependent African economies.
The findings also reveal a “human capital-misery trap,” where higher levels of secondary school enrolment were associated with greater macroeconomic instability. This paradox suggests that a gap between human capital development and economic structure can lead to underemployment and frustration, contributing to economic instability. It emphasizes the importance of matching educational policies to labor market realities, promoting vocational training and technical education that can drive structural transformation and industrialization. Inflation and government consumption were also found to exert mixed but significant effects on macroeconomic stability, underscoring the critical role of prudent fiscal and monetary management.
From these results, several policy implications arise. First, financial reforms must be sequenced carefully and accompanied by stronger regulatory and supervisory frameworks. Best practices can be drawn from countries such as Kenya and Ghana, where gradual liberalization was complemented by regulatory strengthening, compared to Nigeria, where abrupt liberalization in the 1980s and 1990s heightened instability. Strengthening central bank independence, improving risk-based supervision, and deepening capital markets can help ensure that financial reforms deliver stability rather than volatility.
Second, the management of natural resource rents requires stronger institutional frameworks and fiscal rules that ensure that revenues are directed into stabilization funds, sovereign wealth funds, or productive investments. Botswana offers an example of prudent management of diamond revenues, which helped sustain macroeconomic stability over decades, in contrast with Nigeria, where oil revenue dependence amplified instability during price downturns. SSA governments should also expand non-resource tax bases to reduce vulnerability to commodity price shocks and pursue long-term diversification through targeted investments in agriculture, manufacturing, and services.
Third, to address the human capital-misery trap, education policies should be reoriented toward demand-driven skills. Expanding vocational and technical training linked to industrial policies can provide immediate employment opportunities while aligning labor supply with productive sectors. This approach is essential to prevent underemployment of educated youth, which often contributes to social unrest and political instability.
Finally, macroeconomic stability requires coordinated policy frameworks that balance fiscal and monetary policy. Inflation management should avoid excessive reliance on monetary tightening that stifles growth; instead, governments must complement monetary policy with disciplined fiscal expenditure and measures to reduce structural inflationary pressures, such as food price volatility. Public spending should focus on social and economic infrastructure that enhances productivity while being implemented under strong accountability frameworks to minimize rent-seeking.
Therefore, this study contributes to the literature by showing that the relationship between financial reforms, natural resources, and macroeconomic stability in SSA is conditional on income levels and institutional quality. It underscores that one-size-fits-all policy prescriptions are inappropriate for the region. Effective policies must be country-specific, sequenced carefully, and accompanied by institutional strengthening. Only then can financial reforms and natural resource wealth serve as instruments of stability and long-term development rather than sources of fragility.
| Low-Income Economies | Lower-Middle Income Economies | Upper-Middle Income Economies |
| Burkina Faso | Cameroon | South Africa |
| Ethiopia | Cote d’Ivoire | |
| Kenya | Ghana | |
| Madagascar | Nigeria | |
| Mozambique | Senegal | |
| Tanzania | ||
| Uganda | ||
| Zimbabwe |