A Cointegrating Linkage of Financial Inclusion, Institutional Quality and Economic Growth in Sub-Saharan African Countries

Abstract

This study investigates the cointegrating relationships among financial inclusion, institutional quality, and economic growth in 20 Sub-Saharan African nations from 2008 to 2024. Employing the Pooled Mean Group (PMG) estimator in an Autoregressive Distributed Lag (ARDL) panel, the analysis showed a significant and favourable long-term association between economic growth, financial inclusion and institutional quality. In particular, regardless of the proxy for economic growth, the long-term association between financial inclusion and economic growth is positive and statistically significant. Similarly, institutional quality demonstrates a favourable and significant long-run linkage to economic growth, suggesting that improvements in institutional frameworks are related to sustained economic expansion. In contrast, short-run dynamics differs. There is a short-term correlation between institutional quality and economic growth but not between financial inclusion and economic growth. These findings show the importance of institutional quality as a catalyst for economic growth in the region. Consequently, the study recommends that governments in Sub-Saharan Africa should prioritise setting up strong institutions and policies to foster financial inclusion, which has a correlation with sustainable economic growth. This is crucial for both overall economic development and the creation of job opportunities.

1. Introduction

Acknowledging the significance of financial accessibility in promoting equitable development, development partners such as the World Bank have continued to promote and implement deliberate policies focused on providing access to the unbanked population, particularly in developing nations. This has resulted in a considerable increase in the level of global inclusion across regions. Financial inclusion and institutional quality have emerged as fundamental elements in discussions about long-term economic success. It is generally acknowledged that improving access to formal financial services is an essential measure to mobilise savings, effectively allocate capital, boost entrepreneurship, reduce income inequality and improve macroeconomic stability. Empirical and policy-oriented research from established and emerging economies consistently shows that inclusive financial systems promote growth by allowing households and businesses to invest, manage risks, and participate constructively in economic activities. However, the growth-enhancing linkages of financial inclusion are neither automatic nor universal; they are heavily correlated by the quality of institutions that control financial markets and economic activities. By reducing transaction costs, upholding contracts, and fostering trust in financial intermediaries, strong institutions demonstrated by efficient regulation, the rule of law, the prevention of corruption and government accountability foster an environment that allows financial inclusion to lead to long-term economic progress (Yakubu et al., 2025).

However, while nations in Latin America and the Caribbean (LNC) and the Middle East and North Africa (MEA) are believed to have these prospects and sustained economic expansions, their counterparts in Sub-Saharan Africa (SSA) have essentially nothing to exhibit (Kama & Adigun, 2013). Although these findings vary among SSA’s sub-regions, the combined aggregates remain matters of concern. For example, the global financial inclusion index indicates that SSA countries are relatively lagging behind their peers in terms of financial inclusiveness, while Eastern and Southern African countries are recording significant progress in reaching out to the unbanked population than in Western and Central African sub-regions, which have the least financial inclusion (Soumare et al., 2016). In Africa, particularly in SSA, financial inclusion and institutional quality are even more critical. Historically, the region has had low levels of financial intermediation, considerable financial exclusion, and poor institutional frameworks as compared to other developing regions despite recent advancements, particularly through digital financial services in some countries such as the M-Pesa in Kenya, which demonstrates how digital financial innovation can complement and, in some circumstances, expand the original promise of microcredit. The Kenyan M-Pesa model indicates that in SSA, successful financial inclusion has increasingly expanded beyond conventional microcredit to digital financial ecosystems. Fintech-driven inclusion has enabled broader economic involvement than traditional microcredit alone by lowering transaction costs, removing geographical barriers, and integrating low-income users into formal financial networks, underscoring the need of financial inclusion for development (De Mariz, 2022). Nonetheless, huge portions of the population and small businesses in the region are still excluded from formal financial institutions. The efficacy of banking sector reforms and economic policy is also being undermined by governance issues. While financial inclusion has the prospect of boosting economic progress, empirical data from SSA indicates that institutional quality significantly amplifies or dampens its impact, highlighting the need to examine these factors together rather than separately (Ifediora et al., 2022; Chinoda & Kapingura, 2024).

The SSA region is unique in ways that make it difficult to apply traditional growth theories and empirical conclusions from other regions. First, many institutional deficiencies in SSA are deeply founded in colonial history, which bequeathed exploitative governance arrangements, weak legal systems, and fragmented administrative organisations that still exist in various forms today. These historical legacies have reduced state capacity, hindered regulatory enforcement, and undermined public institution legitimacy, all of which have had a direct impact on financial sector development and economic performance. Second, SSA’s financial systems continue to be highly fragmented, with formal banking institutions coexisting with semi-formal arrangements and widespread informal financial markets. This fragmentation diminishes the depth and effectiveness of financial intermediation, weakening the transfer of financial inclusion to productive investment and growth (Diallo, 2024; Jima & Makoni, 2023; Magwedere & Marozva, 2022). Third, SSA economies are especially exposed to exogenous disturbances such as changes in commodity prices, international financial cycles, climate-related shocks and geopolitical disruptions. These vulnerabilities frequently interact with inadequate institutions to exacerbate macroeconomic uncertainty and derail growth trajectories.

These structural and historical differences suggest that classic growth theories, which frequently presume relatively stable institutions, deep financial markets, and effective policy transmission mechanisms, may fail to effectively explain the evolving link between financial inclusiveness and growth in SSA. Similarly, empirical results from Asia, Latin America, or advanced economies cannot be readily applied to the area without accounting for institutional fragility, informality and shock sensitivity. Recent SSA-focused research increasingly recognises this restriction, emphasizing that financial inclusion may deliver limited or delayed growth effects in the absence of enabling institutional frameworks (Yakubu et al., 2025; Chinoda & Kapingura, 2024; Osuma, 2025).

Despite a growing amount of research regarding financial inclusion and economic progress within SSA, significant gaps persist. A large number of existing research studies either look at the direct connection between financial inclusion and economic growth independent of institutional quality, or they regard institutions as peripheral control variables rather than essential components of the economic process. Furthermore, many empirical evaluations rely on static or short-run frameworks that fail to reflect the long-term equilibrium linkages and adjustment processes between inclusion in financial services, institutional strength and growth of the economy. This is especially troublesome in SSA, where institutional reforms and financial deepening processes develop gradually and are more likely to have a long-term impact rather than an immediate one. The purpose for this work is the need to give a more integrated and region-specific knowledge of access to finance, institutional capacity and economic growth of the economy of SSA nations.

Numerous research studies have been conducted to support the underlying problems affecting economic prospects in the SSA, highlighting weak institutional quality as the proximate cause. By establishing a favourable correlation between inclusion in financial services and economic expansion, others have demonstrated the significance of financial accessibility to the region’s development. In order to ascertain the cointegrating links of financial inclusion and growth of the economy of SSA nations, along with institutional quality and economic expansion in the chosen SSA nations, this paper employs panel ARDL. Empirical evidence currently available does not adequately articulate the cointegrating relationships between institutional quality, access to financial services and economic expansion in SSA nations. This article’s subsequent sections are organised as follows: Section 2 presents the literature; Section 3 presents the methodology; Section 4 presents the data analysis, findings and discussion; and Section 5 presents the conclusion and recommendations.

2. Review of the Literature

The purpose of this article is to add to the expanding body of research on how financial inclusion and institutional quality support economic growth and development in Sub-Saharan African (SSA) economies. Financial inclusivity and institutional quality have been conceptualised in a variety of ways. For example, according to North (1991), institutions are made up of laws, compliance protocols, and moral and ethical standards intended to limit people’s actions in order to maximise the wealth or utility of principals. According to Yildirim and Gokalp (2016), institutions are customs that impose restrictions on our behaviour through established social norms and organisations that guide our behaviour and social interactions. Adegboye et al. (2020) state that institutional quality is regarded as a crucial element that may be utilised to ensure that foreign direct investment (FDI) flows inward. Institutions thus serve as the foundation for shaping and enforcing societal norms, behaviours and patterns.

However, it is established that inclusive financial services play a crucial role in promoting economic success and development. There are many financial products available that are provided by various financial services companies (such as banks, insurance companies, capital market instruments and pension products), which contribute to the difficulties in coming up with a definition of financial accessibility, which is generally accepted. Financial inclusion means that every adult citizen in a country can access financial offerings efficiently, helpfully and reliably without experiencing difficulties in opening a bank account or obtaining credit. Onaolapo (2015) defines financial inclusion as a cycle that guarantees everyone may easily access, use, and get formal financial goods from an economy. Financial inclusion, according to Sahay et al. (2015), is the availability, utilisation and provision of broad-based financial products to weak, vulnerable and disadvantaged segments of the population at reasonable costs. We therefore defined financial inclusion as ensuring that everyone has unrestricted rights and access to suitable financial services without facing any form of discrimination, especially the economically vulnerable groups.

Similarly, achieving and sustaining inclusive growth in emerging economies such as the SSA has been an issue of concern. Economic growth implies an annual rise in the monetary worth of all products and services generated in a nation, typically expressed as GDP (Gross Domestic Product). According to Amponsah et al. (2021), inclusive growth is growth in an economy that guarantees equitable income distribution to the population and generates opportunities. Economic growth, according to Abubakar (2020), is the expansion of a nation’s social and economic activity throughout time. According to Hall and Jones (1999) growth differences between countries are related to differences in social infrastructure. The social infrastructure includes laws, regulations, property rights, norms, habits, practices and social conventions.

The current concern is about the determinants of economic growth and development particularly for SSA countries. Solow (1956) and Swan (1956), or the neoclassical growth model, believed that technological progress from outside an economy determines growth for the economy. However, the new growth theory, pioneered by Romer (1986) and Lucas (1988), highlights three main forces behind economic growth. These are technological advancement, knowledge spill-overs and human capital (Udeaja & Obi, 2015). These proponents believed that economic growth is determined by technological progress which comes from within the economy, hence the name endogenous growth hypothesis. In the endogenous growth hypothesis, innovative advancement is not the only conceivable reason for economic growth over the long run. Factors such as, for example, human resources, insurance of intellectual innovation rights, state support for the advancement of science and innovation as well as government establishment of a conducive investment environment and pulling in new advances matter. According to Masoud (2014) the theory explained the variation between the rates of long-term increases in output and per capita income among countries worldwide. Romer (1994) asserts that economic growth is a result of technological breakthroughs. That is, production of goods in an economy can be improved by combining advances in human capital with current knowledge. The endogenous growth hypothesis supports the connection between finance and growth. According to Marwa and Zhanje (2015) finance influence economic growth by means of various channels through capital accretion. The capital aggregated is invested in finance technical advancements and improves innovative progress, which will promote economic growth. The endogenous growth model is critical because institutional quality and financial inclusion are variables that can be developed inside an economy.

In line with this theoretical background, this paper reviewed the literature on inclusion in financial services, institutional effectiveness and growth of the economy. Levine (1996) posits that financial development is crucial to economic progress because it allows for more efficient resource allocation, increases productivity and promotes economic inclusiveness. Specifically, the delivery and accessibility of financial products and services among broader groups in a society is crucial to promoting equitable and sustainable economic growth. Levine emphasises that well-functioning financial systems contribute to economic growth not just by mobilising savings and distributing capital efficiently but also by providing the required financial services that empower individuals and enterprises across diverse income levels (Levine, 1996). According to Demirguc-Kunt et al. (2018), increasing the availability of formal financial services enables individuals and companies to engage more actively in the economy, which boosts productivity, resilience and total economic growth. In other words, more financial inclusion encourages broader economic involvement, which supports sustainable economic success and development.

Many empirical studies on the finance–growth nexus are increasingly recognising institutions’ conditional function. Yakubu et al. (2025) investigate whether institutional quality drives the influence of financial inclusion on the growth of the economy. Drawing on panel data from global development indicators and governance databases, the authors use advanced panel econometric techniques to show that inclusion in financial services is associated with a positive and meaningful influence on economic expansion only under circumstances defined by excellent institutional quality. They advocate improving governance structures alongside financial sector reforms. The study’s main feature is its explicit interaction approach, yet its worldwide reach restricts region-specific insights to SSA, where institutional restrictions are more prominent.

Chinoda and Kapingura (2024) study the institutional dimension of financial inclusion–growth nexus, with a specific focus on SSA. Using panel data from SSA nations and dynamic panel estimation methods, the research discovered that institutional effectiveness considerably drives the growth outcomes of financial inclusion. The authors contend that inadequate governance institutions in many SSA countries negate the developmental benefits of expanding financial access. While the study has significant regional relevance, its focus on aggregate financial inclusion metrics hampers understanding of the complex nature of inclusion. Nonetheless, it is extremely pertinent to research on co-dependent institutional and financial interactions within SSA. Ifediora et al. (2022) use macro-panel data from the World Bank and IMF databases to empirically demonstrate the direct outcomes of inclusion in financial services on economic expansion in SSA. The authors reported a statistically significant and desirable correlation between financial access indices and economic development using panel regression techniques. They discovered that availability, penetration and composite dimensions of access to finance exerts significant and positive outcomes in terms of growth of the economy, whereas the usage aspect slightly boosts economic expansion, and the effect is not significant. Conversely, the presence of bank branches and ATMs strongly supports economic success. While deposit accounts and outstanding loans supports growth, they exert minimal influence. On the other hand, outstanding deposits appear to hinder growth of the economy. Ifediora et al. (2022) advocate for measures that increase access to banking and digital financial services. The study’s large national coverage and robust econometric analysis are major strengths, but the removal of institutional quality as a conditioning variable represents a critical shortcoming since it presupposes homogeneity in governance systems among SSA countries.

Osuma (2025) expands the analysis to include welfare results, conducting a comparative study on digital financial services, poverty alleviation and growth of the economy of SSA nations. Relying on panel data and comparative econometric methodologies, the investigation demonstrates that digital inclusive finance exerts greater outcomes on growth and poverty reduction in economies with superior regulatory and institutional settings. To maximise development effects, the report suggests that digital finance expansion be aligned with institutional transformation. Although it does not directly model institutional quality as an endogenous variable, it makes a significant contribution by emphasising the distinct effects of conventional and digital forms of financial services.

Jima and Makoni (2023) adopt a more methodologically rigorous technique in their paper “Financial Inclusion and Economic Growth in SSA: A Panel ARDL and Granger Non-Causality Approach”. The authors use panel ARDL and Granger non-causality approaches to establish both short-term and long-term correlations between financial inclusion and economic growth. The study provides evidence of long-term cointegration and bidirectional causality, emphasising the relationship’s dynamic nature. However, the model excludes institutional quality, limiting the explanatory power of the findings in governance-sensitive scenarios like SSA. Similarly, an investigation by Chinoda and Kapingura (2024) explores how digital financial inclusion influences economic growth in SSA, specifically considering institutions and governance indicators. They discover that digital financial accessibility considerably boosts economic progress, especially among countries with robust institutional and governance frameworks, using panel data and dynamic estimate methodologies. Although it focuses more on governance than on more general institutional aspects like law enforcement and corruption control, the study’s value is its explicit attention on the linkages between digital finance and governance.

Afolabi and Oyeleke (2024) use panel ARDL methodologies to investigate institutional effectiveness and inclusive growth in SSA with regard to inclusive growth. The study shows that institutional strength has important sustainable effects on inclusive growth outcomes even though financial inclusion is not the main focus. The results support the claim that institutions serve as essential channels for the transmission of financial and economic policies’ effects on development. Although the study’s indirect treatment of financial inclusion is a shortcoming, its methodological congruence with panel ARDL approaches makes it relevant. In addition, as part of a master’s thesis at MIT Sloan, Diallo (2024) creates a multidimensional financial inclusion index for SSA using micro- and macro-level data. The study underlines that access, utilisation, and quality characteristics react differentially to institutional settings and draws attention to the complexity and variability of financial inclusion among SSA countries. The study offers a strong measurement methodology that supports empirical research on financial inclusion even though it does not directly evaluate growth benefits. Its primary drawback is the lack of a formal growth model, yet it is relevant for creating thorough inclusion indicators in research with an SSA focus.

The inability of traditional banks to raise capital, which is crucial for advancing financial inclusion through mainstream institutions, is another distinctive feature of the SSA countries that merits discussion. As seen by their low valuation multiples, banks and microfinance institutions in SSA struggle more than those in other countries to raise capital for growth and expansion. Microfinance institutions are fundamentally sound, but their unique business models, high operating costs, reliance on developmental funding and restricted access to diversified capital conditions, all of which are especially noticeable in SSA, help explain their lower valuation multiples and greater difficulty in raising capital for growth and expansion (O’Donohoe et al., 2009).

The reviewed research concludes that inclusion in financial services is favourably associated with growth of the economy; nevertheless, institutional quality plays a major role in shaping this correlation. Stronger and more consistent growth benefits are reported by studies that specifically include institutions (Yakubu et al., 2025; Chinoda & Kapingura, 2024). In terms of methodology, more sophisticated insights into long-term and short-term interactions are offered by recent studies using dynamic panel and panel ARDL approaches (see, for example, Magwedere & Marozva, 2025; Mugodzva & Marozva, 2025a, 2025b). Nevertheless, there are still gaps in the comprehensive integration of institutional quality, financial inclusion and economic growth within a single, SSA-specific cointegration framework. By concurrently modeling these variables to represent their long-run equilibrium relationships and region-specific dynamics, this study fills this gap.

3. Methodology

The paper investigates the cointegrating relationships between economic growth, financial inclusion and institutional quality using a sample of twenty carefully chosen SSA countries. Ghana, Mauritius, Kenya, Uganda, South Africa, Nigeria, Botswana, Rwanda, Gambia, Gabon, Cameroon, Equatorial Guinea, Tanzania, Zimbabwe, Angola, Guinea, Namibia, Zambia, and Mozambique are the countries that have been chosen out of 48 SSA countries as classified by the World Bank (Bethell, 2016). Based on data accessibility and to ensure adequate representation of the region, which is fundamentally and technically essential for reliable cointegration and panel ARDL estimation, these countries were chosen. Due to significant data gaps and inconsistent reporting, many SSA countries do not meet this requirement. However, the selected countries offer comparatively complete, consistent and continuous time-series data on financial inclusion indicators, institutional quality measures (like governance, regulatory robustness and rule of law) and key macroeconomic variables over sufficiently long periods. The chosen nations, which span all major sub-regions, income groups, and institutional profiles, effectively capture the structural and institutional heterogeneity of SSA. They range from resource-rich but institutionally constrained and macroeconomically vulnerable nations (such as Nigeria, Angola, Equatorial Guinea, Zimbabwe, Guinea and Mozambique) to relatively strong and advanced systems (such as Mauritius, Botswana and South Africa) through reform-driven and financially expanding economies (such as Kenya, Rwanda and Ghana). This variety maintains analytical coherence while ensuring that the sample represents a wide range of SSA development experiences.

This paper uses three economic growth indicators as the dependent variables (GDP growth rate, real GDP, and per capita GDP) obtained from the World Development Indicators (WDI) database. Institutional quality is measured by the World Governance Indicators (WGI) and WDI, whereas the International Monetary Fund (IMF) Financial Access Survey (FAS) and WDI provide information on financial inclusion. Moreover, all the data are accessed for the period 2008–2024. Institutional quality is represented by accountability and citizen voice, political stability and absence of violence, effectiveness of government, quality of regulations, corruption control, adherence to rule of law, ease of doing business, human rights protection and protection of civil liberties variables; number of ATMs per 100,000 adults, number of commercial bank branches per 100,000 adults, number of ATMs per 1000 km, number of commercial bank branches per 1000 km, and domestic bank lending to the private sector (expressed as a percentage of GDP) are the financial inclusion variables.

A single composite index capturing both institutional strength and financial inclusion was constructed for each of the 20 countries using Principal Component Analysis (PCA). This index combines various indicators of financial inclusion like ATMs and bank branches per 100,000 adults, ATMs and branches per 1000 km, and domestic credit to the private sector, while institutional quality indices include voice and accountability, political stability and absence of violence, government effectiveness, quality of regulations, control of corruption, rule of law, human rights protection, ease of doing business and civil liberties (see for example Marozva & Maloa, 2026; Magwedere & Marozva, 2025).

Following the determination of a mixed order of integration devoid of any I(2) series, a heterogeneous panel model, panel ARDL approach is employed for the estimate. Mean Group (MG) estimates, Dynamic Fixed Effects (DFE) estimates, or Pooled Mean Group (PMG) estimates must be established in order to use panel ARDL (Pesaran & Smith, 1995; Pesaran et al., 1999). The MG approach was developed to correct the biases arising from heterogeneous slopes in dynamic panel models. It calculates long-run parameters for the panel by averaging the long-run estimates obtained from ARDL models for each country individually (Rafindadi & Yosuf, 2013). The MG approach places no restrictions on the coefficients, allowing them to vary freely across countries in both short and long runs. Nevertheless, a sufficiently large time dimension (T) is necessary to ensure the reliability and consistency of the estimates. In other words, MG is inconsistent and performs poorly when either cross-sectional dimension (N) or time dimension (T) is limited in size (Pesaran et al., 1999).

They further proffer a solution to the heterogeneity issue across countries associated with MG by establishing the Pool Mean Group (PMG). Although the DFE approach and PMG estimator are strikingly similar, the DFE estimator limits the cointegrating vector’s coefficient to be identical throughout time across all panels. The FE model additionally requires that the short-run coefficient and the speed of adjustment coefficient be comparable. Panel-specific intercepts are permitted by the DFE model. Additionally, it accounts for intragroup correlation while calculating the standard error. Baltagi et al. (2000) show how the correlation between the error term and lagged dependent variables leads to simultaneous-equation bias in FE approaches. It is simple to assess the degree of this endogeneity using the Hausman test.

The primary difference between the MG and PMG estimators lies in their treatment of heterogeneity across cross-sectional units. The MG estimator estimates separate equations for each country (N) and obtains consistent long-run estimates by averaging the individual country coefficients (Pesaran et al., 1999). In contrast, the PMG estimator combines the advantages of MG and DFE approaches, pooling long-run parameters while allowing short-run dynamics and error variances to differ across countries (Pesaran et al., 1999). The PMG estimator assumes that regression residuals are independent across countries, the error terms are serially uncorrelated, and there exists a long-run equilibrium relationship between dependent and explanatory variables, with long-run coefficients being homogeneous across nations (Loayza & Ranciere, 2006; Oyelami et al., 2017).

Furthermore, the PMG method accommodates country-specific heterogeneity in short-run coefficients, intercepts, error variances, and the speed of adjustment toward the long-run equilibrium while maintaining uniformity in long-run slope coefficients across countries (Loayza & Ranciere, 2006; Pesaran et al., 1999). To identify the most appropriate estimator between MG and PMG, the Hausman test is typically employed. The standard panel model is specified below:

$$Y_{it}=\alpha \sum _{j=1}^{p-1}{Y}_{i,t-j}+\delta \sum _{j=0}^{q-1}{X}_{i,t-j}+{\varphi }_1{Y}_{i,t-1}+{\varphi }_2{X}_{i,t-1}+{\mu }_i+{\epsilon }_{it}$$

(1)

Here, $Y_{it}$ indicates the dependent variable, $X_{i,t-j}$ denotes the vector containing the independent variables for the group, $p$ and $q$ represent the lag length, i and ${\mu }_i$ are the fixed effect (see Pesaran et al., 1999). The ECM model parameters for the ARDL system after cointegration is formed are shown below:

$$\Delta Y_{it}=\varphi i(Y_{i,t-1}-{\beta }_i{X}_{i,t-1})+\Omega 1\sum _{j=1}^{p-1}\Delta Y_{i,t-j}+\Omega 2\sum _{j=0}^{q-1}\Delta X_{i,t-j}+{\mu }_i+{\epsilon }_{it}$$

(2)

In the model, ${\beta }_i$ represents the long-run coefficients, while $\varphi i$ represents the error correction terms that adjust deviations from the equilibrium. The vector $Y$ includes the proxies for economic growth, namely real GDP, GDP per capita and GDP growth rate, whereas $X$ consists of the explanatory variables, including measures of institutional quality and financial inclusion. The coefficients 1 and 2 correspond to the short-run links of the dependent and independent variables, respectively. The subscripts $i$ and $t$ indicate the country and time dimensions. The long-run relationship is specified within the brackets. Substituting the study variables into the specification and incorporating both constant term and trend yields the following model:

$$\begin{array}{cc}\hfill \mathsf{\Delta }Log\left(E{G}_t\right)=& {\alpha }_i+{\alpha }_{i}T+{\beta }_{1}Log\left(E{G}_{i,t-1}\right)+{\beta }_{2}Log\left(F{I}_{i,t}\right)+{\beta }_{3}Log\left(I{Q}_{i,t}\right)+{\beta }_{4}Log\left(IF{R}_{i,t}\right)+\hfill \\ & {\beta }_{5}Log\left(TO{P}_{i,t}\right)+{\beta }_{6}Log\left(UE{R}_{i,t}\right)+{\beta }_{7}Log\left(IN{V}_{i,t}\right)+{\beta }_{8}Log\left({LIL}_{i,t}\right)+{\beta }_{9}Log\left(TNR{R}_{i,t}\right)+\hfill \\ & {\displaystyle \sum _{i=1}^n}{\mathsf{\Omega }}_{1i}\mathsf{\Delta }Log\left(E{G}_{i,t-i}\right)+{\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{2ii}\mathsf{\Delta }Log\left(F{I}_{i,t-i}\right)+{\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{3i}\mathsf{\Delta }Log\left(I{Q}_{i,t-i}\right)+\hfill \\ & {\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{4i}\mathsf{\Delta }Log\left(IF{R}_{i,t-i}\right)+{\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{5i}\mathsf{\Delta }Log\left(TO{P}_{i,t-i}\right)+{\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{6i}\mathsf{\Delta }Log\left(UE{R}_{i,t-i}\right)+\hfill \\ & {\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{7i}\mathsf{\Delta }Log\left(IN{V}_{i,t-i}\right)+{\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{8i}\mathsf{\Delta }Log\left(LI{L}_{i,t-i}\right)+{\displaystyle \sum _{i=0}^n}{\mathsf{\Omega }}_{9i}\mathsf{\Delta }Log\left(TNR{R}_{i,t-i}\right)+{\phi }_{i}EC{T}_{i,t}+{\epsilon }_{it}\hfill \end{array}$$

(3)

where EG denotes the (logarithm) level of economic growth (that is, real GDP, real per capita GDP, and GDP growth rate as the case may be); FI captures financial inclusion indicators including domestic credit to private sector, ATMs per 100,000 adults, bank branches per 100,000 adults, ATMs per 1000 km and bank branches per 1000 km. IQ stands for institutional quality, measured through voice and accountability, political stability and nonviolence, effectiveness of government, quality of regulations, corruption control, rule of law, protection of human rights, ease of doing business and civil liberties; IFR refers to inflation rate; TOP to trade openness; UER to unemployment rate; INV to investment expenditure measured as gross capital formation; LIL to literacy level; and TNRR to total natural resource rent. In a similar vein, the long-term parameters are denoted as βs and the short-term coefficients as Ωs. Every variable is expressed in its natural logarithmic method.

4. Analysis and Discussion of Results

Here, we present the empirical findings of the paper. Unit root tests are covered after descriptive statistics, followed by the cointegration analysis and findings.

Table 1. Summary descriptive statistics.

Variables	Mean	Std. Dev.	Jarque-Bera	Observations
Financial Inclusion Variables
FIIN_INDEX	0.00	1.95	1448.00 ***	340
ATM	13.80	17.33	256.04 ***	340
CBB	5.84	4.55	222.65 ***	340
NATM	13.31	43.78	4554.66 ***	340
NCBB	8.67	20.72	3786.54 ***	340
DCPS	21.94	21.16	396.50 ***	340
Institutional Quality Variables
IQ_INDEX	0.00	2.52	10.43 ***	340
VA	−0.50	0.76	15.64 ***	340
PS	−0.37	0.86	9.75 ***	340
GE	−0.56	0.66	14.57 ***	340
RQ	−0.49	0.67	0.16	340
CC	−0.59	0.69	27.17 ***	340
RL	−0.58	0.70	11.89 ***	340
HR	0.50	0.21	0.08	340
SB	65.15	17.80	27.77 ***	340
CL	0.62	0.14	17.94 ***	340
Economic Growth Variables
RGDP	57,300,000,000	107,000,000,000	994.82 ***	340
GDPGR	4.15	5.47	2568.18 ***	340
PCRGDP	3082.85	3268.28	230.88 ***	340

*** p < 0.1%. Source: Author’s computation using Stata 19.0.

below displays the descriptive statistics for the 20 sampled nations from 2004 to 2024.

The descriptive statistics utilising a sample of 20 nations with 340 observations over the years 2008–2024 are summarised in Table 1 above.

According to the findings, the index’s mean value is approximately zero (0.00), indicating an extremely low rate of financial inclusion in all the study’s chosen nations. This means that while there are efforts to improve financial inclusion, there has been little or no significant change. Meanwhile, the standard deviation of the index is about 1.95, which indicates that it is not close to the mean value. This indicates that the values fall above the mean, which implies that financial inclusion rates are widely spread across the region. The Jarque-Bera (JB) test, however, suggests that the data are not from a normal distribution because the results indicate rejection of the null hypothesis even at a 1% level of significance. For other measures of financial inclusion, the data distribution scenario is comparable.

The institutional quality index, which measures institutional quality, ranges from −4.81 to 8.65. The index’s mean score of zero (0.00) indicates that institutional quality is low in all the study’s chosen nations. The average values of these findings further support this conclusion as each of the components of the index which have negative values except for the ease of doing business (SB), Civil Liberties (CL) and Human Rights protection (HR) indicators, which have positive means. In addition, the standard deviation of the index is about 2.52, which indicates that data points are significantly above the mean value. This implies that the performance in terms of institutional quality varies significantly across the region. However, the Jarque-Bera (JB) statistics indicate that the data do not have a normal distribution given the rejection of the null hypothesis.

Economic growth as measured by real GDP (RGDP) has an average of about 57.30 billion across the countries in the sample for the period under evaluation, whereas the standard deviation is about 10.70 billion, which is below the mean value. This implies the existence of data points within the sampled countries that are below the average value. However, GDP growth rate (GDPGR), used as a proxy for economic growth, recorded an average of 4.15%, which shows that though the distribution is negatively skewed with a heavy tail, it is also not normally distributed since the JB test supports the rejection of the null hypothesis at a 1% significance threshold. In addition, the standard deviation is 5.47%, which shows that data points amongst sampled countries are above the mean value. The result shows that the average inflation rate among the selected countries is about 7.42. This suggests that the majority of the region’s nations are facing a positive inflation rate on average, which is, however, single digit. The standard deviation of inflation is 5.97%, which is below the mean value, which indicates that inflation rates amongst the chosen nations are clustered around the mean value. In addition, the distribution of the inflation data from the result was found to be positively skewed with a heavy tail, and as a consequence are not normally distributed, which is indicated by the JB statistics where the null hypothesis is rejected. Gross domestic investment across the countries has an average of about 12 billion. The distribution of the data for the gross domestic investment does not have a normal distribution as depicted by the JB statistics. The standard deviation is about 20.4 billion, which indicates that gross domestic investment is highly dispersed around the mean.

The degree of stationarity of the data utilised in this investigation is shown by the unit root statistics reports in

Table 2. Panel unit root test.

Variables	LLC	I(D)	IPS	I(D)
FIINDEX	−3.61168 ***	I(1)	−3.54626 ***	I(1)
INSTDEX	−4.60973 ***	I(1)	−5.76798 ***	I(1)
L.RGDP	−5.68075 ***	I(1)	−5.03561 ***	I(1)
GDPGR	−7.53995 ***	I(1)	−6.21315 ***	I(1)
L.PCRGDP	−5.79986 ***	I(1)	−5.09919 ***	I(1)

Notes: *** is statistically significant at the levels of significance of 1% level. Source: Author’s computation using Stata 19.0.

below.

The variables’ stationarity test results are displayed in Table 2, along with the integration order required to render the data stationary. With the exception of inflation, which remained constant at level I(0) during all testing, the test findings showed that each variable is integrated of order one, I(1).

The

Table 3. Summary of the Pooled Mean Group (PMG) with GDPGR, PCRGDP and RGDP as the dependent variables.

	PMG (1)	PMG (2)	PMG (3)
Variables	D.GDPGR	D.LPCRGDP	D.LRGDP
Long-run
FIINDEX	−12.71 ***
	(−5.29)
L.FIINDEX L2.FIINDEX		0.0992 *** (9.11)	0.104 *** (7.88)
L.INSTDEX	1.896 ***	−0.0400 ***
L2.INSTDEX	(4.55)	(−5.38)	0.0671 *** (4.14)
ECT	−0.706 ***	−0.142 ***	−0.135 ***
	(−7.99)	(−4.54)	(−3.84)
Short-run
D.FIINDEX	−577.1	−2.523	−4.336
	(−0.78)	(−0.70)	(−0.78)
D.INSTDEX	4.038 *	0.0147 *	0.0158 *
	(2.47)	(1.98)	(2.10)
_cons	0.480	0.461 ***	1.385 ***
	(0.68)	(4.15)	(4.01)
N	320	320	320
Hausman test p-value	0.6883	0.9654	0.99987

Notes: t statistics in parentheses * p < 0.10, *** p < 0.01. Proxies for economic growth include GDPGR (GDP Growth Rate), PCRGDP (Per Capita Real GDP), and RGDP (Real GDP); the financial inclusion index is called FIINDEX; and the institutional quality index is called INSTDEX. The long-term impacts are seen in the first panel. The results of the short-run impacts and the error correction term (ECT) are shown in the second panel. D stands for the difference operator and L for the lag operator. Source: Author’s computation using Stata 19.0.

below reports the PMG estimation results, illustrating the cointegrating correlations between the study’s primary variables—financial inclusion, institutional quality and economic growth—across the sampled nations from 2008 to 2024.

Results from the Hausman test shown above indicate that PMG is preferred. The PMG estimator is predicated on the idea that while the short-term association may vary by nation, the long-term connection between financial inclusion, institutional quality growth (shown by GDPGR, PCRGDP, and RGDP) is constant across nations.

According to findings displayed in Table 3, there exists a long-term favourable connection between institutional quality, financial inclusion and growth of the economy. The linkage is positive and significant at the 1% level for all of the study’s economic growth proxies. It demonstrates that sustained economic growth is related to increased financial inclusion. Accordingly, the economy is expected to expand when a greater number of economic agents gain access to practical and reasonably priced financial services. This outcome is consistent with the hypothesis of economic development by Schumpeter (1983), which underscores the significance of financial advancement in stimulating growth through innovation. This also confirms the findings of Balele (2019) and Odeleye and Olusoji (2016). In addition, Allen et al. (2016) pointed out that access to and adoption of formal banking services allows individuals and enterprises to participate more effectively in economic activities, which is critical for overall economic growth. They argued that people that engage in formal financial services are better equipped to own, save, invest and manage risks, which boosts productivity, which is strongly connected with economic growth.

Conversely, Dahiya and Kumar (2020) draw the conclusion that inclusion in financial services has no significant desirable effect on economic progress; this discrepancy in conclusions may be due to variations in the environment, context, period and study scope. Aligning with the findings of Allen et al. (2016), this investigation demonstrates that greater access to formal financial services across all segments of society is associated with more inclusive and sustainable long-term economic growth. Financial inclusion gives individuals and firms better access to credit and payment systems, which play a critical role in job generation and the broader development of the economy.

In a similar vein, institutional effectiveness and economic growth have a desirable long-term link that is significant at the % level. This suggests that long-term improvements in institutional quality is associated with economic progress. There was an innate anticipation that institutional strength and economic expansion would be favourably correlated, with an increase in institutional quality being predicted to share a strong positive relationship with growth of the economy. These results are consistent with those reported by Shchegolev and Hayat (2018). It goes to further strengthen the position that governments of developing countries of SSA should build institutions and establish mechanisms to guide the conduct and behaviour of individuals and business entities to support their development objectives.

The PMG results indicate a negative and significant error correction term. The economic growth rate adjusts to any shocks in financial inclusion and institutional quality towards its long-run equilibrium, adjusting at a rate of 70.6%, 14.2% and 13.5% when GDP growth rate, per capita real GDP and real GDP are employed to gauge economic growth, respectively. According to Gujarati and Porter (2009), the ECT must be negative and large in order to correct the short-term divergence to the long-term equilibrium’s convergence. It is evident from the data that none of the investigation’s error terms were positive, indicating that the time series did not eventually deviate from its equilibrium. With error coefficients that are negative, statistically significant and larger than −2, the study’s results also satisfy the PMG criteria for dynamic stability (long-run relationship) (Loayza & Ranciere, 2006). Similarly, Magwedere (2019) found that every ECT outcome was statistically significant and unfavourable. Furthermore, Utile et al. (2021) found that the error correction coefficient was negative and demonstrated statistical relevance in any disequilibrium caused by institutional quality, suggesting that economic growth could adjust toward the long-run equilibrium over time.

Nevertheless, our findings indicate that there is no short-term correlation between financial inclusion and economic expansion (irrespective of proxy adopted). The latent and cumulative nature of financial inclusion connection with growth is reflected in the absence of a short-term association linking financial inclusion and economic growth. Stated differently, rather than immediate productive investment that can change aggregate production, greater availability of financial services in the short term is correlated with consumption smoothing, precautionary savings or micro-scale transactions. Conversely, the findings indicated a short-term cointegration between institutional effectiveness and economic expansion. At 10%, there is a substantial short-term correlation between institutional strength and economic progress. This short-term cointegration between economic growth and institutional effectiveness suggests that changes in institutions have more direct linkage with macroeconomic growth. Investor expectations, policy credibility and economic coordination mechanisms are all directly connected to improvements in institutional quality, such as improved regulatory efficacy, a stronger rule of law, or decreased corruption. Even over short time horizons, these advancements quickly change transaction costs, investment choices, governmental spending efficiency, and private sector confidence, all of which share a relationship with the output.

As could be expected, for each measure of economic growth, the error correction coefficient is negative and significant. Stated differently, there is long-term cointegration amongst the variables at the 1% significance level for all economic growth proxies. Therefore, for GDP growth rate, per capita real GDP, and real GDP, any disturbance from the long-run equilibrium is rectified and adjusted at a pace of 70.6%, 14.2%, and 13.5%, respectively. The findings showed a significant connection between growth of the SSA’s economies and institutional quality and inclusion in financial services.

5. Conclusions and Recommendations

This study employs the Pooled Mean Group (PMG) estimator to examine both the short-run and long-run relationships among financial inclusion, institutional quality and economic growth in SSA nations. The findings reveal a strong and positive long-term association between economic growth and both financial inclusion and institutional quality, underscoring their pivotal roles in promoting sustainable development. Enhanced access to affordable financial services is shown to have a statistically meaningful link with long-term economic growth, while institutional quality also exhibits a significant and favourable relationship with growth. The error correction term further indicates that deviations from the long-run equilibrium are adjusted at varying speeds, depending on the economic growth indicator used. It is crucial to deduce from the results that although inclusion in financial services is a vital related factor in long-term progress, its advantages could take longer to manifest since extensive behavioural adjustments and infrastructural improvements are required. However, more rapid changes in the business environment, investment climate and general economic productivity might be associated with reforms targeted at increasing institutional quality, such as bolstering legal frameworks, improving governance and combating corruption. Because they guarantee stability, lower transaction costs and foster a more predictable environment for both domestic and foreign investors, strong institutions are essential for long-term economic growth. Even before the financial system becomes more inclusive, these reforms normally have a quicker and more noticeable effect on economic performance, promoting growth.

The findings highlight the need to achieve long-term economic growth in SSA through integrated policy initiatives that strengthen both financial inclusion and institutional integrity. The established cointegrating correlations between financial inclusion, institutional quality and economic growth indicate that while financial inclusion promotes growth, its outcomes are inextricably linked to institutional quality. Therefore, although financial inclusion is related to long-term growth, institutional quality reforms are more critical and yield faster and more visible growth outcomes. Fostering sustainable economic development requires a synergistic strategy that emphasises a strong connection with both enhancing institutions and increasing financial access.

6. Limitations and Suggestions for Future Research

This study has many limitations that should be taken into account when assessing the findings, despite its empirical and policy significance. First, there may be substantial intra-country heterogeneity hidden by the research’s reliance on aggregate country-level measures of financial inclusion and institutional quality. SSA has significant differences in financial access and institutional efficacy across regions, income groups and urban–rural divides, and macro-panel data cannot adequately represent these micro-level phenomena. Second, while the cointegration and panel ARDL frameworks are ideally suited for investigating long-run and short-run interactions, they do not adequately address potential endogeneity associated with reverse causation running from economic growth, institutional quality and financial inclusion.

Future research could therefore address these limitations in a number of ways. Initially, the utilisation of household or firm-level micro-level data would allow for a more detailed examination of how financial inclusion interacts with institutional quality and is related to economic outcomes within countries. Second, future research may use different econometric methodologies, such as panel vector autoregression or instrumental variable approaches, to better capture cointegrating linkages. Third, additional study might break down financial inclusion into traditional and digital components to see if institutional quality is associated with growth differently. Lastly, extending the study to incorporate external shock variables like the volatility of commodity prices or climate-related hazards could advance our understanding of the relationship between growth resilience in SSA and institutional quality and financial inclusion.