A Panel Data Analysis of Determinants of Financial Inclusion in Sub-Saharan Africa (SSA) Countries from 1999 to 2024

Abstract

Globally, financial inclusion is regarded as being crucial for balancing an economy’s financial system. However, despite the significance of financial inclusion, it still needs to be clarified to identify what factors are responsible for the diverse trend of financial inclusion in the forty-five Sub-Saharan Africa (SSA) countries from 1999 to 2024. The main rationale of the study empirically investigated these determinants of financial inclusion in forty-five Sub-Saharan Africa (SSA) countries from 1999 to 2024, which covers three distinct periods: which is the pre-COVID, 2020–2022 is the COVID period, and the post-COVID period from 2023 onward, but examined as a whole from 1999 to 2024 for easy policy formulation for SSA countries. The study was anchored on two main research objectives: firstly, to examine the factors influencing financial inclusion in Sub-Saharan Africa (SSA) in these three distinct periods, and lastly, to present the policy implications of the result of these factors in enhancing financial inclusion in the post-COVID era in SSA. The study used the Panel Least Squares (PLS) technique in the data analysis. The result revealed that economic growth (GRO), Islamic banking (ISMAIC), money supply (MSS), internet users (USERS), and credit availability (CREDIT) positively and significantly enhance financial inclusion with coefficients of 0.001298, 4.926809, 1.08 × 10⁻⁶, 0.459388, and 0.657431, respectively, with significant p-values of 0.0008, 0.0023, 0.0000, 0.0000, and 0.000, respectively. On the flip side, internet servers (SERVER) have a negative coefficient value of 4.63 × 10⁻⁶ with a p-value of 0.000. Though inflation (INFL) and interest rate (INT.) have negative coefficient values of −0.02853 and −0.08317, they have insignificant p-value impacts of 0.2841 and 0.2501, respectively. The result indicates that many of the variables have a significant impact on financial inclusion. This is shown from the probabilities of the t statistics of each of the independent variables in the estimated model, which are significant at the 5% level. The policy implications of these results include the following: firstly, SSA governments should promote economic growth through investment in productive sectors, infrastructure development, and job creation programs to indirectly improve financial inclusion. Secondly, SSA countries’ policymakers should maintain price stability through sound monetary and fiscal policies to ensure inflation does not hinder access to financial services. Thirdly, SSA countries’ governments and central banks should promote lower interest rates and enhance credit accessibility, especially for marginalized groups, through subsidized loans and targeted credit schemes. Fourthly, policymakers should support the expansion of Islamic finance by improving regulatory frameworks and increasing awareness about Sharia-compliant financial products.

1. Introduction

Financial inclusion refers to the proportion of individuals and enterprises that actively use financial services (Oanh, 2024). It particularly emphasizes enabling marginalized and vulnerable populations to access and utilize these services at affordable rates (Sahay et al., 2015). According to Gallego-Losada et al. (2023), it implies universal access to essential financial services. Building an inclusive financial ecosystem brings about two key benefits (Cecchetti & Schoenholtz, 2020). Firstly, financial inclusion can integrate excluded individuals into the economy, fostering growth. Secondly, economic expansion leads to increased participation in both the labor market and financial institutions (Schuetz & Venkatesh, 2020). Kim et al. (2018) argue that inclusive financial systems can alleviate poverty by encouraging economic progress, enhancing access to savings and entrepreneurial activities, minimizing risk exposure, and improving overall living standards. To broaden financial access, innovations such as microfinance institutions, mobile money platforms, and payment banks have emerged to serve the unbanked (Banna et al., 2021).

Based on the financial inclusion data extracted from the World Development Indicators (WDI) database (2025), shown in Figure 1 below, presents a computed trendline illustrating financial inclusion growth trajectories in 45 SSA from 1999 to 2024. These graphs indicate that some Sub-Saharan African (SSA) countries are experiencing financial inclusion growth and why some are not witnessing financial inclusion growth. This therefore forms the rationale for this research to investigate factors responsible for why some Sub-Saharan African (SSA) countries seem to experience growth and why some are not witnessing growth in their financial inclusion process. The computed financial inclusion trendline was derived by employing Principal Component Analysis (PCA) based on the combination of the three dimensions of financial inclusion in the 45 SSA countries, which are banking penetration (Dimension 1), which is proxied by the total number of bank accounts (banked population), followed by availability of banking services (Dimension 2), proxied by the number of bank branches and the number of banks per 1000 km, and lastly, usage of banking services (Dimension 3), proxied by the credit volume to GDRP ratio and the deposit volume to GDRP ratio. Note that there is no universal consensus for measuring financial inclusion as of the time of writing this manuscript. See Appendix A for the pictorial graph of the financial inclusion variables used in the computation of the financial inclusion for SSA countries.

The above presents the crux for this study to investigate the factors responsible for the mixed trend of financial inclusion among SSA countries from 1999 to 2024. The rationale for selecting 1999 to 2024 is because it covers three distinct periods: 1999–2019 denotes the pre-COVID period, followed by 2020–2022, which is the COVID period, and lastly the post-COVID period, which starts from 2023 onward. Therefore, the study aims to empirically explore the key drivers of financial inclusion in Sub-Saharan African (SSA) countries across three defined periods: the pre-COVID era (1999–2019), the COVID-19 pandemic period (2020–2022), and the post-COVID era beginning in 2023.

The research is structured around three core objectives: first, to identify the determinants influencing financial inclusion in SSA during these distinct timeframes; second, to evaluate the impact of digital financial services on financial inclusion in the post-pandemic period; and third, to examine the socio-economic and institutional factors that shape financial inclusion in the post-COVID context. To address these objectives, the study is guided by the following research questions: (1) What socio-economic and institutional variables affect financial inclusion in SSA across the three identified periods? (2) What are the policy implications derived from the findings on the determinants of financial inclusion in SSA following the COVID-19 crisis? The structure of the paper is organized as follows: the next section presents a review of relevant literature, followed by a detailed discussion of the study’s methodology and empirical results. The Section 6 highlights the study’s key contributions to the literature and its implications for policy development.

This study contributes meaningfully to the growing body of literature on financial inclusion by examining its determinants across Sub-Saharan Africa (SSA) over a 25-year period (1999–2024). While previous research (e.g., Allen et al., 2016; Demirguc-Kunt et al., 2018) has highlighted the role of income levels, infrastructure, and regulatory quality in shaping financial inclusion globally, there remains a notable gap in region-specific empirical analysis for SSA, a region characterized by high financial exclusion and significant socio-economic disparities. This study addresses this gap by providing updated and comprehensive evidence using a panel dataset that spans before and after key digital financial interventions (e.g., M-Pesa), policy shifts, and global financial reforms that have influenced the region.

Moreover, many earlier studies focused on cross-sectional data or limited time frames, thereby missing long-term trends and policy impacts. By extending the analysis from 1999 to 2024, this research captures the evolution of financial inclusion in response to technological innovation, institutional reforms, and macroeconomic developments within SSA. It aligns with and extends existing frameworks such as the World Bank’s Global Findex and the IMF’s Financial Access Survey by not only identifying the main drivers of inclusion—such as mobile phone penetration, education, income inequality, and institutional quality—but also analyzing their dynamic interplay over time.

Furthermore, this study has policy relevance, as it offers empirically backed insights for governments, central banks, and development partners aiming to achieve the United Nations Sustainable Development Goals (SDGs), particularly SDG 1 (No Poverty), SDG 8 (Decent Work and Economic Growth), and SDG 9 (Industry, Innovation, and Infrastructure). It enriches academic discourse while providing region-specific recommendations to close the financial access gap, thus contributing both to scholarly understanding and practical policymaking in Sub-Saharan Africa.

2. Literature Review on Determinants of Financial Inclusion in Sub-Saharan Africa

This section presents information relevant to determinants of financial inclusion in SSA countries. Chummun and Ojah (2016) explored the relationship between financial inclusion and aggregate savings in developing countries. They proposed that financial inclusion initiatives tend to be more impactful in nations with a higher tendency to save—that is, where larger savings pools exist. Their analysis suggested that such savings reserves can be accessed by households and small to medium-sized enterprises (SMEs), thereby fostering increased welfare and productivity. The presence of substantial national savings supports the accumulation of funds available for lending, which in turn can enhance household financial participation. This broader inclusion can lead to improved access to essential services such as healthcare and education, ultimately contributing to a better quality of life through mechanisms such as consumption smoothing. The authors also identified potential strategies for mobilizing savings to support more effective financial inclusion.

In an emerging economy such as Zimbabwe, Thulani et al. (2014) looked at how mobile money usage has expedited financial inclusion in rural communities and how it is a proxy for other factors that determine financial inclusion. Using a concurrent dominant status design and a mixed methods approach, the study used both quantitative and qualitative methods simultaneously, with the quantitative approach holding a dominant position. A straightforward random sample procedure was used to choose the Midlands Province, where the study was conducted. The research population consisted of 262,493 homes from eight districts in the province, and a pilot sample size of 37 households was selected. The study employed a survey approach to gather data, with focus groups and a questionnaire serving as the primary tools. It was discovered that the unbanked rural population uses mobile money extensively, particularly for sending and receiving remittances. However, mobile money’s loan and savings features were not very well-liked. Users continued to use their conventional borrowing and saving strategies. The consequence is that in order to convince this particular market to switch from old methods to safe and secure means of preserving their meagre income, service providers need to step up their awareness campaigns. Furthermore, their loan eligibility will be based on how they save.

Ajide (2017) conducted a study examining the factors influencing financial inclusion, with a specific focus on institutional factors across eighteen (18) Sub-Saharan African (SSA) countries. The research employed a dynamic system of Generalized Method of Moments (SYS-GMM) to analyze the dataset. The findings consistently emphasized the importance of institutions, along with other control variables such as GDP per capita, inflation, bank concentration, and z-score, as crucial drivers of financial inclusion. Additionally, the study emphasized the significance of utilizing dimension-specific indicators of governance, alongside a composite governance index, instead of relying solely on the latter for guiding policy decisions, as they produce different policy implications.

Chikalipah (2017) investigated the determinants of financial inclusion in Sub-Saharan Africa (SSA) using World Bank country-level data from twenty SSA countries for the year 2014. The empirical results indicated that illiteracy significantly hinders financial inclusion in SSA. The insights from this research offer valuable insights for governmental agencies and international development organizations, aiding in the enhancement and acceleration of financial inclusion strategies among SSA countries.

Sanderson et al. (2018) evaluated the factors influencing financial inclusion in Zimbabwe. The study identified age, education, financial literacy, income, and internet connectivity as positively associated with financial inclusion. However, the research found that the documentation required for opening bank accounts and the distance to the nearest access point had a negative effect on financial inclusion.

Asuming et al. (2019) conducted a comparative analysis of financial inclusion in 31 Sub-Saharan African countries using data from the Global Findex database. The study observed a notable increase in the overall level of financial inclusion between 2011 and 2014, albeit with variations in both the level and pace of improvement among the countries. Individual-level factors such as age, education, gender, and wealth, as well as macroeconomic indicators such as GDP growth rate and the presence of financial institutions, along with business freedom, were identified as significant predictors of financial inclusion. The findings suggest that financial inclusion policies should target specific demographics such as women and young people.

Mhlanga and Denhere (2020) examined the factors influencing financial inclusion in Southern Africa, with a particular focus on South Africa. Financial inclusion has gained global attention due to the challenges posed by financial exclusion in tackling socio-economic issues such as poverty. Utilizing a logit model, the study identifies key drivers of financial inclusion, including age, education level, income (proxied by total salary), race, gender, and marital status. Among these factors, gender is the only variable found to have a negative impact, while all other significant variables positively influence financial inclusion. To address these disparities, African governments should promote financial products and services tailored to women, Black Africans, Coloureds, and young people. Enhancing financial access for these groups can contribute significantly to reducing poverty, inequality, and unemployment in the region.

Rashdan and Eissa (2020) investigated the factors influencing financial inclusion in Egypt, using the World Bank’s Global Findex 2017 database and logistic regression analysis. The findings show that gender does not have a significant impact on the level of financial inclusion in Egypt. However, wealthier, more educated, and older individuals are more likely to be included in the financial system. The study also identifies the primary barrier to financial inclusion as a lack of money, which prevents individuals from opening formal accounts, savings accounts, or credit accounts. The paper suggests that through targeted policy measures, a progressive approach to improving financial literacy and awareness is essential for financial inclusion to contribute positively to economic growth in Egypt.

Oumarou and Celestin (2021) examined the factors influencing financial inclusion in West African Economic and Monetary Union (WAEMU) countries and proposed a method for measuring financial inclusion within the region. The study used panel regression analysis, which indicates that real GDP, mobile phone penetration, and literacy rates positively contribute to financial inclusion. In contrast, a larger rural population and interbank credit are negatively correlated with financial inclusion levels. Additionally, agricultural financing through bank-issued credit to the government appears to enhance financial inclusion. The study also highlights the beneficial impact of rural-focused literacy programs on improving financial inclusion.

Using a panel of 46 countries for the years 2004–2018, Sarpong and Nketiah-Amponsah (2022) investigated the quantitative link between financial inclusion and inclusive development in Sub-Saharan Africa. The data indicates that, in comparison to financial service availability and knowledge, the use of financial services, among other factors, has a measurable and noticeable effect on inclusive growth. Sub-Saharan Africa’s inclusive growth is improved by 0.03 units for every unit increase in the use of financial goods and services. The study adds to the body of knowledge by using the Arellano–Bover/Blundell–Bond system Generalized Method of Moment estimator to estimate the distinct quantitative effects of three kinds of financial inclusion indicators on inclusive growth. Policymakers must create inclusive, sustainable, and creative financial institutions that can fairly distribute the advantages of growth, according to the research. Better access to corporate and retail loans, mortgages, overdrafts, credit cards, letters of credit, and user-friendly financial technology, together with reasonable lending rates and transaction fees, can help achieve this.

Bashiru et al. (2023) employed a dynamic panel analysis; this study investigates the factors influencing financial inclusion in Sub-Saharan Africa (SSA) from 2000 to 2017. Their findings indicated that financial globalization and literacy rates significantly enhance financial inclusion. Conversely, rural population growth has a strong negative effect. Additionally, while bank profitability, bank stability, and economic growth exhibit a negative relationship with financial inclusion, their impact is statistically insignificant. The study highlights the policy significance of financial globalization, suggesting that integrating local financial systems with global financial markets can play a crucial role in advancing financial inclusion across SSA.

Bekele (2023) provided a comparative analysis of the factors influencing financial inclusion in Kenya and Ethiopia at both macro and micro levels. Using a generalized linear model, it examines the determinants and obstacles to financial inclusion based on data from the 2017 Global Findex Database, while a descriptive analysis highlights macro-level differences. The findings revealed that Kenya has a higher level of financial inclusion than Ethiopia. Key macro-level factors contributing to this disparity include differences in financial liberalization policies, gross domestic product, rural population share, and the expansion of mobile money services. At the micro level, variations in literacy rates and payment methods, such as government transfers, help explain the differences between the two countries. Additionally, factors such as gender, age, employment status, and mobile phone ownership positively impact financial inclusion. However, significant barriers include lack of documentation, low levels of trust, and financial constraints.

Nsiah and Tweneboah (2023) examined the determinants of financial inclusion in Africa by considering demand, supply, and infrastructure-related factors. Using Generalized Method of Moments (GMM) and Ordinary Least Squares (OLS) estimation techniques, the analysis is based on panel data covering the period from 2004 to 2020. The data, sourced from the World Development Indicators compiled by the World Bank, includes 20 purposively selected countries based on data availability. The findings indicate that gross national income (GNI) per capita, domestic credit to the private sector, and institutional quality significantly influence financial inclusion in Africa. Additionally, GNI per capita, money supply, and institutional quality contribute to reducing barriers to financial inclusion across the continent. Unlike previous studies that focused solely on either demand or supply factors, this research integrates demand, supply, and infrastructure-related determinants within a single model, providing a more comprehensive perspective. Given these insights, policymakers and development partners in the selected countries should implement strategies to enhance financial inclusion by strengthening institutions and adopting targeted measures to eliminate barriers to financial access.

Bekele (2023) conducted a comparative analysis of the determinants of financial inclusion in Kenya and Ethiopia at both macro and micro levels. The study employed a generalized linear model to examine the factors influencing and hindering financial inclusion, drawing on data from the 2017 Global Findex Database. Additionally, a descriptive analysis was utilized to explore macro-level disparities. Kenya exhibited a higher level of financial inclusion compared to Ethiopia. Various macro-level differences, including variations in financial liberalization policies, GDP, the proportion of rural population, and the expansion of mobile money services, contributed to this discrepancy. At the micro level, disparities in literacy rates and payment receipt methods, such as government transfers, elucidated some of the distinctions between the two countries. Furthermore, gender, age, employment status, and mobile phone ownership demonstrated significant and positive associations with financial inclusion. However, challenges such as lack of documentation, trust issues, and financial constraints posed notable barriers to financial inclusion in both contexts.

Eshun and Kočenda (2025), using a dynamic panel data approach, investigated the determinants of financial inclusion in Sub-Saharan Africa (SSA) while using Organization for Economic Cooperation and Development (OECD) countries as a comparative benchmark. Utilizing the system generalized method of moments estimator, our study examines 31 SSA and 38 OECD nations from 2000 to 2021. Their finding indicated that factors such as literacy rate, trade openness, political stability, banking efficiency, income levels, and remittances play significant roles, though their effects vary across regions. Additionally, we demonstrate that different aspects of the financial system—access, usage, and quality—are influenced by distinct factors to varying degrees. We also consider the impact of major global events during this period, including the global financial crisis and the COVID-19 pandemic. Our study underscores the need for well-structured literacy policies and a more efficient financial system to enhance financial inclusion. We advocate for the strengthening of institutional frameworks to support trade openness through improved regulatory policies.

3. Data Source and Methodology

The study employed annual country data for a period from 1999 to 2024, which was obtained from the World Bank’s World Development Indicators (WDI). The model to determine the determinants of financial inclusion in 45 SSA countries was anchored on (Evans’s 2016) model, which is as follows:

$$\begin{array}{c}{\mathrm{F}\mathrm{I}\mathrm{N}\mathrm{C}}_{\mathrm{i},\mathrm{t}}={\mathsf{\tau }}_0+{\mathsf{\tau }}_1\left({\mathrm{G}\mathrm{D}\mathrm{P}\mathrm{C}}_{\mathrm{i},\mathrm{t}}\right)+{\mathsf{\tau }}_2\left({{\mathrm{M}}_2\mathrm{G}\mathrm{D}\mathrm{P}}_{\mathrm{i},\mathrm{t}}\right)+{\mathsf{\tau }}_3\left({\mathrm{C}\mathrm{R}\mathrm{E}\mathrm{D}\mathrm{I}\mathrm{T}}_{\mathrm{i},\mathrm{t}}\right)+{\mathsf{\tau }}_4\left({\mathrm{I}\mathrm{N}\mathrm{T}\mathrm{E}\mathrm{R}\mathrm{E}\mathrm{S}\mathrm{T}}_{\mathrm{i},\mathrm{t}}\right)+{\mathsf{\tau }}_5\mathrm{I}\mathrm{N}\mathrm{F}\mathrm{L}+{\mathsf{\tau }}_6\mathrm{L}\mathrm{I}\mathrm{T}\mathrm{E}\mathrm{R}+{\mathsf{\tau }}_7\mathrm{U}\mathrm{S}\mathrm{E}\mathrm{R}\mathrm{S}\\ +{\mathsf{\tau }}_8\mathrm{S}\mathrm{E}\mathrm{R}\mathrm{V}\mathrm{E}\mathrm{R}\mathrm{S}+{\mathsf{\tau }}_9\mathrm{P}\mathrm{O}\mathrm{P}\mathrm{U}\mathrm{L}\mathrm{A}\mathrm{T}\mathrm{I}\mathrm{O}\mathrm{N}+{\mathsf{\tau }}_{10}\mathrm{I}\mathrm{S}\mathrm{L}\mathrm{A}\mathrm{M}\mathrm{I}\mathrm{C}+{\mathsf{ϵ}}_{\mathrm{i},\mathrm{t}}\end{array}$$

(1)

where FINC is financial inclusion (number of depositors with commercial banks per 1000 adults); GDPC is GDP per capita; M2GDP is money supply (% of GDP); and CREDIT is the credit to the private sector (% of GDP). INFLATION is headline inflation, USERS is the number of internet users, SERVER is secure internet servers, and INTEREST is the deposit interest rate. ISLAMIC is a dummy variable that takes 1 if the country has an Islamic banking presence and activity and 0 otherwise. Ε are the residuals. The subscript i is the i-th country, and the subscript t is the t-th year.

Table 1. Apriori expectation.

S/N	Variable	Expected Sign
1	GDP per capita	+
2	M2GDP is the money supply (% of GDP)	+
3	CREDIT is the credit to the private sector (% of GDP)	+/−
4	INFLATION is headline inflation	+/−
5	USERS is the number of internet users	+/−
6	SERVER is secure internet servers	+/−
7	LITERACY is the adult literacy rate	+/−
8	Islamic banking presence	+/−

Source: Created by authors.

below presents the apriori expectation of variables in line with existing theories and empirical findings.

4. Results

To estimate the determinants of financial inclusion in SSA, the study begins with the descriptive analysis. Results of the descriptive statistics are reported in

Table 2. Summary of descriptive statistics for the determinants of financial inclusion in Sub-Saharan Africa from 1999 to 2024.

	FIIM	GRO	INFL	INT	ISIC	MSS	SERVER	USERS	CRED
Mean	21.40	1941.3	11.7584	7.2809	1	11,212	31,531	11.183	18.688
Median	11.72	959.70	6.24870	6.7381	1	26,877	701,638.8	4.65	15.035
Max	133.8	19,482	557.201	61.882	1	2.04	5,105,718	81.593	70.381
Minimum	0.001	−17.00	−16.8597	−81.132	1	0	0.68503	0	0.0015
Std. Dev.	24.41	2802.5	36.0521	13.166	0	25,203	6,892,717	15.070	14.927
Obs.	598	598	598	598	598	598	598	598	598

Source: Created by authors.

. The summary of statistics is important to explore the time-series distribution of the data collected on each of the variables. The table indicates that all variables used as endogenous variables for financial inclusion are positive. This reveals that on average all the endogenous variables are positive. For instance, the mean distribution of financial inclusion in SSA countries. This is a pointer to the fact that SSA countries’ financial inclusion is a result of these variables.

Results in

Table 3. Correlation matrix for determinants of financial inclusion in SSA countries from 1999 to 2024.

	FII	GRO	INFL	INT	ISIC	MSS	SERVER	USERS	CRE
FIIM	1
GRO	0.464	1
INFL	−0.080	−0.055	1
INT	−0.057	−0.079	−0.539	1
ISMAIC	0	0	0	0	1
MSS	0.030	0.103	−0.023	−0.015	0	1
SERVER	0.039	0.128	−0.030	−0.023	0	0.989	1
USERS	0.445	0.622	−0.017	−0.087	0	0.380	0.410	1
CRE	0.491	0.462	−0.154	−0.029	0	0.216	0.270	0.443	1

Source: Created by authors.

show that headline inflation and interest rates have a negative relationship with financial inclusion. Other variables such as economic growth, money supply (% of GDP), secure internet servers, number of internet users, and credit to the private sector (% of GDP) have a positive relationship with financial inclusion. This positive relationship between financial inclusion and other independent variables indicates that a rise in any of the aforementioned variables will increase financial inclusion, while a rise in headline inflation and internet rate will retard financial inclusion. While positive relationships exist between financial inclusion and GDP per capita, money supply (% of GDP), secure internet servers, number of internet users, and credit to the private sector (% of GDP), the degree of association shows that all independent variables can be included in the model without the fear of multicollinearity.

Various studies, such as (Anifowose et al., 2019) among others, have advised researchers to always use more than one method of panel unit root test in order to be sure of the order of integration of the variables to be included in a particular model. The reason behind this might not be unconnected to the fact that a non-stationary variable constitutes an outlier among other variables, and the inclusion can significantly influence the outcome of the empirical analysis. For this study, both the IPS, LLC, and ADF methods of panel unit root tests are adopted for consistency’s sake. Their results are presented in

Table 4. Panel unit root tests for determinants of SSA countries’ financial inclusion from 1999 to 2024.

	Levin et al. (2002)				Im et al. (2003)
	Level		First Diff		Level		First Diff
Variables	Stat.	p-Val.	Stat.	p-Val.	Stat.	p-Val.	Stat	p-Val.
FII	−1.20247	0.1146	−10.3185	0.0000	−1.10034	0.1356	−10.4873	0.0000
GRO	−1.02950	0.1516	−15.9196	0.0000	0.17815	0.5707	13.4058	0.0000
INFL	−1.20438	0.1143	−16.0053	0.0000	0.11704	0.5466	−13.6685	0.0000
INT	−1.39839	0.0810	−12.0931	0.0000	−1.67129	0.0473	−13.3773	0.0000
ISMAIC	13.3771	1.0000	−1.57036	0.582	9.31774	1.0000	−3.81231	0.0001
MSS	−2.67451	0.0037	-	-	−3.33891	0.0004	-	-
SERVER	−1.876221	0.0303	-	-	−2.2048	0.0137	-	-
USERS	0.24073	0.5951	−12.9703	0.0000	0.15184	0.5603	−9.17874	0.0000
CRE	−6.6041	0.0000	-	-	−5.80599	0.0000	-	-

Source: Author’s computation.

.

It is evident from Table 4 that all the variables are either stationary at levels or after the first difference. The implication of this is that they are suitable for all the analyses adopted in the study. The methods of the panel unit root test give the same levels of integration for each variable. This speaks volumes of the consistency level of the panel unit root results. Furthermore, the results indicate that GDP per capita and headline inflation are stationary at levels.

5. Pooled Regression Analysis for the Determinant of Financial Inclusion in SSA Countries

The essence of pool regression analysis is to verify if there will be a need to use panel data analysis for the estimation of the equation or not. Panel data application might not be necessary if there is no problem of cross-sectional dependence. In other words, if the estimated pool regression model does not have a specific effect, then pool regression will suffice for the analysis, but if otherwise, then panel data analysis is more suitable to be used for the estimation. One of the shortcomings of the pool regression is the problem of heterogeneity, which is not present in the panel data.

The result in

Table 5. Pooled regression results for determinants of SSA countries’ financial inclusion from 1999 to 2024.

Dependent Variable: AFII
Method: Panel Least Squares
Sample (adjusted): 1999–2024
Periods included: 24
Cross-sections included: 34
Total panel (unbalanced) observations: 598
Variable	Coefficient	Std. Error	t-Statistic	Prob.
GRO	0.001298	0.000384	3.383239	0.0008
INFLATION	−0.02853	0.026604	−1.072215	0.2841
INTEREST	−0.08317	0.072245	−1.151253	0.2501
ISMAIC	4.926809	1.611131	3.057981	0.0023
MSS	1.08 × 10−6	2.30 × 10−7	4.679163	0.0000
SERVER	−4.63 × 10−6	8.59 × 10−7	−5.391912	0.000
USERS	0.459388	0.07508	6.118623	0.0000
CREDIT	0.657431	0.066294	9.916842	0.000
Root MSE	19.18573	R-squared	1436
Mean dependent var	21.40235	Adjusted R-squared	0.374097
S.D. dependent var	24.41458	S.E. of regression	19.31536
Akaike info criterion	8.772966	Sum squared resid	220,119.1
Schwarz criterion	8.831743	Log likelihood	−2615.12
Hannan-Quinn criterion	8.795851	Durbin-Watson stat	0.123501

Source: Created by authors.

revealed that economic growth (GRO), Islamic banking (ISMAIC), money supply (MSS), internet users (USERS), and credit availability (CREDIT) positively enhance financial inclusion with positive coefficients of 0.001298, 4.926809, 1.08 × 10⁻⁶, 0.459388, and 0.657431, respectively, with significant p-values of 0.0008, 0.0023, 0.0000, 0.0000, and 0.000, respectively. On the flip side, internet servers (SERVER) have a negatively significant coefficient value of 4.63 × 10⁻⁶ with a p-value of 0.000. Though inflation (INFLATION) and interest rate (INTEREST) have negative coefficient values of −0.02853 and −0.08317, respectively, they have insignificant p-values of 0.2841 and 0.2501, respectively. It is an indication that many of the variables have a significant impact on financial inclusion. This is shown from the probabilities of the t statistics of each of the independent variables in the estimated model, which are significant at the 5% level. Adoption of the Gross Domestic Product per capita, presence of Islamic financing, money supply (% of GDP), number of internet users, and credit to the private sector (% of GDP) have shown significant impact on SSA countries’ financial inclusion. The result is in line with the findings of Bashiru et al. (2023).

6. Conclusions and Policy Implications

The findings on the determinants of financial inclusion in Sub-Saharan Africa show that economic growth (GRO), Islamic banking (ISMAIC), money supply (MSS), internet users (USERS), and credit availability (CREDIT) positively enhance financial inclusion with a significant positive coefficient of 0.001298 with a p-value of 0.0008, 4.926809 with a p-value of 0.0023, 1.08 × 10⁻⁶ with a p-value of 0.0000, 0.459388 with a p-value of 0.0000, and 0.657431 with a p-value of 0.000, respectively. On the flip side, internet servers (SERVER) have a negative coefficient value of 4.63 × 10⁻⁶ with a p-value of 0.000. Though inflation (INFLATION) and interest rate (INTEREST) have negative coefficient values of −0.02853 and −0.08317, respectively, they have insignificant p-value impacts of 0.2841 and 0.2501, respectively. The policy implications of these results include the following: First, SSA governments should promote economic growth through investment in productive sectors, infrastructure development, and job creation programs to indirectly improve financial inclusion.

Secondly, SSA countries’ policymakers should maintain price stability through sound monetary and fiscal policies to ensure inflation does not hinder access to financial services. Thirdly, SSA countries’ governments and central banks should promote lower interest rates and enhance credit accessibility, especially for marginalized groups, through subsidized loans and targeted credit schemes. Fourthly, policymakers should support the expansion of Islamic finance by improving regulatory frameworks and increasing awareness about Sharia-compliant financial products.

In addition, SSA countries’ monetary policies should aim to expand the money supply, particularly through financial sector deepening, digital banking, and mobile money penetration, as well as investments in digital infrastructure, which must be complemented by financial literacy programs and affordable internet access to ensure the population benefits from online banking and financial services. Policymakers should promote internet penetration by lowering data costs, expanding broadband coverage, and incentivizing fintech innovations that leverage internet access for financial services. Governments should enhance credit availability by supporting microfinance institutions, small business loans, and mobile banking credit facilities.

In conclusion, the overall policy recommendations for SSA countries include, but are not limited to, promoting digital financial services through internet expansion and mobile banking adoption. Strengthening financial sector regulations to encourage both conventional and Islamic finance models, enhancing financial literacy programs to ensure people can effectively use financial services, and lastly, stabilizing macroeconomic factors such as inflation and interest rates to create a conducive environment for financial inclusion.