A Panel Data Analysis of Determinants of Financial Inclusion in Sub-Saharan Africa (SSA) Countries from 1999 to 2024
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".