A Panel Data Analysis of Determinants of Financial Inclusion in Sub-Saharan Africa (SSA) Countries from 1999 to 2024
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».