Financial Intermediation by Microfinance Banks in Rural Sub-Saharan Africa: Financial Intermediation Theoretical Approach
Bibliographic record
Abstract
Premised on Meta analysis of financial intermediation theory by Gurley and Shaw (1960), Leland and Pyle (1977), Diamond and Dybvig (1983), Allen and Santomero (1996), Scholtens and van Wensveen (2000), the main purpose of this study is to test for the predictive power of each of the dimensions of financial intermediation of market penetration and quality of financial services on financial inclusion of the poor by microfinance banks in rural sub-Saharan Africa grounded on the financial intermediation theory. This study adopted a cross-sectional research design and data were collected from 400 poor households located in rural Uganda. The data were analyzed using ordinary least square hierarchical regression (OLS) in SPSS (statistical packages for social sciences) to generate the explanatory power of each of the dimensions of financial intermediation on financial inclusion based on coefficient of determination (R²). In addition, results from analysis of variances (ANOVA) were also generated to establish the differences in the perceptions of the poor towards being financially included through financial intermediation. The results revealed that market penetration and quality of financial services as dimensions of financial intermediation significantly explains 22 percent of the variation in financial inclusion of the poor in rural Uganda. Additionally, when individual effects were considered, both market penetration and quality of financial services had significant and positive effects on financial inclusion of the poor in rural Uganda. Accordingly, our study contributes and recommends specific policies toward the role of financial intermediaries in financial deepening, especially in rural sub-Saharan Africa where there are limited presence of traditional banking structures to serve the unbanked rural poor households.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".