The Impact of Microfinance Institutions on Poverty Alleviation
Bibliographic record
Abstract
Microfinancing has been targeted as a tool to address Poverty through the provision of credit to the poor and marginalised economic functions. However, the main objective upon which these institutions are founded is yet to manifest primarily in developing economies. This study examined the role of microfinancing in poverty alleviation by employing a Vector Error Correction Model on quarterly time-series data. The results reveal a significant long-run relationship among the variables poverty, microfinancing, SMEs, and agricultural growth. Contrary to expectations, Microfinancing was found to increase poverty in the long run. SMEs and agricultural development were found to reduce the level of poverty in the long run. In the short run, regression results reveal that SMEs’ growth alleviates poverty, and poverty increases the growth of microfinance loans in the country. The increase in SMEs is a tool for alleviating poverty, and the growth in microfinance institutions is also being driven by poverty. This suggests that continued improper microfinancing can escalate the poverty levels to undesired heights. The findings imply that the growth of microfinance loans is not being put to its intended and efficient use. These findings bring to the fore that it is not only the provision of funds that matters.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".