Social Versus Financial Performance of Microfinance: Bangladesh Perspective
Bibliographic record
Abstract
Microfinance is a tool designed for poverty alleviation by providing financial services more specifically small credit to the poor household for income generating activities. One of the better ways to help poor people for poverty alleviation is through giving them financial services that cannot be done in traditional banking system. However, there is a big question whether it is possible to provide those services for a financial institution without being sustainable financially. How far it can go with free lunch that is depending on donors’ fund. These two patterns place microfinance at the intersection. One may wonder whether the microfinance compromises a trade-off between serving the poor as social objective and attaining financial sustainability as financial objective. If microfinance institute wishes to get financial sustainability through profit maximization rather ignoring intended social objective of alleviating poverty, than it loses its momentum and becomes like other traditional financial institute. Fulfilling social objective with financial sustainability will be the optimum outcome of microfinance. Microfinance has been pioneered primarily in Bangladesh and later replicated in rest of the world. By this time, over 33 million of clients are being served with various financial and non-financial services by over 700 registered microfinance institute in Bangladesh. This study intent to measure the social outreach versus financial sustainability of microfinance institute in Bangladesh through panel data analysis. To do this, we have analyzed the relationship between financial performance and depth of outreach of top 20 microfinance institutes of Bangladesh from 2015 to 2017. Our results show that the relationship is positive or neutral in some cases. Therefore, microfinance in Bangladesh has been attaining both social and financial objectives and there appears no mission drift.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".