Measuring the Outreach Level of Micro-finance Institutions in Bangladesh
Bibliographic record
Abstract
Reaching the poor is one of the main objectives embedded in the programs of microfinance institutions (MFIs). However, there is the question of how well MFIs have fared in terms of meeting this objective, which has been heavily surveyed as an issue by many researchers. In Bangladesh, while not discounting other factors such as the financial assistance received from institutions such as the IMF/World Bank, Asian Development Bank (ADB), and Islamic Development Bank (IDB), the impetus for the speedy reduction in the number of poor people in the country can be attributed to the existence of MFIs. This study attempts to investigate the depth of MFIs’ outreach level in the country. Specifically, using an econometric model, it examines the determinants of the outreach level of MFIs operating in Bangladesh. Overall, this study looks at the eleven (11) biggest MFIs in Bangladesh in terms of their share of active borrowers. The data are compiled from the most reliable sources pertaining to the economic activities of MFIs. The results indicate that the number of years an MFI has spent serving clients, its ratio of borrowers to staff, the size of its assets, and the number of branches all has a positive effect on its outreach level. In contrast, the average loan balance per borrower and cost per borrower have a negative effect on the outreach level of MFIs in Bangladesh. Indeed, as far as outreach level and its relationship with the independent variables are concerned, all of the results obtained in this study are consistent with the expected signs, thereby implying that MFIs in Bangladesh are no different from the conventional wisdom.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".