Questioning Bangladesh's Microcredit
Bibliographic record
Abstract
Abstract The microcredit (MC) program of Bangladesh has been a well-known success story for generation of self-employment, and poverty alleviation. Variants of this MC model are being implemented in more than 60 different countries of the world. It has become almost a universal antidote for poverty especially from 2006 when Professor Muhammad Yunus the founder of the Grameen Bank (GB) and the bank itself shared the Nobel peace prize. Although the GB is the pioneer of MC program in Bangladesh, there are many other nongovernmental organizations (NGO)s that offer the same program in Bangladesh in different forms and names. The providers of MCs claim that the overwhelming majority of the borrowers are using the loan funds profitably for productive purposes, repaying the loans and interest regularly, and thus improving their socio-economic conditions steadily.The findings of the present study are somewhat contrary and disturbing to the claim of the MC programs. This study finds that a bulk of the MC is borrowed for non productive purposes. About one quarter of the borrowers use the credit exclusively for consumption and debt repayment purposes. Nearly half of them use the credits entirely for investment purposes. For all, the return on investment is very meager. About two-third of the borrowers, on average have an impressive 83% net return on investment available for payment of interest and dividend in addition to the principal. But in case of as high as one third of them, average return on investment is not enough even to cover the most minimum or tolerance level of wages for family labors, let alone paying any interest and making any profit after keeping aside the principal.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".