Financial Sustainability of Non-Governmental Microfinance Institutions (MFIs): A Cost-Efficiency Analysis of BRAC, ASA, and PROSHIKA from Bangladesh
Bibliographic record
Abstract
Microcredit for poverty alleviation program got huge success in many parts of the world in reducing income-poverty and human-poverty. The progress on human poverty reduction was faster than the income-poverty. There are insignificant researches of financial efficiency of microcredit delivery programs undertaken by the Microfinance Institutions (MFI). The objective of this paper is to measure the cost-efficiency of three leading micro-finance providers: BRAC, ASA, and PROSHIKA from Bangladesh. The paper intends to achieve five years X-efficiency scores for the above-mentioned micro-finance providers. In the light of this main objective, the specific two objectives of this study are: (i) To find efficiency score of these three NGOs for the study years to identify the best and poor practices; and, (ii) To analyse the potential improvement. We investigate financial efficiency of the group-based lending institutions by using data envelopment analysis (DEA), which is a frontier approach. The study finds that the credit program of BRAC-1998, BRAC-2000, and PROSHIKA-1998 were 100% efficient in output maximization and that PROSHIKA-1998, BRAC-1998, and PROSHIKA-2000 were 100% efficient in input minimization. However, the micro credit program of BRAC, ASA, and PROSHIKA were not efficient for the year 1999. The other study period for the three micro-finance providers were less than 100% efficient in both input minimisation and output maximization problem. Our findings imply that the improvement of micro-finance programs in poverty alleviation requires changes both at program & providers levels as well as at the policy level of donors and/or other organizations who provide funds to these microfinance providers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".