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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".