A Cost Function Approach to MFI Efficiency: The Role of Subsidy and Social Output Measures
Bibliographic record
Abstract
Microfinance institutions (MFIs) strive to reach many poor clients while remaining financially sustainable. Most MFIs have quasi-equity owners who expect the organization to be financially self-sufficient, but do not necessarily expect competitive return on their equity. This chapter analyses the efficiency in MFIs by developing a model to identify the optimal MFI size that takes into account the outreach and the sustainability aspects of MFI performance. Moreover, we assess how results vary when we account for the cost of capital subsidy, measured by the opportunity cost of equity. Since MFIs across the world operate in diverse environments, we also estimate optimal MFI size by region and control for a number of internal and external characteristics. By identifying the optimal size of an MFI based on its location and other characteristics, this research could help donors understand the role of size in MFIs’ performance and help practitioners cut costs and achieve their social mission.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".