Profit-orientation and efficiency in microfinance industry: an application of stochastic frontier approach
Bibliographic record
Abstract
The aim of this study is to ascertain the effect of the current wave of commercialization of microfinance institutions (MFIs) on their financial and social efficiency. We hypothesize that during the period of analysis, while the for-profit institutions achieve higher levels of financial efficiency, the non-profit MFIs achieve better social efficiency levels. In order to realize the underlined objective of this study, we use a sample of 162 MFIs gathered from Microfinance Information eXchange (MIX) market database, for the period 2007–2013 from 30 countries located in four regions Africa, MENA, Asia and Latin America. The analysis is based on a two-stage model. The first stage consists on classifying MFIs into two groups according to their main orientations (for-profit and non-profit). We design afterward two models to obtain both social and financial estimates for each particular type of MFIs, using the stochastic frontier analysis model. In the second stage, a regression analysis is carried out to determine which variables have an effect on financial and social efficiency.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".