Can Combined Microfinance Boost Economic Results? An Empirical Cross-sectional Analysis
Bibliographic record
Abstract
Worldwide, microcredit organizations are gradually transforming to multi-servicing organizations offering additional financial services. This paper examines whether combining microcredit with insurance and/or savings enhances their economic performance measured by their efficiency, productivity, sustainability or portfolio quality indicators. Using cross-sectional data from 250 microfinance institutions (MFIs) from Latin America and the Caribbean, it compares MFIs offering credit only with those combining credit with respectively savings and/or insurance. A cross-sectional multiple regression analysis shows positive effects of both savings and insurance on the efficiency and productivity of MFIs. Taking into account various risks, this can be attributed to economies of scope, especially in a context of large and mature MFIs which exhibit organisational readiness. Surprisingly, the research didn't observe significant results relating to possible effects on the sustainability and portfolio quality of MFIs.
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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 | 0.002 | 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 | 0.001 |
| 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".