Why microfinance institutions exist: lending groups as a mechanism to enhance informational symmetry and enforcement activities
Bibliographic record
Abstract
Abstract In this paper, we focus on the economic motivation for the existence of microfinance institutions (MFIs). In doing so, our study contributes to the debate regarding why MFIs exist and, especially, what mechanisms are used to address the risks associated with their operation. In examining the reasons why some individuals are regarded as “non-bankable”, we lay out the basic economic logic that motivates the exclusion of this population from formal credit markets. Next, we show how the lending group methodology overcomes the credit dilemma which sustains and increases the exclusion of the poorest from these formal credit sources. Through this, we point out the microfinance founding mechanisms: the increase of both informational symmetry and enforcement capacity of MFIs through the enhancement of their screening, monitoring and enforcement activities. We also highlight the importance of context and gender for the success of lending groups. Finally, we analyze these mechanisms in the Brazilian context.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".