Microfinance Complementarity and Trade-Off between Financial Performance and Social Impact
Bibliographic record
Abstract
There is a widespread belief that providing access to financial services (microfinance) or reaching the poor with microcredit are perfect solutions to establish a sustainable economy or to help kick-start a bottom-up recovery and social development animated by the poor themselves through self-employment and microenterprises. Microfinance has therefore become an important instrument for poverty alleviation and for improving the welfare of the poor in both developing and transition economies. Due to the difficulty of targeting the poor, who have a lack of collateral, microfinance institutions (MFIs) are called on to achieve a balance between social impact (poverty reduction) and positive financial performance. This paper assumes that the financial objectives of MFIs operate in opposition to each other and that a trade-off is inevitable. Unbalanced panel data of MFIs for the period 1995-2013 has been extracted from the MIX Market website. In order to solve the endogeneity problem, this paper employed the dynamic system GMM (generalized method of moments) of Blundell and Bond (1998) that is considered as the new methodology currently in use in the empirical investigation of the financial performance in banking and finance. This paper outlines some of the parameters that affect the nature of trade-offs and complementarities between social and financial objectives in microfinance performance, and provides empirical evidence from cross-country analysis. Sustainability has a positive link with outreach. MFIs tend to expand their outreach in order to achieve sustainability, based on the advantages of the economies of scale. However, a threshold which makes the trade-offs or complementarities between financial and social objectives reverse if it goes beyond a certain point is also observed.
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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) | 0.001 | 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".