Bibliographic record
Abstract
The micro-finance revolution has changed attitudes towards helping the poor in many countries and in some has provided substantial flows of credit, often to very low-income groups or households, who would normally be excluded by conventional financial institutions. Bangladesh is starkest example of a very poor country, where currently roughly one quarter of rural households are direct beneficiaries of these programs (Khandker 2003). Much has been written on the range of institutional arrangements pursued in different organizations and countries and in turn a vast number studies have attempted to assess the outreach and poverty impact of such schemes. However, amongst the academic development community there is a recognition that perhaps we know much less about the impact of these programs than might be expected given the enthusiasm for these activities in donor and policy-making circles. To quote a recent authoritative volume on micro finance: “MFI field operations have far surpassed the research capacity to analyze them, so excitement about the use of micro-finance for poverty alleviation is not backed up with sound facts derived from rigorous research. Given the current state of knowledge, it is difficult to allocate confidently public resources to micro-finance development.” (Zeller and Meyer 2002). This is a very strong statement of doubt and in part reflects lack of accurate data, but also in part methodological difficulties associated with assessing exactly what proportion of income and other effects on the beneficiaries of micro credit can actually be attributed to the programs themselves. In recognition of this uncertainty this paper aims to bring together some of the recent evidence that has been accumulating on the impact of micro finance activities on poverty reduction. In particular we ask what is the evidence on three specific issues - the extent to which micro finance initiatives have made a lasting difference in pulling households out of poverty on a permanent basis; - the extent to which micro finance programs reach only the better-off amongst the poor, leaving the ‘core poor’ unaffected; - how far micro finance is a cost-effective means of transferring income to the poor.
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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) | 0.000 | 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 | 0.001 |
| 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".