Factors Influencing Sustainable Poverty Reduction: A Systematic Review of the Literature with a Microfinance Perspective
Bibliographic record
Abstract
This research examined factors that help microfinance achieve sustained poverty reduction based on a systematic literature review (SLR). A search was conducted on the SCOPUS database up to December 2023. After analyzing hundreds of documents, a subset of 30 articles was subject to in-depth analysis, exploring factors and corresponding measurement indicators for sustainable poverty reduction in microfinance contexts. This article emphasizes that sustained poverty reduction is a gradual process requiring ongoing efforts from both Microfinance Institutions (MFIs) and governments. Two key success factors are empowering borrowers and ensuring the microfinance programs themselves are profitable. When implemented in an integrated and coordinated manner, these factors can empower individuals to escape poverty by fostering self-employment and income generation, ultimately reducing dependence on external support. Additionally, the study highlights the role of personality traits in influencing long-term entrepreneurial success. The findings provide valuable tools for MFIs and policymakers. MFIs gain a practical framework to guide their interventions towards sustained poverty reduction. Policymakers can leverage the identified factors and indicators when designing and implementing microfinance policies with a long-term focus on poverty alleviation. This study breaks new ground by presenting an operational framework that categorizes and integrates two critical factor groups: empowerment and beneficiary profitability. Furthermore, it links these factors to corresponding measurement indicators within a unified framework, enabling a more holistic assessment of poverty reduction efforts.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".