Gender, Credit Risk and Performance in Sub-Saharan African Microfinance Institutions
Bibliographic record
Abstract
The involvement of women in business in developing countries has become a subject of great interest for many researchers. In particular, female involvement in microfinance institutions has received special attention from governments and development institutions given its potential impact on poverty alleviation. This paper assesses the effect of gender on the credit risk and performance of microfinance institutions in sub-Saharan Africa. A sample of 43 microfinance institutions from 19 sub-Saharan African countries was selected and data was collected over the period 2010–2016. Seemingly unrelated regressions (SURs) were performed to examine how gender affects the credit risk and performance of microfinance institutions. The findings do not show any significant impact of female loan officers on credit risk, financial performance or social performance. Thus, all else being equal in the countries analyzed, female loan officers do not impact the credit risk and performance differently compared to male credit officers. The contribution of this paper is to shed light on the debate on the impact of gender on the performance of microfinance institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".