Internal governance mechanisms and the performance of decentralized financial systems in Niger
Bibliographic record
Abstract
Purpose The purpose of this paper is to study the effect of internal governance mechanisms on the financial and social performance of Niger’s decentralized financial systems (DFS). Design/methodology/approach This paper investigated the impact of the board size and the CEO/chairman duality on financial performance and sustainability, respectively, measured by the return on assets (ROA) and operational self-sufficiency on one side and social performance measured by the size of loans granted and the percentage of female borrowers on the other side. Findings The results show that board size positively and significantly affects the ROA. The author also concludes that the duality of decision and control functions promotes the financial viability of the DFS. Regarding the impact of internal governance on social performance, the author finds that board size positively and significantly affects loan size. Research limitations/implications This study focuses on Niger’s 13 largest DFSs. However, an analysis that also includes smaller firms may show different results. Practical implications A board size of between 5 and 15 members is recommended. This would help to incorporate key skills and the active involvement of all members. Originality/value This research highlights the importance of including internal governance mechanisms, underscores an interesting problem and answers questions raised in the existing literature by invalidating or confirming the results that have been obtained thus far. As the players in the microfinance sector recognize that sound governance is an important factor for a successful outcome in any microfinance institution objective, the paper helps shed some light on the situation of DFS in Niger.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".