The influence of external governance mechanisms on the performance of microfinance institutions in Togo
Bibliographic record
Abstract
Purpose This paper aims to assess the effects of external governance mechanisms on the performance of microfinance institutions (MFIs) in Togo. Design/methodology/approach Using annual time series data from a sample of 30 MFIs during the period 2011–2015, the authors apply panel data econometrics in their estimations. Findings The results indicate that the notation by a rating agency positively and significantly affects the financial return of MFIs. The quality and the regularity of the audits negatively and significantly influence the financial performance (measured by return on assets and operating self-sufficiency) but favorably and significantly influence social performance (increased number of active borrowers (NAB) and reduced size of loans). Furthermore, supervision increases the amount of individual loans but decreases the NAB, which means deterioration in social performance. Overall, this paper shows that external governance mechanisms significantly affect the performance of Togolese MFIs, but with varying effects depending on the mechanism considered. Research limitations/implications The sample size of 30 MFIs is small, and the geographic coverage of the study is restricted to MFIs operating in the city of Lomé, Togo. The authors did not have access to the information regarding the portfolio at risk at 30 days, even though it is a measure of financial performance. Likewise, we did not have access to the appendices to the financial statements for the calculation of prudential ratios. This method, which consists of asking the institutions using a questionnaire if they comply with prudential standards, may be biased because this study cannot verify the authenticity of the responses given that the standards are quantitative. Practical implications The study findings advocate that improving the financial and social performance of MFIs requires improving the quality of external governance mechanisms. MFIs should then pay close attention to well-functioning external governance mechanisms. Social implications As MFIs are key social actors in a society, all mechanisms that contribute to their efficiency benefit society. Originality/value This study contributes to the corporate governance literature by showing that external governance mechanisms influence performance. These external mechanisms are complementary disciplinary measures to internal governance mechanisms and other tools.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".