The Determinants of the Efficiency of Microfinance Institutions in Africa
Bibliographic record
Abstract
Over the past few decades, microfinance institutions have attracted the interest of governments and academics alike, given their unique nature of being financial institutions with a dual mission of promoting social development and reducing poverty. However, concerns have been raised about their effectiveness in achieving these goals while remaining financially sustainable. In this study, we attempt to examine the factors that have the greatest impact on the social, financial, and overall efficiency of microfinance institutions in African regions. We adopt a two-step approach: First, we assess the efficiency scores of 95 microfinance institutions in Africa between 2005 and 2018 using a data envelopment analysis (DEA) approach. We then regress their efficiency scores on a set of determinant variables, capturing the microfinance institutions’ characteristics. Our findings suggest that a majority of institutions prioritize profitability over social outreach. Furthermore, the panel data regression indicates that factors such as profitability, equity capitalization, types of loans, and low gross domestic product (GDP) have a positive influence on microfinance institutions’ efficiency. Conversely, variables including their risk portfolio, grants, microfinance institution status (Non-Governmental Organization (NGO), cooperative, etc.), operational area, political environment, and size exert a negative impact on efficiency. Through this study, we seek to enhance our understanding of microfinance institutions and to identify the factors that impact their operational efficiency, thereby reinforcing their crucial role in advancing financial inclusion, empowering marginalized communities, and fostering inclusive economic growth.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".