Capital Structure, Financial Performance, and Sustainability of Micro-Finance Institutions (MFIs) in Bangladesh
Bibliographic record
Abstract
Capital structure plays an important role in organizational performance. Sources of funds for micro-finance institutions (MFIs) and their performance and financial sustainability become an important topic for the MFIs and poverty alleviation initiatives to achieve sustainable development goals of the UN. We explored the following question: Does the financial structure in terms of financial leverage affect the financial performance: Financial sustainability, depth, and breadth of outreach of MFIs? Our research focuses on studying the relationship between capital structure and financial performance of micro-finance institutions as well as achieving the objectives of this program by reaching out to the deserving clients without collaterals. A dataset of 187 MFIs is used to establish the relationship between the capital structure and performance of MFIs. Panel data regression analysis has been used for this study using the Random effect and Fixed effect models. Return on Asset (ROA), and Net Income to Expenditure (NIER) have been used as measures of financial performance. The findings indicate that Equity to Asset Ratio (EAR), Debt to Loan Ratio (DTL), Risk, and Size are the factors that influence NIER. Furthermore, EAR, and DTL have a positive effect on ROA, and Risk has a negative effect. The findings of this study will enable MFIs to configure their capital structure by creating a portfolio of sources of their capital from market-based sources of funds that can maximize their financial performance and reach out to poor clients without collaterals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".