Explorations Into Loans Granted To Microfinance Institutions By Financial Entities: Observations From The Indian Context
Bibliographic record
Abstract
The research paper seeks to analyze the patterns of loans disbursed to Microfinance Institutions (MFIs) by various financial entities over the past 15 years. To achieve this objective, the study utilizes secondary data from NABARD and establishes a comparative framework for the growth rate and percentage distribution of loans extended to MFIs by different commercial and public banks as well as financial institutions. The study's findings indicate that over the last 15 years, commercial banks have been the predominant contributors, accounting for 50 percent of the loans disbursed to MFIs. Additionally, the growth rate of loans disbursed by commercial banks has been the highest at 26.58 percent. However, the analysis in the study uncovers that the onset of COVID-19 led to a decrease in loan disbursements, followed by a subsequent increase in the post-pandemic period. This shift is attributed to the heightened demand for financial services from businesses and households that experienced economic setbacks during the pandemic
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".