Financial sector development and microcredit to small firms
Bibliographic record
Abstract
• Negative relationship between financial development and MSME lending by MFIs. • Improvement in financial sector development decreases micro-lending to small firms. • In a less developed financial sector, MFIs lend more to MSME. This article investigates the relationship between countries’ financial sector development and the loans extended to micro, small, and medium-sized enterprises (MSMEs or small firms ) by microfinance institutions (MFIs). Using 4,801 MFI-year observations worldwide, we find a negative relationship between financial sector development and MSME lending by microfinance institutions. In other words, improvement in financial development, defined as a combination of depth, access, and efficiency, decreases micro-lending to small firms due essentially to intense competition from banks. Moreover, looking at the ownership status of microfinance institutions, we find that the intense competition between profit-oriented microfinance institutions and banks mainly drives the observed negative relationship. For nonprofit microfinance institutions, financial sector development is not significantly associated with their lending to small firms. In a less developed financial sector, microfinance institutions lend more to small firms, fulfilling their social mission.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".