P2P lending and banking credit for MSMEs and Non-MSMEs after COVID-19 pandemic: Does it matter?
Bibliographic record
Abstract
This paper proposes an original view to determine the effect of P2P loans on MSME and non-MSME bank loans after the COVID-19 pandemic as a whole and then focuses on the island of Java (more developed areas) and outside Java (areas which are still undeveloped). The approach used in this study uses panel data regression from 33 provinces in Indonesia during Jan-Dec 2022 after the COVID-19 pandemic. The results of this study confirm that P2P lending is not a disrupter for bank credit, the details of the results are: (1) P2P lending has a significant positive effect on overall MSME banking credit, but has no significant effect on overall non-MSME banking credit; (2) P2P lending has no significant effect on MSME banking credit in Java, but has a significant positive effect on non-MSME banking credit in Java after the COVID-19 pandemic; (3) P2P lending has a significant positive effect on MSME banking credit outside Java after the COVID-19 pandemic, but has no significant effect on non-MSME banking credit in Java post the COVID-19 pandemic.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".