Determinants of Successful Access to Bank Loans by Vietnamese SMEs: New Evidence from the Red River Delta
Bibliographic record
Abstract
A key target of Small and Medium sized Enterprise (SME) development is facilitating their access to finance, therefore drivers in SME credit decision making by banks are important to understand in every region and country. This paper provides an empirical analysis of the factors affecting the availability and affordability of SME loans in Vietnam. We use Ordinary Least Squares (OLS) and Logit as measures for analysing results from a survey of 20 banks and 180 SMEs conducted in 2012. The results indicate that collateral and relationship lending have positive impacts on successful access. In addition, developing relationships with lenders or seeking a guarantee from a third party can help firms mitigate stringent terms and conditions for credit approvals. On the demand side, the sector where firms operate has an influence on barriers to finance. From the supply side, with various sizes and ownerships, has different perceptions about legal uncertainties and requirements in SME lending.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".