Financial Institution Type and Firm-Related Attributes as Determinants of Loan Amounts
Bibliographic record
Abstract
Access to formal credit remains critical for business operations, particularly for firms unable to generate sufficient funds internally. Using the World Bank’s Enterprise Survey dataset, 2018, we analyzed 230 Kenyan firms that applied for loans. These loans are sourced from banks (private, commercial, or state-owned) or non-banking financial institutions. Specifically, the paper explores the effect of financial institution type and firm-related characteristics on loan amounts advanced. The results show that the preferred credit provider matters, with the sensitivity level varying among the three institutional types. Additionally, the collateralization value, the owner’s equity proportion of fixed assets, and any existing credit facility correlate positively with the outcome variable. There is an inverse relationship between the largest shareholder’s ownership and the loan amount. The study uses the new product (service) launches to measure innovation. The findings suggest that firms in the innovation process access higher loan amounts than their non-innovative peers. Be that as it may, the difference in amount effect size between the two groups is small based on Cohen’s d rule. The paper highlights the theoretical and practical implications of these findings.
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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 | 0.002 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".