The Effect of Accessibility to Bank Branches on Small- and Medium-Sized Enterprise Capital Structure: Evidence from Swedish Panel Data
Bibliographic record
Abstract
This paper aims to investigate the effects of accessibility to bank branches on the capital structure of small- and medium-sized enterprises (SMEs) by analysing the change in three different leverage measures (total, short-term and long-term leverage). The analysis was conducted using random effects models on two data samples. The full sample consisted of 19,064 SMEs while the other sample used to estimate the long-term leverage consisted of 8707 SMEs over two years, 2007 and 2013. The results show that the distance to the nearest bank branch has a negative relationship with total leverage and short-term leverage but a small positive relationship with long-term leverage. An interesting result from the robustness test shows that the distance to the nearest bank negatively affects the long-term leverage of SMEs in rural areas. SME owners and policymakers may benefit from this research amidst the changing banking landscape; policymakers can help increase access to other types of funding for SMEs in bank deserts by increasing the volume of governmental loans. To the best of the authors’ knowledge, the distance to the nearest bank branch office has not been examined in the earlier literature as a determinant of the leverage of SMEs.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".