Efficiency and Determinants of Capital Structure in the Greek Pharmaceutical, Cosmetic and Detergent Industries
Bibliographic record
Abstract
The purpose of this paper is to investigate the relationship between a firm’s capital structure (i.e., leverage) and its operating environment, taking into account firm (i.e., efficiency, asset structure, profitability, size, age and risk) and industry effects. For a sample of Greek pharmaceutical, cosmetic and detergent (PCD) enterprises, firm efficiency was estimated using bootstrapped data envelopment analysis (DEA), and a leverage model was produced using ordinary least squares (OLS) regression. The findings confirm the significance of firm efficiency (i.e., the franchise-value hypothesis over the efficiency-risk hypothesis) and asset structure on leverage. Efficiency and overall and short-term leverage have a significant negative relationship, indicating that more efficient firms tend to choose a relatively low debt ratio. Pharma firms are more affected since they are less efficient than cosmetics and detergents firms. Furthermore, asset structure and short- and long- term leverage have a significant negative and positive relationship, respectively, indicating that the firms with more tangible assets have less short-term debt and more long-term debt in their capital structure. Cosmetic and detergent firms, which have slightly more tangible assets than pharma firms, appear to be able to substitute high-cost, short-term debt with the low-cost, long-term debt by using such assets as collateral.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".