Capital Structure and Its Determinants—A Comparison of European Top-Rated CSR and Other Companies
Bibliographic record
Abstract
Corporate social responsibility (CSR), ethics, and sustainability have become an inseparable part of the discourse of modern business. Applying linear regression and comparison of intervals of beta-coefficients, we focused on the mediating role of CSR in the relations between capital structure and its determinants. Examining the sample of European large caps, we observed that CSR companies are significantly more leveraged than non-CSR ones. The influence of the corporate income tax rate and depreciation and amortization on leverage does not differ significantly between CSR and non-CSR companies. Moreover, tax shields seem to be insignificant for both CSR and non-CSR companies. However, we should stress that, for depreciation and amortization, the beta coefficient has a different significance in the model of CSR companies, compared to the model of non-CSR companies. Also, the difference between the models regarding the relations of leverage and asset tangibility is worth noting. Non-CSR companies with a higher proportion of fixed assets have lower leverage. This result was not confirmed for CSR companies. The hypothesis that CSR replaces the role of collateral cannot be confirmed. Available cash influences leverage negatively in both models, supporting the pecking-order theory. This result is much stronger for non-CSR companies compared to CSR ones. This study found fewer statistically significant differences between CSR and non-CSR companies regarding capital structure determinants than were expected.
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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 | 0.002 | 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 | 0.000 |
| 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".