Corporate Social Responsibility and Capital Structure
Bibliographic record
Abstract
Objective: We analyze the effects of capital structure influence on corporate social responsibility (CSR) performance, represented by the ESG score. Prior studies have investigated distinct factors to settle CSR adoption. Nonetheless, corporate social responsibility literature has not yet achieved common consent. Method: This study uses a quantitative research approach. We used a sample of listed companies from the United States of America, China, Japan, Germany, India, the United Kingdom, France, Italy, Brazil, and Canada. Three estimators were applied in the regression model, OLS pooled, IV 2SLS, and GMM 2SLS. Results: Our findings indicate a positive and significant relationship between Capital Structure and CSR. Furthermore, we understand that the positive and statistically significant findings in the relationship between market value and corporate social responsibility index are because corporate social responsibility has an intangible asset in its constitution: the corporate reputation. Therefore, these results converge into accepting these practices, which generate a firm’s value, justifying the positive and significant association. Finally, it is essential to highlight that the variations found between countries, especially companies from nations with higher GDP, need a more substantial capital structure than smaller ones to obtain a positive CSR index. Contributions: The paper argues that the capital structure can be introduced related to adopting corporate social responsibility. It is worth noting that this research aims not to provide an optimal set of factors that affect corporate social responsibility practices but to highlight the intangible effect of corporate reputation generated by the capital structure that other studies can investigate.
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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 | 0.007 |
| 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) | 0.003 | 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".