Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically examine the relation between incentives from CEO inside debt (deferred compensation and pension benefits) and corporate social responsibility (CSR). Design/methodology/approach Instrumental variable (IV-GMM) regressions are used to estimate the relation between CEO inside debt and CSR. Findings The results of this paper indicate that CEOs with large inside debt tend to invest more in CSR. Analysis of CSR strengths and concerns supports this finding and shows that CEO inside debt is significantly positively (negatively) associated with CSR strengths (concerns). Further tests indicate that CEO inside debt exerts a positive and significant effect on all five dimensions of social performance (diversity, community, product, employee relations and environment). Research limitations/implications The results of this study are based on US corporations. Future research should investigate if these results hold for firms in other countries in order to better our understanding of the relation between CEO inside debt and CSR. Practical implications CEOs use CSR as a risk management strategy to reduce corporate risk in order to protect the value of their inside debt. Social implications The results in this paper provide a practical tool to boards of corporations to increase investment in CSR. The results suggest that boards can encourage CEOs to invest in CSR by increasing incentives from inside debt. Originality/value This study contributes to the literature that examines the relation between inside debt and CSR by showing that CEO inside debt exerts a positive impact on CSR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".