Executive compensation linked to corporate social responsibility and firm risk
Bibliographic record
Abstract
Purpose The purpose of this study is to empirically examine the relationship between executive compensation linked to corporate social responsibility (CSR) and firm risk. It also explores the moderating role of CSR-linked compensation on the relationship between risk-taking incentives provided in executive compensation and firm risk. Design/methodology/approach This study uses Ordinary Least Squares (OLS) and firm-fixed effects regressions to estimate the association between CSR-linked compensation and firm risk. Furthermore, it employs instrumental variable, propensity score matching and first-order difference approaches to address concerns about endogeneity and sample selection. Findings Benchmark results show that CSR-linked compensation reduces both total and idiosyncratic measures of risk. Further results indicate that CSR-linked compensation reduces firm risk only when risk is above the optimal level and has no significant effect when risk is below the optimal level. Additionally, tests show that CSR-linked compensation also mitigates the positive effect of Vega of executive compensation on risk and this mitigation effect is significant only when risk is above the optimal level. Practical implications The empirical results of this study show that boards can use CSR-linked compensation not only to induce higher social performance but also as a risk management tool to manage risk, especially when risk is above value increasing optimal levels. Furthermore, boards can use CSR-linked compensation to mitigate excessive risk-taking induced by option compensation. Originality/value This study contributes to the emerging literature on CSR-linked compensation and firm risk. To our knowledge, this is the first study that documents the direct risk-reducing effect of CSR-linked compensation and its mitigating effect on the relation between Vega of executive compensation and firm risk.
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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 | 0.000 |
| 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".