Corporate governance and the rise of integrating corporate social responsibility criteria in executive compensation: Effectiveness and implications for firm outcomes
Bibliographic record
Abstract
Research Summary This study examines the integration of corporate social responsibility (CSR) criteria in executive compensation, a relatively recent practice in corporate governance. We construct a novel database of CSR contracting and document that CSR contracting has become more prevalent over time. We further find that the adoption of CSR contracting leads to (a) an increase in long‐term orientation; (b) an increase in firm value; (c) an increase in social and environmental initiatives; (d) a reduction in emissions; and (e) an increase in green innovations. These findings are consistent with our theoretical arguments predicting that CSR contracting helps direct management's attention to stakeholders that are less salient but financially material to the firm in the long run, thereby enhancing corporate governance. Managerial Summary This paper examines the effectiveness and implications of integrating environmental and social performance criteria in executive compensation (CSR contracting)—a recent practice in corporate governance that is becoming more and more prevalent. We show that CSR contracting mitigates corporate short‐termism and improves business performance. Firms that adopt CSR contracting experience a significant increase in firm value, which foreshadows an increase in long‐term operating profits. Furthermore, firms that adopt CSR contracting improve their environmental and social performance, especially with respect to the environment and local communities. Overall, our findings suggest that CSR contracting directs management's attention to stakeholders that are less salient but financially material to the firm in the long run, thereby improving a firm's governance and its impact on society and the natural environment.
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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 | 0.022 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".