Are ESG performance-based incentives a panacea or a smokescreen for excess compensation?
Bibliographic record
Abstract
Purpose This paper aims to examine how the use of environmental, social and governance (ESG) incentives intersects with top management power and various corporate governance mechanisms to affect excess annual cash bonus compensation. Design/methodology/approach The authors use a novel artificial intelligence (AI) technique to obtain data about ESG incentives use by firms in the S&P 500. The authors test the hypotheses with an endogenous treatment-regression and a contrast test. Findings When the top management team has power and uses ESG incentives, there is a 32% reduction in excess annual cash bonuses implying ESG incentives are an effective corporate governance tool. However, nuanced analyses reveal that when powerful management teams with ESG incentives are from environmentally sensitive industries, have a corporate social responsibility (CSR) committee or have long-term view institutional shareholders, they derive excess bonuses. Practical implications Stakeholders will better understand management’s motivations for the inclusion of ESG incentives in executive compensation contracts and be able to identify situations which require closer scrutiny. Social implications Given the increased popularity of ESG incentives, society, regulators, boards of directors and management teams will be interested in better understanding when these incentives might be effective and when they might be abused. Originality/value To the best of the authors’ knowledge, this study is the first to examine the use of ESG incentives in relation to excess pay. The authors contribute to both the CSR and executive compensation literatures. The work also uses a new methodological technique using AI to gather difficult-to-obtain data, opening new avenues for research.
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".