How Good are Our 'Best Practices' When it Comes to Executive Compensation? A Review of Forty Years of Skyrocketing Pay, Regulation, and the Forces of Good Governance
Bibliographic record
Abstract
The recent growth in executive compensation plays a significant part in discussions of corporate governance. The dominant narrative uses agency cost theory to explain executive pay in terms of self-dealing executives and directors too weak or conflicted to stop them. The wide-spread acceptance of this explanation supports an industry of corporate governance advisors, as well as justifying activist shareholder campaigns and regulatory interventions. In fact, the actual cause of the increase in executive compensation over the past four decades has been the increasing use of equity incentives and pay-for-performance schemes. These constitute the “best practices” promoted by shareholders and governance activists over the relevant time period. Empirical studies conducted on these pay practices suggests there is little evidence they improve corporate performance and considerable evidence they produce a variety of deleterious effects. The most obvious solution is to increase board autonomy in setting pay. Indeed, the narrowness of the best pay practices promoted by the governance industry reflects a lack of sophistication both about incentives and the variety of real-world factors relevant to corporate compensation structures.
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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 | 0.010 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".