Framed! The Failure of Traditional Agency Cost Explanations for Executive Pay Practices
Bibliographic record
Abstract
This is the second article in a series exploring the empirical evidence arising from the increasing use of certain executive compensation best practices. The first article, “How Good Are Our ‘Best Practices’ When It Comes to Executive Compensation?” summarizes research findings that these best practices are responsible for most of the growth in executive compensation, and lead to suboptimal corporate performance. It also suggests that the best practices currently in widespread use contradict practices that are often very helpful to directors in setting appropriate incentives in real world circumstances.This article goes on to argue that failures in executive compensation are the result, not of overly powerful CEOs confronting supine boards, but rather of directors and management earnestly striving to follow bad “best practices” promulgated by the corporate governance industry. This can be seen in: (1) the pattern of cause and effect distinguishable in the history of changing North American and British pay practices; (2) the link between these questionable pay practices and various measures of board independence and managerial weakness; and (3) the increasing use of these pay practices in circumstances of increased shareholder power. The most obvious solution is to increase board autonomy in setting pay. Regulatory steps for doing so lay close at hand, and in some cases have been discussed for years.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".