UPWARD RATCHETING OF CEO COMPENSATION IN LARGE CANADIAN CORPORATIONS: ALTERNATIVE EXPLANATIONS
Bibliographic record
Abstract
Anecdotes, business press, and some empirical studies suggest that the 1993 OSC requirement for executive salary disclosure, coupled with traditional market forces, has led to information efficiency of executive labor market, resulting in an upward ratcheting of CEO compensation in large Canadian public corporations. Based on the tenets of managerial power, social comparison, and hidden action, we maintain that the information efficiency-based argument is partial, and that psychological, organizational, and social forces are even more powerful influencers of upward ratcheting. We present the case of a board’s compensation committee as an ideal context for supporting our argument. Over the last couple of decades, the relationship between CEO compensation and firm performance has generated a great deal of controversy. Besides becoming a topic of both interest and scrutiny in the business press, this relationship has also inspired a rich stream of debate among academic researchers in finance, organization theory, and strategy. A consistently high rate of growth in the average earnings of CEOs in Canada and the US over the recent past has stirred this controversy. Although the notion that rewards given to the heads of our public corporations should
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".