Executive Compensation and Tax Policy: Lessons for Canada from the Experience of the United States in the 1990s
Bibliographic record
Abstract
Until now the legal and regulatory measures that have been taken in the United States and Canada to combat excessive executive compensation have been largely ineffectual. The one possible exception is the tax deductibility cap of ý162(m) of the US Internal Revenue Code, which was introduced in 1993. A similar but improved provision ought to be considered by Canadian policymakers. There are several lessons Canadian policymakers can take from the US experience with ý162(m). First, policymakers should consider tightening, although not eliminating, the performance-based exemption. Second, policymakers should not anticipate a deductibility cap to raise a considerable amount of tax revenue or totally prevent CEOs from engaging in rent-seeking behaviour. Third, policymakers should strongly consider prohibiting executives from unravelling the incentives associated with performance-based compensation by entering into hedging transactions. Finally, Canadian policymakers would be wise to carefully consider the effects a deductibility cap would have on the competitive international environment in which Canada competes for corporate patronage.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.017 | 0.005 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| 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".