CEO Perquisites in Canada, 1971-2008: Certainly Not Pure Managerial Excess
Bibliographic record
Abstract
This paper explores Canadian market data on CEO perquisites gathered by a large consulting firm over the period from 1971 to 2008. Perquisites are one of the least documented total compensation components in the academic literature on executive pay. Scant existing literature may be due to the relatively recent and limited corporate disclosures on CEO perquisites, as well as to the comparatively modest monetary value of perquisites relative to other total CEO compensation components. Meanwhile, CEO perquisites regularly capture the public’s imagination in the media because of some perceived excesses, such as immoderate personal use of corporate aircraft (see Rajan & Wulf, 2006). We document a significant evolution in CEO perquisites practices over the period. Consistent with a nascent body of literature, this paper supports empirically hypotheses arguing that CEO perquisites do not uniquely occur as a result of an agency problem, the main theoretical explanation for their existence as of yet, but that they can also serve a legitimate, value-creating, business purpose for the benefit of shareholders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".