The Growing Executive Compensation Advantage of Private Versus Public Companies
Bibliographic record
Abstract
Private companies have a natural governance advantage over public companies—one that stems mainly from the presence on their boards of their largest owners. This governance advantage is reflected in the greater effectiveness of private company executive pay plans in balancing the goals of management retention and incentive alignment against cost. Private company investor‐directors are more likely to make these tradeoffs efficiently because they have both a much stronger interest in outcomes than public company directors and more company‐specific knowledge than public company investors. Furthermore, private company boards do not have to contend with the external scrutiny of CEO pay and the growing number of constraints on compensation that are now faced by the directors of public companies. Such constraints focus almost entirely on one dimension of compensation governance—cost—in the belief that such constraints are required to limit the ability of directors to overpay their CEOs. In practice, any element of compensation can serve to improve retention or alignment, as well as being potentially costly to shareholders. Furthermore, any proscribed compensation element can be “worked around” by plan designers, provided the company is willing to deal with the complexity. For this reason, rules intended to deter excessive CEO pay are now effectively forcing even well‐intentioned public company boards to adopt suboptimal or overly complex compensation plans, while doing little to prevent “captured” boards from overpaying CEOs. As a result, it is increasingly difficult for public companies to put in place the kinds of simple, powerful, and efficient incentive plans that are typically seen at private companies—plans that often feature bonuses funded by an uncapped share of profit growth, or upfront “mega‐grants” of stock options with service‐based vesting.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".