Three Versions of Perfect Pay for Performance (Or The Rebirth of Partnership Concepts in Executive Pay)
Bibliographic record
Abstract
During the 19th century and the first half of the 20th, the compensation of non‐founder managers of U.S. public companies was guided by partnership concepts. Andrew Carnegie made his senior staff coowners by selling them stock at book value. And Alfred Sloan gave the senior staff of General Motors a fixed percentage of the company's “economic profit.” But in the years since World War II, such partnership concepts have largely disappeared from executive pay. The current view of executive pay is guided by the concepts of “competitive pay” and pay components. But unlike the partnership models of the past, today's “human resources model” of executive pay fails to provide useful guidance to companies on how to achieve a consistent relationship between pay and corporate performance, as reflected in returns to shareholders. As the author argues, the model's insistence on providing “competitive pay” packages that are (1) based on size (that is, on revenue not profitability) and (2) “recalibrated” every year regardless of past performance has the effect of undermining management's incentives by rewarding poor past value performance with increases (instead of reductions) in sharing percentage, and penalizing superior value performance with reductions (instead of increases) in sharing percentage. In recent years, however, three different model pay plans have been proposed that provide both competitive pay and fixed pay leverage in relation to shareholder value. The author is the source of one of the three “perfect” pay plans. The other two are (1) the Dynamic Incentive Account proposed by Alex Edmans of London Business School and Xavier Gabaix of NYU and (2) the investment manager fee structure developed and used by Don Raymond, the chief investment strategist of the Canada Pension Plan. The author shows that cumulative pay under all three plans can be expressed as a function of cumulative market compensation (that is, the pay earned by one's peers over the life of the plan, thus reflecting pay levels for average performance) and cumulative value added (as reflected, say, in the company's TSR relative to the average of its peers' over the life of the plan)—and in the case of plans with equity‐like leverage, cumulative pay is the simple sum of cumulative market compensation and a fixed share of the cumulative value added. The plans reconcile retention and performance objectives more effectively than current practice because they provide competitive pay only for average performance, while using the partnership concept of fixed sharing of the value added to provide strong incentives.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".