CEO Compensation in the U.S.: Are CEOs Underpaid or Overpaid ?
Bibliographic record
Abstract
This study investigates the adequacy of CEO compensation from the perspective of using accounting measures to assess the performance of CEOs. The main objective of this research is to determine to what extent compensation packages received by American CEOs represent an underpayment of CEOs based on the performance of their firms when firm performance is defined in terms of accounting measures. CEO compensation data are obtained from Compustat, 10K SEC filings, and Forbes listing of CEO data. The analysis covers a two-phased time period i.e., before and after the financial crisis in the USA. CEO compensation data are analyzed for the years 2004, 2005, 2006, and 2007 (pre-financial crisis) and for years 2009 to 2013 (post financial crisis). Multiple regression models consisting of six accounting performance measures are used to perform the analysis to determine the extent of CEO underpayment or overpayment. Having examined 1151 CEO compensation packages to determine if CEO underpayment exist in light of what is an overwhelming literature supporting CEO overpayment, the results show that 67.33% of the CEOs were in fact underpaid based on their firms performance, and only 32.67% (376 CEOs) were overpaid based on firm performance.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".