Bibliographic record
Abstract
Purpose This paper aims to examine the impact of public scrutiny on chief executive officer (CEO) compensation at Standard & Poor’s (S&P) 500 firms. Design/methodology/approach This paper uses the unique opportunity provided by the 2008 financial crisis and, in particular, government support and legislated compensation restrictions in the US Department of the Treasury’s Troubled Asset Relief Program (TARP). It aggregates monetary and non-monetary executive compensation information from 2006 to 2012, with firm- and manager-level data. It presents univariate summary compensation results and uses multivariate regression analysis to isolate the impact of public scrutiny and legislated compensation restrictions on executive pay. Findings Overall, the results are consistent, with increased public scrutiny having a lasting impact on perks and temporary impact on wage and legislated compensation restrictions having a temporary impact on wage. Changes in specific perk items provide evidence on which perks firms perceive as excessive and which provide common value. Originality/value The paper contributes to the discussion of perks as excess by introducing a novel data set of perk compensation at S&P500 firms and by studying how firms choose to alter levels of specific perk items in response to increased public scrutiny and legislated compensation restrictions. The paper contributes to the literature on executive pay as there has been little inquiry into the impact of public scrutiny on compensation. Public scrutiny could be an important source of external governance if firms change behavior in response to explicit and implicit scrutiny costs.
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 | 0.004 | 0.032 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".