CEO–Employee Pay Gap, Productivity and Value Creation
Bibliographic record
Abstract
This study examines the effect of the CEO–employee pay gap on productivity and performance. Using extensive data of 751 constituents of the Standard and Poor’s (S&P) 1500 index between the years 1992–2016, we found a cubic relationship between salary differential and corporate productivity, with a rising gap adversely affecting productivity principally when it is both too low, as well as too high; intermediate pay inequality levels are less influential. A contrast in the productivity effects of the CEO–worker pay gap for firms with high average salaries and more employees was noticeable, whereas positive productivity gains were present even with a high salary gap. Thus, big companies with a highly skilled workforce are able to achieve tangible benefits through higher salary differentiation. On the other hand, companies with lower average salaries and lower capital intensity were characterized by the negative effects of wage dispersion on productivity. As a result, increasing inequality aversion is an important issue affecting performance among smaller, lower skilled labor dependent firms. Additionally, female CEOs had a significant and positive lagged effect on productivity. Finally, firm market valuation was positively stimulated by the increasing pay gap.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".