CEO Pay-Performance Sensitivity: A Multi-Equation Model
Bibliographic record
Abstract
This study examines the variables influencing CEO compensation in the technology sector using both exclusively exogenous and interchangeably exogenous and endogenous variables. The study was confined to a single industry to isolate industry compensation practices which may be smoothed out in multi-industry studies. Multiple equations in a vector autoregressive model were used to explain compensation in recognition of the endogeneity of variables such as sales growth, stock returns and net income. Using US firms listed on the NASDAQ, we find that CEO compensation (measured separately as salary only, stock option grants only and total compensation from all sources) to be significantly explained by firm size, the ability to reduce debt, the ability to fund growth, net income and personal characteristics. CEOs are rewarded for achieving profitability. While there is an expectation of innovation in the technology sector with research and development expenditure increasing both sales and stock returns, such innovation only contributes to CEO compensation if it is translated into rising net income in an environment of debt-reduction. Further, CEOs are rewarded for implementing disruptive technology as a competitive strategy. The ability to fund growth is pertinent for the technology sector which may be restricted in its access to debt. Increases in age, tenure and the existence of celebrity status of the CEO led to increased compensation underscoring the importance of personal characteristics.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".