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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".