Executive compensation and compensation risk: evidence from technology firms
Why this work is in the frame
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Bibliographic record
Abstract
Purpose The purpose of this research is to investigate factors that contribute to technology firms paying higher compensation than non-technology firms, and why the mix of compensation at technology firms is different than the compensation packages at non-technology firms. Design/methodology/approach This research used a sample of 1,009 firm-year observations for the five-year period from 2001 to 2005 and random-effects regression models. Findings It was found that the total compensation paid to the CEOs of technology firms is higher than the total compensation paid to the CEOs of non-technology firms, and that the value of the stock options granted to the former is greater than the value of the stock options granted to the latter. Research limitations/implications The results are largely consistent with the labour market efficiency perspective. The higher compensation paid to CEOs in technology firms seems to be commensurate with the higher compensation risk that CEOs in technology firms bear. Practical implications Compensation designers should consider both the benefits and costs of granting stock and stock options to executives. An increased portion of stock options definitely aligns the interests of shareholders and CEOs together, and could maximize the retentive effect if CEOs have a significant amount of their wealth in unvested in-the-money options. Social implications Consistent with the literature, a CEO could earn much higher pay if he or she also serves as the chair of the board of directors. Practically, firms do not require all governance mechanisms. They just require one set of suitable governance mechanisms. Originality/value This paper is the first to investigate factors that contribute to technology firms paying higher compensation than non-technology firms, and that do explain why the mix of compensation at technology firms is different than the compensation packages at non-technology firms.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it