Are CEOs compensated for value destroying growth in earnings?
Bibliographic record
Abstract
Prior research has shown that firms who generate earnings growth by improving profitability create value for shareholders, while firms who generate earnings growth through investment destroy value. This paper examines whether compensation committees take this into account while determining CEO compensation. We first confirm the results from prior research that internally generated growth is perceived by markets to add value while investment-driven growth does not. We find that while internally generated growth is positively associated with compensation, so is investment-driven growth. There is evidence to suggest that the weight placed on investment-driven growth in earnings is higher despite such earnings growth not delivering value to shareholders. We find partial support for the hypothesis that institutional investors act as monitors to ensure that managers are compensated efficiently. The presence of institutional ownership increases the weight on internally generated growth, but does not reduce the weight on investment-driven growth. We further show that value oriented institutional ownership increases the sensitivity of compensation growth to internally generated earnings growth and reduces the sensitivity to investment-driven earnings growth. Contrary to this, growth oriented institutional ownership increases the sensitivity of compensation growth to investment-driven earnings growth. Our results indicate the importance of understanding the different sources of earnings growth in the determination of executive compensation.
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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 | 0.002 | 0.021 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".