Bibliographic record
Abstract
This thesis tests multitask agency theory in which the agent has multi-dimensional tasks and both an undistorted and a distorted performance measure are available for contracting. Accounting earnings is modeled as a distorted performance measure and stock returns as a relatively undistorted measure. First, I demonstrate that the marginal products of CEOs' earnings management actions with respect to accounting earnings, in general, are not equal to the marginal products of their earnings management actions with respect to stock returns. In doing so, I provide evidence in support of the hypothesis that accounting earnings is a distorted performance measure because of CEOs' earnings management actions. This distortion measure is then used to investigate cross-sectional variation in the relative sensitivity of CEO compensation to accounting earnings. I find that the weight placed on accounting earnings relative to stock returns in CEO compensation decreases as earnings distortion increases. Overall, the results provide evidence consistent with multitask agency theory, which predicts that optimal compensation contracts put less weight on the distorted performance measure as its distortion increases in order to improve the efficiency of CEOs' effort allocation across tasks.
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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 | 0.016 |
| 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) | 0.005 | 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".