Determinants and Consequences of the Severity of Executive Compensation Clawbacks*
Bibliographic record
Abstract
ABSTRACT We examine the determinants and consequences of the severity of executive compensation clawbacks. As one of the most substantial, prolonged, and controversial proposals to reform executive compensation, clawback rules recently regained the SEC's focus. We construct an intuitive clawback severity score and find that clawbacks are considerably heterogeneous and not homogenous as assumed in the literature. Our determinants analyses suggest that clawback severity is increasing in firms with greater board effectiveness and with higher cash and stock awards in director compensation. In contrast, higher director stock option compensation and more powerful CEOs attenuate the severity of clawbacks. The consequences analyses indicate that while severe clawbacks deter financial restatements, management circumvents severe clawbacks by reducing R&D expenses to avoid earnings decreases. One consistent finding throughout our analyses is that the associations are entirely driven by more severe clawbacks. However, we observe that the financial reporting benefits of severe clawbacks can be diminished by the dynamics in the boardroom. Our study extends extant clawback literature, makes a timely contribution to the SEC's decision to reinitiate discussion on clawbacks, and informs various stakeholders interested in the efficacy of clawbacks. Finally, our clawback score can be used to evaluate clawback policies.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".