Ex Post Settling Up in Cash Compensation: New Evidence
Bibliographic record
Abstract
ABSTRACT This paper provides new evidence on whether and how boards solve costly ex post settling up to recover CEO cash compensation for unrealized gains that fail to materialize. Our analyses are motivated by the likely expanding role for ex post settling up as the risk of compensating executives for unrealized gains that may never materialize increases in a more intangibles‐based economy, as well as by the conflicting evidence of prior research. We provide evidence consistent with ex post settling up by (i) using alternative truncation methods to derive observations most likely to fall within the theoretically motivated incentive zone; (ii) replicating and reconciling the conflicting results of prior research that supports (Leone et al. 2006) and fails to support (Shaw and Zhang 2010) ex post settling up; (iii) using Incentive Lab data with contract‐specific information, allowing strong identification of observations in the incentive zone; and (iv) documenting predictable cross‐sectional variation, with ex post settling up being more pronounced for firms with stronger corporate governance, less conservative accounting earnings, and a larger proportion of total pay in the form of cash compensation. Overall, we conclude that evidence is strong in support of the ex post settling up hypothesis.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".