Top Management Team Incentive Dispersion and Earnings Quality*
Bibliographic record
Abstract
ABSTRACT This paper examines the relation between the dispersion in pay‐performance sensitivities (PPS) among top management team (TMT) members and earnings quality. Prior research suggests that the PPS from executives' equity compensation induce earnings manipulation incentives. Most of this research, however, focuses on the PPS of individual executives, even though the financial reporting process requires the coordination among a broad range of executives. Focusing on the dispersion in PPS among TMTs, we develop a model that shows that, due to the coordination incentives embedded in compensation arrangements, managers with more closely aligned PPS will be more willing to work together to manipulate earnings as the rewards are shared more evenly among them. We empirically test the implications from our model and find a positive relation between PPS dispersion and earnings quality. We further find that the stock price reaction to firms' reported earnings relates to the earnings manipulation incentives based on PPS dispersion. Our results suggest that differences in PPS among TMT members hamper the coordination necessary to manipulate earnings and that investors are aware of this impact. Our study has important implications related to compensation‐related disclosures (e.g., those under section 953a of the Dodd‐Frank Act of 2010) and their potential impact on investors' understanding of earnings quality.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".