The Effects of Governance on Classification Shifting and Compensation Shielding
Bibliographic record
Abstract
Abstract Prior research (e.g., Dechow, Huson, and Sloan ) documents that, on average, compensation practices appear to shield CEO pay from income‐decreasing special items. In some circumstances, compensation shielding can be efficient. For example, it may encourage CEOs with earnings‐sensitive pay to take an action that reduces current earnings but nevertheless enhances value. Compensation shielding can be inefficient in other circumstances, such as when a board of directors is captured by an overly powerful CEO or the magnitude of negative special items has been overstated (e.g., by shifting core expenses into special items). This paper explores whether strong governance can explain cross‐sectional variation in compensation shielding, and whether stronger governance and auditing are associated with less shifting of expenses. We find that strong corporate governance mechanisms, as captured by board (and committee) independence, the Sarbanes‐Oxley (2002) Act (SOX) and its related governance reforms, and switches to Big 4 auditors, are all associated with less compensation shielding. While our evidence suggests that strong overall governance is associated with a reduction in manipulation of core earnings through classification shifting in the cross‐section, we find inconclusive evidence to suggest that board independence or SOX influence classification shifting.
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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 | 0.003 | 0.027 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".