Compensation in the Post‐<scp>FIN</scp> 48 Period: The Case of Contracting on Tax Performance and Uncertainty
Bibliographic record
Abstract
Abstract Academic and anecdotal evidence indicates that incentive systems often provide short‐term payouts without regard for long‐term consequences. New detailed disclosures mandated by FIN No. 48, Accounting for Uncertainty in Income Taxes, enable us to use a tax setting to investigate whether boards adjust performance‐based pay for uncertainty. We find managers’ bonus payouts are positively associated with tax performance; however, bonus payouts are lower when measures of ex ante tax uncertainty are higher. Our results are robust to tests of alternative explanations including financial reporting aggressiveness, overall firm risk, and other forms of compensation. Further, we document that the relation between bonus compensation and tax performance has changed in the post‐FIN No. 48 period. Specifically, we identify a significant association between bonus payout and GAAP ETR only in the pre‐FIN No. 48 period and a significant association between bonus payout and cash ETR only in the post‐FIN No. 48 period, suggesting that the relation between compensation and tax avoidance should be examined carefully with particular attention to the post‐FIN No. 48 period.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".