Interim Effective Tax Rate Estimates and Internal Control Quality
Bibliographic record
Abstract
ABSTRACT This study examines whether the volatility of interim estimates of the annual effective tax rate (ETR) provides ex ante information about the quality of firms' internal control environments. Recent research suggests that some firms selectively disclose internal control weaknesses (ICWs). Given the negative consequences associated with ICWs, it is important for capital market participants to be able to identify firms with ineffective internal controls in a timely manner. We find that firms with more volatile annual ETR estimates are more likely to report both tax‐ and nontax‐related ICWs in the current year. Our results also indicate that the volatility of annual ETR estimates declines following the remediation of tax‐related ICWs, but not following the remediation of nontax‐related ICWs. In addition, we find that ETR volatility in the current year is associated with the likelihood that a firm will report an ICW in the following year. Finally, we provide evidence that the volatility of annual ETR estimates is associated with the likelihood that a firm has an undisclosed ICW. In combination, our results suggest that the volatility of interim estimates of the annual ETR provides an ex ante signal of the likelihood that a firm's internal controls are ineffective.
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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 | 0.009 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".