Deciphering Tax Avoidance: Evidence from Credit Rating Disagreements
Bibliographic record
Abstract
Abstract This study investigates the role of tax avoidance in the credit‐rating process and whether differences exist in how rating agencies account for the risk relevance of tax avoidance. Using a sample of initial credit ratings assigned to public debt issuances during 1994–2013, our evidence is consistent with Moody's Investors Service and Standard & Poor's assessing the costs and benefits associated with tax avoidance differently from one another, resulting in more frequent and pronounced rating agency disagreement. Rating agency disagreement over tax avoidance is most evident when it is accompanied by relatively high levels of uncertain tax positions, foreign activities, research and development activities, or tax footnote opacity. We also find evidence that decreases (increases) in tax avoidance or tax footnote disclosure opacity are positively (negatively) associated with the convergence of split ratings. This suggests that firms can exacerbate or mitigate rating agency disagreement subsequent to bond issuance. Our study complements prior research by examining why sophisticated information intermediaries disagree about the risk relevance of tax avoidance. It also sheds light on how firms can influence rating agencies’ understanding of tax avoidance.
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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 | 0.014 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".