Audit Committee Financial Expertise, Litigation Risk, and <scp>Auditor‐Provided</scp> Tax Services*
Bibliographic record
Abstract
Abstract The Sarbanes‐Oxley Act (SOX) greatly expanded audit committees' oversight responsibilities by requiring that they preapprove all non‐prohibited non‐audit services (NAS). Using data from 2003 to 2011, we find that tax NAS are significantly lower when accounting financial experts (ACT‐FEs) serve on the audit committee, suggesting that ACT‐FEs consider auditor independence risk, perceived and/or real, more than other members, including supervisory experts, to the point of not accepting any tax NAS, not even compliance. However, in firms with higher ex ante litigation risk, ACT‐FEs approve relatively more tax NAS than other members, suggesting that they accept the costs of a perceived lack of auditor independence from tax NAS in return for the potential benefits of increased financial reporting quality arising from tax NAS. Our analysis by subperiod (2003–2006 vs. 2007–2011) shows that this result is significant only in the second period. ACT‐FEs' differential evaluation of the trade‐off between the benefits and costs of joint audit and tax NAS provision between the two periods suggests the need for additional research in later post‐SOX years.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.010 | 0.001 |
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".