The Impact of Trade Secrecy Protection on Audit Pricing
Bibliographic record
Abstract
Because auditors have access to corporate information, a firm’s decision to protect material trade secrets should, in principle, not influence audit effort. We analyze the effects of trade secrecy protection on the audit fees, documenting that firms with redacted information pay significantly higher fees than those that do not redact information. In cross-sectional tests, we further document that the relationship between redaction and audit fees is significantly influenced by both auditor and client characteristics. Consistent with the premise that redaction increases the complexity of the audit—particularly if auditors view redacted disclosures as indicators of potential business or litigation risk—the regression results indicate that the main effect is moderated by auditor factors such as specialization, tenure, and quality, as well as client factors like existing relationships, bargaining power, and reporting quality. These insights contribute to ongoing discussions in audit policy by illustrating how confidential disclosure practices affect audit effort and costs. Overall, our results inform policymakers seeking to reconcile firms’ proprietary information protection with public interest in transparent and credible financial reporting.
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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 | 0.012 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".