How Certain Engagement Letter Clauses Affect the Auditor’s Assessment of Perceived Engagement Risk for Nonissuers
Bibliographic record
Abstract
AICPA auditing standards (e.g., AU-C210, par. 11) for audits of nonissuers require CPA auditors to use engagement letters or another suitable written understanding to clarify their and their clients' duties; these letters also minimize CPAs' potential legal liabilities to clients. But, the SEC, PCAOB and other authoritative bodies prohibit those clauses-fearing their use would impair auditors' independence. We survey 209 CPAs' responses to increasing risk across three engagement letter clauses that can change the level of auditor risk exposure, and measure the amount of data and fees auditors gather relative to changing engagement letter clauses in normal and unusual risk scenarios. We find that CPAs say they increase both the quantity of evidence gathered and their engagement fees in response to increasing risk, which suggests that CPAs' level of work depends much more on their assessment of perceived risks than on the three clauses minimizing their legal liabilities. Auditors further respond to an engagement's aggregate risk in the presence of risk-reducing clauses. We also find that while the clauses can lower the cost of the engagement's risk, auditors do not lower the amount of evidence gathered under the no-unusual-risk scenario, and significantly increase evidence when perceiving an increase in risk. Thus, support exists for authoritative bodies to permit such engagement letter clauses. While PCAOB standards affect only public companies, our results should be of interest to policy makers who oversee attestation services affecting both public and private companies.
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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 | 0.020 | 0.155 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".