Do adjustments bring auditors peace of mind? The effect of previous audit adjustments on current-year audit pricing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper aims to investigate the effect of prior years’ audit adjustments, a proxy for auditors’ private information regarding the persistence of their clients’ audit risk, on audit pricing in the current year. Design/methodology/approach The authors use unique data sets of audit adjustments and audit fieldwork days from China, and a regression approach, to test their hypothesis. Findings The authors find that larger previous audit adjustments are associated with higher current-year audit fees, which is partially attributed to increased audit effort. The authors further document that the results are more pronounced when audit adjustments are consistently made in the same direction or more recent; in these cases, a larger percentage of the total effect is also attributable to the risk premium, instead of audit effort. Finally, the authors find that the effect of previous audit adjustments on current-year audit fees is stronger for firms with younger chief executive officers and specialist auditors. Originality/value To the authors’ best knowledge, they are the first to test the implication of auditors’ private information in setting audit fees. In addition to demonstrating that audit fees consist of a risk premium and a component to cover related costs, the authors further show variations in the relative importance between costs and risk premium under various contexts.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 it