The Effect of Audit Experience on Audit Fees and Audit Quality
Bibliographic record
Abstract
Prior research on audit experience focuses on behavioral studies that are conducted by running experiments. Although these studies provide evidence on the role of experience in completing specific audit tasks, they do not shed light on how experience affects a complete audit engagement. We conduct an archival study to examine the effect of audit experience on audit fees and audit quality. Using unique data from China, where the signees of the audit report can be identified and linked with a government database containing personal information about certified public accountants, we find that experience is positively associated with audit fees and negatively associated with absolute discretionary accruals. Furthermore, we extend the research on personal characteristics of audit partners by considering the incremental effects of gender, education, engagement tenure, industry specialization, and client importance after controlling for overall audit experience. Overall, our results suggest that the auditors’ personal characteristics may serve as a signal of the level of care that will be exercised during the audit process. Our results also have implications for China’s recently announced regulation that would require localization of Big 4 offices in China.
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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 | 0.003 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".