What is the Relationship Between Audit Partner Busyness and Audit Quality?
Bibliographic record
Abstract
Abstract Contemporaneous studies generally find a negative relationship between audit partner busyness ( APB ), measured as the number of clients in an audit partner's portfolio, and audit quality. Their argument is that a busy partner does not devote sufficient time to properly audit his average client. Contrary to these studies, we argue that when busyness is optimally chosen by the partner, in equilibrium, there is no causal relationship between APB and audit quality. Using Australian data for the 1999–2010 period, we show that APB is not reliably linked to audit quality, consistent with this equilibrium theory. We argue that causality can be ascribed to the APB ‐audit quality relationship when accounting scandals exogenously shocked the Australian audit market during the 2002–04 period and APB likely deviated from optimum levels. Supporting this disequilibrium view, we find that higher APB reduces a partner's propensity to issue first‐time going‐concern opinions during this period. Our evidence highlights the importance of the equilibrium condition in testing empirical associations between audit outcomes and endogenous auditor attributes, and shows that the detrimental effect of APB on audit quality is not as pervasive as contemporaneous studies suggest.
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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 | 0.006 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".