Evidence of Industry Scale Effects on Audit Hours, Billing Rates, and Pricing
Bibliographic record
Abstract
ABSTRACT Using a proprietary data set consisting of all private firm audit engagements in 2000 from one Big 4 firm in Belgium, we investigate (i) whether audit office industry scale is associated with a reduction of total, partner, and staff audit hours and thus with efficiency gains triggered by organizational learning from servicing more clients in an industry and (ii) whether the extent of efficiency pass‐on from the auditor to its clients depends on the audit firm's market power. We find that auditor office industry scale is associated with efficiency gains and a reduction of the variable costs (i.e., fewer total audit hours, partner hours, and staff hours), ceteris paribus. Our results also suggest that, on average, realized efficiencies are entirely passed on, as evidenced by a nonsignificant effect of auditor industry scale on the auditor's billing rate. Furthermore, we find that the extent of the efficiency pass‐on decreases with the market power of the audit firm in the industry market segment as we document a higher billing rate for auditors with high market power (versus low market power). In addition, we find that the lower audit hours associated with auditor industry scale do not compromise audit quality.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".