How Big-4 Firms Improve Audit Quality
Bibliographic record
Abstract
This paper studies whether and how Big-4 firms provide higher-quality audits than non-Big-4 firms. Specifically, we first examine a Big-4 effect and then explore three sources of the Big-4 effect. To test the Big-4 effect, we use a unique data set of individual audit partners for a large sample of private companies and a novel research design exploiting the fact that auditees may follow the auditor who switches affiliation from a non-Big-4 firm to a Big-4 firm. Thus, we compare audit quality and audit fees of the same partner–auditee pairs before and after the switch. The results show that the Big-4 effect exists in the private-firm segment. More important, we find evidence for three sources of the Big-4 effect. First, Big-4 firms are able to recruit non-Big-4 partners who deliver higher audit quality than other non-Big-4 partners in the preswitch period. Second, enhanced learning has taken place after the switch. Third, the increased audit quality can also be attributed to stronger incentives/monitoring. These are new findings to the literature.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".