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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".