<scp>PCAOB</scp> Inspections and the Differential Audit Quality Effect for Big 4 and <scp>Non–Big</scp> 4 <scp>US</scp> Auditors*
Bibliographic record
Abstract
ABSTRACT In this study, we investigate whether the increase in regulatory scrutiny epitomized by the initial PCAOB inspection impacted audit quality differentially for Big 4 and non–Big 4 auditors to better understand the consequences of PCAOB inspections for different audit firm types. Because of competing views on the effect of PCAOB inspections, the relation between PCAOB inspections and the audit quality differential between Big 4 and other auditors is an empirical issue. Empirically, we take the endogenous choice of auditor as a given and utilize a difference‐in‐differences specification that takes into account the staggered timing of the initial PCAOB inspection for different‐sized auditors in the United States. Our results suggest that the initial PCAOB inspection improved audit quality more for Big 4 auditors than for other annually inspected or triennially inspected non–Big 4 auditors. We also examine annually and triennially inspected non–Big 4 auditors separately, and find that the pre‐post Big 4/non–Big 4 differential audit quality effect is more pronounced for the triennially inspected non–Big 4 firms. In the larger context of the highly concentrated US audit market, our findings that PCAOB inspections accentuate the Big 4/non–Big 4 audit quality differential are of potential interest to public company audit clients contemplating an auditor change, investors interested in learning about the consequences of PCAOB inspections, regulators concerned about the Big 4 dominance of the US audit market, and academics investigating audit quality differences.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".