Can Big 4 Versus Non-Big 4 Differences in Audit-Quality Proxies be Attributed to Client Characteristics?
Bibliographic record
Abstract
This study examines whether differences in proxies for audit quality between Big 4 and non-Big 4 audit firms could be a reflection of their respective clients’ characteristics. In our analyses, we use three audit-quality proxies—discretionary accruals, the ex ante cost-of-equity capital, and analyst forecast accuracy—and employ propensity-score and attribute-based matching models in attempt to control for differences in client characteristics between the two auditor groups while estimating the audit-quality effects. Using these matching models, we find that the effects of Big 4 auditors are insignificantly different from those of non-Big 4 auditors with respect to the three audit-quality proxies. Our results suggest that differences in these proxies between Big 4 and non-Big 4 auditors largely reflect client characteristics and, more specifically, client size. We caution the reader that this study has not resolved the question, although we hope that it encourages other researchers to explore alternative methodologies that separate client characteristics from audit-quality effects.
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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 | 0.005 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".