Can Big 4 versus Non-Big 4 Differences in Audit-Quality Proxies Be Attributed to Client Characteristics?
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
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.
- Candidate categories
- Metaresearch, Meta-epidemiology (narrow)
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.631
- Threshold uncertainty score
- 1.000
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.233 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- The Accounting Review
- Topic
- Auditing, Earnings Management, Governance
- Field
- Business, Management and Accounting
- Canadian institutions
- University of Toronto
- Funders
- not available
- Keywords
- AuditQuality auditAccrualMatching (statistics)AccountingEquity (law)Big FourBusinessQuality (philosophy)Big dataPropensity score matchingComputer scienceStatisticsEarningsData mining
- Has abstract in OpenAlex
- yes