Office Size of Big 4 Auditors and Client Restatements
Bibliographic record
Abstract
Francis and Yu (2009) and Choi, Kim, Kim, and Zang (2010) report evidence that Big 4 audits are of higher quality when the engagement office is of larger size. Specifically, client earnings quality is higher and auditors in larger offices are more likely to issue going‐concern audit reports. We extend this line of research to test if larger Big 4 offices have fewer client restatements. A client restatement provides more direct evidence of a low‐quality audit than earnings quality metrics or going‐concern reports, because a restatement indicates the client's auditor did not effectively enforce the correct application of GAAP at the time the original financial statements were issued. We analyze 2,557 firm‐year restatements in a sample of 23,190 financial statements originally issued by U.S. firms from 2003 to 2008. We find that Big 4 office size is associated with fewer client restatements after controlling for innate client characteristics that may affect restatements (client size, financial performance, industry membership, nonfinancial measures, off‐balance sheet activities, and market‐related measures), and a set of controls for other auditor factors such as fees and industry expertise. The study raises important questions about the ability of smaller offices to deliver high‐quality audits for SEC registrants.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".