Do <scp>PCAOB</scp> Inspections of Foreign Auditors Affect Global Financial Reporting Comparability?*
Bibliographic record
Abstract
ABSTRACT This study investigates whether PCAOB inspections of foreign auditors affect global financial reporting comparability. Foreign auditors may adjust audit methodologies to address PCAOB inspection findings, which could affect financial reporting of local clients. Exploiting both within‐ and cross‐country variation in PCAOB inspections, we predict and find that non‐US‐listed foreign companies' financial reporting becomes more comparable to their US and non‐US industry peers after their auditors undergo an initial inspection. However, there is a decrease in comparability compared to local peers whose auditors have not been inspected. Subsample tests suggest that the improvement in comparability is driven by (i) auditors that satisfactorily address deficiencies and (ii) auditors that do not publicly push back against deficiencies. The effects are dampened after local audit regulators begin inspection programs. Overall, our evidence suggests that the PCAOB international inspection program affects audit methodologies of inspected auditors in a consistent way, improving comparability across jurisdictions. The improved comparability implies that the PCAOB international inspection program may unintentionally help meet accounting regulators' goals of cross‐country financial reporting convergence, which potentially promotes efficient cross‐country capital allocation.
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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 | 0.017 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".