Does Convergence with International Standards on Auditing Improve Audit Quality?
Bibliographic record
Abstract
ABSTRACT Many countries have converged their domestic auditing standards with International Standards on Auditing (ISA). This study provides global empirical evidence on first-order determinants of audit quality by examining whether and how convergence affects audit quality through utilizing data on 41 jurisdictions and using a staggered difference-in-differences approach. We find that ISA convergence leads to higher audit quality on average. The positive effect is stronger for clients of domestic audit firms, in jurisdictions with stronger enforcement, and when the ISA convergence level is higher. Insights from textual features suggest that changes in principle-orientation, comparability, readability, and size (or length) of auditing standards are positively related to audit quality. Exploratory analyses of textual content using machine learning reveal that the emphases of ISA on going-concern assessment and legal compliance, fraud risk assessment and internal control evaluation, and related-party transactions and subsequent events contribute to enhanced audit quality. JEL Classifications: M40; M42; M48.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".