Do Big 4 Firms Provide Higher Audit Quality in Government Audits? Evidence from Canadian Provincial Consolidated Financial Statements
Bibliographic record
Abstract
SUMMARY Audit quality is influenced by both the demand for and the supply of audits. A major challenge in audit quality research involves isolating supply effects. The audits of Canadian provincial governmental entities present an appealing setting, where there is low variation in the demand for audit quality. Our analysis, employing various audit quality metrics, reveals that Big 4 firms underperform both government auditors and non-Big 4 firms in our setting. We find robust evidence that less government audit knowledge is a key channel through which Big 4 auditors underperform. Additionally, the weaker performance of Big 4 firms may be due to lower effort. Insights from interviews with government audit executives and audit committee members provide evidence supporting the quantitative results. Our results are robust to propensity score matching and to tests that address alternative explanations. Our findings have important implications for governments, audit committees, and auditors in the government sector. JEL Codes: M41; M42.
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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 | 0.006 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".