A MULTI-METHOD INVESTIGATION OF INDEPENDENT AUDIT REGULATORS: EVIDENCE AND IMPLICATIONS
Bibliographic record
Abstract
This thesis uses multi-methods to examine the effectiveness and implications of independent audit regulators, such as the Canadian Public Accountability Board (CPAB) in Canada and the Public Company Accounting Oversight Board (PCAOB) in the United States, whose mandates include improving audit quality through periodic inspections of auditors’ work. In Study 1, I use experimental method to examine how regulators’ inspection feedback anchor interacts with audit firms’ national office style to impact audit partners’ commitment to the goal or reducing aggressive client income and the strategy audit partners intend to undertake to negotiate with their clients. I find that, conditional on having an antagonistic regulator – one that provides feedback anchored on failure – audit partners are less committed to their goal when they work with a collaborative national office than when they work with a prescriptive national office, and they are more inclined to seek win-win with the client. I also find that conditional on working with a collaborative national office, audit partners are more committed to their goals if the audit regulator’s feedback anchors on opportunities to improve rather than on failure. In Study 2, I use archival method to empirically examine CPAB’s inspection results for Canadian audit firms. Based on hand-collected data from CPAB’s and PCAOB’s inspection reports, I find that CPAB consistently reports a lower deficiency rate than PCAOB for the same large accounting firms under inspection (i.e., Canadian Big 4 accounting firms), and reports fewer areas of deficiency than PCAOB for the same large accounting firms inspected. Further analysis shows that CPAB also discloses much less information than PCAOB, consistent with what one would expect from a regulator being captured by the interest of large regulatees. Together, these studies extend existing accounting literature on audit regulations and have practical implications for audit regulators and audit firms.
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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 | 0.434 | 0.648 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".