Discussion of “Does the Identity of Engagement Partner Matter? An Analysis of Audit Partner Reporting Decisions”
Bibliographic record
Abstract
Abstract The authors of this provocative study apply commonly used audit quality surrogate measures to a large and unique set of financial and other data on statutory audits of small private companies in Sweden. The paper has received unparalleled attention by the financial press and the PCAOB for its presumed support for regulatory intervention in standards for U.S. public company audits. In this Discussant Comment, I review the paper's content, analyze its predictive validity, and discuss its multiple implications plus, following Conference instructions, I provide constructive suggestions for improvements. Based on predictive validity analysis, I conclude that engagement partner assignment strategy is an important and acknowledged omitted variable that affects the study's internal validity via both the independent variable (partner's prior performance measure) and the dependent variable (borrower's cost of debt capital). The omission also affects construct validities and, if audit firms are applying a plausible assignment strategy, then interpretation of the study's main results would be reversed. Finally, the lack of a standards intervention noted by the authors and the extreme size and other differences between audits of Swedish private companies and U.S. public companies impair external validity and generalization to the U.S. intervention. As to improvements, I suggest that the authors (i) ask Swedish lenders to validate their presumed use of partner performance ratings in determining a borrower's interest rate, and (ii) ask Swedish Big 4 audit firms to provide a few internal partner performance ratings for comparison with the external performance measures used in the study. This two‐pronged, multimethod approach might confirm or deny critical assumptions underlying the present study and may substantively inform standards setters, evidence‐based standards, and fellow researchers about the validity of commonly applied surrogates for audit quality and the study's stated conclusion.
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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 | 0.059 | 0.219 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".