Does Auditor Industry Specialization Improve Audit Quality? Evidence from Comparable Clients
Bibliographic record
Abstract
ABSTRACT: The objective of this study is to examine the relation between auditor industry specialization and audit quality using an alternative research design to mitigate the influence of client characteristics. After matching clients of specialist and non-specialist auditors according to industry, size and performance, I find no significant differences in audit quality between these two groups of auditors. My findings are robust to using alternative matching approaches, to using various proxies for auditor industry specialization and audit quality, and to controlling for the effect of imperfectly matched characteristics. In addition, I perform two analyses that do not rely primarily on matched samples. First, in examining a sample of Arthur Andersen clients that switched auditors in 2002, I find no evidence of industry-specialization effects following the auditor change. Second, I observe that the industry-specialization effects are simulated by randomly assigning clients to auditors. Overall, these findings do not imply that industry knowledge is not important for auditors, but that the extant methodology may not fully parse out the effects of auditor industry expertise from client characteristics. * This paper is based on the first chapter of my dissertation at the University of Toronto, Rotman School of Management. I gratefully acknowledge the guidance provided by my co-chairs Gordon Richardson and Ping Zhang, as well as the other members of my dissertation committee, Jeffrey Callen and Gus De Franco. I thank Yiwei Dou,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".