Bibliographic record
Abstract
Purpose Fears over public accounting becoming increasingly concentrated have inspired several attempts to study the relationship between competition and audit quality. These studies have yielded conflicting results without a clear reason as to why. This paper aims to propose a new approach and empirically demonstrate a non-monotonic association between competition and audit quality. Design/methodology/approach Using metropolitan statistical area level data from the USA over the period of 2000–2014, the author shows that the effect that changes in the competition will have on audit quality depends upon the current competitive state of the market. Findings Audit quality is at its highest level when competition is neither too high nor too low. In addition, the point of inflection at which competition turns from being helpful to harmful is influenced by the saturation of the Big 4 auditors in the market. Practical implications These findings can help explain the mixed results of the literature and provide insight into the role that regulators can play in modulating competition. Originality/value This is the first paper to document a non-monotonic relationship between competition and audit quality. By introducing and exploring the validity of a non-monotonic component in the audit quality equation, the authors can better determine, which competitive structures generate desired levels of audit quality.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".