Does audit market concentration harm the quality of audited earnings? Evidence from audit markets in 42 countries
Bibliographic record
Abstract
Audit regulators around the world have expressed concern over market dominance by Big 4 accounting firms and the potential adverse effect it may have on the quality of audited financial statements. We use cross-country variation in the audit market structure of 42 countries to examine two separate aspects of Big 4 dominance: (1) Big 4 market concentration as a group relative to non-Big 4 auditors; and (2) concentration within the Big 4 group in which one or more of the Big 4 firms is dominant relative to the other Big 4 firms. We find that in countries where the Big 4 (as a group) conduct more listed company audits, both Big 4 and non-Big 4 clients have higher quality audited earnings compared to clients in countries with smaller Big 4 market shares. In contrast, in countries where there is a greater concentration within the Big 4 group, we find that Big 4 clients have lower quality audited earnings compared to countries with more evenly distributed market shares among the Big 4. Thus concentration within the Big 4 group appears to be detrimental to audit quality in a country and of legitimate concern to regulators and policymakers. However, Big 4 dominance per se does not appear to harm audit quality and is in fact associated with higher earnings quality, after controlling for other country characteristics that potentially affect earnings quality. © 2012 The Canadian Academic Accounting Association.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".