Exploring the audit quality and audit fee impacts of joining different types of non-Big Four accounting networks and associations: evidence from China
Bibliographic record
Abstract
Purpose This paper aims to explore the audit quality and fee implications of joining a global accounting firm network and association (“AF N&A”). Design/methodology/approach A hand-collected sample focusing upon the pre- and post-periods around the time when an independent audit firm joins an AF N&A is developed. A propensity score-matched sample is created to address the endogeneity and self-selection bias. OLS regression is used on a sample of around 2,000 firm-year observations from 2003 to 2014. Findings Membership with an AF N&A is associated with higher levels of audit quality and audit fees. Furthermore, audit quality and fee increases are more pronounced for audit firms that become members of a larger, more formal AF N&A. Originality/value This paper provides additional insights into the conflicting results regarding the audit quality implications of membership with AF N&As in China. This paper also extends the discussion by exploring the audit quality and fee differentials among the non-Big Four AF N&As. These findings have significant implications for independent audit firms pursuing membership with an AF N&A and regulators seeking to reduce market concentration around the Big Four.
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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 | 0.009 | 0.141 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".