The role of the Big 4 and second-tier international networks in redeveloping China’s audit market
Bibliographic record
Abstract
Purpose This study aims to explore the audit quality supplied by the Big 4, large indigenous Chinese (LIC) and five largest second-tier international network (Tier 2) audit firms in China during the second phase of their audit market development. Design/methodology/approach Ordinary least squares regression is used on an archival sample of firm-year observations. Endogeneity and self-selection bias are addressed by creating a propensity score matched sample and using two-stage regression with the inverse Mills’ ratio. Findings Strong evidence is found for higher levels of actual audit quality for the Big 4 relative to both LIC and Tier 2 audit firms. Weak evidence is found regarding the audit quality superiority of Tier 2 relative to LIC audit firms. Furthermore, the actual audit quality differential between the Big 4 relative to the LIC and Tier 2 firms widens after adopting International Financial Reporting Standards, which is contrary to the intention of Chinese regulators. Originality/value To the best of the authors’ knowledge, this is the first known empirical study to trisect Big N and non-Big N audit firm proxies into the Big 4, LIC and Tier 2. Currently, only qualitative studies have fully appreciated the unique regulatory roles of these three firm structures in developing China’s audit market, which reflect tensions between reliance on foreign expertise and self-determination. In addition, this study adds to the ongoing global dialogue on Tier 2 as an alternative to the Big 4 and the benefits of international accounting network membership.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".