The Impact of IFRS on Accounting Quality in a Regulated Market
Bibliographic record
Abstract
As more countries consider the adoption of International Financial Reporting Standards (IFRS) that are based on practices prevalent in the English-speaking countries with free markets, it’s increasingly important to understand the impact of IFRS on countries of different institutional, economic, and political environments. This article reports a study that examines the impact of IFRS on accounting quality in a regulated market, China, where new substantially IFRS-convergent accounting standards became mandatory for listed firms in 2007. Accounting quality is examined for the period 2005 to 2008 with only firms mandated to follow the new standards. The empirical results generally indicate that accounting quality improved with decreased earnings management and increased value relevance of accounting measures in China since 2007. Firms audited by the Big Four are expected to have higher quality before the standard change evidenced quality improvement to a smaller extent. Further analysis shows that such changes are less likely to result from changes in economic conditions but from the changes of the standards. Through the analysis of China’s adoption of the new substantially IFRS-convergent standards, the study provides direct evidence on the question of whether IFRS can be relevant to markets that are still disciplined mainly by regulators rather than by market mechanisms.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".