Adaptability to fair value accounting in an emerging economy
Bibliographic record
Abstract
Purpose This paper seeks to assess the feasibility and desirability of a major emerging economy adopting and implementing fair value accounting (FVA), as codified in the International Financial Reporting Standards (IFRS), by studying China's recent experience. Design/methodology/approach The paper examines the extent of FVA adoption in China's new accounting standards (“2007GAAP”), reasons for differences from the International Accounting Standard Board's IFRS, and how 2007GAAP has been implemented in practice. Data are obtained from content analyses of IFRS and 2007GAAP FVA requirements, critical assessments of standard setters' official statements, and analyses of empirical evidence from official reports, media, and academic research. Findings The authors find a high degree of adoption of IFRS FVA standards in China's 2007GAAP for financial instruments, but many differences for non‐financial long‐term asset investments. Standard setters justify this divergence by fundamental characteristics of the Chinese environment. The resulting differences from IFRS in the 2007GAAP FVA standards, and in their implementation, challenge official claims of “substantial convergence” between 2007GAAP and IFRS. Hence, the benefits desired by Chinese regulators from adopting FVA and international accounting convergence to IFRS may not be realized. Research limitations/implications The findings are derived from aggregated data in government reports. These findings can be extended in future research by examining specific implementation outcomes in company financial statements. Originality/value The paper contributes a timely critical examination of a major emerging economy's convergence with the controversial FVA requirements, which supports the IFRS's standing as a high quality set of accounting standards. The findings provide new insights into factors that can impede international accounting convergence in emerging economies.
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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 | 0.004 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".