IFRS and accounting quality: legal origin, regional, and disclosure impacts
Bibliographic record
Abstract
Purpose The purpose of this study is to examine whether the quality of financial reporting has improved after the adoption of International Financial Reporting Standards (IFRS) in Europe and across the world. The study investigates the impact of IFRS on income smoothing and earnings management in different geographic regions under different legal origins and disclosure environments. Design/methodology/approach To measure income smoothing in the pre- and post-IFRS periods, the authors use the coefficient of variation and the panel unit root model proposed by Imet al.(2003) for testing whether net income is stationary throughout the sample period. The study uses a dynamic panel estimation framework, as it captures the dynamics of IFRS on discretionary accruals efficiently. Discretionary accruals are used to measure earnings management. Findings The results suggest that the adoption of high quality standards, such as IFRS, reduces income smoothing and earnings management. In addition, the study finds that earnings management has decreased in the post-IFRS period, in particular, for French and Scandinavian civil law countries, but not for German civil law countries and common law countries. The latter can be explained by the fact that common law countries have strong investor protection laws, strict law enforcement and high disclosure levels of financial information. The study also finds empirical evidence that the adoption of IFRS reduces earnings management in countries with high levels of financial disclosure. Overall, the study shows that the adoption of IFRS improved the quality of financial reporting. Originality/value This study is useful for accounting standard setters across the world, including those countries that have not yet decided to adopt IFRS. The study contributes to the literature by examining the adoption of IFRS in income smoothing and earnings management under different legal regimes and disclosure environments by using advanced empirical methodologies.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".