Deviations from the Mandatory Adoption of IFRS in the European Union: Implementation, Enforcement, Incentives, and Compliance
Bibliographic record
Abstract
ABSTRACT In this paper, we evaluate the common assumption that European Union (EU) firms began using international financial reporting standards (IFRS) in 2005 when the EU formally adopted IFRS. Although the incidence of firms using local (or some other) GAAP declined between 2005 and 2012, it is still non‐trivial. By 2012 the incidence of non‐IFRS financial statements was still in excess of 17 percent (87 percent of which were fully consolidated). We estimate a model of the non‐adoption of IFRS as a function of implementation features of the IFRS regulation, country‐specific enforcement, and firm‐specific reporting incentives. As expected, being specifically required by EU‐wide and country‐specific rules to adopt IFRS is positively associated with IFRS adoption but does not constitute a complete explanation. Proxies for enforcement are significantly associated with non‐adoption, but the marginal effects of the enforcement variables are weak. We find that larger firms, firms with foreign operations and more analyst following, and firms that issue new debt and equity were more likely to adopt IFRS, both when the regulation was initially imposed and in subsequent years. We conclude that many EU firms do not use IFRS; that some firms exploited definitions, exemptions, and deferrals to avoid adopting IFRS while some firms simply failed to comply with the regulation; and that firms responded to their incentives in deciding whether to adopt IFRS.
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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 | 0.026 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".