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Record W2887950655 · doi:10.1111/1911-3846.12415

Deviations from the Mandatory Adoption of IFRS in the European Union: Implementation, Enforcement, Incentives, and Compliance

2018· article· en· W2887950655 on OpenAlexvenueno aff
Grace Pownall, Maria Wieczynska

Bibliographic record

VenueContemporary Accounting Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEnforcementInternational Financial Reporting StandardsBusinessAccountingIncentiveEquity (law)European unionEconomicsInternational tradeMarket economy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.073
GPT teacher head0.332
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations51
Published2018
Admission routes1
Has abstractyes

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