IFRS 18 AND THE GLOBAL STANDARDIZATION OF RESULT SUBTOTALS: A MULTI-JURISDICTIONAL ECONOMETRIC ANALYSIS IN THE LIGHT OF INSTITUTIONAL THEORY
Bibliographic record
Abstract
This study investigates the impact of IFRS 18 on the standardization of performance subtotals through the lens of institutional theory, critically addressing methodological limitations inherent to the short post-implementation period. Using a hybrid sample of 425 companies (85 per country) and aggregated data from regulators in the United Kingdom, Australia, Canada, South Korea, and Brazil over the period 2015-2024, we implement a multilevel econometric strategy combining triple fixed-effects panel models with difference-in-differences approaches, instrumental variables, and mediation analysis. Our results, robust to extensive sensitivity tests including statistical power analysis (1-β = 0.89), indicate that the adoption of IFRS 18 is associated with a 42.3% reduction in cross-country dispersion of operating profit (p < 0.01), an effect mediated by 65% through the quality of institutional enforcement. Heterogeneity analyses reveal that the effects are 73% larger in jurisdictions with high-quality enforcement, corroborating institutional theory assumptions. Additionally, we document a 58.7% contraction in heterogeneous Management Performance Measures (MPMs) intensity (p < 0.01) and a 34.5% increase in the value relevance of standardized operating profit. We explicitly discuss the implications for accounting standardization theory and propose an integrated framework for evaluating international regulatory reforms.
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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 | 0.018 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".