Earnings Quality: Evidence from Canadian Firms' Choice between <scp>IFRS</scp> and U.S. <scp>GAAP</scp>
Bibliographic record
Abstract
Abstract For fiscal years starting on or after January 1, 2011, Canada abandoned Canadian Generally Accepted Accounting Principles (GAAP) and adopted International Financial Reporting Standards (IFRS), but permitted firms cross‐listed in the United States to adopt U.S. GAAP instead. We document that the number of Canadian firms reporting under U.S. GAAP increased after Canada adopted IFRS. We find that cross‐listed firms are more likely to choose IFRS, if IFRS is the standard most commonly used by the leading global firms in their industry. In addition, we find that firms more likely to choose IFRS are larger, of civil law legal origin, have less U.S. operations, report exploration expense, have fewer U.S. shareholders, and report higher stockholders' equity under Canadian GAAP than under U.S. GAAP. Of these, we find that the convergence benefits of comparability with industry peers are the most significant determinant in firms' choice of standard. Further, we are unable to document changes in earnings quality from cross‐listed firms adopting IFRS or U.S. GAAP or that earnings quality changed for firms adopting IFRS relative to firms adopting U.S. GAAP.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".