Earnings Quality under Rules‐ versus Principles‐based Accounting Standards: A Test of the Skinner Hypothesis / LA QUALITÉ DES RÉSULTATS SELON QUE LES NORMES COMPTABLES SONT AXÉES SUR LES RÈGLES OU SUR LES PRINCIPES: VÉRIFICATION DE L'HYPOTHÈSE DE SKINNER*
Bibliographic record
Abstract
ABSTRACT We provide preliminary evidence, consistent with Skinner (1995), that Canada's relatively principles‐based GAAP yield higher accrual quality than the United States' relatively rules‐based GAAP. These results stem from a comparison of the Dechow‐Dichev (2002) measure of accrual quality for cross‐listed Canadian firms reporting under both Canadian and U.S. GAAP. However, we document lower accrual quality for Canadian firms reporting under U.S. GAAP than for U.S. firms, which are subject to stronger U.S. oversight, reporting under U.S. GAAP. The latter results suggest that stronger U.S. oversight compensates for inferior accrual quality associated with rules‐based GAAP. Consistent with the positive effect of Canada's principles‐based GAAP and the offsetting negative effect of Canada's weaker oversight, we find no overall difference in accrual quality between Canadian firms reporting under Canadian GAAP and U.S. firms reporting under U.S. GAAP. Our results imply that (1) policymakers who wish to compare the effectiveness of oversight across jurisdictions must control for the GAAP effect; and (2) accounting standard‐setters who wish to compare the effectiveness of principles‐ versus rules‐based GAAP must control for oversight strength.
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 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 | 0.021 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".