Canadian Evidence on the Constructive Capitalization of Operating Leases*
Bibliographic record
Abstract
ABSTRACT One type of relevant ex ante research supporting the accounting standard‐setting process is the study of a proposed standard's impact on reported figures. The International Accounting Standards Board recently decided to review the lease accounting standard, which will naturally involve consideration of the G4 + 1 recommendation to capitalize all noncancellable lease contracts, including operating leases. National evidence of the impact of the G4 + 1 proposals provides feedback for the international standard‐setter. This study developed and used a refined constructive capitalization method, in which company‐specific assumptions — interest rate, total/expired/remaining lives of leased assets, and tax rate — were used to compute the impact of operating‐lease capitalization on key financial indicators for a sample of Canadian public companies. The results indicate that capitalizing operating leases would lead to the recognition of important additional assets and liabilities on the balance sheet. It would therefore significantly increase the debt‐to‐asset ratio and significantly decrease the current ratio. These results were noted across all industry segments in the sample. Income statement effects were generally less material. Significant impacts on return on assets, return on equity, and / or earnings per share were noted in only three industry segments: merchandising and lodging, oil and gas, and financial services. Intercompany comparability would not be affected overall nor within industries, because of similar rankings for each financial indicator before and after operating‐lease capitalization.
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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 | 0.005 | 0.041 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".