Did the Adoption of IFRS Affect Corporate Tax Avoidance?
Bibliographic record
Abstract
This article investigates whether the adoption of international financial reporting standards (IFRS) affected corporate tax avoidance in Canada. Based on a 3,200 firm-year data set of 400 publicly listed Canadian firms that adopted IFRS and 400 listed US firms, matched one-to-one using propensity score matching, the authors' regression results show that IFRS adoption was followed by a decrease in corporate tax avoidance in Canada, at least in the short run. The study finds a significant increase in cash tax paid in the post-adoption period by Canadian firms that adopted IFRS compared to US firms that used US generally accepted accounting principles. Additional regression results based on a small control sample of Canadian firms that did not adopt IFRS present collaborative evidence. The authors further test specific taxpayer attributes and accounting issues identified in Canada Revenue Agency internal memorandums—in particular, concerns that the adoption of IFRS may increase the risk of tax avoidance. While the authors find evidence that the IFRS firms that engaged in accrual management paid more taxes in the post-adoption period, their analysis provides no evidence of statistically significant relationships between IFRS adoption and tax avoidance associated with revenue management, ownership of foreign operations, industry membership, profitability, or impairment losses or writeoffs. Taken together, the authors' findings present preliminary but strong empirical evidence that IFRS adoption is associated with a decrease in corporate tax avoidance, at least in the short run.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".