Did the Adoption of BEPS Country-by-Country Reporting Affect Multinational Tax Avoidance? Evidence from Canada
Bibliographic record
Abstract
Since 2016, Canadian multinational corporations (MNCs) with consolidated revenues exceeding [euro]750 million (equivalent to Cdn$1.1 billion) in the preceding fiscal year have been subject to country-by-country reporting (CbCR) under Canada's adoption of action 13 of the base erosion and profit shifting (BEPS) project. The objective of this study is to examine empirically whether the adoption of the CbCR obligation has had an impact on the tax-avoidance practices of Canadian MNCs that are subject to this obligation—that is, whether there has been a reduction in tax avoidance by these MNCs since the adoption of CbCR. The study uses a sample that contains all publicly listed Canadian MNCs for a period of 10 years: 5 years before the adoption of CbCR (2011 to 2015) and 5 years after the adoption of CbCR (2016 to 2020). The sample is split into two groups: a treatment sample, which contains all the MNCs subject to the CbCR obligation, and a control sample, which contains all the MNCs that are not subject to this obligation. Using the difference-in-difference method, the study finds no evidence that the adoption of CbCR under BEPS action 13 has had an effect on tax avoidance by Canadian MNCs subject to this obligation. However, additional analyses by sector show that, contrary to expectations, MNCs subject to CbCR in the energy and materials sectors continued to avoid taxes even after the adoption of CbCR in 2016. In other words, the adoption of CbCR seems to have had no deterrent effect on tax avoidance by MNCs subject to this obligation in the energy and materials sectors. Since 2015, these two sectors have also been subject to another type of CbCR under the Canadian Extractive Sector Transparency Measures Act. The study results, which are robust when tested by various measures of tax avoidance and CbCR, provide important feedback to Canadian tax authorities and the Organisation for Economic Co-operation and Development on the effectiveness/ineffectiveness of BEPS action 13.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".