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Did the Adoption of BEPS Country-by-Country Reporting Affect Multinational Tax Avoidance? Evidence from Canada

2022· article· en· W4391298014 on OpenAlexaffvenueabout
Anis Maaloul

Bibliographic record

VenueCanadian Tax Journal/Revue fiscale canadienne · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsMultinational corporationObligationBusinessRevenueSample (material)AccountingLawPolitical scienceFinance

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.004
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.019
GPT teacher head0.198
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes3
Has abstractyes

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