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Record W2984158833 · doi:10.1108/jaar-01-2019-0014

The determinants of tax-haven use: evidence from Canada

2019· article· en· W2984158833 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Applied Accounting Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversité TÉLUQUniversité du Québec à Montréal
Fundersnot available
KeywordsTax havenAccountingBusinessCapitalizationSample (material)EconomicsLeverage (statistics)Corporate governanceValue-added taxPublic economicsTax avoidanceFinance

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate the determinants of tax-haven use of publicly listed Canadian firms. Design/methodology/approach Based on alternative measures of tax havens (TH) and referring to a sample of 235 Canadian firms over the period of 2014–2015, probit-regression analyses are used to examine the determinants of tax-haven use. Findings The authors provide evidence that multinationality, intangible assets, thin capitalization, withholding taxes, equity-based management remuneration and tax fees paid to auditing firms are positively associated with TH use. Furthermore, the authors show that the variable relating to R&D intensity is positively associated with TH use. The authors also document that strong corporate-governance structures are negatively associated with TH use. Research limitations/implications This study is only limited to Canadian firms, so the results may not be generalizable to other countries. Practical implications The results may assist tax watchdogs in their efforts to understand the tax behavior held by Canadian firms. They may also be interesting for tax authorities in planning enforcement activities. Originality/value This study uses a sample from publicly listed financial and non-financial firms. It also uses various lists of TH published by various competent sources (IMF, 2000, 2007; TJN, 2005; OECD, 2012). The findings corroborate the recent media attention about the extensive use of TH by Canadian firms.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.132
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.301
Teacher spread0.239 · 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