The determinants of tax-haven use: evidence from Canada
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.
Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it