Bibliographic record
Abstract
Why this book? Taxation of Interest under Domestic Law, EU Law and Tax Treaties, comprising the proceedings and working documents of the annual seminar held in Milan in November 2021, is a detailed and comprehensive study on the tax treatment of cross-border interest payments. The book starts with a comparative analysis of the definition of the term “interest” and a tax policy overview of the inclusion of cross-border interest in income. It then focuses on the most interesting open issues in the application of the EU Interest-Royalties Directive, including the concept of beneficial owner, the subject-to-tax test and the definition of “company of a Member State”, as well as hybrid financing. Next, the book thoroughly examines the taxation of interest under tax treaties. Beginning with a detailed account of the history of Article 11 of the OECD Model Tax Convention, the tax treaty section of the book discusses the definition of the term “interest” under tax treaties, the rules governing source, and issues of non-discrimination arising in relation to interest. Taking into account the inputs coming from various national reports, the book also maps some of the most relevant departures from the OECD Model Tax Convention that can be spotted in the actual treaty practice of different countries. Finally, the interactions between the beneficial ownership clause and the principal purpose test rule are scrutinized. Moreover, individual country reports provide an in-depth analysis of the specific domestic tax regimes and tax treaty practice of several EU and non-EU countries, namely Australia, Austria, Belgium, Brazil, Canada, Finland, France, Germany, India, Italy, the Netherlands, South Africa, Spain, Sweden, Switzerland, the United Kingdom and the United States. This book provides a unique and detailed analysis of some of the most important issues concerning cross-border taxation of interest, with a special focus on tax treaty matters. As such, it is an essential reference for international tax students, practitioners and academics. This book is part of the EC and International Tax Law Series View other titles in the series Downloads Sample excerpt, including table of contents Contributor (s) Autilia Arfwidsson, Brian Arnold, Valentin Bendlinger, Gabriel Bez-Batti, Yariv Brauner, Chloe Burnett, Felicity Cullen, Robert J. Danon, Rébecca Dorasamy, Werner Haslehner, Marjaana Helminen, Georg Kofler, Sarah Lauer, Geoffrey Loomer, Thomas Louvel, A.J. Marais, Félix Daniel Martinez Laguna, Markus Mittendorfer, Gerald Montagu, Bart Peeters, Irene Sarzi Sartori, Jacques Sasseville, Luís Eduardo Schoueri, Jonathan Schwarz, D.P. Sengupta, Cesare Silvani, Stan Stevens, Florian Striefler, C John Taylor, Lars Vanneste
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.134 | 0.045 |
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".