International trends in company tax collective investment vehicles
Bibliographic record
Abstract
The world has become an increasingly integrated and global place, creating opportunities for businesses to expand their networks beyond physical borders. This presents both opportunities and challenges to the corporate tax system, as the rise of intangibles and the digital economy creates difficulties in assessing and taxing profits and capital. This study aims to provide a cross-country comparison, drawing out the similarities and differences between corporate tax systems. Australia's corporate tax system was chosen as the centre of this study. The major North American (United States and Canada), European (United Kingdom, Germany, France, Netherlands and Ireland) and Asian (China, India, New Zealand, Japan, Korea, Hong Kong, Singapore and Indonesia) economies were selected, as they were identified as major players in the global economy and important trading partners to Australia. This is not an in-depth cross-country analysis, rather this study aims to provide a snapshot of key trends of corporate tax systems around the world. Four common indicators have been chosen for this study. The ratio of company tax to GDP has been chosen as an indicator to assess a country's reliance on the company tax base. The statutory company tax rate and thin capitalisation rules have been chosen as levers available to governments in shaping the corporate tax system. Collective investment vehicles were also chosen as an interesting and alternate lever available to governments in attracting foreign investment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".