Bibliographic record
Abstract
I examine one way of taxing international corporate income that has not previously been studied, “residence-based formulary apportionment” or RBFA. I first offer a new taxonomy of different ways of taxing corporate income by reference to individual shareholders, and distinguish what I call the “shareholder attribution” approach from integration, pass-through, and other approaches. I then argue that although traditional international legal norms had led international tax design to avoid taxing foreign corporations “unconnected” with the taxing jurisdiction (e.g. foreign corporations earning only foreign income), these legal norms have gone through substantial transformations in recent years. The exercise of jurisdiction over foreign corporations has vastly expanded in the sphere of international taxation, as has the extent of mutual assistance in tax collection. Consequently, the choice between taxing foreign corporations and taxing shareholders should be made mainly on administrative (including enforceability) grounds other than international legal norms. Against this new landscape of international tax law, I compare the relative administrative advantages of two forms of tax design that implement exclusively-individual-shareholder-residence-based taxation of corporate income: the shareholder attribution approach, and RBFA. I conclude that while otherwise promising, RBFA is infeasible because it is incompatible with most corporations’ need to make pro rata distributions.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".