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Record W2896513441

Residence-Based Formulary Apportionment: (In-)Feasibility and Implications

2018· article· en· W2896513441 on OpenAlexaff
Wei Cui

Bibliographic record

VenueeYLS (Yale Law School) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsShareholderInternational taxationJurisdictionBusinessTaxable incomeIncome taxDouble taxationCorporate taxAccountingInternational lawApportionmentPublic economicsTax avoidanceLaw and economicsEconomicsCorporate governanceFinanceLawTax reformPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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 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)0.000
metaresearch head score (Gemma)0.000
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 score0.161
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.034
GPT teacher head0.257
Teacher spread0.223 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2018
Admission routes1
Has abstractyes

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