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Record W4411148362 · doi:10.1016/j.tncr.2025.200127

Tax us, if you can: a game theoretic approach to the European Union's political impasse on a new corporate tax system

2025· article· en· W4411148362 on OpenAlexvenueno aff
Joana Andrade Vicente

Bibliographic record

VenueTransnational Corporation Review · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
FundersFundação para a Ciência e a Tecnologia
KeywordsPoliticsEuropean unionCorporate taxPolitical scienceEconomicsPolitical economyTax avoidanceDouble taxationPublic economicsInternational tradeLaw

Abstract

fetched live from OpenAlex

In this paper we theoretically analyse the European Union’s ongoing political impasse regarding the choice of a single method to allocate multinational enterprises’ profits across countries and we find that this strategic situation resembles a coordination game with distributional consequences. The two Nash equilibria involve no efficiency trade-off, but the conflictual distribution of welfare gains and the presence of heterogeneous preferences have been preventing the implementation of a long-term comprehensive tax policy reform. A unitary taxation approach with formulary apportionment in the European Union is better suited to tackle artificial profit shifting via transfer pricing and would mean an evolutionary change without disrupting the current international tax policy environment. It would restore faith in fairness of the European tax system, while also allowing for further global coordinated actions to tackle the gradual decline and inadequacy of the current international transfer pricing standard-based regulatory model (based in legally non-binding standards and guidelines).

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.007
Scholarly communication0.0080.006
Open science0.0020.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.031
GPT teacher head0.243
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2025
Admission routes1
Has abstractyes

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