MétaCan
Menu
Back to cohort
Record W4405349602 · doi:10.1093/jla/laae008

Strategic Incentives for Adopting the Global Minimum Tax

2024· article· en· W4405349602 on OpenAlexfundno aff
Wei Cui

Bibliographic record

VenueThe Journal of Legal Analysis · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsIncentiveBusinessIndustrial organizationPublic economicsMicroeconomicsEconomics

Abstract

fetched live from OpenAlex

Abstract The USA, alongside many other nations, presently faces a vital policy choice: should it adopt the global minimum tax proposed by the Organization for Economic Cooperation and Development, purportedly to ensure basic levels of corporate taxation of large multinationals? I set out a framework for analyzing and predicting global minimum tax adoption by self-interested, national-income-maximizing governments. Contrary to both popular and prior scholarly claims, the global minimum tax is incentive incompatible: countries from which multinationals originate will likely suffer deep losses; the tax’s purported enforcement tool, even read in an aggressive, controversial fashion, is ineffective. The global minimum tax may unravel despite initial adoption. (JEL codes: F23, F55, H25, H73, H87, K34).

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.261
Teacher spread0.217 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Legal AnalysisSame topicFiscal Policy and Economic GrowthFrench-language works237,207