MétaCan
Menu
Back to cohort
Record W4324133232 · doi:10.1093/isq/sqad012

Where Should Multinationals Pay Taxes?

2023· article· en· W4324133232 on OpenAlexafffund
Vincent Arel‐Bundock, André Blais

Bibliographic record

VenueInternational Studies Quarterly · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsUniversité de Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLegitimacyTax reformTax avoidanceEconomicsTechnocracyConsistency (knowledge bases)Public economicsPolitical economyPolitical scienceInternational economicsLawPolitics

Abstract

fetched live from OpenAlex

Abstract The international tax system is a pillar of the post-war economic order, but it faces major challenges with the rise of global value chains, digitalization, and tax avoidance. Debates over international tax reform usually occur within a small epistemic community of experts and technocrats. In this article, we step outside this restricted circle to assess the sources of bottom-up legitimacy and support for the rules that govern where multinationals must report profits and which governments are entitled to tax those profits. We conduct survey experiments in Brazil, France, and the United States to assess mass attitudes toward the allocation of the tax base across countries. We find that people’s views clash with the core principles of the current regime, but are aligned with reform proposals that allocate more taxing rights to market jurisdictions. These findings are strikingly consistent across three countries and three distinct studies. At first glance, the consistency of attitudes across countries could spell good things for international cooperation in this arena. However, we also find a significant level of “home bias” in the public’s views on tax allocation. These results shed new light on the legitimacy of tax reform and on the prospects for cooperation in a key area of international economic relations.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.996

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)0.0010.005

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 head0.076
GPT teacher head0.321
Teacher spread0.245 · 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.

Study designNot applicable
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

Citations7
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueInternational Studies QuarterlySame topicCorporate Taxation and AvoidanceFrench-language works237,207