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Record W2261966010

Responding to Sovereign Funds: Are We Looking in the Right Place?

2009· article· en· W2261966010 on OpenAlexaff
Wei Cui

Bibliographic record

VenueeYLS (Yale Law School) · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicState Capitalism and Financial Governance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSovereign wealth fundBusinessInstitutional investorTax revenuePensionPrivate pensionInternational economicsMarket economyMonetary economicsEconomicsFinancePublic economicsIncentiveCorporate governance
DOInot available

Abstract

fetched live from OpenAlex

At a superficial glance, Internal Revenue Code Section 892 appears to favor sovereign wealth funds (SWFs) over foreign private investors by exempting the former from tax on a significant range of US investments. This has recently led to calls for its abolition. Several authors, however, have challenged this view by pointing out that the impact of US tax on the relative competitiveness of SWFs and private investors should be analyzed in terms of the investors’ comparative, not absolute, advantage. And such analysis hinges on whether foreign private investors are taxed by their home countries on a worldwide basis, as well as on how SWFs are taxed in other countries where they invest. In support of these challenges, I discuss two hitherto under-noticed facts: the prevalence of the practice of worldwide taxation among countries generating the most investments into the US, and the fact that SWFs themselves may be taxed at home. Both buttress the conclusion that current US tax law is unlikely to have disadvantaged private investors. Moreover, the institutional characteristics SWFs imply that they lie in between foreign government pension funds and commercial state-owned enterprises. Changing current US tax law would unjustifiably hurt the former group of foreign investors, while having no policy effect on the latter, more controversial group of investors.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.231
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.

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
Published2009
Admission routes1
Has abstractyes

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