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Record W2616348526 · doi:10.1111/eufm.12118

Tax Havens, Tax Evasion and Tax Information Exchange Agreements in the OECD

2017· article· en· W2616348526 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEuropean Financial Management · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsTax evasionTax creditMonetary economicsEconomicsInternational economicsValue-added taxAd valorem taxIndirect taxDouble taxationTax reformPreferenceBusinessPublic economicsMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Using data on Foreign Portfolio Investment (FPI), we find a positive relationship between higher tax burden and OECD residents’ tax evasion, especially via tax havens. Contrary to established investor preference for certain country characteristics, we find they are less important to tax evaders who value privacy and want to remain undetected by their home tax authorities. We find very limited evidence that OECD Tax Information Exchange Agreements (TIEAS) reduce tax evasion, controlling for other determinants of overall OECD FPI. Without the US in the OECD sample, tax havens play a lesser role and OECD policies appear to make a marginal impact.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

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

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 head0.039
GPT teacher head0.224
Teacher spread0.185 · 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