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Record W3012863868 · doi:10.5267/j.ac.2020.2.006

Conceptualizing the effects of corporate tax rate differentials on transfer pricing activities of FDI enterprises in Vietnam

2020· article· en· W3012863868 on OpenAlexvenueno aff
Hong Nhat Nguyen, Jacquline Tham, Ali Khatibi, S. M. Ferdous Azam

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsTransfer pricingBusinessForeign direct investmentCorporate taxTax rateMonetary economicsIndustrial organizationInternational economicsEconomicsDouble taxationTax avoidanceMultinational corporationMacroeconomicsFinance

Abstract

fetched live from OpenAlex

The purpose of this paper is to evaluate the differentials effects of the tax rate on transfer pricing activities in foreign direct investment enterprises in Vietnam. The study then suggests further research on the determinants over transfer pricing activities of these enterprises to have better solutions in dealing with transfer mispricing in Vietnam. A quantitative research method involving selfadministered closed-ended questionnaires were extended to Managing Directors/Chief Executive Officers, Tax Managers/ Directors, Chief Finance Officers or Heads of Finance from foreign direct investment enterprises in Vietnam. Findings indicate a strong relationship between corporate tax rate differentials and the transfer pricing activities in foreign direct investment enterprises in Vietnam. The findings support Vietnamese policymakers, academic researchers, auditors, investors to have further study on the effect of the tax rate on transfer pricing activities of the enterprises. Nevertheless, tax officials and accounting representatives can have in-depth knowledge with regards to transfer pricing activities which the outcome of this study aspires as guidance for better understanding the aspects of transfer pricing while doing business in Vietnam.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

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

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 head0.025
GPT teacher head0.215
Teacher spread0.190 · 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 designBench or experimental
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

Citations3
Published2020
Admission routes1
Has abstractyes

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