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Record W2781616120 · doi:10.16980/jitc.13.6.201712.497

A Comparative Analysis of Customs Duties Burden in Major OECD Countries

2017· article· en· W2781616120 on OpenAlexaboutno aff
Seuk-Do Kie

Bibliographic record

VenueKorea International Trade Research Institute · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Arbitration and Investment Law
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessInternational tradeInternational economicsEconomics

Abstract

fetched live from OpenAlex

This study examines income elasticity of tax revenue for major OECD countries such as Korea, Canada, France, Japan, Norway, Spain, the United Kingdom and the United States, empirically analyzing their customs duties burden with the use of the annual data from 1990 to 2015, and clarifying their implications. Using the method of empirical analysis, the effect of tax law revision was not considered in estimating income elasticity of tax revenue (buoyancy). By comparing and analyzing the results with major OECD countries, policy implications of customs duties burden are clarified. According to the empirical results, elasticity of customs duties on the GDP of five countries, namely, Korea, Canada, Japan, Norway and the United States, showed a positive relationship between GDP and customs revenue. However, France, Spain and the United Kingdom reflected a negative relationship between GDP and customs revenue. After removing autocorrelation, almost all countries except Norway and Japan showed the same results as those before autocorrelation. Korea had a lower level of income tax and general consumption tax than the OECD average whereas it had a higher level of corporate tax, property tax, customs and import duties. Notably, the tax revenue as a percentage of total taxation of customs and import duties was the highest in Korea among OECD countries. Based on these results, implications of the need for an appropriate tax policy and directions for future research can be gleaned.

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: none
Teacher disagreement score0.963
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
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.114
GPT teacher head0.386
Teacher spread0.272 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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