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Record W1977919084 · doi:10.1080/13504850500426301

The effect of the corporate tax rate on the trade balance

2007· article· en· W1977919084 on OpenAlexaff
Koichi Yoshimine, Stefan C. Norrbin

Bibliographic record

VenueApplied Economics Letters · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsEconomicsCorporate taxMonetary economicsBalance of tradeInternational economicsBalance (ability)Exchange rateValue-added taxMacroeconomicsTax avoidance

Abstract

fetched live from OpenAlex

Abstract Past research indicates that MNCs may be engaging in income shifting practices. Such practices could bias the trade balance, if the intrafirm trade is substantial. This article examines the effect of the tax differential on the trade balances of OECD countries. The results indicate that the trade balance is adversely affected when the tax differential is positive, for a number of countries. Notes 1 For example, Soyoung (Citation2001) utilizes a VAR approach in examining the effects of the monetary innovations on the trade balance. 2 Operating surplus is defined as: Gross output at producer's value less intermediate consumption, compensation of employees, consumption of fixed capital and indirect taxes reduced by subsidies. 3 According to Pricewaterhouse Coopers (Citation2001), the only OECD countries that adopt progressive rates are Belgium, Japan, Korea, the United Kingdom and the United States. 4 The progressive tax structure for Japan and the USA appears to dominate the results, therefore these countries were eliminated from the results. Note, in Table 2, that the remaining trade share is still large even without these countries. 5 Because of the possibility that the variables in the regression may be nonstationary we also tested the residuals in Table 2 for stationarity. All residuals were found to be stationary, thus avoiding a possible spurious regressor problem. See Yoshimine and Norrbin (Citation2004) for details.

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 categoriesnone
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 score0.739
Threshold uncertainty score0.301

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)0.0000.000

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 head0.010
GPT teacher head0.170
Teacher spread0.160 · 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 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

Citations3
Published2007
Admission routes1
Has abstractyes

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