MétaCan
Menu
Back to cohort
Record W1550948238 · doi:10.5539/res.v7n7p69

The Influence of Value Added Tax (Vat) Assessment on Income Distribution of Consumer of Garment in West Java

2015· article· en· W1550948238 on OpenAlexvenueno aff
Stellamaris Metekohy

Bibliographic record

VenueReview of European Studies · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicTaxation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsPurchasing powerDistribution (mathematics)Index (typography)TariffIncome distributionValue-added taxValue (mathematics)Income taxLabour economicsInequalityPublic economicsMacroeconomicsInternational economics

Abstract

fetched live from OpenAlex

Act No. 18 of 2000 regarding burden of Value Added Tax is bore by the consumer, but the implementation indicates that there is industrial policy to bear a part or an overall of VAT. Accordingly, if burden distribution is progressive, the higher household income means a higher VAT burden bore by the consumer, conversely, the lower consumer income, the lower VAT bore by him. Consequently, it will cut real income down, and it affects consumer’s purchasing power. Research objectives are to find out if distribution of industrial VAT burden is progressive, regressive or proportional, and to find out to what extend influence of VAT on distribution of household income. The influence of VAT on distribution of consumer’s household income will be tested by applying effective tariff, Gini index, and progressivity index. The findings indicate that effective tariff of VAT is progressive (positive value) meaning that consumer and industrial VAT burden is increasing. Calculation of Gini index before and after VAT assessment make consumer’s earning gaps smaller, while calculation of progressivity index indicates industrial tax system is progressive (positive value) indicating that most of consumers come from middle to upper class.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.324

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.082
GPT teacher head0.323
Teacher spread0.241 · 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 designObservational
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
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueReview of European StudiesSame topicTaxation and Compliance StudiesFrench-language works237,207