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Record W2210581846 · doi:10.5539/ijef.v8n1p217

The Effect of Value Add Tax on Economic Growth and Its Sources in Developing Countries

2015· article· en· W2210581846 on OpenAlexvenueno aff
Seyed Hossein Ghaffarian Kolahi, Zaleha Bt Mohd Noor

Bibliographic record

VenueInternational Journal of Economics and Finance · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDeveloping countryProductivityCapital accumulationWelfarePanel dataCapital (architecture)Value (mathematics)Growth modelMonetary economicsMacroeconomicsHuman capitalMarket economyEconomic growthEconometrics

Abstract

fetched live from OpenAlex

Today the role of economic growth for its effect on social welfare is undeniable. For this reason, the factors influencing the economic growth are taken into account by policy makers and researchers. On the other hand, the VAT has been considered by most of the countries due to its numerous advantages and benefits. Hence, investigating how this type of tax affects the economic growth seems to be indispensable, particularly in developing countries. In this study, the effect of value added tax on economic growth was examined especially on the developing countries. In details, the effects of VAT on the economic growth of 19 developing countries for duration of 1995 to 2010 were investigated. For analysing the data, the GMM panel was employed because of the structure of the model. Afterwards, the effect of VAT through the channel of saving on the capital accumulation and productivity and ultimately the economic growth was examined. The results revealed that VAT has a negative effect on capital accumulation growth in the level; the positive effect of VAT on the level of economic growth seems to have been imposed through channels other than the increase of saving and its effect on capital accumulation.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.473

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 head0.021
GPT teacher head0.230
Teacher spread0.208 · 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

Citations14
Published2015
Admission routes1
Has abstractyes

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