MétaCan
Menu
Back to cohort
Record W3119164630 · doi:10.5267/j.ac.2020.12.012

The effect of tax revenues on GDP growth in Jordan

2021· article· en· W3119164630 on OpenAlexvenueno aff
Khaled Abdalla Moh’d AL-Tamimi, Ashraf Bataineh

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsRevenueTax revenueEconomicsValue-added taxIndirect taxMonetary economicsTax reformIncome taxPublic economicsInternational economicsBusinessFinance

Abstract

fetched live from OpenAlex

The research aims to identify the impact of tax revenues on the growth of the gross domestic product (GDP) in Jordan during the period 2000-2018. The research reaches a set of results, which is that the greater the value of tax revenues by one unit, the greater the value of the GDP in Jordan by 7.257 units during the same period. There is also a positive effect of tax revenues on the growth and increase of the GDP in Jordan, however, there is no common integration between tax revenues and the GDP in Jordan. Moreover, there is a correction from the short term to the long term and there is an effect of the long-term correction of the relationship between tax revenues and the GDP during the study period. The study recommends the need to work to facilitate the procedures for individuals to pay taxes through modern technological means, work to develop and simplify tax services and raise the level of transparency in tax dealings with all individuals, the need to make amendments to the tax law in order to match the living conditions of individuals and achieve the highest efficiency in collecting due taxes, increasing tax exemptions that are offered to foreign investment to encourage them for investments in all economic, commercial, service and industrial fields in Jordan, working to diversify sources of income for the Jordanian economy and not to rely entirely on tax revenues as a primary source of income.

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)0.001
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 score0.212
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueAccountingSame topicFiscal Policy and Economic GrowthFrench-language works237,207