MétaCan
Menu
Back to cohort
Record W2622661212 · doi:10.5430/afr.v6n3p10

The Impact of Public Expenditure and Public Debt on Taxes: A Case Study of Jordan

2017· article· en· W2622661212 on OpenAlexvenueno aff
Ateyah Mohammad Alawneh

Bibliographic record

VenueAccounting and Finance Research · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsCapital expenditureEconomicsPublic expenditurePublic financeAggregate expenditureDebtRevenueMonetary economicsPublic economicsFinanceMacroeconomics

Abstract

fetched live from OpenAlex

The study aimed to estimate the impact of capital expenditure, current expenditure and external and internal public debt on taxes in Jordan during the period 2001–2014. It adopted the multiple linear regression method by E-views program to study the impact of the independent variables (represented by capital expenditure, current expenditure, external and internal public debit) on the dependent variable (taxes). The statistical analysis showed a statistically significant, positive impact of both the capital expenditure and the current expenditure on taxes. The study also found a statistically significant, positive relationship between external and internal public debt on taxes in Jordan. The study presented a number of recommendations, most importantly for the public sector, taking into account the capital expenditure, the current expenditure and the external and internal public debt, which directly affect the tax increases in Jordan. There is a need to use non-traditional alternatives to finance capital expenditures instead of external public debt and internal sources, such as Sukuk Murabaha Islamic participation, to finance capital expenditure for the Government to build schools, hospitals and other government services. The Government should take into account the current expenditure of tax revenues, while capital expenditure should be covered by non-traditional means.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.159
GPT teacher head0.368
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.

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

Citations13
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAccounting and Finance ResearchSame topicFiscal Policy and Economic GrowthFrench-language works237,207