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Record W4286517428 · doi:10.18280/ria.360306

Foreign Direct Investment and Information Communication Technology Taxation Effects on Tax Income Growth

2022· article· en· W4286517428 on OpenAlexvenueno aff
Cordelia Onyinyechi Omodero, Promise Iheanyichukwu Ujah, F. O. Iyoha

Bibliographic record

VenueRevue d intelligence artificielle · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
FundersCovenant University
KeywordsForeign direct investmentInformation and Communications TechnologyTax revenueEconomicsDouble taxationIndirect taxIncome taxRevenuePublic economicsInternational economicsBusinessTax avoidanceDescriptive statisticsState income taxMonetary economicsTax reformMacroeconomicsAccounting

Abstract

fetched live from OpenAlex

The necessity to confirm the effectiveness of ICT tax and FDI to government income from tax in Nigeria. The policy to levy ICT-based enterprises appears to raise firms' tax burdens, however it requires rigorous analysis to determine if this policy should be amended or supported. The purpose of this study is to look into the boost that ICT taxes and FDI inflows have brought to tax revenue collection in Nigeria. The study employs descriptive and inferential statistics, different diagnostic tests, and ordinary least squares techniques to analyze the efficacy of ICT taxes and FDI in increasing general tax income. The analysis shows that ICT tax has a positive and considerable impact on tax revenue, whereas FDI inflows have a somewhat favorable impact but have no effect on tax revenue. The global growth of ICT and the accompanying tax in Nigeria has certain economic and financial ramifications that have not been examined. Several researches on ICT and FDI have been published, however this unusual taxing feature has yet to be explored in the current studies.

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 score0.460
Threshold uncertainty score0.633

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 head0.022
GPT teacher head0.214
Teacher spread0.191 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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