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Record W3109228873 · doi:10.5430/ijfr.v11n6p37

The Impact and Contribution of FDI to Saudi Economy During King Abdullah Regime

2020· article· en· W3109228873 on OpenAlexvenueno aff
Khaled Jadeaf Alanazi, Salawati Mat Basir

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentIncentiveRepatriationCapital (architecture)Language changeInvestment (military)Government (linguistics)BusinessInternational economicsPoliticsEconomicsEconomic policyMarket economyMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

Foreign Direct Investment resulted in the disclosure of different investment chances and opportunities through active investment promotion agencies. A country must execute various reforms capable of improving the fundamental determinants of FDI for achieving a high percentage of Foreign Direct Investment. These reforms among others include improving investment laws, reducing political risk and level of corruption, establishing a consistent legitimate and regulatory environment, freeing repatriation of funds and capital, as well as opening up to international trade. Saudi Arabia adopted generous incentive policies for attracting foreign capital and invite Foreign Direct Investment during king Abdullah regime. These policies present positive incentives while eliminating negative disincentives. Positive incentives consist free custom duties, reductions of tax and export zones, by the government of Saudi Arabia. Disincentives elimination to investments indicates the removal of overlong and rigid systems as they can delay visas issuance, restraint travel and complicate the licensing and registration of a project. This paper discusses the impact of FDI on Saudi economy during King Abdullah regime and finally, ascertains the contribution of FDI to Saudi Economy during King Abdullah regime.

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)0.006
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 score0.362
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
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 science0.0010.000
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 head0.055
GPT teacher head0.428
Teacher spread0.372 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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