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Record W2560919019 · doi:10.5539/ijef.v9n1p106

Foreign Direct Investment and Economic Growth in the Arab Region: The Case of Knowledge Spillover Effects

2016· article· en· W2560919019 on OpenAlexvenueno aff
Nayef Al‐Shammari, Huda Al-Rashid

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsSpillover effectForeign direct investmentAbsorptive capacityIncentiveEconomicsHuman capitalInternational economicsLanguage changeInvestment (military)Knowledge spilloverMonetary economicsInternational tradeMacroeconomicsMarket economyIndustrial organizationPolitics

Abstract

fetched live from OpenAlex

This research aims to focus on how institutional barriers in the Arab region may account for losses in FDI inflows along with their potential technology spillover effects, as well as to show how the deficiency of absorptive capacities serve as an important factor for attracting inflows. The analysis relies on endogenous growth models at an aggregate regional level and a microeconomic firm-level. Findings based on linear OLS regressions, reveal a positive correlation between improved institutional factors and potential FDI spillovers, with significance varying in certain countries. Policy implications involve having targeted FDI policies to enhance absorptive capacities, improving information asymmetry to reduce corruption, and enhancing the labor market regulatory framework to improve human capital development as an incentive for FDI inflows.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.012
GPT teacher head0.211
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2016
Admission routes1
Has abstractyes

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