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Record W2726344105 · doi:10.5539/ijef.v9n8p33

Institutional Dimension of Investment Profile, Natural Resources & Foreign Direct Investments: A Case of MENA Oil Producing Countries

2017· article· en· W2726344105 on OpenAlexvenueno aff
Sahar Hassan Khayat

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNatural Resources and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentEndowmentEconomic rentNatural resourceEconomicsPanel dataResource curseInternational economicsDimension (graph theory)International tradeEconometricsMacroeconomicsMarket economyPolitical science

Abstract

fetched live from OpenAlex

The economic development of countries depends on the flow of foreign direct investment. The natural resources are responsible for maximum attraction of FDI in MENA countries. The study has aimed to examine the impact of institutional dimension of investment profile and natural resources on the flow of foreign direct investment in MENA countries. The study has included 17 MENA countries for the generation of incomplete and unbalanced panel data for the years 1960-2012. The study has considered FDI as dependent variable; while, the independent variables include location dimension, institutional dimension, new theory trade, and other economic determinants. The basic dunning OLI paradigm is combined with different variables; and the results were compared with previous studies. The flow of FDI in MENA countries is affected by different natural resources. The application of resource curse to FDI flow in MENA countries represents the negative correlation between energy endowment and FDI flow. The results showed that oils rents are not statistically significant. Moreover, investment profile and oil relative production were negatively correlated. The importance of natural resources and FDI determinants in MENA countries has been determined in the present study.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.749
Threshold uncertainty score0.644

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.236
Teacher spread0.211 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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