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Record W2117889655 · doi:10.5539/ijef.v5n6p157

Foreign Direct Investments and Economic Growth in Sub-Saharan African Countries: A Comparative Analysis between Landlocked Countries and Countries Having Access to the Sea

2013· article· en· W2117889655 on OpenAlexvenueno aff
Luc Nembot Ndeffo, David Kamdem, Roger Tsafack Nanfosso

Bibliographic record

VenueInternational Journal of Economics and Finance · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsLandlocked countryForeign direct investmentOpenness to experiencePanel dataInternational economicsDeveloping countryEconomicsBusinessInternational tradeDevelopment economicsEconomic growthMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

Institutional reforms implemented since the beginning of the Nineties resulted in a substantial increase in Foreign Direct Investments (FDI) inflow into Sub-Saharan Africa. The present study uses data on 32 countries to evaluate the impact of FDI on economic growth through panel data regressions for the period 1988-2008. The study captures the incidence of commercial openness through a comparison between the landlocked countries and those having access to the sea. The results show that FDI have a positive and significant effect on economic growth in countries that have access to the sea whereas for the landlocked countries, the results are not significant. It is therefore recommended that African countries continue to implement policies favorable to the attraction of FDI. Landlocked countries should lay a particular emphasis on the construction of infrastructures (roads, railways, airports, and phone) that facilitate the flow of goods towards the different ports for shipment to countries where their goods are more demanded.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.249
Teacher spread0.218 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

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