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Record W3129360900 · doi:10.5430/ijfr.v12n2p389

The Impact of Trade Openness and Foreign Direct Investment on Economic Welfare in sub-Saharan Africa

2021· article· en· W3129360900 on OpenAlexvenueno aff
F.B. Adegboye, Olumide S. Adesina, F. O. Olokoyo, Stephen Ojeka, Victoria Abosede Akinjare

Bibliographic record

VenueInternational Journal of Financial Research · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
FundersCovenant University Centre for Research, Innovation and DiscoveryCovenant University
KeywordsOpenness to experienceForeign direct investmentInternational economicsEconomicsProductivityWelfareEconomic integrationForeign capitalTrade barrierInternational tradeDevelopment economicsEconomic growthMacroeconomicsMarket economy

Abstract

fetched live from OpenAlex

The sub-Saharan African region is characterized by a high relative degree of openness to trade. The region is also identified with increased inflows of foreign investments with no significant welfare improvement. Economic development emphasizes that the lack of domestic investment in the developing economies could be boosted by trade openness and inflow of Foreign Direct Investment (FDI) for impactful enhancement of capital formation. In this article, the impact of trade openness and foreign capital inflow on economic welfare was examined on a sub-regional analysis for sub-Saharan Africa. The study also appraised the effect of openness to trade and FDI inflow on the region's economic welfare. The data for 30 countries from 2000 to 2018 were collected and analyzed, with the Generalized Least Square (GLS) technique to fit the model developed. The study showed that openness to trade has a significant impact on economic welfare for all sub-Saharan Africa regions, while FDI is only significant for the Western sub-region. Hence, the study recommends that the government of the countries in the sub-Saharan Africa region should boost trade openness to enhance efficiency in productivity, and improve industrial development.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.770
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.085
GPT teacher head0.340
Teacher spread0.256 · 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

Citations9
Published2021
Admission routes1
Has abstractyes

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