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Record W3107168035 · doi:10.3390/jrfm13110291

Global FDI Inflow and Its Implication across Economic Income Groups

2020· article· en· W3107168035 on OpenAlexvenueno aff
Udi Joshua, Mathew Ekundayo Rotimi, Samuel Asumadu Sarkodie

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentEconomicsOpenness to experiencePanel dataStock (firearms)Generalized method of momentsExternal debtMonetary economicsDebtOrdinary least squaresInternational economicsMacroeconomicsEconometrics

Abstract

fetched live from OpenAlex

Foreign direct investment (FDI) as a driver of growth is important in today’s globalized economy. It is extremely difficult for economies to grow sustainably without economic interactions outside their borders. However, there has been a debate on the impact of FDI inflow on economic expansion. Hence, this study investigated the influence of FDI on economic growth for a selection of 200 economies around the world for the period 1990–2018. We subdivided the sample into World Bank income group clusters to aid comparison across income blocs. The study employed panel estimation techniques including pooled ordinary least squares (POLS), dynamic panel estimation with fixed-effects and random-effects and generalized method of moments (GMM). The study found that FDI, debt stock and official development assistance are promoters of growth in the selected countries—although debt stock weakly impacts economic growth. In contrast, trade openness and exchange rates had a mixed (negative and positive) influence on economic growth. The study suggests that the creation of a conducive business environment and economic policies will attract FDI inflows. Additionally, borrowing from external sources could be minimized despite its perceived positive influence on growth to achieve financial independence.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.008
GPT teacher head0.224
Teacher spread0.216 · 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

Citations21
Published2020
Admission routes1
Has abstractyes

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