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Record W2555573793 · doi:10.1080/09638199.2016.1249392

Trade openness, foreign direct investment, and finance-growth nexus in the Eurozone countries

2016· article· en· W2555573793 on OpenAlexaff
Rudra P. Pradhan, Mak B. Arvin, John H. Hall, Mahendhiran Nair

Bibliographic record

VenueJournal of International Trade & Economic Development · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsTrent University
Fundersnot available
KeywordsForeign direct investmentEconomicsOpenness to experienceNexus (standard)Error correction modelInternational economicsMonetary economicsDebtInvestment (military)MacroeconomicsCointegration

Abstract

fetched live from OpenAlex

The paper investigates causal relationships between trade openness, foreign direct investment, financial development, and economic growth in 19 Eurozone countries over the period 1988–2013. Using a panel vector error-correction model (VECM), the empirical results show that these variables are cointegrated. The study shows that a combination of opening the Eurozone countries for trade and fostering their financial and economic development have elevated inflows of foreign direct investment into the region in the long run. At the same time, increasing inflows of foreign direct investment in the short run have propelled economic growth, which in return has strengthened the role of financial development and international trade to sustain economic growth in the region through feedback effects. The empirical results have important policy implications for countries in the Eurozone, especially those who face challenges as a result of lack of confidence in their financial system and those who face a sovereign debt crisis.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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

Citations58
Published2016
Admission routes1
Has abstractyes

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