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Record W2997305025 · doi:10.6000/1929-7092.2019.08.114

The Effect of Financial Crises on Growth and FDI in some African Countries: A Panel VECM Approach

2019· article· en· W2997305025 on OpenAlexvenueno aff
Mary Onyemowo Oche, Yohane Khamfula, Gisele Mah

Bibliographic record

VenueJournal of Reviews on Global Economics · 2019
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsForeign direct investmentPanel dataMonetary economicsMacroeconomicsInternational economicsEconometrics

Abstract

fetched live from OpenAlex

This study investigates the effects of financial crises on economic growth and foreign direct investment in some African countries.A panel vector error correction model is used for the analysis of annual time series data for the period 1994 to 2014.From economic growth model, in the long run, it is observed that gross domestic product per capita is positively influenced by investment, trade and foreign direct investment; with investment and trade being statistically significant.Gross domestic product per capita has a negative significant relationship with real effective exchange rate.On the other hand, in the long run, the investment model shows that investment has a significant positive relationship with both gross domestic product per capita and investment; while it has a negative significant relationship with real effective exchange rate and trade.Also observed from the results is that financial crisis has a negative relationship with both economic growth and foreign direct investment.This study recommends more openness of the economy so as to promote both economic growth and inflow of foreign direct investment in countries.It also recommends the need to encourage more gross fixed capital formation in order to promote both economic growth and foreign direct investment.

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)0.000
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 score0.614
Threshold uncertainty score0.402

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

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