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Record W4397037826 · doi:10.5539/jsd.v17n3p81

Resilience of Developing Economies to External Shocks: Empirical Evidence from CEMAC Countries

2024· article· en· W4397037826 on OpenAlexvenueno aff
Peter Ajonghakoh Foabeh, Vesarach Aumeboonsuke

Bibliographic record

VenueJournal of Sustainable Development · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional resilience and development
Canadian institutionsnot available
Fundersnot available
KeywordsResilience (materials science)EconomicsDeveloping countryEmpirical evidencePsychological resilienceMonetary economicsEconomic growthPsychology

Abstract

fetched live from OpenAlex

This study explores the effects of the 1994 CFA currency depreciation, the 2008 Global Financial Crisis (GFC), and instances of political coups on the relationships between FDI inflow, economic growth, and governance in the Central African Economic and Monetary Community (CEMAC) countries. By examining the impact of these events on FDI, growth, and governance, this paper provides important details of responses to external shocks and internal political disruptions. We employ panel VAR analysis with data from 1990 to 2019 to explore the dynamic relationships among these variables. The results show that growth and governance are not determining factors for attracting FDI in the CEMAC sub-region. Governance, on the other hand, stands as a determining factor for growth. Our findings also suggest that the 1994 CFA currency depreciation, 2008 GFC, and coups had no significant impact on FDI, growth, and governance. Although these events’ effects may expose the countries’ vulnerability to external shocks influencing the dynamics of FDI, economic growth, and governance, their impact did not seem to be evident. However, political instability, evidenced by coups, emerges as a crucial factor shaping the interactions between FDI, growth, and governance in CEMAC countries. Our analysis was conducted using the EViews software package.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.414
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.289
Teacher spread0.236 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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