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Record W2763832723

Measuring the Cost of Financial Integration in the GCC: Lessons from the Global Crisis

2017· article· en· W2763832723 on OpenAlexvenueno aff
Mahmoud Haddad, Sam Hakim

Bibliographic record

VenueReview of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial crisisFinancial systemEquity (law)Financial integrationStock (firearms)Financial marketEconomicsEconomic slowdownBusinessMonetary economicsFinanceInternational economicsMacroeconomicsGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

In the wake of the global financial crisis, several leading countries of the Gulf Cooperation Council (GCC) experienced considerable economic slowdown. Equity prices tumbled, bank credit dried up, GDP growth rates came to a halt, spreads on sovereign bonds soared, and risk aversion increased dramatically. These events have demonstrated the negative consequences of financial integration which combined with financial innovation and deregulation have increased vulnerabilities in the GCC and created heightened systemic risks. Using data between 2001 and 2009, we calculate a measure of financial stress for GCC countries and estimate the harm caused by the financia l cris is to the region¡¯s real economy. Our results show that between 2008 and 2009, economic activity in the GCC slowed by 2.6% after controlling for a variety of factors such as oil and stock price movements. We discuss how policymakers can initiate countercyclical policies to stave off the damage from future financial 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)0.010
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 score0.077
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.001
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 head0.043
GPT teacher head0.259
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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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