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Record W3122369052

Financial deglobalisation in banking

2017· article· en· W3122369052 on OpenAlexaboutno aff
Robert N. McCauley, Agustín S. Bénétrix, Patrick McGuire, Goetz von Peter

Bibliographic record

VenueRePEc: Research Papers in Economics · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsDeleveragingInternational bankingSubsidiaryBusinessFinancial systemBalance sheetFinancial integrationFinanceFinancial marketDebt
DOInot available

Abstract

fetched live from OpenAlex

This paper argues that the decline in cross-border banking since 2007 does not amount to a broad-based retreat in international lending ("financial deglobalisation"). We show that BIS international banking data organised by the nationality of ownership ("consolidated view") provide a clearer picture of international financial integration than the traditional balance-of-payments measure. On the consolidated view, what appears to be a global shrinkage of international banking is confined to European banks, which uniquely responded to credit losses after 2007 by shedding assets abroad - in particular, reducing lending - to restore capital ratios. Other banking systems' global footprint, notably those of Japanese, Canadian and even US banks, has expanded since 2007. Using a global dataset of banks' affiliates (branches and subsidiaries), we demonstrate that the who (nationality) accounts for more of the peak-to-trough shrinkage of foreign claims than does the where (locational factors). These findings suggest that the contraction in global lending can be interpreted as cyclical deleveraging of European banks' large overseas operations, rather than broad-based financial deglobalisation.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.004
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.045
GPT teacher head0.302
Teacher spread0.256 · 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 designNot applicable
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

Citations7
Published2017
Admission routes1
Has abstractyes

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Same venueRePEc: Research Papers in EconomicsSame topicBanking stability, regulation, efficiencyFrench-language works237,207