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Record W2343806928 · doi:10.1111/iere.12173

EFFICIENCY AND BARGAINING POWER IN THE INTERBANK LOAN MARKET

2016· article· en· W2343806928 on OpenAlexaffabout
Jason Allen, James Chapman, Federico Echenique, Matthew Shum

Bibliographic record

VenueInternational Economic Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsBank of Canada
Fundersnot available
KeywordsInefficiencyInterbank lending marketBargaining powerEconomicsLoanMarket powerMonetary economicsArbitrageSample (material)Financial crisisValue (mathematics)Core (optical fiber)BusinessMicroeconomicsFinanceMarket liquidityMacroeconomics

Abstract

fetched live from OpenAlex

Using detailed transactions‐level data on interbank loans, we examine the efficiency of an overnight interbank lending market and the bargaining power of its participants. Our analysis relies on the equilibrium concept of the core, which imposes a set of no‐arbitrage conditions on trades in the market. For Canada's Large Value Transfer System, we show that although the market is fairly efficient, systemic inefficiency persists throughout our sample. The level of inefficiency matches distinct phases of both the Bank of Canada's operations as well as phases of the 2007–8 financial crisis. We find that bargaining power tilted sharply toward borrowers as the financial crisis progressed and (surprisingly) toward riskier borrowers.

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)0.005
metaresearch head score (Gemma)0.015
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.004
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.256
Teacher spread0.235 · 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

Citations7
Published2016
Admission routes2
Has abstractyes

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