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Record W4411919122 · doi:10.1016/j.jet.2025.106050

Money and imperfectly competitive credit

2025· article· en· W4411919122 on OpenAlexafffund
Allen Head, Timothy Kam, Sam Ng, Guangqian Pan

Bibliographic record

VenueJournal of Economic Theory · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsQueen's University
FundersAustralian Research CouncilSocial Sciences and Humanities Research Council of CanadaUniversity of California, IrvineUniversity of California, RiversideQueen's UniversitySogang UniversityUniversity of Western AustraliaSungkyunkwan UniversityKeio University
KeywordsEconomicsMonetary economicsMicroeconomics

Abstract

fetched live from OpenAlex

We develop a monetary economy in which banks have market power emanating from search frictions. Distributions of both deposit and loan interest rates are equilibrium phenomena exhibiting dispersion consistent with new micro-level evidence on U.S. consumer loans and deposits. The theory accounts for incomplete pass-through of monetary policy to the distributions of loan and deposit rates through a novel channel. Imperfect competition links monetary policy to real consumption and welfare through its effects on interest rate spreads driven by market power and individual liquidity risk. Market power in deposits erodes the insurance banks provide against liquidity risk, while market power in lending enables banks to extract surplus from goods market trades. For a given inflation target, welfare gains arise if a central bank uses state-contingent monetary injections to reduce lenders' market power in response to fluctuations in aggregate demand.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.001
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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 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

Citations3
Published2025
Admission routes2
Has abstractyes

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