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Record W4404160337 · doi:10.1515/rne-2024-0047

Effects of Shoe-Leather Cost on Consumer Cash Withdrawal Behavior

2024· article· en· W4404160337 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Network Economics · 2024
Typearticle
Languageen
FieldEngineering
TopicTransportation and Mobility Innovations
Canadian institutionsBank of Canada
Fundersnot available
KeywordsCashBusinessWork (physics)FinanceEngineering

Abstract

fetched live from OpenAlex

Abstract This paper studies an empirical model of shoe-leather cost applied to consumer cash withdrawal. The unique feature is to estimate the effect of shoe-leather cost from the cash inventory model by filtering out free-type consumers who do not incur shoe-leather costs. When compared to the costly-type consumers, the free-type do not need to go out of their ways from home to visit banks to withdraw cash because they can economise their travel costs by combining withdrawals with other activities, such as, one-stop multi-purpose trip on either their ways to work or shopping. We find that the cash withdrawal frequency significantly decreases with the travel distance; otherwise the estimated shoe-leather cost without distinguishing between free- and costly-types is close to zero and insignificant. This finding suggests that in order to maintain cash accessibility, the policy need not only consider the supply of physical branch infrastructure, but also account for consumer’s travel pattern.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.879
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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