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Record W3005302591 · doi:10.34989/swp-2020-4

A Spatial Model of Bank Branches in Canada

2020· preprint· en· W3005302591 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueRePEc: Research Papers in Economics · 2020
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicSpatial and Panel Data Analysis
Canadian institutionsBank of Canada
Fundersnot available
KeywordsCompetitor analysisSpatial distributionCompetition (biology)Economic geographyMarket structureDistribution (mathematics)Spatial dependenceGeographySpatial econometricsMarket sizeBanking industryBusinessEconometricsEconomicsIndustrial organizationMathematicsFinancial systemStatisticsCommerceMarketingEcology

Abstract

fetched live from OpenAlex

This study explores the market structure of the Canadian banking industry at the postal-code level. In particular, we study the effect of geographic and industrial concentration on the density of bank branches. Our analysis makes use of a novel dataset of bank branch locations across Canada over the period 2008 to 2018. We employ a spatial panel model with two-way fixed effects that accounts for spatial spillovers across adjacent postal codes. This encompassing model allows us to disentangle the effect of market structure from spillovers in adjacent regions. Our main finding is that market structure is not correlated with the density of bank branches. However, we do find that branch density is significantly correlated with socioeconomic characteristics of the postal code. We also find that the big five banks tend to avoid markets dominated by smaller banks and credit unions. Similarly, smaller banks and credit unions avoid markets with a high concentration of the big five banks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.566
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.078
GPT teacher head0.266
Teacher spread0.188 · 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