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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 OpenAlexaffabout
Matthew Strathearn

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.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.157

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0160.001

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 head0.078
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

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 designSimulation or modeling
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

Citations2
Published2020
Admission routes2
Has abstractyes

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