The changing landscape of financial services in Manitoba: a location analysis of payday lenders, banks and credit unions
Bibliographic record
Abstract
The Changing Landscape of Financial Services in Manitoba: A Location Analysis of Payday Lenders, Banks and Credit Unions ABSTRACT This study traces the emergence and expansion of payday lending outlets in Winnipeg and the rural Manitoba communities of Brandon, Portage la Prairie, Thompson and Dauphin during the period 1980-2009, in order to look for shifts over time in the site location strategies of payday lenders relative to mainstream banks. Location analysis, in the context of financial exclusion theory, is used to examine the spatial void hypothesis that mainstream banks have played a role in the rise of payday lending in poor neighbourhoods where traditional bank branches are absent or under-represented. It also considers evidence for the spatial complement hypothesis that payday lenders are not geographic substitutes for mainstream banks but are instead spatial complements, serving different segments of shared markets. Results of the goodness-of-fit test and location analysis based on population data suggest that the payday lending industry in Manitoba is not exclusively located in lower income neighbourhoods or solely located in areas where there is an absence or reduced presence of bank and credit union branches. Moreover, newer, suburban and rural payday lender outlets are almost always located next to mainstream banks and credit unions. The exception would be Winnipeg’s inner-city, where payday lenders are more densely located and where mainstream banks have gradually retreated. While multi-service establishments are shown to have first gained a foothold in poor neighbourhoods as cheque-cashers, this study examines the extent to which a focus on payday loans as the lead product has been accompanied by a shift to middle-income, suburban neighbourhoods and rural communities over the study period. The results of descriptive and OLS multivariate regression analyses provide further evidence of the changing relationship of location patterns of payday lenders to neighborhood characteristics, including mainstream bank presence, income level, poverty status, population density, age, education, family type and ethnicity. The implications these findings have for ongoing policy discussions about the status of the payday loan industry in Canada are discussed. JEL Classification code: G21 - Banks; Other Depository Institutions; Microfinance Institutions; Mortgages
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".