Examining payday loan utilization among households with mainstream credit access
Bibliographic record
Abstract
Purpose This study aims to gain insight into the motivations behind the decision to use high-cost payday loans by households who possess mainstream credit and to determine whether this behavior has changed over time. Design/methodology/approach Using data from Statistics Canada’s Surveys of Financial Security, probit models are used to examine the sociodemographic and financial indicators associated with payday loan use. Findings The analysis uncovers the sociodemographic and financial characteristics of payday loan-user households with access to lower-cost short-term loans. The findings indicate that the likelihood of payday loan use has risen over time. Additional analysis reveals that indicators of financial instability are positively associated with payday loan use among this group. Research limitations/implications This research highlights the dichotomy of payday loan users and recommends policymakers tailor solutions to the specific needs of different types of payday loan users. Practical implications This research highlights the distinguishing sociodemographic and financial characteristics of payday loan user households and recommends policymakers tailor solutions to the specific needs of different types of payday loan users. Originality/value This is the first study, to our knowledge, to focus analysis on payday loan use of those with access to lower-cost short-term credit alternatives in Canada and to include measures of financial instability in the analysis. This research is timely given the current economic environment of high interest rates and high levels of household debt.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".