From balances to behaviors: insights into credit card repayment patterns among Canadian households
Bibliographic record
Abstract
Purpose This study investigates the credit card repayment behaviors of Canadian households, identifying factors affecting balance carrying, payment making and repayment delays. Design/methodology/approach The study uses data from five waves of the Survey of Financial Security (SFS) spanning 1999–2019. Simple and multinomial probit models are employed to estimate the likelihood of balance carrying, skipping or delaying payments and other repayment behavior among Canadian credit card users. Findings One-third of Canadian credit card users carry balances, with over two-thirds skipping or delaying payments due to financial difficulties. Younger female-headed households with larger families are less likely to pay off monthly balances. Education significantly impacts repayment behavior, with heads holding a university degree about 13% more likely to pay off balances and 19% less likely to skip or delay payments than those without a high school diploma. Higher debt burdens and costly loans negatively affect repayments. Households with a mortgage are more likely to make payments below the minimum than outright homeowners. In 2019, payday loan borrowers were 25% less likely to pay off balances and 28% more likely to skip or delay payments than non-borrowers. Owning liquid financial assets, such as stocks and savings, enhances repayment likelihood. Households expecting worse future financial conditions are more likely to pay off balances. Financial education and access to affordable credit could improve repayment behaviors. Originality/value This study provides new insights into the determinants of credit card repayment behavior in Canadian households, offering evidence on the role of education, debt burden and access to affordable credit in shaping repayment outcomes. By examining these dynamics over two decades, the study contributes to a deeper understanding of household financial behavior and potential policy interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".