Minimum Payments Alter Debt Repayment Strategies Across Multiple Cards
Bibliographic record
Abstract
U.S. households currently hold $770 billion in credit card debt, often managing repayments across multiple accounts. The authors investigate how minimum payment requirements (i.e., the requirement to allocate at least some money to each account with a balance) alter consumers’ allocation strategies across multiple accounts. Across four experiments, they find that minimum payment requirements cause consumers to increase dispersion (i.e., spread their repayments more evenly) across accounts. The authors term this change in strategy “the dispersion effect of minimum payments” and provide evidence that it can be costly for consumers. They find that the effect is partially driven by the tendency for consumers to interpret minimum payment requirements as recommendations to pay more than the minimum amount. While the presence of the minimum payment requirement is unlikely to change, the authors propose that marketers and policy makers can influence the effects of minimum payments on dispersion by altering the way that information is displayed to consumers. Specifically, they investigate five distinct information displays and find that choice of display can either exaggerate or minimize dispersion and corresponding costs. They discuss implications for consumers, policy makers, and firms, with a particular focus on ways to improve consumer financial well-being.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".