Minimum Payment Warnings and Information Disclosure Effects on Consumer Debt Repayment Decisions
Bibliographic record
Abstract
Public policy makers encourage lenders to disclose loan cost information as a way of enabling borrowers to make more-informed debt repayment decisions. For example, current regulation requires credit card lenders to include a “minimum payment warning” on borrowers' monthly statements, with the goal of encouraging borrowers to make larger monthly repayments each month and, consequently, decrease their debt levels. This research examines the effect of disclosing such information about future interest costs and time to pay off debt on consumers' repayment decisions. The results indicate that disclosing information about the effects of repaying the minimum has little impact on repayment decisions. However, disclosing information about the effect of choosing an alternative course of action (i.e., a larger repayment amount) yielded a robust effect on repayment decisions. The findings suggest that cost information increases repayment amount for some borrowers, whereas time information may decrease repayment for others, especially those with little knowledge of interest compounding. This research provides some initial evidence of the impact of the CARD Act as well as that of similar regulations in Australia and Canada.
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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 | 0.010 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
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".