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Record W3196308402 · doi:10.1177/00222429211047237

Minimum Payments Alter Debt Repayment Strategies Across Multiple Cards

2021· article· en· W3196308402 on OpenAlexaff
Samuel Hirshman, Abigail B. Sussman

Bibliographic record

VenueJournal of Marketing · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsBooth University College
Fundersnot available
KeywordsPaymentDebtCredit cardBusinessDispersion (optics)Consumer debtEconomicsMonetary economicsPublic economicsActuarial scienceMarketingFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.261
Teacher spread0.247 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2021
Admission routes1
Has abstractyes

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