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Record W2914258438 · doi:10.1002/jcpy.1098

Compared to Dematerialized Money, Cash Increases Impatience in Intertemporal Choice

2019· article· en· W2914258438 on OpenAlexafffund
Rod Duclos, Mansur Khamitov

Bibliographic record

VenueJournal of Consumer Psychology · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicDecision-Making and Behavioral Economics
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCashEconomicsIntertemporal choicePatienceDynamic inconsistencyMonetary economicsMicroeconomicsPsychologyFinanceSocial psychology

Abstract

fetched live from OpenAlex

When it comes to trading time for money (or vice versa), people tend to be impatient and myopic. Often dramatically so. For illustration, half of people would rather collect $15 now than $30 in 3 months. This willingness to forego 50% of the reward to skip a 3‐month wait corresponds to an annual discount rate of 277%. This article investigates how money's physical form biases intertemporal choice. We ask, what happens to (im)patience (i.e., discount rates) when time is traded against cash rather than against an equivalent sum of dematerialized money? We find that intertemporal decisions pitting time against cash (rather than against dematerialized money) increase impatience. The underlying mechanism relates to the pain of parting from money. Letting go of cash (dematerialized money) we can have now is psychologically more (less) painful, which in turn reduces (increases) our willingness to wait for larger‐later payoffs. Importantly, heightening prevention focus (i.e., concerns for safety and security) moderates this bias. The article concludes by discussing the implications of the research, particularly for the psychology of saving behavior.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.141
GPT teacher head0.475
Teacher spread0.333 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2019
Admission routes2
Has abstractyes

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