Compared to Dematerialized Money, Cash Increases Impatience in Intertemporal Choice
Bibliographic record
Abstract
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 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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".