Language and economic behaviour: Future tense use causes less not more temporal discounting
Bibliographic record
Abstract
Previous studies have found cross-cultural correlations between linguistic obligations for talking about future events and economic decisions like saving money. The hypothesis is that a grammatical obligation to use the future tense (e.g. will) causes speakers to perceive future rewards as temporally distal and therefore less valuable ("temporal discounting"). However, no studies have tested whether speakers actually temporally discount as a function of the extent to which they use the future tense. We present two studies which use a novel language-elicitation paradigm to do this, involving speakers of English (which obliges the future tense) and Dutch (which does not). We used mediation analysis to test how language-level differences in the grammatical obligation to use the future tense impact economic decisions via individual language use habits. However, we found that English speakers who habitually make greater use of the future tense actually discount less, not more. These results suggest obligatory future tense use is not responsible for previously-reported cross-cultural correlations. Instead, we suggest that a better explanation involves modal notions of certainty (the probability of an event occurring) rather than temporal distance (when an event will occur). Future tenses express high certainty, which makes the correct prediction that obligatory tense marking should cause less discounting. In contrast, the cross-cultural differences may be driven by variation in other aspects of future time reference, such as low-certainty modal terminology (e.g. may, might).
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".