Are the benefits of prosocial spending and buying time moderated by age, gender, or income?
Bibliographic record
Abstract
In the last two decades, social psychologists have identified several key spending strategies that promote happiness such as making time-saving purchases (buying time) and spending money on others (prosocial spending). Although the emotional benefits of these two spending strategies are well-documented in the current literature, it is unclear whether the effectiveness of these strategies vary depending on individual characteristics. To address this research gap, we surveyed an economically diverse sample of 15,545 Americans about their subjective well-being, spending behavior, personal values and beliefs, as well as demographics including age, gender, and income. Across demographic groups, spending money on others was robustly related to happiness. Spending money on others was also associated with greater happiness regardless of whether participants believed that they would be happier spending money on others. In contrast, the relationship between buying time and happiness was somewhat less reliable. Although gender and personal income did not moderate the relationship between buying time and happiness, the relationship was only marginally significant for men, and non-significant within each income bracket. Our results also indicated that those who valued money over time were significantly happier when they used money to buy time, whereas those who valued time over money reported similar levels of happiness whether or not they bought time. Taken together, the present research shows that the relationship between prosocial spending, buying time, and subjective well-being is largely consistent across the different demographic groups we examined.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".