Under What Conditions Does Prosocial Spending Promote Happiness?
Bibliographic record
Abstract
Under what conditions does prosocial spending promote happiness? In a series of well-powered and pre-registered experiments, the present research revisited the role of impact, social connection, and perceived choice in maximizing the emotional benefits of spending money on others. In two exploratory studies, we found that happy (vs. less happy) prosocial spending experiences were marked by higher levels of impact, social connection and perceived choice (Study 1a and 1b). Consistent with these initial findings, three pre-registered studies revealed confirmed that spending money on others was particularly rewarding when people were able to see the difference their generosity made (Study 2); when they felt a sense of social connection to the person or cause they were helping (Study 3); and when they felt that the decision to help was freely chosen (Study 4). Together, our findings corroborate previous research on impact, social connection and perceived choice, and highlight the importance of considering these key variables when evaluating old and new evidence on prosocial spending on happinessthe emotional benefits of prosocial spending. In addition, our findings suggest that the present work urges charitable organizations and policymakers should to review their current solicitation strategies and pay more attention to people’s sense of impact, connection and choice when seeking charitable donations.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".