The Emotional Rewards of Prosocial Spending Are Robust and Replicable in Large Samples
Bibliographic record
Abstract
Past studies show that spending money on other people— prosocial spending—increases a person’s happiness. However, foundational research on this topic was conducted prior to psychology’s credibility revolution (or “replication crisis”), so it is essential to ask whether the evidence supporting this claim is robust and replicable. Here, we consider all 15 published preregistered experiments on prosocial spending to evaluate whether there is causal evidence for the idea that spending money on other people promotes happiness. Although the evidence appears somewhat mixed, we argue that the emotional benefits of prosocial spending are robust and replicable in large samples. These benefits are particularly likely when people have some choice about whether or how to give and when they understand how their generosity makes a difference. This review provides renewed support for the idea that prosocial spending promotes happiness and offers a template for revisiting phenomena that were established prior to the credibility revolution.
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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 | 0.104 | 0.296 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".