A narrative review of the consistency, rigor and generalizability of experiments on prosocial behaviors and happiness
Bibliographic record
Abstract
Past experimental research shows that prosocial behavior promotes happiness. But do past findings hold up to current standards of consistent, rigorous, and generalizable evidence? In this review, we considered the evidentiary value of past experiments examining the happiness (i.e., subjective well-being; SWB) benefits of prosocial action, such as spending money on others or acts of kindness, in non-clinical samples. Specifically, we examined: (1) how consistent findings are across meta-analyses, (2) the conclusions of pre-registered, well-powered experiments, and (3) if the SWB benefits of prosociality are detectable beyond WEIRD (White-Western, Educated, Industrialized, Rich, Democratic) samples. Across the two meta-analyses we found, prosocial behavior led to a small consistent increase in happiness, yet estimates were based primarily on underpowered and WEIRD samples. We identified a growing number of pre-registered experiments (19/71 conducted to date), in which: (1) roughly half were well-powered; (2) only two recruited non-WEIRD samples, both underpowered and collectively showing mixed results; and (3) most examined prosocial spending (79%) over other prosocial behaviors, with happiness gains observed most consistently in well-powered studies on prosocial spending. Finally, we found that just 19% of all experiments recruited non-WEIRD samples, most of which were underpowered and presented mixed results, with acts of prosocial spending demonstrating the most consistent evidence of happiness benefits. We join other researchers in urging for more well-powered pre-registered experiments examining various prosocial behaviors, particularly with Global Majority samples, to ensure that our understanding of the SWB benefits of prosociality are firmly grounded in solid and inclusive evidence.
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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 | 0.051 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".