Greater wealth is associated with higher prosocial preferences and behaviours across 76 countries
Bibliographic record
Abstract
Prosocial preferences and behaviours – defined as those that benefit others – are essential for health, well-being, and a society that can effectively respond to global challenges. Research has therefore focussed on factors that may increase or decrease them. How objectively wealthy an individual is, as well as how subjectively wealthy someone feels, may be crucial in determining prosociality. However, previous studies have often relied on small non-representative samples and/or on a limited range of measures. In addition, experience of precarity (uncertainty in meeting basic needs) could change how wealth correlates with prosociality, yet its impact remains unknown. Using data from 80,337 people across 76 countries, we show that both objective wealth (household income), and subjective wealth (financial well-being), are positively and consistently associated with higher prosociality. Objective wealth was positively associated with altruism, positive reciprocity, donating money, volunteering, and helping a stranger, but negatively associated with trust. Subjective wealth was positively associated with all aspects of prosociality, including trust. Experience of precarity reduced associations between wealth and prosocial preferences yet increased them for prosocial behaviours. These findings could have important implications for enhancing prosociality, critical for a healthy and adaptive society.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".