Demographic variation in charitable giving and helping across 22 countries in the Global Flourishing Study
Bibliographic record
Abstract
Prosocial behaviors play a vital role in promoting individual and societal well-being. Charitable giving and helping strangers are two important expressions of prosociality; yet we know little about how these behaviors differ across sociodemographic indicators cross-nationally. Using data from the Global Flourishing Study, a diverse and international sample of 202,898 individuals across 22 countries, we examined distributions of charitable giving and helping (binary variables, yes/no) across nine demographic factors (age, gender, marital status, employment status, religious service attendance, education, immigration status, race and ethnicity, and religious affiliation) and culturally diverse countries. Unadjusted proportions of charitable giving and helping in the past month varied substantially between countries for charitable giving (from 0.10 [Japan] to 0.79 [Indonesia]) and helping (from 0.11 [Japan] to 0.83 [Nigeria]). Random effects meta-analyses confirmed that rates of charitable giving and helping differed among some demographic groups (e.g., more charitable giving with older age, less helping with older age, increased charitable giving and helping with more education) and that rates of charitable giving and helping across all demographic factors differed between countries. Better understanding how various sociodemographic factors may be associated with prosocial behaviors, and how these associations differ internationally, may help to inform interventions designed to enhance prosociality around the world.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".