Diversity and Donations: The Effect of Religious and Ethnic Diversity on Charitable Giving
Bibliographic record
Abstract
We explore the effects of local ethnic and religious diversity on individual donations to private charities.Using 10-year neighborhood-level panels derived from personal tax records in Canada, we find that diversity has a detrimental effect on charitable donations.A 10 percentage point increase in ethnic diversity reduces donations by 14%, and a 10 percentage point increase in religious diversity reduces donations by 10%.The ethnic diversity effect is driven by a within-group disposition among non-minorities, and is most evident in high income, but low education areas.The religious diversity effect is driven by a within-group disposition among Catholics, and is concentrated in high income and high education areas.Despite these large effects on amount donated, we find no evidence that increasing diversity affects the fraction of households that donate.Over the period studied, ethnic diversity rises by 6 percentage points and religious diversity rises by 4 percentage points; our results suggest that charities receive about 12% less in total donations.As areas like North America continue to grow more diverse over time, our results imply that these demographic changes may have significant implications for the charitable sector.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".