How Individuals’ Health and Wealth Are Associated with Their Donation Behavior and Motivations
Bibliographic record
Abstract
In this article, we examine the differences in charitable donating behaviors among three groups: a nationally representative American sample (N = 513), individuals with an annual household income greater than $250,000 (N = 253), and individuals with significant illness (heart disease or cancer; N = 516). We then use a validated donor motivations scale to examine whether these groups’ reasons for donating money to nonprofits differ. While the extant literature provides information on who is likely to give and under what contexts, it treats donors as a homogenous group, only differentiating them by certain demographic variables. The current study examines two different groups based on two fundamental attributes: wealth and health. We hypothesized that systematic differences in giving behavior and self-reported motivations exist across these groups compared to a nationally representative sample. Instead, we found that only high-income individuals differed in their giving behaviors and motivations. These results show that donor behavior and motivations may depend on their wealth. This research may help fundraisers and development professionals better understand how and why different prospects donate.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".