High economic inequality leads higher-income individuals to be less generous
Bibliographic record
Abstract
Research on social class and generosity suggests that higher-income individuals are less generous than poorer individuals. We propose that this pattern emerges only under conditions of high economic inequality, contexts that can foster a sense of entitlement among higher-income individuals that, in turn, reduces their generosity. Analyzing results of a unique nationally representative survey that included a real-stakes giving opportunity (n = 1,498), we found that in the most unequal US states, higher-income respondents were less generous than lower-income respondents. In the least unequal states, however, higher-income individuals were more generous. To better establish causality, we next conducted an experiment (n = 704) in which apparent levels of economic inequality in participants' home states were portrayed as either relatively high or low. Participants were then presented with a giving opportunity. Higher-income participants were less generous than lower-income participants when inequality was portrayed as relatively high, but there was no association between income and generosity when inequality was portrayed as relatively low. This research finds that the tendency for higher-income individuals to be less generous pertains only when inequality is high, challenging the view that higher-income individuals are necessarily more selfish, and suggesting a previously undocumented way in which inequitable resource distributions undermine collective welfare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".