Rewarding Altruism? A Natural Field Experiment
Bibliographic record
Abstract
We present evidence from a natural field experiment involving nearly 100,000 individuals on the effects of offering economic incentives for blood donations.Subjects who were offered economic rewards to donate blood were more likely to donate, and more so the higher the value of the rewards.They were also more likely to attract others to donate, spatially alter the location of their donations towards the drives offering rewards, and modify their temporal donation schedule leading to a short-term reduction in donations immediately after the reward offer was removed.Although offering economic incentives, combining all of these effects, positively and significantly increased donations, ignoring individuals who took additional actions beyond donating to get others to donate would have led to an under-estimate of the total effect, whereas ignoring the spatial effect would have led to an over-estimate of the total effect.We also find that individuals who received a reward by surprise were less likely to donate after the intervention than subjects who received no reward, suggesting that for some individuals a surprise reward adversely affected their intrinsic motivations.We discuss the implications of these findings for understanding pro-social behavior.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".