Time for Blood: The Effect of Paid Leave Legislation on Altruistic Behavior
Bibliographic record
Abstract
Organizations and public agencies that promote pro-social activities constantly struggle to attract and encourage more contributions. In this article, we study the effects of an explicit reward in the context of blood donation. Specifically, we analyze the effects of a legislative provision that grants a one-day paid leave of absence to blood donors who are employees in Italy, using a unique data set with the complete donation histories of the blood donors in an Italian town. The across-donor variation in employment status, and within-donor changes over time are the sources of variation that we employ to study whether the paid-day-off incentive affects the frequency of their donations. Our analysis indicates that the day-off privilege leads donors who are employees to make, on average, one extra donation per year, which represents an increase of around 40%. We also find that the provision has persistent effects, with donors maintaining higher donation frequencies even when they cease to be eligible for the incentive. We discuss the implications of our findings for policies aimed at reducing the shortages in the supply of blood and, more generally, for organizations that try to motivate voluntary contributors. (JEL: D12, D64, I18)
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".