Preferences over the Fair Division of Goods: Information, Good, and Sample Effects in a Health Context
Bibliographic record
Abstract
Greater recognition by economists of the influential role that concern for distributional equity exerts on decision making in a variety of economic contexts has spurred interest in empirical research on the public judgments of fair distribution. Using a stated-preference experimental design, this paper contributes to the growing literature on fair division by investigating the empirical support for each of five distributional principles — equal division among recipients, Rawlsian maximin, total benefit maximization, equal benefit for recipients, and allocation according to relative need among recipients — in the division of a fixed bundle of a good across settings that differ with respect to the good being allocated (a health care good — pills, and non-health care but still health-affecting good — apples) and the way that alternative possible divisions of the good are described (quantitative information only, verbal information only, and both). It also offers new evidence on sample effects (university sample vs. community samples) and how the aggregate ranking of principles is affected by alternative vote-scoring methods. We find important information effects. When presented with quantitative information only, support for the division to equalize benefit across recipients is consistent with that found in previous research; changing to verbal descriptions causes a notable shift in support among principles, especially between equal division of the goods and total benefit maximization. The judgments made when presented with both quantitative and verbal information match more closely those made with quantitative-only descriptions rather than verbal-only descriptions, suggesting that the quantitative information dominates. The information effects we observe are consistent with a lack of understanding among participants as to the relationship between the principles and the associated quantitative allocations. We also find modest good effects in the expected direction: the fair division of pills is tied more closely to benefit-related criterion than is the fair division of apples (even though both produce health benefits). We find evidence of only small differences between the university and community samples and important sex-information interactions.
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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 | 0.054 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".