Markets and Morals: An Experimental Survey Study
Bibliographic record
Abstract
Most societies prohibit some market transactions based on moral concerns, even when the exchanges would benefit the parties involved and would not create negative externalities. A prominent example is given by payments for human organs for transplantation, banned virtually everywhere despite long waiting lists and many deaths of patients who cannot find a donor. Recent research, however, has shown that individuals significantly increase their stated support for a regulated market for human organs when provided with information about the organ shortage and the potential beneficial effects a price mechanism. In this study we focused on payments for human organs and on another "repugnant" transaction, indoor prostitution, to address two questions: (A) Does providing general information on the welfare properties of prices and markets modify attitudes toward repugnant trades? (B) Does additional knowledge on the benefits of a price mechanism in a specific context affect attitudes toward price-based transactions in another context? By answering these questions, we can assess whether eliciting a market-oriented approach may lead to a relaxation of moral opposition to markets, and whether there is a cross-effect of information, in particular for morally controversial activities that, although different, share a reference to the "commercialization" of the human body. Relying on an online survey experiment with 5,324 U.S. residents, we found no effect of general information about market efficiency, consistent with morally controversial markets being accepted only when they are seen as a solution to a specific problem. We also found some cross-effects of information about a transaction on the acceptance of the other; however, the responses were mediated by the gender and (to a lesser extent) religiosity of the respondent--in particular, women exposed to information about legalizing prostitution reduced their stated support for regulated organ payments. We relate these findings to prior research and discuss implications for public policy.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".