Bidder Motives in Cause Related Auctions
Bibliographic record
Abstract
Abstract. A cause-related auction is different from a list price setting in two major ways: First, the donation percentage directly affects the price of the item in that consumers ’ value for charitable contributions enters their bids. Second, charitable consumers have a price externality on non-charitable consumers, so even a segment of consumers who place a premium on charitable contributions can significantly affect prices for everybody else. The purpose of this paper is to characterize both the charity premium and the impact of the charitable segments with a careful field design. We examine individual choice between pairs of simultaneous auctions identical in all but percentage of the proceeds donated to charity. We investigate the extent to which individuals are willing to pay a charity premium in choice between auctions of various donation percentages. We use a mixture model approach to allow for different types of individual preferences. In analysis of choices between pairs of auctions, we find that individuals fall into three segments—bargain seekers and two altruistic segments. The altruistic segments, which drive up the charity premium, can in turn be classified into warm glow bidders—who derive a pleasure from doing the right thing—and other-regarding bidders—who are sensitive to the percentage given to charity. Further analysis shows that warm glow bidders positively contribute to the charity premium in pairs of auctions that do not differ much in donation percentages, whereas the
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".