Auctions for risk-averse non-profits
Bibliographic record
Abstract
Fundraising for non-profit organizations (NPOs) often involves converting in-kind donations into cash, commonly through charity auctions. However, little attention has been paid to the revenue distributions these mechanisms generate, an obvious concern for risk-averse NPOs deciding on a fundraising strategy. This paper introduces a theoretical framework that evaluates the first two moments of the revenue distributions accruing to ten auction formats under conditions that permit endogenous bidder participation. The resulting “revenue frontier” generated by these mechanisms reveals a robust and sizeable mean-variance tradeoff that NPOs should consider when selecting which mechanism(s) to employ. We test the model’s predictions for each auction format using both laboratory and field experiments and find evidence of a substantial (and similar) tradeoff in each setting. Additionally, we show how bidder participation can modulate an NPO’s risk exposure, conditional on the auction formats selected. These results offer important insights into how NPOs might optimize their fundraising strategies through methods of mechanism diversification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".