Market Responses to Genuine Versus Strategic Generosity: An Empirical Examination of NFT Charity Fundraisers
Bibliographic record
Abstract
Crypto donations now represent a significant fraction of charitable giving worldwide. Nonfungible token (NFT) charity fundraisers, which involve the sale of NFTs of artistic works with the proceeds donated to philanthropic causes, have emerged as a novel development in this space. A unique aspect of NFT charity fundraisers is the significant potential for donors to reap financial gains from the rising value of purchased NFTs. Questions may arise about donors' motivations in these charity fundraisers, potentially resulting in a negative social image. NFT charity fundraisers thus offer a unique opportunity to understand the economic consequences of a donor’s social image. We investigate these effects in the context of a large NFT charity fundraiser, leveraging random variation in transaction processing times on the blockchain to identify the causal effect of purchasing a charity NFT on a donor’s later market outcomes. Further, we demonstrate a clear pattern of heterogeneity based on an individual’s decision to relist (versus hold) the purchased charity NFT (a sign of perceived strategic generosity) and based on an individual’s social exposure within the NFT marketplace. We show that charity-NFT ‘re-listers’ experience significant market penalties, with an estimated 15.3% decrease in the prices they can command for other NFTs in their portfolio. This negative effect is particularly pronounced among those who are more socially exposed. Two controlled online experiments (one incentive-compatible and one scenario-based) corroborate our findings, demonstrating that the re-listing of a charity NFT for sale at a profit leads onlookers to perceive the initial donation as strategic generosity and reduces their willingness to purchase NFTs from the donor. Our study underscores the growing importance of digital visibility and traceability, features that characterize crypto-philanthropy and online philanthropy more broadly.
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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 | 0.011 | 0.095 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".