The Contingent Financial Impact of Company Philanthropy under High Uncertainty
Bibliographic record
Abstract
Studies routinely show that companies benefit from engaging in philanthropy and these benefits are enhanced when actions are perceived as generous and sincere. However, firms are increasingly being asked to respond to urgent and unpredictable issues, like pandemics and natural disasters, which lack clear stakeholder expectations for what constitutes an appropriate response. In the face of this uncertainty, we argue that the material features of the philanthropic action are not useful for assessing a company’s response, and audiences will rely on cues, heuristics unrelated to the donation. Based on an analysis of corporate responses to every epidemic, disaster, and terrorist attack worldwide from 2007-2019, we find that the financial outcomes of disaster philanthropy strongly reflect the reputation of the first firm to donate. Well-regarded first donors benefit from philanthropy, regardless of how much they donate, while ill-regarded first donors are punished. These judgments then transfer to followers that match these donations. Regardless of their own reputations, firms that match well-regarded first donors benefit from philanthropy, while firms that match ill-regarded first donors are punished. Our findings have implications for research on corporate philanthropy in uncertain contexts and offer managers practical advice for how the firm can benefit from its giving.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".