How the Uncertainty Associated with Social Issues Influences the Returns of Corporate Philanthropy
Bibliographic record
Abstract
This study examines whether the varying financial returns to philanthropy can be explained by the uncertainty associated with the issues to which a firm donates. We start with the premise that stakeholders react favorably to donations they view as effective and appropriate for specific social needs, which can lead to financial advantages for the donor firm. However, the reliance on various cues for such assessments may differ based on the uncertainty surrounding social issues. For stable issues, where the social need and redress strategies are relatively clear and direct, we expect that proximate cues such as the donation amount and a firm’s donation experience are likely indicators of philanthropic effectiveness, thereby predicting its financial returns. Conversely, when donations target uncertain issues where the social need is unclear or evolving, these cues become less informative, prompting stakeholders to consider broader cues, such as firm reputation. Our analysis introduces a method for measuring the country- and time-specific uncertainty of issues and applies it to evaluate donations from the world’s largest 2,000 firms from 2007 to 2018. The significance of our study is underscored by the increasing engagement of firms in social issues fraught with high uncertainty.
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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 | 0.013 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".