Dirty air, empty coffers? Nonprofit donations and volunteering in the context of severe air pollution
Bibliographic record
Abstract
Purpose Poor environmental conditions pose profound societal challenges, yet their impact on the nonprofit sector, particularly in terms of financial and organizational sustainability, remains largely unexplored. To address this concern, we investigate how hazardous air quality influences donations and volunteering in nonprofit organizations. Design/methodology/approach Combining county-level US EPA air quality data with 1.63 million IRS Form 990 filings by 248,749 nonprofits between 2010 and 2022, we examine the impact of air pollution on organizations across time and space. Instrumental variables regression and entropy-balancing techniques are employed to address potential endogeneity concerns. Additional analyses examine the roles of industry sub-sector, service orientation, donor sophistication and government funding in conditioning the relationship between air pollution and our two outcome variables. Findings Organizations in areas experiencing more hazardous air quality days are associated with lower levels of donations but higher levels of volunteering. Additional cross-sectional analyses suggest that environmental, health and human service nonprofits experience increased donations in the context of severe air pollution while arts and mutual benefit organizations see increased volunteering. Additional analyses on the conditioning effects of donor sophistication, service orientation, and government funding provide further insights into the empirical relationship between air pollution and nonprofit engagement. Originality/value This study provides one of the first large-N examinations of the organizational impact of localized environmental factors on charitable activity. In providing evidence for how air quality appears to be a robust, but underexplored factor affecting donor and volunteer behavior, our study underscores the importance of understanding how environmental challenges influence civic engagement.
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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 | 0.006 | 0.001 |
| 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 | 0.000 |
| 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".