The cost of noise: noise pollution and nonprofit expenses
Bibliographic record
Abstract
Purpose This study examines the financial impacts of noise pollution on nonprofit organizations, focusing on how increased noise levels influence total spending and expenditures in the fundraising, administrative and program categories. By exploring these effects, the study aims to learn whether and how nonprofits are reallocating resources to address the adverse consequences of environmental noise. Design/methodology/approach Utilizing IRS Form 990 data for 149,595 US nonprofits from 2020, this study employs OLS regressions and robustness tests, including instrumental variables and entropy balancing, to analyze the impact of noise pollution, measured using data from the National Transportation Noise Exposure Map. The research examines total spending as well as spending patterns across three main functional areas: fundraising, administration, and programs. Findings The findings suggest noise pollution has an adverse impact on overall spending as well as fundraising expenses, seemingly at the expense of core program-related functions. Nonprofits in noisier areas appear to require more fundraising investment to counteract donor aversion caused by environmental stressors, leading to reduced capacity for spending on programs. Originality/value This study contributes to the literature by examining the role of environmental factors, and specifically noise pollution, in nonprofit financial health. Using a new dataset on census tract-level ambient noise, we are able, for the first time, to empirically examine the organizational impacts of noise pollution across geographic regions. Our study highlights the importance of considering environmental conditions in financial planning for nonprofits, offering practical implications for nonprofit managers and policymakers to develop strategies that mitigate the financial strain imposed by noise pollution.
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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 | 0.001 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".