Do donors value volunteer commitment in assessing nonprofit effectiveness?
Bibliographic record
Abstract
Abstract Evaluating organizational effectiveness is a significant challenge for nonprofit donors making donation allocation decisions. Donations may be misallocated if organizational effectiveness is inadequately assessed, and donors, who are often organizational outsiders, rely on nonprofit disclosures on IRS Form 990 to make such assessments. We examine whether donors value volunteer commitment, as measured by the number of volunteers that nonprofits disclose on Form 990, alongside financial and governance disclosures in assessing organizational effectiveness. Donors and volunteers prefer to make respective gifts of money and time to nonprofits that are effective in furthering their missions. Based on the premise that volunteers, as organizational insiders, are better positioned than donors to judge the impact of their contributions, we hypothesize that volunteer commitment provides value‐relevant information to donors for use in assessing imprecise effectiveness signals—namely, the program ratio and corporate governance disclosures. Consistent with this, we find that the value relevance of the program ratio and corporate governance disclosures to donors is increasing with the level of volunteer commitment. These results suggest that donors view volunteer commitment as a signal of effectiveness, useful in interpreting other signals of effectiveness. The evidence is more pronounced among nonprofits that report more credible volunteer disclosures, have a larger proportion of sophisticated donors, and are more complex. These findings have implications for regulators considering nonprofit disclosure policies, as well as nonprofit managers and directors engaging volunteers.
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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 | 0.032 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".