Related parties, financial reporting quality, and donations
Bibliographic record
Abstract
Abstract In 2008, the IRS added several schedules to Form 990, including Schedule R, related party transactions. Utilizing Schedule R, we investigate and descriptively document the existence of related parties and the types of transactions engaged in with those related parties. Then, to provide evidence of the usefulness of these disclosures, we tie into the literature on financial reporting quality. Prior research into financial reporting quality shows that donors discount program ratios when a nonprofit organization reports zero fundraising expenses, implying that they find reporting zero fundraising expenses to be a proxy for poor financial reporting quality. A plausible reason for organizations reporting zero fundraising expenses is that a related party conducts fundraising on the organization's behalf. Consistent with this interpretation, we find that when nonprofits disclose that fundraising services are provided by a related entity, they are more likely to report zero fundraising expenses. We also find that disclosure of related party fundraising mitigates donor discounting of the program ratio when zero fundraising expenses are reported. However, we only find that this mitigation occurs in nonprofits with sophisticated donors. In sum, we find evidence consistent with donors—in particular, sophisticated donors—using disclosures provided in Form 990 to supplement the amounts recognized. Our findings demonstrate the importance of, and are consistent with the use of, these related party disclosures. On a broader level, these findings provide insight into how thoroughly donors are willing to review Form 990 to get information relevant to their donation decision.
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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 | 0.046 | 0.225 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".