The Nonprofit Starvation Cycle: The Extent of Overhead Ratios’ Manipulation, Distrust, and Ramifications
Bibliographic record
Abstract
While little evidence supports the notion that financially responsible nonprofits must maintain low overhead ratios, the persistent preference for reduced overhead costs endures. Our study explores (a) the extent of underreporting behaviors, (b) the level of trust (or distrust) that nonprofit leaders have in overhead ratio reports, and (c) the motivations perceived by managers that drive nonprofits to adjust their overhead ratios and the resulting consequences. Experiment results from the “item sum double-list technique” (ISDLT) reveal that nonprofit managers may artificially lower their overhead ratios by approximately 10 percentage points, a range spanning from 7 to 16 percentage points. This adjustment aims to enhance their competitiveness in the funding market. Our vignette-based experiment uncovers significant trust issues related to reported low overhead ratios, potentially indicating accounting manipulation within the field. Complemented by open-ended survey responses from nonprofit managers, our research offers valuable insights into this domain.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".