Institutional Pressures to Provide Social Benefits and the Earnings Management Behavior of Nonprofits: Evidence from the U.S. Hospital Industry
Bibliographic record
Abstract
Abstract This study examines the relationship between institutional pressures to provide social benefits and the discretionary accrual behavior of nonprofit firms. I examine this issue within the context of U.S. nonprofit hospitals, an economically significant and politically rich setting where firms face considerable institutional pressure to provide an important social benefit: charity care. I argue that institutional pressures on nonprofits to provide higher levels of social benefits imply that lower profits should be reported. I develop theory and provide evidence which suggests that, due to competing private incentives to report higher profits, nonprofit managers strategically use discretionary accruals to increase accounting earnings when the social benefits their firms have provided in the current period exceed external stakeholders' normative expectations. The findings from this study inform the ongoing political debate regarding the appropriateness of tax exemptions for U.S. nonprofit hospitals and should therefore be of interest to both regulators and policymakers. In addition, this study provides timely insights for researchers regarding how institutional pressures can affect managers' reporting behaviors in other settings where similar competing reporting incentives exist between managers' private benefits and stakeholder expectations related to social benefits.
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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 | 0.003 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".