The Influence of Ownership and Compensation Practices on Charitable Activities
Bibliographic record
Abstract
Abstract Recent accounting research provides evidence that similar profit‐based compensation incentives are used in for‐profit and nonprofit hospitals. Because charity care reduces profits, such incentives should lead for‐profit hospital managers to reduce charity care levels. Nonprofit hospital managers, however, may respond differently to the same incentives because they face a different set of institutional pressures and constraints. We compare the association between pay‐for‐performance incentives and charity care in for‐profit and nonprofit hospitals. We find a negative and significant association between charity care and our proxy for profit‐based incentives in for‐profit hospitals, and no significant association in nonprofit hospitals. These results suggest that linking manager pay to profitability does not appear to discourage charity care in nonprofit hospitals. Apparently, the nonprofit mission, institutional pressures, and ownership constraints moderate the potentially negative effects of profit‐based incentives. Because this evidence partially alleviates concerns over nonprofit compensation arrangements that mirror those used in for‐profit hospitals, it should be of interest to regulators and policymakers. In addition, this study provides insights into accounting researchers about institutional and organizational influences that affect managerial responses to financial incentives in compensation contracts.
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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 | 0.004 | 0.039 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".