Nonprofit Chief Executive Compensation: Implications of Board Governance Activities
Bibliographic record
Abstract
Using secondary data collected as part of a national survey of nonprofit organizations, this research examines compensation outcomes of 704 nonprofit Chief Executives (CEO), integrating and social and performative aspects of governing/governance to explain compensation (in)equity. Theorizing governance as a socially complex and functionally consequential arena, we examine the impact of social categorization and identity fit between CEO Ethno-Racial Demography and Board Ethno-Racial Variety prior to overlaying the influence of three forms of governance activity: Fiduciary Oversight, Internal Awareness, and External Engagement. We employ serial multiple mediation regression analysis to test direct and indirect effects of demographic diversity and governance activity for nonprofit CEO compensation outcomes. We found compensation of ethno-racialized CEOs is higher when their organizations have diverse boards. Therefore, boards of directors must be cognizant of board composition, the potential for subjectivity and bias, and the impact these factors can have on CEO compensation and compensation equity.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".