To what extent do governance, government funding and chief executive officer characteristics influence executive compensation in U.K. charities? Insights from the social theory of agency
Bibliographic record
Abstract
Abstract This paper draws on agency theory, as extended by the social theory of agency (STA) (Wiseman, Cuevas‐Rodríguez & Gomez‐Mejia, 2012), to examine the association between governance arrangements, reliance on government funding, chief executive officer (CEO) non‐profit experience, and CEO compensation in the UK charity sector. We rely on a hand‐collected data for the largest 240 charities and find that greater trustee board diversity (specifically gender and education diversity) and the existence of a remuneration or nomination committee are positively associated to CEO compensation. The results also show that a reliance on government funding and CEO's non‐profit work experience, together with the presence of a finance/accounting expert on the audit committee are negatively associated to CEO compensation. The existence of an audit committee, internal audit function, use of specialist external auditor and CEO characteristics (gender, ethnicity and managerial experience) are not significant factors. Our findings are largely consistent with the STA's propositions. Specifically, executive compensation levels reflect the CEO's ability to work with a diverse board while a higher reliance on government funding signals the role of the State's pressures in moderating CEO compensation. Finally, in a context characterised by altruism and public benefit, financial rewards are not seen as the dominant ‘value metric’, resulting in lower compensation for CEOs previously working in the sector. Our findings have policy implications, specifically in relation to the role, composition and effectiveness of governance structures (e.g., trustee boards, audit and remuneration committees) in overseeing the design of executive compensation schemes within the charity sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".