Generalist CEOs and conditional accounting conservatism
Bibliographic record
Abstract
Abstract Generalist chief executive officers (CEOs) have accumulated transferrable general skills by working in multiple firms or industries. Recent decades have witnessed an increasing demand for generalist CEOs, which has resulted in a favorable job market for them. Favorable outside job opportunities reduce generalist CEOs’ career concerns and increase their agency problems and risk‐taking incentives. We examine the relation between generalist CEOs and conditional conservatism. On the one hand, conditional conservatism could be positively associated with generalist CEOs because debtholders and shareholders often demand conservatism to alleviate heightened agency problems. On the other hand, a negative association could obtain because generalist CEOs can (1) create better information environments for firms, reduce information asymmetry and lessen stakeholders’ demand for conservatism and (2) have greater bargaining power and reduce the supply of conservatism. We document a positive association between generalist CEOs and conditional conservatism and show that the relationship is more pronounced in firms with a higher demand for conservatism. Our results are robust to a variety of sensitivity tests.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".