What is (s)he Worth? Exploring Mechanisms and Boundary Conditions of the Relationship Between CEO Extraversion and Pay
Bibliographic record
Abstract
Abstract Integrating human capital theories and the status incongruity hypothesis at the upper echelons, we examine for whom extraversion, the personality trait that has been most strongly and consistently implicated in leader success, influences Chief Executive Officer (CEO) pay. To assess the personality traits of CEOs, we used a computerized text analysis approach on the language spoken by CEOs in conference calls. Using a sample of firms listed on the S&P 1500, we find that more extraverted CEOs earned higher pay, indicating that this was a trait valued by boards. Additionally, this relationship was due to enhanced firm performance; specifically, market performance. However, a critical boundary condition is that the relationship between CEO extraversion and pay was weaker for female (vs. male) CEOs, despite the market responding equally positively to extraverted female and male CEOs. Thus, the monetary benefits of higher levels of extraversion did not extend to female CEOs and likely reflects backlash. Supplemental analyses revealed that this devaluation of more extraverted female (vs. male) CEOs was mitigated when the CEO was also the chairperson of the board (i.e., CEO duality) or under conditions of greater female representation on the board of directors.
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".