Executive Gender Pay Gaps: The Roles of Female Risk Aversion and Board Representation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Using a large sample of executives in S&P 1500 firms over 1996–2010, we document significant salary and total compensation gaps between female and male executives and explore two possible explanations for the gaps. We find support for greater female risk aversion as one contributing factor. Female executives hold significantly lower equity incentives and demand larger salary premiums for bearing a given level of compensation risk. These results suggest that females’ risk aversion contributes to the observed lower pay levels through its effect on ex ante compensation structures. We also find evidence that the lack of gender diversity on corporate boards affects the size of the gaps. In firms with a higher proportion of female directors on the board, the gaps in salary and total pay levels are lower. Together, these findings suggest that female higher risk aversion may act as a barrier to full pay convergence, despite the mitigating effect from greater gender diversity on the board.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it