Can't see the Forest for the Trees? Exploring Top Executive Gender Diversity among S&P 500 firms
Bibliographic record
Abstract
Academics have extensively studied women on boards. The number of women among the five named (i.e. best paid) executive officers in the US has also been somewhat explored. However, it appears as if researchers have not paid much attention so far to the topic of 'Executive Gender Diversity': the percentage of women among the full group of top executive as reported to the regulator (i.e.. the SEC in the US). We manually collect this data initially for 2018 S&P 500 firms and compare it to the percentage of women on boards are reported on Bloomberg. We find that firms report, on average, just over 10 executives to the SEC, which shows that studying only the five best paid executives displays less than half of the pictures. Comparing board and top executives, we observe more than 2/3 of firms to have less women among top executives than on the board. Sadly, more than one in six firms does not report any females executives at all to the SEC. The 'Household Durables' and 'Life Sciences' sectors are doing particularly badly from an executive gender diversity perspective, as their median firm has zero female top execs. More generally, less than 1 in 4 firms has more than a quarter of female executives (i.e. more than 75% of the firms have 25% or less female top execs). On a positive note, there are a few firms such as Tiffany & Co. which have more than 40% female top execs but these represent less than 5% of all S&P 500 firms. Our results suggest that executive gender diversity is professionally not as well developed as board gender diversity. For all of us academics studying gender diversity, our results imply that we may want to consider having previously rather often overlooked the forest for the trees. Consequently, we conclude that the topic of executive gender diversity (beyond just the five best paid officers) deserves significantly more attention from academics and research funding bodies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".