The impact of shifting societal attitudes toward women on capital markets and corporations: Evidence from the Harvey Weinstein scandal and the #MeToo movement*
Bibliographic record
Abstract
The underrepresentation of women in leadership positions in corporations, and in other organizations and institutions, is ubiquitous.While business leaders, investors and society in general advocate for greater gender equality at all firm levels, the reality differs: the fraction of female executives remains very low, despite the considerable growth in female representation on company boards over the last few decades.Figure 1 illustrates the low levels of female representation in companies that make up the S&P 1500 index, which consists roughly of the 1500 largest firms in the United States by stock market capitalization.Figure 1A shows that the proportion of firms with at least one female executive among the five highest-paid executives has risen from under 10% in 1992 to 65% by the end of 2023-a significant increase, yet still far below what would be expected if gender were represented proportionately among top executives. 1 Likewise, the fraction of top five executives who are female has also increased substantially over time, but remains at only 17% at the end of 2023 (Figure 1B).Finally, as illustrated in Figure 1C, only 7% of S&P 1500 companies have a female CEO.Why are there so few women in top leadership positions?One possible explanation is that the supply of qualified women is limited.Another is that conscious or unconscious biases lead to female candidates being overlooked for top roles.Of course, these two explanations could both be true, and work to reinforce one another: if female candidates are systematically passed over for top 1 In a scenario of equal gender distribution (and labor supply), only 3.1% of all firms would have no women in leadership positions (assuming a 50% probability of selecting a male executive, the likelihood of choosing five male executives would be 0.5 5 = 3.1%).
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".