Investors’ response to the #MeToo movement: does corporate culture matter?
Bibliographic record
Abstract
Abstract This paper provides evidence that the #MeToo movement revised investors’ beliefs about the costs (benefits) of fostering an exclusive (inclusive) culture, as reflected by the absence (presence of a critical mass) of women directors in the board room. Tracking a timeline of events associated with the #MeToo movement that begin with the Harvey Weinstein exposé in October 2017 in the New York Times , we document contrasting market reactions to the movement depending on the existing culture of the firm. Firms that historically excluded women from their board experienced a negative market response as momentum for the cause increased, whereas investors responded favorably to firms that historically embraced the inclusion of women on their boards. In contrast, we do not detect differences in the market’s response to randomly generated pseudo-events during the same time frame when comparing firms with exclusive and inclusive cultures. In the context of increased regulator attention to board gender diversity, as well as the ESG activist campaigns by large institutional investors, our study documents a shift in investors’ beliefs about the risks associated with sexual misconduct and about the value of having women in the boardroom shaping the culture of the firm.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".