Gender-diverse boards get better performance on mergers and acquisitions
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
In recent years, the composition of boards, particularly the appointment of female directors to the boardroom has attracted significant political and social debate. Despite several studies that have examined links between the representation of women on boards and the corporate performance, research on the board gender diversity in merger contexts is limited. We assess whether the presence of women on corporate boards affects merger and acquisition (M&A) performance. Using acquisition bids by public Canadian companies during 2012-2017, we find that an increasing number of female directors in acquiring companies is associated with an enhanced merger performance and a reduced bid premium. After controlling for gender diversity on executive teams, the value added by having women on boards is particularly noticeable when acquiring firms have few women in the executive teams, and where overconfidence is prevalent. Thus, there is a substitutive relation between gender diversity on the board and gender diversity on the executive team.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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