Better in the Shadows? Media Coverage and Market Reactions to Female CEO Appointments
Bibliographic record
Abstract
Combining media coverage data from a set of approximately 17,000 unique media outlets with the full population of CEO appointments for US publicly traded firms between 2000 and 2014, we investigate whether female CEO appointments garner more media attention compared to male appointments, and if so, whether this increased attention can help to make sense of the previously reported negative market reaction to these events. Contrary to prior reports, our data do not indicate that the appointments of female CEOs elicit negative market responses, on average. Our results do highlight an important moderating role of attention, however. We demonstrate that greater media coverage (even when exogenously determined) contributes to negative market reactions for female CEO appointments but positive market reactions for male CEOs, all else held constant. Additionally, female CEO appointments that attract little attention garner positive responses in the market, compared both to females that draw significant attention and to males drawing comparable, limited attention. Our results help to reconcile contrasting empirical findings on the effects of gender in executive leadership, and parallel recent research on second-order discrimination and anticipatory bias in alternative empirical contexts. We argue that gender is an important lens through which investors interpret heightened attention surrounding organizational events. Increased attention is interpreted positively when associated with males, but negatively when associated with females. Implications for research on media attention, gender bias, and executive succession are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".