Glass ceiling or glass cliff: an examination of the role of female board members on market performance in Poland
Bibliographic record
Abstract
We examine gender bias related to the effect of women in executive management leadership. Specifically, we examine when women are recruited to executive positions and follow companies’ market performance before and after their appointment. Our study is conceptually situated within the field of queuing theory, suggesting certain patterns of labour market queuing behaviour according to race, gender, and class. We formulate two hypotheses: (1) the presence of female executives is associated with lower firm value; and (2) companies with female-centric executive boards are valued lower by investors than companies with male-centric boards. We test these hypotheses employing panel data, using a unique sample of 159 companies listed on the Warsaw Stock Exchange in the years 2006–2015, with hand-collected data on the number of female directors on executive boards. Our results suggest that market queuing behaviour is evident in the case of Poland. Men are more often hired in executive roles than women, whereas females are more likely to be appointed to executive boards in firms which are performing poorly. Moreover, higher participation of women in executive positions is associated with resulting lower value in the long run. According to our interpretation, isomorphism and gender bias diffuse through the reproduction and valuation of capitalist markets.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".