Female top managers and layoffs: examining the contingency effects of the COVID-19 pandemic and country-level institutions
Bibliographic record
Abstract
Purpose Previous research suggests that female leaders, who generally demonstrate higher levels of empathy toward employees than their male counterparts, are more likely to steer their firms away from layoffs. However, female leaders may also struggle with more significant resource constraints, potentially increasing the likelihood of layoffs in their firms. To reconcile these conflicting predictions, we develop a contingency model that considers the impact of the COVID-19 pandemic and country-level institutions on how top manager gender affects layoffs. Design/methodology/approach We use a combined dataset of the World Bank’s Enterprise Survey and the COVID-19 Pandemic Survey, which generates 1,283 firm observations across ten countries. Findings We found that, before the pandemic, the gender of top managers did not significantly affect layoffs in their firms, but during the pandemic, female-led firms experienced significantly higher layoff rates. Further, during the pandemic, the quality of country-level formal institutions attenuated the positive relationship between female top managers and layoffs, but country-level empathy (as an informal institution) did not have a significant effect on this relationship. Originality/value Overall, this pattern of findings highlights the importance of a contingency approach to understanding the roles of women in leadership.
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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".