Gender diversity of board of directors and shareholders: Machine learning exploration during COVID-19
ISSN: 1754-2413
Article publication date: 6 September 2023
Issue publication date: 12 March 2024
Abstract
Purpose
European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.
Design/methodology/approach
The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.
Findings
XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.
Originality/value
This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.
Keywords
Acknowledgements
This manuscript was supported by the Faculty of Management, Comenius University in Bratislava, Slovakia and by VEGA 1/0393/21 titled Impact Analysis of Restrictive Measures and Government Aid Associated with Coronavirus on Financial Health of Small and Medium-Sized Enterprises in Slovakia.
Funding statement: This manuscript was funded by VEGA 1/0393/21 titled Impact Analysis of Restrictive Measures and Government Aid Associated with Coronavirus on Financial Health of Small and Medium-Sized Enterprises in Slovakia.
Data availability statement: The data that support the findings of this study are available from FinStat s.r.o., but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the corresponding author upon reasonable request and with permission of FinStat s.r.o.
Conflict of interest disclosure: The authors declare that they have no conflict of interests.
CRediT authorship contribution statement: Lenka Papíková: Writing – original draft, Writing – review and editing, Visualization, Validation; Project administration; Mário Papík: Writing – original draft. Conceptualization, Methodology, Investigation, Software, Visualization.
Citation
Papíková, L. and Papík, M. (2024), "Gender diversity of board of directors and shareholders: Machine learning exploration during COVID-19", Gender in Management, Vol. 39 No. 3, pp. 345-369. https://doi.org/10.1108/GM-02-2023-0034
Publisher
:Emerald Publishing Limited
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