Vol. 9 No. 2 (2021): Business & Management Studies: An International Journal

The regional and industrial dimensions of gender inequality in firm top management positions: A classification tree and random forest analysis

Mehmet Güney Celbiş
Asst. Prof. Dr., Yeditepe University

Published 2021-06-25


  • Ekonomik Coğrafya, Cinsiyet Eşitsizliği, Firmalar, Üst Yönetim, Makine Öğrenmesi.
  • Economic Geography, Gender Inequality, Firms, Top Management, Machine Learning.

How to Cite

Celbiş, M. G. (2021). The regional and industrial dimensions of gender inequality in firm top management positions: A classification tree and random forest analysis. Business &Amp; Management Studies: An International Journal, 9(2), 439–455. https://doi.org/10.15295/bmij.v9i2.1777


The issue of gender inequality in the labour market is a topic attracting attention in Turkey and globally. One specific manifestation of gender inequality is observed in relation to top management positions. In the present study, the underlying effects that create gender inequality in top management positions are examined to identify the conditions that lead to an unbalanced distribution. Using tree-based machine learning methods, the present study identifies the institutional, industrial, and regional attributes related to gender inequality in top management positions of firms in Turkey. Alongside numerous other findings, we observe that the top managers in firms facing problems related to crime, corruption, access to land and licenses, and establishments located in Northeast and Southeast Anatolia tend to be male. In addition, we also observe that under certain regional and institutional conditions, top female managers are more common in the retail, restaurant and hotel, textiles, clothing, and manufacturing industries. Together with identifying the regions and industries with the highest female-male inequality, the findings also highlight the institutional factors that lead to these inequalities. The results, alongside opening new research paths regarding the analysis of specific determinants, also have the potential of helping the development of new policies to tackle the problem. In the models presented in this study, data on 162 firms and 254 variables presented in the Enterprise Survey-2019 are analyzed using algorithmic models. In addition to the content-related relevance of the findings, the present study presents tree-based machine learning methods as new methodological alternatives concerning assessing the research question.


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