Genderový index v českých veřejných firmách

Velková, Eliška

This bachelor thesis examines the extent of gender inequality in the Czech labour market. More specifically, it explores the under-representation of women on board positions using an analysis prepared by Open Society which contains data of more than 500 Czech public firms. The data analysis is made using the method of Kohonen self-organizing maps. SOMs represent a type of artificial neural network which allows to uncover possible patterns in a dataset and also visualize the multi-dimensional input data as a two-dimensional mapping while preserving topological properties of the input. To date, there is no academic paper examining gender inequality on decision-making positions in the Czech labour market using the method of Kohonen maps. The used dataset includes 77 Czech regions and 14 variables. A choice of appropriate factors that may influence the participation of women in the labour market is essential. The results are presented in 5 clusters of regions which differ in level of gender gap. In conclusion, our results prove that self-organizing maps are a useful data mining tool which can simply interpret high-dimensional data sets. 1

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