Gender Index in the Czech Public Firms
|Author:||Bc. Eliška Velková|
|Year:||2021 - winter|
|Leaders:|| doc. Ing. Tomáš Cahlík CSc.
|Work type:|| Bachelors
|Awards and prizes:|
|Abstract:||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.