Sirvi Autor "Kadri, Oluwagbemi Omobolanle" järgi

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Gender-based segregation in company boards and well-being
(Tartu Ülikool, 2020) Kadri, Oluwagbemi Omobolanle; Sharma, Rajesh, juhendaja; Kungas, Peep, juhendaja; Tartu Ülikool. Loodus- ja täppisteaduste valdkond; Tartu Ülikool. Arvutiteaduse instituut
Segregation is an act of division from a supreme body to smaller groups because of the characteristics of the body. In order terms among humans, we can refer to it as an unwarranted detachment or separation resulting in traits a person possesses; for example, gender, occupation, race, resident, income, religion, age, etc. Analyses based on segregation impact in our society have increased over the years. It has spawned enormous controversial discussions in our modern-day world; and elicited several researchers’ interests to identify the origin of segregation. In this thesis, we investigated if gender and age segregation exist in Estonian companies’ boards and its relationship with the labour market. In addition, we examine if it leads to high credit risks and a negative correlation to the well-being of Estonian society. The key measurement factors for comparison and drawing conclusions are the unemployment rate measured as the labour market, financial key performance indicators measured as credit risk, a well-deprivation index measured as well-being and segregation indexes from ‘SCube’ data model measured as segregation. ‘SCube’ originated from a model created by researchers at the University of Pisa, it uses a data science framework to deal with the problem of social and occupational segregation. Analysis from the ‘SCube’ data-set will be measured with segregation indexes ranging from 0 to 1 in accordance to this range high level of segregation means high value of the segregation index meaning a value close to 1. The Estonian statistics ready-made data-set is used in conjunction with the data-set from ‘SCube’ model to examine and draw conclusions of the occupational segregation problem discussed in this work. In addition, statistical techniques correlation and causal inference are used to determine the relationship and causal effects between segregation and the various factors.