Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/23040
Title: Analytical Modelling of Graduated Economists' Employment
Authors: Peovski, Filip 
Trpeski, Predrag 
Ivanovski, Igor 
Cvetkoska, Violeta 
Keywords: labour market
student employment
Machine Learning
Issue Date: 2022
Publisher: Springer Nature Switzerland AG 2022
Project: „Моделирање на сегментите на пазарот на труд низ перспективите на дипломираните економисти на Економски факултет - Скопје“
Journal: Lecture Notes in Networks and Systems - Sustainable Business Management and Digital Transformation: Challenges and Opportunities in the Post-COVID Era
Abstract: Higher education institutions are fully engaged in producing adequate labour supply. Targeting the persistently high youth unemployment should be among the top governmental priorities in a mutual benefiting environment for the economy and the HEIs. Earlier studies and practical experience for the national labour market imposes indications for a possible labour market mismatch. As the main objective, this research targets the two labour market segments (employment and self-employment) in North Macedonia for graduated economists at the Faculty of Economics – Skopje at Ss. Cyril and Methodius University in Skopje. Through the utilisation of machine learning and cluster analysis techniques for graduated economists between 2017 and 2021, we found that age, academic suc-cess, family’s income, and having some type of informal education, significantly determine the employment status of graduated economists. Moreover, there is no clear-cut evidence that the gender and the bachelor programme are one of the main determinants, even though positive linkage for females, studying financial management and Accounting and auditing, and employment is observed. The empirical findings provide clear direction for policy creation regarding study pro-grammes and higher youth employment, both for the national government and the higher education institutions.
URI: http://hdl.handle.net/20.500.12188/23040
ISSN: 2367-3370
Appears in Collections:Faculty of Economics 03: Journal Articles / Статии во научни списанија

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