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![http://economic-research.pl/Journals/index.php/eq/article/view/1968/1911 [zdalny]](images/mime-icons/binary.gif "1911")
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Research background: The employment rate of young individuals in the labour market has considerably decreased in developed countries recently. Due to lower labour capital, skills, and generic and job-specific work experience, youth consider finding suitable job challenging. If they fail to succeed in the labour market soon after graduation, it leads to long-term unemployment, unstable and low-quality jobs, and even social exclusion.
Purpose of the article: This paper aims to analyse the unemployment rate of high schoolgraduated students and the factors impacting this unemployment rate, such as GDP per capita, total unemployment rate, apartment price per square meter and results from state exams. Identifying the determinants affecting youth unemployment is crucial for theoretical knowledge and for policymakers to ensure youth inclusion in the economic mainstream. As a result, society can reduce social and economic costs and avoid structural problems in the future.
Methods: Data about 464 Slovak high schools from National Institute for Certified Educational. Data include the graduate unemployment rate for each high school in Slovakia. Furthermore, two logistic regression models have been developed to investigate the impact of selected factors on high school graduates' unemployment rate immediately after graduation and nine months after graduation.
Findings & value added: This paper indicates the existence of statistical dependency between unemployment of high school graduates and overall unemployment rate in the region, GDP per capita in the region, quality of high school education and cost of living in the region immediately after graduation. Analysis of the period nine months after graduation has shown the important decline of education quality provided by high schools. To reduce youth unemployment, the state should focus primarily on improving overall unemployment itself by implementing a duallearning system, simplifying business opportunities, making part-time work available, or introducing lifelong learning to help transform the economy into a knowledge base. (original abstract)
Purpose of the article: This paper aims to analyse the unemployment rate of high schoolgraduated students and the factors impacting this unemployment rate, such as GDP per capita, total unemployment rate, apartment price per square meter and results from state exams. Identifying the determinants affecting youth unemployment is crucial for theoretical knowledge and for policymakers to ensure youth inclusion in the economic mainstream. As a result, society can reduce social and economic costs and avoid structural problems in the future.
Methods: Data about 464 Slovak high schools from National Institute for Certified Educational. Data include the graduate unemployment rate for each high school in Slovakia. Furthermore, two logistic regression models have been developed to investigate the impact of selected factors on high school graduates' unemployment rate immediately after graduation and nine months after graduation.
Findings & value added: This paper indicates the existence of statistical dependency between unemployment of high school graduates and overall unemployment rate in the region, GDP per capita in the region, quality of high school education and cost of living in the region immediately after graduation. Analysis of the period nine months after graduation has shown the important decline of education quality provided by high schools. To reduce youth unemployment, the state should focus primarily on improving overall unemployment itself by implementing a duallearning system, simplifying business opportunities, making part-time work available, or introducing lifelong learning to help transform the economy into a knowledge base. (original abstract)
Twórcy
autor
- Comenius University in Bratislava, Slovakia
autor
- Comenius University in Bratislava, Slovakia
autor
- Comenius University in Bratislava, Slovakia
Bibliografia
- Arslan M., & Zaman R. (2014), Unemployment and its determinants: a study of Pakistan economy (1999-2010). Journal of Economics and Sustainable Development, 5(13), 20-24. doi: 10.2139/SSRN.2515075.
- Aquilino, W. S. (2005). Impact of family structure on parental attitudes toward the economic support of adult children over the transition to adulthood. Journal of Family Issues, 26(2), 143-167. doi: 10.1177/0192513X04265950.
- Bal-Domańska, B. (2021). The impact of macroeconomic and structural factors on the unemployment of young women and men. Economic Change and Restructuring. Advance online publication. doi: 10.1007/s10644-021-09341-9.
- Bayrak, R., & Tatli, H. (2018). The determinants of youth unemployment: a panel data analysis of OECD Countries. European Journal of Comparative Economics, 15(2), 231-248. doi: 10.25428/1824-2979/201802-231-248.
- Brada, J. C., Marelli, E., & Signorelli, M. (2014). Young people and the labor market: key determinants and new evidence. Comparative Economic Studies, 56(4). 556-566. doi: 10.1057/ces.2014.30.
- Brzinsky-Fay, C. (2007). Lost in transition? Labour market entry sequences of school leavers in Europe. European Sociological Review, 23, 409-422. doi: 10.1093/esr/jcm011.
- Buttler, D. (2019). Employment status and well-being amongst youth: explaining. In: B. Hvinden, J. O'Reilly, M. A. Schoyen & C. Hyggen (Eds.). Negotiating early job insecurity. Cheltenham: Edward Elgar Publishing, 19-45. doi: 10.4337/9781788118798.00008.
- Caroleo, F. E., Rocca, A., Neagu, G., & Keranova, D., (2022). NEETs and the process of transition from school to the labor market: a comparative analysis of Italy, Romania, and Bulgaria. Youth & Society, 54, 109-129. doi: 10.1177/00441 18X211056360.
- Cvecic, I., & Sokolic, D. (2018). Impact of public expenditure in labour market policies and other selected factors on youth unemployment. Economic Research-Ekonomska Istraživanja, 31(1), 2060-2080. doi: 10.1080/1331677X.2018.1480970.
- Danacica, D.-E. (2014). Determinants of youth unemployment spells and exit destinations in Romania and Hungary. Acta Oeconomica, 64(3), 335-365. doi: 10.1556/AOecon.64.2014.3.4.
- Dagume, M. A., & Gyekye A. (2016). Determinants of youth unemployment in South Africa: evidence from the Vhembe district of Limpopo province. Environmental Economics, 7(4). doi: 10.21511/ee.07(4).2016.06.
- Demidova, O., & Signorelli, M. (2012). Determinants of youth unemployment in Russian regions. Post Communist Economies, 24(2), 191-217. doi: 10.1080/14631377.2012.675155.
- Dimian, G. C. (2011). The role of education in explaining youth labour market imbalances in CEE countries. Journal on Efficiency and Responsibility in Education and Science, 4(3), 105-115.
- Dolado J. J., Jansen, M., Felgueroso, F., Fuentes, A., & Wölf, A. (2013). Youth labour market performance in Spain and its determinants. OECD Economics Department Working Papers. doi: 10.1787/18151973.
- Draper, N. R., & Smith, H. (2014). Applied regression analysis. Wiley series in probability and statistics. John Wiley & Sons. doi: 10.1002/9781118625590.
- Ductor, L., & Grechyna, D., (2020). Determinants of ins and outs of unemployment. Applied Economics Letters, 27, 892-898. doi: 10.1080/13504851.2019.1646858.
- Dvouletý, O., Lukeš, M., & Vancea, M. (2020). Individual-level and family background determinants of young adults' unemployment in Europe. Empirica, 47, 389-409. doi: 10.1007/s10663-018-9430-x.
- Egessa, A., Nnyanzi, J. B., & Muwanga, J. (2021). Determinants of youth unemployment in Uganda: the role of gender, education, residence, and age. IZA Journal of Labor Policy, 11, 20210008. doi: 10.2478/izajolp-2021-0008.
- European commission (2020). Unemployment statistics. Retrieved from https://ec.europa.eu/eurostat/statistics-explained/index.php/Unemployment_statistics
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Bibliografia
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