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NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries

Author

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  • Fulvia Pennoni

    (University of Milano-Bicocca)

  • Beata Bal-Domańska

    (Wroclaw University of Economics and Business)

Abstract

Young people’s place in the labor market has been a topic of interest to the European Union and national governments for many years. This study analyzes young people who are Not in Employment nor in Education or Training (NEET) and Youth Unemployment (YU) in the European Union member states, through data collected over a period of sixteen years, considering the influence of some macroeconomic factors through an hidden Markov model. This approach is based on maximum likelihood estimation of the model parameters, and provides a dynamic classification of the countries into clusters representing different levels of the phenomena. We discover three clusters of countries, and we show that whereas Italy was the worst performing country in terms of both NEETs and YU, the Czech Republic was the best performing country in reducing NEETs, and Poland and Slovakia were the best performing in reducing YU.

Suggested Citation

  • Fulvia Pennoni & Beata Bal-Domańska, 2022. "NEETs and Youth Unemployment: A Longitudinal Comparison Across European Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 739-761, July.
    • Handle: RePEc:spr:soinre:v:162:y:2022:i:2:d:10.1007_s11205-021-02813-5
    DOI: 10.1007/s11205-021-02813-5
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    References listed on IDEAS

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    7. F. Bartolucci & A. Farcomeni & F. Pennoni, 2014. "Rejoinder on: Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 23(3), pages 484-486, September.
    8. Fulvia Pennoni & Ewa Genge, 2020. "Analysing the course of public trust via hidden Markov models: a focus on the Polish society," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 29(2), pages 399-425, June.
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    Cited by:

    1. Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2024. "Using machine learning for NEETs and sustainability studies: Determining best machine learning algorithms," Socio-Economic Planning Sciences, Elsevier, vol. 94(C).
    2. Oğuz Başol & Hüseyin Sevgi & Esin Cumhur Yalçın, 2023. "The Effect of Digitalization on Youth Unemployment for EU Countries: Treat or Threat?," Sustainability, MDPI, vol. 15(14), pages 1-15, July.
    3. Giorgia Casalone & Eliana Baici, 2023. "Education, Off-the-Job Vocational Training, and Early Employment Outcomes: Evidence from Italy," Merits, MDPI, vol. 3(2), pages 1-15, May.

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    More about this item

    Keywords

    Expectation-maximization algorithm; Discrete latent variable; Hidden Markov model; Model-based clustering; Panel data; NEET; Youth unemployment;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • J80 - Labor and Demographic Economics - - Labor Standards - - - General
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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