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A model for predicting determinants factors for NEETs rates: Support for the decision-makers

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  • Berigel, Muhammet
  • Boztaş, Gizem Dilan
  • Rocca, Antonella
  • Neagu, Gabriela

Abstract

The purpose of this paper is to analyze the determining factors of the high levels of NEETs both in EU member states and in partner countries, to support policy steering and increase socio-economic cohesion. The use of longitudinal data (2005–2020) from Eurostat and World Bank databases and selected and the testing of a number of 19 factors likely to influence the rate of NEETs show us that the effectiveness of public policy solutions focused on this category of population increases when complex factors and not singular elements are targeted. From a methodological point of view, we will use MARS models and fixed effects panel models. To account for countries’ heterogeneity, these models are applied to homogeneous groups of countries, identified through cluster analysis. Social cohesion and sustainability measures for policy steering have higher chances if the action of the responsible institutions targets both meso and macro levels, if it acts not only on a factor but also on the causes that favor its manifestation. Our analysis demonstrated that the measures aimed at increasing the chances of NEETs in order to facilitate their access to education, the labor market, and social inclusion must be coordinated with those of support for combating poverty and any type of exclusion, the support given to employers (subsidizing jobs, for example), the family and the community to which the young person belongs or local authorities. Also, the research results show us that there are more common elements between countries when we analyze the factors likely to increase the rate of NEETs than when we focus on their analysis by geographical criteria, based on EU membership status or EU partner status, etc.

Suggested Citation

  • Berigel, Muhammet & Boztaş, Gizem Dilan & Rocca, Antonella & Neagu, Gabriela, 2023. "A model for predicting determinants factors for NEETs rates: Support for the decision-makers," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    • Handle: RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123001052
    DOI: 10.1016/j.seps.2023.101605
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    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).

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