Export Publication

The publication can be exported in the following formats: APA (American Psychological Association) reference format, IEEE (Institute of Electrical and Electronics Engineers) reference format, BibTeX and RIS.

Export Reference (APA) 
Ellena, A. M., Rocca, A., Mazzocchi, P., Zambelli, M., Marzana, D., Pizzolante, F....Marta, E. (2025). Unveiling the complexity of Italian NEET status through Latent Class Analysis: Examining NEET profiles and their engagement with Public Employment Services (PES). Social Indicators Research. 179 (3), 1665-1686
Export Reference (IEEE) 
A. M. Ellena et al.,  "Unveiling the complexity of Italian NEET status through Latent Class Analysis: Examining NEET profiles and their engagement with Public Employment Services (PES)", in Social Indicators Research, vol. 179, no. 3, pp. 1665-1686, 2025
Export BibTeX 
@article{ellena2025_1764921080638,
	author = "Ellena, A. M. and Rocca, A. and Mazzocchi, P. and Zambelli, M. and Marzana, D. and Pizzolante, F. and Simões, F. and De Luca, G. and Marta, E.",
	title = "Unveiling the complexity of Italian NEET status through Latent Class Analysis: Examining NEET profiles and their engagement with Public Employment Services (PES)",
	journal = "Social Indicators Research",
	year = "2025",
	volume = "179",
	number = "3",
	doi = "10.1007/s11205-025-03684-w",
	pages = "1665-1686",
	url = "https://link.springer.com/journal/11205"
}
Export RIS 
TY  - JOUR
TI  - Unveiling the complexity of Italian NEET status through Latent Class Analysis: Examining NEET profiles and their engagement with Public Employment Services (PES)
T2  - Social Indicators Research
VL  - 179
IS  - 3
AU  - Ellena, A. M.
AU  - Rocca, A.
AU  - Mazzocchi, P.
AU  - Zambelli, M.
AU  - Marzana, D.
AU  - Pizzolante, F.
AU  - Simões, F.
AU  - De Luca, G.
AU  - Marta, E.
PY  - 2025
SP  - 1665-1686
SN  - 0303-8300
DO  - 10.1007/s11205-025-03684-w
UR  - https://link.springer.com/journal/11205
AB  - This study delves into the complex issue of NEET (Not in Employment, Education, or Training) status in Italy, using Latent Class Analysis (LCA) to identify distinct profiles within this heterogeneous group. The research, based on a survey of 970 young Italian NEETs, goes beyond broad categorizations to explore the socio-demographic, economic, and motivational factors that contribute to their situations. Six distinct classes of NEETs were identified, each with unique characteristics, including “Affluent Rural Caregivers in Northern Italy,” “Young Educated Unemployed,” “Long-Term NEETs in Southern Italy,” “Disadvantaged Caregivers in Southern Italy,” “Disadvantaged Rural Men in Northern Italy,” and “Affluent Discouraged NEETs”. The study investigates the relationship of these profiles with Public Employment Services (PES), revealing different motivations for seeking assistance and varied willingness to accept employment conditions such as relocation or temporary work. Significant associations were found between NEET class and the motivation for economic support, willingness to move abroad or within Italy, and acceptance of part-time or temporary work. The findings emphasize the need for tailored interventions that address the specific needs and barriers of each NEET subgroup, rather than a one-size-fits-all approach. The research highlights the complex interplay of geographic, economic, educational, and gender factors in shaping NEET status in Italy.
ER  -