Predictors of becoming not in education, employment or training: A dynamic comparison of the direct and indirect determinants

Daniel Gladwell, Gurleen Popli and Aki Tsuchiya ()

Journal of the Royal Statistical Society Series A, 2022, vol. 185, issue S2, S485-S514

Abstract: This paper uses a dynamic latent factor model to investigate the determinants of not in education, employment or training (NEET) status among adolescents in the United Kingdom. We bring together within one framework various determinants of NEET status, such as educational achievements, non‐cognitive skills, family socio‐economic factors, aspirations, mental health and local labour market conditions. We model the educational progress over multiple periods through the life of the young person, up to the completion of compulsory schooling. By taking into account this progression, we can determine the direct and indirect impacts of different determinants of NEET status, and the stage in the life of the young person at which each determinant is important. Our findings suggest that cognitive ability (as measured by educational achievements) remains the key predictor of NEET status. Further, while a range of individual and family factors determines NEET status, the impact of most of these factors is largely indirect, through ability formation and not necessarily direct. To gauge the relative contributions of various determinants, we conduct simulations to predict the probability of the young person being NEET under different scenarios and assumptions. The exercise indicates that the effects of aspirations of the young person, their school engagement, and the local youth unemployment rate on the likelihood of the young person being NEET are as large as boosting their cognitive skills.

Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/rssa.12961

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:185:y:2022:i:s2:p:s485-s514

Ordering information: This journal article can be ordered from
http://ordering.onli ... 1111/(ISSN)1467-985X

Access Statistics for this article

Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples

More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().