Counted But Not Understood: A Rapid Review of What Administrative and Research Data Can and Cannot Tell Us About Young People Who Are NEET In England
Abstract
Background: Being not in education, employment, or training (NEET) is associated with poorer educational, health and socio-economic outcomes, yet why young people become NEET, how they exit NEET status and who remains at risk are poorly understood. In England, gaps in the evidence base are compounded by a fragmented data infrastructure. Methods: We systematically reviewed the academic (Ovid, EBSCOhost, Scopus, Web of science, British Education Index, EconLit and Google scholar) and grey literature databases (Office for National Statistics, Administrative Dara Research, Closer, Department for Work and Pensions, Department for Education, and UK Data Service) from 1990 to 2026, identifying 861 records, of which 36 datasets met the inclusion criteria of data sources collecting data from young people aged 16-24 in England; published, collected or updated from 2000 onwards; sources where NEET status can be derived or is directly measured; administrative data and longitudinal surveys and grey literature. We then analysed and mapped our datasets across five categories: prevalence and monitoring; profiling; trajectories; explanatory and outcome. Results: No dataset combined comprehensive population coverage, robust NEET measurement, longitudinal follow-up, and sufficient domain breadth to investigate NEET entry, persistence and trajectories. England therefore has substantial capacity to monitor the prevalence of NEET status, reasonably accurately, but considerably less capacity to profile who becomes NEET and when, understand trajectories into and out of NEET status, or investigate the factors associated with persistence. Conclusions: This limitation constrains research into the mechanisms underlying NEET status and the longer-term consequences of disengagement. Strategic linkage of complementary datasets, including underused administrative sources and major longitudinal birth cohorts, could substantially strengthen the evidence base for understanding NEET trajectories and informing prevention. Data linkage alone, however, will not be sufficient. Understanding factors associated with why young people become and remain NEET will require a data infrastructure designed to capture their trajectories, circumstances and outcomes across the life course.
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BibTeX
@misc{lombardo2026counted,
title = {{Counted But Not Understood: A Rapid Review of What Administrative and Research Data Can and Cannot Tell Us About Young People Who Are NEET In England}},
author = {Rose Lombardo and Alice Walker and Vassilis Sideropoulos},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/uxy27_v1},
url = {https://osf.io/preprints/psyarxiv/uxy27_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Counted But Not Understood: A Rapid Review of What Administrative and Research Data Can and Cannot Tell Us About Young People Who Are NEET In England
AU - Rose Lombardo
AU - Alice Walker
AU - Vassilis Sideropoulos
PY - 2026
DA - 2026/09/02
PB - PsyArXiv
DO - 10.31234/osf.io/uxy27_v1
UR - https://osf.io/preprints/psyarxiv/uxy27_v1/
AB - Background: Being not in education, employment, or training (NEET) is associated with poorer educational, health and socio-economic outcomes, yet why young people become NEET, how they exit NEET status and who remains at risk are poorly understood. In England, gaps in the evidence base are compounded by a fragmented data infrastructure. Methods: We systematically reviewed the academic (Ovid, EBSCOhost, Scopus, Web of science, British Education Index, EconLit and Google scholar) and grey literature databases (Office for National Statistics, Administrative Dara Research, Closer, Department for Work and Pensions, Department for Education, and UK Data Service) from 1990 to 2026, identifying 861 records, of which 36 datasets met the inclusion criteria of data sources collecting data from young people aged 16-24 in England; published, collected or updated from 2000 onwards; sources where NEET status can be derived or is directly measured; administrative data and longitudinal surveys and grey literature. We then analysed and mapped our datasets across five categories: prevalence and monitoring; profiling; trajectories; explanatory and outcome. Results: No dataset combined comprehensive population coverage, robust NEET measurement, longitudinal follow-up, and sufficient domain breadth to investigate NEET entry, persistence and trajectories. England therefore has substantial capacity to monitor the prevalence of NEET status, reasonably accurately, but considerably less capacity to profile who becomes NEET and when, understand trajectories into and out of NEET status, or investigate the factors associated with persistence. Conclusions: This limitation constrains research into the mechanisms underlying NEET status and the longer-term consequences of disengagement. Strategic linkage of complementary datasets, including underused administrative sources and major longitudinal birth cohorts, could substantially strengthen the evidence base for understanding NEET trajectories and informing prevention. Data linkage alone, however, will not be sufficient. Understanding factors associated with why young people become and remain NEET will require a data infrastructure designed to capture their trajectories, circumstances and outcomes across the life course.
ER -