Do young people not in education, employment or training experience long-term occupational scarring? A longitudinal analysis over 20 years of follow-up

Abstract

Not in education, employment or training (NEET) is a contested concept in the literature. However, it is consistently used by policy-makers and shown in research to be associated with negative outcomes. In this paper we examine whether NEET status is associated with subsequent occupational scarring using the Scottish Longitudinal Study which provides a 5.3% sample of Scotland, based on the censuses of 1991, 2001 and 2011. We model occupational position, using CAMSIS, controlling for the influence of sex, limiting long-term illness, educational attainment and geographical deprivation. We find the NEET categorisation to be a strong marker of subsequent negative outcomes at the aggregate level. This appears to be redolent of a Matthew effect, whereby disadvantage accumulates to the already disadvantaged. Our results also show that negative NEET effects are variable when stratifying by educational attainment and are different for men and women. These findings confirm that there are negative effects on occupational position associated with prior NEET status but that outcomes are heterogeneous depending on levels of education and gender.

Keywords:

• NEET
• occupation
• employment
• scarring
• Scotland

Acknowledgements

We would like to thank Professor Paul Lambert for suggestions made to a draft of this paper. We would also like to thank two anonymous reviewers for the very valuable contributions they made. The help provided by staff of the Longitudinal Studies Centre – Scotland (LSCS) is acknowledged.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Dr Kevin Ralston The author has a methodological focus on longitudinal data analysis and quantitative methods, interests include population and fertility, mortality and inequality with a particular interest in occupational classifications.

Dr Zhiqiang Feng His research interests cover population geography, health geography, health inequalities, GIS, spatial analysis, longitudinal models, multilevel models, migration, commuting, and fuzzy classification.

Dawn Everington Dawn works for the University of Edinburgh. In earlier work she was involved in health-related research such as risk factors for the Creutzfeldt-Jakob disease and survival of cancer patients.

Professor Chris Dibben Chris is interested in researching poverty, deprivation and inequalities; evaluation of area-based initiatives; small area statistics; risk, vulnerability and hazards.

ORCiD

Kevin Ralston http://orcid.org/0000-0003-4344-7120

Zhiqiang Feng http://orcid.org/0000-0003-4077-3668

Chris Dibben http://orcid.org/0000-0003-1769-3774

Notes

1 There is >44% attrition in the baseline sample. Causes for this are death, emigration, item missing and case missing. An analysis of missing suggests a slight bias towards the more advantaged categories, with those lost to attrition or item missing most likely to come from the less advantaged groups, including NEET. If this is the case the analysis here will be likely to underestimate the NEET effects outlined and could therefore be interpreted as conservative estimates.

2 A small number of individuals in the data are recorded as retired. Given the age range of NEET, this may be a recording error.

3 We calculated the rate from full population data downloaded from CASWEB, replicating the method of the Scottish Executive (2006).

4 This is statistically equivalent to including a multiplicative interaction term. However, we take this non-conventional approach. We do this because we have found the concept, of including a multiplicative interaction term and main terms, the main terms being the association compared to being in category 0 on both the variables, then explaining that we add the interaction term to these to derive the magnitude of different combinations of associations, less effective when communicating with a non-technical audience. It is simpler to compare coefficients to a reference category.

5 Small area geography, 42,604 in Scotland (Vickers & Rees, 2006).

Additional information

Funding

The LSCS is supported by the ESRC/JISC, the Scottish Funding Council, the Chief Scientist’s Office and the Scottish Government. The author is responsible for the interpretation of the data. Census output is Crown copyright and is reproduced with the permission of the Controller of HMSO and the Queen’s Printer for Scotland. This research was supported by the Economic and Social Research Council: [Grant Number ES/K000454/2].