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

(2016) - Kevin Ralston, Zhiqiang Feng, Dawn Everington, Chris Dibben

Journal: Contemporary Social Science
Link:: https://www.tandfonline.com/doi/full/10.1080/21582041.2016.1194452
DOI:: 10.1080/21582041.2016.1194452
Links::
Tags:: #paper #NEET #Unemployment #LabourMarket
Cite Key:: [@Ralston2016]

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 longterm 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.

Notes

“examine whether NEET status is associated with subsequent occupational scarring” (Ralston et al., 2016, p. 203)

“We model occupational position, using CAMSIS, controlling for the influence of sex, limiting longterm illness, educational attainment and geographical deprivation” (Ralston et al., 2016, p. 203)

“strong marker of subsequent negative outcomes at the aggregate level” (Ralston et al., 2016, p. 203)

“negative NEET effects are variable when stratifying by educational attainment and are different for men and women” (Ralston et al., 2016, p. 203)

“negative effects on occupational position associated with prior NEET status but that outcomes are heterogeneous depending on levels of education and gender” (Ralston et al., 2016, p. 203)

“In a UK context Croxford and Raffe (2000) found NEET young people not to be disengaged but to be actively seeking employment” (Ralston et al., 2016, p. 203)

“Roberts (2011) argues that the trajectories that young people take do not fit simply into definitions of NEET or non-NEET” (Ralston et al., 2016, p. 204)

“Although NEET is a contested concept research findings suggest a spell of NEET status is associated with a range of negative outcomes” (Ralston et al., 2016, p. 204)

“The relationship between NEET status and subsequent negative outcomes may be usefully understood as examples of a Matthew effect” (Ralston et al., 2016, p. 204)

“Matthew effect describes a phenomenon whereby often small differences, between individuals, at the start of a career, widen over the life course, as an initial advantage is magnified by incremental advantage over time, which develops a gap between individuals.” (Ralston et al., 2016, p. 204)

“Hillmert (2011) elaborates three concepts related to the process of accumulated dis/advantage” (Ralston et al., 2016, p. 204)

“social closure” (Ralston et al., 2016, p. 204)

“collective polarisation” (Ralston et al., 2016, p. 204)

“selective accumulation” (Ralston et al., 2016, p. 204)

“long-term negative consequences related to NEET status are also indicative of scarring. There is a large literature examining scarring (e.g. Arulampalam, Gregg, & Gregory, 2001; Gregory & Jukes, 2001; Knabe & Rätzel, 2011), much of it concerned with the effect of a period of unemployment on subsequent wage level or employment (Nilsen & Reiso, 2011); there has been little engagement, within this literature, with the concept of NEET per” (Ralston et al., 2016, p. 204)

“The research in this paper aims to assess the evidence for occupational scarring, for those aged 36–39 at 2011, who were recorded as NEET aged 16–19 in 1991.” (Ralston et al., 2016, p. 205)

“The analyses are further stratified by level of educational attainment, as the relationship between NEET, and occupational position, may be different for people with different skills (see, Burgess et al., 2003).” (Ralston et al., 2016, p. 205)

“this context, a negative association between NEET status and CAMSIS position in 2011 would provide evidence that the concept is useful as a policy construct (at least as a marker of disadvantage), despite apparent theoretical and substantive deficiencies in the concept” (Ralston et al., 2016, p. 205)

“However, in Scotland the NEET definition is usually applied to 16–19 year olds only. It had also been the case that the level of NEET in Scotland was consistently recorded as higher than in England and Wales across the period of this analysis. For instance, Furlong (2007) reports a NEET rate of 14% in Scotland compared to 9% in England and Wales.” (Ralston et al., 2016, p. 205)

“is NEET status associated with occupational scarring? Do people who were NEET experience worse occupational outcomes within levels of education?” (Ralston et al., 2016, p. 205)

“Hypothesis 1, NEET status was associated with a disadvantaged occupational position in 2011 by comparison to non-NEET status. Hypothesis 2, NEET status (at 1991) and subsequently being as not in education employment and training, when next observed (2001), is an indication of accumulating disadvantage and is associated with a relatively worse occupational outcome at 2011 than NEET, but subsequently active (at 2001). Hypothesis 3, scarring is evident within levels of education.” (Ralston et al., 2016, p. 205)

“NEET classification is based upon an economic activity variable included in the 1991 census.” (Ralston et al., 2016, p. 206)

“Those who are in employment are coded as nonNEET, as are those who are students, those on training schemes and waiting to start a job. The unemployed, permanently sick, retired,2 looking after home/family and other inactive are coded as NEET” (Ralston et al., 2016, p. 206)

“The CAMSIS (Cambridge Social Interaction and Stratification Scale) measure of occupational position is included as an outcome (Prandy & Jones, 2001). CAMSIS is a measure of the occupational structure based upon social interaction patterns. The theoretical basis is that the social distance between occupations, which is revealed by analysing social interaction patterns, represents an important dimension of social stratification, or relative social advantage (Prandy & Lambert, 2003).” (Ralston et al., 2016, p. 206)

“The analysis is split by gender. Women may have different occupational trajectories to men related to the types of occupations they enter, child caring roles and levels of educational attainment (Blau, Brummund, & Liu, 2013” (Ralston et al., 2016, p. 206)

“relative advantage of occupations by gender (Prandy & Lambert, 2003)” (Ralston et al., 2016, p. 206)

“This composite variable of NEET status in 1991 and the equivalent status in 2001 gives a variable with 4 levels. The reference category is those who are non-NEET and who are economically active at 2001 (i.e. the most advantaged group). This contrasts with those who are NEET at 1991 and subsequently economically inactive. There are also two ‘switcher’ categories,” (Ralston et al., 2016, p. 206)

“Success in education is known to relate to successful transitions from school to work (Bynner & Parsons, 2002; Croll, 2009). Educational attainment is measured at 2001 census when the sample was aged between 26 and 29. The majority of the sample will therefore have passed through the education system.” (Ralston et al., 2016, p. 206)

“only whether an individual has a degree or higher degree” (Ralston et al., 2016, p. 206)

“The 1991 Carstairs deprivation index is included in the model (Carstairs & Morris, 1990). Carstairs is a measure of areal deprivation constructed from four census variables at the level of census output area” (Ralston et al., 2016, p. 208)

“Finally, the models also include age and measures of long-term limiting illness (LLTI).” (Ralston et al., 2016, p. 208)

“CAMSIS is modelled using ordinary least squares regression. To check for possible selection bias a two-step selection model is also fitted, selecting whether people are in work or not at the 2011 time point (Heckman, 1979). This is potentially important because occupational status is observed only if an individual is employed. Individuals select themselves into employment. If unobserved variables are associated with both employment status and occupational positions, then the model, without adjusting the selection process, will bias the relationship between NEET status and occupational status.” (Ralston et al., 2016, p. 209)

“A variable measuring the employment rate at output area” (Ralston et al., 2016, p. 209)

“This shows that NEET status at 1991 is significantly associated with economic inactivity in 2011 for both men (Phi=.21) and women (Phi=.15)” (Ralston et al., 2016, p. 209)

“These results tend to support hypotheses 1 and NEET status at 1991 and economic inactivity at 2001 are both associated with occupational scarring” (Ralston et al., 2016, p. 209)

“Table 5 reports the results of two-step models accounting for selection into work. The results show a large selection effect from men, but the model for women is non-significant.” (Ralston et al., 2016, p. 210)

“The results do not fully support hypothesis 3, that occupational scarring, associated with NEET status, is evident within levels of education.” (Ralston et al., 2016, p. 212)

“Overall, hypothesis 3 holds best for women, but also applies to men in the two lowest educational categories.” (Ralston et al., 2016, p. 212)