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  • Author or Editor: Morag Henderson x
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Despite an increase in living standards and material comforts in industrialised societies, today's ‘emerging adults’ (aged from late teens to mid-to-late 20s) face greater challenges than ever before. The aim of this paper is to explore the relationship between labour market status and mental health for the ‘millennial generation’ in England, and whether it varies by gender and ethnicity. This study will be the first to draw on the results from the 2015 sweep of Next Steps data when the sample members are aged 25 and, together with the previous seven sweeps, bring the debate up to date by providing first estimates of the life condition of contemporary emerging adults. We find black and minority ethnic groups have lower odds of reporting mental ill-health at age 25 than the white group. With respect to labour market status, we find that net of socio-economic characteristics, educational attainment, behavioural variables and income at age 25, those who are unemployed are more than twice as likely to report symptoms of poor mental health as those who are employed. Shift workers and those on zero-hours contracts are also at a greater risk of mental ill-health by 47% and 44% respectively than those who are not shift workers or zero-hours workers. We find no significant difference for those who have a second job or are on a permanent contract for mental health at age 25 compared to those who do not have a second job or are on a temporary contract.

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Non-response is common in longitudinal surveys, reducing efficiency and introducing the potential for bias. Principled methods, such as multiple imputation, are generally required to obtain unbiased estimates in surveys subject to missingness which is not completely at random. The inclusion of predictors of non-response in such methods, for example as auxiliary variables in multiple imputation, can help improve the plausibility of the missing at random assumption underlying these methods and hence reduce bias. We present a systematic data-driven approach used to identify predictors of non-response at Wave 8 (age 25–26) of Next Steps, a UK national cohort study that follows a sample of 15,770 young people from age 13–14 years. The identified predictors of non-response were across a number of broad categories, including personal characteristics, schooling and behaviour in school, activities and behaviour outside of school, mental health and well-being, socio-economic status, and practicalities around contact and survey completion. We found that including these predictors of non-response as auxiliary variables in multiple imputation analyses allowed us to restore sample representativeness in several different settings, though we acknowledge that this is unlikely to universally be the case. We propose that these variables are considered for inclusion in future analyses using principled methods to explore and attempt to reduce bias due to non-response in Next Steps. Our data-driven approach to this issue could also be used as a model for investigations in other longitudinal studies.

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