There is still conflicting evidence as

There is still conflicting evidence as to whether the degree of negative mental health outcomes experienced by the underemployed is similar to that experienced by the unemployed (Friedland & Price, 2003; Monfort, Howe, Nettles, & Weihs, 2015). Underemployment has been conceptualised in many ways including involuntary part-time employment (Dooley & Prause, 2004), insufficient income or wages (Eamon & Wu, 2011), and subjective job amyloid (Creed, Lehmann, & Hood, 2009; Monfort et al., 2015). The question of whether inadequate employment is better for mental health than no employment at all is still not confirmed, although some studies have suggested that poor quality work can be as harmful as the unemployment experience (Broom et al., 2006; Butterworth et al., 2011). Indeed, underemployment itself may represent a barrier for individuals in reaping the positive benefits typically attributed to employment. Therefore, understanding the mechanisms through which unemployment and underemployment affect mental health is essential for targeting intervention and social policy. Three key variables that have been identified as playing a role in explaining the association between employment status and poor mental health are financial hardship, a sense of mastery, and social support (Butterworth, Olesen, & Leach, 2012; Creed & Bartrum, 2008; Creed & Moore, 2006). The current study builds upon this previous research, including our recent work considering how employment status influences the mental health of a cohort of young adults in Canberra, Australia (Crowe & Butterworth, 2016). Understanding the roles played by these factors might offer a leverage point for intervention, to help limit the negative mental health impacts of both unemployment and underemployment. This may particularly be the case in young adults who are more susceptible than older age groups to unemployment, underemployment, and poor mental health, and are, therefore, a key group for interventions which target modifiable risk factors (Fergusson, Horwood, & Woodward, 2001; Orygen Youth Health Research Centre, 2014).
Methods
Results Descriptive characteristics of the sample are shown in Table 1, stratified by gender and age. Longitudinal data was collected from 9382 respondents (48% men) across three years: 2003, 2004 and 2007. Respondents that were aged between 20 and 34 years during these years were included in the analysis. Rates of unemployment in the HILDA dataset mirrored national rates (ABS: 2015), with the highest rates of unemployment occurring in the youngest age group, and declining with age. This pattern was also observed with financial hardship, with a lower proportion of respondents experiencing financial hardship in older age groups. Overall, females were more likely to report poor mental health and were slightly more likely to be part-time employed, looking for full-time employment. Mastery was consistent for males and females across the age groups. Table 2 presents prevalence rates for poor mental health, and also the univariate associations between poor mental health and employment status, as well as the key explanatory variables. The overall prevalence of poor mental health was 10.6%. However, this rate was elevated for those who were unemployed (27%), NLF MA (20%), PTLFT (13%), experiencing financial hardship (17%), and those who reported little perceived control over their lives (25%). These figures are starkly contrasted with much lower prevalence rates of poor mental health amongst the employed (8%), those who did not experience financial hardship (7%), and for those who reported a high sense of perceived control (4%). This was confirmed by the univariate analyses which showed elevated odds ratios of poor mental health for all employment states relative to the employed. In addition, the analyses also showed that financial hardship, low mastery, and low social support were associated with increased odds of poor mental health. An additional analysis was conducted to test for possible gender differences for mental health and employments status. While not displayed in the tables, the results revealed that there were no gender differences observed for those who were unemployed or PTLFT. However, there was a significant interaction effect between gender and NILF, indicating that the association between NILF and poor mental health was stronger for men than tap root was for women. While not a key focus of the current study, this may be an interesting finding to explore in future research.