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Pathways between health, education and income in adolescence and adulthood
  1. Emily J Callander
  1. Correspondence to Dr Emily Callander, Australian Institute of Tropical Health and Medicine, James Cook University, Discipline of Tropical Health and Medicine, Building 41, Douglas Campus, Townsville, QLD 4811, Australia; emily.callander{at}jcu.edu.au

Abstract

Objective To quantify the impact of household income, and physical and mental health in adolescence on education attainment, household income and health status in adulthood.

Design Path analysis and regression models using waves 1–12 of the Household, Income and Labour Dynamics in Australia survey.

Participants Individuals aged 17 or 18 in 2001, 52% were males (n=655) and 48% were female (52%). Of those participating in wave 1, five did not respond in wave 12.

Main outcome measures Education attainment, household income, physical and mental health at age 29/30.

Results For females, physical health at age 17/18 was significantly related to level of education attainment at age 29/30 (standardised total effect 0.290, p<0.001), with this influence being greater in magnitude than that of household income at age 17/18 on level of education attainment at age 29/30 (standardised total effect 0.159, p=0.022). Females' physical health at age 17/18 was also significantly related to household income at age 29/30 (standardised total effect 0.09, p=0.018). Both adjusted for initial household income at age 17/18. For males, the total standardised total effect of physical health at age 17/18 had a greater impact than household income at age 17/18 on education attainment at age 29/30 (0.347, p<0.001 for physical health and 0.276, p<0.001 for household income). The OR of achieving a year 12 or higher level of education attainment was 4.72 (95% CI 1.43 to 15.58, p=0.0110) for females with good physical health at age 17/18 and 5.05 (95% CI 1.78 to 14.36, p=0.0024) for males, compared with those with poor physical health at age 17/18.

Conclusions As physical health in adolescence appears to have a stronger influence on education attainment in adulthood than household income, equity strategies for education attainment should also target those with poor health.

  • Adolescent Health
  • Costing
  • Health Economics
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What is already known on this topic?

  • Ill health has a negative impact on labour force participation in adults.

  • Children from poorer families have higher risk of developing chronic health conditions.

What this study adds?

  • Physical health in adolescence has a significant influence on level of education attainment in adulthood.

  • This level of influence is greater than the influence of household income in adolescence on education attainment in adulthood.

Introduction

The path from education to employment among Australia's youth is precarious—with youth unemployment within this country reaching 14%,1 which is similar to that experienced in the UK.2 While this is not as high as some European countries, and is slightly lower than the European Union (EU) average,2 this age group has the highest unemployment rate within Australia. This comes at a time when governments and key policy guidelines stress the need to maximise labour force participation due to imminent workforce shortages and budgetary pressures imparted by the impending retirement of the older working cohort.3 The ageing of the working population is currently being experienced by most developed nations.4

Surprisingly, however, little is known about the time period between education exit and labour force entry within Australia. A recent systematic review has documented the negative influence poor health has on education and employment; however, most studies identified were from the USA and income outcomes were not specifically considered.5 The transition from adolescence to adulthood is a complex time period influenced by higher education attainment, personal income and family income. However, chronic health conditions and health status also have a major impact on income, labour force capacity and also capacity for higher education.6 Furthermore, chronic health conditions are also more common in lower income, poorly educated families,7 thus potentially leading to a widening inequality gap where children for lower socioeconomic backgrounds may not have the opportunity to improve their living standards due to health restraints.

This highlights the complexity of the health–education—income–employment relationship among youth and demonstrates the need for research on this area to have a cross-disciplinary focus, and use longitudinal data. To date there is only a fragmented literature on youth transitions, with researchers often using cross-sectional data and focusing on one or two factors, such as youth's education attainment and labour force outcomes, and do not assess the full, complex web of inter-relationships.8–12 The wider literature focusing on all age groups supports the idea that there is a significant relationship between health and income; however, many of these studies are based on cross-sectional analysis.6 ,13–17 The few longitudinal studies on adults that do exist have demonstrated that the effect of income on health was very small,18 or have only focused on the influence one chronic health condition has on income.19–22 As such, there is a large gap in the literature regarding directional pathways of influence between health status, income and employment, particularly among young people who will likely yield different results to adults as they may also be undertaking higher education.

This paper aims to assess the relationship between household income and individual physical and mental health status at ages 17 and 18, and the influence these have on education attainment, household income, personal income, physical health status and mental health status at ages 29 and 30. The outcomes of this research will have direct policy relevance, as it will give insight into the causal pathways that impact on youth poverty, at a time when governments around the world are seeking to reduce welfare dependency through maximising education attainment and labour force participation.23–27

Methods

To capture the complex interrelationship between health, education, income and employment between the ages of 17 and 30, this study will employ path analyses to analyse the longitudinal Household Income and Labour Dynamics in Australia (HILDA) survey data focusing on the sample aged 17 or 18 at wave 1 (conducted in the year 2001) and follow their education attainment, health status and labour force participation through to wave 12 (year 2012), when they are 29 or 30. Path analysis looks at more than one dependent variable at a time, and also allows variables to be dependent in some cases and independent in others.

Data set sampling and weighting

The HILDA survey is a longitudinal survey of private Australian households conducted annually since 2001. The data are nationally representative of the Australian population living in private dwellings and aged 15 years and over.28 The survey sampling unit for wave 1 was the household, with all members of the household being part of the sample that would be followed over the life of the survey. The reference population for wave 1 was all members of private dwellings in Australia, except overseas residents, including diplomatic personnel, in Australia; residents of institutions such as hospitals, military and police barracks, correctional institutions and monasteries and non-private dwellings such as hotels; and people living in very remote sparsely populated areas.

Income, health and education measures

Individual income was measured in each wave from total regular individual income, which was composed of regular private income (wages and salary, business income, investment income, and private pensions and transfers), Australian government public transfers (government income support payments and other government payments, such as family or carer payments), other public payments such as scholarships, and foreign pensions for all members of the household, less tax. Household income is the sum of the individual income of all individuals living in the one household. This total household income was then equivalised for the number and age of household members using the Organisation for Economic Co-operation and Development (OECD)-modified equivalence scale. Household income only refers to the income of the household in which the individual resides. If a young person has moved away from the parental household, then they are considered to be in a separate household and the parent's income is not included in this analysis.

Health status was measured in each wave using the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores from the SF-36 health scale,29 which was available from each wave of the HILDA survey. The PCS was used to measure physical health, and MCS was used to measure mental health. Those with poor physical health were defined as having a PCS <75% of the average for their age group; those with poor mental health were defined as having an MCS <75% of the average for their age group. This definition of what constitutes ‘poor health’ is arbitrary. No research has been conducted to confirm what cut-off points should be used to define those with ‘poor health’ and those with ‘good health’ when using the SF-36.

Education attainment was measured in each wave based on the stated highest level of education completed: year 5 or below, year 6, year 7, year 8, year 9, year 10, year 11, year 12, certificate III or IV, diploma or advanced diploma, bachelor's degree, postgraduate certificate of diploma or doctoral degree.

Statistical analysis

To estimate the two-way relationship between health and income, we fitted cross-lagged path analysis models of physical and mental health in wave 1 and household income in wave 1 with different lag periods. Cross-lagged path analysis models measure the impact of variable A at time t-1 on variable B at time t; and vice versa (the impact of variable B at time t-1 on variable A at time t).30 Household income, personal income, education attainment, physical health status and mental health status in wave 4, wave 8 and wave 12 were included as dependent and independent variables (with the exception of wave 12 variables that were only dependent variables). The model was stratified by sex to show the results for males and females separately. As the data were collected using the same survey instruments in successive years, the error terms for health and income were allowed to covary. The goodness of fit of the models was assessed by the χ2 test, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI) and root mean square error of approximation (RMSEA). Standardised total effects were measured, along with standardised direct and indirect effects. The total effect is the sum of the direct and indirect effect. The direct effect measures the direct impact of one variable on another, whereas the indirect effect measures the impact of one variable on another through a third variable. The model tested whether equivalised household income in wave 1 directly influenced physical and mental health in wave 4, wave 8 and wave 12; and whether physical and mental health in wave 1 influenced household income and personal income in wave 4, wave 8 and wave 12, both directly and indirectly through level of education attainment in wave 4, wave 8 and wave 12. The model was constructed so that household income in wave 4, wave 8 and wave 12 was adjusted for household income in wave 1, and similarly physical and mental health in wave 4, wave 8 and wave 12 was adjusted for physical and mental health in wave 1, respectively.

Logistic regression analysis was then conducted to show the OR of achieving a year 12 or higher level of education attainment, which is seen as a minimum level of ‘sufficient’ education attainment within Australia to ensure better likelihood of labour force participation and remaining out of income poverty,31 ,32 for those with good physical health compared with those with poor physical health at age 17/18. This model was adjusted for mental health status and household income at age 17/18. The model was then repeated using the measure of mental health, adjusting for physical health status and household income at age 17/18.

The analyses were performed using IMB SPSS AMOS V.22.0.0 and SAS V.9.4. Statistical significance was set at the 5% level.

Results

Table 1 shows the characteristics of the study sample (n=1251). The sample has a similar proportion of females as the actual Australian population aged 17 and 18 in 2001, in which 49% are female.33 The mean equivalised household income for the sample in each year from 2001 to 2012 was similar to the Australian population in the same age group.34

Table 1

Characteristics of the study sample (n=1251)

Females

The cross-lagged path analysis model of equivalised annual household income and physical health between 2001 and 2012 for females is shown in figure 1 and the standardised effects for the model shown in table 2. The model showed a good fit χ2 (48)=58.736, p=0.138; TLI=0.97; CFI=0.99; RMSEA=0.019. For females, physical health in wave 1, at age 17/18, was significantly related to education attainment at age 20/21 (p=0.003), at age 24/25 (p<0.001) and at age 28/29 (p<0.001). While household income in wave 1 was significantly related to education attainment at age 24/25 (p=0.044) and at age 28/29 (p=0.022), the total standardised impact of physical health at age 17/18 had a greater impact than household income at age 17/18 on education attainment at age 24/25 (0.279 for physical health and 0.140 for household income) and at age 28/29 (0.290 for physical health and 0.159 for household income). Physical health at age 17/18 was also significantly related to household income at age 24/25 (standardised total effect: 0.176, p=0.024) and at age 28/29 (standardised total effect: 0.09, p=0.018), and household income at age 17/18 also had significant impact on household income at age 24/25 (standardised total effect: 0.3, p<0.001). There was no significant relationship found between household income at age 17/18, and physical or mental health at ages 20/21, 24/25 and 28/29 (table 3).

Table 2

Standardised regression weights from path analysis model, females aged 17 or 18 in 2001 (wave 1)

Table 3

Standardised regression weights from path analysis model, males aged 17 or 18 in 2001 (wave 1)

Figure 1

Cross-lagged model of equivalised household income, mental health and physical health between 2001 and 2012. Analysis was conducted separately for males and females. e=error.

Males

The cross-lagged path analysis model of equivalised annual household income and physical health between 2001 and 2012 for males is shown in figure 1 and the standardised effects for the model shown in table 3. The model showed a reasonable fit χ2 (48)=67.367, p=0.034; TLI=0.96; CFI=0.99; RMSEA=0.025. For males, physical health at age 17/18 was significantly related to education attainment at age 20/21 (p<0.001), at age 24/25 (p<0.001) and at age 28/29 (p<0.001). While household income at age 17/18 was significantly related to education attainment at age 20/21 (p=0.01), at age 24/25 (p<0.001) and at age 28/29 (p<0.001), the total standardised impact of physical health at age 17/18 had a greater impact than household income at age 17/18 on education attainment at age 20/21 (0.295 for physical health and 0.177 for household income), at age 24/25 (0.326 for physical health and 0.262 for household income) and at age 28/29 (0.347 for physical health and 0.276 for household income). Physical health at age 17/18 was also significantly related to household income at age 24/25 (standardised total effect: 0.148, p=0.037), and household income at age 17/18 also had a significant impact on household income at age 20/21 (standardised total effect: 0.478, p<0.001), at age 24/25 (standardised total effect: 0.237, p=0.013) and at age 28/29 (standardised total effect: 0.181, p=0.019). Household income at age 17/18 also had a significant impact on physical health at age 28/29 (standardised total effect: 0.167, p=0.049).

Education attainment

For females, the OR of achieving a year 12 or higher level of education attainment was 4.72 (95% CI 1.43 to 15.58, p=0.0110) for those with good physical health at age 17/18, compared with those with poor physical health at age 17/18, controlling for mental health status and household income at age 17/18.

For males, the OR of achieving a year 12 or higher level of education attainment was 5.05 (95% CI 1.78 to 14.36, p=0.0024) for those with good physical health at age 17/18, compared with those with poor physical health at age 17/18, controlling for mental health status and household income at age 17/18. No significant relationship was found for those with and without poor mental health at age 17/18 for females (p=0.7421) or males (p=0.6789).

Discussion

The results of the cross-lagged path analysis models have shown that there are significant pathways between physical health at ages 17 and 18, and highest level of education attainment at ages 20/21, 24/25 and 29/30 and also for household income at age 24/25 for males, and 24/25 and 29/30 for females. No evidence was found for level of household income at age 17/18 influencing later physical or mental health for females, but some evidence was found a positive relationship between level of household income at age 17/18 and physical health status at age 29/30 for males. Mental health at age 17/18 also appeared to have no influence on later income or education attainment, which is in contrast to the results of previous studies.5

A key limitation of this study is that the dataset that was used did not fully explore why or how health status affected young people's income and education attainment. Thus, this study was unable to investigate why physical health affected income and education attainment and not mental health status. Follow-up qualitative research or surveys specifically designed to measure the barriers posed by ill health would help overcome this limitation.

The sample used in this study had similar characteristics to the entire Australian population of the same age group, and thus the results are likely to also be reflective of the population as a whole. Furthermore, the results are also likely to be generalisable to most European countries. The proportion of Australians aged 25–34 years who have completed at least upper secondary education attainment is similar to that in the EU, although a slightly higher proportion of people aged 25–34 years have a tertiary degree in Australia than in EU countries. Employment rates for those who have completed a tertiary education are also similar between Australia and EU countries, as is the relative earning of workers with tertiary degree and lower secondary degree relative to workers with upper secondary degrees.35 Australia's health profile is also similar to the UK, and other OECD countries.36

The results have shown that for both males and females, physical health status at ages 17 and 18 had a greater influence on level of highest education attainment in adulthood than household income at ages 17 and 18. Previous studies have highlighted that negative cycle of disadvantage, whereby adolescents from poorer families are less likely to obtain higher levels of education and thus are less likely to achieve higher paying jobs creating an intergeneration transmission of poverty.37–39 However, this paper has shown that physical health status at ages 17 and 18 has more of an influence that household income. Thus, policies to ensure equity of access to higher education attainment should also be targeted at adolescents with poor health outcomes, particularly physical ones, in addition to adolescents from families with lower incomes. Given the known impact of poor health during adolescence on labour force participation in adulthood,8–12 ,40–45 achieving a good level of education attainment may be particularly important for those with poor health to help overcome the barriers to labour force participation imposed by their health status.

The significance of the impact of different levels of education attainment on labour force participation and earning potential within Australia has been highlighted by a recent report that estimated that individuals who obtain a bachelor's degree will earn nearly twice as much over their working life as an individual who achieves less than year 12; even those who do obtain only a year 12 level of highest education attainment will still earn 20% more than those who achieved year 11 or less.46 Thus, the finding that those with poor physical health at ages 17 and 18 are less likely to complete even year 12 indicates that this subpopulation of Australian youth are facing compounding barriers to labour force participation, having both poor health and a low level of education attainment.

References

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Footnotes

  • Contributors EC is the sole author of this paper, and conceived the original idea, undertook the data analysis and drafted the manuscript. EC acts as guarantor for this study.

  • Funding The funding for this project came from a National Health and Medical Research Council (NHMRC) Early Career Fellowship.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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