Counting the Cost:
The Impact of Young
Men’s Mental Health
on the Australian
Economy
Counting the Cost
Acknowledgements
The ‘Counting the Cost – The Impact of Young Men’s Mental Health on
the Australian Economy’ report is the result of a partnership between
the Inspire Foundation (Inspire) and Ernst & Young
to demonstrate the impact of costs associated with poor mental
health amongst young men on the Australian economy. The project
was undertaken as an initiative of the Young and Well Cooperative
Research Centre (YAW-CRC).
Report Authors
Jo Degney (Inspire Foundation)
Blair Hopkins (Ernst & Young)
Aram Hosie (Inspire Foundation)
Simon Lim (Ernst & Young)
Asmita Verma Rajendren (Ernst & Young)
Gillian Vogl (Inspire Foundation)
Mental Health Advisory Committee
Jane Burns (YAW-CRC)
Tracey Davenport (Brain & Mind Research Institute)
John Mendoza (ConNetica Consulting)
Cathy Mihalopolous (Health Economist)
Jonathan Nicholas (Inspire Foundation)
David Roberts (Ernst & Young)
Steve Watson (Ernst & Young)
The authors would also like to thank the following people for their
contribution. These individuals were instrumental in not only providing
real life insights to our economic modelling findings but also invested
their own time providing additional research, modelling and report
editing assistance which brought this report together:
Sarah Metcalf, Bonny Bayne, Kitty Rahilly, Uttara Nataraj, Hayley
Power, Robert Menzies, Catherine Pattison, Axel Levitan, Saru
Pasupathy, Gan Pasupathy, Christian Russo, Houston Lau, Philip Thai,
Alexander Babich, Anthony Saliba, Ben Barrett, Bradley Stevenson,
Chris Faustino, Edward Alexander, Hardik Dalal, Jason Cheah,
Jonathan Ho, Josh Frank, Mark Romanos, Owen Tan, Pu Shen Xin,
Ryan Druitt, Simon Arabian, Timothy Coates, William Xu.
For further information contact:
Aram Hosie (aram@inspire.org.au) or
David Roberts (david.roberts@au.ey.com)
© 2012 Inspire Foundation and Ernst & Young
Counting the Cost
Contents
EXECUTIVE SUMMARY 1
REPORT AIMS 3
YOUNG MEN’S MENTAL HEALTH 5
THE COST OF MENTAL HEALTH 6
THE ECONOMIC IMPACTS OF POOR MENTAL HEALTH 6
MENTAL ILLNESS AND WORK 6
Case Study 9
METHODOLOGY AND RESULTS 10
MODEL DESIGN 11
Approach 11
Model Scope 13
Assumptions and Limitations 14
DETAILED METHODOLOGY AND RESULTS 15
1.0 Health Cost Category 15
2.0 Employment Cost Category 16
3.0 Unemployment Cost Category 22
4.0 Imprisonment Cost Category 24
5.0 Disability Cost Category 27
6.0 Mortality Cost Category 29
FINDINGS AND CONCLUSIONS 30
SUMMARY OF FINDINGS 31
Cost and Impact: Individuals 32
Cost and Impact: Employers 33
Cost and Impact: Government 34
CONCLUSIONS 35
REFERENCES 40
Counting the Cost
4
Counting the Cost
1
The human and economic costs of mental illness in Australia can no
longer be ignored.
The Australian Institute of Health and Welfare reports that
adolescent depression is one of the most frequently reported mental
health problems, representing approximately 26.5% (one in four
young people in this age group).
In spite of this, rates of help-seeking among young Australians,
and particularly among young men, remain low. Tragically, suicide
continues to be the leading cause of death for young men in
Australia, accounting for 22% of all deaths; with male youth suicide
rates in rural areas double those of metropolitan areas1.
This report analyses the resultant cost and impact on the Australian
economy, highlighting the threat to productivity from poor mental
health among young men. In presenting this new evidence, this
report provides a call-to-action, demonstrating the importance of
a community-wide response to raising awareness, prevention and
treatment of young men’s mental illness.
The cost of mental illness on the Australian economy
Our research identifies costs and impacts to the Australian economy
and productivity which are borne across a range of sectors and
institutions.
The findings of our research and modelling reveal the broader costs
to individuals and employers:
• Mental illness in young men aged 12-25 costs the
Australian economy $3.27 billion per annum or $387,000
per hour across a year in lost productivity
• The Federal Government bears 31% of this cost via direct
health costs, disability welfare payments, unemployment
benefits and the direct costs of imprisonment
• Australia loses over 9 million working days per annum to
young men with mental illness. On average they have an
additional 9.5 days out of role per year
• Young men with mental illness have much lower rates of
educational attainment compared to their peers, further
limiting their skills development and long term reduced
earning potential by $559 million per year
Government incurs significant costs associated with the provision of
mental health services:
• In 2008, the overall cost of spending on mental health care
was $5.32 billion, with the Australian government spending
$1.92 billion and the states and territories spending $3.22
billion
• In addition to the costs associated directly with specialist
mental health care, the government also bears a broad range
of costs required to support people with mental illness -
including income support, housing services, domiciliary care
and employment and training opportunities
• The 2010 National Health Report estimated that with
government costs alone, for every dollar spent on specialised
mental health care, an extra $2.30 is spent on other services
to support people with mental illness – equating to $4.4 billion
(2008 prices)
Executive Summary
Mental illness in young
men aged 12-25 costs
the Australian economy
$3.27 billion per annum or
$387,000 per hour across a
year in lost productivity
Counting the Cost
2
Our recommendations
The reality is that the costs of young men’s poor mental health are already being felt throughout Australia’s economy.
In uncovering these costs, this report provides new insights that can be used to guide further reforms and investment
decisions. Failure to act presents a serious threat to Australia’s future productivity and to the individual prosperity
of young men affected with poor mental health. Coordinated activity from all sectors – business, government, and
communities – holds the promise of considerable economic and individual benefits.
The findings of this study point to both the productivity opportunities and risks associated with the mental health of
young men.
Recommendation 1: Efforts should be made by all sectors of the community to
support the engagement of young men to achieve higher levels of education.
• 1.1 Improve secondary, tertiary and vocational educators’ levels of understanding of mental health, including
the identification of disorders and awareness of support and referral services available. This should include
professional development and tools for teachers and other educators
• 1.2 Increase awareness and access for young men to educational alternatives such as apprenticeships
• 1.3 Strengthen cross sector partnerships between employers and education providers to create stronger
pathways from school to work for young men with mental illness. This should include focus on key transition
points such as moving from school to further studies or employment
Recommendation 2: Efforts should be made by all sectors of the community to
support young men with mental illness to engage in more productive employment.
• 2.1 Improve employers’ level of understanding of mental health, including the identification of disorders and
awareness of support and referral services available
• 2.2 Initiate new partnership models between government, mental health service providers, NGOs, employers
and business groups to create strategies that proactively support employees’ good mental health and ongoing
engagement in the workforce
• 2.3 Identify new partnership models between employers, business groups, government and NGOs to drive a
whole of community response. This includes creating new collaborative funding and service delivery models
Recommendation 3: Efforts should be made by all sectors of the community to
evaluate the effectiveness of current policy responses and investments in mental health.
• 3.1 Undertake further targeted research to evaluate the efficacy of existing mental health programs and
interventions with a particular emphasis on prevention and early intervention
• 3.2 Undertake return on investment analysis to inform future investment in young men’s mental health with a
particular emphasis on prevention and early intervention
• 3.3 Enhance reporting of government funded initiatives targeted at supporting young men with mental illness to
achieve full benefits of investment. Key objectives of these enhancements are to drive greater accountability of
public spend and to provide better transparency and access to program performance and evaluation
Counting the Cost
3
In 2010, the Inspire Foundation embarked on an ARC Linkage Research project with the Brain and Mind Research
Institute (BMRI)2 to better understand the help-seeking attitudes and experiences of young men. This initial research
was triggered by a desire to better understand the ‘why’ behind young men’s significantly lower rates of help seeking,
a phenomenon that was evident in the under representation of young men using Inspire’s ReachOut.com service.
In sharing the preliminary findings of this research, the Inspire Foundation received feedback from business leaders
in particular who said that whilst the personal cost of such low levels of help seeking was clear, there was a need to
better understand and explain the economic impacts - if any - of young men’s poor mental health and help seeking.
It was apparent that until such impacts were made clear, the poor mental health of young men would continue to be
seen as primarily a health issue for the attention of the government and community sectors.
Based on the insights gathered from this research and in collaboration with a community of supporters, Inspire
developed a strategy with the aim of building community awareness of the impacts of young men’s poor mental health
and increasing levels of help-seeking in young men and reducing male youth suicide.
Three primary initiatives were identified:
1. National Awareness Campaign. In partnership with the communications sector, develop a national
awareness campaign that challenges young men’s ideas of masculinity and reframe what it means to be a
fit and healthy man
2. Innovative Service Design. Through the ReachOut.com platform, trial new and innovative services to
provide information, support and community to young men (including an online self help tool ‘WorkOut
Mental Fitness Tool’)
3. Demonstrated Impact. Enlist the support of key corporate and academic partners, to undertake economic
modelling focused on revealing the costs associated with poor mental health amongst young men
The aim of this report is to address the third initiative of demonstrating the impact to the broader community on the
real costs of mental illness in young men. The outcomes of the economic analysis are intended to be used as a
foundation stone for the mental health sector - including the Young and Well CRC, Inspire and BMRI – to assist the
focus on building strategies to improve the mental health and wellbeing of Australian young men.
Report Aims
In sharing the preliminary findings of this research, the
Inspire Foundation received feedback from business
leaders in particular who said that whilst the personal cost
of such low levels of help-seeking was clear, there was
a need to better understand and explain the economic
impacts - if any - of young men’s poor mental health and
help seeking. It was apparent that until such impacts were
made clear, the poor mental health of young men would
continue to be seen as primarily a health issue for the
attention of the government and community sectors.
Counting the Cost
4
Counting the Cost
5
Globally, strong evidence demonstrates that the prevalence of mental health problems results in widespread economic
and societal burdens. Findings from the World Health Organisation, World Mental Health Surveys (WMH) show that
mental disorders occur commonly within the general population and frequently begin in adolescence3. Merikangas
et al, found in a review of recent international community surveys that approximately one in four young people had
experienced a mental disorder a year prior to the survey. Evidence from these surveys shows that much of the burden
caused by mental illness could be averted with best-practice treatment, yet fewer than half of the young people with
current disorders captured in those surveys had received any specific treatment.
In Australia, the prevalence of mental illness is also high, particularly amongst young people, with one in four young
Australians experiencing a mental health disorder. The majority of mental illnesses, including depression, have their
onset in adolescence and early adulthood4.
While the impact of poor mental health is significant across the whole population, it is particularly visible among men.
Suicide is the largest single cause of death in young Australian males aged 15–24 years. It accounts for 22% of
deaths; with male youth suicide rates in rural areas double those of metropolitan areas1. In addition, mortality rates in
young men with mental illness are significantly higher than those without mental illness.
While both young men and women suffer from anxiety and depression, young men have higher rates of completed
suicide, antisocial behaviour and drug and alcohol problems than young women. Findings from the 2007 Australian
National Survey of Mental Health and Wellbeing found that while young people (aged 16-24 years) had the highest
prevalence of mental disorders, they also had the lowest rates of receiving services in the 12-month period prior to
the survey. The rate of service use was especially low for young men, with only 13.2% accessing help and support
services, in spite of a 12 month prevalence rate of 22.8%5.
Findings from a number of studies suggest that even when young men are able to identify sources of help, there is
frequently a reluctance to use this help6. Both structural and individual factors provide barriers to men’s help-seeking,
with young men’s reluctance influenced by a fear of stigma, embarrassment, an over-emphasis on being self-reliant7
and internalised gender norms. Social norms encourage young men to hide their vulnerabilities and to strive for
independence.
Consequently, perceptions around masculinity mean that many young men equate masculinity with self-reliance.
Seeking help is perceived as the opposite to being independent8 and, by extension, masculine, resulting in young men
being unlikely to seek help during their formative adult years5.
This is concerning considering that evidence suggests intervening in the first episode of depression is possibly crucial
in preventing recurring episodes of depression. 75% of all serious mental health conditions start before the age of 25,
and preventatively focused interventions targeted to young people aged 12-25 have the potential to create significant
personal, social and economic benefits.
National expenditure on men’s mental health increases significantly from 15-25 years ($205m) to 25-34 years ($306m)
and again for 35-44 years ($268m), before declining until the 75+ group9. This pattern of expenditure may suggest that
the flow on impacts of mental illness, including drug and alcohol disorders, antisocial behaviour, loss of employment
and relationship breakdown become increasingly evident the longer mental illness is untreated.
Young men with mental illness also experience higher incarceration rates than young men without mental illness5. In
the NSW 2009 Inmate health survey of a random sample of 996 prisoners, a majority of participants were assessed as
having a mental illness (commonly mild depression) and yet had not had any contact with a mental health service in
the three months prior to their incarceration10.
Young Men’s
Mental Health
Counting the Cost
6
The cost of mental health
In 2008, the overall cost of spending on mental health care was $5.32 billion, with the Australian government spending
$1.92 billion and the states and territories spending $3.22 billion11.
In addition to the costs associated directly with specialist mental health care, the government also bears a broad
range of costs required to support people with mental illness - including income support, housing services, domiciliary
care and employment and training opportunities.
The 2010 National Health Report estimated that with government costs alone, for every dollar spent on specialised
mental health care, an extra $2.30 is spent on other services to support people with mental illness – equating to $4.4
billion11 (2008 prices).
The economic impacts of poor mental health
In Australia, the 2010 ‘Suicide and Suicide Prevention in Australia: Breaking the Silence’12 report put the financial cost
to Australia as a result of suicide and suicidal behaviour at $17.5 billion. At the time of publication this represented
1.5% of Gross Domestic Product, or $795 per person, per year. While not all of this cost is attributable to mental
illness, mental health is a key contributing factor to this cost.
The presence of mental illness has a significant influence on an individual’s productivity, with a close association
between productivity and the presence of mental illness in adolescence13.
A recent Foresight Mental Capital and Wellbeing Project (2008), commissioned by the Government Office for Science,
London highlighted the strong link between mental health and wellbeing and the production of capital, the role of
mental health in national prosperity, and the development of mental wealth14.
Such findings are especially pertinent in Australia which has seen deterioration in national productivity over the last
decade15. Whereas Australian labour productivity growth was in line with OECD averages in the 1990’s, in the 2000’s,
it has been 0.5% below the OECD average.
This reduction in growth has seen Australia fall from ranking 11th out of 25 OECD countries in the 1990’s to 17th out
of 34 countries in the 2000’s15. Growth in productivity is important as it accounts for the main source of improvement in
living standards over time16. As such, labour productivity serves as a very important measure of a country’s economic
and social wellbeing offering a measure of economic growth, competitiveness and living standards within a country16.
Mental illness and work
The psychological impact of being excluded from the workforce is greater for young people than older adults.
Research has shown that education and training opportunities can act as a protective factor against mental health
issues17, whilst secure and good employment outcomes provide young people with the possibility of financial
independence, a sense of control, self-confidence and social contact18.
However, unemployment, insecure employment and ‘bad’ working conditions are associated with poor self-esteem
and poor physical health, with unemployment in particular being associated with anxiety, depression, higher rates of
smoking and higher suicide rates among young people19.
Some studies suggest work that is both stressful and insecure can increase the risk of depression up to 14 times
relative to jobs in which individuals feel a sense of control and are securely employed20, potentially compounding the
difficulties faced by a person with a pre-existing mental illness.
Education plays a significant role in the employment outcomes of young men who experience mental illness. ‘Men
not at Work21 an analysis of Australian men outside the labour force’ found that individuals who have a degree or a
higher qualification have wages 30 to 45% higher than people with otherwise similar characteristics who have not
completed Year 12. A university education increases men’s wages by approximately 38% and also increases the
probability of employment by 15-20%. Education levels were also found to influence the types of employment men are
able to obtain.
It is significant that mental illness typically begins in adolescence/early adulthood - a time when individuals are
completing their education and pursuing employment options22. The impact of youth mental illness on schooling
through factors such as increased absenteeism, dropout rates and difficulty learning can compound the potential
negative impacts on employment outcomes23.
Counting the Cost
7
Many young people with mental illness have lower levels of educational qualifications, and when they do gain
employment, they tend to obtain lower skilled poorly paid roles. Individuals also accumulate skills – both job specific
and those broader in nature - through education that makes them more productive in the workplace. And whilst higher
education is positively linked to wages and productivity, higher wages in turn also have an impact on health and
education through providing the resources to access educational and health services24.
A number of international and Australian studies provide support for the assertion that untreated mental illness impairs
employment functioning11.
In an Australian study, Butterworth et al25 used five waves of Australia’s nationally representative Hilda survey for
5,846 respondents to analyse the role of mental illness in influencing future employment status. The researchers
followed a sample of respondents who were not unemployed at the start of the study to explore whether baseline
mental health was linked to further unemployment.
They found that for both men and women, their baseline mental health was significant in determining overall time
spent unemployed. Men and women who experienced common mental disorders spent more time unemployed over
the next four years than their more mentally healthy counterparts25.
For people who are employed with mental illness, their condition can negatively impact on their work performance
through increased absenteeism and/or their ability to function productively at work. This loss can be characterised as
the value of the production ‘lost’, including any premium that must be subsequently paid to get someone else to carry
out that work, as well as staff turnover and costs that are expended in training another person to carry out the role of
the individual if they are away for an extended period of time23.
While presenteeism is more difficult to measure than absenteeism, it is estimated to be much higher. The negative
impact of labour productivity losses due to presenteeism spills into the wider economy, resulting in a reduction in
levels of exports, imports and investments26.
Presenteeism not only reduces the productivity of the affected person but can also have an impact on co-workers. For
many workplaces, a significant form of work organisation is teamwork27. Studies have shown that workers who suffer
from depression are more likely to experience difficulties in focusing on work tasks and the levels of work required
of them. The negative impact that poor mental health has on the individual may extend to co-workers who may
experience increased stress through having to carry out additional work tasks.
Imprisonment further compounds the barriers that young men who experience mental illness face with regard to
employment opportunities. Not only do young people who are incarcerated have lower rates of education, but many
do not have the social capital to facilitate transition into employment as they reach their adult years29.
It is clear from the existing research that mental illness in young men can have a far reaching impact, affecting every
aspect of their lives. Significantly, these impacts radiate beyond the individual and into society, with implications for
government service provision and economic productivity.
75% of all serious mental health conditions start before
the age of 25, and preventatively focused interventions
targeted to young people aged 12-25 have the potential to
create significant personal, social and economic benefits.
Counting the Cost
8
Counting the Cost
9
Case Study – Jeff
“Jeff” is a young man in his early thirties. Jeff grew up in a violent household and was abused by
both his parental and step-fathers during his childhood.
He left school after repeated difficulties with teachers and school authorities before he
completed Year 10. Jeff believes his mental health problems were developing at school. The
only response from schools was ‘behaviour management’ including suspensions. Within three
years of leaving school, Jeff had a criminal record.
He has been in and out of jail for the past fourteen years.
Jeff has four children from previous relationships, and with “Theresa” (his present partner) he
has two children and another soon to be born.
Jeff has developed several serious mental illnesses, including substance abuse disorder. He
has had several periods of homelessness and very little sustained employment.
Jeff has no formal qualifications. His experience with his employment service provider has
resulted in him being directed to undertake courses that do not align with his interests, and
to apply for jobs for which he does not have suitable skills. Jeff and Theresa believe that his
criminal history and lack of qualifications are significant barriers to his employment.
Jeff and Theresa receive tens of thousands of dollars in various government support payments,
rental assistance, and service providers in employment, housing, child safety and family and
community services. Yet none of these are able to assist effectively and enable Jeff to gain and
sustain employment.
Through support provided by a Federally funded wrap-round service, progress is being made
for the first time with Jeff. He is now enrolled in a course that interests him and aligns with his
existing abilities in auto mechanics. He is looking forward to undertaking this program.
Jeff and Theresa believe that in fourteen years, this is the first time that Jeff has received
respectful, non-judgmental assistance that is tailored to his needs. Jeff is working extremely
hard to not reoffend and both are extremely thankful and relieved to be receiving support from
the wrap-round service team.
Jeff believes that he and his family will have a more positive and financially independent future
as a result.
Counting the Cost
10
This section describes the model methodology in detail, and is broken into two parts:
• An outline of the model design including the approach, key components and general assumptions made
• The detailed methodology outlining the assumptions and calculation for each cost category. The result for each
cost category is also provided
Methodology
and Results
Counting the Cost
11
Model Design
Approach
Cost of illness studies are conducted in order to measure the economic burden of a disease or diseases. While they
do not provide any information regarding the cost-effectiveness or return on investment of particular approaches or
policies, they do provide a useful body of evidence about the magnitude of costs associated with a particular disease
or condition and by extension, an estimate of the amount of savings that could be achieved by interventions or policies
which impact the costs included in the model.
Accordingly, the objective of this cost of illness economic model is to provide a quantification of the costs for the 2011
calendar year relating to mental illness in young males aged 12 to 25, as incurred by different sections of society.
The model is not intended to be a comprehensive study of all the costs and impacts of mental illness on the general
economy and as a result contains a number of limitations and assumptions and tends to represent a conservative
estimate only. As with any economic model, a number of limitations exist with availability and quality of data and
assumptions need to be made (these are described later in this section). This results in the model tending to
understate the actual cost of mental illness.
The first decision which needs to be made with regards to the development of any economic cost of illness model is to
determine the economic perspective to be adopted by the model30. We have largely adopted a societal perspective for
this model as it was desired that as broad a range of costs as possible be included.
A societal perspective essentially means that all costs associated with the disease/disorder in question is included in
the estimates, to ensure any important effects are not missed. Before discussing precisely which costs are included in
the model it is worth mentioning how costs are categorised more generally in the health economics literature.
Counting the Cost
12
For this model we adopted the four cost categories defined by Drummond et al30:
• C1 costs refer to government health sectors such as medical, pharmaceutical, hospitalisation etc. costs
• C2 costs refer to costs in other sectors such as welfare organisations, forensic services, educational services etc.
• C3 costs refer to any out of pocket expenses incurred by patients and their families such as travel, co-payments
(e.g. for medical services or drugs) expenditure in the home and time
• C4 costs refer to productivity costsa. These are defined as the ability to participate in the paid workforce as well as
productivity impacts while at work
We have developed this model to address all four categories.
In the current context a human capital approach was used as it best represents the total costs (from an individual and
employer perspective). This approach is based on estimated output losses due to cessation or reduction of production
due to morbidity and mortality. This is estimated from employee earnings (which involves various assumptions about
the relationship between employee wages and production) in the case of the paid workforce31.
The values of other nonmarket activities such as leisure, study etc. are also indirect costs, however, such costs are
usually excluded in the calculation of indirect costs due to the difficulty of measuring and defining them. This method
also excludes other psychosocial costs of illness such as pain, suffering, and stress etc., which impact on quality of
life. Such impacts are picked up in the outcome measure of economic evaluations are sometimes included as costs in
cost of illness studies.
The procedure in this study involved the determination of three sets of costs:
• Mortality costs due to premature death
• Morbidity costs due to work absence (including sick days and unemployment benefits to government if the
person is unemployed)
• Morbidity costs due to presenteeism (being present at work but not performing tasks at a maximum capacity)
Notably the mortality costs (in terms of the lifetime stream of income are mostly an individual cost – with a cost to
government as well in terms of less taxes), whereas the costs due to absenteeism and presenteeism are an employer
cost. The resources (within each of the cost categories identified above) and their associated costs used by young
men with mental illness, are added together to produce a total cost.
For the purpose of this study, a ‘bottom up’ as opposed to a ‘top down’ method to calculate costs was preferred as it
provides a more detailed and potentially more accurate depiction of the cost drivers. In the current context, bottom up
costing refers to an aggregation of costs.
Bottom-up costing usually involves the specification of an event pathway, the probability of different events occurring
for the population of interest and a cost associated with the event. In contrast, top-down costing takes an aggregated
total (usually health expenditure as identified in government accounts) and divides this into categories. The biggest
disadvantage with top down approaches is that important costs are often missed or misallocated. Importantly, some of
the unit costs used in the current model (such as health costs) were derived using a top-down approach, resulting in a
hybrid model.
A key concept underlying the model is that the costs are only applied to the marginal number of people affected by a
certain cost categories in the focus cohort. In the unemployment category, as an example, if the focus cohort did not
have mental illness, whilst they would have lower unemployment rates, they would still experience the unemployment
rate applicable to people without mental illness. The difference in the number of unemployed people represents the
marginal number of unemployed and it is to this group that the cost due to mental illness is quantified.
Based on the ABS Survey of Mental Health, it was found that mentally ill people experience higher rates for all cost
categories (e.g. unemployment or disability) relative to people without mental illness. It is assumed that the difference
between the mentally ill and non-mentally ill rate represents the impact due to mental illness. Therefore costs have
been derived by multiplying the marginal people who incur the cost by the monetary value of the cost (sometimes
referred to as the unit cost).
a Productivity costs tend to be used to describe the impact of absence from work, related to premature mortality and/or morbidity. The impacts can be on
individuals (e.g. they do not realise their earning potential), employers (the productivity of their firm is not as good as it can be or they need to replace
(either permanently or temporarily) workers who cannot perform their duties), and government (in terms of welfare payments). This definition is consistent
with the Productivity Commission’s (Productivity Commission 2006) use of the term ‘human capital stream’. The human capital stream in this report is
concerned with “workforce participation and productivity”. Therefore in the current context productivity gains/refers to the effect of mental illness on a
young man’s ability to participate in the paid work force, as well as productivity impacts while at work.
Counting the Cost
13
Explanations of the scope of the marginal cohort affected by each cost category - as well as the unit costs used for
each cost category - are further described in the detailed methodology section that follows.
A mental health advisory committee comprising mental health specialists, health economists and health and financial
modelling experts was convened to test and validate the model for comprehensiveness and validity. A series of
quality review checks were conducted on the model and underlying data to ensure the model met the desired level of
accuracy.
Model Scope
The outcome of this process is the cost categories as detailed in Table 1. Intangible costs or the traditional clinical
impacts of mental illness are not included in the current model, due to the contentious nature of placing a monetary
value of such impacts30.
The focus cohort consists of males aged 12 to 25 who suffer from mental illness. The size of this group as at
December 2011 (496,000) was derived by applying general population growth factors32 to an equivalent cohort
published by Access Economics in 200923.
Access Economics quantified the size of this cohort in 2009 by combining ABS and Australian Institute of Health and
Welfare (AIHW) data. The ABS and AIHW definitions of mental illness vary in scope which prompted the two datasets
to be combined to develop an expanded definition of mental Illnessb.
According to the ABS Survey of Mental Health 22.8% of males aged 16 to 24 suffer from a form of mental illness.
We have further split the cohort group into each cost category, and calculated the applicable costs for the specific
cohort in the model.
b Mental illness: is a clinically diagnosable disorder that significantly interferes with an individual’s cognitive, emotion, and social abilities. Mental illness
encompasses short and longer term conditions, including Anxiety disorders, Affective or mood disorders (e.g. depression) and Substance Use disorders
(e.g. Alcohol Dependence). Depending on the disorder and its severity, people may require specialist management, treatment with medicine and/or
intermittent use of health care services b. It should be noted that the ABS and AIHW definitions of mental illness vary in scope. This prompted the two data
sets used in the economic model to be combined to develop an expanded definition of mental illness. The definition includes the ABS definition (anxiety,
affective and substance use disorders) and AIHW definition (childhood, eating, personality and psychotic disorders).
c Welfare payments are often excluded in cost estimates from a societal perspective since they represent a transfer of income rather than an opportunity
cost of resources. However, from a more limited government economic perspective transfer payments do have an opportunity cost and have been
included in this model.
Not for reproduction or public release
18
Model Scope
The outcome of this process is the cost categories as detailed in Table 1. Intangible costs or
the traditional clinical impacts of mental illness are not included in the current model, due to
the contentious nature of placing a monetary value of such impacts5.
The focus cohort consists of males aged 12 to 25 who suffer from mental illness. The size of
this group as at December 2011 (496,000) was derived by applying general population
growth factors27 to an equivalent cohort published by Access Economics in 200928
Access Economics quantified the size of this cohort in 2009 by combining ABS and Australian
Institute of Health and Welfare (AIHW) data. The ABS and AIHW definitions of mental illness
vary in scope, which prompted the two datasets to be combined to develop an expanded
definition of mental Illness.
According to the ABS Survey of Mental Health 22.8% of males aged 16 to 24 suffer from a
form of mental illness.
We have further split the cohort group into each cost category, and calculated the applicable
costs for the specific cohort in the model.
Table 1: Model cost categories
Cost category Sub category Drummond et
al (2005)
classification
Description Cohort size
1 Health 1.1
Health costs
C1 , C3 Recurring and non-capital health cost
expenditure (includes out of pocket costs).
496,000
2 Employment 2.1
Personal leave
C
30
4 Cost of additional personal leave taken by
the mentally ill cohort
294,000
2.2
Reduced
personal income
C4 Reduced personal income reflected in
reduced wages at the same education
level
2.3
Reduced
education
C4 Reduced earnings due to lower education
level
3 Unemployment 3.1
Lost income
C4 Lost income during the period of
unemployment
24,000
3.2
Welfare benefits
C2 Unemployment welfare benefits paid by
the government to the unemployed
4 Imprisonment 4.1
Direct cost
C2 Prison operational costs 3,000
4.2
Lost income
C4 Lost income during the period of
imprisonment
5 Disability 5.1
Welfare
benefits
C2 Welfare benefits paid by the government
to the disabled
139,000
6 Mortality 6.1
Mortality
C4 Lost income over the life of an individual
due to mental illness related mortality
400
5 Drummond et al 2005
6 Welfare payments are often excluded in cost estimates from a societal perspective since they represent a transfer
of income rather than an opportunity cost of resources. However, from a more limited government economic
perspective transfer payments do have an opportunity cost and have been included in this model.
c
Counting the Cost
14
Assumptions and Limitations
As with any economic model, a number of limitations exist with availability and quality of data and assumptions need
to be made.
Where possible primary data sources have been used as a basis for analysis. This was not always the case due to
factors such as reliability, availability and/or quality of data. Extensive use of the ABS 2007 National Survey of Mental
Health and Wellbeing and findings from the Access Economics report were made in populating the model parameters.
The following assumptions are general assumptions that apply to all aspects of the model. Additional assumptions
specific to components of the model are described in the appropriate section.
• All costs in the model are expressed in 2011 dollars
• If a particular statistic (e.g. unemployment or disability) for a mentally ill cohort is different to a non-mentally ill
cohort, the difference was assumed to be caused by mental illness
• The number of young men with mental illness as a proportion of the general population has not changed since
2009 (most recent available data)
• Adopted future inflation and discount rates as shown in the following figure. Inflation rates were based on Access
Economics23 forecasts and future discount rates based on no arbitrage forward rates implied by the market prices
of Commonwealth Government bonds as at 31 December 2011. This is detailed in Figure 1.
Figure 1: Adopted inflation rates as at 30 December 2011
rDatoewnloads:[Adopted rates table for pg 14.xlsb]Sheet1
0.035
0.029
0.031
0.036
0.037
0.04
0.041
0.043
0.046
0.048
0.048
0.048
0.048
0.048
0.048
0.048
0.048
0.048
0.048
0.048
0.048
0
0.01
0.02
0.03
0.04
0.05
0.06
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031
AWE Yield curve
Counting the Cost
15
Detailed Methodology
and Results
1.0 Health Cost Category
1.1 Health costs
A top down approach was used to calculate the mental health care costs of young men. Total cost per person was
derived based on Access Economics23 data. This was adjusted for age and gender to align with the cohort in the study.
ABS Health CPI inflation33 was applied to inflate costs to 31 December 2011.
The cost categories included in these derived unit costs are:
• High level residential care
• Hospital expenditures
• Out of hospital expenditure
• Pharmaceutical costs
Cost categories excluded are:
• Expenditure on non-mental health related community care
• Capital expenditure
• Public health programs
• Health administration; and
• Health aids and appliances
The model does not quantify any additional non-mental health related health expenditure that may be incurred by
young men with mental illness.
Result
The method of allocating costs to the focus cohort and inflating the costs to 31 December 2011 is shown in Table
2. Note that the costs in Access Economics are a cost per person in the general population. As the current report
is focused on young people with mental illness this cost was divided by the proportion of young people with mental
illness so that a cost per person with mental illness applicable to the cohort is defined.
The total direct health costs have been calculated to be $555.8m per annum.
67.8% of this cost is born by government, with the remaining 32.2% out-of-pocket payments by individuals23. Individual
costs comprise claims paid by health insurance companies and payments by injury compensation insurers.
d 22.8% of males 16-24 suffer from mental illness (ABS National Survey of Mental Health and Wellbeing 2007)
Not for reproduction or public release
Result
The method of allocating costs to the focus cohort and inflating the costs to 31 December
2011 is shown in Table 2. Note that the costs in Access Economics are a cost per person in
the general population. As the current report is focused on young people with mental illness
this cost was divided by the proportion of young people with mental illness so that a cost per
person with mental illness applicable to the cohort is defined.
The total direct health costs have been calculated to be $555.8m per annum.
67.8% of this cost is born by government, with the remaining 32.2% out-of-pocket payments
by individuals31. Individual costs comprise claims paid by health insurance companies and
payments by injury compensation insurers.
Table 2: Direct health costs
2004-05 31 December 2011
Age
range
Focus
cohort
(‘000)
Mental Health
expenditure per all
males
($/person/year)
Health expenditure
per mentally ill
male
($/person/year)
Health
expenditure per
mentally ill male
($/person/year)
Direct health
costs ($m)
12-14 38.1 30 132 176 6.7
15-19 222.0 205 899 1200 266.4
20-25 235.6 205 899 1200 282.7
Total 495.7 555.8
d
Counting the Cost
16
2.0 Employment Cost Category
2.1 Personal Leave
According to the ABS National Survey of Mental Health and Wellbeing, people with mental illness are 2.3 times more
likely to be out of rolee compared to those without mental illness. The assumption is made that a mentally ill person
is more likely to take personal leave if they are in employment. As personal leave is paid by employers - with no
associated productivity benefit - this results in a cost burden to the employer.
These calculations show that those in the mentally ill cohort take an additional 9.5 days of personal leave per year
over the general population.
Result
The cost associated with additional personal leave was calculated by multiplying the marginal number of personal
leave days to the earnings applicable for those within the focus cohort (Table 5).
cost represents a $236.6m annual cost to employers.
e Days out of role: The number of days that a person was unable to work or carry out normal activities or had to cut down what they did because of their
health (ABS National Survey of Mental Health and Wellbeing 2007)
Not for reproduction or public release
22
2. Employment Cost Category
2.1 Personal Leave
According to the ABS National Survey of Mental Health, people with mental illness are 2.3
times more likely to be out of role compared to those without mental illness. The assumption
is made that a mentally ill person is more likely to take personal leave if they are in
employment. As personal leave is paid by employers - with no associated productivity benefit
- this results in a cost burden to employer.
Table 3: ABS 'Days out of Role' by mental health status34
Days out of role Ave. Days Males - no mental illness Males - mentally ill
0 days 0 76% 59%
1 to 7 days 4 18% 24%
More than 7 days 19 6% 16%
Ave days out of role (30 day period) 1.8 4.1
Ratio 2.3
These calculations show that those in the mentally ill cohort take an additional 9.5 days of
personal leave per year over the general population.
.
Table 4: Annual days out of role taken by mental illness status
General population No Mental illness Mental illness
% of employable males34 79.7% 20.3%
Average personal leave (days
per year) 9.3 7.4 16.9
Marginal number of personal leave (days per year) 9.5
Result
The cost associated with additional personal leave was calculated by multiplying the marginal
number of personal leave days to the earnings applicable for those within the focus cohort
(Table 5).
This cost represents a $236.6m annual cost to employers.
Table 5: Cost of personal leave
Age Range Number employed
(‘000)
AWE – Mentally ill males
($/week)
Cost – Personal leave
12-14 0.0 0 0.0
15-19 109.6 333 49.7
20-25 184.0 745 186.8
Total 293.6 236.6
35
Not for reproduction or public release
22
2. Employment Cost Category
2.1 Personal Leave
According to the ABS National Survey of Mental Health, people with mental illness are 2.3
times more likely to be out of role compared to those without mental illness. The assumption
is made that a mentally ill person is more likely to take personal leave if they are in
employment. As personal leave is paid by employers - with no associated productivity benefit
- this results in a cost burden to employer.
Table 3: ABS 'Days out of Role' by mental health status34
Days out of role Ave. Days Males - no mental illness Males - mentally ill
0 days 0 76% 59%
1 to 7 days 4 18% 24%
More than 7 days 19 6% 16%
Ave days out of role (30 day period) 1.8 4.1
Ratio 2.3
These calculations show that those in the mentally ill cohort take an additional 9.5 days of
personal leave per year over the general population.
.
Table 4: Annual days out of role taken by mental illness status
General population No Mental illness Mental illness
% of employable males34 79.7% 20.3%
Average personal leave (days
per year) 9.3 7.4 16.9
Marginal number of personal leave (days per year) 9.5
The cost associated with additional personal leave was calculated by multiplying the marginal
number of personal leave days to the earnings applicable for those within the focus cohort
(Table 5).
This cost represents a $236.6m annual cost to employers.
Table 5: Cost of personal leave
Age Range Number employed
(‘000)
AWE – Mentally ill males
($/week)
Cost – Personal leave
12-14 0.0 0 0.0
15-19 109.6 333 49.7
20-25 184.0 745 186.8
Total 293.6 236.6
35
Not for reproduction or public release
22
2. Employment Cost Category
2.1 Personal Leave
According to the ABS National Survey of Mental Health, people with mental illness are 2.3
times more likely to be out of role compared to those without mental illness. The assumption
is made that a mentally ill person is more likely to take personal leave if they are in
employment. As personal leave is paid by employers - with no associated productivity benefit
- this results in a cost burden to employer.
Table 3: ABS 'Days out of Role' by mental health status34
Days out of role Ave. Days Males - no mental illness Males - mentally ill
0 days 0 76% 59%
1 to 7 days 4 18% 24%
More than 7 days 19 6% 16%
Ave days out of role (30 day period) 1.8 4.1
Ratio 2.3
These calculations show that those in the mentally ill cohort take an additional 9.5 days of
personal leave per year over the general population.
.
Table 4: Annual days out of role taken by mental illness status
General population No Mental illness Mental illness
% of employable males34 79.7% 20.3%
Average personal leave (days
per year) 9.3 7.4 16.9
Marginal number of personal leave (days per year) 9.5
Result
The cost associated with additional personal leave was calculated by multiplying the marginal
number of personal leave days to the earnings applicable for those within the focus cohort
(Table 5).
This cost represents a $236.6m annual cost to employers.
Table 5: Cost of personal leave
Age Range Number employed
(‘000)
AWE – Mentally ill males
($/week)
Cost – Personal leave
12-14 0.0 0 0.0
15-19 109.6 333 49.7
20-25 184.0 745 186.8
Total 293.6 236.6
35
Counting the Cost
17
2.2 Reduced Personal Income
A 2010 Productivity Commission study24 found that on average young men with mental illness have 4.7% lower hourly
wages relative to males without mental illness, controlling for factors including:
• Demographic variables (e.g. age and level of education)
• Employment
• Experience
• Physical health
• Unemployment history
By considering hourly wages this methodology allows for the differences in unemployment and underemployment
rates between mentally ill people and non-mentally ill people.
The proportion of young men who are actively participating in the work force (participation rate) by either being
employed or looking for employment (termed unemployed) was also determined.
To achieve this, the general male population labour force participation rates by age36 were applied to the focus cohort
to split the group between those in the labour force and those who are not participating. This is detailed in Table 6.
General population participation rates were applied to the model rather than mentally ill participation rates due to two
key reasons:
• The publicly available ABS Survey of Mental Health did not contain mentally ill specific labour force participation
rates by age. Labour force participation rates specific to a mentally ill cohort were available only as an average
over all ages
• Given the large variation in participation rates across the age bands, it was necessary to select age specific rates
However, the participation rates for a mentally ill cohort averaged over all ages is not dissimilar to a non-mentally ill
cohort (Table 7). The assumption was made that this applies to the 15 to 25 age bands.
23
A Productivity Commission study35 found that on average young men with mental illness have
4.7% lower hourly wages relative to males without mental illness, controlling for factors
including:
Demographic variables (e.g. age and level of education)
Employment
Experience
Physical health
Unemployment history
By considering hourly wages this methodology allows for the differences in unemployment
and underemployment rates between mentally ill people and non-mentally ill people.
The proportion of young men who are actively participating in the work force (participation
rate) by either being employed or looking for employment (termed unemployed) was also
determined.
To achieve this, the general male population labour force participation rates by age 36 were
applied to the focus cohort to split the group between those in the labour force and those who
are not participating. This is detailed in Table 6.
Table 6: Focus cohort by labour force status
Age
range
Focus cohort
(‘000)
Participation rate
(%)36
Labour force
('000)
Non-labour force
('000)
12-14 38.1 0% 0.0 38.1
15-19 222.0 53% 118.5 103.5
20-25 235.6 84% 198.8 36.7
Total 495.7 317.4 178.4
General population participation rates were applied to the model rather than mentally ill
participation rates due to two key reasons:
The publicly available ABS Survey of Mental Health did not contain mentally ill specific
labour force participation rates by age. Labour force participation rates specific to a
mentally ill cohort were available only as an average over all ages
Given the large variation in participation rates across the age bands, it was necessary to
select age specific rates.
However, the participation rates for a mentally ill cohort averaged over all ages is not
dissimilar to a non-mentally ill cohort (Table 7). The assumption was made that this applies to
the 15 to 25 age bands.
Counting the Cost
18
The actual costs associated with lost personal income were derived using ABS average weekly earnings (AWEs).
AWEs at December 2011 were derived based on 2010 ABS AWEs by age37, inflated to December 2011 using:
• ABS AWE inflation38 to August 2011
• An assumed AWE inflation rate of 4.3% between August 2011 and December 2011 (detailed in Table 8).
Result
This reduction in earnings of the employed group within the focus cohort is $445.2m per annum, as shown in Table 9.
Not for reproduction or public release
24
Table 7: Labour force participation rates34 , 36
Gender – Age Participation rate
Males 12-14 0%
Males 15-19 53%
Males 20-25 84%
Males 15-19 General population 53%
Males 15-64 General population 83%
All Persons 16-85 Mental illness 70%
All persons 16-85 No mental illness 67%
The actual costs associated with lost personal income were derived using ABS average
weekly earnings (AWEs). AWEs at December 2011 were derived based on 2010 ABS AWEs
by age40, inflated to December 2011 using:
ABS AWE inflation41 to August 2011
An assumed AWE inflation rate of 4.3% between August 2011 and December 2011
(detailed in Table 8).
Table 8: Average Weekly earnings by age as at December 2011 (Males only)
Age range AWE Males December 2011 ($/week)
15–19 349
20–24 782
25–29 1,156
30–34 1,358
35–39 1,593
40–44 1,612
45–49 1,592
50–54 1,531
55–59 1,486
60–64 1,335
65 and over 1,094
Result
This reduction in earnings of the employed group within the focus cohort is $445.2m per
annum, as shown in Table 9.
Table 9: Cost of reduced earnings
Age
range
Number employed
(‘000)
AWE General
males 2011
($/week)
AWE – Mentally ill
males ($/week)
Cost – Reduced
productivity ($m)
12-14 0.0 0 0 0.0
15-19 109.6 349 333 93.6
20-25 184.0 782 745 351.6
Total 293.6 445.2
Not for reproduction or public release
24
Table 7: Labour force participation rates34 , 36
Gender – Age Participation rate
Males 12-14 0%
Males 15-19 53%
Males 20-25 84%
Males 15-19 General population 53%
Males 15-64 General population 83%
All Persons 16-85 Mental illness 70%
All persons 16-85 No mental illness 67%
The actual costs associated with lost personal income were derived using ABS average
weekly earnings (AWEs). AWEs at December 2011 were derived based on 2010 ABS AWEs
by age40, inflated to December 2011 using:
ABS AWE inflation41 to August 2011
An assumed AWE inflation rate of 4.3% between August 2011 and December 2011
(detailed in Table 8).
Table 8: Average Weekly earnings by age as at December 2011 (Males only)
Age range AWE Males December 2011 ($/week)
15–19 349
20–24 782
25–29 1,156
30–34 1,358
35–39 1,593
40–44 1,612
45–49 1,592
50–54 1,531
55–59 1,486
60–64 1,335
65 and over 1,094
Result
This reduction in earnings of the employed group within the focus cohort is $445.2m per
annum, as shown in Table 9.
Table 9: Cost of reduced earnings
Age
range
Number employed
(‘000)
AWE General
males 2011
($/week)
AWE – Mentally ill
males ($/week)
Cost – Reduced
productivity ($m)
12-14 0.0 0 0 0.0
15-19 109.6 349 333 93.6
20-25 184.0 782 745 351.6
Total 293.6 445.2
Not for reproduction or public release
24
Table 7: Labour force participation rates34 , 36
Gender – Age Participation rate
Males 12-14 0%
Males 15-19 53%
Males 20-25 84%
Males 15-19 General population 53%
Males 15-64 General population 83%
All Persons 16-85 Mental illness 70%
All persons 16-85 No mental illness 67%
The actual costs associated with lost personal income were derived using ABS average
weekly earnings (AWEs). AWEs at December 2011 were derived based on 2010 ABS AWEs
by age40, inflated to December 2011 using:
ABS AWE inflation41 to August 2011
An assumed AWE inflation rate of 4.3% between August 2011 and December 2011
(detailed in Table 8).
Table 8: Average Weekly earnings by age as at December 2011 (Males only)
Age range AWE Males December 2011 ($/week)
15–19 349
20–24 782
25–29 1,156
30–34 1,358
35–39 1,593
40–44 1,612
45–49 1,592
50–54 1,531
55–59 1,486
60–64 1,335
65 and over 1,094
Result
This reduction in earnings of the employed group within the focus cohort is $445.2m per
annum, as shown in Table 9.
Table 9: Cost of reduced earnings
Age
range
Number employed
(‘000)
AWE General
males 2011
($/week)
AWE – Mentally ill
males ($/week)
Cost – Reduced
productivity ($m)
12-14 0.0 0 0 0.0
15-19 109.6 349 333 93.6
20-25 184.0 782 745 351.6
Total 293.6 445.2
Counting the Cost
19
2.3 Reduced Education
The ABS Survey of Mental Health34 identified that people with mental illness have lower levels of education. According
to a Productivity Commission study24, average hourly wages are correlated with the level of education, adjusting for
demographic and other employment related factors.
To quantify this cost the following approach was adopted:
• Step 1: The employed cohort was divided into groups differentiated by age and education
• Step 2: Earnings by education levels were derived
• Step 3: Total yearly earnings of the cohort with educational attainment levels applicable to mentally ill and nonmentally
ill people were determined
The difference in earnings represents the cost of reduced education.
Step 1: Table 10 illustrates the employment levels within the focus cohort classified by education levels, using both
mentally ill and non-mentally ill education levels. An assumption was made that the earnings growth as an individual
ages is the same at all education levels.
26
2.3 Reduced Education
The ABS Survey of Mental Health42 identified that people with mental illness have lower
levels of education. According to a Productivity Commission study43, average hourly wages
are correlated with the level of education, adjusting for demographic and other employment
related factors.
To quantify this cost the following approach was adopted:
Step 1: The employed cohort was divided into groups differentiated by age and
education
Step 2: Earnings by education levels were derived
Step 3: Total yearly earnings of the cohort with educational attainment levels applicable
to mentally ill and non-mentally ill people were determined.
The difference in earnings represents the cost of reduced education.
Step 1: Table 10 illustrates the employment levels within the focus cohort classified by
education levels, using both mentally ill and non-mentally ill education levels. An assumption
was made that the earnings growth as an individual ages is the same at all education levels.
Table 10: Education level mix by mental health status (ABS Survey of Mental Health)
Education levels
Number employed in focus
cohort aged 15-19
(‘000)
Number employed in focus
cohort aged 20-25
(‘000)
Education level Mental
illness No mental
illness
Mentally ill
rates
Nonmentally
ill
rates
Mentally ill
rates
Nonmentally
ill
rates
Bachelor degree or
above
16.9% 20.7% 18.6 22.7 31.2 38.1
Advanced
diploma/Diploma
9.3% 8.3% 10.2 9.1 17.2 15.3
Certificate 25.6% 25.3% 28.1 27.8 47.1 46.6
No non-school
qualification
48.1% 45.6% 52.7 50.0 88.5 83.9
Total 100% 100% 109.6 109.6 184.0 184.0
34
Counting the Cost
20
Step 2: Earnings by education level by age were derived via three steps:
1. 2003 hourly wages by education level as published in a Productivity Commission study24 were inflated to 31
December 2011 using general male AWE inflation
2. Hourly wages by education level were scaled to reflect the ages within our focus cohort based on the
earnings relativities by age of the general population37, as shown in Table 11
Table 11: General population average weekly earnings by age (full time only)
Step 3: The resultant average weekly earnings applicable to the focus cohort by age are presented in Table 12 and
Table 13.
3. Hourly wages were converted to male average weekly earnings allowing for the following factors:
• Males aged 15-19 and males aged 20-24 have 1.1% and 3.2% higher full time average weekly
earnings relative to the general population at the same age level respectively37
• Average full time hours worked per week: 45.939
• Average part time hours worked per week: 14.8 (15-19 age band), 19.7 (20-25 age band)
• The proportions of workers working part time, by age
• 55% part time for Males 15-19
• 30% part time for Males 20-24
Not for reproduction or public release
27
Step 2: Earnings by education level by age were derived via three steps:
1. 2003 hourly wages by education level as published in a Productivity Commission
study45 were inflated to 31 December 2011 using general male AWE inflation.
2. Hourly wages by education level were scaled to reflect the ages within our focus cohort
based on the earnings relativities by age of the general population46, as shown in
Table 11
Table 11: General population average weekly earnings by age (full time only)
Age range AWE (2010 $)37 Relativity against all ages
AWE 15-19 gen pop 555 44%
AWE '20-24 gen pop 866 69%
AWE all ages 1263
3. Hourly wages were converted to male average weekly earnings allowing for the following
factors:
Males aged 15-19 and males aged 20-24 have 1.1% and 3.2% higher full
time average weekly earnings relative to the general population at the same
age level respectively 48
Average full time hours worked per week: 45.949
Average part time hours worked per week: 14.8 (15-19 age band), 19.7 (20-
25 age band)
The proportions of workers working part time, by age
55% part time for Males 15-19
30% part time for Males 20-24
Not for reproduction or public release
Step 3: The resultant average weekly earnings applicable to the focus cohort by age are
presented in Table 12 and Table 13.
Table 12: Average weekly earnings by education level (aged 15-19)
Education level General
population
earnings
(inflated to 2011
$/hr) 24
General
population Age
15-19 earnings
(2011 $/hr)
Male Age 15-19
earnings
(2011 $/hr)
Male Age 15-19
AWE
(2011 $/week)
Bachelor degree or above 38.0 12.8 13.0 373
Advanced diploma/Diploma 29.2 12.8 13.0 373
Certificate 27.7 12.2 12.3 355
No non-school qualification 25.9 11.4 11.5 331
Overall
349
Table 13:Average weekly earnings by education level (aged 20-24)
Education level General
population
earnings
(inflated to 2011
$/hr)
General
population Age
20-24 earnings
(2011 $/hr)
Male Age 20-24
earnings
(2011 $/hr)
Male Age 20-24
AWE
(2011 $/week)
Bachelor degree or above 38.0 26.0 26.9 1,021
Advanced diploma/Diploma 29.2 20.0 20.7 786
Certificate 27.7 19.0 19.6 746
No non-school qualification 25.9 17.8 18.3 697
Overall
782
Result
24
Counting the Cost
21
Result
The difference in earnings represents the cost of reduced education for the cohort, calculated at $113.7m per annum
of reduced earnings (Table 14).
28
population
earnings
(inflated to 2011
$/hr)
General
population Age
20-24 earnings
(2011 $/hr)
Male Age 20-24
earnings
(2011 $/hr)
Male Age 20-24
AWE
(2011 $/week)
Bachelor degree or above 38.0 26.0 26.9 1,021
Advanced diploma/Diploma 29.2 20.0 20.7 786
Certificate 27.7 19.0 19.6 746
No non-school qualification 25.9 17.8 18.3 697
Overall
782
Result
The difference in earnings represents the cost of reduced education for the cohort, calculated
at $11.7m per annum of reduced earnings (Table 14).
Table 14: Cost due to reduced education
Male AWE Total yearly earnings ($m)
AWE 15-19 AWE 20-24 Mentally ill
rates
Nonmentally
ill
rates
Cost -
reduced
education
levels ($ m)
Bachelor degree
or above
373 1,021 2,016.3 2,466.6 450.3
Advanced
diploma/Diploma
373 786 901.6 804.4 (97.2)
Certificate 355 746 2,347.8 2,320.4 (27.3)
No non-school
qualification
331 697 4,116.7 3,904.7 (212.0)
Total 349 782 9,382.4 9,496.1 113.7
24
Counting the Cost
22
3.0 Unemployment Cost Category
Two annual lost income costs are calculated:
• Where an individual is unemployed
• Unemployment benefits paid from the government to the individual
These costs are applied to the marginal number of unemployed, i.e. the additional number of unemployed people in
the focus cohort due to mental illness.
The approach taken to quantify these costs is as follows:
• The labour force is multiplied by the difference in mentally ill and non-mentally ill unemployment rates to derive
the marginal number of unemployed
• For the lost income component, the number of marginal unemployed is multiplied by average weekly earnings
and the average duration unemployed40
• For the unemployment benefits component, the number of marginal unemployed40 is multiplied by the average
duration unemployed and the weekly unemployment benefits
Statistics from the ABS National Survey of Mental Health and Wellbeing34 were used as a basis to identify
unemployment rates by mental illness status. The 2007 rates were applied to the general population unemployment
rate as at November 2011. This assumes that the relativities applied in 2007 still apply to 2011.
The unemployment rate for people with mental illness was found to be higher than the unemployment rate for people
without mental illness34.
A geometric (proportional) rather than arithmetic (fixed) relativity was chosen to measure the relative risk of
unemployment for this cohort so that the gap is proportional to the general population unemployment rate. A geometric
relativity of 1.6 means the cohort has 1.6 times more prevalence of unemployment relative to a non-mentally ill
population. For example, if the non-mentally ill unemployment rate was higher at 10%, the mentally ill unemployment
rate would be 16%.
Table 16 depicts the number of unemployed within the focus cohort. The number of marginally unemployed was then
calculated using the gap derived above. This value represents the additional number of unemployed people in the
focus cohort due higher unemployment rates relative to a non-mentally ill cohort.
29
These costs are applied to the marginal number of unemployed, i.e. the additional number of
unemployed people in the focus cohort due to mental illness.
The approach taken to quantify these costs is as follows:
The labour force is multiplied by the difference in mentally ill and non-mentally ill
unemployment rates to derive the marginal number of unemployed
For the lost income component, the number of marginal unemployed is multiplied by
average weekly earnings and the average duration unemployed52
For the unemployment benefits component, the number of marginal unemployed is
multiplied by the average duration unemployed53 and the weekly unemployment
benefits54
The lost earnings due to employment cost applies to the marginally unemployed based on
their average duration of unemployment and average earnings. Unemployment benefits also
apply to the marginal number of employed.
Statistics from the ABS National Survey of Mental Health and Wellbeing 2007 were used as a
basis to identify unemployment rates by mental illness status. The 2007 rates were applied to
the general population unemployment rate as at November 2011. This assumes that the
relativities applied in 2007 still apply to 2011.
The unemployment rate for people with mental illness was found to be higher than the
unemployment rate for people without mental illness55.
Table 15: Unemployment rate
Year General
population Mentally Ill Non-mentally ill Relativity Gap
2007 3.8%36 5.4%36 3.4% 1.6
2011 5.3%42 7.5% 4.7% 1.6 2.8%
Proportion 22.2%34 77.8%
A geometric (proportional) rather than arithmetic (fixed) relativity was chosen to measure the
relative risk of unemployment for this cohort so that the gap is proportional to the general
population unemployment rate. A geometric relativity of 1.6 means the cohort has 1.6 times
more prevalence of unemployment relative to a non-mentally ill population. For example, if
Not for reproduction or public release
the non-mentally ill unemployment rate was higher at 10%, the mentally ill employment rate
would be 16%.
Table 15 depicts the number of unemployed within the focus cohort. The number of
marginally unemployed was then calculated using the gap derived above. This value
represents the additional number of unemployed people in the focus cohort due to higher
unemployment rates relative to a non-mentally ill cohort.
Table 16: Marginal unemployment cohort
Age
range
Focus
cohort
(‘000)
Labour
force
('000)
Unemployment
rate Mentally ill
Unemployment
rate Non
mentally ill
Marginal
unemployed
(‘000)
12-14 38.1 0.0 0.0
15-19 222.0 118.5 7.5% 4.7 3.3
20-25 235.6 198.8 7.5% 4.7 5.6
Total 495.7 317.4 23.8 8.9
Result
The overall cost associated with unemployment is presented in Table 17:
Lost income to individuals of $167.8m per annum
Welfare benefits opportunity cost to the government of $62.1m per annum
Not for reproduction or public release
the non-mentally ill unemployment rate was higher at 10%, the mentally ill employment rate
would be 16%.
Table 15 depicts the number of unemployed within the focus cohort. The number of
marginally unemployed was then calculated using the gap derived above. This value
represents the additional number of unemployed people in the focus cohort due to higher
unemployment rates relative to a non-mentally ill cohort.
Table 16: Marginal unemployment cohort
Age
range
Focus
cohort
(‘000)
Labour
force
('000)
Unemployment
rate Mentally ill
Unemployment
rate Non
mentally ill
Marginal
unemployed
(‘000)
12-14 38.1 0.0 0.0
15-19 222.0 118.5 7.5% 4.7 3.3
20-25 235.6 198.8 7.5% 4.7 5.6
Total 495.7 317.4 23.8 8.9
Result
The overall cost associated with unemployment is presented in Table 17:
Lost income to individuals of $167.8m per annum
Welfare benefits opportunity cost to the government of $62.1m per annum
Counting the Cost
23
Result
The overall cost associated with unemployment is presented in Table 17:
• Lost income to individuals of $167.8m per annum
• Welfare benefits opportunity cost to the government of $62.1m per annum
30
range
cohort
(‘000)
force
('000)
rate Mentally ill
rate Non
mentally ill
unemployed
(‘000)
12-14 38.1 0.0 0.0
15-19 222.0 118.5 7.5% 4.7 3.3
20-25 235.6 198.8 7.5% 4.7 5.6
Total 495.7 317.4 23.8 8.9
Result
The overall cost associated with unemployment is presented in Table 17:
Lost income to individuals of $167.8m per annum
Welfare benefits opportunity cost to the government of $62.1m per annum
Table 17:Cost of unemployment lost income and welfare benefits
Age Marginal
unemployed
(‘000)
Ave. weeks
unemployed40
AWE Males
($/week)
Unemp.
benefits
($/week)41
Unemp. lost
income ($m)
Unemp.
Welfare
benefits ($m)
12-14 0.0 0 0 243 0.0 0.0
15-19 3.3 22 349 243 25.7 17.9
20-25 5.6 33 782 243 142.1 44.2
Total 8.9 167.8 62.1
Counting the Cost
24
4.0 Imprisonment Cost Category
The ABS National Survey of Mental Health and Wellbeing34 shows people with mental illness experience higher
imprisonment rates relative to people without mental illness. The model quantified the costs associated with
imprisonment by considering:
• The lost income of the individual during the period of imprisonment
• The direct cost of imprisonment (operational costs)
These costs were applied to the marginal number imprisoned, i.e. the additional number of imprisoned people in the
focus cohort due to higher imprisonment rates.
The ABS National Survey of Mental Health and Wellbeing34 reports that 5% of all mentally ill people have ever
been incarcerated in their lifetime, relative to 1.8% of the non-mentally ill. This reflects a relativity of 2.8 times the
prevalence of all young men.
This relativity was applied to the general population male imprisonment rates to calculate the imprisonment rates
applicable to the mentally ill cohort.
Similar to the unemployment costs calculated in the previous section, a geometric (proportional) rather than arithmetic
(fixed) relativity was chosen to measure the relative risk of this cohort so that the gap is proportional to the general
population imprisonment rate.
The marginal number of people imprisoned relates to the additional number of imprisoned people in the focus cohort
due to higher imprisonment rates. This was calculated as the difference in imprisonment rates between the mentally ill
and non-mentally ill cohorts multiplied by the number of people in the focus cohort.
Not for reproduction or public release
31
4. Imprisonment Cost Category
The ABS National Survey of Mental Health62 shows people with mental illness experience
higher imprisonment rates relative to people without mental illness. The model quantified the
costs associated with imprisonment by considering:
The lost income of the individual during the period of imprisonment
The direct cost of imprisonment (operational costs)
These costs were applied to the marginal number imprisoned, i.e. the additional number of
imprisoned people in the focus cohort due to higher imprisonment rates.
The ABS National Survey of Mental Health63 reports that 5% of all mentally ill people have
ever been incarcerated in their lifetime, relative to 1.8% of the non-mentally ill. This reflects a
relativity of 2.8 times the prevalence of non-mentally ill young men.
This relativity was applied to the general population male imprisonment rates to calculate the
imprisonment rates applicable to the mentally ill cohort.
Table 18: Imprisonment rates by age
Age range General population male
imprisonment rates43
Mentally ill imprisonment
rate
Non-mentally ill
imprisonment rate
<18 0.024% 0.048% 0.017%
18 0.207% 0.411% 0.147%
19 0.349% 0.692% 0.247%
20-25 0.518% 1.029% 0.367%
Similar to the unemployment costs calculated in the previous section, a geometric
(proportional) rather than arithmetic (fixed) relativity was chosen to measure the relative risk
of this cohort so that the gap is proportional to the general population imprisonment rate.
The marginal number of people imprisoned relates to the additional number of imprisoned
people in the focus cohort due to higher imprisonment rates. This was calculated as the
difference in imprisonment rates between the mentally ill and non-mentally ill cohorts
multiplied by the number of people in the focus cohort.
Table 19: Marginal number of focus cohort imprisoned
Age range Focus cohort
(‘000)
Mentally ill
imprisonment
rate
Non-mentally ill
imprisonment
rate
Num
imprisoned
(‘000)
Marginal num
imprisoned
(‘000)
<18 171.2 0.05% 0.017% 0.08 0.05
18 44.5 0.41% 0.147% 0.18 0.12
19 44.5 0.69% 0.247% 0.31 0.20
20-25 235.6 1.03% 0.367% 2.42 1.56
Total 495.7 0.56% 0.20% 3.00 1.93
31
The ABS National Survey of Mental Health62 shows people with mental illness experience
higher imprisonment rates relative to people without mental illness. The model quantified the
costs associated with imprisonment by considering:
The lost income of the individual during the period of imprisonment
The direct cost of imprisonment (operational costs)
These costs were applied to the marginal number imprisoned, i.e. the additional number of
imprisoned people in the focus cohort due to higher imprisonment rates.
The ABS National Survey of Mental Health63 reports that 5% of all mentally ill people have
ever been incarcerated in their lifetime, relative to 1.8% of the non-mentally ill. This reflects a
relativity of 2.8 times the prevalence of non-mentally ill young men.
This relativity was applied to the general population male imprisonment rates to calculate the
imprisonment rates applicable to the mentally ill cohort.
Table 18: Imprisonment rates by age
Age range General population male
imprisonment rates43
Mentally ill imprisonment
rate
Non-mentally ill
imprisonment rate
<18 0.024% 0.048% 0.017%
18 0.207% 0.411% 0.147%
19 0.349% 0.692% 0.247%
20-25 0.518% 1.029% 0.367%
Similar to the unemployment costs calculated in the previous section, a geometric
(proportional) rather than arithmetic (fixed) relativity was chosen to measure the relative risk
of this cohort so that the gap is proportional to the general population imprisonment rate.
The marginal number of people imprisoned relates to the additional number of imprisoned
people in the focus cohort due to higher imprisonment rates. This was calculated as the
difference in imprisonment rates between the mentally ill and non-mentally ill cohorts
multiplied by the number of people in the focus cohort.
Table 19: Marginal number of focus cohort imprisoned
Age range Focus cohort
(‘000)
Mentally ill
imprisonment
rate
Non-mentally ill
imprisonment
rate
Num
imprisoned
(‘000)
Marginal num
imprisoned
(‘000)
<18 171.2 0.05% 0.017% 0.08 0.05
18 44.5 0.41% 0.147% 0.18 0.12
19 44.5 0.69% 0.247% 0.31 0.20
20-25 235.6 1.03% 0.367% 2.42 1.56
Total 495.7 0.56% 0.20% 3.00 1.93
Counting the Cost
25
4.1 Direct costs
Direct costs relate to the operational costs associated with running a prison. According to a Corrective Services report
on government services44, total cost per prisoner (comprising net operating expenditure, depreciation, debt servicing
fees and user cost of capital) was $275 per day, or $100,400 per year. This 2009-10 cost was inflated to December
2011 using CPI inflation33 to arrive at a sum of $107,300.
The health costs of caring for mentally ill prisoners have not been included in the current study due to lack of available
data.
Using ABS data43, the average prison duration was then calculated by taking the weighted average by type of crime
using the mix of prisoners by age and by sentence type, and the average expected time to serve. It was assumed that
the length of prison sentences received by the mentally ill cohort is the same as the general population.
The average duration of imprisonment is greater than a year for all age groups.
The direct cost of imprisonment was capped at a 1 year for the model as the intention is to calculate yearly cost.
Result
The total direct cost for imprisonment for this cohort is $206.8m per annum.
32
Using ABS data65, the average prison duration was then calculated by taking the weighted
average by type of crime using the mix of prisoners by age and by sentence type, and the
average expected time to serve. It was assumed that the length of prison sentences received
by the mentally ill cohort is the same as the general population.
4.1 Direct costs
Direct costs relate to the operational costs associated with running a prison. According to a
Corrective Services report on government services66, total cost per prisoner (comprising net
operating expenditure, depreciation, debt servicing fees and user cost of capital) was $275
per day, or $100,400 per year. This 2009-10 cost was inflated to December 2011 using CPI
inflation67 to arrive at a sum of $107,300.
The health costs of caring for mentally ill prisoners have not been included in the current
study due to lack of available data.
The direct cost of imprisonment was capped at a 1 year for the model as the intention is to
calculate yearly cost9.
Result
total direct cost for imprisonment for this cohort is $206.8m per annum.
Table 20: Imprisonment direct costs
Age range Marginal number imprisoned (‘000) Direct imprisonment cost ($m)
<18 0.05 5.7
18 0.12 12.6
19 0.20 21.3
20-25 1.56 167.2
Total 1.93 206.8
9 The average duration of imprisonment is greater than a year for all age groups
Counting the Cost
26
4.2 Lost income
Lost income refers to the potential wages that would have otherwise been earned had the individual not been
imprisoned. This was calculated by applying general male earnings to the marginal number of the focus cohort
imprisoned, adjusting for participation rate and employed rate.
Result
The total cost of lost income to individuals due to imprisonment for this cohort is $53.9m per annum.
Not for reproduction or public release
33
4.2 Lost income
Lost income refers to the potential wages that would have otherwise been earned had the
individual not been imprisoned. This was calculated by applying general male earnings to the
marginal number of the focus cohort imprisoned, adjusting for the participation rate and
employed rate.
The total cost of lost income top individuals due to imprisonment for this cohort is $53.9m per
annum.
Table 21: Imprisonment lost income
Age range Marginal number
imprison ed
(‘000)
AWE Males
($/week)
Participation
rate (%)36
General
population
employment rate
(%)42
Imprisonment
lost income ($m)
<18 0.05 0 0% 0.0
18 0.12 349 53% 95.3% 1.1
19 0.20 349 53% 95.3% 1.8
20-25 1.56 782 84% 95.3% 51.0
Total 1.93 53.9
Counting the Cost
27
5.0 Disability Cost Category
5.1 Welfare Benefits
Welfare payments are often excluded in cost estimates from a societal perspective as they represent a transfer of
income rather than an opportunity cost of resources. However, from a more limited government economic perspective
transfer payments do have an opportunity cost and are of interest to the discussion of the impact of mental illness on
the Australian economy.
According to the ABS Survey of Mental Health and Wellbeing34, people with mental illness have significantly higher
disability rates and are entitled to receive disability welfare payments. The costs associated with disability welfare
payments due to mental illness were quantified by:
• Step 1: Determine the marginal number of disabled with mental illness
• Step 2: Categorise by disability severity
• Step 3: Apply relevant Centrelink welfare rates
Step 1: The marginal number of disabled was calculated by applying the difference in mentally ill and non-mentally ill
disability rates to the focus cohort (Table 22). This represents the additional number of disabled people in the focus.
Step 2: The marginal number of disabled in each disability severity category was then split into age bands reflecting
the eligibility criteria and payment rates published by Centrelink.
It was assumed all disability categories have the same age mix, with the rates applicable to severity levels extracted
from the Youth Disability Supplement (for claimants under 16), and the Disability Support Pension (for claimants 16 or
older).
34
5. Disability Cost Category
5.1 Welfare Benefits
Welfare payments are often excluded in cost estimates from a societal perspective since they
represent a transfer of income rather than an opportunity cost of resources. However, from a
more limited government economic perspective transfer payments do have an opportunity
cost and are of interest to the discussion of the impact of mental illness on the Australian
economy.
According to the ABS Survey of Mental Health70, people with mental illness have significantly
higher disability rates and are entitled to receive disability welfare payments. The costs
associated with disability welfare payments due to mental illness were quantified by:
Step 1: Determine the marginal number of disabled with mental illness
Step 2: Categorise by disability severity
Step 3: Apply relevant Centrelink welfare rates
Step 1: The marginal number of disabled was calculated by applying the difference in
mentally ill and non-mentally ill disability rates to the focus cohort (Table 22). This represents
the additional number of disabled people in the focus.
Table 22: Male disability rates by mental illness status
% of Males34 Number in focus cohort
Mental illness status All males Mentally ill
rate
Non-mentally
ill rate Disabled
Marginal
number in
cohort
(000)
Profound/Severe 2.4% 5.1% 1.8% 25.3 16.1
Moderate/Mild 6.1% 9.6% 5.4% 47.6 20.8
Schooling/Employment
restriction only
5.6% 13.4% 3.9% 66.4 46.8
No disability/No specific
limitations or restrictions
85.8% 71.9% 88.8% 356.5
Total 495.7 83.7
Step 2: The marginal number of disabled in each disability severity category was then split
into age bands reflecting the eligibility criteria and payment rates published by Centrelink.
It was assumed all disability categories have the same age mix, with the rates applicable to
severity levels extracted from the Youth Disability Supplement (for claimants under 16), and
the Disability Support Pension (for claimants 16 or older).
Not for reproduction or public release
Table 23: Marginal number of disabled by age
Marginal number disabled
(‘000)
Age range
Number focus
cohort
(‘000) Profound/Severe Moderate/Mild Schooling/Employment
restriction only
under 16 82.4 2.7 3.5 7.8
16 to 18 88.7 2.9 3.7 8.4
18-20 136.0 4.4 5.7 12.8
>20 188.6 6.1 7.9 17.8
Total 495.7 16.1 20.8 46.8
Step 3: Centrelink disability payment rates were applied to the marginal number of disabled.
Based on actual expenditure on the disability support pension to working age claimants, the
following parameters were chosen:
The profoundly disabled qualify for Centrelink’s maximum rate
The moderately disabled qualifies for 40% of the rate
Counting the Cost
28
Step 3: Centrelink disability payment rates were applied to the marginal number of disabled. Based on actual
expenditure on the disability support pension to working age claimants, the following parameters were chosen:
• The profoundly disabled qualify for Centrelink’s maximum rate
• The moderately disabled qualifies for 40% of the rate
• The schooling/employment restriction only category qualifies for 12.5% of the rate
Result
The total cost of disability welfare payments is $372.5m per annum.
G Centrelink maximum rates, averaged between the at home and independent rates
35
18-20 136.0 4.4 5.7 12.8
>20 188.6 6.1 7.9 17.8
Total 495.7 16.1 20.8 46.8
Step 3: Centrelink disability payment rates were applied to the marginal number of disabled.
Based on actual expenditure on the disability support pension to working age claimants, the
following parameters were chosen:
The profoundly disabled qualify for Centrelink’s maximum rate
The moderately disabled qualifies for 40% of the rate
The schooling/employment restriction only category qualifies for 12.5% of the rate
Table 24: Centrelink Disability Support Pension and Youth Disability Supplement rates (2011)
Maximum rate per year ($)
Age range Maximum rate
($/fortnight)g Profound/Severe Moderate/Mild Schooling/Employment
restriction only
under 16 114 2,964 1,186 371
16 to 18 411 10,678 4,271 1,335
18-20 432 11,239 4,495 1,405
>20 689 17,914 7,166 2,239
Result
The total cost of disability welfare payments is $398.1m per annum.
Table 25: Disability welfare payments
Age range ($m) ($m) ($m) annual cost ($m) Profound/Severe Moderate/Mild Schooling/Employment
restriction only
Total
under 16 7.9 4.1 2.9 14.9
16 to 18 30.8 15.9 11.2 57.9
18-20 49.7 25.6 18.0 93.4
>20 109.9 56.6 39.9 206.4
Total 372.5
10 Centrelink maximum rates, averaged between the at home and independent rates
35
>20 188.6 6.1 7.9 17.8
Total 495.7 16.1 20.8 46.8
Step 3: Centrelink disability payment rates were applied to the marginal number of disabled.
Based on actual expenditure on the disability support pension to working age claimants, the
following parameters were chosen:
The profoundly disabled qualify for Centrelink’s maximum rate
The moderately disabled qualifies for 40% of the rate
The schooling/employment restriction only category qualifies for 12.5% of the rate
Table 24: Centrelink Disability Support Pension and Youth Disability Supplement rates (2011)
Maximum rate per year ($)
Age range Maximum rate
($/fortnight)g Profound/Severe Moderate/Mild Schooling/Employment
restriction only
under 16 114 2,964 1,186 371
16 to 18 411 10,678 4,271 1,335
18-20 432 11,239 4,495 1,405
>20 689 17,914 7,166 2,239
Result
total cost of disability welfare payments is $398.1m per annum.
Table 25: Disability welfare payments
Age range ($m) ($m) ($m) annual cost ($m) Profound/Severe Moderate/Mild Schooling/Employment
restriction only
Total
under 16 7.9 4.1 2.9 14.9
16 to 18 30.8 15.9 11.2 57.9
18-20 49.7 25.6 18.0 93.4
>20 109.9 56.6 39.9 206.4
Total 372.5
10 Centrelink maximum rates, averaged between the at home and independent rates
Counting the Cost
29
6.0 Mortality Cost Category
A major aspect of the human capital approach is the lifetime stream of costs attributable to premature mortality,
normally presented as the stream of income.
In addition, there are also potential cost-offsets associated with premature mortality, such as future health care costs
avoided. These costs were not included in the model.
The Access Economics study23 reported that mortality rates in young men with mental illness were significantly higher
than those without mental illness. The average cost per death was calculated by taking the net present value of all
future earnings from the age at death to the retirement age (65) and offset this by pension costs.
The net present value approach is a process where future cash flows are discounted to the current time to account for
the time value of money. The net present value has been converted to an annualised cost.
The following assumptions were made:
• General population male average weekly earnings by age were averaged to derive earnings for each 5 year age
band
• For each age group (12-14, 15-19, 20-25), average age at death was the midpoint of the age band
• Current life expectancy is 80 years33
Result
This cost was applied to the number of people in the focus cohort that is expected to die annually due to mental illness
related mortality, as summarised in Table 26.
36
A major aspect of the human capital approach is the lifetime stream of costs attributable to
premature mortality, normally presented as the stream of income.
In addition, there are also potential cost-offsets associated with premature mortality, such as
future health care costs avoided. These costs however were not included in the model.
The Access Economics study72 reported that mortality rates in young men with mental illness
were significantly higher than those without mental illness. The average cost per death was
calculated by taking the net present value of all future earnings from the age at death to the
retirement age (65), and offset this by pension costs.
The net present value approach is a process where future cash flows are discounted to the
current time to account for the time value of money. The net present value has been
converted to an annualised 2011 cost.
The following assumptions were made:
General population male average weekly earnings by age were averaged to derive
earnings for each 5 year age band
For each age group (12-14, 15-19, 20-25), average age at death was the midpoint of the
age band
Current life expectancy is 80 years, using 2010 ABS Life Tables
Result
This cost was applied to the number of people in the focus cohort that is expected to die
annually due to mental illness related mortality, as summarised in Table 26.
Table 26: Mortality cost
Age range
Focus cohort
(‘000)
Mortality rate
due to mental
illness23 Marginal deaths
Average
cost/death ($m)
Annual mortality
cost ($m)
12-14 38.1 0.01% 4 2.6 10.0
15-19 222.0 0.08% 178 2.7 482.1
20-25 235.6 0.09% 212 2.7 564.6
Total 495.7 393 1,056.7
Counting the Cost
30
Findings and
Conclusions
Counting the Cost
31
Summary of Findings
The results of our modelling and analysis estimate the
cost of young men’s mental illness in Australia to be
$3.27 billion per year.
Table 27 summarises the costs for each cost category.
The costs identified in the model were allocated by cost bearer to better understand how they are spread across the
community. The study found three bearers of cost - individuals, employers and government.
It is important to note that both costs and impacts radiate beyond the primary cost bearer. For example, the impact
of lower levels of education attainment is experienced directly by individuals through reduced earnings and also by
employers through a corresponding reduction in the skilled labour force.
Not for reproduction or public release
37
Findings and Conclusions
Summary of Findings
The results of our modelling and analysis estimate the cost of young men’s mental illness in
Australia, to be $3.27 billion per year. Table 27 summarises the costs for each cost category.
Table 27: Estimated cost of mental illness in 12 to 25 year old Australian males
Cost category Total cost by
category
($m)
Sub category Annual cost by
sub-category ($m)
Health Health costs 556 556
Employment Personal leave 237 796
Reduced personal income 445
Reduced education 114
Unemployment Lost income 168 230
Welfare benefits 62
Imprisonment Direct cost 207 261
Lost income 54
Disability Welfare benefits 373 373
Mortality Mortality 1,057 1,057
Total 3,271
The costs identified in the model were allocated by cost bearer to better understand how they
are spread across the community. The study found three bearers of cost - individuals,
employers and government.
It is important to note that both costs and impacts radiate beyond the primary cost bearer. For
example, the impact of lower levels of education attainment is experienced directly by
individuals through reduced earnings, and also by employers through a corresponding
reduction in the skilled labour force.
Cost and Impact: Individuals
Our analysis found that individuals bear costs of mental illness of $2.017 billion per annum.
Young men bear the cost of factors associated with health, reduced productivity and
education, lost income and mortality.
Health
The total direct health cost per year is $556 million, of which $179 million is incurred by
individuals
Employment
Young men with mental illness have on average 4.7% lower hourly wages relative to
their peers with same level of educational attainment74. The cost to individuals in
reduced personal income due to lower wages is $445 million per annum
Counting the Cost
32
Cost and Impact: Individuals
Our analysis found that individuals bear costs of mental illness of $2.016 billion per annum.
Young men bear the cost of factors associated with health, reduced productivity and education, lost income and
mortality.
Health
• The total direct health cost per year is $556 million, of which $179 million is incurred by individuals
Employment
• Young men with mental illness have on average 4.7% lower hourly wages relative to their peers with the same
level of educational attainment45. The cost to individuals in reduced personal income due to lower wages is $445
million per annum
• 48.1% of young men within the cohort have no qualifications beyond high school. The cost to individuals in
reduced personal income due to lower wages is $114 million per annum
• Young people with mental illness have lower levels of educational qualifications and when they do gain
employment tend to obtain lower skilled poorly paid roles
Unemployment
• Young men with a mental illness are 1.6 times more likely to be unemployed relative to a person who does not
have a mental illness
• Lost income in young men with mental illness who are actively looking for work but unemployed is $168 million
per annum
Imprisonment
• The ABS National Survey of Mental Health and Wellbeing reports that 5% of all mentally ill people have ever
been incarcerated in their lifetime, relative to 1.8% of the non-mentally ill. This reflects a relativity of 2.8 times the
prevalence of non-mentally ill young men
• Lost income in young men with mental illness who are imprisoned is $54 million per annum
Disability
• The literature shows there are wider indirect costs to individuals with mental illness and their families such as
carers’ costs, psycho social costs such as stress, pain and suffering and other indirect costs such as reduced
income for carers. These costs have not been quantified in this model
Mortality
• Mortality rates are significantly higher for young men with mental illness compared to young men who do not
have mental illness
• Loss of lifetime earnings in young men due to mental illness related mortality – including from death by suicide –
is $1.057 billion per annum
Counting the Cost
33
Cost and Impact: Employers
Our analysis found that employers bear direct costs of mental illness of $237 million per annum. This is primarily due
to the costs associated with additional personal leave taken by the cohort.
There are, however, impacts from other cost categories that have an indirect impact on employer productivity.
Health
• Work that is both stressful and insecure can increase the risk of depression up to 14 times relative to jobs in
which individuals feel a sense of control and are securely employed
• The negative impact that poor mental health has on the individual may extend to co-workers who may
experience increased stress through having to carry out additional work tasks
Employment
• Young men with mental illness take an additional 9.5 days out of role per year over and above people without
mental illness. This equates to a loss of over 9 million working days due to mental illness across Australia per
year
• The marginal cost to employers due to additional days out of role is $237 million per annum
Counting the Cost
34
Cost and Impact: Government
Our analysis found that government bear costs of mental illness of $1.019 billion per annum.
Government bear the cost associated with health, welfare (unemployment and disability pensions) and imprisonment.
Health
• The total direct health cost per year is $556 million, of which $377 million is incurred by government
• Government spend on mental health increases significantly from 15-25 years ($205m) to 25-34 years ($306m)
and again for 35-44 years ($268m), before declining until the 75+ group
Unemployment
• Young men with a mental illness are 1.6 times more likely to be unemployed relative to a person who does not
have a mental illness
• Marginal unemployment payments disbursed to young men with a mental illness cost the government $62 million
per annum
• This is an opportunity cost to government
Imprisonment
• The government incurs $207 million per annum in direct costs related to the higher rates of imprisonment
experienced by young men with a mental illness
• The health costs of caring for mentally ill prisoners is not included in this study due to lack of data
Disability
• Disability welfare payments paid to young men who experience poor mental health cost the government $373
million per annum
Mortality
• Potential cost offsets to government associated with premature mortality (such as future health costs avoided)
were not included in the model
Counting the Cost
35
We have identified the cost to Australia of young
men’s mental illness to be $3.27 billion per annum. We
have brought together research that links this cost to
the human impacts on young men through reduced
employment opportunities when in work, higher risk of
unemployment, higher levels of imprisonment and early
mortality.
These findings represent the economic impact of the complex interplay of the challenges that young men with mental
illness face, illustrating the link between good mental health and national productivity. This cost is being felt throughout
the Australian economy.
Education is a significant contributing factor to the economic cost of mental illness. The improvement of education
attainment levels would play a major role in delivering better employment opportunities for young men with mental
illness, with subsequent improvements in productivity.
The complex interplay between cost bearers is not solely the remit of government to solve. Interconnected problems
require interconnected solutions with coordinated effort across educators, government, mental health service
providers, NGO’s, employers and business groups.
This study has highlighted the opportunity at stake in young men’s mental health. In Australia, spend on men’s
mental health increases significantly as the cohort ages. 75% of onset of mental illness occurs prior to the age of 25.
Australian research shows interventions focused on the ages of 12-25 years have the potential for greater personal,
social and economic benefit23.
Deepening our understanding of the efficacy and return on investment of current policy responses and programs in
mental health is critical to driving targeted investment. Our findings suggest that investing smarter and earlier in young
men has the potential to reduce the cost and impacts on individuals and the Australian economy identified in this
report.
Failure to act presents a threat to Australia’s future productivity and individual prosperity. A coordinated response from
all sectors of the community holds the promise of considerable economic and individual benefits.
Conclusions
Counting the Cost
36
Key Conclusion 1: Education plays a
significant role in the employment outcomes of
young men with mental illness.
Research shows that education and training opportunities can act as a protective factor against mental health issues17,
whilst secure and good employment outcomes provide young people with the possibility of financial independence, a
sense of control, self-confidence and social contact.
Education plays a significant role in the employment outcomes of young men who experience mental illness. In
Australia, individuals who have a degree or a higher qualification earn wages 30 to 45% higher than people with
otherwise similar characteristics who have not completed Year 12. A university education increases men’s wages by
approximately 38% and also increases the probability of employment by 15-20%. Education levels also influence the
types of employment men are able to obtain.
Of particular significance, mental illness typically begins in adolescence/early adulthood - a time when individuals
are completing their education and pursuing employment options22. The impact of youth mental illness on schooling
through factors such as increased absenteeism, dropout rates and difficulty learning can compound the potential
negative impacts on employment outcomes23.
The impact of reduced education is very real for young men with mental illness, earning 4.7% lower hourly wages
compared to their peers, and almost half do not have a qualification beyond high school. As a consequence, young
men with mental illness are often employed in lower skilled, poorly paid roles.
Higher education is positively linked to wages and productivity. Higher wages in turn also have an impact on health
and education through providing the resources to access educational and health services24.
Recommendation 1. Efforts should be made by all sectors of the community to
support the engagement of young men to achieve higher levels of education:
• 1.1 Improve secondary, tertiary and vocational educators’ levels of understanding of mental health, including
the identification of disorders and awareness of support and referral services available. This should include
professional development and tools for teachers and other educators
• 1.2 Increase awareness and access for young men to educational alternatives such as apprenticeships
• 1.3 Strengthen cross sector partnerships between employers and education providers to create stronger
pathways from school to work for young men with mental illness. This should include focus on key transition
points such as moving from school to further studies or employment
Counting the Cost
37
Key Conclusion 2: Employers bear a significant
impact in direct costs of absenteeism and
reduced productivity. Employers and business
groups are crucial stakeholders
All indications show Australia will continue to face productivity challenges into the future, with an ageing population in
particular expected to place increased pressure on Australia’s labour supply. The ‘Australia to 2050: future challenges’
report highlights the need to improve labour participation rates, suggesting that ‘policy responses need to reflect a
sound understanding of the complex nature of mature age participation.’
The report goes on to acknowledge the importance of policies that target improvements in education and health –
factors which are also crucial to the workforce participation of the 496,000 young men experiencing mental illness.
For men who are suffering from poor mental health in particular, research shows that treating or preventing mental
illness can potentially improve their chances of participating in the workforce by up to 30%46.
Addressing poor mental health in the workplace environment has the direct benefit of the avoiding costs of
absenteeism and also has the potential to reduce flow-on effects to co-workers by not having to carry additional worktasks.
Engaging employers and business groups in the development of and delivery of mental health initiatives will assist in
cultivating a larger, higher skilled and more productive Australian labour force.
Recommendation 2. Efforts should be made by all sectors of the community to
support young men with mental illness to engage in more productive employment:
• 2.1 Improve employers’ levels of understanding of mental health, including the identification of disorders and
awareness of support and referral services available
• 2.2 Initiate new partnership models between government, mental health service providers, NGOs, employers
and business groups to create strategies that proactively support employees’ good mental health and ongoing
engagement in the workforce
• 2.3 Identify new partnership models between employers, business groups, government and NGOs to drive a
whole of community response. This includes creating new collaborative funding and service delivery models
Counting the Cost
38
Key Conclusion 3: Deepening our
understanding of the efficacy and return on
investment of current policy responses and
programs in mental health is critical to driving
targeted investment
The cost impact identified in this report suggests that further analysis of current policy responses to young men’s
mental health be undertaken to determine the efficacy and impact of these interventions.
As our findings suggest, investing smarter and earlier in young men has the potential to reduce the mental health cost
and impacts on individuals and the Australian economy. Further research on return on investment for existing mental
health services targeted at young men is essential to inform investment decisions.
Smarter and targeted investments across the spectrum of mental health services will have the added benefit of
improving national productivity. By increasing the capacity of young men with mental illness to meaningfully participate
in work and community life the prosperity of the nation will be improved.
Recommendation 3. Efforts should be made by all sectors of the community to
evaluate the effectiveness of current policy responses and investments in mental health:
• 3.1 Undertake further targeted research to evaluate the efficacy of existing mental health programs and
interventions with a particular emphasis on prevention and early intervention
• 3.2 Undertake return on investment analysis to inform future investment in young men’s mental health with a
particular emphasis on prevention and early intervention
• 3.3 Enhance reporting of government funded initiatives targeted at supporting young men with mental illness to
achieve full benefits of investment. Key objectives of these enhancements are to drive greater accountability of
public spend and to provide better transparency and access to program performance and evaluation
The mental health of the young men
employed by Active is critical to the
success of our business. It is not
only an indicator of their capacity to
be productive employees, but also of
their ability to be part of a safe and
supportive work team.
Brendan Murphy, CEO, Active Tree Services
This report initiates a timely
conversation with business leaders,
highlighting the importance of mental
health for both employees and the
companies they work for.
Richard Murray, CFO, JB Hi Fi
Counting the Cost
40
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Counting the Cost
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Counting the Cost
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Counting the Cost
About Inspire
Tragically, the leading cause of death among young Australians (14-25) is suicide.
To tackle this issue the Inspire Foundation provides services which aim to improve young people’s mental health
and let them know that they don’t have to go through tough times alone. Our flagship service ReachOut.com increases
young people’s knowledge of mental health and wellbeing, increases their help seeking skills and ensures that they
feel less alone. We provide our services online because it offers young people anonymity; it offers help and
support 24 hours a day; it is accessible to young Australians in remote regions and it allows us to help thousands at
any one time.
We also recognise that although targeting young people is crucial to achieving our mission it is only one piece of a
‘whole of community’ approach. That’s why, as well as providing a world class mental health service for young people
through ReachOut.com, we also:
• Lead research on technology, young people and well-being;
• Support schools to foster resilience;
• Help deliver relevant, accessible and appropriate clinical services for young people; and
• Share our expertise within and across sectors through consultancies to help even more young people.
By 2020 we aim to make a global contribution to young people’s mental health and wellbeing with every young
Australian knowing, trusting and using ReachOut.com when they need to.
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About the Young and Well CRC
The Young and Well Cooperative Research Centre (youngandwellcrc.org.au) is an Australian-based, international
research centre that unites young people with researchers, practitioners, innovators and policy-makers from over 70
partner organisations. Together, we explore the role of technology in young people’s lives, and how it can be used
to improve the mental health and wellbeing of young people aged 12 to 25. The Young and Well CRC is established
under the Australian Government’s Cooperative Research Centres Program.
Counting the Cost: The
Impact of Young Men’s
Mental Health on the
Australian Economy