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Please carefully consider your needs when reading the following information about suicide and self-harm. If this material raises concerns for you contact Lifeline on 13 11 14, or see other ways you can seek help.
The information included here places an emphasis on data, and as such, can appear to depersonalise the pain and loss behind the statistics. The AIHW acknowledges the individuals, families and communities affected by suicide each year in Australia.
Aboriginal and Torres Strait Islander readers are advised that information relating to Indigenous suicide and self-harm is included.
The AIHW supports the use of the Mindframe guidelines on responsible, accurate and safe suicide and self-harm reporting. Please consider these guidelines when reporting on statistics on the monitoring of suicide and self-harm.
There is growing evidence that social factors, including education, employment status, income level and wealth have a marked influence on people and generations living, working and contributing to society (WHO, 2020).
It is important to remember that the presence of one or more of these risk factors cannot predict or explain suicide or intentional self-harm as each person’s experience is unique. Experiencing any of these risk factors does not necessarily mean a person has—or ever will—attempt suicide, but establishing whether a person has any of these risk factors can help determine whether they are at increased risk. Also, some people will have suicidal thoughts without having a history of any risk factors.
The purpose of this analysis by the AIHW was to determine the relationship between education and suicide risk in Australia, in light of rising suicide rates and socioeconomic differentials in mortality reported in the US (Case & Deaton, 2015, 2017, 2020; Phillips & Hempstead, 2017) and other countries (e.g. Sweden; Socialstyrelsen National Board of Health and Welfare, 2017). Given the strong association between education and employment opportunity, particularly during prime working age (ages 25–54 years) this analysis explores both these factors, illustrating the utility of integrated data sets, Census 2011 and the Australian Bureau of Statistics (ABS) Causes of death data set, to investigate contextual factors associated with suicide risk. The information on educational attainment and employment are as reported at the time of Census 2011; death by suicide is reported between 2011 and 2017. For details of the methodology see Technical notes. The findings on educational attainment and employment highlight the importance of social determinants in suicide risk, with important prevention implications.
The estimated suicide risk (measured as the age-adjusted cumulative incidence (risk) from 2011–2017) is higher among those with fewer years of education, as reported at Census 2011.
Among males with only secondary school or no education:
cumulative suicide risk is 2.6 times higher than among males with a university degree (Table 1).
The education gradient in female suicide mortality was consistent with that seen for males, but the ratio is smaller (1.6 times) between the highest and lowest levels of educational attainment. These estimates are the first for Australia, and like other countries, show a strong relationship between educational attainment and the risk of suicide.
The estimated suicide risk is higher among males than females at all levels of educational attainment.
The gap is smallest for those with a university degree with the suicide risk for males 2.2 times higher than females.
Estimated suicide risk, by highest educational attainment and labour force status, by sex, aged 25–54 years, Australia, 2011 to 2017.
The vertical bar chart shows age-adjusted cumulative risk of suicide for males and females by highest level of educational attainment (secondary school or lower; diploma, certificate; and bachelor degree or higher). Users can also choose to view age-adjusted cumulative risk or proportion by labour force status. The lowest age-adjusted cumulative suicide risk among males was in those with a bachelor degree or higher while the highest age-adjusted cumulative suicide risk was seen in those with secondary school or lower as their highest level of educational attainment.
Estimated suicide risk (measured as the age-adjusted cumulative incidence from 2011–2017) is lower among those with a job, as reported at Census 2011.
Among males of prime working age (25–54 years) who were not in the labour force (people who are neither working nor looking for work):
For males who were not in the labour force the cumulative suicide risk was actually a little higher (rate ratio of 1.3) than for males who were unemployed at the time of the 2011 Census. This reminds us that in thinking about the relationship between labour force status and suicide, it is important to focus on people of workforce age who are not employed, regardless of whether they are classified as being unemployed.
Among females, employment is also associated with the lowest suicide risk but did not vary greatly between those not in the labour force and those unemployed.
The cumulative suicide risk for females not in the labour force was:
Educational attainment or labour force status
Year 12 and below : Bachelor degree and higher
Unemployed : employed
Not in labour force : employed
Not in labour force : unemployed
A social gradient describes a spectrum from high to low socioeconomic position and shows that, in general, the lower an individual’s socioeconomic position the worse their health (WHO, 2020). While a social gradient was evident in both male and female employment circumstances, the estimated suicide risk is considerably higher for males than females across all 3 labour force categories (Table 2).
Among males not in the labour force:
Males : Females
Year 12 and below
Diploma or Certificate
Bachelor degree and higher
Not in labour force
Future updates to Suicide & self-harm monitoring will include analysis where differences in the circumstances and characteristics of suicide deaths, including educational attainment and labour force status, will be modelled to better identify protective and risk factors.
Monitoring and analysing suicide risk by education and employment status can be very informative. Sweden regularly publishes data on suicide rates by level of education and Case and Deaton (2015, 2017, 2020) have shown that rises in suicide rates in the US are being driven by rises among people with a high school or lower level of education. That said, care is required in drawing causal inferences from the data. Education and employment are clearly associated; for example—adults of working age with a degree or higher level of education are considerably more likely to be employed than those with a high school or lower level of education. This means that some of the apparent association between education and suicide risk is explained by the association between education and employment status. These associations will be drawn out in data modelling.
Blakely et al. (2003) found similar associations between the risk of death by suicide by labour force status for New Zealand using linked data. However, they argue that while being unemployed was associated with a 2- to 3-fold increased relative risk of death by suicide compared with being unemployed, around half of this association might be explained by confounding mental illness.
Addressing socioeconomic inequalities in mortality within countries is a key public health priority globally (WHO 2008). Analyses of education inequalities in Australia in both chronic disease mortality (AIHW 2019) and all-cause mortality (Korda et al. 2019) reveal a clear gradient across differing levels of education with the probability of dying in 2011–12 decreasing as education levels increase. Quantifying inequalities for specific mortality causes will provide a broader understanding of the experience of population groups, the relationships between health and welfare, and insights into underlying reasons for these inequalities.
Australian Institute of Health and Welfare (AIHW) 2019. Indicators of socioeconomic inequalities in cardiovascular disease, diabetes and chronic kidney disease. Cat. no. CDK 12. Canberra: AIHW.
Blakely TA, Collings SCD, and Atkinson J 2003. Unemployment and Suicide. Evidence for a causal association. Journal of Epidemiology and Community Health, 57 (8): 594–600.
Case A & Deaton A 2015. Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. PNAS. 112(49):15078–15083.
Case A & Deaton A 2017. Mortality and morbidity in the 21st century. Brookings Papers on Economic Activity 397.
Case A & Deaton A 2020. Deaths of Despair and the Future of Capitalism. Princeton: Princeton University Press.
Korda RJ, Biddle N, Lynch J, Eynstone-Hinkins J, Soga K, Banks E, Priest N, Moon L, Blakely T 2020. Education inequalities in adult all-cause mortality: first national data for Australia using linked census and mortality data. International Journal of Epidemiology. 49(2): 511–518. https://doi.org/10.1093/ije/dyz191
Phillips JA, Hempstead K 2017. Differences in U.S. Suicide Rates by Educational Attainment, 2000-2014. Am J Prev Med. 2017;53(4): e123–e130. doi:10.1016/j.amepre.2017.04.010
Socialstyrelsen National Board of Health and Welfare. 2017. Regional comparisons 2016; Six questions about Swedish healthcare. Viewed 28–07–2020.
World Health Organisation (WHO) 2008. Closing the Gap in a Generation: Health Equity through Action on the Social Determinants of Health. Geneva: The World Health Organization and Commission on Social Determinants of Health.
WHO 2020. Social determinants of health. Viewed 28–07–2020.
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