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Health, Pensions and retirement

Research articles · Aug 31, 2014

Health&WealthMOD2030: A microsimulation model of the long term economic impacts of disease leading to premature retirements of Australians aged 45–64 years old

Deborah Schofield, Rupendra Shrestha, Simon Kelly, Lennert Veerman, Robert Tanton, Megan Passey, Theo Vos, Michelle Cunich, Emily Callander

NHMRC Clinical Trials Centre, Sydney Medical School, Australia; National Centre for Social and Economic Modelling, The University of Canberra, Australia; School of Population Health, Faulty of Medicine and Biomedical Sciences, The University of Queensland, Australia; University Centre for Rural Health, North Coast, School of Public Health, Sydney Medical School, The University of Sydney PO Box 3074, Lismore, NSW 2480, Australia

Volume 7(2) Autumn 2014

Cite as: D. Schofield, R. Shrestha, S. Kelly, L. Veerman, R. Tanton, M. Passey, T. Vos, M. Cunich, E. Callander; 2014; Health&WealthMOD2030: A microsimulation model of the long term economic impacts of disease leading to premature retirements of Australians aged 45–64 years old; International Journal of Microsimulation; 7(2); 94–118. doi: 10.34196/ijm.00101

Abstract

Policymakers in Australia, like in most OECD countries, have recognised the importance of early retirement due to ill health on individuals and families, as well as on the budget balance when planning for the health needs of an ageing population. In order to understand these effects, a unique microsimulation model, called Health&WealthMOD2030, was built to estimate the impacts of early retirement due to ill health on labour force participation, personal and household income, economic hardship (poverty), and government taxation revenue, spending and GDP in the years 2010, 2015, 2020, 2025 and 2030. This paper describes the construction of Health&WealthMOD2030. The model captures the long term projections of demographic change, changing labour force participation patterns, real wages growth and trends in major illnesses affecting the older working age population. The base population of Health&WealthMOD2030 are the individuals aged 45–64 years with information on their work force status and health from the Australian Bureau of Statistics’ Surveys of Disability, Ageing and Carers (SDAC) 2003 and 2009. Projected estimates of income, taxation, income support payments, savings and superannuation from the National Centre for Social and Economic Modelling (NATSEM’s) dynamic microsimulation model Australian Population and Policy Simulation Model (APPSIM) were synthetically matched with the base population. Health&WealthMOD2030 project forward the economic impacts of early retirement from ill health to 2030. This will fill substantial gaps in the current Australian evidence of health conditions that will keep older working age Australians out of the labour market over the longterm.

This article contains 3 figures and 4 tables.

Figures

Figure 1. Health&WealthMOD2030.
Figure 2. Prevalence of chronic conditions by five year age group and sex, indexed from 2010.
Figure 3. Example of synthetic matching for two cells in Health&WealthMOD2030.

Tables

Table 1. Prevalence of chronic conditions amongst SDAC respondents in 2003 and 2009.
Prevalence of chronic conditions, 2003 SDAC
Sex Age group Ischaemic heart disease Stroke Type 2 diabetes COPD Cancer
Male 45–49 1.7% 0.5% 3.5% 0.6% 0.7%
50–54 1.5% 1.2% 5.5% 0.3% 0.7%
55–59 5.7% 1.7% 7.1% 1.0% 1.5%
60–64 9.3% 4.1% 11.0% 2.2% 3.0%
45–49 0.8% 0.6% 2.5% 0.5% 1.3%
Female 50–54 1.3% 0.8% 3.3% 0.6% 1.6%
55–59 2.6% 2.4% 4.8% 0.9% 2.3%
60–64 4.9% 1.9% 8.2% 2.7% 2.1%
Prevalence of chronic conditions, 2009 SDAC
Ischaemic heart disease Stroke Type 2 diabetes COPD Cancer
Male 45–49 1.4% 0.6% 3.0% 0.6% 0.7%
50–54 3.0% 1.5% 4.7% 0.5% 0.7%
55–59 4.7% 2.2% 9.0% 1.0% 1.9%
60–64 7.7% 3.3% 10.0% 2.0% 3.0%
Female 45–49 0.6% 0.6% 2.1% 0.3% 1.2%
50–54 0.6% 1.4% 4.8% 0.9% 1.8%
55–59 2.2% 2.1% 6.6% 1.3% 2.2%
60–64 3.2% 2.7% 8.1% 1.8% 2.6%
Table 2. Synthetic matching variables and their categories in Health&WealthMOD2030.
Group Matching variables Categories
Labour force status • Employed full-time
• Employed part-time
• Unemployed looking for work
• Not in the labour force
Income unit type a. Married couple with dependents
b. Married couple only
c. One parent with dependents
d. One person
Group A Income quintile a. 1st quintile
b. 2nd quintile
c. 3rd quintile
d. 4th quintile
e. 5th quintile
Receiving age pension a. Yes
b. no
Receiving disability support pension a. Yes
b. no
Sex a. male
b. female
Age group a. 45–49 years
b. 50–54 years
c. 55–59 years
d. 60–64 years
Group B Hours worked per week a. not applicable
b. 1–15 hours
c. 16–24 hours
d. 25–34 hours
e. 35–40 hours
f. +41 hours
Highest level of educational qualification a. university
b. non-university
Home ownership a. yes
b. no
Table 3. Degree of exact synthetic matching of SDAC and APPSIM unit records.
No. of synthetic matching variables that have the categories exactly matched Year 2010 Year 2020 Year 2030
No. of SDAC unit records % No. of SDAC unit records % No. of SDAC unit records %
10 23,356 93.04 23,042 91.79 22,854 91.04
9 24,682 98.32 24,650 98.19 24,777 98.7
8 25,052 99.79 25,061 99.83 25,060 99.83
7 25,102 99.99 25,096 99.97 25,098 99.98
6 25,104 100 25,102 99.99 25,103 100
5 25,104 100 25,104 100 25,104 100
Total unit records 25,104 25,104 25,104
Table 4. Number of SDAC unit records that matched with APPSIM unit records of different categories of synthetic matching variables due to matching with a close neighbour cell.
Synthetic matching variables Year 2010 Year 2020 Year 2030
No. of SDAC unit records matched with APPSIM unit records of different category % No. of SDAC unit records matched with APPSIM unit records of different category % No. of SDAC unit records matched with APPSIM unit records of different category %
Sex 325 1.3% 140 0.6% 211 0.8%
Income unit type 0 0% 0 0% 0 0%
Income quintiles 487 1.9% 629 2.5% 625 2.5%
Receiving age 1 0.0% 494 2% 494 2%
pension
Receiving 1 0.0% 21 0.1% 0 0%
disability support
pension
Age group 281 1.1% 477 1.9% 586 2.3%
Labour force 0 0% 0 0% 0 0%
status
Hours worked per 747 3% 585 2.3% 420 1.7%
week
Highest level of 121 0.5% 35 0.1% 18 0.1%
education
Home ownership 251 1.0% 188 0.7% 274 1.1%