ProductiveLifeMOD: A microsimulation model of the productivity and fiscal impacts of chronic illness and mortality in Australia

The recognition of health as an effective facilitator of productivity growth provides an important avenue for promoting good health and securing funding for health conditions with the greatest impact. Despite health being recognised as a critical policy lever for increasing productivity, there is no cohesive measure of the productivity impacts of health, nor clarity on which conditions result in the greatest productivity loss. While several studies have analysed the impact of health on labour force participation and income, they have used different methods; reported outcomes for a single disease; or combined all health conditions together, making it difficult to rank conditions based on the productivity loss. There has also been relatively little work extending the financial costs of lost productivity to increased welfare dependence or reduced income taxation, or examining the impacts of mortality. We have developed a new Australian microsimulation model, ProductiveLifeMOD, to quantify the national productivity impacts of chronic illness and mortality and the associated financial costs of productivity loss to individuals, families and government. This model integrates morbidity, mortality, fiscal impacts, and productivity effects across the full working-age population.

ProductiveLifeMOD is an extension of our microsimulation model, Health&WealthMOD, the first Australian microsimulation model of the economic impacts of early retirement due to chronic illness, which focused on the impacts on mature-aged workers aged 45 to 64 years. The new model analyses impacts across the whole working-age population, as there is growing evidence that health is also a major factor limiting labour force participation among younger people of workforce age, although the relevant health conditions may differ from those affecting mature-aged workers. The model analyses the productivity impacts of both morbidity and mortality together, as well as the related financial costs to individuals, families and government.

The base population of ProductiveLifeMOD is built from three Surveys of Disability, Ageing and Carers (SDAC) conducted in 2012, 2015 and 2018. These are nationally representative cross-sectional household surveys conducted by the Australian Bureau of Statistics (ABS) and provide detailed data on socio-demographic characteristics, chronic illness, disability and labour force participation. Mortality estimates are obtained from the Person Level Integrated Data Asset (PLIDA), a linked administrative dataset of the Australian population developed by the ABS. The three SDAC datasets are statically aged to form the base population. Statistical matching of the base data with STINMOD output is undertaken using a set of matching variables to create a synthetic dataset for modelling. This paper describes the development of ProductiveLifeMOD and its application for simulating policy impacts.

ProductiveLifeMOD enables direct comparisons of productivity losses and associated financial costs across specific chronic conditions. The model will provide a mechanism to determine where investment in health would generate the greatest productivity gains and economic benefits.