Demographic change and the needs-based planning of government services: projecting small area populations using spatial microsimulation

Changing patterns of longevity, fertility and migration in Australia have driven substantial changes in population age structure and household size and composition. Of the various dimensions of population change, population ageing is expected to present major challenges to the financing and sustainability of welfare state programs in industrialized countries. One key issue for many of these countries will be assessing where particular services will be required in the future. This paper outlines the application of new forecasting techniques that age a spatial microdataset to 2027. Two illustrative examples are provided to highlight the potential capacities of the new modelling approach for government service delivery planners. For many older people, ageing in place is important, but is more difficult when the person is single: and so the first illustrative application focuses on where aged single people will be living in 2027. The second application examines where future childcare places will be required given the projected growth in the number of children aged 3–4 years living in families where all parents are working. This information will be important for Government planners in deciding the best location for childcare places. The creation of synthetic small-area household microdata for future years offers great potential for a number of purposes, such as analysis of the likely future sociodemographic characteristics of individuals and families at the local level and assessment of the future geographic effect of alternative scenarios such as changes in labour force participation or fertility rates.     

Measuring Community Economic Resilience in Australia: Estimates of Recent Levels and Trends

We measure changes in community economic resilience (CER) across geo-locations in Australia between 2006 and 2011, a time span characterised by major natural and economic shocks. We build an index of potential CER that captures communities’ stocks of human, social, natural, physical and financial capitals, levels of economic diversity and accessibility to service centres. Using Census data and the ARIA index, we resort to principal component analysis to generate CER indexes at statistical area level 1, which is our community proxy. Our analysis of index values provides a number of useful insights. First, there was a statistically significant improvement over time in the overall CER index in all states and regions. Second, our CER measures improved at a different pace across regions and states while their rank remained mostly unchanged. Third, CER improved over time in social and physical capital and accessibility terms, but declined in human, natural, financial capital and diversity terms. Fourth, communities with a high economic diversity level reported higher capital stock except for natural capital, and communities with a low accessibility level had lower capital stock except for social and natural capital. Finally, CER has a long-term positive association with household income.     

Estimating the fiscal costs of long‐term jobless families in Australia

Despite substantial costs to society associated with the adverse consequences of joblessness, studies estimating actual costs are rare. This paper identifies the main costs to the Government of long‐term jobless families in Australia. The costs were split into three groups: immediate support costs, immediate opportunity costs, and indirect/intergenerational costs. Using a microsimulation model, the paper estimates the fiscal costs, which include immediate support costs such as the welfare system, and the immediate opportunity costs such as potential revenue from the tax system. We estimate that welfare payments and lost taxation revenue from long‐term joblessness cost the Government AU$5.55 billion per year.