Chapter 10 A Microsimulation Model for Pharmacare: Development, Analysis and Policy Applications

Kapur, Vishnu; Gupta, Anil

doi:10.1016/s1571-0386(06)16010-x

Cited by 2 publications

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“…Such models were initially developed within the discipline of economics (Orcutt et al 1986) and have today become very widely used by governments across the developed world for analysis of the fine-grained distributional effect of possible changes in government programs (Harding 1996;Gupta and Kapur 2000;Mitton et al 2000;Harding and Gupta 2007;Zaidi et al 2010). While most such models still assess the likely winners and losers from reforms in income tax and cash transfer programs (Immervoll et al 2006;Lloyd 2007), the past decade has also seen the rapid expansion of microsimulation models into other subject areas, such as health, housing and aged care (Harding and Gupta 2007;Brown et al 2004;Kapur and Gupta 2007;Xiong et al 2007). However, importantly, the overwhelming majority of these microsimulation models have been national models, constructed on top of national sample survey microdata and predicting the distributional effect of policy change for an entire country, rather than for a small region within a country.…”

Section: Small-area Data and Spatial Microsimulationmentioning

confidence: 99%

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

2011

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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.

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