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Social Modelling and Public Policy: Application of Microsimulation Modelling in Australia

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Abstract

This paper provides an overview of social modelling and in particular a general introduction to and insight into the potential role and usefulness of micro-simulation in contributing to public policy. Despite having made a major contribution to the development of tax and cash transfer policies, there are many important areas of government policy to which microsimulation has not yet been applied or only slow progress has been made. The paper starts with a brief review of some of the main distinguishing characteristics of social models. This provides a contextual background to the main discussion on recent microsimulation modelling developments at the National Centre for Social and Economic Modelling (NATSEM) in Canberra, Australia, and how these models are being used to inform social and economic policy in Australia. Examples include: NATSEM’s static tax and cash transfer model (STINMOD); modelling the Australian Pharmaceutical Benefits Scheme; application of dynamic modelling for assessing future superannuation and retirement incomes; and the development of a regional microsimulation model (SYNAGI). Various technical aspects of the modelling are highlighted in order to illustrate how these types of socio-economic models are constructed and implemented. The key to effective social modelling is to recognise what type of model is required for a given task and to build a model that will meet the purposes for which it is intended. The potential of microsimulation models in the social security, welfare and health fields is very significant. However, it is important to recognise that policy decisions are going to involve value judgements - policies are created and implemented within a political environment. The aim is for social modelling, and in particular policy simulations, to contribute to a more rational analysis and informed debate. In this context, microsimulation models can make a significant contribution to the evaluation and implementation of ‘just and fair’ public policy.

Suggested Citation

  • Laurie Brown & Ann Harding, 2002. "Social Modelling and Public Policy: Application of Microsimulation Modelling in Australia," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 5(4), pages 1-6.
    • Handle: RePEc:jas:jasssj:2002-33-1
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    Citations

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    Cited by:

    1. Linping Xiong & Xiuqiang Ma, 2007. "Forecasting China's Medical Insurance Policy for Urban Employees Using a Microsimulation Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(1), pages 1-8.
    2. Terance J. Rephann & Einar Holm, 2004. "Economic-Demographic Effects of Immigration: Results from a Dynamic Spatial Microsimulation Model," International Regional Science Review, , vol. 27(4), pages 379-410, October.
    3. Sharyn Lymer & Laurie Brown & Ann Harding & Mandy Yap, 2009. "Predicting the need for aged care services at the small area level: the CAREMOD spatial microsimulation model," International Journal of Microsimulation, International Microsimulation Association, vol. 2(2), pages 27-42.
    4. Annie Abello & Sharyn Lymer & Laurie Brown & Ann Harding & Ben Phillips, 2008. "Enhancing the Australian National Health Survey Data for Use in a Microsimulation Model of Pharmaceutical Drug Usage and Cost," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(3), pages 1-2.
    5. Davis, Peter & Lay-Yee, Roy & Pearson, Janet, 2010. "Using micro-simulation to create a synthesised data set and test policy options: The case of health service effects under demographic ageing," Health Policy, Elsevier, vol. 97(2-3), pages 267-274, October.
    6. Templ, Matthias & Meindl, Bernhard & Kowarik, Alexander & Dupriez, Olivier, 2017. "Simulation of Synthetic Complex Data: The R Package simPop," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i10).
    7. M. Esteban Muñoz H. & Ivan Dochev & Hannes Seller & Irene Peters, 2016. "Constructing a Synthetic City for Estimating Spatially Disaggregated Heat Demand," International Journal of Microsimulation, International Microsimulation Association, vol. 9(3), pages 66-88.
    8. Mueller, Michel G. & de Haan, Peter, 2009. "How much do incentives affect car purchase? Agent-based microsimulation of consumer choice of new cars--Part I: Model structure, simulation of bounded rationality, and model validation," Energy Policy, Elsevier, vol. 37(3), pages 1072-1082, March.
    9. Rupendra N Shrestha & Deborah Schofield & Melanie J B Zeppel & Michelle M Cunich & Robert Tanton & Simon J Kelly & Lennert Veerman & Megan E Passey, 2018. "Care&WorkMOD: An Australian Microsimulation Model Projecting the Economic Impacts of Early Retirement in Informal Carers," International Journal of Microsimulation, International Microsimulation Association, vol. 11(3), pages 78-99.
    10. Oliver Mannion & Roy Lay-Yee & Wendy Wrapson & Peter Davis & Janet Pearson, 2012. "JAMSIM: a Microsimulation Modelling Policy Tool," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 15(1), pages 1-8.

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