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Case Studies - Health&WealthMOD: a microsimulation model of the economic impacts of diseases on older workers

Author

Listed:
  • Deborah Schofield

    (Northern Rivers University Department of Rural Health, School of Public Health, Faculty of Medicine, University of Sydney, PO Box 3074, Lismore, NSW 2480)

  • Megan Passey

    (Northern Rivers University Department of Rural Health, School of Public Health, Faculty of Medicine, University of Sydney, PO Box 3074, Lismore, NSW 2480)

  • Arul Earnest

    (Northern Rivers University Department of Rural Health, School of Public Health, Faculty of Medicine, University of Sydney, PO Box 3074, Lismore, NSW 2480)

  • Richard Percival

    (National Centre for Social and Economic Modelling, University of Canberra, ACT 2601, Australia)

  • Simon Kelly

    (National Centre for Social and Economic Modelling, University of Canberra, ACT 2601, Australia)

  • Rupendra Shrestha

    (Northern Rivers University Department of Rural Health, School of Public Health, Faculty of Medicine, University of Sydney, PO Box 3074, Lismore, NSW 2480)

  • Susan Fletcher

    (Northern Rivers University Department of Rural Health, School of Public Health, Faculty of Medicine, University of Sydney, PO Box 3074, Lismore, NSW 2480)

Abstract

The Australian Government is seeking to encourage older workers to remain in the labour force longer to overcome future labour shortages and ensure adequate taxation revenue to fund the needs of an ageing population. Longer labour force participation also has benefits to individuals in terms of available income and capacity to save for a better standard of living in retirement. While the Government recognises that not all older workers are able to participate in the labour force, there is little information on what health conditions keep people from working, or what the costs are to the government or the individual. This case study outlines how a new micro simulation model, Health&WealthMOD, is being designed to fill this information gap by estimating the economic impacts of illness. The general approach adopted is outlined, following which two early applications of the model are presented. The model is used to explore the determinants of the duration spent in retirement and to generate projections of retirement due to illness to 2020.

Suggested Citation

  • Deborah Schofield & Megan Passey & Arul Earnest & Richard Percival & Simon Kelly & Rupendra Shrestha & Susan Fletcher, 2009. "Case Studies - Health&WealthMOD: a microsimulation model of the economic impacts of diseases on older workers," International Journal of Microsimulation, International Microsimulation Association, vol. 2(2), pages 58-63.
    • Handle: RePEc:ijm:journl:v:2:y:2009:i:2:p:58-63
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    File URL: http://ima.natsem.canberra.edu.au/IJM/V2_2/IJM_2_2_6.pdf
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    References listed on IDEAS

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    1. Deborah Schofield & Josh Polette, 1999. "A Comparison of Data Merging Methodologies for Extending a Microsimulation Model," Brazilian Electronic Journal of Economics, Department of Economics, Universidade Federal de Pernambuco, vol. 2(1), January.
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    Cited by:

    1. Xiong Linping & Tang Weidong & Liu Hong, 2011. "Constructing a Basefile for Simulating Kunmings Medical Insurance Scheme of Urban Employees," International Journal of Microsimulation, International Microsimulation Association, vol. 4(3), pages 3-16.
    2. Hannah E Carter & Deborah J Schofield & Rupendra Shrestha, 2014. "Editorial Volume 7(3)," International Journal of Microsimulation, International Microsimulation Association, vol. 7(3), pages 33-52.
    3. Eugenio Zucchelli & Andrew M Jones & Nigel Rice, 2012. "The evaluation of health policies through dynamic microsimulation methods," International Journal of Microsimulation, International Microsimulation Association, vol. 5(1), pages 2-20.
    4. Deborah Schofield & Rupendra Shrestha & Simon Kelly & Lennert Veerman & Robert Tanton & Megan Passey & Theo Vos & Michelle Cunich & Emily 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, International Microsimulation Association, vol. 7(2), pages 94-118.

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