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‘Small area estimates of Subjective Wellbeing: Spatial Microsimulation on the Australian Unity Wellbeing Index Survey’

Author

Listed:
  • Itismita Mohanty

    () (NATSEM, University of Canberra)

  • Robert Tanton

    () (NATSEM, University of Canberra)

  • Yogi Vidyattama

    () (NATSEM, University of Canberra)

  • Marcia Keegan

    () (NATSEM, University of Canberra)

  • Robert Cummins

    () (Deakin University)

Abstract

Subjective wellbeing is an indicator of how satisfied a person is with their life. It is becoming accepted as an indicator of happiness and overall wellbeing. This paper uses spatial microsimulation methods to estimate small area subjective wellbeing (SWB) in Australia. The procedure uses the Australian Unity Wellbeing Index survey and the 2011 Census data to derive small area estimates of SWB. Validation of the results shows that high and normal levels of SWB compare well to another source of SWB for small areas in the Murray Darling Basin, but levels of low SWB are not predicted as well, possibly due to the effect of local factors not available for the modelling on reducing SWB. Aggregate estimates compare well to HILDA estimates of SWB at a State level. These estimates are now available from the Australian Urban Research Infrastructure Network (AURIN) at the University of Melbourne..

Suggested Citation

  • Itismita Mohanty & Robert Tanton & Yogi Vidyattama & Marcia Keegan & Robert Cummins, 2013. "‘Small area estimates of Subjective Wellbeing: Spatial Microsimulation on the Australian Unity Wellbeing Index Survey’," NATSEM Working Paper Series 13/23, University of Canberra, National Centre for Social and Economic Modelling.
  • Handle: RePEc:cba:wpaper:wp1123
    as

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    File URL: http://www.natsem.canberra.edu.au/files/download?id=1175
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    References listed on IDEAS

    as
    1. Robert Tanton & Yogi Vidyattama & Justine McNamara & Quoc Ngu Vu & Ann Harding, 2009. "Old, Single and Poor: Using Microsimulation and Microdata to Analyse Poverty and the Impact of Policy Change among Older Australians," Economic Papers, The Economic Society of Australia, vol. 28(2), pages 102-120, June.
    2. Dolan, Paul & Layard, Richard & Metcalfe, Robert, 2011. "Measuring subjective well-being for public policy," LSE Research Online Documents on Economics 35420, London School of Economics and Political Science, LSE Library.
    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. Robert Cummins, 2003. "Normative Life Satisfaction: Measurement Issues and a Homeostatic Model," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 64(2), pages 225-256, November.
    5. P Williamson & M Birkin & P H Rees, 1998. "The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records," Environment and Planning A, , vol. 30(5), pages 785-816, May.
    6. P Williamson & M Birkin & P H Rees, 1998. "The estimation of population microdata by using data from small area statistics and samples of anonymised records," Environment and Planning A, Pion Ltd, London, vol. 30(5), pages 785-816, May.
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    Cited by:

    1. Robert Tanton, 2014. "A Review of Spatial Microsimulation Methods," International Journal of Microsimulation, International Microsimulation Association, vol. 7(1), pages 4-25.

    More about this item

    Keywords

    Wellbeing; Regional welfare; Spatial Microsimulation;

    JEL classification:

    • I31 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - General Welfare, Well-Being
    • R13 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - General Equilibrium and Welfare Economic Analysis of Regional Economies
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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