Applied Spatial Analysis and Policy

, Volume 1, Issue 2, pp 99–116 | Cite as

2001 Regional Disability Estimates for New South Wales, Australia, Using Spatial Microsimulation

  • S. Lymer
  • L. Brown
  • M. Yap
  • A. Harding


Estimating disability levels in older Australians and their demographic and socio-economic profiles is essential for identifying the need for aged care services and for the development and implementation of effective social policy on ageing. Small area estimates are produced from the spatial microsimulation model ‘CareMod’ which is based on the 1998 ABS Survey of Disability, Ageing and Carers (SDAC; ABS (1999). Disability, Ageing and Carers: User Guide, Australia. Canberra: ABS), and up-rated to 2001. Estimates are generated by reweighting the SDAC confidentialised unit record file to create ‘synthetic’ datasets for each Statistical Local Area in New South Wales (NSW), Australia. Disability levels and the need for aged care in NSW was determined across a range of age groups. The results show that there is significant variation across NSW in disability levels and the need for aged care services by older persons. With increasing age, the presence of severe or profound disability increases and, as a consequence, this subgroup of aged persons has a greater potential need for high level care. The coastal areas of NSW have a greater requirement of care provision with respect to overall numbers, as a consequence of the greater overall population. However, many inland towns have greater rates of disability amongst the elderly. The research findings will assist in the strategic planning and improved targeting of aged care services, especially in identifying areas of unmet need at the small area level.


Ageing Disability Spatial microsimulation Regional effects Aged care services Small area estimation 



The construction of the current version of the CAREMOD model was supported by an Australian Research Council linkage grant (no. LP0349126), and by Industry Partners to this grant - NSW Department of Ageing, Disability and Home Care and the Australian Department of Health and Ageing.


  1. ABS (1999). Disability, Ageing and Carers: User Guide, Australia. Canberra: ABS ABS Cat. 4431.0.Google Scholar
  2. ABS (2001). Australian Standard Geographical Classification (ASGC). Canberra: ABS ABS Cat. 1216.0.Google Scholar
  3. ABS (2004). Disability, Ageing and Carers, Australia: Summary of Findings, 2003. Canberra: ABS ABS Cat. 4430.0.Google Scholar
  4. ABS (2005). Population Projections Australia 2004–2101 Australia. Canberra: ABS ABS Cat. 3222.0.Google Scholar
  5. ABS (2007a). Regional Population Growth Australia 1996 to 2006. Canberra: ABS ABS Cat. 3218.0.Google Scholar
  6. ABS (2007b). Australian Demographic Statistics March Quarter 2007. Canberra: ABS ABS Cat. 3101.0.Google Scholar
  7. ABS (2007c). 2006 Census Tables: New South Wales. Accessed at September 20, 2007,
  8. AIHW (2000). Disability and Ageing: Australian Population Patterns and Implications. Canberra: AIHW AIHW Cat No DIS 19.Google Scholar
  9. AIHW (2002). Older Australia at a Glance 2002 (3rd ed.). Canberra: AIHW AIHW Cat No AGE 25.Google Scholar
  10. AIHW (2003). Australia’s Welfare 2003. Canberra: AIHW AIHW Cat No AUS 41.Google Scholar
  11. AIHW (2004). Carers in Australia: assisting frail older people and people with a disability. Canberra: AIHW AIHW Cat No AGE 41.Google Scholar
  12. Allen Consulting Group (2002). The Financial Implications of Caring for the Aged to 2020. A report commissioned in conjunction with the Myer Foundation project 2020: A Vision for Aged Care in Australia. Melbourne: Allen Consulting Group.Google Scholar
  13. Ballas, D., & Clarke, G. P. (2001). Modelling the local impacts of national social policies: a spatial microsimulation approach. Environment and Planning C, Government and Policy, 19, 587–606.CrossRefGoogle Scholar
  14. Ballas, D., Clarke, G. P., Dorling, D., Rigby, J., & Wheeler, B. (2006). Using Geographical Information Systems and spatial microsimulation for the analysis of health inequalities. Health Informatics Journal, 12(1), 57–72.CrossRefGoogle Scholar
  15. Baltes, P. B., & Smith, J. (2003). New frontiers in the future of aging: From successful aging of the young old to the dilemmas of the fourth age. Gerontology, 49, 123–125.CrossRefGoogle Scholar
  16. Barrett, P., Twitchin, S., Kletchko, S., & Ryan, F. (2006). The living environments of community dwelling older people who become frail: Another look at the living standards of older New Zealanders survey. Social Policy of New Zealand, 28, 133–157.Google Scholar
  17. Brown, L., & Harding, A. (2002). Social modelling and public policy: Application of microsimulation modelling in Australia. Journal of Artificial Societies and Social Simulation, 5(4), 2002.Google Scholar
  18. Brown, L., & Harding, A. (2005). The new frontier of health and aged Care. (in ‘Quantitative Tools for Microeconomic Policy Analysis, Productivity Commission, Commonwealth of Australia, pp. 217–245). Accessed at
  19. Casey, B., Oxley, H., Whitehouse, E., Antolin, P., Duval, R., & Leibfritz, W. (2003). Policies for an Ageing Society: Recent Measures and Areas for Further Reform, OECD Economics Department Working Papers, No. 369. Paris: OECD. DOI  10.1787/737005512385.Google Scholar
  20. Chin, S. F., & Harding, A. (2006). Regional Dimensions: Creating Synthetic Small-area Microdata and Spatial Microsimulation Models. Technical Paper no. 33. Canberra: NATSEM May.Google Scholar
  21. Chin, S. F., Harding, A., Lloyd, R., McNamara, J., Phillips, B., & Vu, Q. (2005). Spatial microsimulation using synthetic small area estimates of income, tax and social security benefits. Australasian Journal of Regional Studies, 11(3), 303–336.Google Scholar
  22. Citro, C. F., & Hanushek, E. A. (1991). The uses of microsimuation modelling, Vol 1: review and recommendations. Washington: National Academy.Google Scholar
  23. Department of the Treasury (2002). Intergenerational Report 2002–03, Budget Paper No. 5, Treasury, Commonwealth of Australia, May.Google Scholar
  24. Elazar, D., & Conn, L. (2005). Small area estimates of Disability in Australia. Canberra: ABS Pub. # 1351.0.55.006.Google Scholar
  25. Gibson, D., Braun, P., & Liu, Z. (2000). Spatial Equity in the Distribution of Aged Care Services. Canberra: AIHW Welfare Division Working Paper No. 25.Google Scholar
  26. Giles, L. C., Cameron, I. D., & Crotty, M. (2003). Disability in older Australians: projections for 2006–2031. MJA, 179(3), 130–133.Google Scholar
  27. Gupta, A., & Harding, A. (2007). Modelling our future: population ageing, health and aged care, international symposia in economic theory and econometrics (vol. 16). Amsterdam: Elsevier.Google Scholar
  28. Gupta, A., & Kapur, V. (2000). Microsimulation in Government Policy and Forecasting, Contributions to Economic Analysis Series. Amsterdam: Elsevier.Google Scholar
  29. Harding, A. (1996). Microsimulation and Public Policy, Contributions to Economic Analysis Series. Amsterdam: North Holland.Google Scholar
  30. Harding, A., & Gupta, A. (2007). Modelling our future: population ageing, social security and taxation. International Symposia in Economic Theory and Econometrics (vol. 15). Amsterdam: Elsevier.Google Scholar
  31. Hogan, W. P. (2004). Review of pricing arrangements in residential aged care Australian Government Department of Health and Ageing. Accessed August 20, 2007, at
  32. Huang, Z., & Williamson, P. (2001). A comparison of synthetic reconstruction and combinatorial optimisation approaches to the creation of small-area microdata. Working Paper 2001/2, Population Microdata Unit, Department of Geography, University of Liverpool, Liverpool L69 3BX. Accessed September 10, 2007, from
  33. Klevmarken, N. A. (2005). “Dynamic microsimulation for policy analysis—problems and solutions”. Paper presented at 34th Conference of Economists, University of Melbourne, Melbourne, Australia, 28 September.Google Scholar
  34. Lymer, S., Brown, L., Harding, A., Yap, M., Chin, S. F., & Leicester, S. (2006). Development of CAREMOD/05A, Technical Paper No 32, NATSEM, March.Google Scholar
  35. Neugarten, B. (1974). Age groups in American society and the rise of the young old. Annals of the Academy of Social and Political Science, 415, 187–198.CrossRefGoogle Scholar
  36. Oliveira Martins, J., Gonand, F., Antolin, P., de la Maisonneuve, C., & Yoo, K. Y. (2005). The impact of ageing on demand, factor markets and growth, OECD Economics Department Working Papers, No. 420. Paris: OECD. DOI  10.1787/545827207132.Google Scholar
  37. Orcutt, G. (1957). A new type of socio-economic system. Review of Economics and Statistics, 58(2), 773–797.Google Scholar
  38. Percival, R., & Kelly, S. (2004). Who’s going to care? Informal Care and an ageing population, Carers Australia, Canberra, June. Accessed September 10, 2007 from
  39. Singh, M. (1995). Disability and handicap among future elderly Australians. Population Geography, 15(1&2), 1–21.Google Scholar
  40. Smith, D. M., Clarke, G. P., Ransley, J., & Cade, J. (2006). Food access and health: a microsimulation framework for analysis. Studies in Regional Science, 35(4), 909–927.CrossRefGoogle Scholar
  41. Tanton, R., Williamson, P., & Harding, A. (2007). Comparing two methods of reweighting a survey file to small area data: generalised regression and combinatorial optimization. Paper presented to the 1st General Conference of the International Microsimulation Association, Vienna, Austria, 20–21 August.Google Scholar
  42. The Hon Peter Costello MP Treasurer (2007). Intergenerational Report 2007, Treasury, Commonwealth of Australia, April. Accessed August 1, 2007, from
  43. Voas, D., & Williamson, P. (2000). An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata. International Journal of Population Geography, 6, 348–366.CrossRefGoogle Scholar
  44. Williamson, P. (1996). Community care policies for the elderly, 1981 and 1991: a microsimulation approach. In G. Clarke (Ed.), Microsimulation for urban and regional policy analysis (pp. 64–87). London: Pion.Google Scholar
  45. Williamson, P. (2002). Synthetic microdata. In M. Rees & P. Williamson (Eds.), The census data system (pp. 231–241). Chichester: Wiley.Google Scholar
  46. Williamson, P., Birkin, M., & Rees, P. H. (1998). The estimation of population microdata by using data from small area statistics and samples of anonymised records. Environment and Planning A, 30(5), 785–816.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2008

Authors and Affiliations

  1. 1.NATSEMUniversity of CanberraCanberraAustralia

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