Geography of Advantage and Disadvantage for Older Australians: Insights from Spatial Microsimulation

1. The geography of advantage and disadvantage for older Australians: insights from spatial microsimulation JUSTINE MCNAMARA, CATHY GONG, RIYANA MIRANTI, YOGI VIDYATTAMA, ROBERT TANTON, ANN HARDING AND HAL KENDIG PRESENTED AT THE BRITISH SOCIETY FOR POPULATION STUDIES CONFERENCE, UNIVERSITY OF SUSSEX, SEPTEMBER 9 – 11 2009

2. Acknowledgements • This paper was funded by a Discovery Grant from the Australian Research Council (DP664429: Opportunity and Disadvantage: Differences in Wellbeing Among Australia's Adults and Children at a Small Area Level). • The authors would like to thank our fellow Chief Investigators on this grant, Professor Fiona Stanley, Professor Bob Stimson, Dr Sharon Goldfeld, and the Australian Bureau of Statistics for their input to the broader project being undertaken through this grant.

3. Background • Australia ranks low in OECD in terms of income ratios of people aged 65 + to those aged 18-64 • BUT income alone not a good measure of economic circumstances for older Australians • Very large differences in the distribution of income, wealth and home ownership • Vulnerabilities of older renters • Increasing interest in spatial dimensions of disadvantage in Australia, but little research on small areas and older people

4. Focus of this research • Geographic dimensions of advantage and disadvantage for older people • Social exclusion-multiple sources of disadvantage (economic aspects) esp moving beyond income • Combine variables measuring (i) income (ii) welfare dependence (iii) housing costs • Use of spatial microsimulation techniques • Work in progress

5. Coverage and definitions • Age cut-off, those aged 65 and above • Two groups (the most vs the least disadvantaged) • relative economic advantage (top two quintiles of equivalised national household disposable income, paying no rent or mortgage, and relying mainly on private household income) • deep economic disadvantage (bottom income quintile, paying private rent, and relying mainly on government income benefits) • Unit of analysis – statistical local area (SLA)

6. National characteristics of older Australians – advantage variables Source: ABS Survey of Income and Housing Costs, 2005/06

7. National characteristics of older Australians – disadvantage variables Source: ABS Survey of Income and Housing Costs, 2005/06

8. National characteristics of older Australians

10. Data source Reweighting process uses three sources of data : • 2006 Census • Survey - SIH 2003-04 and 2005-06

11. Spatial methodology • Spatial microsimulation-SpatialMSM/09C • Synthetic household weights for every SLA • Benchmark variables • Complex process of spatial microsimulation • Aggregate SLAs in Canberra and Brisbane (MAUP) • Caution re Northern Territory results • Further exclusions – (finally use 816 small areas)

12. Spatial Methodology : Reweighting Method turning the national household weights in the SIH 03-04 and 05-06 file into … … household weights for small areas

13. Validation • Checking the accuracy of our estimates against existing data • Small area validation, see next slides • 65 +, bottom gross income quintile, paying rent in the private market • 65 +, top two gross income quintiles, paying neither private rent nor mortgage. • Aggregate data validation, at state/territory level,

14. Small area validation : advantage

15. Small area validation : disadvantage

16. Adjusting reweighting to improve accuracy Work in progress • Inclusion of additional benchmarks: • Age*Income*tenure type (to improve estimation of private renters) • Age*household labour force status (to improve estimation of income source) Challenges: • Additional benchmarks increase non-convergence • Problem of ‘balancing’ tables

17. Preliminary estimates of the distribution of 65+ deeply economically disadvantaged

18. Preliminary estimates of the distribution of 65+ relatively economically advantaged

19. Preliminary estimates of the distribution of the 65+ deep disadvantage and relative economic advantage, Sydney

20. Comparing spatial distributions Does the spatial distribution of deep disadvantage/relative advantage among older Australians mirror that of other population groups? • Compared with ABS SEIFA scores

21. Preliminary estimates

22. Preliminary estimates

23. Discussion • Substantial heterogeneity among older people • Complex patterns geographically • Although private renting affects small proportion now, it may rise in the future (marriage breakdown + lower rates of home ownership)

24. Further work • Further refinement of modelling and validation (sample size, definitions) • Analyse characteristics of the areas with the most concentrated economic disadvantage and advantage (unemployment, industry structure, education levels, age distribution, household composition and poverty rates) • Intergenerational comparisons • Examine the regional effects of policy changes on older people

25. www.natsem.canberra.edu.au