1. Inequality in Australia: Does region matter? Riyana Miranti, Rebecca Cassells, Yogi Vidyattama and Justine McNamara PRESENTED AT THE 2ND GENERAL CONFERENCE OF THE INTERNATIONAL MICROSIMULATION ASSOCIATION, OTTAWA, CANADA, JUNE 8 – 10, 2009

2. 2 Measuring Inequality - Background ●Why we chose this topic ? ●Objectives : ● to provide valuable information about regional inequality at a small area level ● to explore another use of spatial microsimulation and demonstrate its benefits

3. 3 What are we going to do ●Measuring inequality at small area using Gini coefficients ●Reasons for use of Gini coefficients ● Most common measure ● Validation purpose – publicly available at the national and state level ●Expand previous research with improvements : ● disposable household income ● smaller geographical unit than any that has been previously used ●Using spatial microsimulation, as direct data are not available

4. 4 Data source ●Reweighting process uses three sources of data : ● 2006 Census ● Survey - SIH 2003-04 and 2005-06 ●Validation use 2006 Census data, ABS published data and SIH 2005-06 ●Limit the scope of study to New South Wales (NSW) and Victoria (Vic) ●Unit of analysis : small area (Statistical Local Area)

5. 5 Spatial methodology ●Spatial microsimulation – SpatialMSM/09C ●Small area weights for every SLA ●Benchmarks variables ●Complex process of spatial microsimulation

6. 6 Gini coefficient ●Has a value between zero and one ●Zero means perfect equality, everyone has the same level of equivalised income ●One means perfect inequality, one person holds all the income ●Smaller Gini coefficient – more equal ●Equivalised hh disposable income

7. 7 Validation of our estimates ●To see whether our Gini coefficient estimates are reliable ●197 SLAs in NSW, and 198 SLAs in VIC ●Small area validation – equivalised gross household income data, see next slide ●Aggregate data validation, at capital city and balance of state level – equivalised disposable household income data – overall looks good.

8. 8 Validation – small area validation (NSW) The Spearman rank correlation is 0.958

9. 9 Australian map

10. 10 Distribution of small area inequality estimates – New South Wales

11. 11 Distribution of small area inequality estimates – Sydney

12. 12 Distribution of small area inequality estimates– Victoria

13. 13 Distribution of small area inequality estimates - Melbourne

14. 14 Inequality and small area characteristics ●Econometric analysis of determinants of inequality is beyond the scope of this paper. However : ●Previous research in Australia discusses several factors associated with inequality ●We find some similarities but also differences in characteristics among high inequality areas – no “One story fits all” ●Need to look further into particular SLAs, which ones underlying difference

15. 15 Conclusion ●Application of spatial microsimulation ●The validation shows that weights give reasonable results ●Does region matter ? Yes. There are substantial variations in inequality at small area level ●May help the policy makers/service providers to understand differences in order to better develop programs/policy. ●Future work ? Econometric estimation, spatial microsimulation in order to model policy changes

16. www.natsem.canberra.edu.au