1. Poverty and disadvantage among Australian children: a spatial perspective Presentation to the ACT Branch of the Economics Society, 27 June 2006 Ann Harding NATSEM, University of Canberra

2. 2 Innovative features of this study ARC Grant – to create multidimensional measures of disadvantage – “social exclusion” – for children at small area level Awarded to Anne Daly, Ann Harding and Phil Lewis – this is joint work with Justine McNamara, Mandy Yap and Rob Tanton of NATSEM (DP 560192). Child-focused High level of spatial disaggregation – for 1300 Statistical Local Areas across Australia

3. 3 Why use a multidimensional measure of disadvantage? Limitations of income-based poverty measures Increasing acceptance that we need to move beyond income-only measures of disadvantage Strong emphasis internationally on multidimensional measures of disadvantage

4. 4 Social exclusion “Social exclusion happens when people or places suffer from a series of problems such as unemployment, discrimination, poor skills, low incomes, poor housing, high crime, ill health and family breakdown” (British Social Exclusion Unit 1997)

5. 5 Why study disadvantage at a small area level? Sense that the fruits of economic growth have not been equally shared among Australians living in different regions Evidence base to support this belief is not well developed. Need to know what regional differences are, how they develop, and how they can be overcome

6. 6 Data source Australian 2001 Census of Population and Housing (ABS) Chosen because it has adequate information at a small area level Limitations in terms of data detail and coverage of issues (delete SLAs with NT results not accurate

7. 7 Variables Yr 12, Govt school, blue collar, computer were most important 4 variables

8. 8 Developing a composite index Used principal components analysis (PCA) to summarise variables into a single measure of child social exclusion risk. ABS use this technique to create the SEIFA indexes PCA transforms a set of correlated data into a set of new variables or components. The first new variable or component captures most of the variation in the original set of variables, and is used as the index.

9. 9 Interpreting the Child Social Exclusion (CSE) Index Low values = high disadvantage All analysis conducted with child-weighted social exclusion deciles, to overcome problems with different SLA populations across states Bottom social exclusion decile = 10 per cent of Australian children facing highest social exclusion risks

10. 10 Where do Australian children at risk of social exclusion live – what the CSE index tells us

11. 11

12. 12 What percentage of children in each state fall into the top and bottom CSE Index deciles? ‘Bottom CSE decile’ is the 10% of children across all of Australia facing highest risk of social exclusion: 5.3% of NSW children fall into bottom CSE decile

13. 13 Distribution across states & territories of children in the bottom (most excluded) CSE decile Of all those children in the bottom national CSE decile, 17.7% come from NSW (which contains 34% of all children)

14. 14 Capital city and balance of Australia: distribution within CSE deciles Of all those children in the bottom national CSE decile, 49% live in capital cites and 51% live outside capital cities

15. 15 Capital city and balance of Australia: distribution over all CSE deciles 14% of all children who live outside capital cites fall into the bottom (most excluded) CSE decile, while only 1% make it into the ‘least excluded’ decile

16. 16 Components of social exclusion

17. 17 Social exclusion characteristics by capital city/balance of Australia

18. 18 Proportion of children with selected characteristics by CSE decile

19. 19 Comparison between 20 SLAs most and least at risk of child social exclusion

20. 20 Comparing child income poverty and child social exclusion Data limitations Measure of poverty fairly rough – based on gross equivalised income ranges from Census Created child-weighted child income poverty deciles (bottom decile=10% of children living in SLAs with the highest risk of poverty)

21. 21 Transition matrix – CSE deciles and Child Income Poverty deciles Note: Decile 1 = highest risk of social exclusion and highest poverty rate

22. 22 Conclusions Large variations in child social exclusion risk by state Substantial variations in exclusion risk within cities, and between capital cities and balance of Australia Also major differences in specific index components bottom decile children 4 times as likely to live in a blue collar family and 5 times as likely to live in a family where no-one has completed Year 12

23. 23 Conclusions considerable divergence between the CSE index and child income poverty when examining disadvantage half of all the most disadvantaged decile of children as measured by the CSE index fall above the bottom decile of child income poverty. Future work: Statistical significance of spatial clustering Comparison between CSE Index and ABS SEIFA indexes Examination of trends over time