inequality in australia: does region matter?
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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. Measuring Inequality - Background. - PowerPoint PPT PresentationTRANSCRIPT
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
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
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
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
Spatial methodology
● Spatial microsimulation – SpatialMSM/09C
● Small area weights for every SLA
● Benchmarks variables
● Complex process of spatial microsimulation
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
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
Validation – small area validation (NSW)
The Spearman rank correlation is 0.958
y = 0.918x
R2 = 0.897
0.250
0.300
0.350
0.400
0.450
0.500
0.250 0.300 0.350 0.400 0.450 0.500Gini coefficient -
gross household income (SpatialMSM/09c)
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Australian map
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Distribution of small area inequality estimates – New South Wales
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Distribution of small area inequality estimates – Sydney
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Distribution of small area inequality estimates– Victoria
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Distribution of small area inequality estimates - Melbourne
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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
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
www.natsem.canberra.edu.au