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)

Gin

i co

effi

cien

t -

gro

ss h

ou

seho

ld in

com

e (C

ensu

s d

ata)

9

Australian map

10

Distribution of small area inequality estimates – New South Wales

11

Distribution of small area inequality estimates – Sydney

12

Distribution of small area inequality estimates– Victoria

13

Distribution of small area inequality estimates - Melbourne

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

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