The geography of advantage and disadvantage for older Australians: insights

from spatial microsimulationJUSTINE 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

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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.

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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

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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

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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)

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National characteristics of older Australians – advantage variables

Source: ABS Survey of Income and Housing Costs, 2005/06

Top two hh income quintiles 12.7%

No rent or mortgage 79.8%

Main source of hh income not pension

35.2% Relative economic advantage 10.4%

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National characteristics of older Australians – disadvantage variables

Source: ABS Survey of Income and Housing Costs, 2005/06

Bottom income quintile 47.1%

Private renter 6.6%

Pension dependent

64.8%

Deep economicdisadvantage 3.8%

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National characteristics of older Australians

'Deeply disadvantaged'

'Relatively advantaged'

Characteristic % %All persons 65+ 3.8 10.4Females 65+ 4.1 8.7Males 65+ 3.3 12.3Persons 75+ 2.7 10.665+ living in hh with at least one person <65 2.0 16.165+ living in hh with at least one person >=75 5.0 10.565+ living alone 8.5 6.565+ living in hh where anyone working 0.4 24.665+ living in a capital city 3.4 12.165+ not living in a capital city 4.5 7.3

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Data source

Reweighting process uses three sources of data :● 2006 Census ● Survey - SIH 2003-04 and 2005-06

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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)

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Spatial Methodology : Reweighting Method

turning the national household weights in the SIH 03-04 and 05-06 file into …

… household weightsfor small areas

Unit record

Household ID

Weekly income

Weekly rent

Other variables

Household weight

1 1 7 3 . 10292 2 11 4 . 1573 2 11 4 . 1574 2 11 4 . 1575 3 11 0 . 10036 3 11 0 . 10037 4 10 4 . 708 4 12 4 . 709 6 12 0 . 703

10 6 12 0 . 703. . . . . .. . . . . .

53220 15374 . . . .

15,374,000Num of households in Aust

NSW SLA1

NSW SLA2

NSW SLA3

Other SLAs

0 0 0 .0 0 0 .0 0 0 .0 0 0 .

2.45 13.54 16.38 .2.45 13.54 16.38 .

0 0 0 .0 0 0 .

3.27 0 0 .3.27 0 0 .

. . . .

. . . .

. . . .

12465 25853 27940 .

Num of households in small areas

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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,

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Small area validation : advantage

0

5

10

15

20

25

30

0 10 20 30

% of aged 65+ not paying rent/mortgage and in top two quintiles, synthetic estimates

% o

f age

d 65

+ no

t pay

ing

rent

/m

ortg

age

at to

p tw

o qu

intil

es (s

tate

d),

Cen

sus

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Small area validation : disadvantage

0

5

10

15

20

0 5 10 15 20

% of aged 65+ paying private rent and in bottom quintile, synthetic estimates

% o

f age

d 65

+ pa

ying

pri

vate

ren

t and

in

bott

om q

uint

ile (s

tate

d), C

ensu

s

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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

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Preliminary estimates of the distribution of 65+ deeply economically disadvantaged

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Preliminary estimates of the distribution of 65+ relatively economically advantaged

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

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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

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Preliminary estimates

SEIFA Index and Percentage of Deeply Economically Disadvantaged Older Australians

Spearman's correlation=-0.4403

0

5

10

15

20

25

7 9 11 13

Synthetic estimates of percentage of deeply economically disadvantaged older Australians

SE

IFA

sco

re (/

100)

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Preliminary estimates

SEIFA Index and Relatively Advantaged Older Australians

Spearman's correlation=0.7871

0

10

20

30

40

50

60

70

8 8.5 9 9.5 10 10.5 11 11.5 12

Percentage of relatively advantaged older Australians

SE

IFA

sco

re (/

100)

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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)

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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

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