Approaches to measuring disadvantage at a small area level: children and older people

Presentation to Measuring Disadvantage and Outcomes Based Reporting Workshop at Defining Diversity ACTCOSS Conference, November 4 – 5th 2010, Canberra

Justine McNamara

Acknowledgements

● This presentation showcases work funded by ARC Discovery Grant DP1094318, ARC Discovery Grant

DP664429

and ARC Linkage Grant LP775396 ● Many people have contributed to the work presented here, including the investigators and funding partners on the above grants, and the authors of papers from which the material presented here has been drawn 2

Overview of presentation

● Small areas ● Measuring child disadvantage: child social exclusion risk ● Disadvantage among older people: two worlds of ageing 3

Small areas

Increasing interest in Australia in examining geographical differences in advantage and disadvantage: ● Work by Vinson and others ● To what extent was economic boom shared equally?

● Are inequalities widening?

● Neighbourhood effects ● ‘locational disadvantage’ part of social inclusion agenda ● Place-based service planning 4

Challenges in small area measurement

To name a few: ● Data, data, data ● Small sample sizes ● Choice of geographical unit ● ‘Modifiable areal unit problem’ ● Ecological fallacy 5

Child social exclusion risk

6

Conceptualising social inclusion/exclusion

Very large literature on conceptualising and measuring social exclusion, and much debate.

Issues include: Differences between social exclusion and poverty Individual/structural Relational aspects Normative judgements Overlap of risk/causal factors with outcomes How important is persistence/intergenerational issues Wide and deep exclusion 7

Social exclusion and children

● Levitas et al. (2007)UK work on matrix of social exclusion measures which can be applied to different age groups ● UK social exclusion and poverty audit indicators for children (Opportunity for All) ● SPRC Australian work on social exclusion measures related to children ● Small but increasing number of international

small area

indicators of child deprivation/disadvantage (eg UK, South Africa) 8

Measuring child social exclusion risk at a small area level

● Earlier ARC-funded research into child social exclusion, leading to development of NATSEM’s original Child Social Exclusion (CSE) Index ● Work under new grant (2010 – 2012): Further development and refinement of CSE Index Creation of an index of youth social exclusion risk More analysis ● Unit of analysis: Statistical Local Area (SLA) 9

Some additional conceptual and measurement issues

● Data availability, especially for some concepts/dimensions ● The role (and availability) of data on children’s subjective well-being ● Importance of policy relevance ● Composite index vs individual variables ● Use of domains 10

Refining the index

● Re-examination of conceptual and measurement frameworks ● Investigation of new sources of data/variables ● Re-visiting methodology (first version used Principal Components Analysis to create index – similar to SEIFA indexes; this version we are creating domains, using PCA within domains and then equal weighting to combine domains) ● Comparing results ● WORK IN PROGRESS 11

Domains and variables used for original and first revision of NATSEM CSE index

Domains Variables Original CSE index First revision Socio-economic Single parent family In bottom income quintile No family member completing year 12 √ √ √ √ √ √ Engagement Housing Health services & disability Highest occupation of family members No parent working No internet at home No parent volunteering No motor vehicle Public housing High renting cost Ratio of GPs Ratio of dentists Children with disability × × × √ √ × √ √ √ √ √ √ √ √ × √ × √ √ √ 12

Additional proposed variables

Housing: ● ● Overcrowding ? adjustment to housing costs variable Education/development: ● ● literacy/numeracy Australian Early Development Index Transport ● ● ? Forced car ownership ? Fuel price vulnerability Health ● Replace disability with an alternative measure?

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Statistics of main variables, Australia, 2006

Variable Single parent family In bottom income quintile No family member completing year 12 No parent working No internet at home No parent volunteering No motor vehicle High renting cost Children with disability Ratio of GPs Ratio of dentists

Source: ABS Census 2006; authors’ calculations

Unit % of children % of children % of children % of children % of children % of children % of children % of children % of children Per 1000 persons Per 1000 persons Mean 0.20

0.23

0.24

0.16

0.26

0.60

0.07

0.07

0.02

1.71

0.44

SD 0.07

0.12

0.13

0.09

0.17

0.11

0.12

0.05

0.01

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Characteristics for areas with greatest and least risk (n=50)

Mean

Unit

Single parent family No family member completed Yr 12 No parent working No internet at home No motor vehicle No parent volunteering Bottom income quintile High renting cost Children with disability GP to 1000 population Dentist to 1000 population % of children % of children % of children % of children % of children % of children % of children % of children % of children Per 1000 persons Per 1000 persons 50 small areas with highest risk 38.7

50.1

37.9

65.6

37.3

76.8

50.0

11.9

1.7

1.6

0.2

50 small areas with least risk 10.3

4.8

6.8

6.1

1.2

57.3

6.9

3.9

1.2

2.4

0.7

Source: ABS Census 2006; authors’ calculations 15

Two worlds of ageing

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Measuring disadvantage among older Australians

● 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 17

Income distribution by age group

25 Individuals aged 15 to 54 Individuals aged 55+ 20 15 10 5 0 $1-149 $150 249 $250 349 $350 499 $500 649 $650 799 $800 999 $1000 1199 $1200 1399 $1400 1699

Household w eekly disposable incom e

$1700 1999 $2000 2499 $2500 2999 $3000+ Data source: SIH 2005/06 18

Tenure type by age group

80 70 60 50 40 30 20 10 0 Ow ner w ithout mortgage Younger than 55 Ow ner w ith mortgage Public renter 55 plus Private renter Other tenure Data source: SIH 2005/06 19

Coverage and definitions

● Aged 55 and above ● Contrast analysis – narrow definitions ● Two groups (the most vs the least disadvantaged) ●

relative economic advantage

(national top two quintiles of equivalised household disposable income, paying no rent or mortgage, and relying mainly on private household income) ●

deep economic disadvantage

income quintile, paying rent, and relying mainly on government income benefits) (national bottom ● Unit of analysis – statistical local area (SLA) ● Synthetic estimates 20

Spatial Methodology : Reweighting Method

turning the national household weights in the SIH 03-04 and 05-06 file into … Unit record Household ID Weekly incom e Weekly rent Other variables Household w eight

8 9 10 .

53220 .

1 2 3 4 5 6 7 4 6 6 .

15374 .

1 2 2 2 3 3 4 7 11 11 11 11 11 10 12 12 12 .

.

.

3 4 4 4 0 0 4 4 0 0 .

.

.

.

.

.

.

.

.

.

.

.

.

.

.

.

1029 157 157 157 1003 1003 70 70 703 703 .

.

.

15,374,000 Num of households in Aust … household weights for small areas NSW SLA1 NSW SLA2 NSW SLA3 Other SLAs

2.45

2.45

0 0 3.27

3.27

.

.

.

0 0 0 0

12465

0 0 0 0 13.54

13.54

0 0 0 0 .

25853

.

.

Num of households in small areas

16.38

16.38

0 0 0 0 .

.

.

0 0 0 0

27940

.

.

.

.

.

.

.

.

.

.

.

.

.

.

21

● Map or 2 22

Other work includes:

● Interactive maps of child (available now) and older adult (coming soon) wellbeing and synthetic estimates of poverty rates and housing stress: www.natsem.canberra.edu.au

● Measuring persistence of social exclusion among older Australians ● Work on particular aspects of disadvantage (children in households where no parent is in paid work; child housing disadvantage; income poverty among lone person households) ● Youth social exclusion risk 23

References

Abello, A., Gong, C., McNamara, J. and Daly, A. (2010) Spatial dimensions of child social exclusion risk: widening the scope (2010). Presented at the 11th Institute of Family Studies Conference, Melbourne, 7 – 9 July 2010.

Gong, C., McNamara, J. , Vidyattama, Y., Miranti, R., Tanton, R., Harding, A. and Kendig, H. (2009) Two worlds of ageing: spatial microsimulation estimates of small area advantage and disadvantage among older Australians. Paper presented at the ARCRNSISS Methods, Tools and Technologies Workshop, Newcastle, 10-11 December 2009 Harding, A., McNamara, J., Daly, A., and Tanton, R., (2009), 'Child social exclusion: an updated index from the 2006 Census',

Australian Journal of Labour Economics, Volume 12 Number 1

, 41-64 McNamara, J., Gong, C., Miranti, R., Vidyattama, Y., Tanton, R, Harding, A. and Kendig, H. (2009). ‘The geography of advantage and disadvantage for older Australians: insights from spatial microsimulation’. Paper presented at the British Society for Population Studies Annual Conference, University of Sussex, UK, September 9 - 11 2009.

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www.natsem.canberra.edu.au