Poverty and disadvantage among Australian children: a spatial perspective

Presentation to the ACT Branch of the Economics Society, 27 June 2006

Ann HardingNATSEM, University of Canberra

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

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

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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)

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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

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 < 30 children; delete children with missing family values -> NT results not accurate

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VariablesVariable in Census Social Exclusion Measure Developed

Family Type Proportion of children aged 0 – 15 in sole parent family with low income

Schooling Proportion of children aged 5 – 15 in Government school with low income

Education in family Proportion of children aged 0 – 15 with no-one in the family completed Year 12 and low income

Occupation in family Proportion of children aged 0 – 15 with highest occupation in family blue collar worker and low income

Housing tenure Proportion of children aged 0 – 15 in public housing and low income

Parents speak English at home

Proportion of children aged 0 – 15 in family where at least 1 parent speaks a language other than English at home and low income

Labour force status of parents

Proportion of children aged 0 – 15 in family where no parent working and low income

Personal computer usage

Proportion of children aged 0 – 15 in family where no-one used computer at home in last week and low income

Motor Vehicle Proportion of children aged 0 – 15 in household with no motor vehicle and low income

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

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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.

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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

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

11

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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

0.0

5.3

0.0

5.7

17.6

25.1

2.1

36.3

5.4

24.3

6.46.4

6.8

5.7

11.0

13.3

0

10

20

30

40

NSW VIC QLD SA WA TAS NT ACT

% o

f all

child

ren

in s

tate

/terr

itory

Bottom CSE decile Top CSE decile

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Distribution across states & territories of children in the bottom (most excluded) CSE decile

5.1

48.8

13.19.2

0.5

17.7

0.05.7

0

10

20

30

40

50

60

NSW VIC QLD SA WA TAS NT ACT

0

10

20

30

40

50

60

% o

f all

0-15

yea

r ol

d ch

ildre

n (li

ne)

% o

f chi

ldre

n in

bot

tom

CS

E d

ecile

Of all those children in the bottom national CSE decile, 17.7% come from NSW (which contains 34% of all children)

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Capital city and balance of Australia: distribution within CSE deciles

37

63

79

98

63

2

49

46

48

53

54

93

51

54

52 4

746

37

21

70

20

40

60

80

100

Mostexcluded

10%

Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Leastexcluded

10%Child Social Exclusion (CSE) decile

% o

f ch

ildre

n

Capital City Balance of state

Of all those children in the bottom national CSE decile, 49% live in capital cites and 51% live outside capital cities

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Capital city and balance of Australia: distribution over all CSE deciles

16

15

788

13

10

99

65

14

2

14

14

1

10

12

12

17

0

2

4

6

8

10

12

14

16

18

Mostexcluded

10%

Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Leastexcluded

10%Child Social Exclusion (CSE) decile

% o

f ch

ildre

n

Capital City Balance of state

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

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Components of social exclusion

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Social exclusion characteristics by capital city/balance of Australia Capital cities

Balance of Australia

% point difference

Child and family characteristics

Mean proportion of

children

Mean proportion of

childrenCouple family 80.8 81.4 0.6One parent family 19.1 18.4 -0.7Pre-school (aged 0-4) 15.0 14.7 -0.3Government school (aged 5-15) 58.2 70.8 12.6Catholic school (aged 5-15) 19.8 14.1 -5.7Other non-government school (aged 5-15) 12.8 6.8 -6.0Post-school qualifications 62.1 49.0 -13.1Year 12 16.1 15.1 -1.0Not Year 12 18.1 31.5 13.4White collar 40.5 25.1 -15.4Grey collar 28.1 33.0 4.9Blue collar 12.9 20.3 7.4Own home 68.2 63.5 -4.7Rent -public 7.7 7.7 0.0Rent - private 19.8 18.3 -1.5Other than English 21.5 6.9 -14.6English 78.5 93.2 14.7Couple - 1 parent employed 28.8 29.6 0.8Couple - 2 parents employed 44.5 42.6 -1.9Couple - both parents not working 7.0 8.8 1.8Single parent - employed 9.0 7.3 -1.7Single parent - not working 9.7 10.7 1.0Computer used at home 72.2 61.0 -11.2Computer not used at home 24.6 35.5 10.9At least one motor vehicle 93.0 92.4 -0.6No motor vehicle 4.4 5.1 0.7

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Proportion of children with selected characteristics by CSE decile

38.6

7.0

23.8

5.4

43.9

13.4

73.1

49.4

0

20

40

60

80

Mostexcluded

10%

Decile 2 Decile 3 Decile 4 Decile 5 Decile 6 Decile 7 Decile 8 Decile 9 Leastexcluded

10%Child Social Exclusion (CSE) Decile

% o

f chi

ldre

n w

ith c

hara

cter

istic

.No-one completed Year 12 Highest occupation blue collar

No computer use at home In government schools

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

Bottom 20 SLAs Top 20 SLAs % point difference

Child and family characteristicsMean proportion of

childrenMean proportion

of childrenCouple family 72.0 89.5 17.5One parent family 27.9 10.4 -17.5Pre-school (aged 0-4) 11.0 21.0 10.0Government school (aged 5-15) 72.5 42.4 -30.1Catholic school (aged 5-15) 8.1 17.9 9.8Other non-government school (aged 5-15) 4.0 33.3 29.3Post-school qualifications 26.9 83.7 56.8Year 12 17.5 11.4 -6.1Not Year 12 48.7 3.1 -45.6White collar 11.9 63.0 51.1Grey collar 23.4 24.7 1.3Blue collar 28.1 2.0 -26.1Own home 39.6 78.3 38.7Rent -public 33.1 3.5 -29.6Rent - private 19.7 15.2 -4.5Other than English 17.8 17.0 -0.8English 82.2 83.0 0.8Couple - 1 parent employed 31.5 31.1 -0.4Couple - 2 parents employed 24.9 53.8 28.9Couple - both parents not working 14.4 4.4 -10.0Single parent - employed 7.8 6.7 -1.1Single parent - not working 19.3 3.5 -15.8Computer used at home 37.9 89.2 51.3Computer not used at home 56.5 8.5 -48.0At least one motor vehicle 78.8 96.5 17.7No motor vehicle 17.3 1.9 -15.4

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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)

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Transition matrix – CSE deciles and Child Income Poverty deciles

TotalWeighted CSE index decile 1 2 3 4 5 6 7 8 9 10

1 5.0 2.2 2.1 0.3 0.1 0.3 0.0 0.0 0.0 0.0 10.02 2.6 2.2 2.2 1.8 0.7 0.6 0.1 0.0 0.0 0.0 10.33 0.8 3.2 1.6 3.0 0.6 0.4 0.1 0.0 0.0 0.0 9.74 0.5 1.1 1.2 1.1 3.3 2.3 0.4 0.0 0.0 0.0 10.05 0.4 0.4 1.4 2.3 2.2 0.8 2.5 0.1 0.0 0.0 10.06 0.6 0.7 1.2 1.0 1.8 1.3 2.8 0.7 0.0 0.0 10.07 0.1 0.0 0.1 0.3 1.0 3.1 1.5 2.9 0.9 0.0 10.08 0.1 0.0 0.2 0.0 0.5 0.9 2.4 3.5 2.3 0.2 10.09 0.0 0.0 0.1 0.2 0.0 0.1 0.2 2.3 5.2 1.9 10.1

10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.7 1.3 7.9 9.9Total % 10.1 10.0 10.0 10.0 10.2 9.8 10.1 10.2 9.8 10.0 100

Weighted CIP decile

Note: Decile 1 = highest risk of social exclusion and highest poverty rate

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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

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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