Social Inclusion: Issues, Data and Policy Responses Ann Harding Presentation to the 3 rd BITRE Regional Perspectives Conference, Parliament House, Canberra, 17 June 2008 NATSEM, University of Canberra Much of the research presented in this presentation was funded by ARC Grants Nos LP775396, DP664429, and DP

2 Australia 2020 Summit recommendation n That the Federal government should develop a National Action Plan for Social Inclusion With evidence-based goals and measurable targets With evidence-based goals and measurable targets Government to report progress annually Government to report progress annually Government to respond to this idea by the end of this year Government to respond to this idea by the end of this year

3 Reflects international trends to focus upon social exclusion n Focus on broader range of indicators than just income n Reflects realisation that social exclusion and consequent waste of human potential is bad for the country and the economy, as well as for individuals Population ageing means that we need all the workers that we can get! Population ageing means that we need all the workers that we can get!

4 Concern about intergenerational transfer of social exclusion n ‘About 2.5 per cent of every generation seem to be stuck in a lifetime of disadvantage. Their problems are multiple, entrenched and often passed down through generations.’ –Tony Blair in the introduction to the latest UK plan, ‘Reaching Out: An Action Plan on Social Exclusion’, 2006, p. 3

5 Debate coming about social inclusion measures n Possible measures might include: Child poverty/economic wellbeing Child poverty/economic wellbeing Reducing inequalities in health status or access to health services Reducing inequalities in health status or access to health services School retention rates for key sub-groups School retention rates for key sub-groups Workforce participation goals Workforce participation goals n Plans often associated with early intervention and better co-ordination of services

6 Monitoring social inclusion requires good data n National level data will be essential % of households in housing stress up from 19% in to 22.5 % in (AMP.NATSEM Income and Wealth Report, No 19, 2008, p. 9: ‘housing stress’ defined as spending >30% of income on housing) % of households in housing stress up from 19% in to 22.5 % in (AMP.NATSEM Income and Wealth Report, No 19, 2008, p. 9: ‘housing stress’ defined as spending >30% of income on housing) % of persons in income poverty, up from 9.8% in to 11.1% in * % of persons in income poverty, up from 9.8% in to 11.1% in * % of dependent children with jobless parents down from 15.6 % in to 13.8% in (Miranti et al, 2008) % of dependent children with jobless parents down from 15.6 % in to 13.8% in (Miranti et al, 2008) n But small area data will also be crucial *Source: Saunders et al, ‘Poverty in Australia’, SPRC Report 4/08, 2008, p.43.

7 The ‘two speed economy’ leads to differing income growth across Aust Source: AMP.NATSEM Income and Wealth Report No 20, ‘Trends in small area socio-economic inequality, 2001 to 2006’, launching on July 24, 2008.

8 NATSEM’s Child Social Exclusion Index, 2006 n ARC Grant – to create multidimensional measures of disadvantage – “social exclusion” – for children at small area level (DP ). n High level of spatial disaggregation > 1400 Statistical Local Areas across Australia > 1400 Statistical Local Areas across Australia n Uses relevant Census variables % of children where no one in family completed year 12, no parent is working, no motor vehicle, in bottom income quintile % of children where no one in family completed year 12, no parent is working, no motor vehicle, in bottom income quintile

9 J. McNamara et al, ‘Child social exclusion: an updated index from the 2006 Census’, Paper for Presentation at the 10 TH Australian Institute of Family Studies Conference, Melbourne, 9-11 July 2008.

10 Spatial microsimulation for small area needs-based planning indicators n ARC Linkage grant ( ) for examining spatial implications of population ageing over next 20 years (esp. for needs-based planning of govt services), with NSW, Vic, Qld, ACT. n Producing forecasting versions (e.g. for 2020) for Statistical Local Areas Goal is to produce needs-based planning indicators for 2020 and today, as well as more general household data Goal is to produce needs-based planning indicators for 2020 and today, as well as more general household data

11 % of children living in jobless families 2006 Source: Miranti et al, ‘Children with Jobless Parents: National and Small Area Trends for Australia in the Past Decade’, Paper submitted for the 37th Annual Conference of Economists, Gold Coast, 30 September- 3 October th Annual Conference of Economists, Gold Coast, 30 September- 3 October 2008

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13 spatialMSM – synthetic socio- economic household data at SLA level SMALL AREA DATA 2006 Census data at SLA level: - XCP data for SLAs UNIT RECORD DATA (SOURCE) and Surveys - Of Income and Housing UNIT RECORD DATA (AMENDED) - Updated to REWEIGHTING USING LINKING VARIABLES SMALL-AREA ESTIMATES 1) Unit record dataset 2) Set of weights for each SLA

14 Housing stress in Melbourne 2005

15 Predict spatial impact of a policy change n Spatial microdata now linked with NATSEM’s existing microsimulation models to model the immediate distributional/revenue impact of a policy change link synthetic spatial output to STINMOD and model changes to the tax and transfer system for small geographic areas link synthetic spatial output to STINMOD and model changes to the tax and transfer system for small geographic areas Currently modelling changes in Commonwealth Rent Assistance, income tax, social security and family payments Currently modelling changes in Commonwealth Rent Assistance, income tax, social security and family payments

16 Where did the $5bn of tax cuts go? n Updated 2001 population numbers to using ABS estimates pop’n growth by SLA n Updated household incomes and rules of govt programs to

17 Estimated average tax cut per household per week, Sydney SLAs, $ $9.50 per hhold (lightest) $ $13.70 $ $19.30 $ $34.10 per hhold (darkest)

18 Forthcoming research this year n SLA level impact of liberalising Family Tax Benefit income test n SLA level estimates of poverty for adults and children poverty for adults and children children in housing disadvantage children in housing disadvantage housing stress housing stress smoking prevalence smoking prevalence n Small area estimation and spatial microsimulation workshop, 19 Sept

19 Conclusions n Social inclusion is high on the new government’s agenda n Much work to be done on defining indicators to be used to measure social exclusion n Major challenges in finding data to inform measures n Important to have small area as well as national measures

20 Stay in touch with new research NATSEM research freely available from To stay in touch via automatic notification of new research: AMP:NATSEM reports freely available from

21 Selected references Child Social Exclusion Index (small area index of social exclusion specifically developed for children) Daly, A, McNamara, J, Tanton, R, Harding, A and Yap, M 2007,, “Indicators of Risk of Social Exclusion for Australia’s Children: An Analysis by State and Income Group”, available from ‘Other Papers’ section of NATSEM website, including SLA level results Spatial Microsimulation S.F., Chin, A., Harding, R., Lloyd, J., McNamara, B,.Phillips and Q., Vu, 2006, “Spatial Microsimulation Using Synthetic Small Area Estimates of Income, Tax and Social Security Benefits”, Australasian Journal of Regional Studies, vol. 11, no. 3, pp Chin, S.F., Harding, A. and Tanton, R “A Spatial Portrait of Disadvantage: Income Poverty by Statistical Local Area in 2001” ANZRSAI Conference “Heritage and Regional Development” Beechworth, Victoria, September 2006.* Harding, A., Lloyd, R., Bill, A., and King, A., Assessing Poverty and Inequality at a Detailed Regional Level – New Advances in Microsimulation, Research Paper No 2004/26 of the United Nations University World Institute for Development Economics Research (WIDER), April 2004 * Tanton, R. McNamara, J. Harding, A. and Morrison, T. Rich suburbs, poor suburbs? Small area poverty estimates for Australia’s eastern seaboard in Paper for the 1st General Conference of the International Microsimulation Association, Vienna, August 2007.* CuSP Model (spatial microsimulation model of Centrelink’s customers) King, A., 2007, ‘Providing Income Support Services to a Changing Aged Population in Australia: Centrelink’s Regional Microsimulation Model’, in Gupta, A and Harding, A, 2007, Modelling Our Future: Population Ageing, Health and Aged Care, North Holland, Amsterdam. CAREMOD (spatial microsimulation model of aged care needs) Brown, L and Harding, A. 2005, ‘The New Frontier of Health And Aged Care: Using Microsimulation to Assess Policy Options’, Quantitative Tools for Microeconomic Policy Analysis, Productivity Commission, Canberra (available from L, Brown, S, Lymer, M,Yap, M,Singh and A, Harding “Where are Aged Care Services Needed in NSW – Small Area Projections of Care Needs and Capacity for Self Provision of Older Australians”,Aged Care Association of Victoria State Conferences, May 2005 * HOUSEMOD (spatial microsimulation model of housing) McNamara, J, Tanton, R, and Phillips, B, 2007, The regional impact of housing costs oand assistance on financial disadvantage, Final report No 109,, Australian Housing and Urban Research Institute, Melbourne, November ( Kelly, S., Phillips, B. and Taylor, E., “Baseline Small Area Projections of the Demand for Housing Assistance”. Final report, The Australian Housing and Urban Research Institute RMIT-NATSEM AHURI Research Centre. May 2006 (ahuri.edu.au) * Means available on NATSEM website at