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Socio-Economic Characteristics at the Small Area Level: New Models and Data for Policy Makers and for
Needs Based Planning of Government Services
Ann Harding
Presentation to Department of Geography Seminar Series, University of California Santa Barbara, USA, 17
May 2007
National Centre for Social and Economic Modelling(NATSEM), University of Canberra
2
What are microdata and microsimulation models?
Focus on individuals or householdsStart with large microdata sets (admin or sample survey)Primarily used to estimate impact of government policy change on these individuals or households• Impact on small sub-groups• Aggregate impact• Impact on government revenue or expenditure
Static models of taxes and transfers
4
Income tax and social security
STINMOD model is now maintained by NATSEM for Australian government departments (Family and Community Services, Education Science and Training, Treasury, Employment and Workplace Relations)13 years oldSTINMOD simulates all the major income tax and cash transfer programs (age pension, family payments etc)Used regularly in research – distributional impact of welfare state, impact of minimum wage rises, EMTRsConstructed on top of Income Surveys and Expenditure Surveys (2 versions)
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6
7
8
Disposable income of sole parents with one child aged 8+, 2006-07 Impact of 2005 ‘welfare to work’ budget changes
300
400
500
600
700
800
0 100 200 300 400 500 600 700 800Private Income ($ pw)
Dis
posa
ble
Inco
me
($ p
w)
Current policy - disposable income
New policy - disposable income
9
EMTRs of sole parents with one child aged 8+, 2006-07 *
* EMTR of 65% means that person keeps 35 cents from an additional dollar of earnings
0
10
20
30
40
50
60
70
80
0 100 200 300 400 500 600 700 800Private Income ($ pw)
ETR
(%)
Current policy - EMTRs New policy - EMTRs
Dynamic models
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Dynamic microsimulation modelling
Simulates the events that happen to ordinary Australians over their lifetimeStarts in 2001 with 180,000 people (1% of the Australian population – the Census sample)Models individuals (the micro level)Uses regression equations to model human behaviour over time (dynamic)APPSIM (Australian Population and Policy Simulation Model) currently under devt – won ARC grant last year with 13 govt depts as research partnersEarlier version was DYNAMOD3
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DYNAMOD3’s Simulation Cycle
Immigration
Macro-economics Emigration
Annual EarningsAnd Savings
LabourForce
EducationCouple Formation
Y
YNew Quarter?
Pregnancy and Births
Couple Dissolution
DeathsDisability & Recovery
Simulation Clock
Australia 2001
Australia 2050
New Year?
Spatial models
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Characteristics of available datasets
HighHighLowGeographic detail
HighMediumHighPopulation detail
?Census of Popn & Housing
National sample surveys
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Synthetic Spatial Microdata
Solution:
Combine the information-rich survey data with the geographically disaggregated Census data
Using ‘spatial microsimulation’ (synthetic estimation) to:
create detailed unit record data for small areas(synthetic spatial microdata)
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Constructing small-area estimatesSMALL AREA DATA
2001 Census data at SLA level:
- XCP data for SLAs
UNIT RECORD DATA(SOURCE)
1998-99 Household Expenditure Survey
UNIT RECORD DATA(AMENDED)
- Updated to 2001
-
Enhanced income
REWEIGHTINGUSING
LINKINGVARIABLES
SMALL-AREA ESTIMATES
1) Unit record dataset
2) Set of weights for each SLA
Iterative process to identify a set of variables suitable for reweighting
Major data preparation task
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What is ‘reweighting’?turning the national household weights in the HES survey file into …
… household weightsof 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. . . . . .. . . . . .
13964 6892 . . . .7,122,000
Num 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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Linkage variables available in the 2001 Census and 1998-99 HES
Family level variablesFamily typeFamily income
Household level variablesDwelling structureTenure typeHousehold incomeHousehold typeHousehold sizeNumber of dependentsNumber of carsRent paidMortgage repayments
Person level variablesAgeSexSocial marital statusCountry of birthLevel of schoolingNon-school qualificationsEducational institution attendingStudy statusHours workedIndividual incomeOccupationLabour force statusYear of arrivalRelationship in household
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Application 1: Analysis of Specific Population Sub-Groups
Allows – for small areas:
• identification and analysis of specificsocio-demographic groups and characteristics
• analysis at various population levels:e.g. persons, income units, households
Examples – children in low income families; children in jobless families; unskilled youth, those in housing stress
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Ipswich (C) East
wich (C) - CentralWacol
Archerfield
Chermside
Nundah
Clayfield
Nathan
Windsor
Annerley
Fairfield
Inala
Richlands
New Farm
Kelvin Grove
Toowong
Indooroopilly
East Brisbane
Dutton ParkTaringa
Bowen HillsFortitude Valley - Remainder
Milton
St Lucia
South Brisbane
Spring Hill
Highgate HillWest End (Brisbane)Kangaroo Point
City - Remainder
Percentage of householdsin unaffordable housing
0.88% - 6.05%
6.06% - 9.48%9.49% - 14.00%14.01% - 29.27%
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Hervey Bay (C
Boulia (S)
Belyando (S)
Mount Isa (C)
Longreach (S)
Duaringa (S)
Broadsound (S)
Nebo (S)
Emerald (S)
Peak Downs (S)
Pine Rivers (S) Bal
Calliope (S) - Pt A
Guanaba-Currumbin ValleyBeaudesert (S) - Pt A
Cairns (C) - Trinity
Thuringowa (C) - Pt A Bal
Cairns (C) - Northern Suburbs
Tara (S)
Cook (S) (excl. Weipa)
Etheridge (S)Herberton (S)
Wondai (S) Kilkivan (S)
Kolan (S)Miriam Vale (S)
Tiaro (S)
Nanango (S)
Mount Morgan (S)
Households in poverty (%)
0 - 8
9 - 12
13 - 17
18 - 30
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Age profile of those in poverty in postcode in metro Sydney
35-44 years38%
45-54 years16%
55-64 years15%
65+ years2%
25-34 years27%
< 25 years2%
(Aust average 4.1%)
(Aust average 20.3%)
(Aust average 35.1%
(Aust average 17.7%
(Aust average 12.3%)
(Aust average 10.5%)
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Application 2: Predict spatial impact of a policy change
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
• Currently modelling changes in Commonwealth Rent Assistance, income tax, social security and family payments
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Where did the $5bn of 2005-06 tax cuts go?
Updated 2001 population numbers to 2005-06 using ABS estimates pop’n growth by SLAUpdated household incomes and rules of govt programs to 2005-06
Tax threshold Tax rate
Tax threshold Tax rate
$6,000 0.17 $6,000 0.15$21,600 0.3 $21,600 0.3$58,000 0.42 $63,000 0.42$70,000 0.47 $95,000 0.47
2004-05 2005-06
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Legend
CUT_HH_TTA3.5 - 9.59.6 - 13.713.8 - 19.319.4 - 34.1
±20
Km
Estimated average tax cut per household per week, Sydney SLAs, 2005-06
$3.50 - $9.50 (lightest)
$9.51 - $13.70
$13.71 - $19.30
$19.31 - $34.10 (darkest)
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Estimated average dollar tax cut per household per week, by regions, 2005-06
State/
Territory Capital city Major urban Regional town Rural town Rural area All regions
NSW $14.6 $9.1 $8.8 $9.0 $8.0 $12.2 (1.3) (0.8) (0.8) (0.8) (0.7) (1.1)
VIC $12.6 $9.7 $8.3 $8.4 $8.5 $11.5 (1.1) (0.8) (0.7) (0.7) (0.7) (1.0)
QLD $11.0 $9.0 $8.8 $7.1 $9.4 $9.9 (1.0) (0.8) (0.8) (0.6) (0.8) (0.9)
ACT $16.0 na na na $11.7 $16.0 (1.4) (1.0) (1.4)
All four $13.3 $9.1 $8.6 $8.5 $8.6 $11.5 (1.2) (0.8) (0.7) (0.7) (0.7) (1.00)
Note: Regions are aggregates of SLAs. Figures in bracket are multiples of $11.5. Convergent SLAs only.
Example of aggregating the microdata
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HOUSEMOD
Spatial model (SLA)Models receipt of Commonwealth Rent Assistance• Means-tested assistance to low income private renters
Can change rules of CRA and predict spatial impactHas been extended to add:• public renters as well• plus projections for 20 years
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% in unaffordable housing
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Application 3: Where to put govt offices? (access channel planning)
The Centrelink CuSP Model (Customer Service Projection Model)Centrelink needed an evidence based methodology to help:• match services available to customers’ needs and preferences • deliver the service via the most suitable channel and • in most efficient way.
CuSP model assists Centrelink strategic decision-making by:
• producing projections of Centrelink customers and channel use• over the next 5 years• for small areas• and under alternative scenarios about the future
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Projected % changes in customer numbers – 2002-07
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Application 4: Forecasting current & future need for services
CAREMOD model simulates current characteristics of older Australians at a detailed regional level (SLA)• Imputing functional status and thus likely need for different types of
care• Industry partners: NSW Dept of Ageing, Disability and Home Care
and Fed Dept of Health and Ageing (2003-2005)
Also new ARC grant 2007-2009 for examining spatial implications of population ageing over next 20 years (esp. for needs-based planning of govt services) –with four states and territories
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Man ly
Curt in
Eppi ng
Mos man
Burban k
Toow ong
Kenmore
Ipsw i ch
Cronul l a
Rose Ba yVaucl use
Red Hi llAshg ro ve
Lane Cov e
Bel levu e H il l
±
PERCENTAGE OF POPULATION AGED 55
S Y D N E YI N S E T
Quintiles (Percent)2.1 - 7.9
7.9 - 9.0
9.0 - 10.0
10.0 - 11.2
11.2 - 38.6
WITH CARE MODALITY 5
100 0 10050
Kilometres
YEARS AND OVER
% needing high level institutional care
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Where do self-funded retirees live
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Other forthcoming ARC funded & ARCRNSISS initiatives
By end 2006, estimates of poverty, housing stress & smoking expenditure in 03-04 to be available via web to ARCRNSISS members• At labour market area level• At SLA level• Based on new 03-04 Household Expenditure Survey
Developing index of social exclusion for childrenWould like to link the SLA estimates to administrative data about usage of government services• Eg do poor children use public health services more or less than
children from affluent families
Continue to refine the technology
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Evidence based policy making
Growing demand for decision support tools• Reduce risk to policy makers making billion dollar decisions• Assess distributional implications of policy change before
implemented• Improve predictive capacity & strategic planning
NATSEM has now constructed dozens of microsimulation models, based on ABS or admin microdataExciting new developments are• Spatial microsimulation models• Health and housing models• Next generation of dynamic models
For free copies of all publications as released, email hotline@natsem.canberra.edu.au
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Selected referencesSpatial Microsimulation (spatial estimates of poverty, disadvantage etc)Lloyd, R, Harding, A and Greenwell, H, 2001, 'Worlds apart: postcodes with the highest and lowest poverty rates in today's Australia', Eardley, A, and
Bradbury, B (eds), Refereed Proceedings of the National Social Policy Conference 2001, SPRC Report 1/02, pp. 279–97(www.sprc.unsw.edu.au)
Harding, A., Lloyd, R., Bill, A., and King, A., ‘Assessing Poverty and Inequality at a Detailed Regional Level – New Advances in Microsimulation’, in M McGillivray (ed), Perspectives on Human Wellbeing, UN University Press, Helsinki.
Taylor, E, Harding, A, Lloyd, R, Blake, M, “Housing Unaffordability at the Statistical Local Area Level: New Estimates Using Spatial Microsimulation”, Australasian Journal of Regional Studies, 2004, Volume 10, Number 3, pp 279-300
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. 303-336
Chin, S.F. and Harding, A. 2006, Regional Dimensions: Creating Synthetic Small-area Microdata and Spatial Microsimulation Models. Technical Paper no. 33, April*
Child Social Exclusion Index (small area index of social exclusion specifically developed for children)Harding, A., McNamara, J., Tanton, R., Daly, A., and Yap, M., “Poverty and disadvantage among Australian children: a spatial perspective” Paper for presentation at
29th General Conference of the International Association for Research in Income and Wealth , Joensuu, Finland, 20 – 26 August 2006
CuSP Model (spatial model for service delivery and access issues)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 model of 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 (www.pc.gov.au/research/confproc/qtmpa/qtmpa.pdf )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 *Lymer, S., Brown, L. Harding, A. Yap, M. Chin, S.F. and Leicester, S. Development of CareMod/05, NATSEM Technical Paper no. 32, March 2006*
HOUSEMOD (spatial model of housing assistance and housing issues)King, A and Melhuish, T, 2004, The regional impact of Commonwealth Rent Assistance, Final report, Australian Housing and Urban Research
Institute, Melbourne, November (www.ahuri.edu.au)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)DYNAMOD (dynamic microsimulation model - now being replaced by APPSIM)Kelly, S, Percival, R and Harding, A., 2001, ‘Women and superannuation in the 21st century: poverty or plenty?’, Eardley, A, and Bradbury, B (eds),
Refereed Proceedings of the National Social Policy Conference 2001, SPRC Report 1/02, pp. 223–47. (www.sprc.unsw.edu.au)Kelly, S, 2007, ‘ Self Provision In Retirement? Forecasting Future Household Wealth in Australia’ , in Harding, A and Gupta, A., Modelling Our
Future: Population Ageing, Social Security and Taxation (eds), North Holland, Amsterdam.Cassells, R., Harding, A. and Kelly, S., 2006, Problems and Prospects for Dynamic Microsimulation: A Review and Lessons for APPSIM. NATSEM
Discussion Paper no. 63, December *.
* Means available on NATSEM website at www.natsem.canberra.edu.au
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Selected referencesSTINMOD applications (static tax-benefit model)Toohey, M and Beer, G, 2004, Financial incentives to work for married mothers under A New Tax System’, Australian Journal of Labour Economics, vol. 7,
no. 1, p. 53–69, JanuaryHarding, A., Warren, N., Robinson, M. and Lambert, S., 2000, ‘The Distributional Impact of the Year 2000 Tax Reforms in Australia’, Agenda, Volume 7, No
1, pp 17-31. McNamara, J, Lloyd, R, Toohey, M and Harding, A, 2004, Prosperity for all? How low income families have fared in the boom times, Report commissioned
by the Australian Council of Social Service, the Brotherhood of St Laurence, Anglicare NSW, Family Services Australia, Canberra, October.* A. Harding, R. Lloyd & N. Warren, 2006, "The Distribution of Taxes and Government Benefits in Australia", in Dimitri Papadimitriou. (ed), The Distributional
Effects of Government Spending and Taxation, Chapter 7, Palgrave Macmillan, New York., pp. 176-201.Harding, A, Vu, Q.N, Percival, R & Beer, G, “ Welfare-to-Work Reforms: Impact on Sole Parents” Agenda, Volume 12, Number 3, 2005, pages 195-210
(www.agenda.anu.edu)Harding, A., Payne, A, Vu Q N and Percival, P., 2006, ‘Trends in Effective Marginal Tax Rates, 1996-97 to 2006-07’, ,AMP NATSEM Income and Wealth
Report Issue 14, September (available from www.amp.com.au/ampnatsemreports)Lloyd, R, 2007, ‘STINMOD: Use of a static microsimulation model in the policy process in Australia’, in Harding, A and Gupta, A., Modelling Our Future:
Population Ageing, Social Security and Taxation (eds), North Holland, Amsterdam.
CHILDMOD (static child support model)Ministerial Taskforce on Child Support, 2004, In the Best Interests of Children – Reforming the Child Support Scheme, Report of the Ministerial Taskforce
on Child Support, May (see Chap 16 for output from CHILDMOD) (http://www.facsia.gov.au/internet/facsinternet.nsf/family/childsupportreport.htm)
NSW Hospitals Model (spatial model of socio-economic status and hospital usage and costs)Walker, A., Thurect, L and Harding, A. 2006, Changes in hospitalisation rates and costs - – New South Wales, 1996-97 and 2000-01. The Australian
Economic Review, vol. 39, no. 4, pp. 391-408. (Dec)Walker, A. Pearse, J, Thurect, L and Harding, A. 2006, Hospital admissions by socioeconomic status: does the ‘inverse care law’ apply to older
Australians? Australian and New Zealand Journal of Public Health, vol. 30, no. 5, pp 467-73. (October)Thurecht, L, Bennett, D, Gibbs, A, Walker, A, Pearse, J and Harding, A., 2003, A Microsimulation Model of Hospital Patients: New South Wales, Technical
Paper No. 29, National Centre for Social and Economic Modelling, University of Canberra.*Thurecht, L, Walker, A, Harding, A, Pearse, J, 2005, ‘The “Inverse Care Law”, Population Ageing and the Hospital System: A Distributional Analysis’,
Economic Papers, Vol 24, No 1, March A, Walker, R, Percival, L, Thurecht, J, Pearce, 2005 “ Distributional Impact of Recent Changes in Private Health Insurance Policies” Australian Health
Review, 29(2),167-177, May
MediSim (static model of the Pharmaceutical Benefits Scheme)Brown. L., Abello, A., Phillips, B. and Harding A., 2004, "Moving towards an improved microsimulation model of the Australian PBS' Australian Economic
Review., 1st quarterAbello, A., Brown, L., Walker, A. and Thurecht, T., 2003, An Economic Forecasting Microsimulation Model of the Australian Pharmaceutical Benefits
Scheme, Technical Paper No. 30, National Centre for Social and Economic Modelling, University of Canberra.*Harding, A., Abello, A., Brown, L., and Phillips, B. 2004 The Distributional Impact of Government Outlays on the Australian Pharmaceutical Benefits
Scheme in 2001-02, Economic Record, Vol 80, Special Issue, SeptemberBrown, L., Abello, A. and Harding, A.2006. Pharmaceuticals Benefit Scheme: Effects of the Safety Net. Agenda, vol. 13, no. 3, pp211-224
HEALTHMOD (under development: model of hospital, medical and pharmaceutical sectors)Lymer, s, Brown, Payne, and Harding, 2006, ‘Development of HEALTHMOD: a model of the use and costs of medical services in Australia, 8th Nordic
Seminar on Microsimulation Models, Oslo, June (http://www.ssb.no/english/research_and_analysis/conferences/misi/)
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