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
What are microdata and
microsimulation models?
„
„
„
Focus on individuals or households
Start 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
2
Static models of taxes and
transfers
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 old
STINMOD 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, EMTRs
Constructed on top of Income Surveys
and Expenditure Surveys (2 versions)
4
5
6
7
Disposable income of sole parents
with one child aged 8+, 2006-07
Impact of 2005 ‘welfare to work’ budget changes
800
Current policy - disposable income
Disposable Income ($ pw)
700
New policy - disposable income
600
500
400
300
0
100
200
300
400
500
Private Income ($ pw)
600
700
800
8
EMTRs of sole parents with one child
aged 8+, 2006-07 *
80
70
60
ETR (%)
50
40
30
20
10
0
0
100
200
300
400
500
Private Income ($ pw)
Current policy - EMTRs
600
700
800
New policy - EMTRs
* EMTR of 65% means that person keeps 35 cents from an additional dollar of earnings
9
Dynamic models
Dynamic microsimulation modelling
„
„
„
„
„
„
Simulates the events that happen to ordinary
Australians over their lifetime
Starts 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 partners
Earlier version was DYNAMOD3
11
DYNAMOD3’s Simulation Cycle
Australia 2001
Macro-
Y economics
Simulation
Clock
Emigration
Deaths
Y
New
Quarter?
New Year?
Education
Couple
Formation
Immigration
Disability & Recovery
Couple
Dissolution
Pregnancy
and Births
Australia 2050
Annual
Earnings
And Savings
Labour
Force
12
Spatial models
Characteristics of available
datasets
National
sample
surveys
Census of
Popn &
Housing
?
Population
detail
High
Medium
High
Geographic
detail
Low
High
High
14
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)
15
Constructing small-area estimates
SMALL AREA DATA
UNIT RECORD DATA
(SOURCE)
2001 Census data at SLA
level:
1998-99 Household
Expenditure Survey
- XCP data for SLAs
Major data
preparation task
REWEIGHTING
USING
LINKING
VARIABLES
UNIT RECORD DATA
(AMENDED)
- Updated to 2001
Enhanced income
-
Iterative process to identify
a set of variables suitable
for reweighting
SMALL-AREA ESTIMATES
1) Unit record dataset
2) Set of weights for each SLA
16
What is ‘reweighting’?
turning the national household weights
in the HES survey file into …
Unit
record
1
2
3
4
5
6
7
8
9
10
.
.
13964
Household
ID
1
2
2
2
3
3
4
4
6
6
.
.
6892
Weekly
income
7
11
11
11
11
11
10
12
12
12
.
.
.
Weekly
Other
rent
variables
3
4
4
4
0
0
4
4
0
0
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Household
weight
… household weights
of small-areas
NSW
SLA1
NSW
SLA2
NSW Other
SLA3 SLAs
1029
157
157
157
1003
1003
70
70
703
703
.
.
.
0
0
0
0
2.45
2.45
0
0
3.27
3.27
.
.
.
0
0
0
0
13.54
13.54
0
0
0
0
.
.
.
0
0
0
0
16.38
16.38
0
0
0
0
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
7,122,000
Num of
households in
Aust
12465
25853
27940
.
Num of households in small
areas
17
Linkage variables available in the
2001 Census and 1998-99 HES
„
Person level variables
Age
Sex
„
Family level variables
Family type
Family income
Social marital status
Country of birth
Level of schooling
Non-school qualifications
Educational institution attending
Study status
Hours worked
Individual income
Occupation
Labour force status
Year of arrival
Relationship in household
„
Household level variables
Dwelling structure
Tenure type
Household income
Household type
Household size
Number of dependents
Number of cars
Rent paid
Mortgage repayments
18
Application 1: Analysis of Specific
Population Sub-Groups
„
Allows – for small areas:
•
•
„
identification and analysis of specific
socio-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
19
Chermside
Nundah
Percentage of households
in unaffordable housing
Clayfield
0.88% - 6.05%
6.06% - 9.48%
Windsor
9.49% - 14.00%
Kelvin Grove
Bowen Hills
14.01% - 29.27%
Spring Hill
Milton
City - Remainder
Fortitude Valley - Remainder
New Farm
Toowong
South BrisbaneKangaroo Point
West End (Brisbane)
East Brisbane
Highgate Hill
Taringa
St Lucia
Dutton Park
Indooroopilly
Fairfield
Annerley
Nathan
Archerfield
Wacol
wich (C) - Central
Richlands
Inala
Ipswich (C) East
20
Households in poverty (%)
0-8
9 - 12
13 - 17
18 - 30
Cook (S) (excl. Weipa)
Cairns (C) - Northern Suburbs
Cairns (C) - Trinity
Herberton (S)
Etheridge (S)
Thuringowa (C) - Pt A Bal
Mount Isa (C)
Nebo (S)
Belyando (S)
Broadsound (S)
Boulia (S)
Longreach (S)
Peak Downs (S)
Emerald (S)
Mount Morgan (S)
Duaringa (S) Calliope (S) - Pt A
Miriam Vale (S)
Kolan (S)
Hervey Bay (C
Tiaro (S)
Wondai (S) Kilkivan (S)
Nanango (S)
Tara (S)
Pine Rivers (S) Bal
Beaudesert (S) - Pt A
Guanaba-Currumbin Valley
21
Age profile of those in poverty in
postcode in metro Sydney
(Aust average 10.5%)
65+ years
2%
55-64 years
15%
(Aust average 12.3%)
45-54 years
16%
< 25 yea rs (Aust average 4.1%)
2%
25-34 years
27%
(Aust average 20.3%)
(Aust average 17.7%
35-44 yea rs
38%
(Aust average 35.1%
22
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
23
Where did the $5bn of 2005-06 tax
cuts go?
„
„
Updated 2001 population numbers to 2005-06 using
ABS estimates pop’n growth by SLA
Updated household incomes and rules of govt
programs to 2005-06
2004-05
T ax
threshold
2005-06
T ax rate
T ax
threshold
T ax rate
$6,000
0.17
$6,000
0.15
$21,600
0.3
$21,600
0.3
$58,000
$70,000
0.42
0.47
$63,000
$95,000
0.42
0.47
24
Estimated average tax cut per
household per week, Sydney
±
SLAs,
2005-06
20
Km
$3.50 - $9.50 (lightest)
$9.51 - $13.70
Legend
CUT_HH_TTA
$13.71
- $19.30
3.5 - 9.5
9.6 - 13.7
$19.31
- -$34.10
(darkest)
13.8
19.3
19.4 - 34.1
25
Estimated average dollar tax cut
per household per week, by
regions, 2005-06
State/
Territory
NSW
VIC
QLD
ACT
All four
Regional
town
Rural town
Rural area
All regions
$9.1
(0.8)
$9.7
(0.8)
$9.0
(0.8)
na
$8.8
(0.8)
$8.3
(0.7)
$8.8
(0.8)
na
$9.0
(0.8)
$8.4
(0.7)
$7.1
(0.6)
na
$9.1
(0.8)
$8.6
(0.7)
$8.5
(0.7)
$8.0
(0.7)
$8.5
(0.7)
$9.4
(0.8)
$11.7
(1.0)
$8.6
(0.7)
$12.2
(1.1)
$11.5
(1.0)
$9.9
(0.9)
$16.0
(1.4)
$11.5
(1.00)
Capital city Major urban
$14.6
(1.3)
$12.6
(1.1)
$11.0
(1.0)
$16.0
(1.4)
$13.3
(1.2)
Note: Regions are aggregates of SLAs. Figures in bracket are multiples of $11.5. Convergent SLAs only.
Example of aggregating the microdata
26
HOUSEMOD
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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 impact
Has been extended to add:
• public renters as well
• plus projections for 20 years
27
% in unaffordable housing
28
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
29
Projected % changes in customer numbers – 2002-07
30
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
31
% needing high level institutional care
PERCENTAGE OF POPULATION AGED 55 YEARS AND OVER
WITH CARE MODALITY 5
Ashg o
r ve
Red Hi l
Toow ong
Kenm or e
Bur ban k
Ipsw i ch
SYDNEY
INSET
Eppi ng
Man y
l
Lane Cov e
Mos man
Vaucl use
Rose Ba y
Bel levu eH il l
Cr onul l a
Cur t in
Quintiles (Percent)
2.1 - 7.9
7.9 - 9.0
9.0 - 10.0
10.0 - 11.2
11.2 - 38.6
±
10050 0
100
Kilometres
32
Where do self-funded retirees
live
33
Other forthcoming ARC funded &
ARCRNSISS initiatives
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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 children
Would 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
34
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
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„
•
NATSEM has now constructed dozens of
microsimulation models, based on ABS or admin
microdata
Exciting new developments are
• Spatial microsimulation models
• Health and housing models
• Next generation of dynamic models
For free copies of all publications as released, email
[email protected]
35
Selected references
Spatial 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 *.
36
* Means available on NATSEM website at www.natsem.canberra.edu.au
Selected references
STINMOD 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, January
Harding, 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 quarter
Abello, 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, September
Brown, 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/)
37