Creating Socio-Economic Household Data at the Small Area Level: An Introduction to Spatial Microsimulation

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Creating Socio-Economic Household Data at the
Small Area Level An Introduction to Spatial
Microsimulation

• Ann Harding
• Presentation to Department of Geography Seminar
  Series, University of California Santa Barbara,
  USA, 12 May 2008

National Centre for Social and Economic
Modelling (NATSEM), University of Canberra
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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

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Static models of taxes and transfers
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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)

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Disposable income of sole parents with one child
aged 8, 2006-07 Impact of 2005
welfare to work budget changes
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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
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Dynamic models
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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

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DYNAMOD3s Simulation Cycle
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Spatial models
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Characteristics of available datasets
National sample surveys Census of Popn Housing ?
Population detail High Medium High
Geographic detail Low High High
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Synthetic Spatial Microdata

• SolutionCombine the information-rich survey
  data with the geographically disaggregated Census
  data
• Using spatial microsimulation (synthetic
  estimation) tocreate detailed unit record data
  for small areas(synthetic spatial microdata)

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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
UNIT RECORD DATA

REWEIGHTING
(AMENDED)

USING


LINKING

-


Updated to 2001
VARIABLES

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

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What is reweighting?
turning the national household weights in the
HES survey file into
household weights of small-areas

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Linkage variables available in the 2001 Census
and 1998-99 HES

• Family level variables
• Family type
• Family income
• Household level variables
• Dwelling structure
• Tenure type
• Household income
• Household type
• Household size
• Number of dependents
• Number of cars
• Rent paid
• Mortgage repayments

• Person level variables
• Age
• Sex
• 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

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Application 1 Analysis of Specific Population
Sub-Groups

• Allows for small areas
• identification and analysis of specificsocio-demo
  graphic groups and characteristics
• analysis at various population levelse.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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Age profile of those in poverty in postcode in
metro Sydney
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Application 2 Predict spatial impact of a policy
change

• Spatial microdata now linked with NATSEMs
  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 popn growth by SLA
• Updated household incomes and rules of govt
  programs to 2005-06

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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
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
  impact
• Has 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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needing high level institutional care
YEARS AND OVER
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Where do self-funded retirees live
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Other forthcoming ARC funded ARCRNSISS
initiatives

• By end 2008, estimates of poverty, housing stress
  smoking expenditure to be available via web to
  ARCRNSISS members
• At labour market area level
• At SLA level
• Based on new 03-04 Income and Household
  Expenditure Surveys
• Have also developed small area 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

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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
  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
• hotline_at_natsem.canberra.edu.au

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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. 27997
  (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 Centrelinks 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/confp
  roc/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)

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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. 5369, 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/in
  ternet/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