Advances in Microsimulation Modelling: Improving Social

Transcript Advances in Microsimulation Modelling: Improving Social

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

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

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 700 New policy - disposable income 600 500 400 300 0 100 200 300 400 500 Private Income ($ pw) 600 700

EMTRs of sole parents with one child aged 8+, 2006-07 *

80 70 60 50 40 30 20 10 0 0 100 200 300 400 500 Private Income ($ pw) Current policy - EMTRs 600 New policy - EMTRs

* EMTR of 65% means that person keeps 35 cents from an additional dollar of earnings

700 800

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 Y Macro economics Deaths Simulation Clock New Quarter?

Immigration Couple Dissolution Pregnancy and Births Couple Formation Emigration Disability & Recovery New Year?

Y Education Annual Earnings And Savings Labour Force Australia 2050

Spatial models

Characteristics of available datasets

Population detail National sample surveys High Census of Popn & Housing Medium High Geographic detail Low High High

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

2001 Census data at SLA level: - XCP data for SLAs

UNIT RECORD DATA (SOURCE)

1998-99 Household Expenditure Survey

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 Household ID Weekly income Weekly rent Other variables Household weight

10 .

13964 .

7 8 9 1 2 3 4 5 6 6 .

6892 4 4 6 1 2 2 2 3 3 7 11 11 11 11 11 10 12 12 12 .

3 4 4 4 0 0 4 4 0 0 .

1029 157 157 157 1003 1003 70 70 703 703 .

7,122,000 Num of households in Aust … household weights of small-areas NSW SLA1 NSW SLA2 NSW SLA3 Other SLAs

2.45

0 0 3.27

3.27

0 0 0 0

12465

0 0 0 0 13.54

13.54

0 0 0 0 .

25853

Num of households in small areas

16.38

0 0 0 0 .

0 0 0 0

27940

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



Person level variables

Age Sex Social m arital 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 

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 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

Percentage of households in unaffordable housing

0.88% - 6.05% 6.06% - 9.48% 9.49% - 14.00% 14.01% - 29.27%

Chermside Nundah Clayfield Windsor Kelvin Grove Bowen Hills Fortitude Valley - Remainder Spring Hill Milton City - Remainder New Farm Toowong South Brisbane Kangaroo Point West End (Brisbane) Highgate Hill Taringa East Brisbane Indooroopilly St Lucia Dutton Park Fairfield Annerley Nathan Archerfield Ipswich (C) - Central Ipswich (C) - East Wacol Richlands Inala

Households in poverty (%) 0 - 8 9 - 12 13 - 17 18 - 30

Mount Isa (C) Boulia (S)

Cook (S) (excl. Weipa)

Cairns (C) - Northern Suburbs Cairns (C) - Trinity

Herberton (S) Etheridge (S)

Thuringowa (C) - Pt A Bal Longreach (S) Nebo (S) Belyando (S) Broadsound (S) Peak Downs (S) Emerald (S) Duaringa (S)

Mount Morgan (S)

Calliope (S) - Pt A

Miriam Vale (S) Kolan (S) Hervey Bay (C) - Pt B Tiaro (S) Wondai (S) Kilkivan (S) Nanango (S) Tara (S)

Pine Rivers (S) Bal Beaudesert (S) - Pt A Guanaba-Currumbin Valley

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%

(Aust average 17.7%

< 25 years 2%

(Aust average 4.1%)

25-34 years 27%

(Aust average 20.3%)

35-44 years 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 Tax threshold $6,000 $21,600 $58,000 $70,000 Tax rate 0.17

0.3

0.42

0.47

2005-06 Tax threshold $6,000 $21,600 $63,000 $95,000 Tax rate 0.15

0.3

0.42

0.47

Estimated average tax cut per household per week, Sydney

20 Km

$3.50 - $9.50 (lightest) $9.51 - $13.70

Legend

3.5 - 9.5

9.6 - 13.7

$19.31 - $34.10 (darkest)

19.4 - 34.1

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

NSW VIC QLD ACT All four $14.6 (1.3) $12.6 (1.1) $11.0 (1.0) $16.0 (1.4)

$13.3 (1.2)

$9.1 (0.8) $9.7 (0.8) $9.0 (0.8) na $9.1 (0.8) $8.8 (0.8) $8.3 (0.7) $8.8 (0.8) na $8.6 (0.7) $9.0 (0.8) $8.4 (0.7) $7.1 (0.6) na $8.5 (0.7) $8.0 (0.7) $8.5 (0.7)

$9.4 (0.8)

$11.7 (1.0) $8.6 (0.7)

Note:

Regions are aggregates of SLAs. Figures in bracket are multiples of $11.5. Convergent SLAs only.

All regions

$12.2 (1.1) $11.5 (1.0) $9.9 (0.9)

$16.0 (1.4)

$11.5 (1.00)

Example of aggregating the microdata

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 27

% in unaffordable housing

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

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 Ipsw i ch Ashg ro ve Red Hi ll Kenm or e Toow ong YEARS AND OVER Bur ban k WITH CARE MODALITY 5 S Y D N E Y I 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

100 50 0 Kilometres 100 Cur t in Eppi ng Lane Cov e Mos man Man ly Bel levu e H il l Vaucl use Rose Ba y Cr onul l a

Where do self-funded retirees live

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 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 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]

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, Income, Tax and Social Security Benefits

”

“

Spatial Microsimulation Using Synthetic Small Area Estimates of

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

Australians? , 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

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

., 1 st 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.