Spatial microsimulation techniques have become an increasingly popular way of fulfilling the need for generating small-area data estimates. However, the technique also poses numerous methodological challenges, including the utilisation of two different databases simultaneously to produce estimates of population characteristics at the local level. An important but neglected question is whether different distributions of key variables within these two databases may affect the validity of the spatial estimation results. This study uses the significant policy issue of small-area estimates of child poverty rates in Australia to examine this question. The different income distributions for families with children in the two databases and the consequent effect on child-poverty estimates are assessed, while the apparent validity of these synthetic small-area poverty rates is gauged.
2005Household Income and Income Distribution, Australia, 2003–04 Cat. No. 6523.0
3.
2006Census Dictionary Australia 2006 (Reissue) Cat. No. 2901.0
4.
2008Survey of Income and Housing—Confidentialised Unit Record Files, Technical Manual Australia 2005–06 (Second Edition) Cat. No. 6541.0
5.
AndersonB, 2007, “Creating small area income deprivation estimates for Wales: Spatial microsimulation modelling” Chimera WP2007–11, University of Essex, Colchester
6.
Aschan-LeygonieCMathianHSandersLMäkiläK, 2000, “A spatial microsimulation of population dynamics in Southern France: A model integrating individual decisions and spatial constraints”Advances in Complex Systems (ACS)3(1–4) 109–125
7.
AttreeP, 2004, “Growing up in disadvantage: A systematic review of the qualitative evidence”Child: Care, Health and Development30679–689
8.
BallasDClarkeG P, 2001, “Modelling the local impacts of national social policies: A spatial microsimulation approach”Environment and Planning C: Government and Policy19587–606
9.
BallasDClarkeGDorlingDEyreHThomasBRossiterD, 2005, “SimBritain: A spatial microsimulation approach to population dynamics”Population, Space and Place1113–34
10.
BallasDClarkeG PDewhurstJ, 2006, “Modelling the socio-economic impacts of major job loss or gain at the local level: A spatial microsimulation framework”Spatial Economic Analysis1127–146
11.
BellP, 2000GREGWT and TABLE Macros—Users GuideAustralian Bureau of Statistics
12.
BirkinMClarkeG PClarkeM, 1996, “Urban and regional modelling at the microscale” in Microsimulation for Urban and Regional Policy Analysis Ed. ClarkeG P (Pion, London) pp 10–27
13.
BradburyB, 1996, “Are the low-income self-employed poor?”, DP 73, Social Policy Research Centre, University of New South Wales, Sydney
14.
CaldwellS BClarkeG PKeisterL A, 1998, “Modelling regional changes in US household income and wealth: A research agenda”Environment and Planning C: Government and Policy16707–722
15.
CassellsRHardingAMirantiRTantonRMcNamaraJ, 2010, “Spatial microsimulation: Preparation of sample survey and census data for SpatialMSM/08 and SpatialMSM/09”, TP36, National Centre for Social and Economic Modelling, Canberra
16.
ChinS FHardingA, 2007, “SpatialMSM—NATSEM's small area household model for Australia”, in Modelling our Future: Population Ageing Health and Aged Care Eds GuptraAHardingA (Elsevier, Oxford) pp 563–566
17.
ChinS FHardingALloydRMcNamaraJPhillipsBVuQ N, 2005, “Spatial microsimulation using synthetic small area estimates of income, tax and social security benefits”Australasian Journal of Regional Studies11303–336
18.
ClarkeGHardingA, 2012“Conclusions and future research directions”, in Spatial Microsimulation: A Practitioners Handbook Eds TantonREdwardsK (Springer, New York) pp 259–273
GabrielMJacobsKArthursonKBurkeTYatesJ, 2005, “Conceptualising and measuring the housing affordability problem”, background report, Collaborative Research Venture 3: Housing Affordability for Lower Income Australians, Australian Housing and Urban Research Institute, Melbourne
21.
GhoshMRaoJ N K, 1994, “Small area estimation: An appraisal”Statistical Science955–76
22.
HanaokaKClarkeG P, 2007, “Spatial microsimulation modelling for retail market analysis at the small-area level”Computers, Environment and Urban Systems31162–187
23.
HardingAGuptaA, 2007, “Introduction and overview”, in Modelling Our Future: Population Ageing, Social Security and Taxation Eds HardingAGuptaAInternational Symposia in Economic Theory and Econometrics15 (Elsevier, Amsterdam) pp 1–29
24.
HardingASzukalskaA, 2000, “Trends in child poverty in Australia, 1982 to 1995–96”The Economic Record76(234) 236–254
25.
HardingAVuQ NTantonRVidyattamaY, 2009, “Improving work incentives for parents: The national and geographic impact of liberalising the Family Tax Benefit income test”The Economic Record85 (special issue) 48–58
26.
HardingAVidyattamaYTantonR, 2011, “Demographic change and the needs-based planning of government services: Projecting small area populations using spatial microsimulation”The Journal of Population Research28203–224
27.
HeadeyBWoodenM, 2006, “Jobless households: Longitudinal analysis of the persistence and determinants of joblessness”, Report 7, final report prepared for the Australian Government Department of Employment and Workplace Relations under the Social Policy Research Services Agreement, Melbourne Institute
28.
HomelRBurnsA, 1985, “Through a child's eyes: Quality of neighbourhood and quality of life”, in Living in Cities, Eds BurnleyIForrestJ (Allen and Unwin, Sydney) pp 103–115
29.
HynesSMorrisseyKO'DonoghueCClarkeG, 2009, “Building a static farm level spatial microsimulation model for rural development and agricultural policy analysis in Ireland”International Journal of Agricultural Resources, Governance and Ecology8282–299
30.
JohnsonDScutellaR, 2003, “Understanding and improving data quality relating to low-income households”, WP 18/03, Melbourne Institute, University of Melbourne, Victoria
31.
LymerSBrownLYapMHardingA, 2008, “Regional disability estimates for New South Wales in 2001 using spatial microsimulation”Applied Spatial Analysis and Policy1299–116
32.
LymerSBrownLHardingAYapM, 2009, “Predicting the need for aged care services at the small area level: The CAREMOD spatial microsimulation model”International Journal of Microsimulation2227–42
33.
McNamaraJCassellsRWicksPVidyattamaY, 2010, “Children in housing disadvantage in Australia: Development of a summary small area index”Housing Studies25625–646
34.
MarksG N, 2007, “Income poverty, subjective poverty and financial stress” FACSIA Social Policy RP29, FACSIA, Canberra
35.
MirantiRMcNamaraJTantonRHardingA, 2011, “Poverty at the local level: National and small area poverty estimates by family type for Australia in 2006”Applied Spatial Analysis and Policy4(3) 145–171
36.
NakayaTFotheringhamA SHanaokaKClarkeG PBallasDYanoK, 2007, “Combining microsimulation and spatial interaction models for retail location analysis”Journal of Geographical Systems9345–369
37.
PfeffermannD, 2002, “Small area estimation—new developments and directions”International Statistical Review70125–143
38.
RahmanAHardingATantonRLiuS, 2010, “Methodological issues in spatial microsimulation modelling for small area estimation”International Journal of Microsimulation3(2) 3–22
39.
SaundersP, 2005The Poverty Wars: Reconnecting Research with Reality (UNSW Press, Sydney)
40.
SaundersPBradburyB, 2006, “Monitoring trends in poverty and income distribution: Data, methodology and measurement”The Economic Record82258341–364
41.
SaundersPHillTBradburyB, 2008, “Poverty in Australia: Sensitivity analysis and recent trends” SPRC Report 4/08, Social Policy Research Centre, University of New South Wales, Sydney
42.
SiminskiPSaundersPWaseemSBradburyB, 2003, “Assessing the quality and inter-temporal comparability of ABS Household Income Distribution Survey Data”, DP 123, Social Policy Research Centre, University of New South Wales, Sydney
43.
SmithD MClarkeG PHarlandK, 2009, “Improving the synthetic data generation process in spatial microsimulation models”Environment and Planning A411251–1268
44.
SugikiNInomotoMMiyamotoKMoriT, 2005, “A micro-simulation model of metropolitan floor market representing mismatch between building stock and new urban activity generation”Journal of the Eastern Asia Society for Transportation Studies63760–3772
45.
TantonRWilliamsonPHardingA, 2007, “Comparing two methods of reweighting a survey file to small area data: Generalised regression and combinatorial optimisation” presented at the 1st General Conference of the International Microsimulation Association, Vienna; http://www.natsem.canberra.edu.au/storage/Tanton%20et%20al%20final.pdf
46.
TantonRVidyattamaYMcNamaraJVuQ NHardingA, 2009, “Old, single and poor: Using microsimulation and microdata to analyse poverty and the impact of policy change among older Australians”Economic Papers: A Journal of Applied Economics and Policy28102–120
47.
TantonRVidyattamaYNepalBMcNamaraJ, 2011, “Small area estimation using a reweighting algorithm”Journal of the Royal Statistical Society Series A (Statistics in Society)174931–951
48.
TiglaoN C CTsutsumiM, 2005, “Modeling households and location choices in metro Manila using spatial microsimulation approach”Proceedings of the Eastern Asia Society for Transportation Studies62179–2194
49.
van LeeuwenEClarkeG PRietveldP, 2009, “Microsimulation as a tool in spatial decision making: Simulation of retail developments in a Dutch town”, in New Frontiers in Microsimulation Modelling Eds ZaidiAHardingAWilliamsonP (Ashgate, Aldershot, Hants) pp 97–122
50.
VidyattamaYCassellsRCorcoranJ, 2010, “Trapped in jobless household areas: The spatiotemporal dynamics of children in jobless households in metropolitan Australia”Australian Geographer41367–389
51.
VidyattamaYCassellsRHardingAMcNamaraJ, 2011, “Rich or poor in retirement? A small area analysis of Australian private superannuation savings in 2006 using spatial microsimulation”Regional Studies DOI:10.1080/00343404.2011.589829
52.
VoasDWilliamsonP, 2000, “An evaluation of the combinatorial optimisation approach to the creation of synthetic microdata”International Journal of Population Geography6349–366
53.
VuQ NTantonR, 2010, “The distributional and regional impact of the Australian Government's Household Stimulus Package”Australasian Journal of Regional Studies16127–145
54.
WilliamsonP, 2001, “A comparison of synthetic reconstruction and combinatorial optimisation approaches to the creation of small-area microdata” WP2001/2, Population Microdata Unit, Department of Geography, University of Liverpool
55.
WilliamsonPBirkinMReesP, 1998, “The estimation of population microdata by using data from small area statistics and samples of anonymised records”Environment and Planning A30785–816
56.
ZaidiAHardingAWilliamsonP, (Eds), 2009New Frontiers in Microsimulation Modelling (Ashgate, Aldershot, Hants)
