Microsimulation in the UK: the current state of play Dr Paul Williamson Dept. of Geography University of Liverpool

ModelInstitutionStaticDynamicABMTax-benefitDemographicOtherPublic MultipleDWPXXXX MultipleTreasuryXXXX PPIXXX IFSXX OPERAStirlingXXX MOSESLeedsXX SCOTSIMLiverpoolXX SAGEMODSouthamptonXXX SimBritainSheffieldXXX EUROMODEssexXXX LawsonEssexX?XXX AndersonEssex S-ParamicsSIASXXX SimDELTADSCXXX SOCSIMLSEXXX CARESIMUEAXXX FEARLUSMLURIX ADaPTSouthamptonXX ?YorkX ?LeedsXXX Current MSM in the UK: an overview

% of non-fitting synthetic combinations PARTIALLY CONSTRAINED DISTRIBUTIONS DistributionRural (South West) ‘Middling England’ (East Mids.) Deprived industrial (North) Deprived urban (Outer London) SEG / Household composition 0000 SEG / Rooms Household composition / Dependants 0000 Dependants / Tenure0000 Sex / marital status / tenure Illness / sex Synthetically estimated spatial microdata

Telephony: 2005/6 Simulated household weekly telephone bill (landlines) (FES 2005/6) EEDA, LSOA level Ward level comparison with BT billing data (EEDA, Ward level) Spearman rho = , p < 0.001

Simulated ‘work time’ ONS Time-Use Survey (2001) and Census 2001 East of England, LSOA Validation: –(Spearman rho = , p < 0.001) –Strong correlation with Census 2001 ‘work time’

% More happy than usual

Happiness & Life Events EventCorrelation Top 5 negative associations Relationships (mine ending) Death (parent) Healthparent Death (other) Employment job loss Top Five positive associations Relationships (mine starting)0.160 Employment job gain0.097 Finance (house)0.097 Pregnancy (mine)0.084 Pregnancy (child's)0.053

OPERA (1) Costs (% disposable income) of various Local Tax structures

(2) Change in costs given changing Dementia prevalence

Workflow Research Object Portlet MOSES

Simulation Database Aggregate To Migrant Population Aggregate To Vacant Dwellings Migrant generation model Spatial Interaction Model Compute dwelling preference for each migrant Update Location and Dwelling Characteristics ‘Conventional’ migration distribution procedure

ABM in MOSES Observed MSM ABM

Modelling Individual Consumer Behaviour

ABM v. MSM?

t4t4 t1t1 t2t2 t3t3 The Global Epidemic Simulator

Modelling Needs and Resources of Older People to 2030 (MAP2030)

SOCSIM

Average number of living grand children and grand parents (complete)

CARESIM: adding new cohort of people now aged Need to simulate pensions & retirement

CONCLUSION In a number of fields UK MSM is world-leading Challenges All models Maintenance/updating/upgrading/validation Increased collaboration Increasing user base Academic models Greater public policy influence

SPARES

Household Income (y h ) Hours of labour supplied by household (h) h * =max h U= U( h, y h | X ) U Behavioural Labour Supply Modelling

S-Paramics

Yellow: actual forestry Green: suitable for forestry Red: ownership boundaries  Land use is based on more than suitability and (simple) economics i.e. Sociological factors (owner preferences) e.g. Grouse shooting FEARLUS – Land use model (ABM)

Estimated Yield Land Uses Land use selection £ Climate Market Conditions Calculation of Return Before Social Interactions YearlyCycle Land sales Neighbours’ Approval/Disapproval Land use Biophysical properties Pollution Estimated Social Acceptability After FEARLUS-W Return

Year 12Year 14Year 16 Year 17 No Reward With Social Approval

SimDELTA – model processes

Transport test: M18 spur New junction