Spatial microsimulation approach: A journey of explanation and exploration!

Dr Malcolm Campbell

Director Geohealth Laboratory and Department of Geography,

University of Canterbury, Christchurch, NZ

Contents• What is Microsimulation?

• Why might it be useful and policy relevant?

• How does Microsimulation help illuminate wealth and health variations?

• The power of using Spatial Microsimulation

• Policy scenarios

• Future research

• Questions and discussion

Some assumptions• You are here because you are interested

• (bold assumption!)

• You should see the usefulness of microsimulation from some of the examples to follow?

• (or I am in trouble?)

• A basic grasp of stats?

• (or you are in trouble?)

• You may already have some ideas about how microsimulation could be used?

• I am going to try and cover a wide range of areas and use maps – because geography matters!

• Know how to laugh at terrible jokes

What is microsimulation?• Microsimulation

• is a technique used to create simulated data by combining, or merging various datasets to `populate' and therefore create a `new' synthetic population that is as close as possible to the `real’ population

• Spatial Microsimulation

• Same as above but with an inbuilt geography

• Instead of creating one ‘national’ model we create a series of smaller ‘local’ models = complex

Microsimulation ‘flavours’?• Static Microsimulation - create a microdata set and

then policy analysis follows – e.g. Tax and benefit modelling – IFS (UK Budget)

• Static Spatial Microsimulation - Same as above but with an inbuilt geography (model presented here)

• Dynamic microsimulation – effects of policy over time (e.g. CORSIM – Caldwell 1997)

• Dynamic Spatial microsimulation – effects of policy over time and space (e.g. SimBritain – Ballas 2005)

Where is microsimulation used?• for Tax and Benefit modelling in

• Australia (STINMOD)

• Canada (SPSD/M)

• USA (TRIM)

• UK (POLIMOD)

• EU (EUROMOD)

• Norwary (MOSART)

• Germany (SFB3)

• Netherlands (NEDYMAS)

• Belgium (STATION)

• Spain (GLADHISPANIA)

Where is spatial microsimulation used?

• A few select examples

• Sweden (SVERIGE) – dynamic spatial model

• UK – SimCrime, SimHealth, Smoking (Leeds/Bradford), SIMALBA (Scotland), SimBritian

• Ireland – SMILE: Simulation Model for Irish Local Economy

• Australia – SPATIALMSM

• NZ – limited use ... Testing reliability of smoking prevalence in New Zealand .. Watch this space?

A Case Study:How to microsimulate?

• To build the model (SIMALBA) data from the Scottish Health Survey (SHS) and the UK Census of Population were merged to create the `new’ microdata at various spatial scales

• By ... Reweighting existing data using deterministic reweighting techniques (example to follow)

• General formula :

NWi = Wi * CENij / SHSij

- see Ballas 2005; Campbell (2011) – E-thesis; Campbell (forthcoming)

Smaller example: How to microsimulate?

Scottish Health Survey

AGE / TENURE OWN RENT

YOUNG 3 5

OLD 3 1

AGE / TENURE OWN RENT

YOUNG 1 1

OLD 2 1

Census

ID TENURE AGE WEIGHT CALC NEWWEIGHT

1 OWN OLD 1 1 * 3 / 2 1.5

2 OWN OLD 1 1 * 3 / 2 1.5

3 OWN YOUNG 1 1 * 3 / 1 3.0

4 RENT OLD 1 1 * 1 / 1 1.0

5 RENT YOUNG 1 1 * 5 / 1 5.0

NWi = Wi * CENij / SHSij Sum =12

Why microsimulate?• Data doesn’t exist elsewhere

• e.g. In the UK - Income, Smoking rates, Alcohol, Obesity... At the small area and individual level simultaneously

• To explore `what-if’ policy options

• Examine distributional effects of policy (socio-economic and demographics)

• Examine spatial effects of policy (by area – aggregate to appropriate scale)

• Can model policy before implementation to study the effects

Wealth variations using Spatial Microsimulation:An example from Scotland (a similar sized country to NZ?)

Focus on Edinburgh Output Areas

• Large area of Holyrood Park stands out as close to the centre

Map readingNote maps are QUNITILE maps

• Q1 = bottom 20% of distribution for Lothian Health Board

• Q5 = highest 20% of distribution for variable

“NEW” simulated data

• previously only available by Health Board (n=15)

• Microsimulated down to Output Areas (think meshblocks in NZ) - n=42,604 in Scotland

• The minimum OA size is 20 resident households and 50 resident people, target size was 50 households.

Map reading

(New) simulated ‘economic’ variables at output area geography – note: individual data also exists

• Income (not so exciting in NZ? Or is it?)

• Housing and Child Benefits

High Earners: £150,000 or more (50% tax rate – ‘losers’)

High earners appear more concentrated in areas in the west of Edinburgh (Q5), absent from low income areas (Q1) next slide

Low Earners: up to £10,400 (possible 0% tax rate)Low earners appear more concentrated in the areas around north of Edinburgh and to the western edges (Q5)

Policy Scenario: Low Earners: £10,400 (possible 0% tax rate – ‘winners’)

The spatial distribution of those who would gain from an increase in tax free threshold (relevant to NZ?)Can also estimate the income gain in each area and nationally

Health variations using Spatial Microsimulation:An example from Scotland (a similar sized country to NZ?)

Map reading

Four (new) simulated health variables at output area geography – note: individual data also exists

• Mental well-being: GHQ score

• Obesity: BMI

• Smoking

• Alcohol consumption

Mental Health (GHQ12)• GHQ 0 = “happy”

• GHQ 1-3

• GHQ 4 or more = “unhappy”

Mental Health (GHQ12)• `Happy’ (GHQ 0) and

Q5 people in areas clustered around `old town’ and to the south.

Mental Health (GHQ12)• `Unhappy’ (GHQ 4 or

more) people in areas around North (e.g. Leith) – mentally distressed

Obesity (BMI)

• 4 categories

• Underweght

• Normal

• Overweight

• Obese – Focus on this

Obesity (BMI)

• Highest proportions of obese in areas clustered around North of Edinburgh (e.g. Granton, Muirhouse) and around Holyrood Park

Smoking

• Non-smokers

• Ex smokers

• Less than 20 a day

• More than 20 a day

• Non-Smokers in areas clustered around `old town’ and to the south and west.

Smoking

• Smokers in areas around Leith and edges of Edinburgh City

Alcohol consumption• Under (left) and

Over (right) daily alcohol limits

• Female (top) and Male (bottom)

• 21 (14) units for men (women) per week

• Female pattern hard to determine – few clusters

Alcohol consumption

• Female pattern hard to determine – few clusters

• Men over limits in areas clustered to the south of the City.

The `added value’ of Spatial Microsimulation

Policy Scenario: Individual “stories”

By combining survey data with census data

• Glasgow, Single female, Housing association (Ten = HA) property,

• aged 50, Income approx £6,000 (Cat 6, Type = Low),

• Has an illness (Ill = 1)

• Semi-routine job (nssec8 = 6), low level of qualifications (Qual = 1)

• Deprived area (Dep =7)

• Housing benefit (HB = Y), No child benefit (CB = N)

• + all the other Census and survey variables (“value added”)

Area Based Policy Scenario: Lothian and Greater Glasgow Health Boards• Creating customised

queries: Heavy smokers AND heavy drinkers AND mentally distressed AND obese

• top 10% of areas with high risk (red)

Area Based Policy Scenario: Lothian Health Boards

• Top 10% of areas with low risk (blue)

• top 10% of areas with high risk (red)

• If the last slide was too much?

Policy Scenario: Areas of High Suicide Risk?

• Men under 25 years old, with a GHQ score of 4 or more (`unhappy’) a potential suicide risk

• Microsimulation allows a range of scenarios to be modeled

Future Research?• Making more applied use of microdata created –

any suggestions from statistics NZ?

• Dynamic Spatial Microsimulation modeling -predicting changes into the future

• Cross national comparisons – see Campbell (forthcoming)- comparing Japan and UK

• Different contexts for Spatial Microsimulation (NZ – SimAotearoa)

Research IdeasSuggestions from Statistics NZ?

• Particularly looking for feedback from you all on… areas of application and Policy relevance?

• Economic (e.g. tax policy) or Health (e.g. smoking, alcohol, obesity, mental health, suicide) or ….. ?

• Opportunities for collaboration? Talk to me

• ‘adding value’ to existing data – any thoughts?