Transcript Spatial microsimulation approach: A journey of explanation and exploration!

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
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You are here because you are interested
•
•
You should see the usefulness of microsimulation
from some of the examples to follow?
•
•
(bold assumption!)
(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!
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Know how to laugh at terrible jokes
What is microsimulation?
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Microsimulation
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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
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Instead of creating one ‘national’ model
we create a series of smaller ‘local’
models = complex
Microsimulation ‘flavours’?
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Static Microsimulation - create a microdata set and
then policy analysis follows – e.g. Tax and benefit
modelling – IFS (UK Budget)
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Static Spatial Microsimulation - Same as above but
with an inbuilt geography (model presented here)
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Dynamic microsimulation – effects of policy over time
(e.g. CORSIM – Caldwell 1997)
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Dynamic Spatial microsimulation – effects of policy
over time and space (e.g. SimBritain – Ballas 2005)
Where is microsimulation used?
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for Tax and Benefit modelling in
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Australia (STINMOD)
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Canada (SPSD/M)
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USA (TRIM)
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UK (POLIMOD)
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EU (EUROMOD)
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Norwary (MOSART)
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Germany (SFB3)
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Netherlands (NEDYMAS)
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Belgium (STATION)
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Spain (GLADHISPANIA)
Where is spatial microsimulation
used?
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A few select examples
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Sweden (SVERIGE) – dynamic spatial model
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UK – SimCrime, SimHealth, Smoking
(Leeds/Bradford), SIMALBA (Scotland),
SimBritian
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Ireland – SMILE: Simulation Model for Irish
Local Economy
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Australia – SPATIALMSM
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NZ – limited use ... Testing reliability of
smoking prevalence in New Zealand .. Watch
this space?
A Case Study:
How to microsimulate?
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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
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By ... Reweighting existing data using
deterministic reweighting techniques (example
to follow)
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General formula :
NWi = Wi * CENij / SHSij
- see Ballas 2005; Campbell (2011) – E-thesis;
Campbell (forthcoming)
Smaller example: How to microsimulate?
Scottish Health Survey
Census
AGE / TENURE
OWN
RENT
AGE / TENURE
OWN
RENT
YOUNG
3
5
YOUNG
1
1
OLD
3
1
OLD
2
1
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?
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Data doesn’t exist elsewhere
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e.g. In the UK - Income, Smoking rates,
Alcohol, Obesity... At the small area and
individual level simultaneously
To explore `what-if’ policy options
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Examine distributional effects of policy
(socio-economic and demographics)
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Examine spatial effects of policy (by area –
aggregate to appropriate scale)
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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
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Large area of
Holyrood Park
stands out as
close to the
centre
Map reading
Note maps are QUNITILE maps
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Q1 = bottom 20% of distribution for Lothian Health Board
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Q5 = highest 20% of distribution for variable
“NEW” simulated data
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previously only available by Health Board (n=15)
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Microsimulated down to Output Areas (think meshblocks
in NZ) - n=42,604 in Scotland
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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
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Income (not so exciting in NZ? Or is it?)
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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
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Mental well-being: GHQ score
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Obesity: BMI
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Smoking
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Alcohol consumption
Mental
Health
(GHQ12)
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GHQ 0 = “happy”
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GHQ 1-3
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GHQ 4 or more =
“unhappy”
Mental
Health
(GHQ12)
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`Happy’ (GHQ 0) and
Q5 people in areas
clustered around `old
town’ and to the south.
Mental
Health
(GHQ12)
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`Unhappy’ (GHQ 4 or
more) people in areas
around North (e.g.
Leith) – mentally
distressed
Obesity
(BMI)
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4 categories
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Underweght
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Normal
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Overweight
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Obese – Focus on this
Obesity
(BMI)
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Highest proportions of
obese in areas
clustered around
North of Edinburgh
(e.g. Granton,
Muirhouse) and
around Holyrood Park
Smoking
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Non-smokers
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Ex smokers
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Less than 20 a day
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More than 20 a
day
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Non-Smokers in
areas clustered
around `old town’
and to the south
and west.
Smoking
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Smokers in areas
around Leith and
edges of
Edinburgh City
Alcohol
consumption
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Under (left) and
Over (right) daily
alcohol limits
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Female (top) and
Male (bottom)
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21 (14) units for
men (women) per
week
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Female pattern
hard to determine
– few clusters
Alcohol
consumption
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Female pattern
hard to determine
– few clusters
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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
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Glasgow, Single female, Housing association (Ten = HA) property,
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aged 50, Income approx £6,000 (Cat 6, Type = Low),
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Has an illness (Ill = 1)
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Semi-routine job (nssec8 = 6), low level of qualifications (Qual = 1)
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Deprived area (Dep =7)
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Housing benefit (HB = Y), No child benefit (CB = N)
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+ all the other Census and survey variables (“value added”)
Area Based
Policy Scenario:
Lothian and
Greater Glasgow
Health Boards
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Creating customised
queries: Heavy
smokers AND heavy
drinkers AND mentally
distressed AND obese
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top 10% of areas with
high risk (red)
Area Based
Policy Scenario:
Lothian Health
Boards
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Top 10% of areas
with low risk (blue)
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top 10% of areas
with high risk (red)
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If the last slide was
too much?
Policy Scenario:
Areas of High
Suicide Risk?
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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?
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Making more applied use of microdata created –
any suggestions from statistics NZ?
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Dynamic Spatial Microsimulation modeling predicting changes into the future
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Cross national comparisons – see Campbell
(forthcoming)- comparing Japan and UK
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Different contexts for Spatial Microsimulation (NZ
– SimAotearoa)
Research Ideas
Suggestions from Statistics NZ?
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Particularly looking for feedback from
you all on… areas of application and
Policy relevance?
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Economic (e.g. tax policy) or Health
(e.g. smoking, alcohol, obesity,
mental health, suicide) or ….. ?
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Opportunities for collaboration? Talk to
me
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‘adding value’ to existing data – any
thoughts?
Feel free to contact me with questions or suggestions
[email protected]
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Joint venture between:
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Department of Geography, UC
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Health and Disability Intelligence, Ministry of Health
Undertake policy relevant health research which is relevant to
Ministry of Health
Two key functions:
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Research
• Lab on 3rd floor Geog
• Employs staff
• House and supports students
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Studentships
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See website http://www.geohealth.canterbury.ac.nz