Spatial Simulation for Education Policy Analysis in Ireland
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Transcript Spatial Simulation for Education Policy Analysis in Ireland
Spatial Simulation for
Education Policy
Analysis in Ireland
An Initial Exploration
Gillian Golden
University College Dublin
[email protected]
Overview
Individual level modelling for policy
analysis in the education sector –
proof of concept exercise
Exploiting statistical value of
available administrative data and
“Joined up data” – NSB Position
Papers December 2011
Spatial component - important for
planning and efficient resource
provision.
Microsimulation
Representing a system in terms of it’s individual units.
Often generated synthetically using fitting techniquesCensus small areas and PUMS
Model effect of a policy change on individuals and
aggregate the results
Can provide a more insightful picture of a complex
social system
Example – Integration in
Washington DC
Spatial Microsimulation
Statistical table
Ethnic Group
%
White alone
42.90%
Black or African American alone
50.10%
American Indian and Alaska Native
alone
0.60%
Asian alone
Hawaiian and Other Pacific Islander
alone
3.80%
Two or More Races
2.50%
0.20%
Irish Education System
Overall budget of €9 billion
annually.
Primary sector – approximately
3200 schools with 520,000 pupils
Traditional macro analysis – Value
for Money reviews, 2009 Special
Group on Public Service
Expenditure
Can spatial microsimulation add
value?
Data Sources
Irish Census of Population 2011
POWSCAR file
Department of Education and
Skills school XY coordinates
Other school level data
combined from databases
held in the Department
County Mayo chosen as test
geographic area
Methodology
POWSCAR fuzzy northing
and easting
Primary school XY data
Spatial Join Operation
Result - Individual level data
with contextual info on
pupil’s home and school
Data Cleaning Issues
Spatial Join – primary schools located next to each
other.
Geographical information not “fine grained” enough.
Alternative method to assign pupils to schools –
optimisation “bin packing” algorithm
School Census returns 2010-2011 used as “bin volume”
Pupils assigned to schools according to school size.
Primary and post-primary school co-located. Remove
records at random.
Data Cleaning- Bin Packing
Algorithm
Matched dataset – Irish Student
Simulation Model (ISSIM)
Rich Dataset
Many
Possibilities
Comparison with Department of
Education census records
Simulating school
Amalgamations
Can examine hypothetical scenarios
Example analysis – Close all schools
with less than 50 pupils and reassign
pupils to other schools
Distance calculated based on point
distance between school and
randomly generated point in small
area of residence
School Amalgamations
Variations in distance between home and school,
indicator of active school choice.
Proximity table of schools
Pupils reassigned to school nearest the one attended
before amalgamation
“Before and After” analyses of the effects of the
amalgamations
School Locations
Financial Effects
Smaller schools have a higher unit cost
Notional projected future cost of a teacher - €55,000
per annum
Capitation grants for additional school level staff,
school running costs etc
Computation of cost before and after simulating the
amalgamation
Financial Effects
Social Effects
Socio-Economic “Equality” in schools
DEIS Schools
ISSIM useful for targeting resources aimed at
alleviating educational disadvantage
DEIS programme designated schools
Community Effects
Add value to qualitative analysis also
Individual case studies possible
Local “catchment area” of school
Community effects of closure of small schools
ISSIM can add information to contextual analysis – to
what extent does the school serve the local
community?
Evaluation
Comprehensive dataset
Cost-effective insights compared to surveys
Possibilities to convert from microsimulation to agentbased model by including records with uncoded
place of school and assigning records to specific
households
From static to dynamic – enrolment and cost
projections
Evaluation
Data protection - Dataset
currently warehoused in
Department of Education
Strict access controls
Published material based on
POWSCAR must be approved
by CSO.
Next steps
Expand the model to cover all of Ireland
Develop standard “data cleaning” methodologies –
cities may present additional validation issues.
Examine some of the policy issues explored here in
more detail – initial focus on policies affecting primary
schools