Spatial Simulation for Education Policy Analysis in Ireland An Initial Exploration

Gillian GoldenUniversity College Dublin

gillian.golden@ucdconnect.ie

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 techniques-Census 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

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 3.80%Hawaiian and Other Pacific Islander alone 0.20%

Two or More Races 2.50%

Spatial Microsimulation

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)

Many Possibilities

Rich Dataset

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 agent-based 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