Spatial Simulation for Education Policy Analysis in Ireland An Initial Exploration Gillian Golden University College Dublin

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 alone42.90% Black or African American alone50.10% American Indian and Alaska Native alone0.60% Asian alone3.80% Hawaiian and Other Pacific Islander alone0.20% Two or More Races2.50% 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 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