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Spatial Simulation for Education Policy Analysis in Ireland

Spatial Simulation for Education Policy Analysis in Ireland. An Initial Exploration. Gillian Golden University College Dublin gillian.golden@ucdconnect.ie. Overview. Individual level modelling for policy analysis in the education sector – proof of concept exercise

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Spatial Simulation for Education Policy Analysis in Ireland

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  1. Spatial Simulation for Education Policy Analysis in Ireland An Initial Exploration Gillian Golden University College Dublin gillian.golden@ucdconnect.ie

  2. 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.

  3. 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

  4. Example – Integration in Washington DC Spatial Microsimulation Statistical table

  5. 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 microsimulationadd value?

  6. 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

  7. 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

  8. 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.

  9. Data Cleaning- Bin Packing Algorithm

  10. Matched dataset – Irish Student Simulation Model (ISSIM) Rich Dataset Many Possibilities

  11. Comparison with Department of Education census records

  12. 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

  13. 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

  14. School Locations

  15. 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

  16. Financial Effects

  17. Social Effects • Socio-Economic “Equality” in schools

  18. DEIS Schools • ISSIM useful for targeting resources aimed at alleviating educational disadvantage • DEIS programme designated schools

  19. 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?

  20. 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

  21. Evaluation • Data protection - Dataset currently warehoused in Department of Education • Strict access controls • Published material based on POWSCAR must be approved by CSO.

  22. 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

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