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Spatial Simulation for Education Policy Analysis in Ireland

This paper explores the use of spatial microsimulation in education policy analysis in Ireland, specifically focusing on the primary school sector. The study utilizes individual-level modeling and statistical analysis of available administrative data to assess the impact of policy changes on individuals and aggregate the results. The paper discusses data sources, methodology, data cleaning issues, and various analyses conducted using the Irish Education System as an example. The findings highlight the potential of spatial microsimulation in providing a more insightful picture of complex social systems and its value in informing policy decisions. The paper concludes with suggestions for future research and expansion of the model to cover all of Ireland.

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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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