1 / 25

3 rd Annual IE-SEBA Workshop 26-27 October 2012

Projection of the Educational Attainment of the Hungarian Population from 2001 to 2020 Modelling Education with a Dynamic Microsimulation Model – ISMIK Zoltán Hermann – Júlia Varga. 3 rd Annual IE-SEBA Workshop 26-27 October 2012. Outline.

sharon-carney
Download Presentation

3 rd Annual IE-SEBA Workshop 26-27 October 2012

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Projection of the Educational Attainment of the Hungarian Population from 2001 to 2020Modelling Education with a Dynamic Microsimulation Model – ISMIKZoltán Hermann – Júlia Varga 3rd Annual IE-SEBA Workshop 26-27 October 2012

  2. Outline ▪ Microsimulation models for projecting educational attainment ▪ Main characteristics of the ISMIK model ▪ Results of base scenario ▪ Results of other scenarios

  3. What is microsimulation • events can be best explained at the level on which decisions are made • microsimulation models use simulation techniques and take micro-level units as the basic units of analysis • microsimulation models are used for different purposes: projections, evaluations of public policy or designing policy reform etc

  4. Differences in microsimulation models • Base dataset selection– Cross-Sectional or Synthetic • Cohort or Longitudinal Models • Behavioural Equations or Probabilistic Based Modelling • Static or Dynamic Microsimulation • Discrete Time or Continuous Time

  5. Microsimulation models Events in microsimulation: each micro unit in each period has certain (transition) probabilities of experiencing transitions from one state to another. Each of these transition probabilities constitute an event, and depends on each person’s characteristics Drawing in microsimulation: stochastic drawing, often called Monte Carlo technique

  6. Dynamic cross-section microsimulation Simulation by changing micro unit's characteristics based on the model's institutional and behavioural relations -------------------- Dynamic aging: each micro unit is aged for each time period byempirically based survivor probabilities Cross-section with n micro units time: t Cross-section with m micro units time: t + 1

  7. Some dynamic microsimulation models modelling education DYNAMOD - Australia MOSART - Norway LifePath - Canada APPSIM - Asztralia SAGE - UK (LSE) SESIM - Sweden GAMEO - France

  8. Main characteristics of the ISMIK model

  9. ISMIK model Initial population - 50 per cent sample of the 2001 Census data for 5 096 323 individuals defining „Roma” status with microsimulation the number of population with Roma status - corresponds with the number of population being considered to Roma according to representative sociological surveys

  10. ISMIK model • Datasets for estimating transition probabilities • 2006-2009 waves of Hungarian Life Course Survey conducted by TÁRKI- • Educatio Kht.. • - Public Education Statistics of Ministry of Education (KIRTAT). Data for years • 2001-2010 • Individual level Higher Education Application-Admission Statistics of Ministry of • Education (FELVI) , Data for years 2001-2009 • - Higher Education Statistics of Ministry of Education • - 2001 Census • - 2005 Micro Census • - Follow-up Survey of Hungarian Higher Education Graduates (DPR) 2010

  11. Events in the ISMIK models and estimation of transition probabilities

  12. Results of base scenario – „ everything goes on as in the early 2000s” Distribution of 25-64 year olds by highest educational attainment per cent Educational attainment of the 25-64 year olds will be increasing between 2010 and 2020 but slower than between t 2000 and 2010

  13. Results of base scenario In the young age groups the decrease of the ratio of undereducated will stop

  14. Results of base scenario In the young age groups the decrease of the ratio of population whose highest educational attainment is vocational training school will stop

  15. Results of base scenario In the young age groups the increase of the ratio of population whose highest educational attainment is upper secondary school will stop

  16. Results of base scenario In the young age groups the increase of the ratio of population who has attained tertiary education will stop

  17. Results of base scenario - differences by gender

  18. Results of base scenario – differences by gender

  19. what would have been educational attainment of the population had Roma and Non-Roma students had the same opportunities in their schooling career. Results of scenario 2 The ratio of population with at most lower secondary education would be smaller by 3 percentage points

  20. Results of scenario 2 what would have been educational attainment of the population had Roma and Non-Roma students had the same opportunities in their schooling career Ratio of population with at most lower secondary education by regions 23-24 year olds Regional differences in educational attainment would decrease ■Basescenario■ Scenario 2

  21. Thank you for your attention

More Related