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A microsimulation model for forecasting education

Date post: 29-Jun-2015
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A dynamic microsimulation model for forecasting educational patterns is presented. At the level of individuals the model simulates lifetime educational behavior, resulting in a long term forecast of the general educational level in Denmark. The model is a light-weight, dynamic, multithreaded and closed microsimulation model using discrete time.
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A microsimulation model for forecasting education Niels Erik Kaaber Rasmussen, DREAM European Meeting of the International Microsimulation Association Maastricht, 23-24 October
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Page 1: A microsimulation model for forecasting education

A microsimulation model for forecasting education

Niels Erik Kaaber Rasmussen, DREAM

European Meeting of the International Microsimulation AssociationMaastricht, 23-24 October

Page 2: A microsimulation model for forecasting education

Why?

• Education is important• Rich educational data on individual level• Full population data for current and

forthcoming generations – extrogenous• Using machine learning technique to handle

transition probabilities• Light multi-threaded setup for fast computation

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Population Education Labor market DREAM(Economy)

Part of the DREAM-system

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What answers are we looking for?

• What will be the general level of education in Denmark in 2050?

• What if behavoir changes? – Increase in drop out rate– Decrease in enrollment– …

• How does this effect long term fiscal sustainability?

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Model charateristics

• Dynamic microsimulation• Full Danish population• Longitudinal model• Unit-wise updating• Closed model• Stochastic• Discrete time

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ProgramProbabilitiesStatistics

output

PopulationEducation

Person

Full Danish population from 2014 to 2130 (total of 18.8 million people) in 2.5 minutes

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Simulation

[Show visualisation]

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Transition probabilities

• Smoothing• Extrapolation• Grouping with decision trees

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Smoothing transition probabilities

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Extrapolation of trends

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Raw transition probabilities

• Transition probabilty = historical frequency• Behavoir depends on many characteristics• Data is too sparse• Too much noise

• Transition probabilities depends on: Gender, origin, age, highest education, current participation in education (2x5x50x12x12=72.000). And more to come…

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Conditional inference trees• Decision tree• Groups observations in a way so that there’s a:– minimum of variation within a group– maximum variation across groups

• Data-mining approach• Based on statistical tests

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Origin = Immigrant from western country

Yes No

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CTREE algorithm1. Test for independence between any of the

explanatory variables and the responsea) Stop if p>0.05

2. Select the input variable with strongest association to the response.

3. Find best binary split point for the selected input variable.

4. Recursively repeat from step 1 until a stop criterion is reached.

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Current participation in education

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17-65 years olds by highest level of education

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Further developments

• Spartial dimension: 98 municipalities• Social inheritance: Educational level of parents

• Stronger path-dependencies?• In SMILE DK-context: Related educational

events to moving patterns, demography, labour market behavoir and more…


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