An agent-based model of school choice with information asymmetries
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Going from a neighbourhood-based to a choice-based system of school selection can have positive effects on enrolment in higher achievement schools, increasing average student achievement. We develop an Agent-Based model (ABM) that simulates students’ decisions on a heterogeneous agents’ framework with information asymmetries between income levels, allowing to simulate school choice policies and determine their impact on school enrolment and average student achievement. We use data from Santiago schools to initialise the model and study the impact of a discrete information signal of school achievement, as a policy implemented in 2010 in Chile called traffic lights.