Saturday, October 15, 2016: 3:35 PM
An agent-based model is built to investigate the effects and policy implications of the following centralized student-assignment mechanisms on admission distribution in a computational environment reflecting the Taipei Senior School District: the Taipei mechanism (TM), serial dictatorship (SD), deferred acceptance (DA), the Boston mechanism (BM), and the Chinese Parallel mechanism (CP). TM is Taipei’s new school admission mechanism implemented in 2016, which is intended to transit the school system from tracking to partial mixing. SD, DA, and BM are the student assignment mechanisms commonly discussed in the literature. CP is the mechanism implemented by some provinces in China to transit the system from BM to DA. As in the Taipei school District, the model contains two regions; students in the city have higher incomes and test scores than those in the suburban area. There are ten schools in the model, half of which are located in the city area and averagely ranked higher than the schools in the suburban area initially. School rankings depend on the average scores of the senior student agents in each school and will be adjusted annually. Each year, 1,000 high-school candidate agents enter the system. As observed in the Taipei School District, most candidate agents prefer to attend highly ranked schools without considering transportation costs; however, among low-ranked schools, students prefer nearby schools than far-away schools. We simulate their admission results under different assumptions of students’ school-choice strategies and the numbers of choices allowed. For each scenario, we conduct 30 simulation runs, and there are 30 ticks (years) in each run. We then study the simulated distribution of the average family income of the freshmen in each school as the proxy for the distribution of educational opportunity. We also investigate the occurrence of justified envy among the top performing students and the welfare of the student agents in the bottom income quartile in each scenario.