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讲座简介：
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Many countries use a centralized admissions system by which students are admitted to universities. That said, little is known about how changes to the centralized admissions policies impacts the allocation of students to colleges (majors). This paper uses a novel dataset from the University of Costa Rica (UCR) to address this question. A central challenge in doing so is recovering students' preferences over final assignments. The reason is that much like many centralized admissions systems, UCR restricts the number of options a student can report. This gives students an incentive to manipulate their reports. We propose a new methodology to recover a minimal set of preferences that are consistent with the data. To achieve this, we treat the students' decision problem as one associated with a large population, and impose two minimal rationality conditions on students' reports. We apply this methodology to the UCR dataset and use the recovered preferences to address counterfactuals. We show that 72% of the students receive a different allocation from what they would receive if they reported their full preference ranking (the benchmark). Finally, we consider alternate mechanisms for allocating students to seats. We show that a mechanism based on an ascending auction does not generate large distortions. Under that mechanism, only 6% of the students receive a different assignment from the benchmark. Moreover, it only reduces the aggregate welfare from the benchmark in 1% |
