Preference estimation in centralized college admissions from reported lists

This paper proposes a practical and data-driven preference estimation method from reported lists in a deferred acceptance mechanism when there are incentives to report these lists strategically. Data on centralized college admissions from Turkey show many pieces of evidence that students construct their lists strategically according to their admission chances and previous years’ admission outcomes. We develop a preference estimation method to evaluate reported lists within the set of colleges that are considered accessible to each student. This method allows us to create personal choice sets and to estimate student preferences by making valid utility comparisons that are supported by data and theory. We show the robustness of our estimation method compared to the existing estimation methods. A counterfactual admission analysis based on our preference estimates suggests that students from low-SES households are better off under a student sorting rule only based on high school GPAs.