Working Papers

Abstract: We study the assignment of indivisible objects to individuals when transfers are not allowed. Previous literature has mainly focused on efficiency (from both ex-ante and ex-post perspectives) and individually fair assignments. As a result, egalitarian concerns have been overlooked. We draw inspiration from the assignment of apartments in housing cooperatives, where families consider egalitarianism of assignments as a first-order requirement. Specifically, they aim to avoid situations where some families receive their most preferred apartments while others are assigned options ranked very low in their preferences. Building on Rawls' concept of fairness, we introduce the notion of Rawlsian assignments. We prove that a unique Rawlsian assignment always exists. Furthermore, the Rawlsian rule is efficient and anonymous. To illustrate our analysis, we use preference data from housing cooperatives. We show that the Rawlsian assignment substantially improves, from an egalitarian perspective, both the probabilistic serial rule, and the rule currently used to assign apartments in the housing cooperatives.

Abstract: Recent literature shows that dynamic matching mechanisms may outperform the standard mechanisms to deliver desirable results. We highlight an under-explored design dimension, the time constraints that students face under such a dynamic mechanism. First, we theoretically explore the effect of time constraints and show that the outcome can be worse than the outcome produced by the student-proposing deferred acceptance mechanism. Second, we present evidence from the Inner Mongolian university admissions that time constraints can prevent dynamic mechanisms from achieving stable  outcomes, creating losers and winners among students.

Abstract: A classic trade-off that school districts face when deciding which matching algorithm to use is that it is not possible to always respect both priorities and preferences. The student-proposing deferred acceptance algorithm (DA) respects priorities but can lead to inefficient allocations. The top trading cycle algorithm (TTC) respects preferences but may violate priorities. We identify a new condition on school choice markets under which DA is efficient and there is a unique allocation that respects priorities. Our condition generalizes earlier conditions by placing restrictions on how preferences and priorities relate to one another only on the parts that are relevant for the assignment. We discuss how our condition sheds light on existing empirical findings. We show through simulations that our condition significantly expands the range of known environments for which DA is efficient.