Stable Matching with Contingent Priorities

TL;DR

This paper develops a new model for stable many-to-one matchings with contingent priorities, allowing for flexible prioritization based on current assignments, and demonstrates its effectiveness using Chilean school choice data.

Contribution

It introduces a novel framework for implementing contingent priorities in matching markets, including mechanisms, stability analysis, and practical applications.

Findings

Contingent priority mechanisms can increase top preference matches.

The soft priority approach guarantees the existence of stable matchings.

Application to Chilean data shows improved sibling and top-choice assignments.

Abstract

Using school choice as a motivating example, we introduce a stylized model of a many-to-one matching market where the clearinghouse aims to implement contingent priorities, i.e., priorities that depend on the current assignment, to prioritize students with siblings and match them together. We provide a series of guidelines and introduce two natural approaches to implement them: (i) absolute, whereby a prioritized student can displace any student without siblings assigned to the school, and (ii) partial, whereby prioritized students can only displace students that have a less favorable lottery than their priority provider. We study several properties of the corresponding mechanisms, including the existence of a stable assignment under contingent priorities, the complexity of deciding whether there exists one, and its incentive properties. Furthermore, we introduce a soft version of these priorities to guarantee existence, and we provide mathematical programming formulations to find such stable matching or certify that one does not exist. Finally, using data from the Chilean school choice system, we show that our framework can significantly increase the number of students assigned to their top preference and the number of siblings assigned together relative to current practice.