Matching in Dynamic Imbalanced Markets

TL;DR

This paper analyzes dynamic matching in markets with agents of varying match difficulty, showing that greedy policies outperform others in large markets by reducing wait times and increasing match rates, confirmed through kidney transplant data.

Contribution

It demonstrates that in large markets, greedy matching policies are optimal, balancing market thickness and matching speed, with empirical validation from kidney transplant data.

Findings

Greedy policy results in shorter waiting times.

Greedy policy matches more agents than other policies.

Empirical data from the National Kidney Registry supports theoretical results.

Abstract

We study dynamic matching in exchange markets with easy- and hard-to-match agents. A greedy policy, which attempts to match agents upon arrival, ignores the positive externality that waiting agents generate by facilitating future matchings. We prove that this trade-off between a ``thicker'' market and faster matching vanishes in large markets; A greedy policy leads to shorter waiting times, and more agents matched than any other policy. We empirically confirm these findings in data from the National Kidney Registry. Greedy matching achieves as many transplants as commonly-used policies (1.6\% more than monthly-batching), and shorter patient waiting times.