Second-Best Gains from Trade in Matching Markets

We study gains from trade (GFT) in two-sided matching markets with independent private types and arbitrary downward-closed feasibility constraints. The second-best benchmark is the maximum expected GFT achievable by a Bayesian incentive compatible, interim individually rational mechanism that is strongly budget balanced at every report profile. These constraints generally preclude attaining the first-best GFT and raise the question of how much efficiency must be lost. We prove that the second-best GFT is at least one half of the first-best GFT in every such matching market. This recovers and generalizes the recent $1\mathrm{/}2$ guarantee for bilateral trade by Liu et al. (2026) to markets with multiple buyers and sellers and arbitrary downward-closed feasibility constraints. Together with their matching lower bound for bilateral trade, our result establishes a tight worst-case ratio of $1\mathrm{/}2$ for this general class of matching markets. Our proof builds on the virtual-GFT framework of Brüstle et al. (EC 2017) to reduce the problem to a one-parameter Lagrangian. The main step is a geometric, edge-by-edge analysis based on first-best edge-selection regions, combined with a randomized contraction in rank space.