סמינר מחלקתי

Matching and (misaligned) Incentives in Kidney Exchange

Dr. Itai Ashlagi – Stanford University

Abstract:

Growth of live-donor kidney exchange has stagnated although an increasing number of  transplant centers are facilitating exchanges through large national exchanges, in isolation or in small consortiums with other centers.

We first discuss  the problem of dynamic matching in a single kidney exchange program. We show that matching greedily does not harm the match rate among the set of batching policies, however, lack of collaboration between kidney exchange programs does affect the match rate.

In the second part of the talk we will discuss the kidney exchange market in the U.S as a whole:

Using a novel dataset combining all transplants from the US and the largest kidney exchange, we document significant fragmentation and heterogeneity in participation by centers, an adversely selected and hard to match national exchange pool, and inefficient transplants outside the largest exchange. Motivated by these facts, we model a kidney exchange as a producer of transplants that takes donors and recipients supplied by centers as inputs. The model shows that a decentralized market structure may or may not be efficient depending on the economies of scale, and that participation incentives may be misaligned. It also yields a simple sufficient statistics formula, based on a Ramsey-pricing rule, for the optimal mechanism. We then estimate the key statistics of a high-dimensional transplant production function using detailed administrative data from the National Kidney Registry. Our results suggest that the largest exchange is close to efficient scale, while individual transplant centers are considerably smaller. Moreover, the current rules of the exchange are far from optimal with a wide dispersion between marginal products of some pairs and the rewards provided to the transplant centers. This dispersion generates incentives for adverse selection and inefficient market fragmentation that results in an estimated 30% fewer transplants than possible with full participation by centers. Finally, we calculate a rewards scheme that would be implemented by the optimal mechanism.