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Donor Cycle Dynamics

Summary:
It’s an ill-wind that blows no good and in Allocating Scarce Organs, Dickert-Conlin, Elder and Teltser find that repealing motorcycle helmet laws generate large increases in the supply of deceased organ transplants. The supply shock, however, is just the experiment that the authors use to measure demand responses. It’s well known that the shortage of transplant organs has led to a long waiting-list. The waiting-list, however, is only the tip of the iceberg. Many people who could benefit from a transplant never bother getting on the list since their prospects are already so low. In addition, some people have access to substitutes for a deceased organ transplant namely a living donor. Finally, there is a quality tradeoff: as more organs become available the quality of the match may increase

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It’s an ill-wind that blows no good and in Allocating Scarce Organs, Dickert-Conlin, Elder and Teltser find that repealing motorcycle helmet laws generate large increases in the supply of deceased organ transplants. The supply shock, however, is just the experiment that the authors use to measure demand responses. It’s well known that the shortage of transplant organs has led to a long waiting-list. The waiting-list, however, is only the tip of the iceberg. Many people who could benefit from a transplant never bother getting on the list since their prospects are already so low. In addition, some people have access to substitutes for a deceased organ transplant namely a living donor. Finally, there is a quality tradeoff: as more organs become available the quality of the match may increase as people may pass on the first available organ to get a better match. The authors use the supply shock to study all these issues:

We find that transplant candidates respond strongly to local supply shocks, along two dimensions. First, for each new organ that becomes available in a market, roughly five new candidates join the local wait list. With detailed zip code data, we demonstrate that candidates listed in multiple locations and candidates living out-side of the local market disproportionately drive demand responses. Second, kidney transplant recipients substitute away from living-donor transplants. We estimate the largest crowd out of potential transplants from living donors who are neither blood relatives nor spouses, suggesting that these are the marginal cases in which the relative costs of living-donor and deceased-donor transplants are most influential. Taken together, these findings show that increases in the supply of organs generate demand behavior that at least partially offsets a shock’s direct effects. Presumably as a result of this offset, the average waiting time for an organ does not measurably decrease in response to a positive supply shock. However, for livers, hearts, lungs, and pancreases, we find evidence that an increase in the supply of deceased organs increases the probability that a transplant is successful, defined as graft survival. Among kidney transplant recipients, we hypothesize that living donor crowd out mitigates any health outcome gains resulting from increases in deceased-donor transplants.

In other words, increased organ availability increases the quality of the matches for organs that cannot be given by a living donor (hearts, lungs, pancreases, partially liver) but for kidneys some of the benefit of increased organ availability accrues to potential living donors who do not have to donate and this means that match quality does not substantially increase.

The authors also critique the geographic isolation of kidney donation regions. As I wrote when Steve Jobs received a kidney transplant:

Although there is no reason to think that Apple CEO Steve Jobs “jumped the line” to get his recent liver transplant, Jobs did have an advantage: He was able to choose which line to stand in.

Contrary to popular belief, transplant organs are not allocated solely according to medical need. Organs are allocated through a complex system of 58 transplant territories. Patients within each territory typically get first dibs on organs from that territory. That’s great if a patient happens to live in a territory with a lot of organ donors and relatively few demanders, but not so good for a patient living in New York, San Francisco or Los Angeles, where waiting lines are longest.

As a result of these “accidents of geography,” relatively healthy patients in some parts of the country get transplants while sicker patients in other parts of the country die waiting.

The post Donor Cycle Dynamics appeared first on Marginal REVOLUTION.

Alex Tabarrok
Alex Tabarrok is Bartley J. Madden Chair in Economics at the Mercatus Center at George Mason University and a professor of economics at George Mason University. He specializes in patent-system reform, the effectiveness of bounty hunters compared to the police, how judicial elections bias judges, and how local poverty rates impact trial decisions by juries. He also examines methods for increasing the supply of human organs for transplant, the regulation of pharmaceuticals by the FDA, and voting systems.

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