Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys

72 Pages Posted: 9 Nov 2020 Last revised: 18 Nov 2021

See all articles by Nikhil Agarwal

Nikhil Agarwal

Massachusetts Institute of Technology (MIT) - Department of Economics; Yale University - Cowles Foundation

Charles Hodgson

Yale University - Department of Economics

Paulo Somaini

Stanford University - Department of Economics

Date Written: November 2020

Abstract

While the mechanism design paradigm emphasizes notions of efficiency based on agent preferences, policymakers often focus on alternative objectives. School districts emphasize educational achievement, and transplantation communities focus on patient survival. It is unclear whether choice-based mechanisms perform well when assessed based on these outcomes. This paper evaluates the assignment mechanism for allocating deceased donor kidneys on the basis of patient life-years from transplantation (LYFT). We examine the role of choice in increasing LYFT and compare equilibrium assignments to benchmarks that remove choice. Our model combines choices and outcomes in order to study how selection affects LYFT. We show how to identify and estimate the model using instruments derived from the mechanism. The estimates suggest that the design in use selects patients with better post-transplant survival prospects and matches them well, resulting in an average LYFT of 8.78, which is 0.92 years more than a random assignment. However, the maximum aggregate LYFT is 13.84. Realizing the majority of the gains requires transplanting relatively healthy patients, who would have longer life expectancies even without a transplant. Therefore, a policymaker faces a dilemma between transplanting patients who are sicker and those for whom life will be extended the longest.

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Suggested Citation

Agarwal, Nikhil and Hodgson, Charles and Somaini, Paulo, Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys (November 2020). NBER Working Paper No. w28064, Available at SSRN: https://ssrn.com/abstract=3727140 or http://dx.doi.org/10.2139/ssrn.3727140

Nikhil Agarwal (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

50 Memorial Drive
E52-391
Cambridge, MA 02142
United States

Yale University - Cowles Foundation ( email )

Box 208281
New Haven, CT 06520-8281
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Charles Hodgson

Yale University - Department of Economics ( email )

28 Hillhouse Ave
New Haven, CT 06520-8268
United States

Paulo Somaini

Stanford University - Department of Economics ( email )

Landau Economics Building
579 Serra Mall
Stanford, CA 94305-6072
United States

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