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Biological, Behavioural and Spurious Selection on the Kidney Transplant Waitlist

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  • Kastoryano, Stephen

    (University of Reading)

Abstract

The kidney allocation system aims to distribute kidneys from deceased donors in an equitable and potential-life optimising manner. This is a difficult task, not least because intrinsic biological differences, such as a person's ABO blood type, influence the allocation. This paper begins by presenting a curious and undocumented empirical fact: candidates on the kidney transplant waitlist with blood types implying they will more rapidly be offered a kidney display lower pre-transplant survival. The paper investigates whether this difference in pre-transplant survival is due to biological, behavioural, or spurious selection. To that end, we promote a two-in-one randomization design which allows us to credibly fit our empirical setting within a dynamic potential outcomes framework. Using this framework, drawing from economic theory, and noting problematic financial and legal market incentives, the paper systematically evaluates different explanations for pre-transplant survival patterns. Our analysis establishes a small set of behavioural explanations which directly inform debates about how to reduce the excessive discard of viable kidneys in the US transplant market.

Suggested Citation

  • Kastoryano, Stephen, 2024. "Biological, Behavioural and Spurious Selection on the Kidney Transplant Waitlist," IZA Discussion Papers 16995, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp16995
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    References listed on IDEAS

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    1. Tayfun Sönmez & Alvin E. Roth & M. Utku Ünver, 2007. "Efficient Kidney Exchange: Coincidence of Wants in Markets with Compatibility-Based Preferences," American Economic Review, American Economic Association, vol. 97(3), pages 828-851, June.
    2. Nikhil Agarwal & Charles Hodgson & Paulo Somaini, 2020. "Choices and Outcomes in Assignment Mechanisms: The Allocation of Deceased Donor Kidneys," NBER Working Papers 28064, National Bureau of Economic Research, Inc.
    3. Nikhil Agarwal & Itai Ashlagi & Eduardo Azevedo & Clayton R. Featherstone & Ömer Karaduman, 2019. "Market Failure in Kidney Exchange," American Economic Review, American Economic Association, vol. 109(11), pages 4026-4070, November.
    4. Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2023. "Designing Information Provision Experiments," Journal of Economic Literature, American Economic Association, vol. 61(1), pages 3-40, March.
    5. Stephanie Riegg Cellini & Fernando Ferreira & Jesse Rothstein, 2010. "The Value of School Facility Investments: Evidence from a Dynamic Regression Discontinuity Design," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(1), pages 215-261.
    6. Karel Mertens & Morten O. Ravn, 2012. "Empirical Evidence on the Aggregate Effects of Anticipated and Unanticipated US Tax Policy Shocks," American Economic Journal: Economic Policy, American Economic Association, vol. 4(2), pages 145-181, May.
    7. Sarah S. Stith & Richard A. Hirth, 2016. "The Effect of Performance Standards on Health Care Provider Behavior: Evidence from Kidney Transplantation," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 25(4), pages 789-825, December.
    8. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    9. Ham, John C & LaLonde, Robert J, 1996. "The Effect of Sample Selection and Initial Conditions in Duration Models: Evidence from Experimental Data on Training," Econometrica, Econometric Society, vol. 64(1), pages 175-205, January.
    10. Abbring, Jaap H & van den Berg, Gerard J, 2005. "Social experiments and intrumental variables with duration outcomes," Working Paper Series 2005:11, IFAU - Institute for Evaluation of Labour Market and Education Policy.
    11. Nikhil Agarwal & Itai Ashlagi & Michael A. Rees & Paulo Somaini & Daniel Waldinger, 2021. "Equilibrium Allocations Under Alternative Waitlist Designs: Evidence From Deceased Donor Kidneys," Econometrica, Econometric Society, vol. 89(1), pages 37-76, January.
    12. Stephen Kastoryano & Bas van der Klaauw, 2022. "Dynamic evaluation of job search assistance," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(2), pages 227-241, March.
    13. Stacy Dickert-Conlin & Todd Elder & Keith Teltser, 2019. "Allocating Scarce Organs: How a Change in Supply Affects Transplant Waiting Lists and Transplant Recipients," American Economic Journal: Applied Economics, American Economic Association, vol. 11(4), pages 210-239, October.
    14. Lechner, Michael, 2009. "Sequential Causal Models for the Evaluation of Labor Market Programs," Journal of Business & Economic Statistics, American Statistical Association, vol. 27, pages 71-83.
    15. Karel Mertens & Morten O. Ravn, 2009. "Empirical evidence on the aggregate effects of anticipated and unanticipated US tax policy shocks," Working Paper Research 181, National Bank of Belgium.
    16. Keith F. Teltser, 2019. "Do Kidney Exchanges Improve Patient Outcomes?," American Economic Journal: Economic Policy, American Economic Association, vol. 11(3), pages 427-453, August.
    17. Nikhil Agarwal & Itai Ashlagi & Paulo Somaini & Daniel Waldinger, 2018. "Dynamic Incentives in Wait List Mechanisms," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 341-347, May.
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    More about this item

    Keywords

    kidney transplant; expectation effects; dynamic treatment effects; survival models;
    All these keywords.

    JEL classification:

    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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