JOURNAL ARTICLE
Incentivizing Organ Donation Under Different Priority Rules: The Role of Information.
Published In: Management Science (INFORMS), 2025, v. 71, n. 2. P. 1418 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Li, Mengling; Riyanto, Yohanes E. 3 of 3
Abstract
This article investigates how different organ allocation priority rules influence incentives for registering as deceased organ donors, focusing on the role of information about patients' valuations for transplantation. It compares four priority rules—Value Priority (based solely on patients' transplant value), Donor Priority (favoring registered donors), and two hybrid dual-incentive rules combining donor status and value in different orders—under two information environments: one where individuals know their transplant valuation before deciding to donate (observed value) and one where they do not (unobserved value). Theoretical analysis and laboratory experiments with human subjects reveal that when valuations are unobserved, the Donor + Value Priority Rule yields the highest donation rates, whereas when valuations are observed, the Donor Priority Rule outperforms the dual-incentive rules due to incentive imbalances between high- and low-value individuals. These findings underscore the critical impact of information disclosure on the effectiveness of priority-based organ allocation policies and suggest that policy design must carefully consider the interaction between allocation criteria and information environments to enhance organ donation rates.
Additional Information
- Source:Management Science (INFORMS). 2025/02, Vol. 71, Issue 2, p1418
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.01530
- Accession Number:182990749
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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