Anti-selection & Genetic Testing in Insurance: An Interdisciplinary Perspective
Journal of Law, Medicine, and Ethics, Forthcoming
25 Pages Posted: 23 Jun 2021 Last revised: 29 Sep 2021
Date Written: June 9, 2021
Anti-selection occurs when information asymmetry exists between an insurer and an applicant. When an applicant knows that they are at high risk of loss, but the insurer does not, the applicant may try to exploit this knowledge differential to secure insurance at a lower premium that does not match risk. Predictive genetic testing could lead to anti-selection if individuals, but not insurers, learn of genetic risk. Yet, to address fear of discrimination, several countries have, or are considering, limitations on insurers’ use of predictive genetic test results.
In this paper, we discuss anti-selection theory and modeling and illustrate how regulation regarding insurer use of predictive genetic test results could impact anti-selection in insurance markets. The extent of this impact turns on how much individuals alter their insurance purchasing behavior following predictive genetic testing. At first blush it may seem likely that those who learn that they are at high-risk of a genetic condition would attempt to gain greater coverage. However, we highlight several domains of on-the-ground realities that challenge this baseline assumption. These real-world considerations should be incorporated into modeling of anti-selection to truly assess the potential impacts of regulation limiting insurer use of predictive genetic testing.
Note: Funding Statement: Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health under Award Number R00HG008819. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Declaration of Interests: No conflicts of interests to declare.
Keywords: Anti-Selection, Insurance, Genetic Testing, ELSI
JEL Classification: K32, I18, G22
Suggested Citation: Suggested Citation