Behavioral Considerations in Stated Preference Valuation of Human Life Metrics: Cost Vector Effects, Anchoring and Scope-Insensitivity

OBJECTIVES: This study centers on a critical investigation into the robustness of estimates for the value of statistical life (VSL) and the value of statistical life year (VSLY) derived from stated preference valuation methodologies. Our focus primarily rests on comparative analysis, assessing the degree to which selected behavioral effects influence these estimates. The examination encompasses the cost vector effect, the anchoring effect, and scope insensitivity.

METHODS: One of the tested anchors involves a life expectancy table with personalized information on current health risks. This treatment, combined with the outcomes of the applied EuroQol EQ-5D-5L, aims to challenge the assumption that most individuals are unaware of their present health status. Additionally, by varying the cost vector in the presented discrete choice experiment, we analyze sequencing and ordering effects, as well as assess the influence of the initial choice task on subsequent responses. Moreover, in our analyses, we focus on testing whether, depending on the cost vector presented, WTP estimates (for cardiovascular mortality risk reduction) are equally exposed to problems with sensitivity to scope.

RESULTS: Our preliminary analysis indicates differences of up to 330% of VSL estimates - depending on the selection of various cost vectors, which may have far-reaching consequences for the reliability of the obtained estimates.

CONCLUSIONS: This study's significance lies in its potential to enhance the reliability and quality of stated preference health studies. Amidst a proliferation of health-oriented stated preference studies, only a minority delve into fortifying their robustness. While scope sensitivity issues affecting willingness-to-pay estimates are spotlighted in recent literature, VSL and VSLY estimates remain somewhat overlooked – even though they might be more susceptible. Given their central role in health economics' cost-benefit analyses guiding policy decisions, confirming the stability of these measures against behavioral influences assumes paramount importance. Without such validation, the authenticity of findings could be compromised, rendering insights misleading.