The COVID-19 crisis has focused unprecedented attention on the use of benefit-cost analysis and approaches for valuing mortality risk reductions, commonly referred to as the value per statistical life (VSL). However, the appropriate VSL estimate is uncertain. CHDS Deputy Director and Senior Research Scientist Lisa A. Robinson explored these uncertainties in a Risk Analysis article co-authored with Ryan Sullivan and Jason Shogren.
Many COVID-19 analyses rely on a population-average VSL estimate; some adjust VSL for life expectancy at the age of death. Robinson et al. explore this relationship and find that the variation in VSL by age is uncertain. They compare the effects of applying three approaches: (1) an invariant population-average VSL; (2) a constant value per statistical life-year (VSLY); and (3) a VSL that follows an inverse-U pattern, peaking in middle age. When applied to the U.S. age distribution of COVID-19 deaths, these approaches result in average VSL estimates of $10.63 million, $4.47 million, and $8.31 million. The difference among these estimates is sufficient to alter the conclusions of frequently cited social distancing and other analyses.
However, these estimates do not address other characteristics of COVID-19 deaths that may increase or decrease the values. Examples include the health status and income level of those affected, the size of the risk change, and the extent to which the risk is dreaded, uncertain, involuntarily incurred, and outside of one’s control. The effects of these characteristics and their correlation with age are uncertain; it is unclear whether they amplify or diminish the effects of age on VSL. Thus exploring and clearly communicating the effects of these uncertainties on the analytic findings is essential.
Learn more: Watch CHDS’ video on Valuing Statistical Lives.