Numerous analyses of the benefits and costs of COVID-19 policies have been completed quickly as the crisis has unfolded. The results often largely depend on the approach used to value mortality risk reductions, typically expressed as the value per statistical life (VSL). Many analyses rely on a population-average VSL estimate; some adjust VSL for life expectancy at the age of death. We explore the implications of theory and empirical studies, which suggest that the relationship between age and VSL is uncertain. We compare the effects of 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. We find that 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. We explore the extent to which applying these estimates alters the conclusions of frequently cited analyses of social distancing, finding that they significantly affect the findings. However, these analyses do not address other characteristics of COVID-19 deaths that may increase or decrease the VSL estimates. 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.
Keywords: Benefit-cost analysis; COVID-19; VSL; pandemic; value per statistical life; willingness to pay.
© 2020 Society for Risk Analysis.