As a society, we make choices regarding safety and health regulations, environmental regulations and investments, all intended to reduce risks of premature death. If a policy reduces the chance of premature death from 4 in one million to 3 in one million, in a population of one million, then that policy intervention is said to save one statistical life. Currently, social choices with respect to mortality risks are most often made based on a single estimate of the value of a statistical life (VSL). What single value to use, however, is still contested by scholars. Scholars currently rely on a median value, $5.8 million (in 1997 dollars), calculated from the point estimates of 26 diverse studies that range from $0.7 million to $16.3 million. The authors of these studies point out that their estimates are highly context dependent. Yet policymakers have adopted a one-size-fits-all estimate of the VSL because they lack an interpretative and organizing framework for this observed heterogeneity in VSL estimates. We address this gap in the literature by estimating a representative utility function that expresses the value of a statistical life as a function of heterogeneity in several variables.
Our central contribution to the literature is to include four additional categories of variables, which form the basis for the following hypotheses:
1. individuals will value risk reductions differently depending on the conditions of the pre-death state (e.g., level and duration of pain, physical and mental disability, etc.);
2. the non-cost attributes of interventions (e.g., the frequency and discomfort of screenings, therapies, safety procedures, etc.) may significantly change individuals' preference rankings over these interventions;
3. willingness-to-pay for different types of interventions to reduce risk may be affected by individual characteristics including not only age, but also education, wealth, religion and religiosity, and ethnicity, among others; and
4. the presence and prices of substitutes for (and complements to) the intervention in question, or the quality of life after the intervention, will affect individuals' values of an intervention in ways not currently considered by the literature.
Approach: We will estimate a representative utility function using conjoint choice exercises in which we ask survey respondents to choose among three different risk-reduction interventions that mitigate a specific way of dying. Knowledge Networks, Inc. will administer this survey to a random national sample of 2,800 adults using a pre-existing panel maintained. This conjoint choice experiment, which focuses on interventions that produce private benefits for the respondent only, enables us to answer the normative: what should the VSL be for different types of death, interventions and subpopulations? Our conjoint exercises incorporate the baseline risk of premature death, the risk reduction, and the cost of the intervention. We employ innovative visual aids and tutorials to ensure that respondents are responsive to changes in risk reductions (Krupnick et al., 1999). We analyze these conjoint choices within the framework of a random utility (RU) model.
Expected Results: Although state and local policies may affect the attributes of morbidity-mortality events, these decision-makers have little information on how individuals would value changes in these attributes. Previous empirical estimates of the VSL from individual studies have been specific to particular risks and particular interventions. Our proposed approach acknowledges the likelihood that one single standard VSL probably does not exist, and it therefore makes little sense to seek such a one-size-fits-all estimate. Instead, VSLs may be highly context-dependent, with considerable systematic heterogeneity. We seek to provide policymakers with better information on the value of human life so that they may better manage risks to life.