We revisit a 2003 general-population survey designed to determine people’s willingness to bear the costs of public policies to reduce illnesses and avoid premature deaths in their communities. We re-estimate earlier models, omitting all respondent-specific characteristics and adding new county-level data on a variety of contextual variables circa 2003. Then we transfer this model to the context of the 2020-21 COVID-19 pandemic, substituting 2020-era levels of the contextual variables.
Information about preferences for treatment and prevention policies can help policy makers more effectively allocate public health resources. We estimate a random utility model of preferences for treatment and prevention policies and explore sources of systematic heterogeneity in preferences. Marginal utility associated with avoided deaths is about twice as high for prevention policies as for treatment policies, and there is significant heterogeneity with respect to disease type, the group targeted by the policy, and respondent characteristics.
We estimate demand for environmental polices to improve health, using choice experiments concerning community-lvel illnesses and premature deaths avoided, policy duration, and the affected population size. Preferences for policy attributes vary systematically with the scenario design, with the source of risk and type of health threat, and with respondent characteristics. Omission of illness information biases upwards the value of avoided premature deaths, and individuals view avoided deaths and avoided illnesses as substitutes.