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.
Studies of participant attention allocation in choice experimentshave found mixed evidence that participants employ attribute non-attendance (ANA) as a decision heuristic or as a part of a fully rational search strategy. Many studies find that correcting for ANA has considerable effects on willingness-to-pay (WTP) estimates, but the right method of correction depends on the reasons for ANA. We conduct an experiment that, coupled with results from a previous survey, give us a uniquely suitable dataset to explore the heuristic-vs.-rationality debate.
We estimate a utility-theoretic choice model to quantify demand for publicly provided medical treatment policies. We find diminishing marginal utility for increased recoveries and avoided premature deaths. Willingness to pay for different types of treatment policies varies with who would benefit, with each respondent's own characteristics, community ethnic fractionalization and immigrant composition, as well as the respondent's expected private benefits from the policy, attitude toward government interventions and overall health care funding priorities.
Economic researchers often require monetized values of households' willingness to pay for reductions in risks to human life and health. Compared to valuing reductions in the risk of death, there is a smaller literature on valuing reductions in morbidity risks. I compare the requirements for environmental benefit-cost analysis with the limitations of the standard approaches taken in cost-effectiveness analysis in health economics, and I highlight some areas that are ripe for further research.
A choice model based on utility in a sequence of prospective future health states permits us to generalize the concept of the value of statistical life (VSL). Our representative national survey asks individuals to choose between costly risk-reducing programs and the status quo in randomized stated choice scenarios. Our model allows for separate marginal utilities for discounted net income and avoided illness years, post-illness years, and lost life-years. Our estimates permit calculation of overall willingness to pay to reduce risks for a wide variety of different prospective illness profiles. These can be benchmarked against the standard VSL as a special case.
In a survey about health risk reductions, we also collect data about individual time preferences using a choice about payout options for hypothetical lottery winnings.We model individual discount rates as a function of age, other socio-demographic variables and variables to capture health expectations. For older subjects, undesirable current health behaviors are better predicted by their back-casted discount rates at age 21. Changing time preferences as people mature may lead to the development of health habits while they are young that are likely to be inconsistent with the preferences of their older future selves.
The time-tradeoff (TTO) method typically describes some period of years in an adverse health state and asks respondents how many (fewer) years in “full health” they would accept to avoid the longer time period in the adverse health state. We use a large general-population choice experiment designed to permit estimation of willingness to pay (WTP) to reduce health risks with different time profiles. Here, however, we focus on the tradeoffs between discounted time in different health states, rather than tradeoffs between health states and net income.
Survey respondents may assume that the substantive alternative(s) in an SP choice set, in their own particular case, will be different from what the survey instrument describes. We demonstrate a strategy to control and correct for scenario adjustment in the estimation of willingness to pay for a non-market good, using data from follow-up questions, and ex post econometric controls, for each respondent's subjective departures from the intended choice scenario.
We estimate large systematic differences, by type of illness, in individual willingness to pay (WTP) to reduce the risk of the major health threats. These include five types of cancers, chronic heart disease, heart attacks, respiratory disease, strokes, diabetes, Alzheimer’s disease and traffic accidents. Analyses that constrain values to be the same across all illnesses forgo valuable information for benefit-cost analyses of health, environmental and safety policies. The rank ordering of values for illness-specific risk reductions is correlated with spending patterns by government agencies.
Using a large stated preference survey conducted across the U.S. and Canada, we assess differences in individual willingness to pay (WTP) for health risk reductions between the two countries. WTP differs systematically with age, gender, education, and marital status, as well as a number of attitudinal and subjective health-perception variables. Age profiles for WTP are markedly different across the two countries. Canadians tend to display flatter age profiles, with peak WTP realized at older ages.