Differential Attention to Attributes in Utility-theoretic Choice Models

We show in a theoretical model that benefits of allocating additional attention to evaluating the marginal attribute with in choice set depend upon the expected utility loss from making a suboptimal choice as a result of ignoring that incremental attribute. Guided by this analysis, we then develop a very general and practical empirical method for measuring the individual's propensity to attend to attributes. As a proof of concept, we offer an empirical example of our method using a conjoint analysis of demand for programs to reduce health risks. Our results suggest that respondents differentially allocate attention across attributes, as a function of the mix of attribute levels in a choice set. This behavior can cause researchers who fail to model attention allocation to incorrectly estimate the marginal utilities derived from selected attributes. This illustrative example is a first attempt to implement an attention-corrected choice model with a sample of field data from a conjoint choice experiment.conjoint choice, bounded rationality, attention to attributes, choice set design

An Empirical Model of Demand for Future Health States when Valuing Risk-Mitigating Programs

51 p.We develop a structural option price model in which individuals choose among competing risk-mitigating programs to alter their probability of experiencing future years in various degraded health states. The novel aspects of this model include separate estimates of the marginal utilities of avoiding years of morbidity and lost life-years. With these marginal utilities, we may evaluate a broad spectrum of probabilistic health outcomes over any period of an individual’s future life. The model also reduces potential biases associated with singleperiod, single-risk models typically used to produce estimates of the Value of a Statistical Life (VSL) by allowing individuals to substitute risk mitigation across competing sources of risk and across future years of their lives. We evaluate this model using data from a national survey that contains a choice experiment on demand for the mitigation of illness-specific risks.US Environmental Protection Agency (R829485) and Health Canada (Contract H5431-010041/001/SS

Scenario adjustment in stated preference research

AbstractPoorly designed stated preference (SP) studies are subject to a number of well-known biases, but many of these biases can be minimized when they are anticipated ex ante and accommodated in the study's design or during data analysis. We identify another source of potential bias, which we call “scenario adjustment,” where respondents 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 use an existing survey, developed to ascertain willingness to pay for private health-risk reduction programs, to demonstrate a strategy to control and correct for scenario adjustment in the estimation of willingness to pay. This strategy involves data from carefully worded follow-up questions, and ex post econometric controls, for each respondent's subjective departures from the intended choice scenario. Our research has important implications for the design of future SP surveys