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

Employment Benefits from California Climate Investments and Co-investments

From the launch of California Climate Investments in 2013 through 2016, the state appropriated about $2.2 billion to 29 programs aimed at reducing GHGs. Of these, 13 were transportation programs that received nearly$1.8 billion in California Climate Investments, including the High-Speed Rail Project, the Affordable Housing and Sustainable Communities Program, the Transit and Intercity Rail Capital Program, the Clean Vehicle Rebate Project, and otherLow CarbonTransportation investments.How do these programs translate into jobs? Researchers at the UCLA Luskin Center for Innovation conducted the state’s largest study of the employment impacts of CCI transportation investments

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