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Differential Attention to Attributes in Utility-theoretic Choice Models


  • Trudy Ann Cameron

    () (University of Oregon Economics Department)

  • J.R. DeShazo

    () (School of Public Affairs, UCLA)


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.

Suggested Citation

  • Trudy Ann Cameron & J.R. DeShazo, 2008. "Differential Attention to Attributes in Utility-theoretic Choice Models," University of Oregon Economics Department Working Papers 2010-8, University of Oregon Economics Department.
  • Handle: RePEc:ore:uoecwp:2010-8

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    More about this item


    conjoint choice; bounded rationality; attention to attributes; choice set design;

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C9 - Mathematical and Quantitative Methods - - Design of Experiments


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