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The Structure of Consumer Taste Heterogeneity in Revealed vs. Stated Preference Data

Author

Listed:
  • Michael P. Keane

    (University of Oxford, Nuffield College)

  • Nada Wasi

    (University of Michigan, Survey Research Center)

Abstract

In recent years it has become common to use stated preference (SP) discrete choice experiments (DCEs) to study and/or predict consumer demand. SP is particularly useful when revealed preference (RP) data is unobtainable or uninformative (e.g., to predict demand for a new product with an attribute not present in existing products, to value non-traded goods). The increasing use of SP data has led to a growing body of research that compares SP vs. RP demand predictions (in contexts when both are available). The present paper goes further by comparing the structure of consumer taste heterogeneity in SP vs. RP data. Our results suggest the nature of taste heterogeneity is very different: In SP data consumers are much more likely to exhibit either (i) lexicographic preferences, or (ii) “random” choice behavior. And many consumers appear to be fairly insensitive to price. This suggests that caution should be applied before using SP to answer questions about the distribution of taste heterogeneity in actual markets.

Suggested Citation

  • Michael P. Keane & Nada Wasi, 2013. "The Structure of Consumer Taste Heterogeneity in Revealed vs. Stated Preference Data," Economics Papers 2013-W10, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:1310
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    More about this item

    Keywords

    Discrete choice experiments; Stated preference data; Discrete choice models; Consumer demand; Consumer heterogeneity; Mixture models;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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