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Experimental investigation of consumer price evaluations

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  • Sándor, Z.
  • Franses, Ph.H.B.F.

Abstract

We develop a procedure to collect experimental choice data for estimating consumer preferences with a special focus on consumer price evaluations. For this purpose we employ a heteroskedastic mixed logit model that measures the effect of the way prices are specified on the variance of choice. Our procedure is based on optimal design ideas from the statistics literature and on some algorithms for constructing choice designs published in marketing journals. In an empirical application on mobile phone preferences we find evidence that the way prices are specified significantly affects the variance of choice. In a simulation study we show that our design is significantly more efficient than randomly generated designs., which can be regarded as equivalent to most commonly used experimental designs in the literature.

Suggested Citation

  • Sándor, Z. & Franses, Ph.H.B.F., 2004. "Experimental investigation of consumer price evaluations," Econometric Institute Research Papers EI 2004-12, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:1203
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    References listed on IDEAS

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    6. Arora, Neeraj & Huber, Joel, 2001. "Improving Parameter Estimates and Model Prediction by Aggregate Customization in Choice Experiments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(2), pages 273-283, September.
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    8. van Ophem, Hans & Stam, Piet & Van Praag, Bernard M S, 1999. "Multichoice Logit: Modeling Incomplete Preference Rankings of Classical Concerts," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 117-128, January.
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    Cited by:

    1. Hu, Wuyang, 2006. "Effects of Endogenous Task Complexity and the Endowed Bundle on Stated Choice," 2006 Annual meeting, July 23-26, Long Beach, CA 21437, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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    More about this item

    Keywords

    Bayesian design; demand; heterogeneity; quasi-random; task complexity;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • 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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