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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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    1. Sándor, Zsolt & Train, Kenneth, 2004. "Quasi-random simulation of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 313-327, May.
    2. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
    3. Adamowicz, Wiktor & Swait, Joffre & Boxall, Peter & Louviere, Jordan & Williams, Michael, 1997. "Perceptions versus Objective Measures of Environmental Quality in Combined Revealed and Stated Preference Models of Environmental Valuation," Journal of Environmental Economics and Management, Elsevier, vol. 32(1), pages 65-84, January.
    4. DeShazo, J. R. & Fermo, German, 2002. "Designing Choice Sets for Stated Preference Methods: The Effects of Complexity on Choice Consistency," Journal of Environmental Economics and Management, Elsevier, vol. 44(1), pages 123-143, July.
    5. Swait, Joffre & Adamowicz, Wiktor, 2001. "The Influence of Task Complexity on Consumer Choice: A Latent Class Model of Decision Strategy Switching," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 28(1), pages 135-148, June.
    6. Berry, Steven & Levinsohn, James & Pakes, Ariel, 1995. "Automobile Prices in Market Equilibrium," Econometrica, Econometric Society, vol. 63(4), pages 841-890, July.
    7. San Miguel, Fernando & Ryan, Mandy & Scott, Anthony, 2002. "Are preferences stable? The case of health care," Journal of Economic Behavior & Organization, Elsevier, vol. 48(1), pages 1-14, May.
    8. 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.
    9. Scott, Anthony, 2001. "Eliciting GPs' preferences for pecuniary and non-pecuniary job characteristics," Journal of Health Economics, Elsevier, vol. 20(3), pages 329-347, May.
    10. David F. Layton & Gardner Brown, 2000. "Heterogeneous Preferences Regarding Global Climate Change," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 616-624, November.
    11. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt3tb6j874, University of California Transportation Center.
    12. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt1j6814b3, University of California Transportation Center.
    13. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    14. 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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