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Exploring distinct sources of heterogeneity in discrete choice experiment: An application to wine choice across European consumers

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  • Gonçalves, Tânia
  • Pinto, Lígia M. Costa
  • Lourenço-Gomes, Lina

Abstract

This paper explores the role of unobserved heterogeneity using different approaches: mixed, scaled and the generalized mixed logit model, and the latent class model, applied to wine choice. Results show that preference heterogeneity appears to be more relevant than scale heterogeneity, and the model accounting for both performs best. This finding suggests that wine choice is complex, demanding a higher cognitive burden, which may lead to the use of simplification strategies when choosing. For the purpose of wine valuation, the latent class model gives useful insights for market segmentation.

Suggested Citation

  • Gonçalves, Tânia & Pinto, Lígia M. Costa & Lourenço-Gomes, Lina, 2019. "Exploring distinct sources of heterogeneity in discrete choice experiment: An application to wine choice across European consumers," Economics Letters, Elsevier, vol. 178(C), pages 28-32.
  • Handle: RePEc:eee:ecolet:v:178:y:2019:i:c:p:28-32
    DOI: 10.1016/j.econlet.2019.02.019
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    References listed on IDEAS

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    1. Arne Hole & Julie Kolstad, 2012. "Mixed logit estimation of willingness to pay distributions: a comparison of models in preference and WTP space using data from a health-related choice experiment," Empirical Economics, Springer, vol. 42(2), pages 445-469, April.
    2. William H. Greene & David A. Hensher, 2013. "Revealing additional dimensions of preference heterogeneity in a latent class mixed multinomial logit model," Applied Economics, Taylor & Francis Journals, vol. 45(14), pages 1897-1902, May.
    3. Hossain, Ishrat & Saqib, Najam U. & Haq, Munshi Masudul, 2018. "Scale heterogeneity in discrete choice experiment: An application of generalized mixed logit model in air travel choice," Economics Letters, Elsevier, vol. 172(C), pages 85-88.
    4. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    5. Andrew Daly & Stephane Hess & Kenneth Train, 2012. "Assuring finite moments for willingness to pay in random coefficient models," Transportation, Springer, vol. 39(1), pages 19-31, January.
    6. Mara Thiene & Riccardo Scarpa & Jordan Louviere, 2015. "Addressing Preference Heterogeneity, Multiple Scales and Attribute Attendance with a Correlated Finite Mixing Model of Tap Water Choice," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(3), pages 637-656, November.
    7. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.
    8. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.
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    Cited by:

    1. Francesca Bassi & Fulvia Pennoni & Luca Rossetto, 2020. "The Italian market of sparkling wines: Latent variable models for brand positioning, customer loyalty, and transitions across brands' preferences," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 542-567, October.

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    More about this item

    Keywords

    Discrete choice models; Preference heterogeneity; Wine choice;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

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