IDEAS home Printed from https://ideas.repec.org/p/hbs/wpaper/09-049.html
   My bibliography  Save this paper

Taste Heterogeneity, IIA, and the Similarity Critique

Author

Listed:
  • Thomas J. Steenburgh

    (Harvard Business School, Marketing Unit)

  • Andrew Ainslie

    (UCLA Anderson, School of Management)

Abstract

The purpose of this paper is to show that allowing for taste heterogeneity does not address the similarity critique of discrete-choice models. Although IIA may technically be broken in aggregate, the mixed logit model allows neither a given individual nor the population as a whole to behave with perfect substitution when facing perfect substitutes. Thus, the mixed logit model implies that individuals behave inconsistently across choice sets. Estimating the mixed logit on data in which individuals do behave consistently can result in biased parameter estimates, with the individuals' tastes for desirable attributes being systemically undervalued.

Suggested Citation

  • Thomas J. Steenburgh & Andrew Ainslie, 2008. "Taste Heterogeneity, IIA, and the Similarity Critique," Harvard Business School Working Papers 09-049, Harvard Business School.
    • Handle: RePEc:hbs:wpaper:09-049
    as

    Download full text from publisher

    File URL: http://www.hbs.edu/research/pdf/09-049.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Pradeep Chintagunta & Jean-Pierre Dubé & Vishal Singh, 2003. "Balancing Profitability and Customer Welfare in a Supermarket Chain," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 111-147, March.
    3. Thomas J. Steenburgh, 2008. "The Invariant Proportion of Substitution Property (IPS) of Discrete-Choice Models," Marketing Science, INFORMS, vol. 27(2), pages 300-307, 03-04.
    4. 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.
    5. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
    6. Tülin Erdem & Michael Keane & Baohong Sun, 2008. "The impact of advertising on consumer price sensitivity in experience goods markets," Quantitative Marketing and Economics (QME), Springer, vol. 6(2), pages 139-176, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Marije Schaafsma & Roy Brouwer, 2020. "Substitution Effects in Spatial Discrete Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(2), pages 323-349, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paleti, Rajesh, 2018. "Generalized multinomial probit Model: Accommodating constrained random parameters," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 248-262.
    2. Frick, Bernd & Barros, Carlos Pestana & Prinz, Joachim, 2010. "Analysing head coach dismissals in the German "Bundesliga" with a mixed logit approach," European Journal of Operational Research, Elsevier, vol. 200(1), pages 151-159, January.
    3. Epstein, Andrew J., 2010. "Effects of report cards on referral patterns to cardiac surgeons," Journal of Health Economics, Elsevier, vol. 29(5), pages 718-731, September.
    4. Deka, Devajyoti & Carnegie, Jon, 2021. "Predicting transit mode choice of New Jersey workers commuting to New York City from a stated preference survey," Journal of Transport Geography, Elsevier, vol. 91(C).
    5. Useche, Pilar & Barham, Bradford & Foltz, Jeremy, 2006. "A Trait Specific Model of GM Crop Adoption by Minnesota and Wisconsin Corn Farmers," Working Papers 201525, University of Wisconsin-Madison, Department of Agricultural and Applied Economics, Food System Research Group.
    6. Scheufele, Gabriela & Bennett, Jeffrey W., 2010. "Ordering effects and strategic response in discrete choice experiments," Research Reports 107743, Australian National University, Environmental Economics Research Hub.
    7. F Alpizar & F Carlsson & P Martinsson, 2003. "Using Choice Experiments for Non-Market Valuation," Economic Issues Journal Articles, Economic Issues, vol. 8(1), pages 83-110, March.
    8. Stephane Hess & John W. Polak, 2004. "An analysis of parking behaviour using discrete choice models calibrated on SP datasets," ERSA conference papers ersa04p60, European Regional Science Association.
    9. Siikamaki, Juha & Layton, David F., 2007. "Discrete choice survey experiments: A comparison using flexible methods," Journal of Environmental Economics and Management, Elsevier, vol. 53(1), pages 122-139, January.
    10. Campbell, Danny & Hutchinson, W. George & Scarpa, Riccardo, 2006. "Using Discrete Choice Experiments to Derive Individual-Specific WTP Estimates for Landscape Improvements under Agri-Environmental Schemes: Evidence from the Rural Environment Protection Scheme in Irel," Sustainability Indicators and Environmental Valuation Working Papers 12220, Fondazione Eni Enrico Mattei (FEEM).
    11. Erdem, Seda & Rigby, Dan & Wossink, Ada, 2012. "Using best–worst scaling to explore perceptions of relative responsibility for ensuring food safety," Food Policy, Elsevier, vol. 37(6), pages 661-670.
    12. Joan L. Walker & Moshe Ben-Akiva, 2011. "Advances in Discrete Choice: Mixture Models," Chapters, in: André de Palma & Robin Lindsey & Emile Quinet & Roger Vickerman (ed.), A Handbook of Transport Economics, chapter 8, Edward Elgar Publishing.
    13. Ida, Takanori & Goto, Rei, 2009. "Interdependency among addictive behaviours and time/risk preferences: Discrete choice model analysis of smoking, drinking, and gambling," Journal of Economic Psychology, Elsevier, vol. 30(4), pages 608-621, August.
    14. Robert Bartels & Denzil Fiebig & Arthur Soest, 2006. "Consumers and experts: an econometric analysis of the demand for water heaters," Empirical Economics, Springer, vol. 31(2), pages 369-391, June.
    15. Bliemer, Michiel C.J. & Rose, John M., 2010. "Construction of experimental designs for mixed logit models allowing for correlation across choice observations," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 720-734, July.
    16. Walker, Joan & Ben-Akiva, Moshe, 2002. "Generalized random utility model," Mathematical Social Sciences, Elsevier, vol. 43(3), pages 303-343, July.
    17. Siikamaki, Juha & Layton, David F., 2001. "Logit Models For Pooled Contingent Valuation And Contingent Rating And Ranking Data: Valuing Benefits From Forest Biodiversity Conservation," 2001 Annual meeting, August 5-8, Chicago, IL 20616, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    18. Jane Hall & Patricia Kenny & Ishrat Hossain, 2007. "The provision of informal care in terminal illness: An analysis of carers? needs using a discrete choice experiment," Working Papers 2007/12, CHERE, University of Technology, Sydney.
    19. Zsolt Sándor & Michel Wedel, 2002. "Profile Construction in Experimental Choice Designs for Mixed Logit Models," Marketing Science, INFORMS, vol. 21(4), pages 455-475, February.
    20. Revelt, David & Train, Kenneth, 2000. "Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier," Department of Economics, Working Paper Series qt1900p96t, Department of Economics, Institute for Business and Economic Research, UC Berkeley.

    More about this item

    Keywords

    Heterogeneity; Mixed Logit; Independence from Irrelevant Alternatives; IIA; Similarity Critique; Ecological Fallacy;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hbs:wpaper:09-049. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: HBS (email available below). General contact details of provider: https://edirc.repec.org/data/harbsus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.