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Making use of respondent reported processing information to understand attribute importance: a latent variable scaling approach

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  • Stephane Hess
  • David Hensher
Abstract
In recent years we have seen an explosion of research seeking to understand the role that rules and heuristics might play in improving the predictive capability of discrete choice models, as well as delivering willingness to pay estimates for specific attributes that may (and often do) differ significantly from estimates based on a model specification that assumes all attributes are relevant. This paper adds to that literature in one important way—it explicitly recognises the endogeneity issues raised by typical attribute non-attendance treatments and conditions attribute parameters on underlying unobserved attribute importance ratings. We develop a hybrid model system involving attribute processing and outcome choice models in which latent variables are introduced as explanatory variables in both parts of the model, explaining the answers to attribute processing questions and explaining heterogeneity in marginal sensitivities in the choice model. The resulting empirical model explains how lower latent attribute importance leads to a higher probability of indicating that an attribute was ignored or that it was ranked as less important, as well as increasing the probability of a reduced value for the associated marginal utility coefficient in the choice model. The model does so by treating the answers to information processing questions as dependent rather than explanatory variables, hence avoiding potential risk of endogeneity bias and measurement error. Copyright Springer Science+Business Media, LLC. 2013

Suggested Citation

  • Stephane Hess & David Hensher, 2013. "Making use of respondent reported processing information to understand attribute importance: a latent variable scaling approach," Transportation, Springer, vol. 40(2), pages 397-412, February.
    • Handle: RePEc:kap:transp:v:40:y:2013:i:2:p:397-412
    DOI: 10.1007/s11116-012-9420-y
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    References listed on IDEAS

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    1. Mohammed Alemu & Morten Mørkbak & Søren Olsen & Carsten Jensen, 2013. "Attending to the Reasons for Attribute Non-attendance in Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 54(3), pages 333-359, March.
    2. Cantillo, Víctor & Heydecker, Benjamin & de Dios Ortúzar, Juan, 2006. "A discrete choice model incorporating thresholds for perception in attribute values," Transportation Research Part B: Methodological, Elsevier, vol. 40(9), pages 807-825, November.
    3. Hole, Arne Risa, 2011. "A discrete choice model with endogenous attribute attendance," Economics Letters, Elsevier, vol. 110(3), pages 203-205, March.
    4. David A. Hensher, 2008. "Joint Estimation of Process and Outcome in Choice Experiments and Implications for Willingness to Pay," Journal of Transport Economics and Policy, University of Bath, vol. 42(2), pages 297-322, May.
    5. David A. Hensher, 2006. "How do respondents process stated choice experiments? Attribute consideration under varying information load," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 861-878.
    6. Stephane Hess & John Rose, 2012. "Can scale and coefficient heterogeneity be separated in random coefficients models?," Transportation, Springer, vol. 39(6), pages 1225-1239, November.
    7. Fredrik Carlsson & Mitesh Kataria & Elina Lampi, 2010. "Dealing with Ignored Attributes in Choice Experiments on Valuation of Sweden’s Environmental Quality Objectives," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(1), pages 65-89, September.
    8. David Hensher & John Rose & William Greene, 2005. "The implications on willingness to pay of respondents ignoring specific attributes," Transportation, Springer, vol. 32(3), pages 203-222, May.
    9. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    10. Hess, Stephane & Hensher, David A., 2010. "Using conditioning on observed choices to retrieve individual-specific attribute processing strategies," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 781-790, July.
    11. Puckett, Sean M. & Hensher, David A., 2008. "The role of attribute processing strategies in estimating the preferences of road freight stakeholders," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 44(3), pages 379-395, May.
    12. Balcombe, Kelvin & Burton, Michael & Rigby, Dan, 2011. "Skew and attribute non-attendance within the Bayesian mixed logit model," Journal of Environmental Economics and Management, Elsevier, vol. 62(3), pages 446-461.
    13. Riccardo Scarpa & Timothy J. Gilbride & Danny Campbell & David A. Hensher, 2009. "Modelling attribute non-attendance in choice experiments for rural landscape valuation," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 36(2), pages 151-174, June.
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