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Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a latent class approach

Author

Listed:
  • McNair, Ben J.
  • Hensher, David A.
  • Bennett, Jeff

Abstract

There is a growing body of evidence in the non-market valuation literature suggesting that responses to a sequence of discrete choice questions tend to violate the assumptions typically made by analysts regarding independence of responses and stability of preferences. Heuristics such as value learning and strategic misrepresentation have been offered as explanations for these results. While a few studies have tested these heuristics as competing hypotheses, none have investigated the possibility that each explains the response behaviour of a subgroup of the population. In this paper, we make a contribution towards addressing this research gap by presenting an equality-constrained latent class model designed to estimate the proportion of respondents employing each of the proposed heuristics. We demonstrate the model on binary and multinomial choice data sources and find three distinct types of response behaviour. The results suggest that accounting for heterogeneity in response behaviour may be a better way forward than attempting to identify a single heuristic to explain the behaviour of all respondents.

Suggested Citation

  • McNair, Ben J. & Hensher, David A. & Bennett, Jeff, 2010. "Modelling heterogeneity in response behaviour towards a sequence of discrete choice questions: a latent class approach," MPRA Paper 23427, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:23427
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    File URL: https://mpra.ub.uni-muenchen.de/23427/1/MPRA_paper_23427.pdf
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    References listed on IDEAS

    as
    1. DeShazo, J. R., 2002. "Designing Transactions without Framing Effects in Iterative Question Formats," Journal of Environmental Economics and Management, Elsevier, vol. 43(3), pages 360-385, May.
    2. Ladenburg, Jacob & Olsen, Søren Bøye, 2008. "Gender-specific starting point bias in choice experiments: Evidence from an empirical study," Journal of Environmental Economics and Management, Elsevier, vol. 56(3), pages 275-285, November.
    3. Carson, Katherine Silz & Chilton, Susan M. & Hutchinson, W. George, 2009. "Necessary conditions for demand revelation in double referenda," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 219-225, March.
    4. Day, Brett & Pinto Prades, Jose-Luis, 2010. "Ordering anomalies in choice experiments," Journal of Environmental Economics and Management, Elsevier, vol. 59(3), pages 271-285, May.
    5. Scarpa, Riccardo & Rose, John M., 2008. "Design efficiency for non-market valuation with choice modelling: how to measure it, what to report and why," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(3), September.
    6. McNair, Ben J. & Bennett, Jeff & Hensher, David A., 2011. "A comparison of responses to single and repeated discrete choice questions," Resource and Energy Economics, Elsevier, vol. 33(3), pages 554-571, September.
    7. Carlsson, Fredrik & Martinsson, Peter, 2008. "Does it matter when a power outage occurs? -- A choice experiment study on the willingness to pay to avoid power outages," Energy Economics, Elsevier, vol. 30(3), pages 1232-1245, May.
    8. Herriges, Joseph A. & Shogren, Jason F., 1996. "Starting Point Bias in Dichotomous Choice Valuation with Follow-Up Questioning," Journal of Environmental Economics and Management, Elsevier, vol. 30(1), pages 112-131, January.
    9. Bateman, Ian J. & Burgess, Diane & Hutchinson, W. George & Matthews, David I., 2008. "Learning design contingent valuation (LDCV): NOAA guidelines, preference learning and coherent arbitrariness," Journal of Environmental Economics and Management, Elsevier, vol. 55(2), pages 127-141, March.
    10. Kevin J. Boyle & Richard C. Bishop & Michael P. Welsh, 1985. "Starting Point Bias in Contingent Valuation Bidding Games," Land Economics, University of Wisconsin Press, vol. 62(2), pages 188-194.
    11. Richard Carson & Theodore Groves, 2007. "Incentive and informational properties of preference questions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 37(1), pages 181-210, May.
    12. Thomas P. Holmes & Kevin J. Boyle, 2005. "Dynamic Learning and Context-Dependence in Sequential, Attribute-Based, Stated-Preference Valuation Questions," Land Economics, University of Wisconsin Press, vol. 81(1).
    13. Beenstock, Michael & Goldin, Ephraim & Haitovsky, Yoel, 1998. "Response bias in a conjoint analysis of power outages," Energy Economics, Elsevier, vol. 20(2), pages 135-156, April.
    14. Carlsson, Fredrik & Martinsson, Peter, 2006. "How much is too much? - An investigation of the effect of the number of choice sets, starting point and the choice of bid vectors in choice experiments," Working Papers in Economics 191, University of Gothenburg, Department of Economics.
    15. Cameron Trudy Ann & Quiggin John, 1994. "Estimation Using Contingent Valuation Data from a Dichotomous Choice with Follow-Up Questionnaire," Journal of Environmental Economics and Management, Elsevier, vol. 27(3), pages 218-234, November.
    16. McNair, Ben J. & Bennett, Jeff & Hensher, David A., 2010. "Households’ Willingness to Pay for Undergrounding Electricity and Telecommunications Wires," MPRA Paper 23164, University Library of Munich, Germany.
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    More about this item

    Keywords

    Choice experiment; latent class; ordering effects; strategic response; willingness-to-pay;

    JEL classification:

    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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