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Using Attribute Importance Rankings within Discrete Choice Experiments: An Application to Valuing Bread Attributes

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  • Balcombe, Kelvin George
  • Bitzios, Michael
  • Fraser, Iain
  • Haddock-Fraser, Janet

Abstract

We present a new Bayesian econometric speci cation for a hypothetical Discrete Choice Experiment (DCE) incorporating respondent ranking information about attribute impor- tance. Our results indicate that a DCE debrie ng question that asks respondents to rank the importance of attributes helps to explain the resulting choices. We also examine how mode of survey delivery (online and mail) impacts model performance, nding that results are not substantively a¤ected by the mode of survey delivery. We conclude that the ranking data is a complementary source of information about respondent utility functions within hypothetical DCEs.

Suggested Citation

  • Balcombe, Kelvin George & Bitzios, Michael & Fraser, Iain & Haddock-Fraser, Janet, 2013. "Using Attribute Importance Rankings within Discrete Choice Experiments: An Application to Valuing Bread Attributes," 2013 Conference (57th), February 5-8, 2013, Sydney, Australia 152151, Australian Agricultural and Resource Economics Society.
  • Handle: RePEc:ags:aare13:152151
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    References listed on IDEAS

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    1. Bitzios, Michael & Fraser, Iain & Haddock-Fraser, Janet, 2011. "Functional ingredients and food choice: Results from a dual-mode study employing means-end-chain analysis and a choice experiment," Food Policy, Elsevier, vol. 36(5), pages 714-724, October.
    2. Kelvin Balcombe & Iain Fraser & Eugene McSorley, 2015. "Visual Attention and Attribute Attendance in Multi‐Attribute Choice Experiments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(3), pages 447-467, April.
    3. Lindhjem, Henrik & Navrud, Ståle, 2011. "Using Internet in Stated Preference Surveys: A Review and Comparison of Survey Modes," International Review of Environmental and Resource Economics, now publishers, vol. 5(4), pages 309-351, September.
    4. Riccardo Scarpa & Sandra Notaro & Jordan Louviere & Roberta Raffaelli, 2010. "Exploring Scale Effects of Best/Worst Rank Ordered Choice Data to Estimate Benefits of Tourism in Alpine Grazing Commons," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 809-824.
    5. David Hensher & John Rose & William Greene, 2012. "Inferring attribute non-attendance from stated choice data: implications for willingness to pay estimates and a warning for stated choice experiment design," Transportation, Springer, vol. 39(2), pages 235-245, March.
    6. Mazzocchi, Mario & Traill, W. Bruce & Shogren, Jason F., 2009. "Fat Economics: Nutrition, Health, and Economic Policy," OUP Catalogue, Oxford University Press, number 9780199213863.
    7. Danny Campbell & W. Hutchinson & Riccardo Scarpa, 2008. "Incorporating Discontinuous Preferences into the Analysis of Discrete Choice Experiments," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 41(3), pages 401-417, November.
    8. Layton, David F., 2000. "Random Coefficient Models for Stated Preference Surveys," Journal of Environmental Economics and Management, Elsevier, vol. 40(1), pages 21-36, July.
    9. Balcombe, Kelvin & Fraser, Iain & Falco, Salvatore Di, 2010. "Traffic lights and food choice: A choice experiment examining the relationship between nutritional food labels and price," Food Policy, Elsevier, vol. 35(3), pages 211-220, June.
    10. Scott J. Savage & Donald M. Waldman, 2008. "Learning and fatigue during choice experiments: a comparison of online and mail survey modes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(3), pages 351-371.
    11. Puckett, Sean M. & Hensher, David A., 2009. "Revealing the extent of process heterogeneity in choice analysis: An empirical assessment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(2), pages 117-126, February.
    12. Riccardo Scarpa & Mara Thiene & David A. Hensher, 2010. "Monitoring Choice Task Attribute Attendance in Nonmarket Valuation of Multiple Park Management Services: Does It Matter?," Land Economics, University of Wisconsin Press, vol. 86(4), pages 817-839.
    13. 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.
    14. Balcombe, Kelvin & Chalak, Ali & Fraser, Iain, 2009. "Model selection for the mixed logit with Bayesian estimation," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 226-237, March.
    15. 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.
    16. Søren Olsen, 2009. "Choosing Between Internet and Mail Survey Modes for Choice Experiment Surveys Considering Non-Market Goods," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 44(4), pages 591-610, December.
    17. Michael Burton & Dan Rigby, 2012. "The Self Selection of Complexity in Choice Experiments," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(3), pages 786-800.
    18. Riccardo Scarpa & Raffaele Zanoli & Viola Bruschi & Simona Naspetti, 2013. "Inferred and Stated Attribute Non-attendance in Food Choice Experiments," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(1), pages 165-180.
    19. 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, Foundation for the European Review of Agricultural Economics, vol. 36(2), pages 151-174, June.
    20. Hellyer, Nicole Elizabeth & Fraser, Iain & Haddock-Fraser, Janet, 2012. "Food choice, health information and functional ingredients: An experimental auction employing bread," Food Policy, Elsevier, vol. 37(3), pages 232-245.
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    Cited by:

    1. Harmsen - van Hout, Marjolein & Ghosh, Gaurav & Madlener, Reinhard, 2013. "An Evaluation of Attribute Anchoring Bias in a Choice Experimental Setting," FCN Working Papers 6/2013, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    2. Menegaki, Angeliki, N. & Olsen, Søren Bøye & Tsagarakis, Konstantinos P., 2016. "Towards a common standard – A reporting checklist for web-based stated preference valuation surveys and a critique for mode surveys," Journal of choice modelling, Elsevier, vol. 18(C), pages 18-50.
    3. Rebecca Owusu Coffie & Michael P. Burton & Fiona L. Gibson & Atakelty Hailu, 2016. "Choice of Rice Production Practices in Ghana: A Comparison of Willingness to Pay and Preference Space Estimates," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(3), pages 799-819, September.
    4. repec:gam:jsusta:v:9:y:2017:i:10:p:1743-:d:113414 is not listed on IDEAS

    More about this item

    Keywords

    Attribute Importance Rankings; Discrete Choice Experiment; Survey Mode; Food Consumption/Nutrition/Food Safety; Productivity Analysis; Research and Development/Tech Change/Emerging Technologies; C11; C25; L66;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • L66 - Industrial Organization - - Industry Studies: Manufacturing - - - Food; Beverages; Cosmetics; Tobacco

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