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The use of alternative preference elicitation methods in complex discrete choice experiments

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  • Hong il Yoo

    (School of Economics, The University of New South Wales)

  • Denise Doiron

    (School of Economics, The University of New South Wales)

Abstract

We analyse stated preference data over nursing jobs collected from two leading types of best-worst discrete choice experiments (DCEs): a traditional DCE involving choice over alternative jobs (BWL) and a newly-developed DCE where respondents choose best and worst job attributes (BWT). The latter allows identi cation of additional utility parameters and is believed to be cognitively easier. Results suggest that respondents place greater value on pecuniary over non-pecuniary gains in traditional DCE. Rather than caused by the use of heuristics in BWL, we nd that respondents nd it dicult and/or are reluctant to directly compare money with other attributes in BWT.

Suggested Citation

  • Hong il Yoo & Denise Doiron, 2012. "The use of alternative preference elicitation methods in complex discrete choice experiments," Discussion Papers 2012-16, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2012-16
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    Cited by:

    1. José L. Oviedo & Hong Il Yoo, 2017. "A Latent Class Nested Logit Model for Rank-Ordered Data with Application to Cork Oak Reforestation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 68(4), pages 1021-1051, December.
    2. Hong il Yoo, 2012. "The perceived unreliability of rank-ordered data: an econometric origin and implications," Discussion Papers 2012-46, School of Economics, The University of New South Wales.
    3. Pedersen, Line Bjørnskov & Hess, Stephane & Kjær, Trine, 2016. "Asymmetric information and user orientation in general practice: Exploring the agency relationship in a best–worst scaling study," Journal of Health Economics, Elsevier, vol. 50(C), pages 115-130.
    4. Oreoluwa Ola & Luisa Menapace, 2020. "Revisiting constraints to smallholder participation in high‐value markets: A best‐worst scaling approach," Agricultural Economics, International Association of Agricultural Economists, vol. 51(4), pages 595-608, July.
    5. John Buckell & Joachim Marti & Jody L. Sindelar, 2017. "Should Flavors be Banned in E-cigarettes? Evidence on Adult Smokers and Recent Quitters from a Discrete Choice Experiment," NBER Working Papers 23865, National Bureau of Economic Research, Inc.
    6. Aizaki, Hideo & Fogarty, James, 2019. "An R package and tutorial for case 2 best–worst scaling," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
    7. Yan, Jin & Yoo, Hong Il, 2019. "Semiparametric estimation of the random utility model with rank-ordered choice data," Journal of Econometrics, Elsevier, vol. 211(2), pages 414-438.
    8. Juan Carlos Martín & Concepción Román & Cira Mendoza, 2018. "Determinants for sun-and-beach self-catering accommodation selection," Tourism Economics, , vol. 24(3), pages 319-336, May.
    9. Yoo, Hong Il & Doiron, Denise, 2013. "The use of alternative preference elicitation methods in complex discrete choice experiments," Journal of Health Economics, Elsevier, vol. 32(6), pages 1166-1179.
    10. Qinxin Guo & Junyi Shen, 2019. "An Empirical Comparison Between Discrete Choice Experiment and Best-worst Scaling: A Case Study of Mobile Payment Choice," Discussion Paper Series DP2019-14, Research Institute for Economics & Business Administration, Kobe University.
    11. Denise Doiron & Hong Il Yoo, 2020. "Stated preferences over job characteristics: A panel study," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(1), pages 43-82, February.
    12. Jennifer A Whitty & Ruth Walker & Xanthe Golenko & Julie Ratcliffe, 2014. "A Think Aloud Study Comparing the Validity and Acceptability of Discrete Choice and Best Worst Scaling Methods," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-9, April.
    13. Denise Doiron & Hong Il Yoo, 2017. "Temporal Stability of Stated Preferences: The Case of Junior Nursing Jobs," Health Economics, John Wiley & Sons, Ltd., vol. 26(6), pages 802-809, June.
    14. Buttorff, Christine & Trujillo, Antonio J. & Diez-Canseco, Francisco & Bernabe-Ortiz, Antonio & Miranda, J. Jaime, 2015. "Evaluating consumer preferences for healthy eating from Community Kitchens in low-income urban areas: A discrete choice experiment of Comedores Populares in Peru," Social Science & Medicine, Elsevier, vol. 140(C), pages 1-8.

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    More about this item

    Keywords

    complex discrete choice experiments; preference elicitation; rank-ordered data; latent classes; heteroskedastic logits; maximum-di erence models.;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • J44 - Labor and Demographic Economics - - Particular Labor Markets - - - Professional Labor Markets and Occupations

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