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Alternative Models of Preference Heterogeneity for Elicited Choice Probabilities

Author

Listed:
  • Kettlewell, Nathan

    (University of Technology, Sydney)

  • Walker, Matthew J.

    (Newcastle University)

  • Yoo, Hong Il

    (Loughborough University)

Abstract
Discrete choice experiments (DCEs) often present concise choice scenarios that may appear incomplete to respondents. To allow respondents to express uncertainty arising from this incompleteness, DCEs may ask them to state probabilities with which they expect to make specific choices. The workhorse method for analyzing the elicited probabilities involves semi-parametric estimation of population average preferences. Despite flexible distributional assumptions, this method presents challenges in estimating unobserved preference heterogeneity, a key element in non-market valuation studies. We introduce a fractional response model based on a mixture of beta distributions. The model enables researchers to uncover preference heterogeneity under comparable parametric assumptions as adopted in conventional choice analysis, and can accommodate multiplicative forms of heterogeneity that make the semi-parametric method inconsistent. Using a DCE on alternative fuel vehicles, we illustrate the complementary roles of the parametric and semi-parametric approaches. We also undertake a separate analysis in which respondents are randomized to either a DCE employing a conventional choice elicitation format or a parallel DCE employing the probability elicitation format.

Suggested Citation

  • Kettlewell, Nathan & Walker, Matthew J. & Yoo, Hong Il, 2024. "Alternative Models of Preference Heterogeneity for Elicited Choice Probabilities," IZA Discussion Papers 16821, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp16821
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    References listed on IDEAS

    as
    1. Pedersen, Line Bjørnskov & Mørkbak, Morten Raun & Scarpa, Riccardo, 2020. "Handling resolvable uncertainty from incomplete scenarios in future doctors' job choice – Probabilities vs discrete choices," Journal of choice modelling, Elsevier, vol. 34(C).
    2. Bleemer, Zachary & Zafar, Basit, 2018. "Intended college attendance: Evidence from an experiment on college returns and costs," Journal of Public Economics, Elsevier, vol. 157(C), pages 184-211.
    3. Manski, Charles F, 1999. "Analysis of Choice Expectations in Incomplete Scenarios," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 49-66, December.
    4. Koşar, Gizem & Ransom, Tyler & van der Klaauw, Wilbert, 2022. "Understanding migration aversion using elicited counterfactual choice probabilities," Journal of Econometrics, Elsevier, vol. 231(1), pages 123-147.
    5. Jeremy T. Fox, 2007. "Semiparametric estimation of multinomial discrete-choice models using a subset of choices," RAND Journal of Economics, RAND Corporation, vol. 38(4), pages 1002-1019, December.
    6. Zachary Bleemer & Basit Zafar, 2014. "Information heterogeneity and intended college enrollment," Staff Reports 685, Federal Reserve Bank of New York.
    7. Riccardo Scarpa & Claudia Bazzani & Diego Begalli & Roberta Capitello, 2021. "Resolvable and Near‐epistemic Uncertainty in Stated Preference for Olive Oil: An Empirical Exploration," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(2), pages 335-369, June.
    8. Miller, Grant & de Paula, Áureo & Valente, Christine, 2020. "Subjective Expectations and Demands for Contraception," CINCH Working Paper Series (since 2020) 73328, Duisburg-Essen University Library, DuEPublico.
    9. Matthew Wiswall & Basit Zafar, 2018. "Preference for the Workplace, Investment in Human Capital, and Gender," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(1), pages 457-507.
    10. Vikas Soekhai & Esther W. Bekker-Grob & Alan R. Ellis & Caroline M. Vass, 2019. "Discrete Choice Experiments in Health Economics: Past, Present and Future," PharmacoEconomics, Springer, vol. 37(2), pages 201-226, February.
    11. Michael D. Hurd & James P. Smith & Julie M. Zissimopoulos, 2004. "The effects of subjective survival on retirement and Social Security claiming," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(6), pages 761-775.
    12. 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.
    13. McKenzie, David & Gibson, John & Stillman, Steven, 2013. "A land of milk and honey with streets paved with gold: Do emigrants have over-optimistic expectations about incomes abroad?," Journal of Development Economics, Elsevier, vol. 102(C), pages 116-127.
    14. Martin Boyer & Philippe De Donder & Claude Fluet & Marie-Louise Leroux & Pierre-Carl Michaud, 2017. "Long-Term Care Insurance: Knowledge Barriers, Risk Perception and Adverse Selection," NBER Working Papers 23918, National Bureau of Economic Research, Inc.
    15. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555.
    16. F. Thomas Juster, 1966. "Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design," NBER Books, National Bureau of Economic Research, Inc, number just66-2.
    17. 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.
    18. Ruder, Alexander I. & Van Noy, Michelle, 2017. "Knowledge of earnings risk and major choice: Evidence from an information experiment," Economics of Education Review, Elsevier, vol. 57(C), pages 80-90.
    19. Matthew Wiswall & Basit Zafar, 2015. "Determinants of College Major Choice: Identification using an Information Experiment," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(2), pages 791-824.
    20. Joan L. Walker & Moshe Ben-Akiva & Denis Bolduc, 2007. "Identification of parameters in normal error component logit-mixture (NECLM) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(6), pages 1095-1125.
    21. Nathaniel Hendren, 2013. "Private Information and Insurance Rejections," Econometrica, Econometric Society, vol. 81(5), pages 1713-1762, September.
    22. Adeline Delavande & Basit Zafar, 2019. "University Choice: The Role of Expected Earnings, Nonpecuniary Outcomes, and Financial Constraints," Journal of Political Economy, University of Chicago Press, vol. 127(5), pages 2343-2393.
    23. Asher A. Blass & Saul Lach & Charles F. Manski, 2010. "Using Elicited Choice Probabilities To Estimate Random Utility Models: Preferences For Electricity Reliability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 51(2), pages 421-440, May.
    24. David F. Layton & Gardner Brown, 2000. "Heterogeneous Preferences Regarding Global Climate Change," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 616-624, November.
    25. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    26. Ryan, Mandy, 1999. "Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation," Social Science & Medicine, Elsevier, vol. 48(4), pages 535-546, February.
    27. 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.
    28. Herriges, Joseph A. & Bhattacharjee, Subhra & Kling, Catherine L., 2011. "Capturing preferences under incomplete scenarios using elicited choice probabilities," ISU General Staff Papers 201103230700001093, Iowa State University, Department of Economics.
    29. Adeline Delavande, 2008. "Pill, Patch, Or Shot? Subjective Expectations And Birth Control Choice," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 49(3), pages 999-1042, August.
    30. Silvia Ferrari & Francisco Cribari-Neto, 2004. "Beta Regression for Modelling Rates and Proportions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(7), pages 799-815.
    31. Charles F. Manski, 2004. "Measuring Expectations," Econometrica, Econometric Society, vol. 72(5), pages 1329-1376, September.
    32. Lance Lochner, 2007. "Individual Perceptions of the Criminal Justice System," American Economic Review, American Economic Association, vol. 97(1), pages 444-460, March.
    33. Morita, Tamaki & Managi, Shunsuke, 2015. "Consumers’ willingness to pay for electricity after the Great East Japan Earthquake," Economic Analysis and Policy, Elsevier, vol. 48(C), pages 82-105.
    34. Manski, Charles F., 1985. "Semiparametric analysis of discrete response : Asymptotic properties of the maximum score estimator," Journal of Econometrics, Elsevier, vol. 27(3), pages 313-333, March.
    35. Basit Zafar, 2013. "College Major Choice and the Gender Gap," Journal of Human Resources, University of Wisconsin Press, vol. 48(3), pages 545-595.
    36. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    37. Hensher, David & Louviere, Jordan & Swait, Joffre, 1998. "Combining sources of preference data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 197-221, November.
    38. Papke, Leslie E & Wooldridge, Jeffrey M, 1996. "Econometric Methods for Fractional Response Variables with an Application to 401(K) Plan Participation Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 619-632, Nov.-Dec..
    39. Andrew Daly & Stephane Hess & Kenneth Train, 2012. "Assuring finite moments for willingness to pay in random coefficient models," Transportation, Springer, vol. 39(1), pages 19-31, January.
    40. Beggs, S. & Cardell, S. & Hausman, J., 1981. "Assessing the potential demand for electric cars," Journal of Econometrics, Elsevier, vol. 17(1), pages 1-19, September.
    41. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.
    42. Manski, Charles F. & Molinari, Francesca, 2010. "Rounding Probabilistic Expectations in Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 219-231.
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    More about this item

    Keywords

    discrete choice experiment; probability elicitation; mixed logit; beta regression; willingness to pay;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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