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Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data

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  • Buckell, John
  • Hess, Stephane

Abstract

In health, stated preference data from discrete choice experiments (DCEs) are commonly used to estimate discrete choice models that are then used for forecasting behavioral change, often with the goal of informing policy decisions. Data from DCEs are potentially subject to hypothetical bias. In turn, forecasts may be biased, yielding substandard evidence for policymakers. Bias can enter both through the elasticities as well as through the model constants. Simple correction approaches exist (using revealed preference data) but are seemingly not widely used in health economics. We use DCE data from an experiment on smokers in the US. Real-world data are used to calibrate the scale of utility (in two ways) and the alternative-specific constants (ASCs); several innovations for calibration are proposed. We find that embedding revealed preference data in the model makes a substantial difference to the forecasts; and that how models are calibrated also makes a substantial difference.

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  • Buckell, John & Hess, Stephane, 2019. "Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data," Journal of Health Economics, Elsevier, vol. 65(C), pages 93-102.
  • Handle: RePEc:eee:jhecon:v:65:y:2019:i:c:p:93-102
    DOI: 10.1016/j.jhealeco.2019.03.011
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    Cited by:

    1. Scott, Anthony & Witt, Julia, 2020. "Loss aversion, reference dependence and diminishing sensitivity in choice experiments," Journal of choice modelling, Elsevier, vol. 37(C).
    2. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part I. Integrative synthesis of empirical evidence and conceptualisation of external validity," Papers 2102.02940, arXiv.org.
    3. Buckell, John & White, Justin S. & Shang, Ce, 2020. "Can incentive-compatibility reduce hypothetical bias in smokers’ experimental choice behavior? A randomized discrete choice experiment," Journal of choice modelling, Elsevier, vol. 37(C).
    4. Frimpong, Jemima A. & Helleringer, Stephane, 2020. "Financial Incentives for Downloading COVID–19 Digital Contact Tracing Apps," SocArXiv 9vp7x, Center for Open Science.
    5. John Buckell & David A Hensher & Stephane Hess, 2021. "Kicking the habit is hard: A hybrid choice model investigation into the role of addiction in smoking behavior," Health Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 3-19, January.
    6. Donald S. Kenkel & Sida Peng & Michael F. Pesko & Hua Wang, 2020. "Mostly harmless regulation? Electronic cigarettes, public policy, and consumer welfare," Health Economics, John Wiley & Sons, Ltd., vol. 29(11), pages 1364-1377, November.
    7. Kaat de Corte & John Cairns & Richard Grieve, 2021. "Stated versus revealed preferences: An approach to reduce bias," Health Economics, John Wiley & Sons, Ltd., vol. 30(5), pages 1095-1123, May.
    8. Milad Haghani & Michiel C. J. Bliemer & John M. Rose & Harmen Oppewal & Emily Lancsar, 2021. "Hypothetical bias in stated choice experiments: Part II. Macro-scale analysis of literature and effectiveness of bias mitigation methods," Papers 2102.02945, arXiv.org.

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    More about this item

    Keywords

    Stated preference; Revealed preference; Hypothetical bias; Tobacco; Discrete choice experiment; Policy predictions;
    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
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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