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Estimating Biases in Smoking Cessation: Evidence from a Field Experiment

Author

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  • Frank J. Chaloupka IV
  • Matthew R. Levy
  • Justin S. White

Abstract

We conduct a randomized field experiment to quantify biases that affect consumers of addictive goods: present-biased preferences, naïve beliefs regarding present bias, and projection-biased beliefs over future abstinence. These biases reflect departures from the neoclassical benchmark needed to accommodate intertemporal and state-dependent prediction errors and have important theoretical and policy ramifications. Our experiment employs a new approach for remote monitoring to ensure truthful reporting of behavior and valuations, and a novel identification of subjects’ biases based on willingness to pay for future abstinence incentives that serve as partial commitment devices. We find that cigarette smokers overestimate their likelihood of future abstinence by more than 100%, consistent with partially-naïve present-biased preferences. We estimate that on average smokers are present biased and only partially aware of their present bias, with substantial heterogeneity and a positive correlation between the two at the individual level. Smokers mispredict the effects of an abstinence intervention. Ex ante smokers anticipate no effect of the intervention on their future abstinence and ex post fail to recognize the intervention’s positive effect. Our estimates highlight that smokers suffer from a constellation of biases: under their own long-run preferences, smokers’ choices lead to a private welfare loss of $400 per week.

Suggested Citation

  • Frank J. Chaloupka IV & Matthew R. Levy & Justin S. White, 2019. "Estimating Biases in Smoking Cessation: Evidence from a Field Experiment," NBER Working Papers 26522, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26522
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    References listed on IDEAS

    as
    1. Dan Acland & Matthew R. Levy, 2015. "Naiveté, Projection Bias, and Habit Formation in Gym Attendance," Management Science, INFORMS, vol. 61(1), pages 146-160, January.
    2. Acland, Dan & Levy, Matthew R., 2015. "Naiveté, projection bias, and habit formation in gym attendance," LSE Research Online Documents on Economics 66147, London School of Economics and Political Science, LSE Library.
    3. Steffen Andersen & Glenn W. Harrison & Morten I. Lau & E. Elisabet Rutström, 2008. "Eliciting Risk and Time Preferences," Econometrica, Econometric Society, vol. 76(3), pages 583-618, May.
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    Citations

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    Cited by:

    1. Piccoli, Luca & Tiezzi, Silvia, 2021. "Rational addiction and time-consistency: An empirical test," Journal of Health Economics, Elsevier, vol. 80(C).
    2. Sophie Massin & Maxence Miéra, 2020. "Measuring consumer surplus in the case of addiction: A re-examination of the rational benchmark algebra," Economics Bulletin, AccessEcon, vol. 40(4), pages 3171-3181.
    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. Kaufmann, Marc, 2022. "Projection bias in effort choices," Games and Economic Behavior, Elsevier, vol. 135(C), pages 368-393.
    5. Federico Perali & Luca Piccoli, 2022. "An Extended Theory of Rational Addiction," Mathematics, MDPI, vol. 10(15), pages 1-20, July.
    6. Rodemeier, Matthias, 2021. "Buy baits and consumer sophistication: Theory and field evidence from large-scale rebate promotions," CAWM Discussion Papers 124, University of Münster, Münster Center for Economic Policy (MEP).
    7. Hunt Allcott & Charlie Rafkin, 2020. "Optimal Regulation of E-cigarettes: Theory and Evidence," NBER Working Papers 27000, National Bureau of Economic Research, Inc.

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    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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