IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v66y12i2020p5886-5905.html
   My bibliography  Save this article

A Dynamic Model of Rational Addiction with Stockpiling and Learning: An Empirical Examination of E-cigarettes

Author

Listed:
  • Jialie Chen

    (Sam L. Walton College of Business, University of Arkansas, Fayetteville, Arkansas 72701)

  • Vithala R. Rao

    (SC Johnson College of Business, Cornell University, Ithaca, New York 14853)

Abstract

Current regulations on e-cigarettes are minimal compared with cigarette regulations, despite their growing popularity globally. Advocates of e-cigarettes claim that they aid in ceasing smoking habits. However, leaving e-cigarettes unregulated has raised growing health concerns. Policymakers in several countries, including the United States and those in Europe, are considering and experimenting with policy interventions. To evaluate current policies and implement potential regulations on e-cigarettes, policymakers must understand the impact of e-cigarettes on consumers’ smoking behaviors. To address this issue, we construct a dynamic structural model that incorporates consumers’ purchases and consumption behaviors of both cigarettes and e-cigarettes. The results from our proposed model indicate that consumption of e-cigarettes promotes, rather than counteracts, smoking. This is because the less costly e-cigarettes incentivize consumers to build their addiction to nicotine, which, in return, increases future consumption of both cigarettes and e-cigarettes. This finding calls for regulations on e-cigarettes. We then conduct counterfactual analyses to evaluate two policy regulations on e-cigarettes: (1) e-cigarette taxes and (2) price regulation. Because both of these policies have been discussed extensively in both the United States and many countries in the European Union, results of our policy simulations address these policy debates. We find that both are effective in reducing overall consumption of cigarettes and e-cigarettes. We also examine the role of consumers’ heterogeneity on the simulation results as well as the policy implications. We conclude with future research directions, such as inclusion of social influence and cross-selling marketing.

Suggested Citation

  • Jialie Chen & Vithala R. Rao, 2020. "A Dynamic Model of Rational Addiction with Stockpiling and Learning: An Empirical Examination of E-cigarettes," Management Science, INFORMS, vol. 66(12), pages 5886-5905, December.
  • Handle: RePEc:inm:ormnsc:v:66:y:12:i:2020:p:5886-5905
    DOI: 10.1287/mnsc.2019.3490
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/mnsc.2019.3490
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2019.3490?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Rust, John, 1987. "Optimal Replacement of GMC Bus Engines: An Empirical Model of Harold Zurcher," Econometrica, Econometric Society, vol. 55(5), pages 999-1033, September.
    2. Sridhar Narayanan & Puneet Manchanda, 2009. "Heterogeneous Learning and the Targeting of Marketing Communication for New Products," Marketing Science, INFORMS, vol. 28(3), pages 424-441, 05-06.
    3. Igal Hendel & Aviv Nevo, 2006. "Sales and consumer inventory," RAND Journal of Economics, RAND Corporation, vol. 37(3), pages 543-561, September.
    4. Klaus Wertenbroch, 1998. "Consumption Self-Control by Rationing Purchase Quantities of Virtue and Vice," Marketing Science, INFORMS, vol. 17(4), pages 317-337.
    5. Masakazu Ishihara & Andrew T. Ching, 2019. "Dynamic Demand for New and Used Durable Goods Without Physical Depreciation: The Case of Japanese Video Games," Marketing Science, INFORMS, vol. 38(3), pages 392-416, May.
    6. Michael Darden, 2017. "Smoking, Expectations, and Health: A Dynamic Stochastic Model of Lifetime Smoking Behavior," Journal of Political Economy, University of Chicago Press, vol. 125(5), pages 1465-1522.
    7. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    8. Orphanides, Athanasios & Zervos, David, 1995. "Rational Addiction with Learning and Regret," Journal of Political Economy, University of Chicago Press, vol. 103(4), pages 739-758, August.
    9. Ching, Andrew T., 2010. "Consumer learning and heterogeneity: Dynamics of demand for prescription drugs after patent expiration," International Journal of Industrial Organization, Elsevier, vol. 28(6), pages 619-638, November.
    10. Igal Hendel & Aviv Nevo, 2006. "Sales and Consumer Inventory," RAND Journal of Economics, The RAND Corporation, vol. 37(3), pages 543-561, Autumn.
    11. Brett R. Gordon & Baohong Sun, 2015. "A Dynamic Model of Rational Addiction: Evaluating Cigarette Taxes," Marketing Science, INFORMS, vol. 34(3), pages 452-470, May.
    12. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
    13. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    14. Andrew T. Ching & Masakazu Ishihara, 2012. "Measuring the Informative and Persuasive Roles of Detailing on Prescribing Decisions," Management Science, INFORMS, vol. 58(7), pages 1374-1387, July.
    15. Tülin Erdem & Susumu Imai & Michael Keane, 2003. "Brand and Quantity Choice Dynamics Under Price Uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 1(1), pages 5-64, March.
    16. Andrew Ching & Masakazu Ishihara, 2010. "The effects of detailing on prescribing decisions under quality uncertainty," Quantitative Marketing and Economics (QME), Springer, vol. 8(2), pages 123-165, June.
    17. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    18. Gregory S. Crawford & Matthew Shum, 2005. "Uncertainty and Learning in Pharmaceutical Demand," Econometrica, Econometric Society, vol. 73(4), pages 1137-1173, July.
    19. Hai Che & Tülin Erdem & T. Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    20. World Health Organization, 2016. "Electronic Nicotine Delivery Systems and Electronic Non-Nicotine Delivery Systems (ENDS/ENNDS)," University of California at San Francisco, Center for Tobacco Control Research and Education qt2f65f2j5, Center for Tobacco Control Research and Education, UC San Francisco.
    21. Anna E. Tuchman, 2019. "Advertising and Demand for Addictive Goods: The Effects of E-Cigarette Advertising," Marketing Science, INFORMS, vol. 38(6), pages 994-1022, November.
    22. Andrew T. Ching & Matthew Osborne, 2020. "Identification and Estimation of Forward-Looking Behavior: The Case of Consumer Stockpiling," Marketing Science, INFORMS, vol. 39(4), pages 707-726, July.
    23. Hai Che & Tülin Erdem & T. Sabri Öncü, 2015. "Consumer learning and evolution of consumer brand preferences," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 173-202, September.
    24. Becker, Gary S & Murphy, Kevin M, 1988. "A Theory of Rational Addiction," Journal of Political Economy, University of Chicago Press, vol. 96(4), pages 675-700, August.
    25. Stigler, George J & Becker, Gary S, 1977. "De Gustibus Non Est Disputandum," American Economic Review, American Economic Association, vol. 67(2), pages 76-90, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Minjung Kwon & Tülin Erdem & Masakazu Ishihara, 2023. "Counter-cyclical price promotion: Capturing seasonal changes in stockpiling and endogenous consumption," Quantitative Marketing and Economics (QME), Springer, vol. 21(4), pages 437-492, December.
    2. Li, Dong & Dong, Chuanwen, 2022. "Government regulations to mitigate the shortage of life-saving goods in the face of a pandemic," European Journal of Operational Research, Elsevier, vol. 301(3), pages 942-955.
    3. Shuo Zhang & Tat Y. Chan & Xueming Luo & Xiaoyi Wang, 2022. "Time-Inconsistent Preferences and Strategic Self-Control in Digital Content Consumption," Marketing Science, INFORMS, vol. 41(3), pages 616-636, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Learning Models: An Assessment of Progress, Challenges and New Developments," Economics Papers 2013-W07, Economics Group, Nuffield College, University of Oxford.
    2. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2013. "Invited Paper ---Learning Models: An Assessment of Progress, Challenges, and New Developments," Marketing Science, INFORMS, vol. 32(6), pages 913-938, November.
    3. Masakazu Ishihara & Andrew T. Ching, 2019. "Dynamic Demand for New and Used Durable Goods Without Physical Depreciation: The Case of Japanese Video Games," Marketing Science, INFORMS, vol. 38(3), pages 392-416, May.
    4. Song Lin & Juanjuan Zhang & John R. Hauser, 2015. "Learning from Experience, Simply," Marketing Science, INFORMS, vol. 34(1), pages 1-19, January.
    5. Ching, Andrew T. & Erdem, Tülin & Keane, Michael P., 2014. "A simple method to estimate the roles of learning, inventories and category consideration in consumer choice," Journal of choice modelling, Elsevier, vol. 13(C), pages 60-72.
    6. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2020. "How much do consumers know about the quality of products? Evidence from the diaper market," The Japanese Economic Review, Springer, vol. 71(4), pages 541-569, October.
    7. Andrew T. Ching & Matthew Osborne, 2020. "Identification and Estimation of Forward-Looking Behavior: The Case of Consumer Stockpiling," Marketing Science, INFORMS, vol. 39(4), pages 707-726, July.
    8. Yufeng Huang, 2019. "Learning by Doing and the Demand for Advanced Products," Marketing Science, INFORMS, vol. 38(1), pages 107-128, January.
    9. Arjen van Lin & Els Gijsbrechts, 2019. "“Hello Jumbo!” The Spatio-Temporal Rollout and Traffic to a New Grocery Chain After Acquisition," Management Science, INFORMS, vol. 67(5), pages 2388-2411, May.
    10. Andrew Ching & Susumu Imai & Masakazu Ishihara & Neelam Jain, 2012. "A practitioner’s guide to Bayesian estimation of discrete choice dynamic programming models," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 151-196, June.
    11. Andrew T. Ching & Tülin Erdem & Michael P. Keane, 2017. "Empirical Models of Learning Dynamics: A Survey of Recent Developments," International Series in Operations Research & Management Science, in: Berend Wierenga & Ralf van der Lans (ed.), Handbook of Marketing Decision Models, edition 2, chapter 0, pages 223-257, Springer.
    12. Xu, Yan, 2017. "Essays on preference formation and home production," Other publications TiSEM b028fd7e-53ba-4ff6-97eb-4, Tilburg University, School of Economics and Management.
    13. Daniel Ackerberg, 2009. "A new use of importance sampling to reduce computational burden in simulation estimation," Quantitative Marketing and Economics (QME), Springer, vol. 7(4), pages 343-376, December.
    14. Shuo Zhang & Tat Y. Chan & Xueming Luo & Xiaoyi Wang, 2022. "Time-Inconsistent Preferences and Strategic Self-Control in Digital Content Consumption," Marketing Science, INFORMS, vol. 41(3), pages 616-636, May.
    15. van Ewijk, Bernadette J. & Gijsbrechts, Els & Steenkamp, Jan-Benedict E.M., 2022. "The dark side of innovation: How new SKUs affect brand choice in the presence of consumer uncertainty and learning," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 967-987.
    16. Zhu, Z.;, 2023. "The Value of Patients: Heterogenous Physician Learning and Generic Drug Diffusion," Health, Econometrics and Data Group (HEDG) Working Papers 23/12, HEDG, c/o Department of Economics, University of York.
    17. Tat Chan & Chakravarthi Narasimhan & Ying Xie, 2013. "Treatment Effectiveness and Side Effects: A Model of Physician Learning," Management Science, INFORMS, vol. 59(6), pages 1309-1325, June.
    18. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    19. Wesley Hartmann, 2006. "Intertemporal effects of consumption and their implications for demand elasticity estimates," Quantitative Marketing and Economics (QME), Springer, vol. 4(4), pages 325-349, December.
    20. Mantian (Mandy) Hu & Chu (Ivy) Dang & Pradeep K. Chintagunta, 2019. "Search and Learning at a Daily Deals Website," Marketing Science, INFORMS, vol. 38(4), pages 609-642, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:66:y:12:i:2020:p:5886-5905. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.