IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v42y2023i2p233-250.html
   My bibliography  Save this article

GMO and Non-GMO Labeling Effects: Evidence from a Quasi-Natural Experiment

Author

Listed:
  • Aaron Adalja

    (S.C. Johnson College of Business, Cornell University, Ithaca, New York 14853)

  • Jūra Liaukonytė

    (S.C. Johnson College of Business, Cornell University, Ithaca, New York 14853)

  • Emily Wang

    (Department of Resource Economics, University of Massachusetts, Amherst, Massachusetts 01003)

  • Xinrong Zhu

    (Department of Marketing, Imperial College Business School, London SW7 2BU, United Kingdom)

Abstract

The United States recently mandated disclosure labels on all foods that contain genetically modified organisms (GMOs), despite longstanding, widespread use of voluntary third-party non-GMO labeling. We leverage the earlier passage and implementation of a mandatory GMO labeling law in Vermont as a quasi-natural experiment to show that adding this mandatory labeling into a market with pre-existing voluntary non-GMO labels had no effect on demand. Instead, the legislative process made consumers aware of GMO topics and increased non-GMO product sales before the GMO labeling mandate went into effect. The GMO-related legislative processes also increased non-GMO product demand in other states that considered, but did not implement, GMO labeling mandates. We find that 36% of new non-GMO product adoption can be explained by differences in consumer awareness tied to legislative activity. Our findings suggest that voluntary non-GMO labels may have provided an efficient disclosure mechanism without mandatory GMO labels.

Suggested Citation

  • Aaron Adalja & Jūra Liaukonytė & Emily Wang & Xinrong Zhu, 2023. "GMO and Non-GMO Labeling Effects: Evidence from a Quasi-Natural Experiment," Marketing Science, INFORMS, vol. 42(2), pages 233-250, March.
    • Handle: RePEc:inm:ormksc:v:42:y:2023:i:2:p:233-250
    DOI: 10.1287/mksc.2022.1375
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2022.1375
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mksc.2022.1375?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. Kahneman, Daniel & Ritov, Ilana & Schkade, David A, 1999. "Economic Preferences or Attitude Expressions?: An Analysis of Dollar Responses to Public Issues," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 203-235, December.
    2. Bovay, John & Alston, Julian M., 2016. "GM Labeling Regulation by Plebiscite: Analysis of Voting on Proposition 37 in California," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(2), May.
    3. Rebecca L. C. Taylor & Scott Kaplan & Sofia B. Villas‐Boas & Kevin Jung, 2019. "Soda Wars: The Effect Of A Soda Tax Election On University Beverage Sales," Economic Inquiry, Western Economic Association International, vol. 57(3), pages 1480-1496, July.
    4. Christian Rojas & Emily Wang, 2021. "Do Taxes On Soda And Sugary Drinks Work? Scanner Data Evidence From Berkeley And Washington State," Economic Inquiry, Western Economic Association International, vol. 59(1), pages 95-118, January.
    5. Dannenberg, Astrid, 2009. "The dispersion and development of consumer preferences for genetically modified food -- A meta-analysis," Ecological Economics, Elsevier, vol. 68(8-9), pages 2182-2192, June.
    6. Varian, Hal R, 1999. "Commentary on "Economic Preferences or Attitude Expressions?: An Analysis of Dollar Responses to Public Issues" by Kahneman et al," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 241-242, December.
    7. Sherman, Steven J, 1999. "Commentary on "Economic Preferences or Attitude Expressions?: An Analysis of Dollar Responses to Public Issues" by Kahneman, Ritov and Schkade," Journal of Risk and Uncertainty, Springer, vol. 19(1-3), pages 237-239, December.
    8. Dmitry Arkhangelsky & Susan Athey & David A. Hirshberg & Guido W. Imbens & Stefan Wager, 2021. "Synthetic Difference-in-Differences," American Economic Review, American Economic Association, vol. 111(12), pages 4088-4118, December.
    9. Darby, Michael R & Karni, Edi, 1973. "Free Competition and the Optimal Amount of Fraud," Journal of Law and Economics, University of Chicago Press, vol. 16(1), pages 67-88, April.
    10. Mr. Futoshi Narita & Rujun Yin, 2018. "In Search of Information: Use of Google Trends’ Data to Narrow Information Gaps for Low-income Developing Countries," IMF Working Papers 2018/286, International Monetary Fund.
    11. Bovay, John & Alston, Julian M., 2018. "GMO food labels in the United States: Economic implications of the new law," Food Policy, Elsevier, vol. 78(C), pages 14-25.
    12. Christine Moorman & Rosellina Ferraro & Joel Huber, 2012. "Unintended Nutrition Consequences: Firm Responses to the Nutrition Labeling and Education Act," Marketing Science, INFORMS, vol. 31(5), pages 717-737, September.
    13. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    14. Lusk, Jayson L. & Jamal, Mustafa & Kurlander, Lauren & Roucan, Maud & Taulman, Lesley, 2005. "A Meta-Analysis of Genetically Modified Food Valuation Studies," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 30(1), pages 1-17, April.
    15. Alberto Abadie & Alexis Diamond & Jens Hainmueller, 2015. "Comparative Politics and the Synthetic Control Method," American Journal of Political Science, John Wiley & Sons, vol. 59(2), pages 495-510, February.
    16. Ginger Zhe Jin & Phillip Leslie, 2003. "The Effect of Information on Product Quality: Evidence from Restaurant Hygiene Grade Cards," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(2), pages 409-451.
    17. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    18. Smith, Vernon L, 1991. "Rational Choice: The Contrast between Economics and Psychology," Journal of Political Economy, University of Chicago Press, vol. 99(4), pages 877-897, August.
    19. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    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. Dennis Shen & Peng Ding & Jasjeet Sekhon & Bin Yu, 2022. "Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data," Papers 2207.14481, arXiv.org, revised Oct 2022.
    2. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    3. Youngho Kim, 2024. "Payments for Ecosystem Services Programs and Climate Change Adaptation in Agriculture," Economics Series Working Papers 1054, University of Oxford, Department of Economics.
    4. repec:ags:aaea22:335971 is not listed on IDEAS
    5. Masahiro Kato & Akari Ohda & Masaaki Imaizumi, 2023. "Asymptotically Unbiased Synthetic Control Methods by Distribution Matching," Papers 2307.11127, arXiv.org, revised May 2024.
    6. Cummins Joseph & Miller Douglas L. & Smith Brock & Simon David, 2024. "Matching on Noise: Finite Sample Bias in the Synthetic Control Estimator," Journal of Econometric Methods, De Gruyter, vol. 13(1), pages 67-95, January.
    7. Stefano, Roberta di & Mellace, Giovanni, 2020. "The inclusive synthetic control method," Discussion Papers on Economics 14/2020, University of Southern Denmark, Department of Economics.
    8. Billy Ferguson & Brad Ross, 2020. "Assessing the Sensitivity of Synthetic Control Treatment Effect Estimates to Misspecification Error," Papers 2012.15367, arXiv.org, revised Feb 2021.
    9. David Gilchrist & Thomas Emery & Nuno Garoupa & Rok Spruk, 2023. "Synthetic Control Method: A tool for comparative case studies in economic history," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 409-445, April.
    10. Lea Bottmer & Guido Imbens & Jann Spiess & Merrill Warnick, 2021. "A Design-Based Perspective on Synthetic Control Methods," Papers 2101.09398, arXiv.org, revised Jul 2023.
    11. Ruoxuan Xiong & Susan Athey & Mohsen Bayati & Guido Imbens, 2019. "Optimal Experimental Design for Staggered Rollouts," Papers 1911.03764, arXiv.org, revised Sep 2023.
    12. Dallas Dotter & Duncan Chaplin & Maria Bartlett, "undated". "Impacts of School Reforms in Washington, DC on Student Achievement," Mathematica Policy Research Reports 44e95d7566434a21b8d57f951, Mathematica Policy Research.
    13. Bidisha Lahiri & Anurag Deb, 2022. "Impact of the Indian “demonetization” policy on its export performance," Empirical Economics, Springer, vol. 62(6), pages 2799-2825, June.
    14. Yonzan, Nishant & Timilsina, Laxman & Kelly, Inas Rashad, 2024. "Economic incentives surrounding fertility: Evidence from Alaska’s permanent fund dividend," Economics & Human Biology, Elsevier, vol. 52(C).
    15. Parast Layla & Hunt Priscillia & Griffin Beth Ann & Powell David, 2020. "When is a Match Sufficient? A Score-based Balance Metric for the Synthetic Control Method," Journal of Causal Inference, De Gruyter, vol. 8(1), pages 209-228, January.
    16. Bruno Ferman & Cristine Pinto, 2021. "Synthetic controls with imperfect pretreatment fit," Quantitative Economics, Econometric Society, vol. 12(4), pages 1197-1221, November.
    17. Nuno Garoupa & Rok Spruk, 2024. "Populist Constitutional Backsliding and Judicial Independence: Evidence from Turkiye," Papers 2410.02439, arXiv.org.
    18. Yi‐Ting Chen, 2020. "A distributional synthetic control method for policy evaluation," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(5), pages 505-525, August.
    19. Jason Poulos & Shuxi Zeng, 2021. "RNN‐based counterfactual prediction, with an application to homestead policy and public schooling," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(4), pages 1124-1139, August.
    20. Alberto Abadie & Jaume Vives-i-Bastida, 2022. "Synthetic Controls in Action," Papers 2203.06279, arXiv.org.
    21. Guillaume Allaire Pouliot & Zhen Xie, 2022. "Degrees of Freedom and Information Criteria for the Synthetic Control Method," Papers 2207.02943, arXiv.org.

    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:ormksc:v:42:y:2023:i:2:p:233-250. 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.