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Consumption Externalities, Information Policies, And Multiple Equilibria: Evidence For Genetically Engineered Food Markets

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  • Roe, Brian E.
  • Teisl, Mario F.

Abstract

We provide evidence of consumption externalities for foods with genetically engineered ingredients. The probability of choosing bread made exclusively from genetically engineered wheat is significantly higher for individuals who perceive normal bread to contain higher levels of genetically engineered content. The magnitude and significance of the consumption externality depends upon the intensity and nature of individual concern about genetically engineered foods and upon prevailing information policies such as explicit warnings about potential health impacts of genetically engineered foods. The estimated preference structures result in an equilibrium level of genetically engineered ingredients that can be sensitive to the initial level of genetically engineered content in the general marketplace. We discuss possible regulatory implications of such preferences structures.

Suggested Citation

  • Roe, Brian E. & Teisl, Mario F., 2004. "Consumption Externalities, Information Policies, And Multiple Equilibria: Evidence For Genetically Engineered Food Markets," 2004 Annual meeting, August 1-4, Denver, CO 20243, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    • Handle: RePEc:ags:aaea04:20243
    DOI: 10.22004/ag.econ.20243
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    References listed on IDEAS

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    1. Markus Noth & Martin Weber, 2003. "Information Aggregation with Random Ordering: Cascades and Overconfidence," Economic Journal, Royal Economic Society, vol. 113(484), pages 166-189, January.
    2. Lones Smith & Peter Sorensen, 2000. "Pathological Outcomes of Observational Learning," Econometrica, Econometric Society, vol. 68(2), pages 371-398, March.
    3. Huck, Steffen & Oechssler, Jorg, 2000. "Informational cascades in the laboratory: Do they occur for the right reasons?," Journal of Economic Psychology, Elsevier, vol. 21(6), pages 661-671, December.
    4. Karni, Edi & Levin, Dan, 1994. "Social Attributes and Strategic Equilibrium: A Restaurant Pricing Game," Journal of Political Economy, University of Chicago Press, vol. 102(4), pages 822-840, August.
    5. Angela A. Hung & Charles R. Plott, 2001. "Information Cascades: Replication and an Extension to Majority Rule and Conformity-Rewarding Institutions," American Economic Review, American Economic Association, vol. 91(5), pages 1508-1520, December.
    6. Fox, John A & Hayes, Dermot J & Shogren, Jason F, 2002. "Consumer Preferences for Food Irradiation: How Favorable and Unfavorable Descriptions Affect Preferences for Irradiated Pork in Experimental Auctions," Journal of Risk and Uncertainty, Springer, vol. 24(1), pages 75-95, January.
    7. Nikolaos Vettas, 1998. "Demand and Supply in New Markets: Diffusion with Bilateral Learning," RAND Journal of Economics, The RAND Corporation, vol. 29(1), pages 215-233, Spring.
    8. Kim, Hee-Su & Kwon, Namhoon, 2003. "The advantage of network size in acquiring new subscribers: a conditional logit analysis of the Korean mobile telephony market," Information Economics and Policy, Elsevier, vol. 15(1), pages 17-33, March.
    9. George A. Akerlof, 1980. "A Theory of Social Custom, of which Unemployment may be One Consequence," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 94(4), pages 749-775.
    10. Ramon Caminal & Xavier Vives, 1996. "Why Market Shares Matter: An Information-Based Theory," RAND Journal of Economics, The RAND Corporation, vol. 27(2), pages 221-239, Summer.
    11. Louise Allsopp & John D. Hey, 2000. "Two Experiments to Test a Model of Herd Behaviour," Experimental Economics, Springer;Economic Science Association, vol. 3(2), pages 121-136, October.
    12. Becker, Gary S, 1991. "A Note on Restaurant Pricing and Other Examples of Social Influences on Price," Journal of Political Economy, University of Chicago Press, vol. 99(5), pages 1109-1116, October.
    13. Katz, Michael L & Shapiro, Carl, 1986. "Technology Adoption in the Presence of Network Externalities," Journal of Political Economy, University of Chicago Press, vol. 94(4), pages 822-841, August.
    14. Duflo, Esther & Saez, Emmanuel, 2002. "Participation and investment decisions in a retirement plan: the influence of colleagues' choices," Journal of Public Economics, Elsevier, vol. 85(1), pages 121-148, July.
    15. Jonathan Knight, 2003. "Trade war looms as US launches challenge over transgenic crops," Nature, Nature, vol. 423(6938), pages 369-369, May.
    16. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    17. Anderson, Lisa R & Holt, Charles A, 1997. "Information Cascades in the Laboratory," American Economic Review, American Economic Association, vol. 87(5), pages 847-862, December.
    18. Dennis E. Smallwood & John Conlisk, 1979. "Product Quality in Markets Where Consumers are Imperfectly Informed," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 93(1), pages 1-23.
    19. Kim, Youngse, 2003. "Income distribution and equilibrium multiplicity in a stigma-based model of tax evasion," Journal of Public Economics, Elsevier, vol. 87(7-8), pages 1591-1616, August.
    20. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
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