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Noisy Information Signals and Endogenous Preferences for Labeled Attributes

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  • Liaukonyte, Jura
  • Streletskaya, Nadia A.
  • Kaiser, Harry M.

Abstract

Consumer preferences for labeled products are often assumed to be exogenous to the presence of labels. However, the label itself (and not the information on the label) can be interpreted as a noisy warning signal. We measure the impact of “contains” labels and additional information about the labeled ingredients, treating preferences for labeled characteristics as endogenous. We find that for organic-food shoppers, the “contains” label absent additional information serves as a noisy warning signal leading them to overestimate the riskiness of consuming the product. Providing additional information mitigates the large negative signaling effect of the label.

Suggested Citation

  • Liaukonyte, Jura & Streletskaya, Nadia A. & Kaiser, Harry M., 2015. "Noisy Information Signals and Endogenous Preferences for Labeled Attributes," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(2), pages 1-24, May.
    • Handle: RePEc:ags:jlaare:206591
    DOI: 10.22004/ag.econ.206591
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    Cited by:

    1. Kofi Britwum & Amalia Yiannaka, 2019. "Labeling food safety attributes: to inform or not to inform?," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 7(1), pages 1-21, December.
    2. D Adeline Yeh & Miguel I Gómez & Harry M Kaiser, 2019. "Signaling impacts of GMO labeling on fruit and vegetable demand," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-16, October.
    3. Jean‐Sauveur Ay, 2021. "The Informational Content of Geographical Indications," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 523-542, March.
    4. Francisco Scott & Juan P. Sesmero, 2022. "Market and welfare effects of quality misperception in food labels," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(5), pages 1747-1769, October.
    5. Kent D. Messer & Marco Costanigro & Harry M. Kaiser, 2017. "Labeling Food Processes: The Good, the Bad and the Ugly," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 39(3), pages 407-427.
    6. Frederic Ouedraogo & B. Wade Brorsen, 2018. "Hierarchical Bayesian Estimation of a Stochastic Plateau Response Function: Determining Optimal Levels of Nitrogen Fertilization," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 66(1), pages 87-102, March.
    7. Nadia A Streletskaya & Jura Liaukonyte & Harry M Kaiser, 2019. "Absence labels: How does information about production practices impact consumer demand?," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-18, June.
    8. David M.A. Murphy & Dries Roobroeck & David R. Lee & Janice Thies, 2020. "Underground Knowledge: Estimating the Impacts of Soil Information Transfers Through Experimental Auctions†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(5), pages 1468-1493, October.
    9. Liu, Xiaoou & Lopez, Rigoberto & Zhu, Chen, 2015. "Can Voluntary Nutrition Labeling Lead to a Healthier Food Market?," 2016 Allied Social Sciences Association (ASSA) Annual Meeting, January 3-5, 2016, San Francisco, California 212818, Agricultural and Applied Economics Association.
    10. Murphy, David M. A., 2017. "Underground Knowledge: Soil Testing, Farmer Learning, and Input Demand in Kenya," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258372, Agricultural and Applied Economics Association.
    11. Grace Melo & Laura Chomali & Ariun Ishdorj, 2024. "From sweet tooth to healthy choices: How Chilean food policies are changing household diets," Agribusiness, John Wiley & Sons, Ltd., vol. 40(3), pages 550-570, July.
    12. Francisco Scott, 2023. "An Experimental Analysis of Quality Misperception in Food Labels," Research Working Paper RWP 23-11, Federal Reserve Bank of Kansas City.

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    Keywords

    Research and Development/Tech Change/Emerging Technologies;

    JEL classification:

    • L13 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Oligopoly and Other Imperfect Markets
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing
    • Q13 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Markets and Marketing; Cooperatives; Agribusiness
    • Q18 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Agricultural Policy; Food Policy; Animal Welfare Policy

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