IDEAS home Printed from https://ideas.repec.org/a/ags/jlofdr/232294.html
   My bibliography  Save this article

Private Label Products and Consumer Income: Is There a Curvilinear Relationship?

Author

Listed:
  • Jones, Eugene

Abstract

Supermarket scanner data are analyzed for five product categories across three income groups to test the premise of a curvilinear relationship between income and private labels (PLs). The three income groups are lower-, moderate-, and high-income consumers and the premise tested is that moderate-income consumers are far more inclined to purchase PLs than lower- and higher- income consumers. The five product categories selected for this study areL butter and margarine; frozen potatoes; ice cream; jams, jelly and peanut butter; and yogurt. Statistical results derived for these product categories offer no support for a curvilinear relationship between income and PLs. Lower-income consumers are shown to be more prone to purchase PLs than moderate- and higher-icnome consumers across all product groups

Suggested Citation

  • Jones, Eugene, 2016. "Private Label Products and Consumer Income: Is There a Curvilinear Relationship?," Journal of Food Distribution Research, Food Distribution Research Society, vol. 47(1), pages 1-9.
    • Handle: RePEc:ags:jlofdr:232294
    DOI: 10.22004/ag.econ.232294
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/232294/files/4_Jones.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.232294?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. 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.
    2. Drewnowski, A. & Aggarwal, A. & Hurvitz, P.M. & Monsivais, P. & Moudon, A.V., 2012. "Obesity and supermarket access: Proximity or price?," American Journal of Public Health, American Public Health Association, vol. 102(8), pages 74-80.
    3. Eugene Jones, 2014. "Consumer Preferences for National Brands and Private Labels: Do Business Cycles Matter?," Springer Proceedings in Business and Economics, in: Juan Carlos Gázquez-Abad & Francisco J. Martínez-López & Irene Esteban-Millat & Juan Antonio Mondéja (ed.), National Brands and Private Labels in Retailing, edition 127, pages 91-101, Springer.
    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. Noah Gans & George Knox & Rachel Croson, 2007. "Simple Models of Discrete Choice and Their Performance in Bandit Experiments," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 383-408, December.
    2. Andreas Lanz & Gregor Reich & Ole Wilms, 2022. "Adaptive grids for the estimation of dynamic models," Quantitative Marketing and Economics (QME), Springer, vol. 20(2), pages 179-238, June.
    3. Xiong, Siqin & Yuan, Yi & Yao, Jia & Bai, Bo & Ma, Xiaoming, 2023. "Exploring consumer preferences for electric vehicles based on the random coefficient logit model," Energy, Elsevier, vol. 263(PA).
    4. Jones, Eugene, 2014. "An Empirical Assessment of Consumers’ Preferences for Coffee," Journal of Food Distribution Research, Food Distribution Research Society, vol. 45(2), pages 1-26, July.
    5. Susumu Imai & Yuka Ohno, 2018. "Bridging Marketing and Economics: Introduction to Special Issue," The Japanese Economic Review, Springer, vol. 69(3), pages 255-257, September.
    6. Dan Horsky & Sanjog Misra & Paul Nelson, 2006. "Observed and Unobserved Preference Heterogeneity in Brand-Choice Models," Marketing Science, INFORMS, vol. 25(4), pages 322-335, 07-08.
    7. Baxendale, Shane & Macdonald, Emma K. & Wilson, Hugh N., 2015. "The Impact of Different Touchpoints on Brand Consideration," Journal of Retailing, Elsevier, vol. 91(2), pages 235-253.
    8. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    9. Rose, Chelsea M. & Gupta, Shilpi & Buszkiewicz, James & Ko, Linda K. & Mou, Jin & Cook, Andrea & Moudon, Anne Vernez & Aggarwal, Anju & Drewnowski, Adam, 2020. "Small increments in diet cost can improve compliance with the Dietary Guidelines for Americans," Social Science & Medicine, Elsevier, vol. 266(C).
    10. Danatzis, Ilias & Möller, Jana & Mathies, Christine, 2020. "We're So Bad It's Funny - Effects of Using Humour in the Marketing Communication of Low-Quality Service Providers," SMR - Journal of Service Management Research, Nomos Verlagsgesellschaft mbH & Co. KG, vol. 4(2-3), pages 84-99.
    11. ILes, Richard, 2017. "Government Doctor Absenteeism And Its Effects On Consumer Demand In Rural North India," Working Papers 2018-9, School of Economic Sciences, Washington State University, revised 12 2018.
    12. Mark Coppejans & Donna Gilleskie & Holger Sieg & Koleman Strumpf, 2007. "Consumer Demand under Price Uncertainty: Empirical Evidence from the Market for Cigarettes," The Review of Economics and Statistics, MIT Press, vol. 89(3), pages 510-521, August.
    13. Jie Bai, 2016. "Melons as Lemons: Asymmetric Information, Consumer Learning and Seller Reputation," Natural Field Experiments 00540, The Field Experiments Website.
    14. Khwaja, Ahmed & Sloan, Frank & Chung, Sukyung, 2006. "Learning about individual risk and the decision to smoke," International Journal of Industrial Organization, Elsevier, vol. 24(4), pages 683-699, July.
    15. Tat Y. Chan & Jia Li & Lamar Pierce, 2014. "Learning from Peers: Knowledge Transfer and Sales Force Productivity Growth," Marketing Science, INFORMS, vol. 33(4), pages 463-484, July.
    16. Azam, Nouman & Zhang, Yan & Yao, JingTao, 2017. "Evaluation functions and decision conditions of three-way decisions with game-theoretic rough sets," European Journal of Operational Research, Elsevier, vol. 261(2), pages 704-714.
    17. Dayeon Shin & Nitin Shivappa & James R. Hébert & Kyung Won Lee, 2020. "Examining Regional Differences of Dietary Inflammatory Index and Its Association with Depression and Depressive Symptoms in Korean Adults," IJERPH, MDPI, vol. 17(9), pages 1-11, May.
    18. Vishal Narayan & Vithala R. Rao & Carolyne Saunders, 2011. "How Peer Influence Affects Attribute Preferences: A Bayesian Updating Mechanism," Marketing Science, INFORMS, vol. 30(2), pages 368-384, 03-04.
    19. 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.
    20. Yingjie Zhang & Beibei Li & Ramayya Krishnan, 2020. "Learning Individual Behavior Using Sensor Data: The Case of Global Positioning System Traces and Taxi Drivers," Information Systems Research, INFORMS, vol. 31(4), pages 1301-1321, December.

    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:ags:jlofdr:232294. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/fdrssea.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.