IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/28138.html
   My bibliography  Save this paper

We are all Behavioral, More or Less: A Taxonomy of Consumer Decision Making

Author

Listed:
  • Victor Stango
  • Jonathan Zinman

Abstract

We examine how 17 behavioral biases relate to each other, to other decision inputs, and to decision outputs. Most consumers exhibit multiple biases in our nationally representative panel data. There is substantial heterogeneity across consumers, even within similar demographic/skill groups. Biases are positively correlated within person, especially after adjusting for measurement error, and less correlated with other inputs—risk aversion, patience, cognitive skills, and personality traits—with some expected exceptions. Accounting for this correlation structure, we reduce our 29 decision inputs to eight common factors. Seven common factors load on at least two biases, six on clusters of theoretically related biases, and two or three are distinctly behavioral. All but one common factor is distinct from cognitive skills. Factor scores strongly conditionally correlate with decisions and outcomes in various domains. We discuss several potential implications of this taxonomy for various approaches to modeling influences of behavioral biases on decision making.

Suggested Citation

  • Victor Stango & Jonathan Zinman, 2020. "We are all Behavioral, More or Less: A Taxonomy of Consumer Decision Making," NBER Working Papers 28138, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:28138
    Note: AG DEV EH IO LE PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w28138.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

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


    Cited by:

    1. Kaiser, Tim & Lusardi, Annamaria, 2024. "Financial Literacy and Financial Education: An Overview," IZA Discussion Papers 16926, Institute of Labor Economics (IZA).
    2. Carvajal, Daniel & Franco, Catalina & Isaksson, Siri, 2024. "Will Artificial Intelligence Get in the Way of Achieving Gender Equality?," Discussion Paper Series in Economics 3/2024, Norwegian School of Economics, Department of Economics, revised 31 Oct 2024.
    3. Dietrichson, Jens & Gudmundsson, Jens & Jochem, Torsten, 2022. "Why don’t we talk about it? Communication and coordination in teams," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 257-278.
    4. Garber, Gabriel & Mian, Atif & Ponticelli, Jacopo & Sufi, Amir, 2024. "Consumption smoothing or consumption binging? The effects of government-led consumer credit expansion in Brazil," Journal of Financial Economics, Elsevier, vol. 156(C).
    5. Esplin, Ryan & Best, Rohan & Scranton, Jessica & Chai, Andreas, 2022. "Who pays the loyalty tax? The relationship between socioeconomic status and switching in Australia's retail electricity markets," Energy Policy, Elsevier, vol. 164(C).
    6. Ertl, Antal & Horn, Dániel & Kiss, Hubert János, 2024. "Economic Preferences across Generations and Family Clusters: A Comment," I4R Discussion Paper Series 105, The Institute for Replication (I4R).
    7. Andrew Caplin & David J. Deming & Søren Leth-Petersen & Ben Weidmann, 2023. "Economic Decision-Making Skill Predicts Income in Two Countries," NBER Working Papers 31674, National Bureau of Economic Research, Inc.
    8. Lewis Davis & Dolores Garrido & Carolina Missura, 2023. "Inherited Patience and the Taste for Environmental Quality," Sustainability, MDPI, vol. 15(5), pages 1-10, February.
    9. Marcus Roel & Manuel Staab, 2021. "The benefits of being misinformed," AMSE Working Papers 2108, Aix-Marseille School of Economics, France.
    10. Little, Andrew T., 2022. "Information Theory and Biased Beliefs," OSF Preprints vfqy2, Center for Open Science.
    11. Byrne, David P. & Martin, Leslie A., 2021. "Consumer search and income inequality," International Journal of Industrial Organization, Elsevier, vol. 79(C).

    More about this item

    JEL classification:

    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D90 - Microeconomics - - Micro-Based Behavioral Economics - - - General
    • E70 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nbr:nberwo:28138. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.