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

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  • 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
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    Cited by:

    1. 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.
    2. 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.
    3. 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).
    4. 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).
    5. Lewis Davis & Dolores Garrido & Carolina Missura, 2023. "Inherited Patience and the Taste for Environmental Quality," Sustainability, MDPI, vol. 15(5), pages 1-10, February.
    6. Marcus Roel & Manuel Staab, 2021. "The benefits of being misinformed," AMSE Working Papers 2108, Aix-Marseille School of Economics, France.
    7. Little, Andrew T., 2022. "Information Theory and Biased Beliefs," OSF Preprints vfqy2, Center for Open Science.
    8. 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

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