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We Are All Behavioural, More, or Less: A Taxonomy of Consumer Decision-Making

Author

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  • Victor Stango
  • Jonathan Zinman

Abstract

We examine how 17 behavioural biases relate to each other, to three standard measures of risk and time preferences, to cognitive skills, personality, and demographics, and to outcomes in household finance, well-being, and health. Most consumers in our nationally representative panel data exhibit multiple biases, with substantial cross-person heterogeneity. Biases are positively correlated within person, especially after adjusting for measurement error. From that correlation structure, we reduce our 20 bias and standard preference measures to four behavioural common factors. Each BCF reflects a group of related biases regarding beliefs, decision quality, discounting, or risk/uncertainty attitudes. The first two BCFs also strongly correlate with each other (positively) and cognitive skills (negatively). The first three BCFs and cognitive skills strongly correlate with various outcomes in the expected directions. Our results support processing-based models where basic limitations in cognition and/or attention produce multiple biases, and they have several other implications for theory and practice.

Suggested Citation

  • Victor Stango & Jonathan Zinman, 2023. "We Are All Behavioural, More, or Less: A Taxonomy of Consumer Decision-Making," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(3), pages 1470-1498.
    • Handle: RePEc:oup:restud:v:90:y:2023:i:3:p:1470-1498.
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    File URL: http://hdl.handle.net/10.1093/restud/rdac055
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    Citations

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    Cited by:

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

    Keywords

    Behavioural economics; Multiple biases; Factor analysis; Measurement error instrumental variables; Risky choice; Intertemporal choice;
    All these keywords.

    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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