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We Are All Behavioral, More or Less: Measuring and Using Consumer-Level Behavioral Sufficient Statistics

Author

Listed:
  • Victor Stango
  • Jonathan Zinman

Abstract

Can a behavioral sufficient statistic empirically capture cross-consumer variation in behavioral tendencies and help identify whether behavioral biases, taken together, are linked to material consumer welfare losses? Our answer is yes. We construct simple consumer-level behavioral sufficient statistics??B-counts??by eliciting seventeen potential sources of behavioral biases per person, in a nationally representative panel, in two separate rounds nearly three years apart. B-counts aggregate information on behavioral biases within-person. Nearly all consumers exhibit multiple biases, in patterns assumed by behavioral sufficient statistic models (a la Chetty), and with substantial variation across people. B-counts are stable within-consumer over time, and that stability helps to address measurement error when using B-counts to model the relationship between biases, decision utility, and experienced utility. Conditional on classical inputs?risk aversion and patience, life-cycle factors and other demographics, cognitive and non-cognitive skills, and financial resources?B-counts strongly negatively correlate with both objective and subjective aspects of experienced utility. The results hold in much lower-dimensional models employing ?Sparsity B-counts? based on bias subsets (a la Gabaix) and/or fewer covariates, illuminating lower-cost ways to use behavioral sufficient statistics to help capture the combined influence of multiple behavioral biases for a wide range of research questions and applications.

Suggested Citation

  • Victor Stango & Jonathan Zinman, 2019. "We Are All Behavioral, More or Less: Measuring and Using Consumer-Level Behavioral Sufficient Statistics," Working Papers 19-14, Federal Reserve Bank of Philadelphia.
  • Handle: RePEc:fip:fedpwp:19-14
    DOI: 10.21799/frbp.wp.2019.14
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    Cited by:

    1. James J. Heckman & Tomáš Jagelka & Timothy D. Kautz, 2019. "Some Contributions of Economics to the Study of Personality," NBER Working Papers 26459, National Bureau of Economic Research, Inc.
    2. Sandro Ambuehl & B. Douglas Bernheim & Axel Ockenfels, 2019. "Projective Paternalism," NBER Working Papers 26119, National Bureau of Economic Research, Inc.
    3. Sophia Soyoung Jeong & Yuanyuan Gong & Alexandra Henderson, 2023. "Sympathy or distress? The moderating role of negative emotion differentiation in helping behavior," Asia Pacific Journal of Management, Springer, vol. 40(4), pages 1429-1458, December.
    4. Tomáš Jagelka, 2024. "Are Economists’ Preferences Psychologists’ Personality Traits? A Structural Approach," Journal of Political Economy, University of Chicago Press, vol. 132(3), pages 910-970.
    5. Alexander W. Cappelen & Benjamin Enke & Bertil Tungodden, 2025. "Universalism: Global Evidence," American Economic Review, American Economic Association, vol. 115(1), pages 43-76, January.
    6. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2019. "Predicting Insurance Demand from Risk Attitudes," NBER Working Papers 26508, National Bureau of Economic Research, Inc.
    7. Leandro Carvalho & Arna Olafsson & Dan Silverman, 2024. "Misfortune and Mistake: The Financial Conditions and Decision-Making Ability of High-Cost Loan Borrowers," Journal of Political Economy, University of Chicago Press, vol. 132(9), pages 3173-3213.
    8. Merkoulova, Yulia & Veld, Chris, 2022. "Stock return ignorance," Journal of Financial Economics, Elsevier, vol. 144(3), pages 864-884.
    9. Johannes G. Jaspersen & Marc A. Ragin & Justin R. Sydnor, 2022. "Predicting insurance demand from risk attitudes," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 89(1), pages 63-96, March.
    10. Alexander W. Cappelen & Benjamin Enke & Bertil Tungodden, 2022. "Moral Universalism: Global Evidence," NBER Working Papers 30157, National Bureau of Economic Research, Inc.

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    Keywords

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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • D1 - Microeconomics - - Household Behavior
    • D6 - Microeconomics - - Welfare Economics
    • D9 - Microeconomics - - Micro-Based Behavioral Economics
    • E7 - Macroeconomics and Monetary Economics - - Macro-Based Behavioral Economics
    • G4 - Financial Economics - - Behavioral Finance

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