• Medientyp: E-Book
  • Titel: We are all Behavioral, More or Less : Measuring and Using Consumer-level Behavioral Sufficient Statistics
  • Beteiligte: Stango, Victor [VerfasserIn]; Zinman, Jonathan [Sonstige Person, Familie und Körperschaft]
  • Körperschaft: National Bureau of Economic Research
  • Erschienen: Cambridge, Mass: National Bureau of Economic Research, 2019
  • Erschienen in: NBER working paper series ; no. w25540
  • Umfang: 1 Online-Ressource; illustrations (black and white)
  • Sprache: Englisch
  • DOI: 10.3386/w25540
  • Identifikator:
  • Reproduktionsnotiz: Hardcopy version available to institutional subscribers
  • Entstehung:
  • Anmerkungen: System requirements: Adobe [Acrobat] Reader required for PDF files
    Mode of access: World Wide Web
  • Beschreibung: 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
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