IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/31152.html

Judging Nudging: Understanding the Welfare Effects of Nudges Versus Taxes

Author

Listed:
  • John A. List
  • Matthias Rodemeier
  • Sutanuka Roy
  • Gregory K. Sun

Abstract

While behavioral non-price interventions (“nudges”) have grown from academic curiosity to a bona fide policy tool, their relative economic efficiency remains under-researched. We develop a unified framework to estimate welfare effects of both nudges and taxes, while allowing for normative ambiguity about how nudges map into utility. We showcase our approach by creating a database of more than 300 carefully hand-coded point estimates of non-price and price interventions in the markets for cigarettes, influenza vaccinations, and household energy. While nudges are effective in changing behavior in all three markets, they are not necessarily the most efficient policy. When nudges are debiasing, they are more efficient in the market for cigarettes, while taxes are more efficient in the vaccine and energy market. Interestingly, these conclusions also often hold when nudges are deceptive rather than debiasing. We identify two key factors that govern the difference in results across markets: i) an elasticity-weighted standard deviation of the behavioral bias, and ii) the magnitude of the average externality. Nudges dominate taxes whenever i) exceeds ii). Finally, we consider cases in which nudges cause direct psychic costs or benefits to consumers.

Suggested Citation

  • John A. List & Matthias Rodemeier & Sutanuka Roy & Gregory K. Sun, 2023. "Judging Nudging: Understanding the Welfare Effects of Nudges Versus Taxes," NBER Working Papers 31152, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:31152
    Note: EEE PE
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w31152.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. Antinyan, Armenak & Asatryan, Zareh, 2019. "Nudging for tax compliance: A meta-analysis," ZEW Discussion Papers 19-055, ZEW - Leibniz Centre for European Economic Research.
    2. James Alm & Lilith Burgstaller & Arrita Domi & Amanda März & Matthias Kasper, 2023. "Nudges, Boosts, and Sludge: Using New Behavioral Approaches to Improve Tax Compliance," Economies, MDPI, vol. 11(9), pages 1-22, September.
    3. Lenders, Marc, 2025. "Nudging the intrinsic motivation of moral consumers," VfS Annual Conference 2025 (Cologne): Revival of Industrial Policy 325435, Verein für Socialpolitik / German Economic Association.
    4. Hernández, Francisco & Jaime, Marcela & Vásquez, Felipe, 2024. "Nudges versus prices: Lessons and challenges from a water-savings program," Energy Economics, Elsevier, vol. 134(C).
    5. Rodemeier, Matthias, 2023. "Willingness to Pay for Carbon Mitigation: Field Evidence from the Market for Carbon Offsets," IZA Discussion Papers 15939, IZA Network @ LISER.
    6. Antinyan, Armenak & Corazzini, Luca, 2023. "Breaking the Bag Habit: Testing Interventions to Reduce Plastic Bag Demand in a Developing Country," Cardiff Economics Working Papers E2023/7, Cardiff University, Cardiff Business School, Economics Section.
    7. Löschel, Andreas & Rodemeier, Matthias & Werthschulte, Madeline, 2023. "Can self-set goals encourage resource conservation? Field experimental evidence from a smartphone app," European Economic Review, Elsevier, vol. 160(C).
    8. Daniel Reck & Arthur Seibold, 2023. "The Welfare Economics of Reference Dependence," CRC TR 224 Discussion Paper Series crctr224_2023_450, University of Bonn and University of Mannheim, Germany.
    9. Chadimová, Kateřina, 2024. "Deterrence strength in TV fee enforcement: Field evidence from the Czech Republic," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 112(C).
    10. Alt, Marius, 2024. "Better us later than me now —," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 108(C).

    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D61 - Microeconomics - - Welfare Economics - - - Allocative Efficiency; Cost-Benefit Analysis
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

    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:31152. 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.