IDEAS home Printed from https://ideas.repec.org/p/inf/wpaper/2025.8.html
   My bibliography  Save this paper

Estimating Behavioral Inattention

Author

Listed:
  • Jonathan Benchimol

    (Bank of Israel)

  • Lahcen Bounader

    (World Bank)

  • Mario Dotta

    (Sao Paulo School of Business Administration, Getulio Vargas Foundation)

Abstract

Bounded rationality and limited attention significantly influence expectation formation and macroeconomic dynamics, yet empirical quantification of these behavioral phenomena remains challenging. This paper provides the first cross-country estimation of both micro- and macro-level attention parameters using a structurally identified behavioral New Keynesian model. Employing Bayesian techniques on harmonized data from 22 OECD countries (1996-2019) and ensuring robust parameter identification, we document substantial heterogeneity in behavioral inattention across countries. Our cognitive discounting estimates range from 0.76 to 0.98, with higher values indicating greater attention. We establish three key empirical regularities: (1) attention parameters are positively associated with macroeconomic volatility, supporting rational inattention theory; (2) surprise movements in key macroeconomic variables and online information-seeking behavior significantly influence attention allocation; and (3) institutional quality, particularly government effectiveness, is correlated with attention levels. These findings reveal that attention is both a behavioral and a structural phenomenon, responding to institutional factors and economic conditions. Our results provide an empirical foundation for calibrating country-specific models and yield important implications for the design and transmission of monetary policy under bounded rationality, showing that policy effectiveness may systematically vary with the macroeconomic environment.

Suggested Citation

  • Jonathan Benchimol & Lahcen Bounader & Mario Dotta, 2025. "Estimating Behavioral Inattention," Working Papers 2025.8, International Network for Economic Research - INFER.
  • Handle: RePEc:inf:wpaper:2025.8
    as

    Download full text from publisher

    File URL: https://infer-research.eu/wp-content/uploads/2025/06/WP2025.08.pdf
    File Function: First version, 2025
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Cognitive discounting; myopia; attention; Bayesian estimation; behavioral macroeconomics;
    All these keywords.

    JEL classification:

    • E - Macroeconomics and Monetary Economics

    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:inf:wpaper:2025.8. 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: Pedro Cerqueira The email address of this maintainer does not seem to be valid anymore. Please ask Pedro Cerqueira to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/inferea.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.