IDEAS home Printed from https://ideas.repec.org/a/aea/aerins/v7y2025i3p376-95.html
   My bibliography  Save this article

Interpreting TSLS Estimators in Information Provision Experiments

Author

Listed:
  • Vod Vilfort
  • Whitney Zhang

Abstract

In information provision experiments, researchers often estimate the causal effects of beliefs on actions using two-stage least squares (TSLS). This paper formalizes exclusion and monotonicity conditions that ensure that TSLS recovers a positive-weighted average of causal effects. We assess common TSLS estimators for both passive and active control designs from the literature; we find that two commonly used passive control estimators generally allow for negative weights. The choice of passive control estimator affects the magnitude and significance of estimates in simulations and in an empirical application. We give practical recommendations for addressing these issues.

Suggested Citation

  • Vod Vilfort & Whitney Zhang, 2025. "Interpreting TSLS Estimators in Information Provision Experiments," American Economic Review: Insights, American Economic Association, vol. 7(3), pages 376-395, September.
    • Handle: RePEc:aea:aerins:v:7:y:2025:i:3:p:376-95
    DOI: 10.1257/aeri.20240353
    as

    Download full text from publisher

    File URL: https://www.aeaweb.org/doi/10.1257/aeri.20240353
    Download Restriction: Access to full text is restricted to AEA members and institutional subscribers.
    File URL: https://doi.org/10.3886/E211442V2
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/materials/23762
    Download Restriction: no
    File URL: https://www.aeaweb.org/articles/materials/23763
    Download Restriction: no
    File URL: https://libkey.io/10.1257/aeri.20240353?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    More about this item

    JEL classification:

    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
    • D21 - Microeconomics - - Production and Organizations - - - Firm Behavior: Theory
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

    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:aea:aerins:v:7:y:2025:i:3:p:376-95. 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: Michael P. Albert (email available below). General contact details of provider: https://edirc.repec.org/data/aeaaaea.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.