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Identifying Causal Effects in Information Provision Experiments

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  • Dylan Balla-Elliott

Abstract

Standard estimators in information provision experiments place more weight on individuals who update their beliefs more in response to new information. This paper shows that, in practice, these individuals who update the most have the weakest causal effects of beliefs on outcomes. Standard estimators therefore understate these causal effects. I propose an alternative local least squares (LLS) estimator that recovers a representative unweighted average effect in a broad class of learning rate models that generalize Bayesian updating. I reanalyze six published studies. In five, estimates of the causal effects of beliefs on outcomes increase; in two, they more than double.

Suggested Citation

  • Dylan Balla-Elliott, 2023. "Identifying Causal Effects in Information Provision Experiments," Papers 2309.11387, arXiv.org, revised Jan 2026.
    • Handle: RePEc:arx:papers:2309.11387
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    References listed on IDEAS

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    1. Sonja Settele, 2022. "How Do Beliefs about the Gender Wage Gap Affect the Demand for Public Policy?," American Economic Journal: Economic Policy, American Economic Association, vol. 14(2), pages 475-508, May.
    2. Leonardo Bursztyn & Alessandra L. González & David Yanagizawa-Drott, 2020. "Misperceived Social Norms: Women Working Outside the Home in Saudi Arabia," American Economic Review, American Economic Association, vol. 110(10), pages 2997-3029, October.
    3. repec:dau:papers:123456789/1908 is not listed on IDEAS
    4. Dylan Balla‐Elliott & Zoë B. Cullen & Edward L. Glaeser & Michael Luca & Christopher Stanton, 2022. "Determinants Of Small Business Reopening Decisions After Covid Restrictions Were Lifted," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 41(1), pages 278-317, January.
    5. Sonja Settele, 2022. "How Do Beliefs About the Gender Wage Gap Affect the Demand for Public Policy?," ECONtribute Discussion Papers Series 179, University of Bonn and University of Cologne, Germany.
    6. Matthew Masten & Alexander Torgovitsky, 2014. "Instrumental variables estimation of a generalized correlated random coefficients model," CeMMAP working papers 02/14, Institute for Fiscal Studies.
    7. Alberto Cavallo & Guillermo Cruces & Ricardo Perez-Truglia, 2017. "Inflation Expectations, Learning, and Supermarket Prices: Evidence from Survey Experiments," American Economic Journal: Macroeconomics, American Economic Association, vol. 9(3), pages 1-35, July.
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    Cited by:

    1. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.

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