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Identifying Causal Effects in Information Provision Experiments

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  • Dylan Balla-Elliott

Abstract

Information provision experiments are a popular way to study causal effects of beliefs on behavior. Researchers estimate these effects using TSLS. I show that existing TSLS specifications do not estimate the average partial effect; they have weights proportional to belief updating in the first-stage. If people whose decisions depend on their beliefs gather information before the experiment, the information treatment may shift beliefs more for people with weak belief effects. This attenuates TSLS estimates. I propose researchers use a local-least-squares (LLS) estimator that I show consistently estimates the average partial effect (APE) under Bayesian updating, and apply it to Settele (2022).

Suggested Citation

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

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    1. 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.
    2. repec:dau:papers:123456789/1908 is not listed on IDEAS
    3. 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.
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