Identifying Causal Effects in Information Provision Experiments
Author:
Dylan Balla-Elliott
Keyword:
Economics, Econometrics, Econometrics (econ.EM)
journal:
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date:
2023-09-19 16:00:00
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).PDF: Identifying Causal Effects in Information Provision Experiments.pdf
