Identifying Causal Effects in Information Provision Experiments

Dylan Balla-Elliott

Papers from arXiv.org

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).

Date: 2023-09, Revised 2023-11
New Economics Papers: this item is included in nep-ecm and nep-exp
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2309.11387 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2309.11387

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().