Identifying Causal Effects in Information Provision Experiments

Dylan Balla-Elliott

Papers from arXiv.org

Abstract: Information treatments often shift beliefs more for people with weaker belief effects. Since standard TSLS and panel specifications in information provision experiments have weights proportional to belief updating in the first-stage, this dependence attenuates existing estimates. This is natural if people whose decisions depend on their beliefs gather information before the experiment. I propose a local least squares estimator that identifies unweighted average effects in several classes of experiments under progressively stronger versions of Bayesian updating. In five of six recent studies, average effects are larger than-in several cases more than double-estimates in standard specifications.

Date: 2023-09, Revised 2025-06
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: View citations in EconPapers (1)

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 ().