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Unconditional Treatment Effects in Information Provision Experiments

Last registered on October 13, 2025
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Pre-Trial

Trial Information

General Information

Title
Unconditional Treatment Effects in Information Provision Experiments
RCT ID
AEARCTR-0016983
Initial registration date
October 13, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
October 13, 2025, 11:15 AM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Jiannan Zhou
Affiliation
Shandong University
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Other Primary Investigator(s)

PI Name
Pei Kuang
PI Affiliation
University of Macau
PI Name
Carola Binder
PI Affiliation
University of Texas at Austin
PI Name
Li Tang
PI Affiliation
University of Reading

Additional Trial Information

Status
In development
Start date
2025-10-27
End date
2026-06-02
Keywords
Behavior
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study examines how policy information affects economic expectations in survey experiments, focusing on "unconditional" effects that account for real-world limits on people's attention and exposure to news. Unlike standard approaches that assume full information receipt, we adjust estimates to reflect varying awareness levels among participants.

We apply this to monetary policy, testing how different interest rate decisions influence consumer views on inflation and the economy. Using a two-wave online survey with U.S. adults, we randomly assign participants to control or treatment groups with different scenarios. We measure expectations before and after providing info.

This method bridges experimental controls with practical realities, aiding policymakers in evaluating communication impacts.
External Link(s)

Registration Citation

Citation
Binder, Carola et al. 2025. "Unconditional Treatment Effects in Information Provision Experiments." AEA RCT Registry. October 13. https://doi.org/10.1257/rct.16983-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The interventions consist of providing participants with hypothetical monetary policy scenarios related to interest rate decisions by the Federal Open Market Committee. Specifically, in the first wave of the survey, participants in treatment groups are exposed to information about potential interest rate changes (e.g., increases or decreases of varying magnitudes in basis points). This information is presented as part of a randomized survey experiment to assess its impact on economic expectations.
Intervention Start Date
2025-10-28
Intervention End Date
2026-06-01

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes are participants' expectations about inflation and other key macroeconomic variables (e.g., interest rates). These are measured before and after exposure to the information in the first wave, with revisions calculated as differences. In the second wave, we also measure awareness and recall of the actual decision by the Federal Open Market Committee, used to adjust for unconditional effects.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This is a two-wave online survey experiment with U.S. adults conducted around a Federal Open Market Committee (FOMC) meeting. The first wave is conducted shortly before the meeting. Participants are randomly assigned to a control group or one of four treatment groups involving hypothetical interest rate scenarios (adjustments of ±25 or ±50 basis points). We measure expectations about inflation and macroeconomic variables before and after providing the information, allowing estimation of conditional effects. The second wave follows shortly after the FOMC announcement. We assess whether participants heard and correctly recalled the actual decision, which can be used to adjust for unconditional effects.
Experimental Design Details
Not available
Randomization Method
Individual-level randomization will be implemented by a survey software to ensure allocation is unbiased and not observable to participants or researchers.
Randomization Unit
Individual respondent
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
2500 respondents in each of the two waves
Sample size (or number of clusters) by treatment arms
500 respondents per treatment group
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
University of Reading Research Ethics Committee
IRB Approval Date
2025-05-09
IRB Approval Number
N/A
Analysis Plan