Back to History Current Version

Does the Messenger of Central Bank Communication Matter? Effects on Belief Updating.

Last registered on January 09, 2023

Pre-Trial

Trial Information

General Information

Title
Macroeconomic Expectations and Belief Updating
RCT ID
AEARCTR-0010727
Initial registration date
January 09, 2023

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
January 09, 2023, 5:33 PM EST

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

Locations

Primary Investigator

Affiliation
University of Oxford

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2023-01-09
End date
2024-09-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This paper studies primarily individuals' expectations, updates to their expectations, and their sentiment about institutions and the economy.
External Link(s)

Registration Citation

Citation
Wabitsch, Alena. 2023. "Macroeconomic Expectations and Belief Updating." AEA RCT Registry. January 09. https://doi.org/10.1257/rct.10727-1.0
Sponsors & Partners

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information
Experimental Details

Interventions

Intervention(s)
Intervention (Hidden)
There are two parts to the analysis:

1) A Short Survey that is sent out via Twitter. The purpose is to elicit economic expectations and their uncertainty, as well as users' sentiments about institutions and policy.

2) The RCT: An information experiment via survey with participants from different EA nationalities. Their core task is to perform macroeconomic forecasting (e.g. inflation prediction, GDP, unemployment rate). The treatment consists of a randomly varying source of information that helps participants with the forecasting task. The outcome of interest is the extent to which this information (i.e. signal) is integrated into individuals' (Bayesian) Updating of their forecasts (i.e. beliefs).
Intervention Start Date
2023-02-01
Intervention End Date
2023-10-31

Primary Outcomes

Primary Outcomes (end points)
1) Short Survey: inflation expectations and variance, sentiments towards monetary policy (institution), economy, inflation

2) RCT: Economic Forecast (prior), Economic Forecast Revision after treatment (posterior), institutional trust and knowledge
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
A short survey, as well as an information treatment survey experiment are run to test how individuals form and update economic expectations.
Experimental Design Details
There are two parts to the analysis:

1) A Short Survey that is sent out via Twitter. The purpose is to elicit economic expectations and their uncertainty, as well as users' sentiments about institutions and policy. in addition to point expectations, also ranges of selected expectations are elicited to capture uncertainty. The collected data will then be matched to empirically calculated values of these expectations using NLP. Particularly, measures of users' beliefs and the precision of beliefs. The key here is to understand whether and how users' tweets reflect their actual beliefs, expectations and sentiments, but also understand which expectations are mostly reflected (past, short-run, long-run inflation?). In short: how informative is data on Twitter for economic expectations? This aims to also provide some information on individuals' economic narratives.

2) The RCT: I'm running a survey experiment in which participants from different nationalities are being tasked to perform a macroeconomic forecast (e.g. inflation prediction, GDP, unemployment rate). Participants indicate a point and variance forecast. Participants will be rewarded by the accuracy of their forecast (incentivised). There are two treatments. In treatment "european", participants will get related forecast information from the European Central Bank. In treatment "national", participants will get the same related forecast information but from their own national bank (e.g. Deutsche Bundesbank, Banque de France, etc.). Participants get either "european" or "national". After the information treatment, participants get the opportunity to revise their forecast.
The idea here is to see how Bayesian participants are, when they update their forecasts after the information treatment. This design allows to test, whether there are differences between nationalities in how participants use information from monetary policy institutions to update their expectations. Further, it can be tested whether information from the national banks are used differently from the european bank. Particularly, whether there exists something like an "ingroup effect", where participants who share their nationality with the head of the ECB update differently to the european bank's information than participants in the "outgroup". In addition, this allows to test whether the update to the national bank's information of "outgroup" participants is comparable to the "ingroup's" update to the european bank's information. If this is the case, this gives rise to the policy recommendation of using national central bank communication more strongly.
In addition to the experimental intervention, this survey will also collect information on trust and knowledge about the monetary policy institutions (but also commonly used ability tests) to make inferences about potential mechanisms of the effect of interest.

2-4 nationalities: french and german (+ possible extension with spanish and italian)
Randomization Method
1) Short Survey: no treatment, so no treatment randomisation; only some randomisation component done by computer regarding question on some extra information

2) RCT: Randomisation done by the computer (potentially via a survey company)
Randomization Unit
1) Short Survey: individual.

2) RCT: individual.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1) n/a

2) RCT: 2-4 nationalities
Sample size: planned number of observations
for Short Survey: at least 100 Twitter users, up to 500 for the RCT: at least 100 participants per nationality, up to 500 per nationality
Sample size (or number of clusters) by treatment arms
1) Short Survey: at least 20 Twitter users by language (5 languages) - no treatment

2) RCT: at least 50 participants per treatment (2 treatments) and per nationality (2-4 languages)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Department of Economics Departmental Research Ethics Committee
IRB Approval Date
2021-11-05
IRB Approval Number
ECONCIA21-22-24

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials