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Political Identities, Information Demand and Processing
Last registered on October 28, 2020

Pre-Trial

Trial Information
General Information
Title
Political Identities, Information Demand and Processing
RCT ID
AEARCTR-0006670
Initial registration date
October 26, 2020
Last updated
October 28, 2020 9:15 AM EDT
Location(s)
Region
Primary Investigator
Affiliation
University of Michigan
Other Primary Investigator(s)
PI Affiliation
Goethe University Frankfurt
PI Affiliation
Johannes Gutenberg University Mainz
PI Affiliation
Goethe University Frankfurt
Additional Trial Information
Status
In development
Start date
2020-10-27
End date
2020-11-03
Secondary IDs
Abstract
We study the role of social identity and "groupiness'' in the processing of and demand for information using an online experiment with a representative sample of the US population, deployed the week before the upcoming 2020 US presidential election on November 3. Specifically, we ask whether (i) people update their beliefs in reaction to consuming news in a biased way, (ii) whether this bias increases if news source labels are observable, (iii) whether groupiness as measured by the other-other allocation game moderates both these effects, and (iv) whether exposure to news articles from a neutral source or the other side increases prediction accuracies. In our online experiment, approximately 1,000 participants have to predict both the outcome of the 2020 presidential election and policy-sensitive statistics a year after the election conditional on its outcome. To update their initial predictions, individuals receive or can select articles on the respective topics from different news sources. Our key experimental variation is whether the articles come with a label of the news sources or not, where news sources differ in their political leaning.
External Link(s)
Registration Citation
Citation
Bauer, Kevin et al. 2020. "Political Identities, Information Demand and Processing." AEA RCT Registry. October 28. https://doi.org/10.1257/rct.6670-1.0.
Sponsors & Partners

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Experimental Details
Interventions
Intervention(s)
In our online experiment, participants have to predict both the outcome of the 2020 presidential election and policy-sensitive statistics a year after the election conditional on its outcome. To update their initial predictions, individuals receive or can select articles on the respective topics from different news sources. Our key experimental variation is whether the articles come with a label of the news sources or not, where news sources differ in their overlap with the participants' political identity.

The main experiment will be implemented the week before the 2020 presidential election. We will contact the participants again in the last week of January 2021 to pay them for the accuracy of their predictions of the election outcome, as well as the last week of October 2021 to pay them for their predictions of policy-relevant statistics.
Intervention Start Date
2020-10-27
Intervention End Date
2020-11-03
Primary Outcomes
Primary Outcomes (end points)
1. (Prior beliefs) Differences in the answers in the prediction tasks between the two different election outcomes before consuming additional news.
2. (Posterior beliefs) Differences in the answers in the prediction tasks between the two different election outcomes after consuming additional news.
3. (Change of beliefs) Change of the differences in the answers in the prediction tasks between the two different election outcomes before and after consuming additional news.
4. (WTP for partisan news) Money spent to affect the probabilities in the news demand task.
5. (Prediction accuracy) A dummy variable which equals one if a prediction is correct, and zero otherwise.
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
1. In stage 1, we elicit a groupiness measure using the minimal group paradigm, priors for two prediction tasks, and participants' political identities.

2. In stage P (information processing), we examine how labels of news sources influence the processing of information.

3. In stage D (information demand) we test how labels affect the demand for information, and end by measuring their updating tendencies for neutral information.
Experimental Design Details
Randomization Method
randomization done online by a computer
Randomization Unit
individual participant
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
1,000 participants
Sample size: planned number of observations
1,000 participants
Sample size (or number of clusters) by treatment arms
500 participants in the control condition and 500 in the treatment condition
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Using the standard criteria of $\alpha = 0.05$, $\beta = 0.20$, and the minimum detectable effect size is somewhere between 17% to 20% of a standard deviation, we will need a sample size of will need a sample size of 392-543 participants per condition, respectively.
Supporting Documents and Materials

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Documents
Document Name
IRB Exempt Letter
Document Type
irb_protocol
Document Description
File
IRB Exempt Letter

MD5: b0dc2a0e67536d5873190e335573cf58

SHA1: 0ed6e88d9867c764562b94e92392f5d45aa17d82

Uploaded At: October 27, 2020

IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Goethe University Frankfurt IRB
IRB Approval Date
2020-10-22
IRB Approval Number
N/A
Analysis Plan

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Post-Trial
Post Trial Information
Study Withdrawal
Intervention
Is the intervention completed?
No
Is 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