Political Identities, Information Demand and Processing

Last registered on October 28, 2020

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Pre-Trial

Trial Information

General Information

Title

Political Identities, Information Demand and Processing

RCT ID

AEARCTR-0006670

Initial registration date

October 26, 2020

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 28, 2020, 9:15 AM EDT

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

Locations

Country

United States of America

Region

Primary Investigator

Name

Yan Chen

Affiliation

University of Michigan

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Other Primary Investigator(s)

PI Name

Michael Kosfeld

PI Affiliation

Goethe University Frankfurt

Contact Investigator

PI Name

Florian Hett

PI Affiliation

Johannes Gutenberg University Mainz

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PI Name

Kevin Bauer

PI Affiliation

Goethe University Frankfurt

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Additional Trial Information

Status

In development

Start date

2020-10-27

End date

2020-11-03

Keywords

Behavior, Electoral, Lab

Additional Keywords

social identity, political identity, information processing, information demand, motivated cognition

JEL code(s)

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

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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

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?

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