Political Endorsements and Trust in Science during the COVID-19 Pandemic

Last registered on August 02, 2021
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Pre-Trial

Trial Information

General Information

Title
Political Endorsements and Trust in Science during the COVID-19 Pandemic
RCT ID
AEARCTR-0007007
Initial registration date
July 27, 2021

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
August 02, 2021, 12:39 PM EDT

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

Last updated
August 02, 2021, 4:35 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

Country
United States of America
Region

Primary Investigator

Name
Floyd Zhang
Affiliation
Stanford University
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2021-07-27
End date
2021-08-09
Keywords
Behavior, Electoral, Health
Additional Keywords
Trust in Science; COVID-19; Trust; Endorsement;
JEL code(s)
D83
Secondary IDs
Abstract
The effect of political endorsement on the endorser is under0studied. How do political endorsements made by scientific organizations affect (1) public trust in the endorsing organization, (2) demand for scientific information provided by the endorsing organization, (3) trust in scientific expertise in genera;? How do the effects vary by participants' prior beliefs about the political issues that are the subject of the endorsement?
I investigate these question by conducting an online experiment treating participants with information about the scientific journal Nature's endorsement of Joe Biden in the 2020 presidential election, when the COVID-19 pandemic, which the endorsement statement highlights, was an extremely salient issue. I then examine participants' stated trust in Nature, demand for COVID-19 related information from Nature, as well as receptiveness toward Nature's scientific communication unrelated to COVID-19. I focus on how effects differ by participants' prior views about Biden vs. Trump.
I structure my analyses with a simple model of information communication with repetitional concerns.
External Link(s)

Registration Citation

Citation
Zhang, Floyd. 2021. "Political Endorsements and Trust in Science during the COVID-19 Pandemic." AEA RCT Registry. August 02. https://doi.org/10.1257/rct.7007-1.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2021-07-27
Intervention End Date
2021-08-09

Primary Outcomes

Primary Outcomes (end points)
Stated trust in Nature's (1) impartiality and (2) competence. (2 outcome variables)
Primary Outcomes (explanation)
Each of the outcome variables is constructed based on one survey question by treating the five response options as a (linear) five-point scale then normalizing it as a z-score.

Secondary Outcomes

Secondary Outcomes (end points)
1, Demand for COVID-related information published by Nature, as measure the rate at which they choose Nature articles from a menu of different information sources from which they can learn about emerging COVID-19 variants and vaccine efficacy against them. (1 outcome variable)

2, Persuasiveness of Nature's scientific message about climate change. (2 outcome variables)

3, Relative assessments of Biden's pandemic response versus that of Trump's; evaluation of two candidate's ability to make use of scientific information. (3 outcome variables)

4, Stated trust in scientists' (1) impartiality and (2) competence in general. (1 outcome variable)
Secondary Outcomes (explanation)
With two exceptions, each of the outcome variables is constructed based on one survey question by treating the five (or seven) response options as a (linear) five- (seven-)point scale then normalizing it as a z-score.

The two exceptions:
(1) The measure of demand for COVID-related information published by Nature is coded as binary. The dummy equals one if only if the list of sources the participant chooses include Nature.
(2) One of the two questions about trust Nature's scientific message on climate change is also coded as binary. The question asks participants what they think is share of climate scientists who believe in human-caused climate change, after informing them Nature says the percentage is 97%. The dummy is coded as 1 if and only if they select " > 90%", which is what the Nature message entails.

Experimental Design

Experimental Design
The experiment is in the form of an online survey with participants recruited using Lucid Theorem.
I first solicit participants' prior beliefs about politics, including which 2020 U.S. presidential candidate they support.
Next I provide the treatment group with information about Nature's endorsement of Joe Biden.
I then ask further questions measuring all outcomes variables.
Experimental Design Details
The experiment is in the form of an online survey with participants recruited using Lucid Theorem.
I first solicit participants' prior beliefs about politics, including which 2020 U.S. presidential candidate they support. I also elicit priors about whether they think it is likely that Nature made any political endorsement, and who they think Nature would endorse.
Next I provide the treatment group with information about Nature's endorsement of Joe Biden, which includes a (partial) summary of what the endorsement statement says and a link to the endorsement statement on Nature's official website. The control group sees some information about Nature's new visual design, presented in similar format as the endorsement information that the treatment group see.
I then ask further questions measuring all outcome variables.
Randomization Method
Qualtrics randomizer
Randomization Unit
Individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
4,000 individuals.
Sample size: planned number of observations
4,000 individuals.
Sample size (or number of clusters) by treatment arms
2,000 control, 2,000 treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Stanford University IRB
IRB Approval Date
2021-07-01
IRB Approval Number
IRB-60462
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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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