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Field
Abstract
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Before
The effect of political endorsement on the endorser has been understudied. 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 general. 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 examines participants' stated trust in Nature, demand for COVID-19 related information from Nature, as well as receptiveness toward Nature's unrelated scientific communication. I ask 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.
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After
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.
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Last Published
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Before
August 02, 2021 12:39 PM
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After
August 02, 2021 04:35 PM
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Experimental Design (Public)
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Before
The experiment is in the form of an online survey with participants recruited using Lucid Theorem.
I first solicited participants' prior beliefs about politics, including which candidate they supported for the 2020 U.S. presidential election.
Next I provide the treatment group with information about Nature's endorsement of Joe Biden.
I then ask further questions measuring all outcomes variables.
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After
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.
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Intervention (Hidden)
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Before
The treatment receives 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.
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After
The treatment group receives 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.
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Secondary Outcomes (Explanation)
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Before
With two exceptions, each of the outcome variable 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.
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After
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.
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