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Abstract Identity groups often hold incorrect and biased beliefs about competing groups. Examples include Democrats and Republicans in the US, or Israelis and Arabs in the Middle East. In India, the setting for this study, Hindu nationalists commonly believe that Muslims are untrustworthy, or that the Muslim population is growing so fast that their population will overtake Hindus. These beliefs may be persistent and difficult to correct. Why is that? One reason, we hypothesize, is that people exposed to information counter to their group identity may work to rebias themselves by increasing their selective exposure--their consumption of biased news and information. We design an experiment in which we randomly provide Hindu nationalists on Facebook with information to correct a biased belief about Muslims, using informational videos. We first confirm that the videos shift their beliefs during the experiment. We then examine their demand for new information—having been de-biased, are they more likely to seek information from a biased source? Finally, we check whether this demand for new biased information changes when subjects are incentivized to answer a factual question correctly. Identity groups often hold incorrect and biased beliefs about competing groups. Examples include Democrats and Republicans in the US, or Israelis and Arabs in the Middle East. In India, the setting for this study, Hindu nationalists commonly believe that Muslims are untrustworthy, or that the Muslim population is growing so fast that their population will overtake Hindus. These beliefs may be persistent and difficult to correct. Why is that? One reason, we hypothesize, is that people exposed to information counter to their group identity may work to rebias themselves by increasing their selective exposure--their consumption of biased news and information. We design an experiment in which we randomly provide Hindu respondents with information to correct a biased belief about Muslims, using informational videos. We first confirm that the videos shift their beliefs during the experiment. We then examine their demand for new information—having been de-biased, are they more likely to seek information from a biased source?
Trial End Date December 31, 2021 August 31, 2022
Last Published December 17, 2020 08:31 PM June 28, 2021 02:48 PM
Intervention Start Date February 22, 2020 June 30, 2020
Intervention End Date March 31, 2020 August 31, 2020
Primary Outcomes (End Points) Demand for biased vs. unbiased news for a long-term news subscription service Demand for biased vs. unbiased news for a long-term news subscription service
Primary Outcomes (Explanation) Demand for biased news in a long-term news subscription service (selective exposure). This takes the form of an offer of a free clipping service—video clippings and alerts via WhatsApp. Subjects may choose between receiving entertainment or news, where the various news packages include left, centrist, and right-wing options. Demand for biased news in a long-term news subscription service (selective exposure). This takes the form of an offer of a free news clipping service—video clippings and alerts via WhatsApp. Subjects may choose to receive news clippings from a list of prominent entertainment and news journalists with commonly known political affiliations (left, centrist, or right wing).
Experimental Design (Public) Study participants will be recruited through Facebook advertisements targeting Indians in Hindi-speaking states who have indicated an interest in Hindu nationalism (“Hindutva”). After collecting simple demographic questions and passing attention-check/screening questions, participants are randomized into seeing either the two treatment videos or two placebo videos on trust or population. - Trust Treatment: The trust game is briefly explained, and participants are told that Muslims return all money twice as often as Hindus. - Trust Placebo: The trust game is briefly explained, and participants are told nothing about the behavior of Hindus or Muslims. - Population Treatment: The video debunks the myth that the Muslim population could overtake the Hindu population in India. - Population Placebo: Participants are provided with information about Buddhist population trends in India. Then, the posterior belief outcomes are elicited (a tertiary outcome). Participants are randomized into two outcome measures: - In the one case, we measure demand for selective exposure -- biased vs. unbiased news for a long-term news subscription service (primary outcome) - In the other one case, we measure demand for biased vs. unbiased news sources for usage in an incentivized fact-finding activity (secondary outcome, in order to assess whether they are conscious of the bias) We hypothesize that: - After treatment, if participants exhibit motivated reasoning (in the sense of experiencing disutility from revising their negative views about Muslims), then they will be more likely to select into the biased/pro-Hindu news subscription, or at least no more likely to select away from it. - After treatment, participants might be less likely to select pro-Hindu/biased journalists in the fact-finding activity, or at least no more likely to select it. Our prediction here is more ambiguous given that, for some respondents, the reward entering a correct answer may not outweigh their demand for biased information. (secondary outcome) - People will immediately update their posteriors to reflect the information they have been given in the treatment (tertiary outcome) Study participants will be recruited through an online survey firm targeting Indians in Hindi-speaking states. Our sample of interest is individuals with strong anti-Muslim sentiments. We use several attitudinal questions to create an index of anti-Muslim bias ranging from 0-12. We aim to recruit 1,000 respondents with a bias score of 8 or more. Given the difficulty of recruiting this sample, it is possible that we will not be able to reach our target N. As such, we will pool data from our small pilot study in our main specification (N = 42). If after pooling we are unable to reach our target sample size, we will include respondents with slightly lower bias scores. After collecting simple demographic questions and passing attention-check/screening questions, participants are randomized into seeing either the two treatment videos or two placebo videos on trust or population. - Trust Treatment: The trust game is briefly explained, and participants are told that Muslims return all money twice as often as Hindus. - Trust Placebo: The trust game is briefly explained, and participants are told nothing about the behavior of Hindus or Muslims. - Population Treatment: The video debunks the myth that the Muslim population could overtake the Hindu population in India. - Population Placebo: Participants are provided with information about Buddhist population trends in India. Then, the posterior belief outcomes are elicited (a secondary outcome). We measure demand for selective exposure -- news subscription for journalists with biased vs. unbiased stances (primary outcome). We hypothesize that: - After treatment, if participants exhibit motivated reasoning (in the sense of experiencing disutility from revising their negative views about Muslims), then they will be more likely to select into the biased/pro-Hindu news subscription, or at least no more likely to select away from it. - People will immediately update their posteriors to reflect the information they have been given in the treatment (secondary outcome) In addition to this primary analysis, we will also collect data on a random sample of respondents with a bias score below this cutoff, and examine treatment effects by level of bias (i.e. heterogeneity analysis by bias).
Planned Number of Clusters 2500-3000 individuals depending on recruitment costs, no clusters 1,000 high-bias individuals depending on recruitment costs, no clusters, plus a similar number of lower-bias respondents. (The specific number is impossible to prespecify because we select a random sample of respondents, and can only specify probabilities of selection into the full online experiment, not a target sample size).
Planned Number of Observations 2500-3000 individuals depending on recruitment costs 1,000 high-bias individuals depending on recruitment costs, plus a similar number of lower-bias respondents.
Sample size (or number of clusters) by treatment arms 625-750 individuals per arm 500 high-bias individuals per arm
Power calculation: Minimum Detectable Effect Size for Main Outcomes We expect to detect a 7 percentage point treatment effect for our primary outcome, using a power level of 0.8, a 5% significance-level and a sample size of 1500 individuals (750 per arm). We expect to detect an 8 percentage point treatment effect for our primary outcome, using a power level of 0.8, a 5% significance level and a sample size of 1,000 individuals (500 per arm).
Keyword(s) Other Other
Secondary Outcomes (End Points) Demand for biased vs. unbiased news sources for usage in a fact-finding activity Tertiary Outcome: Change in posterior beliefs for topics related to the treatment videos, as well as spillovers to associated beliefs Change in posterior beliefs for topics related to the treatment videos, as well as spillovers to associated beliefs.
Secondary Outcomes (Explanation) Demand for biased vs. unbiased news sources for usage in a fact-finding activity. Participants are told that they will need to estimate the percentage of Indians aged under 21 who are Muslim and are allowed to choose one video clipping out of a set. Each clip is shown to contain a prominent journalist with commonly known political affiliation (left, centrist, or right wing). Tertiary Outcome: Participants are asked to estimate statistics and incentivized for correct answers Participants are asked to estimate statistics related to the treatment videos, and incentivized for correct answers.
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