Persistent prejudice: De-biasing and the demand for biased news

Last registered on December 17, 2020

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Pre-Trial

Trial Information

General Information

Title

Persistent prejudice: De-biasing and the demand for biased news

RCT ID

AEARCTR-0005468

Initial registration date

February 19, 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

February 19, 2020, 3:04 PM EST

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

Last updated

December 17, 2020, 8:31 PM EST

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

Locations

Country

India

Region

Primary Investigator

Name

Kailash Rajah

Affiliation

Massachusetts Institute of Technology

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Chinmaya Kumar

PI Affiliation

University of Chicago

Contact Investigator

PI Name

Leonardo Bursztyn

PI Affiliation

University of Chicago

Contact Investigator

PI Name

Christopher Blattman

PI Affiliation

University of Chicago

Contact Investigator

Additional Trial Information

Status

In development

Start date

2020-02-22

End date

2021-12-31

Keywords

Other

Additional Keywords

Motivated Beliefs, Media Bias, Selective exposure, Religious Polarization, Fake News, Experiments, News Consumption, Information, India

JEL code(s)

D91, D83, C91, N35

Secondary IDs

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.

External Link(s)

Registration Citation

Citation

Blattman, Christopher et al. 2020. "Persistent prejudice: De-biasing and the demand for biased news." AEA RCT Registry. December 17. https://doi.org/10.1257/rct.5468-2.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2020-02-22

Intervention End Date

2020-03-31

Primary Outcomes

Primary Outcomes (end points)

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.

Secondary Outcomes

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

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

Experimental Design

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)

Experimental Design Details

Randomization Method

Randomization done by Qualtrics

Randomization Unit

Treatments are randomized at the individual level

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

2500-3000 individuals depending on recruitment costs, no clusters

Sample size: planned number of observations

2500-3000 individuals depending on recruitment costs

Sample size (or number of clusters) by treatment arms

625-750 individuals per arm

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

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).

Supporting Documents and Materials

IRB