Fear and Favoritism in the Time of COVID-19

Last registered on October 08, 2020

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Pre-Trial

Trial Information

General Information

Title

Fear and Favoritism in the Time of COVID-19

RCT ID

AEARCTR-0006571

Initial registration date

October 08, 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 08, 2020, 7:25 AM EDT

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

Locations

Country

Korea (Republic of)

Region

Primary Investigator

Name

Inbok Rhee

Affiliation

KDI School of Public Policy and Management

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Chrysostomos Tabakis

PI Affiliation

KDI School of Public Policy and Management

Contact Investigator

PI Name

Benjamin Ho

PI Affiliation

Vassar College

Contact Investigator

PI Name

Baran Han

PI Affiliation

KDI School of Public Policy and Management

Contact Investigator

Additional Trial Information

Status

In development

Start date

2020-10-12

End date

2021-08-31

Keywords

Behavior

Additional Keywords

JEL code(s)

Secondary IDs

Abstract

This study employs a survey experiment in which we use a standard social psychology induction of feelings of fear in the aftermath of the COVID-19 pandemic in South Korea to test in-group favoritism and out-group bias toward immigrants. This is done by measuring the effect of fear on political attitudes and its impact on in-group favoritism in a standard dictator game. The study will help us understand the importance of fear in inducing anti-immigrant sentiments and could shed some light on the role of the media in creating division.

External Link(s)

Registration Citation

Citation

Han, Baran et al. 2020. "Fear and Favoritism in the Time of COVID-19." AEA RCT Registry. October 08. https://doi.org/10.1257/rct.6571-1.0

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Experimental Details

Interventions

Intervention(s)

Individuals will be randomly assigned to treatment and control groups. 2x3 factorial design where subjects are exposed to a fear/hope induction interacted with exposure to one of three recent media accounts of quarantine violations that differ only by the ethnicity of the quarantine violator.

Intervention (Hidden)

Intervention Start Date

2020-10-12

Intervention End Date

2020-10-23

Primary Outcomes

Primary Outcomes (end points)

Non-native sentiment (in-group favoritism) as measured by dictator game allocations toward a non-native charity in comparison with allocations to a general charity or money kept by the subject.
Non-native sentiment as measured by economic preferences variables of reciprocity, altruism, risk, patience from Pew, Gallop, Global Preferences Survey, etc.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Which groups do you anticipate will display heterogeneous effects?
We are interested in heterogeneity by economic variables such as risk aversion, patience, trust, altruism.
But also by demographics and political views.

Secondary Outcomes (explanation)

Experimental Design

2x3 factorial design where subjects are exposed to a fear/hope induction interacted with exposure to one of three recent media accounts of quarantine violations that differ only by the ethnicity of the quarantine violator.

Experimental Design Details

Randomization Method

Randomization done in office by a computer

Randomization Unit

Individuals

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

No clusters considered, so 4,000 individual adults.

Sample size: planned number of observations

The planned full sample size is 4,000.

Sample size (or number of clusters) by treatment arms

Our full sample size is 4000, and so our 2x3 design has 666 people in each subsample.

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

Using Kritikos and Tan (IZA 2014), who ran a similar style dictator game, we are assuming a donation rate of 20% and a standard deviation of 32%. We assume the usual alpha of .05 and a beta of .80. Our full sample size is 4000, and so our 2x3 design has 666 people in each subsample. This will allow us to detect a treatment size of 5% (e.g. from 20% to 25%) between our subsample treatment. Just comparing our fear (2000 subjects) versus hope (2000 subjects) interventions allows us to detect a treatment effect of 2.8% for an alpha value of 0.1.

Supporting Documents and Materials

IRB