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Inter-ethnic relations in the time of COVID-19
Last registered on June 11, 2020


Trial Information
General Information
Inter-ethnic relations in the time of COVID-19
Initial registration date
June 11, 2020
Last updated
June 11, 2020 8:53 AM EDT
Primary Investigator
Kiel Institute for the World Economy
Other Primary Investigator(s)
PI Affiliation
Max Planck Institute for Research on Collective Goods
PI Affiliation
PI Affiliation
Brown University
Additional Trial Information
In development
Start date
End date
Secondary IDs
The study is the second wave of the Trustlab survey (Murtin et al., 2018), which has already been conducted in the US (and in six other countries). The main objective of the survey is to measure experimentally trust in others and (within a survey) trust in government in a representative sample – according to age, gender, and income - of the US population. As detailed below, the survey includes several games. For the purpose of the present study, we are mainly interested in anonymous Trust Games (TGs) where participants are randomly matched with other US residents, whose ethnic background is either not specified, or specified as belonging to one of the following categories: (a) non-Hispanic white; (b) African American; (c) Hispanic. Furthermore, second movers from ethnic groups (a) through (c) have either an unspecified income level or their income level is specified as belonging to the top 20% of the income distribution. We have thus a total of seven TGs. For the purpose of disentangling individual motivations behind trust choices, we also use seven Dictator Games (DGs) and seven belief elicitation questions on second movers’ behaviour, where the recipient belongs to the same categories listed above.
The main hypothesis we want to study is whether the existential threat posed by COVID-19 leads to greater ingroup bias. We already demonstrated the existence of significant ingroup bias in Cetre et al. (2020), across all of the three ethnic groups that are targeted in our study. In this second wave, we want to ascertain whether ingroup bias is higher in the time of COVID-19 than before. We also decompose ingroup bias into taste-based and statistical discrimination components.

Cetre, Sophie, Yann Algan, Gianluca Grimalda, Fabrice Murtin, Louis Putterman, Ulrich Schmidt, Vincent Siegerink (2020). Ethnic bias, economic success, and trust: findings from large sample experiments in Germany and the U.S. OECD Working Paper series (forthcoming)

Murtin, F., Fleischer, L., Siegerink, V., Aassve, A., Algan, Y., Boarini, R., ... & Kim, S. (2018). Trust and its determinants. OECD Working Paper 2018/02; https://doi.org/10.1787/869ef2ec-en
Registration Citation
Grimalda, Gianluca et al. 2020. "Inter-ethnic relations in the time of COVID-19." AEA RCT Registry. June 11. https://doi.org/10.1257/rct.5995-1.0.
Sponsors & Partners

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Experimental Details
There is no intervention.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Our key Dependent Variable (DVs) is:

- The ingroup bias, defined as the amount transferred to a person from one’s own ethnic group and the amount transferred to a person from a different ethnic group in the TG. Since there are two target ethnic groups different from one’s own, we will generally use repeated observation models. For some analyses we will use the average between the two ingroup biases computed with respect to the two outgroups.
Since trust may depend both on the level of altruism experienced with respect to another ethnic group, and on the expectation over their trustworthiness, we will also analyse ingroup bias in the DGs and in expectations. However, we see these two variables mainly as auxiliary variables that will help us decompose whether the ingroup bias observed in the TG decisions is due to taste-based discrimination (in which case we should observe a significant correlation between TG and DG transfers) or to statistical discrimination (in which case we should observe a significant correlation between TG transfers and expectations on amount returned).
Primary Outcomes (explanation)
Ingroup bias is defined as the difference between transfer to one’s ingroup and one’s outgroup in the TGs (and in the DGs).
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
There are no treatments as such in our online survey. Rather, we have the following blocks of games:
1) A Trust Game (TG), in which participants play as first and second movers;
2) A Public Goods Game (in which participants decide how much to contribute in absolute terms and in relation with the average contribution of others);
3) A Dictator Game (DG);
4) A series of six TGs in which participants act as first movers, knowing that the second mover is (a) non-Hispanic white; or (b) African American; or (c) Hispanic; or (a) through (c) with the second mover belonging to the top 20% of the income distribution.
5) A series of six DGs played with recipients from the same ethnic categories as in (4);
6) A decision to measure risk tolerance.

One among the six tasks is randomly selected to determine the participants’ final payoffs. After block (1) and (4) participants are also asked their beliefs over the second mover’s response to one of the possible actions. This belief elicitation is not monetarily incentivized.
After the experimental choices, participants are involved in a survey inquiring over their demographic characteristics, attitudes towards society (in particular, trust in governments), and their level of exposure to COVID-19. The order with which the series of six games in Block (4) and (5) is administered is randomized.
Experimental Design Details
Not available
Randomization Method
The only randomization concerns the order with which decisions are administered in Block 4 and 5. This is carried out by a computer programme. Matching between participants to determine final payoffs, including which of the tasks a given participant is paid off for, is random and follows the order with which participants enter the survey.
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
We plan no clusters.
Sample size: planned number of observations
We target 1000 participants, which is the sample size used in the first wave of Trustlab. This is considered sufficient to obtain a representative sample of the US population with respect to age, gender and income.
Sample size (or number of clusters) by treatment arms
1000 observations
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Max Planck Institute for Research on Collective Goods Bonn
IRB Approval Date
IRB Approval Number
Block approval for experiments falling into this typology
Analysis Plan

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