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Understanding cooperation to contain a pandemic. A behavioral economics analysis
Last registered on June 10, 2021


Trial Information
General Information
Understanding cooperation to contain a pandemic. A behavioral economics analysis
Initial registration date
April 28, 2020
Last updated
June 10, 2021 11:56 AM EDT
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
University of Nottingham
Additional Trial Information
On going
Start date
End date
Secondary IDs
Containing a pandemic, like the current Covid-19 pandemic, requires people to get vaccinated and to avoid unnecessary physical interactions with each other—that is, they have to stay at home and refrain from many social activities they normally like to undertake in the company of other people. This is a large-scale cooperation problem, where (some) people may have some incentives to break the rules for self-interested reasons but are thereby undermining the collective efficacy of containment strategies. Here, we are interested in the behavioral dimensions of people’s decisions to get vaccinated or not and to stay at home or not. There are several potentially relevant behavioral dimensions: People’s pro-social inclinations, their patience and risk preferences, their trust in the authorities, and their tendency to follow other people’s behavior may all play a part in decisions to get vaccinated and to stay at home. We therefore measure these preferences, using a mixture of tested survey tools (e.g., the Global Preference Survey and the Social Value Orientation test) and incentivized behavioral games (sequential prisoner’s dilemma, dictator game, and public goods game). We will also measure various motives to get vaccinated and to comply or not with the stay-at-home rules. We plan to run the study on Amazon Mechanical Turk in four waves of 600 participants each.
External Link(s)
Registration Citation
ARECHAR, ANTONIO and SIMON GAECHTER. 2021. "Understanding cooperation to contain a pandemic. A behavioral economics analysis." AEA RCT Registry. June 10. https://doi.org/10.1257/rct.5749-2.0.
Experimental Details
We invite 2,400 participants on Amazon Mechanical Turk (Mturk) to take part in an incentivized survey programmed in Qualtrics (see documents attached), implemented in three stages: on 4/30/20 we recruit the first 600 participants and two weeks later, on 5/14/20, we recruit the next 600 new participants. This approach allows us to explore any temporal dynamics between those days, where the number of cases is likely to increase as the lockdown policies relax at the state level. This approach also allows us to test the robustness of our findings while replicating the results from the first stage. The last 1,200 participants are recruited a year later, 600 on 6/10/21 and 600 on 6/24/21, when the lockdown policies have been further relaxed and vaccines have been applied to roughly half of the U.S. population. This last stage will further allow us to test the robustness of our findings and learn whether the behavioral regularities pertaining to social distancing extend to willingness to get vaccinated.
The survey consists of three main sections, with the content of the first two waves and the last two varying slightly. Specifically, in the first section of the first two waves we use three incentivized economic games to measure: i) cooperation using a one-shot public goods game; ii) conditional cooperation using a sequential prisoner’s dilemma game; iii) and altruism using an ad hoc donation task; whereas in the third and fourth waves we do not make use of the public goods game. In the second section we present participants of the first two waves with hypothetical scenarios that also measure standard economic preferences, based on the Global Preference Survey (GPS) by Falk et al. 2018, and the Triple Dominance Measure of Social Value Orientation (SVO) by Van Lange et al. 1997; whereas in the last two waves we only make use of the SVO component. In the third section we survey the participants’ opinions about the current epidemic, the behavior of other people, and the severity of the current crisis, and build an index of compliance with social distancing rules as a proxy for cooperation for the first two waves, and an index of compliance with vaccination as a proxy for cooperation for the third and fourth waves. The last two waves also include an exploratory 2-question experiment in a 2x2 design, where participants first assess the likelihood that a person is to get vaccinated if they live in a [county, close-knit community] where vaccines are available and [80%, 20%] of the people have been vaccinated. Participants then determine what they would do if they were such a person: either get vaccinated, not get vaccinated, or wait until more people are vaccinated.
We conclude the study with an exploratory section where we assess the participants’ prosocial motives based in their demographics.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Overall cooperation / compliance across the three sections
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
This study does not have a treatment or a control, and therefore no between-subject design. Instead, it surveys cooperative outcomes from all participants through different angles, and almost all measures are therefore within-subjects. Design details can be found in the attached Qualtrics surveys (available upon request).
Experimental Design Details
Not available
Randomization Method
Randomization done by the survey software qualtrics (to determine the order of some of the questions).
Randomization Unit
Individual question presented in the survey.
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
2,400 MTurk participants.
Sample size: planned number of observations
2,400 MTurk participants.
Sample size (or number of clusters) by treatment arms
2,400 MTurk participants (600 different participants in each of the two waves).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB Name
IRB Approval Date
IRB Approval Number
IRB Name
The Nottingham School of Economics Research Ethics Committee
IRB Approval Date
IRB Approval Number
Analysis Plan

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