Panic and Preferences
Last registered on April 03, 2020


Trial Information
General Information
Panic and Preferences
Initial registration date
April 02, 2020
Last updated
April 03, 2020 9:11 AM EDT

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Primary Investigator
Other Primary Investigator(s)
Additional Trial Information
On going
Start date
End date
Secondary IDs
We perform two experiments to measure how government declarations and actions affect mental states and behavior. To manipulate the (true) information that participants receive, we will randomize exposure to news about government actions. We will then analyze how the nature of this information affects mental states and behavior.
External Link(s)
Registration Citation
Rafkin, Charlie. 2020. "Panic and Preferences." AEA RCT Registry. April 03.
Experimental Details
We run two separate experiments, with distinct samples, simultaneously. Both experiments manipulate exposure to information about the government.
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Anxiety measured via the Beck Anxiety Inventory, aggregated into one index
Update in predictions about how severe COVID-19 will be (log predicted number of deaths)
Demand for information
Performance on a data entry task
Willingness to pay as a measure of hoarding behavior
Heterogeneity by:
- Baseline Trump support (three groups: oppose trump, support trump, undecided)
- Baseline belief that the government has or acts on own information
- Baseline belief about severity of COVID-19 (predicted number of deaths, predicted death rate).

Additional primary outcomes for Strong vs. Weak:
- Update in predictions about how severe COVID-19 will be (death rate)

Additional primary outcomes for Steady vs. Changing:
- Update in support for Donald Trump

Primary Outcomes (explanation)
Anxiety measure is a standard questionnaire commonly used in psychiatry, which we adapted to focus on Today
Support for Donald Trump is measured as the confidence, on a scale from 0 to 10, that the participant will vote for Trump in the 2020 Presidential election
Predictions about how severe COVID-19 will be (total number of deaths in the US, rate of death among younger and older people infected) are elicited before and after information provision. These predictions are incentivized for accuracy.
Willingness to pay for a notification service about when hand sanitizer, N95 masks, pasta, and sunscreen (as a placebo) become available again on Amazon
Our primary measure of willingness to pay uses a log or sinh transform to handle outliers
Demand for information: we promise to show participants a link to an article at the end of the study. The article can be on four subjects: it can provide: (i) cute animal pictures, (ii) information about COVID cases and deaths in the United States, (iii) information about the effect of the Senate CARES bill on health insurance coverage, and (iv) information about wellness and stress-reduction. Participants choose the article they most want to receive.
Data entry: In a first task, participants are given a list of metropolitan areas’ populations. We ask participants to sort the metro area by population from largest to smallest. In a second task, participants are given a list of positive COVID-19 tests in a list of states. We ask participants to sort the states by positive COVID-19 tests, from largest to smallest. For both tasks, we measure accuracy and speed, as well as a combined index. Participants are randomized to complete one of the two data entry tasks.
We study heterogeneous treatment effects by: (i) baseline Trump support, (ii) belief that the government acts on its own private information, and (iii) baseline beliefs about COVID severity. We bucket Trump support into three groups: opposed to Trump, undecided, and supports Trump.
Secondary Outcomes
Secondary Outcomes (end points)
Updates in perception of how the government is handling the crisis
Change in width of confidence intervals about predicting the number of deaths from coronavirus in the US
Update in predictions about the stock market
Change in self-reported uncertainty about the stock market
Anxiety decomposition
Risk aversion
An exploratory empirical strategy will use our treatments as instrumental variables for anxiety in order to explore the causal effects of anxiety on economic preferences as measured by the global preference survey and dictator game. Because the exclusion restriction seems imperfectly satisfied, this approach will be secondary
The number of self-reported people the person plans to meet with outside the household
Heterogeneity by the same sources as in primary outcomes:
- Baseline Trump support (three groups: oppose Trump, support Trump, undecided)
- Baseline belief that the government has or acts on own information
- Baseline belief about severity of COVID-19 (predicted number of deaths, predicted death rate)
Additional heterogeneity of primary and secondary outcomes by the baseline amount of news the participant has consumed about COVID-19.

Additional secondary outcomes for Strong vs. Weak:
- Update in support for Donald Trump and the government
Additional secondary outcomes for Steady vs. Changing:
- Update in predictions about how severe COVID-19 will be (death rate)

Secondary Outcomes (explanation)
We ask participants about how well the government is managing the crisis (e.g., overreacting vs. taking appropriate action)
Anxiety decomposition: We ask participants to self-report whether they are anxious because of: economic fallout from COVID (effect on themselves and others), consequences of quarantines, the health effects of COVID (becoming sick themselves or others becoming sick), political consequences of COVID, or general COVID-related chaos.
We elicit predictions about the value of the Dow Jones Industrial Average in October, 2020. We reward predictions for accuracy.
Risk aversion: participants choose between a binary choice between $15 for sure and a 50-50 gamble of 0 or $30
We ask participants to report the number of people they plan to meet for social purposes outside the household. Our objective is to study how the treatment affects how people adhere to self-reported social distancing guidelines.
We add additional heterogeneity by the self-reported amount of attention the participant has consumed about COVID-19; our information may be more surprising to people who have consumed less news.
Experimental Design
Experimental Design
We randomize (truthful) information about statements given and actions taken by the government.
Experimental Design Details
Not available
Randomization Method
Randomization in Qualtrics
Randomization Unit
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
2,000 per experiment
Sample size: planned number of observations
2,000 per experiment
Sample size (or number of clusters) by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
IRB Approval Number