Vaccines at Work
Last registered on February 21, 2020


Trial Information
General Information
Vaccines at Work
Initial registration date
February 20, 2020
Last updated
February 21, 2020 11:54 AM EST
Primary Investigator
Other Primary Investigator(s)
PI Affiliation
PI Affiliation
Additional Trial Information
Start date
End date
Secondary IDs
Influenza vaccination could be a cost-effective way to reduce costs in terms of human lives and productivity losses, but low take-up rates and vaccination unintentionally causing moral hazard may decrease its benefits. We ran a natural field experiment in cooperation with a bank in Ecuador, where we modified its vaccination campaign. Experimentally manipulating incentives to participate in this health intervention allows us to study peer effects with organizational data and to determine the personal consequences of being randomly encouraged to get vaccinated. We find that assigning employees to get vaccinated during the workweek roughly doubled take-up compared to employees assigned to the weekend, which indicates that reducing opportunity costs plays an important role in increasing vaccination rates. Coworker take-up also increased individual take-up significantly and is driven by social norms. Contrary to the company’s expectation, vaccination did not reduce sickness absence during the flu season. Getting vaccinated was ineffective with no measurable health externalities from coworker vaccination. We rule out meaningful individual health effects when considering several thresholds of expected vaccine effectiveness. Using a dataset of administrative records on medical diagnoses and employee surveys, we find evidence consistent with vaccination causing moral hazard, which could decrease the effectiveness of vaccination.
External Link(s)
Registration Citation
Chadi, Adrian, Manuel Hoffmann and Roberto Mosquera. 2020. "Vaccines at Work." AEA RCT Registry. February 21.
Experimental Details
Intervention Start Date
Intervention End Date
Primary Outcomes
Primary Outcomes (end points)
Vaccine take-up, sickness, sick days, doctor visits and health-related habits
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
We ran the field experiment in cooperation with a bank in Ecuador. This bank focuses on consumer credit and is one of the largest credit card issuers in the country. Its headquarters are in Quito, Ecuador’s capital, and it has six branches across the country with over 1,300 employees, distributed in 31 divisions with 142 working units. The bank had run small vaccination campaigns in the past. These campaigns included only some employees in crowded areas and ran during the workweek in the bank’s offices. In 2017, the bank decided to extend its annual campaign to all its employees and allowed us to experimentally modify it to investigate how to increase take-up and the effects of vaccination. We implemented three interventions: we changed the vaccine’s price for some employees using income-dependent subsidies, we randomized assignments for on-site vaccinations across weekdays, and we implemented information nudges by varying the content of the emails used to invite employees to vaccinate.
The bank decided to provide the vaccine for free to areas that participated in campaigns in previous years and to partially subsidize it for new participants. Since the company opposed the randomizing subsidies, we used information on employees’ income to allocate this subsidy. Employees who earned less than $750 per month would pay $4.95 to get vaccinated, while those who earned more than $750 would pay $7.43. Note that the vaccine’s full price is $9.99. Employees were informed about the vaccine’s price in their invitation email. This email included basic information about the campaign and informed employees that the payment for the vaccine was directly deducted from their paycheck if they opted to get vaccinated. The email also contained information on the assigned day and time.
To examine the effects of opportunity costs and information, we randomly assigned all employees into one of four groups. First, employees assigned to the control group (Control) were invited to get vaccinated during the workweek (Wednesday, Thursday, or Friday) and were allowed to take time off their duties to get vaccinated. The specific day was selected randomly for each employee.
The first treatment increased the opportunity costs of vaccination by assigning employees to get vaccinated on Saturday. The employees usually do not work during the weekend, so they would incur extra transportation costs and have to arrange their schedules to go to the bank and get vaccinated. Otherwise, this group received the same information as the Control. This treatment was only applied in Quito because all the other branches are substantially smaller (82% of the employees work in Quito), and their employees could get vaccinated in a single day, which was not possible in Quito.
We also implemented two information nudges. We kept the additional messages as unobtrusive as possible to prevent confounding the effect of information with salience or other behavioral factors. The first nudge highlights the social benefits of flu immunization (Altruistic Treatment). In addition to the information provided to the control group, the email included the phrase: “Getting vaccinated also protects people around you, including those who are more vulnerable to serious flu illness, like infants, young children, the elderly and people with serious health conditions that cannot get vaccinated.” The second nudge highlights the individual benefits of flu immunization (Selfish Treatment). In addition to the information provided to the control group, the email included the phrase: “Vaccination can significantly reduce your risk of getting sick, according to both health officials from the World Health Organization and numerous scientific studies.” Employees in these two treatments were assigned to get vaccinated during the workweek, while the specific day was selected randomly.
Experimental Design Details
Randomization Method
Randomization done by a computer
Randomization Unit
Individual employee
Was the treatment clustered?
Experiment Characteristics
Sample size: planned number of clusters
Sample size: planned number of observations
1320 individuals
Sample size (or number of clusters) by treatment arms
Treatment 1 326, treatment 2 348, treatment 3 249, Control 381
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB Name
Study has received IRB approval. Details not available.
IRB Approval Date
Details not available
IRB Approval Number
Details not available
Post Trial Information
Study Withdrawal
Is the intervention completed?
Intervention Completion Date
April 30, 2018, 12:00 AM +00:00
Is data collection complete?
Data Publication
Data Publication
Is public data available?
Program Files
Program Files
Reports, Papers & Other Materials
Relevant Paper(s)