Incentives, economic preferences and vaccination uptake

Last registered on January 27, 2022

View Trial History

Pre-Trial

Trial Information

General Information

Title

Incentives, economic preferences and vaccination uptake

RCT ID

AEARCTR-0008906

Initial registration date

January 27, 2022

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

January 27, 2022, 1:10 PM EST

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

Locations

Country

Sweden

Region

Primary Investigator

Name

Florian Schneider

Affiliation

University of Zurich

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Pol Campos-Mercade

PI Affiliation

PI Name

Armando Meier

PI Affiliation

PI Name

Stephan Meier

PI Affiliation

PI Name

Devin Pope

PI Affiliation

PI Name

Erik Wengström

PI Affiliation

Additional Trial Information

Status

In development

Start date

2022-01-27

End date

2023-12-31

Keywords

Behavior, Health

Additional Keywords

JEL code(s)

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

We study the impact of different incentive schemes on COVID-19 booster shot uptake in Sweden. We look at actual vaccination behavior and vaccination intentions.

External Link(s)

Registration Citation

Citation

Campos-Mercade, Pol et al. 2022. "Incentives, economic preferences and vaccination uptake." AEA RCT Registry. January 27. https://doi.org/10.1257/rct.8906-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

Intervention Start Date

2022-01-27

Intervention End Date

2022-02-28

Primary Outcomes

Primary Outcomes (end points)

Vaccination uptake: Did the participant get a third shot (booster shot) of a COVID-19 vaccine within the first month of trial participation? (0/1)

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)

Secondary Outcomes (explanation)

Experimental Design

To address our research questions, we conduct a survey with a general population sample of the Swedish population.We randomly allocate participants to an intervention. Next, we measure participants' intentions to get vaccinated. Last, we examine whether people did or did not get a booster shot (third dose) using data from administrative registers.

Experimental Design Details

To address our research questions, we conduct a survey with a general population sample of the Swedish population. In the online survey, we first measure participants’ preferences. Then we randomly allocate participants to an intervention. Next, we measure participants' intentions to get vaccinated. Last, we examine whether people did or did not get a booster shot (third dose) using data from administrative registers.

First, we measure participants’ economic preferences, in particular altruism and risk preferences (see pre-analysis plan for details).

Then, we randomly allocate participants to one of five conditions. First, we have a control condition:

• Control condition: We encourage participants to take the third dose of a COVID-19 vaccine.

Second, we study the impact of four incentive schemes which all have an expected payoff of SEK 200. All schemes include the same encouragement as in the control condition plus the treatment:

• Guaranteed payment: We offer people SEK 200 if they get vaccinated within 1 month, which we check using administrative data.
• Lottery incentive: We offer people a lottery ticket with a 1% chance of winning SEK 20,000 (expected value is SEK 200) if they get vaccinated within 1 month, which we check using administrative data.
• Prosocial incentive: We offer people a donation of SEK 200 to the charity Save the Children if they get vaccinated within 1 month, which we check using administrative data.
• Choice of incentive: We offer people the free choice of any of the three incentive schemes (guaranteed payment, lottery or prosocial incentive). We pay people the chosen incentive if they get vaccinated within 1 month, which we check using administrative data.

Next, we ask participants about their intentions to receive the COVID-19 vaccine.

We will be able to match our survey responses with administrative data on vaccination uptake. For all participants we will see whether and when they received the third dose of a COVID-19 vaccine.

Randomization Method

Randomization is done by a computer

Randomization Unit

We randomize at the individual level.

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

We aim to collect data from about 6,000 participants. However, there is some uncertainty about the number of people that can be recruited, and we could end up with a higher or small sample size. Importantly, we will not have access to the vaccination data until we have finished data collection.

Sample size: planned number of observations

Sample size (or number of clusters) by treatment arms

We have a total of five conditions. We allocate 1/5 of the sample size to each condition.

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

With 6’000 observations and using a baseline in which 60% of the participants in the control group vaccinate, we will have 80% power to detect an effect size of about 5 percentage points.

Supporting Documents and Materials

IRB